PREVENT Extreme Weather Conditions/Epidemics (U. Patras)

Epidemics and Extreme Weather Conditions

Prevent Project - Chapter 5

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Chapter description:

This chapter introduces the interconnected challenges of epidemics and extreme weather events in a changing climate. It explores how infectious diseases emerge, spread, and evolve, and how climate-driven hazards—such as heatwaves, floods, droughts, and storms—can amplify health risks and disrupt communities. Through historical examples, transmission routes, and climate-sensitive disease patterns, the chapter highlights the growing vulnerability of human populations to both biological and environmental threats. Emphasis is placed on the importance of preparedness, early warning systems, and innovative technologies that support monitoring, prediction, and effective response.

Extreme Weather Conditions

Epidemics

Group Projects | Extreme Weathe Conditions

Group Projects | Epidemics

01

Epidemics

This chapter explores the relationship between epidemics and climate change, detailing how rising temperatures, changing precipitation patterns, and extreme weather events amplify the risks of infectious disease outbreaks. It covers the types and transmission of diseases, historical and recent epidemic events, and the influence of climate on disease vectors like mosquitoes and rodents. It also emphasizes the growing threats of zoonotic diseases, antimicrobial resistance, and vulnerabilities in health systems, particularly in low-income regions. Finally, it highlights strategies for epidemic management and preparedness, including surveillance systems, early warning tools, and international cooperation led by organizations like WHO.

Diseases

"A disease is a particular abnormal condition that adversely affects the structure or function of all or part of an organism. It is often associated as a medical condition associated with specific symptoms.”

Causes of diseases (etiological categories)

Physical

• Caused by physical factors that damage the body, such as: mechanical injury, pressure, radiation, heat or cold

Genetic

• inherited (passed from parents)
• acquired (mutations that occur during life).

Chemical

• Caused by harmful chemical substances or toxins, such as: poisons, pollutants, heavy metals, industrial chemicals

Biological

• Caused by living organisms — pathogens such as: viruses, bacteria, fungi, parasites

Diseases | Types

Infectious Diseases

“Infectious diseases are caused by pathogenic microorganisms, such as bacteria, viruses, parasites or fungi; the diseases can be spread, directly or indirectly, from one person to another.” (WHO)

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Diseases which place on populations heavy burdens of disability

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Diseases which cause high levels of mortality

Diseases which place on populations heavy burdens of disability

Several infectious diseases have the potential to pose serious risks on local, national, or worldwide scales, often triggering outbreaks or even global epidemics or Epidemics.

Epidemics

An increase, often sudden, in the number of cases of an infectious disease above what is normally expected in a population in a specific area within a short time period.

Epidemics | Historical Events

Plague of Athens (430 BC)

• The first recorded epidemic was the plague of Athens, around 430 B.C.E (described in “History of the Peloponnesian War”, Thucydides.
• Killed up to one-third of Athens' population
• Possible causes: typhoid fever, smallpox, or measles

Smallpox and the Fall of the Aztec Empire (1520)

• Approximately half of the 3.5 million Aztecs died from the epidemic
• The disease severely weakened the Aztec resistance against Spanish forces

• Played a major role in the Spanish conquest of the Aztec Empire

The Black Death (1347–1351)

• Bubonic plague devastated Europe,
• Spreaded throughout the Mediterranean.
• That disease alone accounts for the death of 50-100 million people in just two years - 1348-1350
• Estimated death toll: 75–200 million people (50% of entire population of Europe)

Spanish Flu (1918–1919)

• H1N1 influenza virus
• Infected one-third of the world’s population
• 675,000 Americans lost their lives to the Spanish Flu, > casualties during World War I, World War II, Korea and Vietnam combined.
• Killed 30–40 million people

Epidemics | A Strange Historical Event

Tanganyika laughter epidemic of 1962 A Mysterious Case of Mass Psychogenic Illness

Outbreak Origin: Began on January 30 1962, at a mission-run girls' boarding school in Kashasha, near Lake Victoria.Spread and Scope: Dozens of girls began to laugh and cry uncontrollably. Within weeks, over 1,000 people across 14 schools and several villages were affected. Duration: The episodes lasted from a few hours to 16 days, and the epidemic persisted for 18 months.

Epidemics | A Strange Historical Event

Tanganyika laughter epidemic of 1962 A Mysterious Case of Mass Psychogenic Illness

Cause: Increased stress and pressure on kids. Initiated by stress fear and anxiety. Impacts: Mass Hysteria, schools closed (95 of the 159 pupils were affected), pain, fainting, respiratory problems, and rashes. Research Impact: The case significantly influenced research in psychosomatic medicine, social psychology, and epidemiology.

It highlights the impact of group dynamics and stress on mental health!!!

Epidemics | Routes of Transmission

Vector-borne

Blood and/or body fluids borne

Airborne

spread by bites from mosquitos, fleas, ticks (malaria, dengue, plague, WNV)

spread by contact, blood transfusion, pregnancy, and sexual activity (Ebola virus, HIV)

spread by air and droplets (flu, measles, SARS, MERS)

Food-borne

Zoonotic

Waterborne

spread by food (salmonella, listeria and hepatitis)

spread between animals and people, direct and indirect contact (viruses, bacteria, parasites, and fungi)

spread through contaminated water (cholera)

Epidemics | Routes of Transmission

Source: Han, J.J.; Song, H.A.; Pierson, S.L.; Shen-Gunther, J.; Xia, Q. Emerging Infectious Diseases Are Virulent Viruses—Are We Prepared? An Overview. Microorganisms 2023, 11, 2618. https://doi.org/10.3390/microorganisms11112618

Epidemics | Terminology

DISEASE:A pathological condition of body parts or tissues characterized by an identifiable group of signs & symptoms.

INFECTION: Colonization of a host organism by parasite species. Occurs when an infectious agent enters and begins to reproduce in the body—may or may not lead to disease.

INFECTIOUS DISEASE:Disease caused by infectious agents (bacteria, viruses, protozoa, fungi) that can be transmitted. Also known as communicable, contagious, or transmissible diseases.

INFECTIVITY: The ability of an organism to enter, survive, and multiply in the host.

Virulence: The degree of damage a pathogen causes to its host; higher virulence often correlates with more severe disease.

INFECTIOUSNESS OF DISEASE: Indicates how easily the disease is transmitted to other hosts.

Epidemics | Terminology

PATHOGEN An infectious agent or microorganism capable of causing disease. PATHOLOGY The study of the structural and functional manifestations of disease. PATHOLOGIST A physician specialized in diagnosing diseases through the examination of tissues, organs, and bodily fluids.

PATHOGENICITYThe inherent ability of an organism to cause disease. PATHOGENESIS The sequence of events or mechanisms involved in the development and progression of a disease. HOST An organism that harbors and supports the survival and multiplication of another organism, typically a pathogen.

Epidemics | Koch’s Postulates

Koch developed four essential criteria to establish that a specific disease is caused by a particular microorganism:

✅ Presence: The specific agent must be found in every case of the disease. 🧫 Isolation: The agent must be isolated from the diseased host and grown in pure culture. 🧍‍♂️ Reproduction: When introduced into a healthy, susceptible host, the cultured agent must reproduce the same disease. 🔬 Re-isolation: The same agent must be re-isolated from the newly infected experimental host.

Epidemics | Disease Agents

• Most infectious agents responsible for diseases are small in size, so they are referred to as microbes or microorganisms.

• They are collectively known as Pathogens
• There are several groups of agents that cause diseases:
• Bacteria
• Viruses
• Protozoa (Protists)
• Fungi
• Helminthes
• Prions

Epidemics | Disease Agents

Members of several virus families can provoke emerging and re-emerging epidemics

• newly emerging diseases
• re-emerging/resurging diseases
• deliberately emerging disease

Epidemics | Current state

• During the last years several (at least 11) different viruses have emerged (or reemerged) causing epidemics
• 2013 – 2015 Ebola and chikungunya
• 2016 Zika
• Yellow fever, Monkeypox and Lass provoke outbreaks among several countries (Brazil and Nigeria)

Yellow fever in Brazil: (from July 217): 1257 cases and 394 deaths. Lassa in Nigeria: Since early 2018 more than 120 deaths Monkeypox in Nigeria: Since September 2017, 61 cases

THE QUESTION IS NOT IF A NEW INFECTIOUS DISEASEEPIDEMIC OR PANDEMIC WILL OCCUR, BUT WHEN AND BY WHAT AGENT?

Epidemics | Current state

774 deaths 50b USD

>20.000 cases >400 deaths

Edited image from: The Neglected Dimension of Global Security — A Framework for Countering Infectious-Disease Crises. Peter Sands, M.P.A., Carmen Mundaca-Shah, M.D., Dr.P.H., and Victor J. Dzau, M.D.

Epidemics | Current state

Leading causes of death in 2021 worldwide.

• Top 10 causes of death led to 39 million deaths (57% of the total deaths) globally.
• 7 of the 10 leading causes of deaths were noncommunicable diseases
• Ischaemic Heart Disease:
• Leading cause of death globally (13% of total deaths)
• Increased by 2.7 million deaths since 2000, reaching 9.1 million in 2021
• COVID-19:
• Responsible for 8.8 million deaths in 2021
• Pushed other leading causes of death down by one rank
• Stroke and Chronic Obstructive Pulmonary Disease (COPD):
• Moved to 3rd and 4th positions in 2021 (10% and 5% of deaths, respectively)
• Lower Respiratory Infections:
• 5th leading cause of death

Epidemics | Current state

Leading causes of death in 2021 in low-income countries.

Leading causes of death in 2021 in high-income countries.

Source: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death

Epidemics | Current state

• Globally, The global pool of potential pathogens is vast, yet resources for disease research and development (R&D) are constrained.
• A WHO tool identifies diseases posing significant public health risks due to their epidemic potential or lack of effective countermeasures.
• Current WHO Priority Diseases for R&D:
• COVID-19
• Crimean-Congo Hemorrhagic Fever
• Ebola Virus Disease and Marburg Virus Disease
• Lassa Fever
• Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS)
• Rift Valley Fever
• Zika Virus
• “Disease X” (an unknown pathogen with potential for significant impact)

Epidemics | Impacts

Epidemics and Health Impact

• Epidemics lead to significant increases in morbidity and mortality, with particularly higher mortality rates in Low- and Middle-Income Countries (LMICs).

Economic Consequences of Epidemics:

• Epidemics inflict economic damage through both immediate fiscal shocks and prolonged negative effects on economic growth.

Behavioral Changes and Economic Impact:

• Fear-driven changes in individual behavior, such as avoidance of workplaces and public spaces, are major contributors to economic downturns during Epidemics.

Social and Economic Disruptions:

• Certain pandemic mitigation strategies can lead to substantial social and economic disruptions
• Epidemics disrupt daily life, causing fear, stigma, and mental health crises; school closures and travel restrictions are common.

Political Instability and Epidemics:

• In nations with weak institutions and histories of political instability, Epidemics exacerbate political tensions and stress.
• Outbreak response measures, such as quarantines, can provoke violence and conflict between the state and citizens in such contexts.

Epidemics | Future state

• Population Dynamics and Urban Expansion:
• Increasing population growth, coupled with rapid urbanization and human encroachment into previously untouched environments, heightens the risk of local outbreaks escalating into broader health crises.
• Globalization of Infectious Diseases:
• The extensive movement of people and goods through international travel and trade accelerates the geographic spread of infectious diseases.
• Zoonotic Transmission:
• The rising incidence of zoonoses, where pathogens spill over from animals to humans, particularly in regions with close human-animal interactions, poses a significant threat to global public health.
• Climate Change and Disease Ecology:
• Shifts in climate patterns influence the distribution and behavior of disease vectors, increasing the potential for local outbreaks to affect wider regions.

• Antimicrobial Resistance:
• The growing prevalence of antimicrobial resistance undermines treatment efficacy and complicates efforts to contain infectious diseases, contributing to the risk of pandemics.
• Inadequate Public Health Systems:
• Weak public health infrastructure, including shortages of healthcare personnel in regions experiencing outbreaks, hampers effective response and containment, facilitating the spread of diseases.
• Impact of Civil Unrest:
• Political instability and civil conflict can disrupt health systems, displace populations, and create conditions conducive to the rapid transmission of infectious diseases.

Epidemics | Climate Change

What is Climate? - Climate refers to the long-term patterns and averages of weather conditions observed in a specific region over extended periods—typically decades. - It differs from weather, which describes short-term atmospheric conditions.

Key Elements That Define Climate:

• Temperature – The average heat levels experienced in a region across seasons and years.
• Precipitation – The amount and frequency of rainfall, snowfall, sleet, or hail.

• Humidity – The level of moisture present in the air, influencing how temperatures feel.
• Wind Patterns – Direction and speed of winds that affect regional climates and weather systems.
• Air Pressure – The weight of the atmosphere, which influences weather patterns and storm formation.

Epidemics | Climate Change

• Temperature
• Global temperatures are rising due to increased greenhouse gas emissions.
• More frequent and intense heatwaves are occurring worldwide.
• Precipitation
• Changes in rainfall patterns lead to more extreme events—both heavy downpours and droughts.
• Some regions are becoming wetter, while others face prolonged dry periods.
• Increased rainfall can lead to flooding, soil erosion, and waterborne diseases.

• Humidity
• Warmer air holds more moisture, leading to higher humidity levels in many areas.
• Increased humidity intensifies heat stress and makes extreme temperatures feel hotter.
• High humidity supports the spread of mold and some infectious diseases.
• Wind Patterns
• Shifts in global wind systems alter storm paths and weather patterns.
• More powerful hurricanes and cyclones are occurring due to warmer ocean temperatures.
• Air Pressure
• Climate change affects the distribution of high and low-pressure systems.
• This can lead to more persistent weather extremes, such as stagnant heat domes or prolonged storms.
• Altered pressure patterns influence jet streams and contribute to unpredictable weather

Epidemics | Climate Change

Anticipated Trends

1. Longer and warmer summers
2. Shorter and milder winters
3. Fewer frost days
4. More intense heat waves; less intense cold waves
5. More extreme and unpredictable weather events (severe storms like hurricanes, heavy precipitation, severe droughts, flooding)

Source: https://www.noaa.gov/education/resource-collections/climate/climate-change-impacts

Epidemics | Climate Change

Effects of Climate Change on Human Health

1. Heat-Related Illnesses
1. Increased frequency of heatwaves leads to more cases of heatstroke and dehydration.
2. Vulnerable populations—children, the elderly, and outdoor workers—are most at risk.
3. Rising temperatures can worsen existing conditions like cardiovascular and respiratory diseases.
2. Respiratory and Cardiovascular Issues
1. Higher temperatures and air pollution (e.g., ozone, wildfire smoke) worsen asthma and heart disease.
2. Allergens like pollen become more prevalent with longer growing seasons.
3. Poor air quality contributes to premature deaths and hospitalizations

3. Food and Water Insecurity

1. Droughts, floods, and shifting climates reduce crop yields and food supply.
2. Contaminated water sources increase risks of waterborne diseases like cholera.
3. Malnutrition and undernutrition become more common in affected regions.

4. Mental Health Impacts

1. Extreme weather events and displacement contribute to stress, anxiety, PTSD, and depression (Climate anxiety)
2. Loss of homes, livelihoods, and communities can have long-lasting psychological effects.

Epidemics | Climate Change

Effects of Climate Change on Human Health

Spread of Infectious Diseases

• Warmer climates expand the habitat of disease-carrying vectors like mosquitoes and ticks.
• Increased risk of malaria, dengue fever, Zika virus, and Lyme disease.
• Changing rainfall and humidity can create ideal breeding conditions for pathogens.
• Shifting seasonal temperatures and rainfall patterns alter ecosystems and disease dynamics.
• More frequent and intense extreme weather events can trigger outbreaks and disrupt health services.
• Global changes in weather patterns create favorable environments for disease vectors like mosquitoes and ticks.
• These conditions support the emergence and spread of infectious diseases in new regions.

Infectious diseases sensitive to climate changes include:

• Waterborne
• Foodborne
• Soil- and dust-associated
• Zoonotic
• Vector-borne

Epidemics | Climate Change

Infectious diseases sensitive to climate changes include

Foodborne and Waterborne Diseases• Cryptosporidium • Giardia • Naegleria fowleri (brain-eating amoeba) • Salmonella • Vibrio species • Harmful Algal Blooms (HABs) Soil and Dust-Associated Diseases• Infections linked to airborne spores and pathogens in dry, dusty conditions

Zoonotic and Vector-Borne Diseases • Lyme disease • Dengue fever • West Nile Virus infection • Plague • Rabies • Anthrax Fungal Diseases • Valley fever (Coccidioidomycosis) • Histoplasmosis • Blastomycosis Antibiotic-Resistant Organisms • Resistant bacteria and pathogens that thrive or spread under changing environmental conditions

Epidemics | Climate Change

Common Pathways for Climate-Sensitive Disease Transmission

1. Bites from mosquitoes and ticks
2. Interaction with infected or asymptomatic animals
3. Exposure to mold or other fungi through inhalation or skin contact
4. Ingestion of food or water contaminated with pathogens
5. Contact with polluted water sources

Epidemics | Climate Change

WNV vs climate change (Angelou et al., Comecap 2025)

Source: Angelou A, Stilianakis NI, Kioutsioukis I. Weather Patterns as Predictors of West Nile Virus Infection Risk in Greece. Environmental and Earth Sciences Proceedings. 2025; 35(1):8. https://doi.org/10.3390/eesp2025035008

Epidemics | Climate Change

WNV vs climate change (Angelou et al., Comecap 2025)

Source: Angelou A, Stilianakis NI, Kioutsioukis I. Weather Patterns as Predictors of West Nile Virus Infection Risk in Greece. Environmental and Earth Sciences Proceedings. 2025; 35(1):8. https://doi.org/10.3390/eesp2025035008

Epidemics | Extreme Weather Events and Disease Outbreaks

• Shifts in climate alter environmental conditions, which can lead to an increase in both the prevalence and geographic range of certain diseases.
• Key climate factors like temperature and rainfall affect how disease-causing organisms replicate, interact with hosts, and survive—whether in animals, disease-carrying insects, or the broader environment.
• Extreme weather events such as storms and droughts can change how diseases emerge, trigger outbreaks, and disrupt essential infrastructure needed to manage public health.

"Climate change influences the spread and frequency of infectious diseases in several ways"

Outbreaks of climate-sensitive infectious diseases in the aftermath of extreme climatic events are of high public health concern, particularly in lower- or middle-income countries that are highly vulnerable and exposed to climate change, despite having contributed very little to global greenhouse gas emissions

Epidemics | Extreme Weather Events and Disease Outbreaks

How Disasters Amplify Epidemic Risks

• Flood-Linked Epidemics: Floods increase exposure to pathogens via contaminated water, causing outbreaks of cholera, leptospirosis, and hepatitis A.
• Drought and Dust-Borne Diseases: Dry conditions promote airborne transmission of diseases like Valley fever due to increased dust.
• Healthcare Disruption: Extreme weather events disrupt medical supply chains and healthcare services, delaying outbreak responses.

The transmission and risk of infectious diseases are influenced by a complex interplay of factors, including the disease’s natural ecology, environmental and climate conditions, sanitation and water systems, human behavior, population health, access to healthcare, and social and economic policies.

Epidemics | Extreme Weather Events and Disease Outbreaks

Hydrometeorological parameters associated with Climate-Sensitive Infectious Diseases (CSID) outbreaks

"Many major infectious diseases in tropical regions are spread by vectors that rely on external environmental conditions, making them highly sensitive to changes in temperature and humidity."

Source: Alcayna T., at., 2022, Climate-sensitive disease outbreaks in the aftermath of extreme climatic events: A scoping review

Epidemics | Extreme Weather Events and Disease Outbreaks

Effect of climate factors on vector- and rodent-borne disease transmission.

Source: Intergovernmental Panel on Climate Change. (2001). Potential impacts of climate change (IPCC Third Assessment Report, Working Group II, Chapter 15). https://archive.ipcc.ch/ipccreports/tar/wg2/index.php?idp=358

Epidemics | Extreme Weather Events and Disease Outbreaks

Non-Cholera Vibrio Bacteria

• Health Impact: Non-cholera Vibrio species can cause serious gastrointestinal illnesses, ear and skin infections, and in severe cases, life-threatening conditions like sepsis, tissue-destroying infections, and even death.
• Rising Incidence: Although not currently listed as a notifiable disease in the EU, reported cases are increasing in countries with established surveillance systems.
• Environmental Link: Higher sea surface temperatures have expanded the range of coastal waters with conditions favorable for Vibrio transmission—especially in brackish environments.
• Geographical Spread (as of 2020):
• Baltic Sea coastlines (Sweden, Finland, Estonia, Latvia, Lithuania, Poland): ~97–100% suitability
• Germany (92%) and Denmark (83%) also show high suitability
• Mediterranean countries (e.g., Spain at 4%, Italy at 2%) show low suitability due to saltier waters

Urgency for Action: As environmental conditions become more favorable, early warning systems and proactive public health interventions are crucial to prevent severe outcomes.

Epidemics | Extreme Weather Events and Disease Outbreaks

West Nile Virus (WNV)

• Overview: WNV is a climate-sensitive virus transmitted by mosquitoes (notably Culex spp.), and poses a risk of severe neurological disease and fatalities in humans.
• Recent Trends:
• Major outbreaks have increased in size, frequency, and geographical scope.
• The 2018 outbreak marked the largest on record, with over 1,500 local infections across 11 countries.
• Climate Influence:
• Rising ambient temperatures enhance the mosquito’s ability to transmit the virus, raising the risk of outbreaks.
• Climate modeling (1951–2020) reveals a continuous upward trend in outbreak risk linked to temperature and rainfall changes.

• Regional Risk Growth (1986–2020 vs. 1951–1985):
• Northern Europe: +445%
• Western Europe: +242%
• Southern Europe: +149%
• Central and Eastern Europe: +163%
• Key Insight:
• Although relative risk is rising fastest in the north and west, the absolute risk remains highest in southern and central/eastern Europe.

Epidemics | Extreme Weather Events and Disease Outbreaks

Dengue Virus

• Emerging Threat: Enhanced global mobility and increasingly favorable climatic conditions for mosquito-borne viruses are driving the rise of arboviral diseases such as dengue across Europe.
• Recent Local Transmission: In the past five years, autochthonous (locally transmitted) dengue cases have been reported in countries like Spain and France.
• Potential Impact: Without adequate preparedness, dengue outbreaks could lead to serious health consequences
• Model Insights: A mechanistic model estimates the virus's basic reproduction number (R₀) and the duration of the transmission season based on:
• Temperature
• Rainfall
• Mosquito population density
• Human population density

• Trend (1986–2020 vs. 1951–1985):
• R₀ increased by 17.3% across Europe
• Similar trends observed for Chikungunya and Zika
• Geographical Shift:
• The longest transmission season extension occurred in Central and Eastern Europe, gaining about 0.2 additional suitable months for dengue transmission.

Dengue is the world’s most important vector-borne viral disease!!!

Epidemics | Extreme Weather Events and Disease Outbreaks

Malaria

• Trends in Suitability (1986–2020 vs. 1951–1985):
• Europe experienced a 4.5% increase in suitable months overall
• Northern and Western Europe saw the highest increase:
• +21.6% (1951–85)
• +25.2% (1986–2020)

• Background: Though Europe has been malaria-free since 1974, sporadic local transmission and cases among travelers still occur.
• Need for Vigilance: Surveillance, climate monitoring, and health system preparedness are essential to avoid re-establishment.
• Model Parameters: A threshold model estimates suitable transmission months based on:
• Precipitation
• Relative humidity
• Temperature
• Land cover type

Epidemics | Extreme Weather Events and Disease Outbreaks

• Intense precipitation can transfer pathogenic microorganisms of human or animal fecal origin to the water through discharge of raw and treated sewage and run-off from the soil, increasing the microbial load on surface water.
• Heavy rainfall is strongly linked to the occurrence of waterborne disease outbreaks, particularly during the spring and summer months.
• Groundwater sources and private household water supplies are especially vulnerable to the effects of extreme weather events.
• There is a positive correlation between higher mean annual precipitations (thus higher humidity) and HFRS incidents.
• Changes in precipitation can impact the reproduction, development, and behavior of arthropod vectors, their pathogens, and the non-human vertebrate hosts that carry these pathogens.
• Standing water from heavy rainfall provides breeding sites for mosquitoes, increasing the likelihood of WNV transmission.
• Heavy precipitation event is found to be positively associated with mortality from various infectious diseases.

Epidemics | Floods and Disease Outbreaks

• Beyond immediate physical harm, floods present a major public health risk due to the potential spread of infectious diseases.
• These risks are present both during the flooding event and throughout the recovery period.
• Damage to essential infrastructure, lack of safe drinking water, and use of contaminated sources (e.g., wells, fountains, boreholes) increase vulnerability.
• Direct exposure to sewage-polluted floodwater and a surge in vector and rodent populations intensify health threats.
• Such conditions can lead to spikes in infection rates and the potential for disease outbreaks in affected and surrounding areas.
• In Europe, flooding and the resulting adverse conditions in both urban and rural areas have been associated with the rise and transmission of waterborne, rodent-borne, and vector-borne diseases.

The risk of infectious disease following flooding in high-income countries is lower

Epidemics | Floods and Disease Outbreaks

Waterborne diseases

• Waterborne illnesses occur when drinking water is contaminated with bacteria, viruses, or parasites from human or animal waste.
• Flooding can damage water supply and drainage systems, allowing pathogens to mix with drinking water sources. Use of contaminated water and food.
• Lack of sufficient running water for the cleaning of the flooded houses and infrastructure. Use of
• Such conditions increase the risk of outbreaks, especially in areas with compromised infrastructure.
• European countries have reported more cases of waterborne diseases following flood events, including those caused by microbial contamination.

• Case Study: Waterborne Illness in Amsterdam (2015)
• In 2015, swimmers in Amsterdam developed acute gastroenteritis after a sporting event.
• The illness was linked to water contaminated with multiple strains of norovirus.
• Heavy flooding and sewage overflow in the city’s canals occurred just two days prior to the event.
• The incident highlights the health risks of water contamination following extreme weather

Epidemics | Floods and Disease Outbreaks

Vectorborne diseases

• Floods can contribute to the spread of vector-borne diseases by creating more suitable habitats for disease-carrying insects.
• While floodwaters may initially wash away breeding sites and reduce vector numbers,
• Stagnant water left behind after flooding often becomes prime breeding ground for mosquitoes and other vectors.
• This increases the risk of disease transmission to both local residents and emergency responders.
• Illnesses like West Nile fever may emerge or become more widespread under these conditions.
• Extreme precipitation can increase the number of mosquito breeding sites, higher mosquito abundance, and likelihood of disease outbreaks

Vector-borne diseases (VBDs) are extensively researched in the context of climate change because they are common and highly responsive to changes in climate conditions.

Epidemics | Floods and Disease Outbreaks

Infectious diseases outbreaks in Europe 1910 - 1999

Souce: Mavrouli M et al., 2022. Infectious Diseases Associated with Hydrometeorological Hazards in Europe: Disaster Risk Reduction in the Context of the Climate Crisis and the Ongoing COVID-19 Pandemic. Int J Environ Res Public Health. doi: 10.3390/ijerph191610206. PMID: 36011854; PMCID: PMC9408126.

Epidemics | Floods and Disease Outbreaks

• Coastal areas suitable for Vibrio could increase by 38.000 km by 2100
• The "Vibrio season" will increase by 1 to 4 months globally
• 1.4 billion people will live in coastal areas suitable for Vibrio by 2069

Coastal areas are more vulnerable to infectious diseases!!!

Trends in population living in areas with suitable conditions for Vibrio

% increment by year and country of coastline affected by conditions suitable for Vibrio

Source: Joaquin Trinanes, Jaime Martinez-Urtaza,2021. Future scenarios of risk of Vibrio infections in a warming planet: a global mapping study, The Lancet Planetary Health, Volume 5, Issue 7, https://doi.org/10.1016/S2542-5196(21)00169-8.

Epidemics | Temperature Changes and Disease Outbreaks

Temperature - Sensitive Infectious Diseases

• Expanding Transmission and geographical spread: Warmer temperatures are extending the transmission seasons and allowing mosquitoes to spread further, reaching previously unaffected areas, especially in northern Europe.
• Enhanced reproduction: Higher temperatures accelerate the reproduction cycle of mosquitoes by shortening the time between egg-laying and the development of eggs.
• Emerging Threats: Increased temperatures due to changing climate conditions make Europe more susceptible to diseases like malaria, dengue fever, and West Nile virus.
• Shorter incubation period: Temperature alters pathogen development rates, shortening incubation periods.

Epidemics | Temperature Changes and Disease Outbreaks

Temperature - Sensitive Infectious Diseases

• Occupational Risks: People working in agriculture, forestry, and emergency services are at higher risk of contracting infectious diseases.
• Higher Risk Populations: The elderly, children, and immunocompromised individuals are more likely to suffer severe effects if infected.
• Rising Sea Temperatures: Warming seas, particularly along the Baltic Sea, are favorable for harmful Vibrio bacteria.
• Health Impacts: These bacteria pose a risk to human health through seafood consumption or exposure to contaminated water.

Epidemics | Temperature Changes and Disease Outbreaks

• Mosquito borne diseases (Malaria, dengue etc) expected to spread into new regions.
• The prevalence of these diseases has significantly risen over the past 80 years as global warming has provided the warmer and more humid conditions they thrive in.
• Eightfold increment of dengue cases during the last 20 years(WHO)

Source: European Centre for Disease Prevention and Control (ECDC)

Epidemics | Temperature Changes and Disease Outbreaks

• Monitoring and Early Warnings:
• Continuous monitoring of climate-related health threats is crucial.
• Early warning systems can effectively reduce heat-related fatalities and prevent disease outbreaks.
• Local Health Engagement:
• There is a gap in local-level health and climate adaptation planning across Europe.
• Health and social care providers must be better integrated into climate adaptation strategies.
• Healthcare Facility Preparedness:
• Strengthen health systems' capacity to cope with extreme weather events and increased patient care needs.
• Improve training for healthcare providers on climate-related health risks.
• Action Steps: It is time to shift from planning to implementation, ensuring effective heat-health action plans, disease surveillance, and adaptation strategies.
• Building Resilience: Ensuring that vulnerable groups are better protected and that healthcare systems are equipped to handle increased demands due to climate change.

Epidemics | Droughts and Disease Outbreaks

• Geographic Spread of Meningitis: The range of meningitis in West Africa has grown, potentially due to environmental shifts linked to land use and regional climate
• Impact of Drought on Mosquito-borne Diseases:
• During Droughts: Mosquito activity declines, leading to fewer infections but a growing pool of susceptible individuals.
• After Droughts: When rains return, the high number of non-immune people can cause outbreaks
• Alternative Effects: In some regions, droughts may reduce mosquito predators, increasing mosquito populations
• Other Risk Factors: Stagnant and contaminated water sources during droughts can further elevate short-term disease risks.
• Long-term Trends: Extended dry periods can reduce mosquito-borne diseases like malaria due to limited breeding conditions.

Source: Aguado T, Bertherat E, Djingarey M, Kandolo D, Kieny MP, Kondé K, LaForce FM, Nelson CB, Perea W, Préziosi MP. Meningococcal meningitis. Nat Rev Microbiol. 2005 Jan;3(1):10-1. doi: 10.1038/nrmicro1070. PMID: 15635779.

Epidemics | Overview

• Climate change could aggravate more than 50% of known human pathogens. This is happening now!!!
• Changes in where species are found geographically are among the most frequent ecological signs of climate change.
• Warming at higher latitudes allows vectors and pathogens to survive winter, aggravating outbreaks by several viruses
• Disruptions to natural habitats from rising temperatures, droughts, heatwaves, wildfires, storms, floods, and changes in land use have been linked to increased contact between humans and disease-causing pathogens.
• Climatic hazards bringing people closer to pathogens

Interactive display of the pathways: https://camilo-mora.github.io/Diseases/

Source: Mora, C.et al. Over half of known human pathogenic diseases can be aggravated by climate change. Nat. Clim. Chang. 12, 869–875 (2022). https://doi.org/10.1038/s41558-022-01426-1

Epidemics | Overview

Chikungunya epidemic in Italy in 2017

• Cases associated with increased temperatures in southern Europe
• Chikungunya resurfaced during the summer of 2017 but remained undetected until September 6.
• By late October 2017, a total of 269 chikungunya cases had been confirmed.
• Chikungunya outbreaks were observed in Italy with a 10-year gap.
• It remains unclear whether sporadic cases or small clusters went unnoticed during the 10-year period between epidemics.

Epidemics | Overview

Dengue virus infections in France

• 65 autochthonous cases spread over nine transmission events by 21 October 2022

• Higher than the number of cases observed from 2010 to 2021
• French health authorities have warned of more expected cases.

Source: Cochet A. et al., 2022 Autochthonous dengue in mainland France, 2022: geographical extension and incidence increase.

Epidemics | Overview

West Nile virus in Europe in 2022 (09/2022)

EU/EEA countries have reported a total of 570 human cases of West Nile Virus (WNV) infection:

• Italy: 383 cases
• Greece: 155 cases
• Romania: 21 cases
• Hungary: 7 cases
• Austria: 2 cases
• Germany: 1 case
• Slovakia: 1 case

A total of 36 deaths from WNV have been recorded in EU/EEA countries:

• Italy: 21 deaths
• Greece: 14 deaths
• Romania: 1 death

Source: European Centre for Disease Prevention and Control

Epidemics | Overview

Two devastating cholera epidemics in 2022 and 2023

Floods in Pakistan 2022

• Health officials have warned of potential large-scale disease outbreaks in Pakistan following severe flooding that displaced millions.
• There has been an increase in cases of diarrhea and malaria after heavy rains left many people stranded and without access to clean water.
• Authorities are concerned that the spread of waterborne diseases post-flood will further strain already overwhelmed health facilities.
• Almost 1,200 people have died as a result of the floods.
• Over 880 clinics have been damaged, according to the World Health Organization (WHO).
• The WHO has allocated $10 million (£8.6 million) for emergency health relief efforts.

Malawi's Worst Cholera Outbreak in History 2023

• 59,376 confirmed cases and 1,772 deaths reported, making it the deadliest cholera outbreak in the country's history.
• Outbreak affected all 29 districts of Malawi, with over 14,000 children among the cases.
• Cyclone Freddy in early 2023 worsened the situation by causing flooding and displacing thousands, increasing the risk of cholera transmission.
• Over 880 health clinics were damaged, and the health system faced immense pressure, with a 3% case fatality rate.
• World Health Organization (WHO) allocated $10 million for emergency health relief efforts to combat the outbreak.

Epidemics | Future projections

Predicted percentage change in deaths in the African endemic region in 2050 and 2070 compared to the baseline/current scenario.

• Climate change is already affecting global human and animal populations, with continuing effects expected.
• Climate models predict that temperature and rainfall changes in Africa may create new habitats for mosquitoes, potentially increasing the spread of diseases like yellow fever.
• Yellow fever deaths could rise by up to 25% by 2050 due to these changes.
• Environmental risks are a key focus of the 2026 WHO Global Strategy to Eliminate Yellow Fever Epidemics.
• By 2030, over 80 million more people in Africa will be at risk of malaria due to climate change.

The next epidemic is not a matter of "IF," but "WHEN!!!"

Epidemics | Management

• Understanding Epidemics: Epidemics are outbreaks of disease that spread rapidly within a population, requiring immediate and coordinated responses.
• Importance of Management: Effective epidemic management minimizes health impacts, economic disruption, and societal strain.
• Global Collaboration: Managing epidemics necessitates collaboration among international organizations, national authorities, and local communities.

Ready and able to detect the next outbreak

Source: WHO, International Health Regulations (2005) – Third edition

Epidemics | Management

CLIMADE Consortium

• Purpose: Focuses on bridging knowledge gaps, improving surveillance tools, and expanding interventions to reduce the impact of diseases and epidemics worsened by climate change.
• Primary Goal: Predict, track, and control diseases and epidemics that are exacerbated by human-induced climate change.
• Focus: Targeting countries most affected by climate change-driven health risks.
• Key Objectives:
• Bring together global experts to address climate-related health challenges.
• Improve monitoring and early warning systems for climate-sensitive diseases.
• Develop and implement interventions to mitigate the health impacts of climate change

Epidemics | Management

CLIMADE Consortium

• Mission: Leverage medical, scientific, and public health expertise from the Global South to build a robust surveillance system for early identification and tracking of pathogens to control outbreaks before they escalate.
• Key Objective: Prevent epidemics from becoming pandemics by detecting and tracking pathogens and their evolution.
• Global Collaboration: Partners include public health agencies, academia, and industry with expertise in pathogen genomics and climate-amplified epidemics.
• Collaborating Agencies: Africa CDC, WHO/PAHO
• Expertise: Decades of experience in genomic surveillance, epidemic response, and public health.
• Long-term Goal: Predict, track, and control diseases and epidemics in the most affected countries, using data to prevent new outbreaks and pandemics amplified by climate change.

Epidemics | Management

WHO’s role in global health Leader on health-related issues:

• Collaborates with nations to expand access to prevention, care, and treatment.
• Sets health priorities and strategic plans.
• Leads health emergency responses globally (e.g., via IHR 2005 framework).

Global Health Leadership & Partnerships:

• Coordinates international efforts on critical health issues.
• Fosters collaborations for joint health action.

Research & Knowledge Generation

• Drives innovation through the R&D Blueprint during outbreaks.
• Promotes rapid development of diagnostics, vaccines, and treatments.

Ethical & Evidence-Based Policy Guidance

• Issues global vaccine position papers based on SAGE recommendations.
• Ensures ethical standards through the WHO Ethics Review Committee

Source: WHO, International Health Regulations (2005) – Third edition

Epidemics | Management

Setting Health Standards

• Establishes global norms and best practices (e.g., VHF management, PPE guidelines).
• Encourages adoption through implementation monitoring and guideline review.

Surveillance & Risk Assessment

• Conducts real-time assessments of global health threats.
• Shares outbreak data via platforms like Disease Outbreak News and Weekly Epidemiological Record.

Capacity Building & Technical Support

• Offers training and resources through OpenWHO MOOCs.
• Strengthens health systems for better epidemic preparedness and response.

Source: WHO, International Health Regulations (2005) – Third edition

Epidemics | Management

Pandemic Preparedness and Response Activities by Phase

Early Outbreak Phase

• Identifying initial cases and confirming outbreaks
• Analyzing and identifying the infectious agent
• Communicating risks and engaging local communities
• Managing and controlling infections in animals
• Implementing isolation, quarantine, and tracing contacts
• Monitoring and evaluating outbreak developments

Pre-Pandemic Phase

• Building emergency supply reserves
• Developing operational continuity strategies
• Training health personnel for emergency response
• Conducting preparedness drills and simulations
• Establishing risk-sharing financial instruments
• Maintaining awareness of potential health threats

Widespread Transmission Phase

• Recognizing and declaring the global health emergency
• Sharing ongoing updates and safety guidelines
• Continuing contact tracing, quarantine, and isolation measures
• Applying population-level distancing strategies
• Distributing medical stockpiles as needed
• Rolling out vaccines or antiviral medications
• Continuously assessing the evolving situation

Epidemics | AI in Epidemics Management

• The term AI was first introduced in 1956 by John McCarthy. He co-organized the Dartmouth Conference, which is considered the founding event of AI as a field. At this conference, he proposed the term "Artificial Intelligence", defining it as "the science and engineering of making intelligent machines".
• Artificial Intelligence (AI) refers to the application of computers and technology to mimic intelligent behavior and human-like critical thinking.

Epidemics | AI in Epidemics Management

🧪 Traditional Methods for Infectious Disease Prediction

• Depend on manually reported data from local public health authorities
• Utilize classical compartmental models (e.g., SEIR) to simulate the spread of diseases
• Set model parameters primarily using historical records or prior epidemiological research

⚠️ Limitations of Traditional Methods

• Struggle to provide real-time updates on epidemic status
• Involve time-consuming and labor-intensive data collection and analysis processes
• Experience delays in data processing, leading to slower predictions and reduced efficiency

Epidemics | AI in Epidemics Management

• Early Outbreak Detection: AI systems can analyze vast datasets—including genomic, epidemiological, environmental, and social data—to identify patterns and anomalies that may indicate the emergence of infectious diseases. This enables timely alerts to health authorities, facilitating swift responses.
• Integration of Diverse Data Sources: Information from various sources (electronic health records, social media, and climate data) AI provides a comprehensive view of potential health threats, improving the accuracy of outbreak predictions.
• Predictive Modeling: Machine learning algorithms can forecast the spread of diseases by analyzing trends and transmission patterns, aiding in resource allocation and intervention strategies.
• Identification of Transmission Routes: AI helps in mapping how diseases spread through populations by analyzing travel patterns, social interactions, and other factors, which is crucial for implementing effective measures.
• Monitoring of Vectors and Sources: Through the analysis of environmental and biological data, AI can identify potential sources and vectors of infections, such as specific animal populations or ecological conditions, allowing for targeted preventive actions.

Epidemics | AI in Epidemics Management

How AI Helps Treat Infectious Diseases

• Optimizes Antimicrobial Therapy: AI assists in selecting the most effective antibiotics or antiviral treatments tailored to each patient's specific condition.
• Personalizes Treatment Plans: By analyzing clinical trial data, patient records, and drug-response models, AI recommends individualized treatment regimens.
• Determines Best Dosage and Duration: Machine learning helps calculate the optimal dosage, duration, and method of delivery (e.g., oral, intravenous) for medications.
• Supports Infection Control Measures: AI contributes to preventing the spread of infection by suggesting targeted control strategies based on patient data and hospital trends.
• Integrates Multiple Data Sources: Combines insights from electronic health records, pharmacokinetics, and pharmacodynamics to improve therapeutic decision-making..

Epidemics | AI in Epidemics Management

Data Sources | Satellite Imagery in Disease Prediction

🛰️ Unique Benefits of Satellite Imagery

• Provides a broad, bird’s-eye view of environmental patterns that traditional methods often miss.
• Offers regular, consistent, and unbiased data for long-term environmental monitoring.
• Ideal for detecting subtle environmental changes that influence disease dynamics.

🌿 Environmental Monitoring for Epidemiology

• Enables tracking of health-related environmental factors, like climate and land cover changes.
• Especially useful in studying vector-borne diseases such as malaria and dengue.
• Detects stagnant water bodies, potential breeding grounds for mosquitoes.
• Helps model disease hotspots by integrating satellite data with AI analysis.

🔄 Climate Change and Disease Spread

• AI can process satellite data to reveal how warming temperatures expand disease-prone zones.
• Detects shifts in ecosystems that allow vectors and pathogens to invade new areas.
• Useful for long-term risk forecasting related to changing global climates.

🌍 Real-World Applications Across Regions

• Sub-Saharan Africa: AI used to predict malaria outbreaks via analysis of land cover, humidity, and temperature.
• South America: Deforestation patterns linked to the emergence of new vector-borne diseases.
• Coastal Regions: Satellite monitoring of water temperatures and algae blooms enabled early warnings for cholera outbreaks across multiple nations.

Epidemics | AI in Epidemics Management

Data Sources | Health Records

📁 The Value of Electronic Health Records (EHRs)

• EHRs digitally store comprehensive patient data: medical history, diagnostics, treatments, and prescriptions.
• They offer a rich source of information for medical research and public health monitoring.
• Traditional analysis methods struggle with the complexity and volume of EHRs.

🤖 AI’s Role in Unlocking EHR Potential

• AI detects early disease indicators by analyzing massive datasets for hidden patterns.
• Capable of handling unstructured data like physician notes, imaging, and genomic info using NLP and deep learning.
• Enables more precise and timely predictions than traditional tools.

🧬 Advancing Personalized Medicine

• AI analyzes individual health records in the context of broader population data.
• Delivers personalized risk profiles and preventive healthcare strategies.
• Supports tailored treatment plans based on genetics, behavior, and environment.

🛑 Outbreak Detection and Public Health Response

• AI flags emerging symptom patterns in local records, indicating potential disease outbreaks.
• Offers early alerts for public health action, limiting disease spread.
• Essential tool in infectious disease surveillance and crisis response.

Epidemics | AI in Epidemics Management

Data Sources | Mobility Data

🌍 What Is Travel and Mobility Data?

• Includes flight records, border crossings, and public transport usage.
• Can also involve granular data from smartphones and wearable devices.
• Helps build a detailed map of population movement patterns.

🔍 Why It's Important for Disease Prediction

• Global travel accelerates disease spread, as seen in recent pandemics.
• Traditional contact tracing methods are too slow and limited in scale.
• AI can process massive mobility datasets to detect and predict disease spread faster.

🤖 AI Enhancing Outbreak Modeling

• Analyzes movement patterns to identify potential secondary outbreak regions.
• Models how infections may spread from an epicenter to other locations.
• Helps forecast high-risk zones before outbreaks become visible.

🚦 Impact on Public Health Strategy

• Informs proactive interventions like targeted screening at travel hubs.
• Supports early allocation of healthcare resources in predicted hotspots.
• Enables faster, more data-driven decision-making during health crises.

Epidemics | AI in Epidemics Management

Data Sources | Genomic and Pathogen Data

🧬 What Are Genomic and Pathogen Data?

• Genomic data: Full genetic sequences of viruses, bacteria, and other pathogens.
• Pathogen data: Biological traits such as life cycle, transmission methods, and drug resistance.

🔎 Why Genomic Data Matter

• Reveal mutations linked to drug resistance or immune evasion.
• Help predict the virulence and spread potential of pathogens.
• Support the design of targeted treatments and vaccines.

🌍 Tracking Disease Origins and Spread

• Phylogenetic analysis traces evolutionary links between pathogen strains.
• Enables tracking of disease migration and identification of outbreak sources.

🤖 AI's Role in Interpreting These Data

• Processes large genomic/pathogen datasets rapidly and accurately.
• Identifies high-risk mutations or traits linked to outbreaks.
• Predicts how pathogens might respond to environmental or medical pressures.

📈 Impact on Public Health

• Enhances outbreak prediction and response strategies.
• Aids in surveillance of emerging strains and drug resistance.
• Informs precision medicine and global health planning.

Epidemics | AI in Epidemics Management

Data Sources | Social Media

📱 What Social Media Contributes

• Platforms like Twitter, Facebook, and others provide vast, real-time health-related data.
• Users often share symptoms, treatments, or reactions before official data becomes available.
• Offers early signals of emerging health trends and outbreaks.

🔍 AI-Powered Disease Detection

• AI analyzes spikes in health-related posts to identify potential outbreaks.
• Detects patterns in location, symptoms, and timing that may suggest disease spread.
• Faster than traditional surveillance methods, offering near-instant alerts. health education efforts.

💬 Sentiment Analysis for Public Health

• AI interprets the emotion behind user posts (e.g., fear, resistance, support).
• Useful in assessing public attitudes toward vaccines, treatments, or health policies.
• Enables targeted communication and intervention strategies.

🌐 Real-World Applications

• A spike in social posts about flu symptoms may indicate a regional outbreak.
• Public concerns about air quality linked to respiratory issues can prompt environmental health checks.
• Tracking misinformation and public hesitancy helps guide health education efforts.

Epidemics | AI in Epidemics Management

Data Sources | Deep Learning

🔄 Neural Network Architectures

• CNNs (Convolutional Neural Networks): Best for analyzing images (e.g., tumor detection, radiology).
• RNNs (Recurrent Neural Networks): Designed for time-sequenced data (e.g., tracking disease progression over time).

📊 Why It Matters

• Does not rely on rigid assumptions like traditional models.
• Learns patterns autonomously from raw data.
• Enhances early detection and enables proactive public health responses.

🧠 What Is Deep Learning?

• A machine learning approach using multi-layered artificial neural networks.
• Mimics the human brain’s structure to process data through interconnected layers.
• Ideal for analyzing complex, high-volume datasets.

🏥 Applications in Healthcare

• Medical Imaging: Detects subtle disease signs in X-rays, MRIs, etc., often surpassing human accuracy.
• Disease Outbreak Prediction: Analyzes diverse data sources (e.g., climate, social media) to foresee outbreaks.

Epidemics | AI in Epidemics Management

Tools| Time Series

📈 What Is Time Series Analysis?

• Statistical method focused on analyzing data points collected at regular time intervals.
• Especially relevant in tracking temporal patterns in infectious diseases.

🦠 Why It Matters in Epidemiology

• Many diseases (e.g., influenza, dengue) show seasonal or cyclical trends.
• Helps predict outbreaks, guiding timely interventions and resource planning.

🧮 Traditional Uses

• Identify recurring cycles or outbreak patterns (e.g., annual flu spikes).
• Support vaccine distribution, staffing, and public health readiness.

🤖 Modern Enhancements with AI & ML

• ARIMA Models: Predict future cases based on past trends; effective for diseases like COVID-19, malaria.
• Fourier Analysis: Detects seasonal trends by breaking data into frequency components.

📊 Impact

• Boosts forecasting accuracy.
• Supports proactive health planning and early warning systems.

Epidemics | AI in Epidemics Management

Tools | Geospatial Analysis

📊 Definition & Importance

• Geospatial analysis involves techniques to analyze spatial data, crucial in public health and epidemiology.
• Focuses on how location and spatial relationships impact disease spread and predictions.

💻 Technological Advancements

• GIS and remote sensing technologies enable precise collection, analysis, and visualization of spatial data.
• These technologies have revolutionized disease prediction and surveillance.

🌍 Integration of Diverse Data Sources

• Satellite imagery provides insights on land use, vegetation, and water bodies to predict vector-borne diseases.
• Population density, transportation networks, and mobility data reveal human movement patterns, key to understanding disease transmission.
• Combining these data sources helps identify disease hotspots, patterns, and risk factors.

🤖 Impact of AI & Machine Learning

• Machine learning techniques enhance geospatial analysis by processing large datasets efficiently.
• Deep learning algorithms analyze satellite images, detecting changes like deforestation or urbanization, linked to increased disease risk.

⏳ Spatial-Temporal Models

• These models capture both spatial distribution and time-based changes in disease dynamics.
• Allow for more accurate predictions of disease outbreaks, enabling proactive public health responses.
• For example, analyzing influenza patterns helps forecast disease spread and determine the best time for interventions.

Epidemics | AI in Epidemics Management

Tools | Reinforcement Learning (RL)

🔍 Definition & Core Principles

• Operates on the idea that agents take actions in an environment to maximize cumulative rewards.
• RL uses a trial-and-error-based learning method, making it suitable for various applications

🏥 Application in Disease Prediction & Management

• An agent (the learning model) interacts with its environment and adjusts based on rewards or penalties.
• This ability to adapt to new data over time offers RL a unique advantage in the evolving field of epidemiology.

🎮 Optimizing Intervention Strategies

• RL models can simulate intervention strategies in disease outbreaks, such as quarantine or resource allocation.
• By running thousands of simulations, RL helps identify the most effective strategies to manage the spread of diseases. This allows public health officials to make informed decisions that lead to better outcomes.

💊 Personalized Medicine

• Analyze real-time data from wearable health devices and continuous patient monitoring providing personalized health recommendations.
• RL identifies patterns in vital signs and medication intake to suggest optimized treatment plans or lifestyle changes.

💉 Drug Discovery & Treatment Optimization

• RL accelerates drug discovery by simulating interactions between drug compounds and biological systems
• Optimize treatment regimens (dosages and drug combinations) to ensure the best therapeutic outcomes while minimizing side effects.

Epidemics | AI in Epidemics Management

Developed Tools

BlueDot Disease Monitoring Platform

• Developed by Canadian health technology company BlueDot
• Designed to evaluate regional public health risks and identify potential disease outbreaks
• Successfully detected early signs of a global threat just weeks before the COVID-19 pandemic

Epidemics | AI in Epidemics Management

Developed Tools

MetaBiota Disease Intelligence Platform

• A U.S.-based company that leverages natural language processing (NLP) and other AI techniques
• Analyzes unstructured data from social media to evaluate infectious disease severity
• In early March 2020, accurately predicted the scale of the COVID-19 outbreak
• Forecast deviated from the actual case count by only around 37,000 cases

Epidemics | AI in Epidemics Management

Developed Tools GLEAM

• GLEAM is a computational modeling framework designed to simulate the spread of infectious diseases on a global scale. It integrates various data sources, including:
• Mobility data: Patterns of human movement, such as commuting and international travel.
• Epidemiological data: Disease-specific parameters like transmission rates and incubation periods.
• Population data: Detailed information about population distribution worldwide.

• By combining these data sources, GLEAM can model how diseases spread geographically and temporally, assisting in forecasting outbreaks and evaluating intervention strategies.

Key Tools and Applications

• GLEAM offers several tools to support researchers and policymakers:
• GLEAMviz: A user-friendly software that allows users to create and visualize epidemic scenarios.
• EpiRisk: A web application that assesses the risk of disease spread based on air travel networks.
• EpiPop: A tool for simulating disease spread in metapopulation networks, exploring temporal evolution.

• These tools have been used to model various outbreaks, including H1N1 influenza, Ebola, Zika, and COVID-19, providing valuable insights into disease dynamics and informing public health responses.

Epidemics | AI in Epidemics Management

Study Cases | Climate Engine – Environmental Monitoring and Prediction Platform

• Purpose:
• Supports agriculture, water resource management, and public health
• Analyzes global climate and environmental data
• Technology Used:
• Satellite remote sensing
• Meteorological data
• Climate models

• Key Function:
• Generates early warnings for mosquito-borne disease outbreaks
• Identifies high-risk areas using AI
• AI Application:
• Environmental and climate forecasting

Epidemics | AI in Epidemics Management

Study Cases | HealthMap – Infectious Disease Monitoring Platform

• Purpose
• Monitors global infectious disease outbreaks in real-time
• Technology Used
• Real-time data collection and analysis
• Methodology
• Cluster similar outbreak events to identify patterns
• Natural Language Processing (NLP): Identify key entities and extract keywords related to outbreaks
• Frequency analysis, Assess disease outbreak risks in specific areas
• Training classification models: Evaluate the link between symptoms and diseases
• Reveal hidden transmission patterns to predict future outbreaks
• AI Application
• Regional epidemic monitoring and prediction

02

Extreme Weather Conditions (EWC)

This chapter summarizes the causes, types, and impacts of extreme weather events (EWEs), linking their growing intensity to climate change. It discusses key examples like the 2003 European heatwave and Storm Daniel (2023), and explores how advanced technologies like AI and satellites improve forecasting, early warnings, and disaster management.

Definitions and understanding of EWC

• An extreme weather event is the occurrence of a value of a weather or climatic variable above (or below) a threshold value near the upper (or lower) ends of the range of observed values of the variable. (IPCC)
• Climatology refers to the statistical distribution of weather data collected over long periods (>30 years).
• Distinctions from normal weather patterns: normal weather patterns embody the average conditions in a regions while EXE signifies rare anomalies that have significant impacts challenging the boundaries of what is considered typical
• These events are not a sign of climate change by itself (as they always existed) but the occurrence and severity of some of these events have increased
• The rising frequency is directly correlated with climate change
• Grasping the patterns and impacts of EWE is crucial for effective disaster preparedness, adaptation strategies and public policy to mitigate adverse effects on health, infrastructure and ecosystems along with socioeconomic impacts

Definitions and understanding of EWC

• Heat & Cold Waves
• Tropical Cyclones/Storms
• Blizzards
• Extreme Precipitation
• Floods
• Drought

Definitions and understanding of EWC

• Extreme weather and climate events encompass heatwaves, cold waves, floods, heavy rainfall, droughts, tornadoes and tropical cyclones, among others.

• Human-induced climate change, exceeding natural climate variability, has led to more frequent and intense extreme events.

• These changes have resulted in widespread adverse impacts, causing significant losses and damages to both nature and people (IPCC, 2022).

Definitions and understanding of EWC

• The global climate is warming, primarily due to increasing greenhouse gas concentrations in the atmosphere (IPCC, 2021).
• Early reports linked the European flood to climate change, noting increased intensity and higher likelihood of future occurrences (Kreienkamp et al., 2021).
• No detailed attribution studies exist yet for the tornado outbreak (warmer winter temperatures attributed to climate change are projected to create conditions that make tornadoes more likely)

A changing climate leads to changes in extreme weather and climate events

Definitions and understanding of EWC

• The 2019 heatwaves were highly unlikely without human-induced climate change:
• Estimated return time in today’s climate: 1 in 400 years
• Estimated return time in pre-industrial climate: 1 in 1000 years
• Though differing in nature and meteorological causes, all events caused severe damage.
• Understanding the impact of climate change on various extremes is vital for:
• Anticipating future risks
• Minimizing loss of life and property damage

A changing climate leads to changes in extreme weather and climate events

Definitions and understanding of EWC

• Climate change influences extremes by increasing their frequency, or by intensifying them (warmer heatwaves, stronger rainfall, longer droughts, etc.)
• Either the curve is flatter (red curve) or the whole distribution shifts in its totality (blue curve)
• Impact: uncommon values in the present climate become common

Normal distribution of maximum daily temperature

Definitions and understanding of EWC

• Extreme events are analyzed by comparing them to the climatological distribution.
• A return time estimate is calculated based on where the event falls in the distribution's tail.
• This method is known as probabilistic attribution:
• Assesses the probability of an event occurring with or without climate change.
• Statistical significance requires a large number of climate model simulations

• Extreme events are also defined/categorised by impact, not just meteorological conditions.
• Impact depends on geography, human actions, land use, and mitigation options.
• Not all high-impact events are tied to rare or extreme meteorological conditions (van der Wiel et al., 2020).

Characterizing an Extreme Event

• Magnitude
• Measures deviation from a baseline or predefined threshold
• Reflects the extremity of the event
• Baselines and thresholds defined by NMHSs at national & sub-national levels
• Stored in an official database for consultation
• Duration
• Individual location: Time difference between event start and end
• Wider scale: Difference between first and last recorded station
• Helps assess persistence and impact of the event
• Extent
• Defined as the geographical area affected
• Can be expressed as a percentage of stations recording the event
• Requires well-distributed station density for accuracy
• If station density is poor, a gridding method should be used

Characterizing an Extreme Event

• Magnitude
• Measures deviation from a baseline or predefined threshold
• Reflects the extremity of the event
• Baselines and thresholds defined by NMHSs at national & sub-national levels
• Stored in an official database for consultation
• Duration
• Individual location: Time difference between event start and end
• Wider scale: Difference between first and last recorded station
• Helps assess persistence and impact of the event
• Extent
• Defined as the geographical area affected
• Can be expressed as a percentage of stations recording the event
• Requires well-distributed station density for accuracy
• If station density is poor, a gridding method should be used

Characterizing an Extreme Event

• Magnitude
• Measures deviation from a baseline or predefined threshold
• Reflects the extremity of the event
• Baselines and thresholds defined by NMHSs at national & sub-national levels
• Stored in an official database for consultation
• Duration
• Individual location: Time difference between event start and end
• Wider scale: Difference between first and last recorded station
• Helps assess persistence and impact of the event
• Extent
• Defined as the geographical area affected
• Can be expressed as a percentage of stations recording the event
• Requires well-distributed station density for accuracy
• If station density is poor, a gridding method should be used

Heat Waves | Introduction

Understanding Heat Waves

• Heat Waves: Prolonged periods of excessively high temperatures. There is no universally accepted definition of heatwave according to World Meteorological Organization (WMO)
• Relative to Normal Climate: Occur in specific regions compared to typical conditions.
• Duration: Can last for days to weeks.
• Impact Areas:
• Public Health: Increased heat-related illnesses.
• Agriculture: Crop damage and reduced yields.
• Infrastructure: Strain on power grids and transportation.

Significance of Heat Wave Research

• Crucial for public safety.
• Aids strategic planning in key sectors:
• Emergency services (preparedness & response).
• Agriculture (crop protection & sustainability).
• Urban development (infrastructure resilience).
• Understanding heat wave patterns helps mitigate adverse effects.

WMO defines that a heatwave can be considered as a period of abnormally hot weather, often defined with reference to a relative temperature threshold, lasting from two days to months

Heat Waves | Mechanisms

• Dominant Driver: Greenhouse gas forcing is the dominant factor in increasing the intensity, frequency, and duration of warm extremes, while decreasing cold extremes.
• Global-Scale Warming: General global warming is influenced by large-scale atmospheric circulation patterns.
• Modulating Factors:
• Soil moisture-evapotranspiration–temperature feedback
• Snow/ice-albedo–temperature feedback
• Local forcings (e.g., land-use change, aerosol concentrations)
• Changes in temperature extremes at regional and local scales can have heterogeneous spatial distributions

Human-induced climate change beyond natural climate variability, including more frequent and intense extreme events, has caused widespread adverse impacts and related losses and damages to nature and people (IPCC, 2022).

Heat Waves | Mechanisms and Principles

• Meteorological Conditions:
• High-pressure systems: Trap warm air, leading to heat waves.
• Reduced cloud cover: Increases solar radiation absorption.
• Atmospheric Circulation Patterns:
• Blocking patterns: Contribute to sustained heat accumulation.
• Influence of climatic factors: El Niño and La Niña impact circulation dynamics.
• Physical Properties of Heat Waves:
• Humidity: Exacerbates heat stress, affecting human health.
• Wind patterns: Influence intensity and duration.
• Land surface characteristics: Affect heat wave intensity and duration

Heat Waves | Past, Current, and Future States

Historical Heat Wave Events

• Significant episodes, such as the 1936 North American Heat Wave and the 2003 European heat wave, exemplify the deadly impact of extreme temperatures and enhanced awareness about climate risks.

Current Trends

• Recent data show a disturbing increase in the frequency and intensity of heat waves worldwide. This trend correlates with rising global temperatures, posing severe risks to human, ecological, and economic systems.

Future Projections

• Climate models predict that as global temperatures rise due to climate change, heat waves will become more common and severe, with regions not previously affected by intense heat being at risk.

With current mitigations, the Earth’s climate is on track to warm 2.7°C above pre-industrial levels by the end of the century.

Heat Waves | Notable Past Events, 2003 in Western Europe

• The surface temperature anomaly over Western Europe in summer 2003, during the period 20 July to 20 August.
• The anomaly is calculated by subtracting from the 2003 measurements the average of observations made during cloudless days in 2000, 2001, 2002 and 2004.
• France: the average summer temperature was approximately 19°C over the period from 1983 to 2002, and increased to 22–23°C in 2003 during this dramatic event.
• Switzerland: The average temperature in the summer of 2003 was approximately 22°C, much higher than the average temperature for the period from 1863 to 2003, which was about 17°C.
• Mass of Alpine glaciers decreased by up to 10% in 2003
• Low river flows and lake levels
• Increased forest fire risk

Source: Mélières, M.-A., & Maréchal, C. (2015). Climate change: Past, present and future. Wiley-Blackwell. https://www.researchgate.net/publication/291338150_Climate_change_Past_Present_and_Future

Heat Waves | Notable Past Events, 2003 in Western Europe

Development of Extreme Temperature Events

• Triggered by persistent anticyclonic patterns (atmospheric blocking)
• These patterns disrupt normal weather flow
• Can persist for days or weeks
• Lead to self-reinforcing, heat-accumulating dynamic processes
• Reduced cloud cover → increased solar radiation
• Stagnant air → less heat dispersion
• Soil moisture depletion → reduced evaporative cooling
• Amplified by thermodynamic changes, including:
• Global warming
• Raises baseline temperatures
• Increases intensity of heat extremes
• Land cover change
• Urbanization and deforestation reduce natural cooling
• Result: Stronger and longer-lasting heatwaves during anticyclonic events

Source: Black, E., Blackburn, M., Harrison, G., Hoskins, B., & Methven, J. (2004). Factors contributing to the summer 2003 European heatwave. Weather, 59, 217–223. https://doi.org/10.1256/wea.74.04

Heat Waves | Notable Past Events, 2010 in Moscow

Health & Mortality Impact:

• ~11,000 excess deaths (non-accidental).
• High mortality in 65+ age group, but younger groups also affected.
• Increased risk of cardiovascular, respiratory, nervous, and genitourinary diseases.
• Moscow’s daily mortality rate (~300 deaths/day) spiked.

Heatwave Frequency:

• Definition: 3+ consecutive days above threshold.
• 2006–2009: Six heatwaves.
• 2010: Two major heatwaves (6 days in June + 44 days in

July-August).

Causes of the Heatwave:

• Record-breaking temperatures, minimal rainfall, and crop loss.
• Peat and forest fires worsened the situation.
• A persistent high-pressure system led to prolonged heat.
• "Blocking pattern" prevented normal weather movement.
• NOAA: Most extreme & longest-lasting blocking since 1920.

Key Statistics:

• Duration: 44 days (6 July – 18 August 2010).
• Temperature:
• 24-hour averages: 24°C to 31°C.
• Max temperature: Above 38°C (First time in history for Moscow).
• Diurnal mean: Above 30°C.
• Monthly mean: Above 26°C.
• Soil surface temperature: Above 60°C.

Heat Waves | Notable Past Events, 2010 in Moscow

Monthly mean air temperature in Moscow (a) in July and (b) in August (Lokoshchenko, M. A. (2012). Catastrophic heat of 2010 in Moscow from data of ground‑based meteorological measurements. Izvestiya, Atmospheric and Oceanic Physics, 48(5), 463‑475. https://doi.org/10.1134/S0001433812050076)

• Average July Temperatures: Typically, Moscow's average temperature in July hovers around 18.4°C. In July 2010, the average soared to 26.0°C, indicating a significant anomaly.​
• Peak Temperature: On July 29, 2010, Moscow recorded its highest temperature ever at 38.2°C, surpassing all previous records in over 130 years of meteorological observations
• Sustained Heat: Throughout July and August 2010, the city experienced prolonged periods where daily mean temperatures exceeded 30°C, a rare and extreme occurrence for the region.​
• Precipitation Levels: July 2010 saw only 7.4 mm of rainfall, a stark contrast to the usual monthly average of approximately 80 mm, exacerbating drought conditions and contributing to widespread wildfires.​

Heat Waves | Impact of Climate Change

Increased Frequency and Severity Analysis shows that with climate change, we can expect not only more frequent heat waves globally but also higher peak temperatures and prolonged duration, significantly impacting our ecosystems. IPCC Findings Reports from the Intergovernmental Panel on Climate Change underscore the critical relationship between rising greenhouse gas emissions and the escalation of heat wave events globally. Data on Temperature Extremes Data illustrates an alarming rise in temperature extremes associated with heat wave events, revealing the growing challenges for health and urban infrastructure resulting from such changes.

In the European region, results propose evidence of a European mean summer lengthening of 2.4 days per decade for the 1950–2012 period

Cold Extremes | Introduction

• A cold wave is a weather event marked by a sudden and significant drop in temperature
• Often leads to dangerous weather conditions, including frost and ice formation.
• Lack of a clear and consistent definition for cold wave events.
• Extreme cold events are driven by a combination of thermodynamics (cold airmass formation) and dynamics (the large-scale circulation, advection)
• Is associated with invasion of very cold air caused by a polar or high-latitude air mass displacement to lower latitudes.
• Cold events are described by temperature, wind, snow and ice, duration, intensity
• Cold waves report decreasing frequency or intensity over most land areas
• Human-induced greenhouse gas forcing is the main driver of the observed changes in cold extremes
• May become more frequent or intense in certain regions for limited periods, such as during stronger cold air movement from polar to lower latitudes.

Droughts | Introduction

Definition of Drought Drought is characterized as a prolonged period of abnormally low precipitation relative to the climatic norm, resulting in water shortages that can affect both natural ecosystems and human activity.

Types of Drought Droughts can be classified into several categories including meteorological, agricultural, hydrological, and socio-economic, each defined by different factors and endpoints for severity.

Importance of Studying Droughts Understanding droughts is critical for developing effective water resource management strategies, safeguarding food security, and planning for climate resilience as they pose significant risks to ecosystems and human societies.

Droughts | Mechanisms and Principles

• Impact on Ecosystems:Drought significantly disrupts ecosystems, leading to reduced primary productivity, altered species composition, and increased vulnerability to pests and diseases, ultimately jeopardizing ecosystem health and stability. • Biodiversity Loss: As drought conditions intensify, many species struggle to adapt to changing habitats and water availability, precipitating the risk of extinction for sensitive flora and fauna, thereby diminishing biodiversity. • Soil Degradation: Continuous drought can impair soil structure, diminish nutrient availability, and foster erosion, which can have downstream effects on land productivity and sustainability, creating a vicious cycle of degradation.

Droughts | Climate Change Impact

• Due to the varied definitions of drought, consistent global observations are limited. No robust global trends in drought conditions could be confirmed.
• Higher temperatures increase evaporation rates, leading to drier conditions and increasing the risk of droughts.
• Climate change is altering precipitation patterns, causing some regions to become drier and others wetter, causing drought.
• Reduced snowpack in mountain regions decreases water availability in spring and summer, exacerbating drought conditions.
• Droughts can create feedback loops that worsen climate change, such as reduced carbon sequestration and increased wildfires.
• Addressing climate change is essential to mitigate the risk of future droughts and protect water resourcesworldwide.

IPCC Special Report on Extremes reports that droughts will intensify in the 21st century in some seasons and areas, due to reduced precipitation and/or increased evapotranspiration

Droughts | Mechanisms and Principles

• Impact on Ecosystems:Drought significantly disrupts ecosystems, leading to reduced primary productivity, altered species composition, and increased vulnerability to pests and diseases, ultimately jeopardizing ecosystem health and stability. • Biodiversity Loss: As drought conditions intensify, many species struggle to adapt to changing habitats and water availability, precipitating the risk of extinction for sensitive flora and fauna, thereby diminishing biodiversity. • Soil Degradation: Continuous drought can impair soil structure, diminish nutrient availability, and foster erosion, which can have downstream effects on land productivity and sustainability, creating a vicious cycle of degradation.

Droughts | Historical Events | 2020–2023 North American drought

• Severe Drought by 2020: The Western U.S. experienced its worst drought since a similar event seven years earlier.
• Historic Dry Conditions (2020–2021): Some experts considered this drought one of the worst in modern history for the region.
• Expanding Dryness (Spring 2021): By late spring, nearly all of California and parts of Nevada faced extreme drought conditions.
• Reservoir Levels Drop (July 2021): After two extremely dry winters, Lake Powell fell to its lowest level since 1969, and Lake Mead hit a critical point, triggering federal water cuts for Arizona and Nevada.
• Record Dry Start to 2022: The first quarter of 2022 was the driest ever recorded in California and Nevada, with June bringing slight relief but overall dryness persisting.
• Lake Mead Rises Slightly (July 2022): Despite an intense monsoon season, Lake Mead saw only a 1% increase in water levels.
• Drought Emergency Declared (Dec 2022): Southern California entered a drought emergency, leading to water restrictions.

Droughts | Current State in USA (02/2025)

• The Palmer Drought Severity Index (PDSI) uses readily available temperature and precipitation data to estimate relative dryness.
• It is a standardized index that generally spans -10 (dry) to +10 (wet).
• t uses temperature data and a physical water balance model, it can capture the basic effect of global warming on drought through changes in potential evapotranspiration

Droughts |The Millennium Drought in southeast Australia (2001–2009)

• Worst Drought on Record: The Millennium Drought was the most severe drought in southeast Australia’s history.
• Historic Low River Murray Flows: Water levels in the River Murray dropped to record lows.
• Minimal Rainfall & Inflows: A combination of extremely low rainfall and the lowest inflows ever recorded intensified the crisis. The drought began in 1997 in southern regions like Victoria during the El Niño event.
• Severe Water Restrictions: 4,000 South Australian irrigators faced extreme water limitations, straining agriculture, horticulture, and regional communities.
• Wetland Disconnections: 33 wetlands were cut off temporarily to conserve water, risking long-term environmental damage.
• Impacts: Major river systems dried up, leading to unprecedented agricultural losses.
• Record-High Salinity: Rising salinity levels harmed ecosystems and threatened water supplies for people and livestock.
• The drought ended: in 2010 with record-high rainfall across much of Australia.

Murray Mouth and Lake Alexandrina 2008

Droughts |The Millennium Drought in southeast Australia (2001–2009)

Droughts | The Path Forward: Towards a Resilient Future

• Managing water resources using holistic ways that consider environmental, social, and economicfactors for more sustainable future.
• Restoring ecosystems can provide essential services, like flood control, water purification, and drought mitigation.
• Developing and implementing new technologies to conserve water, improve prediction and forecasting, and enhance monitoring.
• Creating strong water governance frameworks and regulations to ensure equitable access, allocation, and sustainability of resources.
• Supporting communities so that they can adapt, innovate, and collaborate in facing the challenges of floods and droughts.

Extreme Precipitation | Impacts

• Flooding: Extreme precipitation frequently manifests as floods, which can lead to extensive damage to both built and natural environments. Flooding disrupts ecosystems, displaces communities, and can contaminate water supplies, bringing with it public health concerns. • Infrastructure Damage: Critical infrastructure such as roads, bridges, and drainage systems can suffer catastrophic failure during extreme precipitation events. The costs for repair and the timeline for recovery from such damage can be extensive, highlighting the need for resilient design principles in urban planning. • Ecosystem Effects: The abrupt influx of water into ecosystems can lead to erosion, sediment displacement, and alterations in species habitats, affecting biodiversity. The long-term ecological impacts can be profound, changing local flora and fauna dynamics significantly. • Human Health Risks: Extreme precipitation events elevate the risk of health emergencies stemming from waterborne diseases, mental health crises due to displacement, and injuries related to flooding. The interconnectedness of environmental factors and public health underscores the urgency of effective risk management strategies.

As regards 2020, floods accounted for 51.67% of all incidents (ranking first) 33.71% of the population affected (ranking second after storms), and 29.95% of economic losses in billion USD (ranking second after storms) by all disasters caused by natural hazards worldwide. (Mavrouli et al., 2022)

Extreme Precipitation | Mechanisms

• Extreme rainfall events are often triggered by strong upward air movement and large amounts of moisture in the atmosphere.
• These events are linked to various weather systems like tropical and extratropical cyclones, monsoons, atmospheric rivers, and local storms.
• Global warming increases the atmosphere's ability to hold moisture, leading to more intense rainfall overall.
• Climate patterns like El Niño, the North Atlantic Oscillation (NAO), and others influence extreme rain by changing weather conditions and storm behavior.
• Latent heat released in storms can make them more powerful, sometimes leading to rainfall even heavier than predicted by temperature-based models.
• The ability of the atmosphere to turn moisture into rain isn't fixed and can change, affecting how intense precipitation becomes.
• Warming oceans and changing sea surface temperatures (SSTs) shift rainfall patterns, especially near coasts.
• Land use changes (like deforestation or urbanization) are now shown to influence local heavy rainfall events.
• Warming also affects cloud processes and rainfall characteristics, though the exact impacts on rain efficiency are still uncertain.
• There's growing evidence that human activities, including greenhouse gas emissions and pollution, are influencing extreme precipitation patterns on a continental scale.

Extreme Precipitation | Future Projections

Source: IPCC. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (S. Solomon et al., Eds.). Cambridge University Press. Retrieved from https://archive.ipcc.ch/publications_and_data/ar4/wg1/en/ch11s11-1.html

Extreme Precipitation | Future Projections

Source: IPCC. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (S. Solomon et al., Eds.). Cambridge University Press. Retrieved from https://archive.ipcc.ch/publications_and_data/ar4/wg1/en/ch11s11-1.html

Extreme Precipitation | Historical Events | Volos 2023

• Storm Daniel formed on September 3, 2023, over the Northeastern Aegean Sea, bringing intense rainfall for the next four days. The Peneus basin in Thessaly, Central Greece, experienced an average precipitation of approximately 780mm (47% of its mean annual precipitation

• ~36.7% of the basin has been identified as Areas with Potential Significant Flood Risk according to the Flood Directive 2007/60/EC

• From 3 to 8 September 2023, a depression (named Daniel) affected the Southeastern Mediterranean region.

Source: Dimitriou E, Efstratiadis A, Zotou I, Papadopoulos A, Iliopoulou T, Sakki G-K, Mazi K, Rozos E, Koukouvinos A, Koussis AD, et al. Post-Analysis of Daniel Extreme Flood Event in Thessaly, Central Greece: Practical Lessons and the Value of State-of-the-Art Water-Monitoring Networks. Water. 2024; 16(7):980. https://doi.org/10.3390/w16070980

Extreme Precipitation | Historical Events | Volos 2023

• Storm Overview: Storm Daniel emerged as a significant meteorological phenomenon that affected a wide geographic area, leading to considerable socio-economic and ecological consequences. • Event Timeline: The storm primarily impacted the region from September 3-8, 2023, marking a period of severe weather conditions. • Regional Impact: The storm had devastating effects on the Southeastern Mediterranean, leading to flooding, infrastructure damage, and humanitarian crises.

Source: Dimitriou E, Efstratiadis A, Zotou I, Papadopoulos A, Iliopoulou T, Sakki G-K, Mazi K, Rozos E, Koukouvinos A, Koussis AD, et al. Post-Analysis of Daniel Extreme Flood Event in Thessaly, Central Greece: Practical Lessons and the Value of State-of-the-Art Water-Monitoring Networks. Water. 2024; 16(7):980. https://doi.org/10.3390/w16070980

Extreme Precipitation | Historical Events | Volos 2023

• Upper-Level Barometric Low Formation
• The Storm Daniels was created due to a pronounced upper-level barometric low along the west coast of the Iberian Peninsula creating favorable conditions for storm development.

• Jet Stream Dynamics
• Alterations in the jet stream’s trajectory (bend and weaken) played a significant role, leading to stagnations and enhancing storms intensity.
• Moist Air transportation

• An upper-level ridge begins to strengthen, transporting, moist air from North Africa to Europe, fueling the storm system growth and enhancing precipitation

• Omega Blocking pattern
• This situation favored the establishment of a blocking anticyclone that spread over Western, Central, and Northern Europe.

Extreme Precipitation | Historical Events | Volos 2023

• Storm Daniel & Omega Block Effects
• Omega Block & Extreme Weather
• Persistent omega block caused extreme September temperatures in Western Europe.
• Deep upper lows on either side led to heavy rainfall in Iberian & Balkan Peninsulas.
• The omega blocking patterns are associated with a long-lasting period of stable weather: hot and clear sky in the middle under the ridge of the omega block and rain and clouds in the areas around the troughs on either sides
• The omega block, forming in warm atmospheric and oceanic conditions, caused prolonged extremetemperatures in Western Europe.
• Formation & Early Development
• Storm Daniel formed over Greece as a deep upper-level low.
• Moved southwest into the Ionian & Southern Mediterranean.
• SSTs in the region were +2 to +3°C above climatological averages.
• Intensification & Rainfall
• Warm SSTs fueled Daniel, strengthening the storm.
• Strong easterly flow transported warm, moist air from Aegean & Black Seas.
• Led to low-level convergence, orographic lifting, and heavy rainfall (4–7 Sept).
• Tropical Characteristics & Landfall
• SSTs of 26–28°C and a cut-off trough helped Daniel develop a tropical structure.
• Features included an eye and spiral clouds (observed on 9 Sept).
• Made landfall in Benghazi, Libya, on 10 Sept.
• Storm track estimated from ERA5 mean sea-level pressure analysis.

Extreme Precipitation | Historical Events | Volos 2023

• Spatial distribution of daily rainfall 4 – 7/09/2023
• On Monday, 4 September, the storm initially impacted the southeastern part of the basin.
• It then moved westward, where the highest accumulated rainfall was recorded.
• The storm was significantly less intense in both the southern and northern parts of the basin.

Source: Dimitriou E, Efstratiadis A, Zotou I, Papadopoulos A, Iliopoulou T, Sakki G-K, Mazi K, Rozos E, Koukouvinos A, Koussis AD, et al. Post-Analysis of Daniel Extreme Flood Event in Thessaly, Central Greece: Practical Lessons and the Value of State-of-the-Art Water-Monitoring Networks. Water. 2024; 16(7):980. https://doi.org/10.3390/w16070980

Extreme Precipitation | Historical Events | Volos 2023

• Impact Summary: The extensive impact of Storm Daniel serves as a reminder of the vulnerabilities in the Southeastern Mediterranean area and the need for enhanced preparedness. • Lessons Learned: Insights gained from the storm's behavior and impact can guide improved forecasting and mitigation strategies for future extreme weather events. • Meteorological Considerations: Future studies should focus on the influence of climate change on storm patterns and the implications for resilience planning in affected regions.

Tropical Cyclons| Introduction

• Definition of Tropical Cyclones: Tropical cyclones are intense circular storms formed over warm ocean waters, characterized by low atmospheric pressure, high winds, and heavy rains, that can cause significant damage as they make landfall. • Key Characteristics: Tropical cyclones exhibit three distinct phenomena: powerful winds that can exceed 120 km/h, heavy precipitation leading to flooding, and storm surges which elevate sea levels significantly during a storm's approach (determined by satellite images and in situ measurements). • Formation Zones: These storms typically develop in tropical and subtropical regions where warm, moist air rises from the ocean surface, creating an environment conducive to cyclogenesis. • Historical Tracks of TCs: Visual historical tracking of TCs demonstrates their pathways and intensities, offering insight into their origin, movement, and the areas most vulnerable to impacts.

NOAA definition: “A warm-core non-frontal synoptic-scale cyclone, originating over tropical or subtropical waters, with organized deep convectionand a closed surface wind circulation about a well-defined center.”

Tropical Cyclons| Introduction

• Closed Surface Wind Circulation: Tropical cyclones are characterized by a closed circulation of winds that spirals around a central area of the lowest pressure (eyewall), crucial for their maintenance and intensity. • The Eye and Eyewall: The cyclone's eye is a distinct calm center surrounded by the eyewall, where the highest winds and most intense rainfall occur, forming a critical component of the storm's structure. • Wind Speed Characteristics: Wind speeds within tropical cyclones vary significantly (up to 345, km/h, 2015 Patricia), with maximum sustained winds indicating the storm's intensity, impacting the potential for damage during landfall.

Cyclone Chapala 30/10/2015 from MODIS

Wind is one of the major hazards associated with Tropical Cyclons

Tropical Cyclons| Clasification

Saffir-Simpson Hurricane Wind Scale This scale categorizes tropical cyclones based on their sustained wind speeds, providing a valuable framework for assessing storm strength and potential damage.

Categories 1 to 5 The categories range from 1, indicating minimal damage, to 5, indicating catastrophic conditions, delineating the expected impact based on wind speed.

Tropical Storms and Depressions Understanding the thresholds distinguishing tropical storms and depressions aids in understanding storm evolution and potential for strengthening.

SSHWS Scale Chart Visual representation of the SSHWS provides an accessible reference for understanding the categories and their respective impacts on infrastructure and safety.

Tropical Cyclons| Impacts

• Wind Damage to Infrastructure: High winds in tropical cyclones can lead to extensive damage, including structural failures, downed power lines, and destruction of critical infrastructure. • Storm Surges and Waves: The combination of high winds and low pressure can elevate sea levels drastically, leading to dangerous storm surges that inundate coastal areas. • Impact on Ecosystems and Inland Areas: Tropical cyclones can wreak havoc beyond the coast, causing inland flooding, disrupting ecosystems, and leading to long-term environmental changes. • Damage from TCs: Visual impact assessments detail the extensive damage inflicted by cyclones, drawing attention to the human and environmental costs associated with these phenomena.

Tropical Cyclons| Historical Events |Bhola Cyclone 1970

• Origin and Development of Cyclone Bhola
• Originated from a tropical depression in the southern Bay of Bengal and hit East Pakistan on Nov 12-13, 1970.
• Influenced by remnants of Tropical Storm Nora from the western Pacific.
• Tracked by satellite via the Indian Meteorological Department (IMD).
• Dvorak Technique for cyclone strength was newly introduced only in the U.S., limiting intensity estimates.
• Drifted slowly northward; began to accelerate by November 10 and hit East Pakistan on Nov 12-13, 1970.

• Storm Intensification and Limited Warning
• By November 12, storm moved rapidly north-northeast.
• Ship reports estimated sustained winds of 205 km/h.
• India-Pakistan hostilities affected meteorological communication!!!!
• Warnings were issued by Pakistani meteorological services, but:
• Public response was minimal.
• Many lacked access to shelters or means to evacuate.

Track of the Bhola Cyclone 1970 (Unisys)

Tropical Cyclons| Historical Events |Bhola Cyclone 1970

• Impact of Cyclone Bhola
• Cyclone made landfall with a 10.5 m storm surge.
• Devastated islands including Bhola Island (the largest).
• Entire fishing communities were destroyed.
• Death toll estimated between 300,000–500,000 people.
• Caused US$86 million in damages
• Declared the world’s deadliest tropical cyclone by WMO’s Weather and Climate Extremes Archive.

• Political Consequences and Aftermath
• Government response was delayed; India’s aid offers were rejected.
• Deepened West-East Pakistan tensions.
• Led to civil unrest, violence, and declaration of independence by East Pakistan in March 1971.
• Resulted in the Indo-Pakistani War and creation of Bangladesh by December 16, 1971.
• A natural disaster that triggered geopolitical transformation.

Tropical Cyclons | Overview

• Tropical Cyclone Disasters (Past 50 Years)
• 1,942 disasters linked to tropical cyclones.
• 779,324 deaths recorded.
• $1.4 trillion in economic losses (avg. 43 deaths & $78M damage daily).

• Achievements & Challenges
• Improved tropical cyclone forecasts & warnings, saving thousands of lives.
• More action needed as climate change, rising sea levels, and urban growth increase vulnerabilities.
• WMO shifting focus to impact-based forecasting – predicting what weather will DO rather than just what it will BE.
• foundation of WMO’s Tropical Cyclone Programme (TCP).
• Goal: Ensure early warnings reach the most vulnerable to prevent disasters like Bhola from recurring.
• Prompted UN resolutions for disaster mitigation | laid the foundations of WMO’s Tropical Cyclone Programme. https://trello.com/b/2HvMthoS/tropical-cyclones

Tropical Cyclons | Climate Change Impact

• No clear long-term trends detected in tropical cyclone numbers, intensity, or overall activity.
• Some regions with high-quality data show clearer trends.
• Certain studies report an increase in Category 4 and 5 storm frequency.
• Tropical cyclones are projected to intensify as global temperatures rise.
• Uncertainty remains high for future cyclone frequency in individual basins.
• Precipitation associated with tropical cyclones is expected to increase.
• Coastal flood risk is projected to rise due to:
• Sea level rise
• Increasing cyclone intensity
• High-resolution global models can now simulate tropical cyclones with reasonable accuracy (e.g., Shaevitz et al., 2014).
• Main challenges: high computational cost and the need for many simulation years for statistical confidence.

Introduction to Climate Adaptation

• Definition of climate adaptation: Definition of climate adaptation: Climate adaptation refers to the adjustments made in socio-economic, environmental, and infrastructural systems in response to the actual or expected adverse impacts of climate change. This process involves resilience-building strategies that reduce vulnerability to climate disturbances and enhance the capacity of communities and ecosystems to cope with climatic changes.
• Importance of technology in climate resilience: Technology plays a pivotal role in climate resilience by providing tools and methodologies that enhance monitoring, forecasting, and analysis of climate data. These technologies enable better decision-making and resource allocation to minimize disaster impacts, protect vulnerable communities, and sustain economic growth in the face of changing environmental conditions.
• Overview of deep technologies: Deep technologies, such as AI, IoT, and blockchain, are pivotal in crafting innovative solutions, creating smarter infrastructures and systems that bolster resilience against climate-induced adversities.

Deep learning models are revolutionizing extreme weather prediction by leveraging diverse data sources to accurately forecast events such as cyclones, heatwaves, heavy rainfall, and severe storms

Introduction to Climate Adaptation

Data analysis for climate modeling AI-driven data analysis plays a crucial role in climate modeling by processing complex datasets that originate from satellite images, ground sensors, and historical records. This enhanced data processing power enables researchers to simulate climate system behavior effectively and assess the potential impacts of future climate scenarios under varying conditions.

AI applications in weather predictionArtificial intelligence enhances weather forecasting by analyzing vast datasets to identify patterns and predict weather events with greater accuracy. By leveraging machine learning algorithms, AI provides real-time updates and anticipates extreme weather situations, thereby facilitating proactive measures and timely responses by decision-makers.

Case studies of AI in disaster response Several successful applications of AI in disaster response demonstrate its transformative potential. For instance, AI has been utilized in real-time risk assessment during natural disasters, including optimizing evacuation routes, resource allocation, and logistics management, which is crucial for saving lives and reducing economic losses in affected regions.

Introduction to Climate Adaptation

Weather Insights Weather prediction involves analyzing atmospheric conditions to forecast future weather events. Accurate predictions help individuals and organizations prepare for various weather scenarios.

Significance Weather prediction is crucial for agriculture, disaster management, and daily planning. It plays a vital role in ensuring public safety and optimizing resource allocation.

Fundamental Principles Weather prediction relies on data collection from satellites, radar, and weather stations. Meteorologists use mathematical models to interpret this data and make forecasts.

Introduction to Climate Adaptation | Data Collection Methods

Weather Stations Collect meteorological data at fixed locations. They measure temperature, humidity, wind speed, and precipitation.

Satellites Provide a comprehensive view of weather patterns from space. They monitor cloud cover, sea surface temperatures, and atmospheric conditions.

Radar Systems Detect precipitation and storm systems in real-time. They provide data on the intensity and movement of weather events.

Weather balloons Weather balloons carry instruments into the atmosphere to gather data at various altitudes. They measure temperature, humidity, and pressure, offering insights into upper-atmosphere conditions.

Buoys Buoys are floating devices that collect oceanographic data, such as wave height and water temperature. They are essential for monitoring marine weather and climate conditions.

Introduction to Climate Adaptation | Challenges in Weather Prediction

• Complex Phenomena
• Weather systems involve many interacting variables.
• These interactions create unpredictable outcomes.

• Traditional Limitations
• Conventional models often fail to capture full complexity.
• They rely on historical data. This data may not accurately predict future events.

• Non-linear Dynamics
• Small changes in initial conditions can lead to vastly different outcomes.
• This sensitivity makes long-term predictions challenging.

Introduction to Climate Adaptation | Advanced Computing for Climate Modeling

• Climate Simulations
• High-performance computing enables the simulation of complex climate scenarios with greater accuracy.
• These simulations help predict future climate patterns and assess potential impacts.
• Data Analysis
• Advanced computing techniques facilitate the analysis of vast amounts of climate data.
• This analysis is crucial for understanding trends and making informed predictions.
• Decision Support
• High-performance computing provides essential tools for decision-makers in climate policy.
• It supports the evaluation of different strategies to mitigate climate change effects.

Introduction to Climate Adaptation | Key Technologies for EWC

• The landscape of climate adaptation is significantly shaped by various advanced technologies. He Key technologies that are making a difference:
• Artificial Intelligence (AI): AI algorithms analyze vast amounts of climate data to predict weather patterns, optimize resource allocation, and enhance disaster response strategies.
• Drones: Equipped with sensors and cameras, drones are utilized for real-time monitoring of environmental changes, assessing damage post-disaster, and collecting data from hard-to-reach areas.
• Earth Observation: Satellites and ground-based sensors provide critical data on land use, vegetation health, and atmospheric conditions, aiding in climate modeling and risk assessment.
• Advanced Computing: High-performance computing enables complex climate simulations, allowing researchers to model future climate scenarios and assess potential impacts on ecosystems and communities.
• Internet of Things (IoT): IoT devices facilitate real-time monitoring and data collection, improving decision-making processes in urban planning and environmental management.
• Virtual and Augmented Reality: These technologies create immersive experiences for education and awareness, helping communities visualize the impacts of climate change and engage in proactive measures.

Introduction to Climate Adaptation | Internet of Things (IoT)

• Environmental Monitoring
• IoT devices collect real-time data on environmental conditions.
• This helps assess air quality and humidity levels.
• Create extensive networks of connected sensors that transform raw data into actionable insights

• Early Warning Systems
• IoT networks provide timely alerts for natural disasters.
• They improve community preparedness and response.

• Smart Infrastructure
• IoT technology enhances infrastructure management with data insights.
• This promotes efficiency and sustainability in urban planning.

Introduction to Climate Adaptation | Artificial intelligence for extreme weather events

Artificial Intelligence (AI) is a transformative tool in meteorology

• Supports detection, forecasting, and analysis of extreme weather events
• Enables generation of worst-case event scenarios
• Advances attribution studies and risk communication
• Improves explanation and understanding of extreme weather risks
• ML, RNN, ANN, CNN-LSTM models

Introduction to Climate Adaptation | Role of Deep Learning in Weather Prediction

• Deep Learning Fundamentals:
• Consists of multiple layers of artificial neurons
• Learns complex patterns from raw data without manual feature engineering
• Forecast Accuracy Challenges:
• Weather systems are non-linear and highly complex
• Traditional models (statistical, dynamical, numerical) have limitations in capturing detailed patterns
• Advantages of Deep Learning:
• Revolutionize EWE predictions (cyclones, heatwaves, heavy rainfall, severe storms etc)
• Enhances prediction accuracy, especially for severe weather events
• Analyze complex patterns and relationships in weather data
• Enhance early warning systems for extreme weather events
• Enable proactive mitigation strategies
• Help minimize societal and environmental impacts
• Integrates diverse data sources (satellites, radars, weather stations)
• Offers real-time, comprehensive meteorological insights
• Supports proactive disaster mitigation and public safety efforts

Introduction to Climate Adaptation | Comparison of Traditional and Deep Learning Methods

Introduction to Climate Adaptation | Applications of Drones in EWC

• Data collection: Drones can gather a plethora of atmospheric data, including temperature, humidity, wind speed, and precipitation levels over a defined area (up to 50% increased data collection efficiency). They operate outside of conventional data collection methods, providing hyper-local real-time insights that improve the precision of weather models.

• Real-time monitoring: In times of severe weather, drones are imperative for continuous monitoring of storm systems. Equipped with sensors, they transmit vital information back to meteorological teams, allowing for dynamic assessment of changing weather patterns and helping to inform immediate response actions.

• Disaster response: Post-disaster, drones play a crucial role in assessing damage and aiding recovery efforts. They can survey affected areas, identify hazards, and deliver supplies. Their capability to swiftly navigate disaster zones makes them fundamental for effective emergency management and resource allocation.

Introduction to Climate Adaptation | Applications of Drones in EWC

• Regulatory issues

The deployment of drones, especially in emergency situations, is often hindered by regulatory constraints. These can vary by region and may restrict flight paths, altitudes, and data usage. Navigating these regulations requires collaboration between drone operators and governing bodies to create adaptable frameworks that support technological growth while ensuring safety.

• Technological limitations

Despite their advances, drones remain limited by factors such as battery life, payload capacity, and adverse weather conditions that can inhibit their functionality. Ongoing research and development are needed to overcome these limitations and optimize their performance for various scenarios related to weather prediction.

• Future potential

Looking ahead, the potential for drones in the field of meteorology is vast. Innovations in AI, machine learning, and enhanced communications can boost data accuracy and collection efficiency. With further investment in research and development, drones could form the backbone of next-generation weather forecasting systems and disaster response operations.

Introduction to Climate Adaptation | Satellite Imaging in EWC

• Observing Storm Formation: Satellite imaging allows for real-time observation of storm systems as they develop, offering valuable data for assessing their intensity and potential impact.

• Hurricane and Typhoon Tracking: Advanced satellite technology enables meteorologists to track the path and strength of hurricanes and typhoons, aiding in evacuation and safety planning efforts.

• Evaluating Drought and Flood Conditions: Satellite imagery plays a key role in assessing and mapping drought or flood situations, providing critical insights for resource management and disaster response.

Copernicus Sentinel-3 observes wildfires in Portugal

Mapping the Amazon

Introduction to Climate Adaptation | Satellite Imaging in EWC

• Preparing for Disasters
• Satellite data enhances disaster preparedness by providing early warning systems that inform communities and authorities before extreme weather strikes.

• Real-Time Emergency Response Data
• The integration of satellite imaging with emergency services allows for immediate data access, significantly improving situational awareness during disasters.

• Adapting to Climate Change
• Long-term satellite data analysis supports strategies for climate change adaptation, enabling communities to develop resilient practices against shifting weather pattern

Introduction to Climate Adaptation | Pyramid of Climate Adaptation Strategies

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Epidemics | Group Projects

Study Case 1: Climate and Vector-Borne Disease Prediction Using Climate Engine

• Objective: Use Climate Engine to analyze how climate variables (e.g., temperature, precipitation, vegetation) may contribute to the outbreak of a vector-borne disease (e.g., malaria, dengue).
• Background: Climate conditions can significantly influence the spread of vector-borne diseases. Tools like Climate Engine allow public health researchers to use real-time satellite and climate data to model risk zones.
• Exercise Tasks:
• Choose a vector-borne disease and a geographic region (e.g., dengue in Southeast Asia).
• Use Climate Engine (https://www.climateengine.org/) to:
• Analyze historical temperature and precipitation trends for the selected region.
• Generate NDVI (Normalized Difference Vegetation Index) maps to evaluate vegetation density.
• Identify patterns that could increase the risk of disease outbreaks (e.g., higher rainfall supporting mosquito breeding).
• Write a short report (2–3 pages) including:
• Visual outputs from Climate Engine (screenshots or exported maps)
• A discussion on how AI could integrate these environmental indicators into early warning systems
• Policy recommendations for local health authorities

Study Case 2: Real-Time Epidemic Surveillance with HealthMap

• Objective: Investigate how AI-driven platforms like HealthMap collect and visualize real-time data on infectious diseases using natural language processing and data mining.
• Background: HealthMap uses AI to scan news articles, social media, and official reports to track global disease outbreaks in near real-time.
• Exercise Tasks:
• Visit HealthMap (https://www.healthmap.org/en/)and select a recent outbreak (e.g., measles, COVID-19, or cholera).
• Investigate:
• Which countries or regions are most affected?
• What are the sources of the outbreak data?
• Analyze how the platform uses unstructured data sources (e.g., news, social media) for disease surveillance.
• In a short report (2–3 pages), address:
• Strengths and limitations of AI in real-time epidemic tracking
• How data quality and bias affect HealthMap's outputs
• Suggestions for improving HealthMap’s AI capabilities in detecting underreported outbreaks

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EWC | Group Projects

Study Case | Catraia Wildfire (south Portugal, July 2012)

• Burned area: ~250 km2
• 18-21 July 2012
• Climate Data Records of Land Surface variables allow detailed inspection of the anomalies and their spatial extent
• Use EUMETSAT database to:
• [1 - Load and browse LSA SAF Fire Risk Map v2 data]
• [2 - Visualise the LSA SAF Fire Risk Map v2 data]
• [3 - Load and browse LSA SAF P2000 data]
• [4 - Visualise the LSA SAF P2000 data]
• [5 - Load and browse LSA SAF P2000a data]
• [6 - Visualise the LSA SAF P2000a data]

Study Case | Catraia Wildfire (south Portugal, July 2012)

• Wildfires have clear signatures in most land surface variables
• Northwest Portugal has denser vegetation. Most of the south vegetation desiccates over the summer (savanna-like)
• FVC pronounced negative anomaly (~-0.4)
• LST pronounced positive anomaly (up to 10 °C) within the scar.

• Overall positive anomaly

Study Case | Workflow

• Identify and describe a past wildfire
• Download the 10-daily Normalised Difference Vegetation Index (NDVI) during the last 10 or 20 (if available) years
• Calculate NDVI anomaly for the last decade (or 20years) for month that the event took place
• Visualize the NDVI anomaly (as shown in previous slide)
• Download LST data during the last 10 or 20 (if available) years and repeat steps 3 and 4 for LST
• Discuss and explain the results for the examined month and focus on LST anomalies 3 days before the event
• Download and plot FRMv2 product during the last 7 days before the event and discuss the results
• Discuss the association between FRMv2, NDVI and LST anomalies. Propose prevention and mitigation measures
• What could we do to mitigate or even prevent fire impacts?