Editing IPCC:AR6/WGII/Chapter-2 (section)

=== 2.6.4 Adaptation for Increased Risk of Disease ===

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Low-probability events can have a very high impact (e.g., the transmission of SARS-CoV-2 from wild animals to humans, causing the Covid-19 pandemic ). A robust disease risk reduction policy would include utilising One Biosecurity ( [[#Meyerson--2002|Meyerson and Reaser, 2002]] ; [[#Hulme--2020|Hulme, 2020]] ; [[#MacLeod--2020|MacLeod and Spence, 2020]] ) or One Health ( [[#Monath--2010|Monath et al., 2010]] ; [[#Deem--2018|Deem et al., 2018]] ; [[#Destoumieux-Garzón--2018|Destoumieux-Garzón et al., 2018]] ; [[#Zinsstag--2018|Zinsstag et al., 2018]] ) approaches with actions to reduce disease risk across multiple sectors and from a variety of anthropogenic drivers, including climate change, even if there is high uncertainty in projected risk (see Cross-Chapter Boxes ILLNESS in this chapter, COVID in [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] and DEEP in Chapter 17). [[#Kraemer--2019|Kraemer et al. (2019)]] found that vector importation was a key risk factor and that the focus should be on preventing the introduction of invasive species. Furthermore, many neglected tropical diseases (NTDs) are also VBDs, and the UN SDG of good health and well-being explicitly calls for increased control and intervention with a focus on emergency preparedness and response ( [[#Stensgaard--2019a|Stensgaard et al., 2019a]] ).

Online tools are being developed to warn conservation biologists when species of conservation concern are at a greater risk of disease outbreaks due to environmental changes, for example, for Hawaiian honeycreepers and avian malaria ( [[#Berio%20Fortini--2020|Berio Fortini et al., 2020]] ) and coral diseases ( [[#Caldwell--2016|Caldwell et al., 2016]] ). Forecasting models to warn of human disease outbreaks like malaria and dengue are also now available, with findings that multiple-model ensemble forecasts outperform individual models ( [[#Lowe--2013|Lowe et al., 2013]] ; [[#Lowe--2014|Lowe et al., 2014]] ; [[#Lowe--2018|Lowe et al., 2018]] ; [[#Zhai--2018|Zhai et al., 2018]] ; [[#Johansson--2019|Johansson et al., 2019]] ; [[#Tompkins--2019|Tompkins et al., 2019]] ; [[#Muñoz--2020|Muñoz et al., 2020]] ; [[#Colón-González--2021|Colón-González et al., 2021]] ; [[#Petrova--2021|Petrova et al., 2021]] ). Improving VBD and NTD public health responses will require multi-disciplinary teams capable of interpreting, analysing, and synthesising diverse components of complex ecosystem-based studies for effective intervention ( [[#Mills--2010|Mills et al., 2010]] ; [[#Rubin--2014|Rubin et al., 2014]] ; [[#Valenzuela--2018|Valenzuela and Aksoy, 2018]] ), broad epidemiological and entomological surveillance ( [[#Depaquit--2010|Depaquit et al., 2010]] ; [[#Lindgren--2012|Lindgren et al., 2012]] ; [[#Springer--2016|Springer et al., 2016]] ) as well as community-based disease control programmes that build local capacity ( [[#Andersson--2015|Andersson et al., 2015]] ; [[#Jones--2020b|Jones et al., 2020b]] ).

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'''Cross-Chapter Box ILLNESS | Infectious Diseases, Biodiversity and Climate: Serious Risks Posed by Vector- and Water-Borne Diseases'''
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Authors: Marie-Fanny Racault (UK/France, Chapter 3), Stavana E. Strutz (USA, Chapter 2), Camille Parmesan (France/UK/USA, Chapter 2), Rita Adrian (Germany, Chapter 2), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Sarah Cooley (USA, Chapter 3), Meghnath Dhimal (Nepal), Luis E. Escobar (Guatemala/USA), Adugna Gemeda (Ethiopia, Chapter 9), Nathalie Jeanne Marie Hilmi (Monaco/France, Chapter 18), Salvador E. Lluch-Cota (Mexico, Chapter 5), Erin Mordecai (USA), Gretta Pecl (Australia, Chapter 11), A. Townsend Peterson (USA), Joacim Rocklöv (Germany/Sweden), Marina Romanello (UK/Argentina/Italy), David Schoeman (Australia, Chapter 3), Jan C. Semenza (Italy, Chapter 7), Maria Cristina Tirado (USA/Spain, Chapter 7), Gautam Hirak Talukdar (India, Chapter 2), Yongyut Trisurat (Thailand, Chapter 2)

''Climate change is altering the life cycles of many pathogenic organisms and changing the risk of transmission of vector- and water-borne infectious diseases to humans ('' high confidence ''). The rearrangement and emergence of some diseases are already observed in temperate-zone and high-elevation areas and coastal areas ('' medium confidence to high confidence, depending upon region ''). Shifts in the geographic and seasonal range suitability of pathogens and vectors are related to climatic-impact drivers (warming, extreme events, precipitation and humidity) ('' very high confidence ''), but there are substantial non-climatic drivers (LUC, wildlife exploitation, habitat degradation, public health and socioeconomic conditions) that affect the attribution of the overall impacts on the prevalence or severity of some vector- and water-borne infectious diseases over recent decades ('' high confidence ''). Adaptation options that involve sustained and rapid surveillance systems as well as the preservation and restoration of natural habitats with their associated higher levels of biodiversity, both marine and terrestrial, will be key to reducing the risk of epidemics and the large-scale transmission of diseases ('' medium confidence '').''

Since AR5, further evidence is showing that climate-related changes in the geographic and seasonal range suitability of pathogens and vectors and the prevalence or new emergence of vector- and water-borne infectious diseases have continued across many regions worldwide and are sustained over decadal timescales ( ''low confidence to high confidence,'' depending upon region)(Sections 2.4.2.5, 3.5.5.3, 7.2, 7.3, 9.10.1.2.1) ( [[#Harvell--2009|Harvell et al., 2009]] ; [[#Garrett--2013|Garrett et al., 2013]] ; [[#Burge--2014|Burge et al., 2014]] ; [[#Guzman--2015|Guzman and Harris, 2015]] ; [[#Baker-Austin--2018|Baker-Austin et al., 2018]] ; [[#Watts--2019|Watts et al., 2019]] ; [[#Semenza--2020|Semenza, 2020]] ; [[#Watts--2021|Watts et al., 2021]] ). Ecosystem-mediated infectious diseases at risk of increase from climate change include water-borne diseases associated with pathogenic ''Vibrio'' spp. (e.g., those causing cholera and vibriosis) and harmful algal blooms (e.g., ciguatera fish poisoning) (Sections 3.5, 5.12, Table SM3.3) ( [[#Bindoff--2019|Bindoff et al., 2019]] ); ( [[#Baker-Austin--2013|Baker-Austin et al., 2013]] ; [[#Levy--2015|Levy, 2015]] ; [[#Trtanj--2016|Trtanj et al., 2016]] ; [[#Ebi--2017|Ebi et al., 2017]] ; [[#Mantzouki--2018|Mantzouki et al., 2018]] ; [[#Nichols--2018|Nichols et al., 2018]] ), and VBDs associated with arthropods (e.g., malaria, dengue, chikungunya, Zika virus, West Nile virus and Lyme disease), helminths (e.g., schistosomiasis) and zoonotic diseases associated with cattle and wildlife (e.g., leptospirosis) ( ''low confidence to very high confidence'' , depending upon disease and region) (Sections 2.4.2.7, 3.5, 7.2, 7.3, 9.10.1.1.1, 13.7.1.2, 14.4.6, Cross-Chapter Box COVID in Chapter 7; Table Cross-Chapter Box ILLNESS.1) ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Ebi--2021|Ebi et al., 2021]] ).

The attribution of observed changes in disease incidence, partly or fully, to climatic-impact drivers remains challenging because of the difficulty of accurately capturing the contributions of multiple, interacting and often nonlinear underlying responses of host, pathogen and vector, which can be influenced further by non-climate stressors and the long history of anthropogenic disturbance. Disease emergence in new areas requires independent drivers to coincide (i.e., increasing climate suitability for pathogen or vector survival and competence/capacity, and introduction of the pathogen, that is often via the mobility of human populations). Furthermore, the extent to which changes in ecosystem-mediated diseases impact human health is highly dependent on local socioeconomic status, sanitation, medical systems and practices ( [[#2.4.2.5|Section 2.4.2.5]] , Figure FAQ2.3.1) ( [[#Gething--2010|Gething et al., 2010]] ; [[#Lindgren--2012|Lindgren et al., 2012]] ; [[#Mordecai--2013|Mordecai et al., 2013]] ; [[#Liu-Helmersson--2014|Liu-Helmersson et al., 2014]] ; [[#Bhatt--2015|Bhatt et al., 2015]] ; [[#Morin--2015|Morin et al., 2015]] ; [[#Ryan--2015|Ryan et al., 2015]] ; [[#Wesolowski--2015|Wesolowski et al., 2015]] ; [[#Stanaway--2016|Stanaway et al., 2016]] ; [[#Yamana--2016|Yamana et al., 2016]] ; [[#Mordecai--2017|Mordecai et al., 2017]] ; [[#Tesla--2018|Tesla et al., 2018]] ; [[#Ryan--2019|Ryan et al., 2019]] ; [[#Shah--2019|Shah et al., 2019]] ; [[#Iwamura--2020|Iwamura et al., 2020]] ; [[#Mordecai--2020|Mordecai et al., 2020]] ; [[#Colón-González--2021|Colón-González et al., 2021]] ; [[#Ryan--2021|Ryan et al., 2021]] ).Thus, risk reduction is more effective when links between climate change, ecosystem change, health and adaptation are considered concurrently (Sections 2.4, 3.5.3, 7.2, 7.3, 4.3.3, 6.2.2.3, Table SM2.1).

'''Table Cross-Chapter Box ILLNESS.1 |''' Observed climate change impacts on cholera, dengue and malaria incidence. (1) Cholera: endemicity based on ( [[#Ali--2015|Ali et al., 2015]] ). Changes (2003–2018) in suitability for coastal ''Vibrio cholerae'' estimated from model observations driven by sea-surface temperature (SST) and chlorophyll ''a'' (CHL) concentration ( [[#Escobar--2015|Escobar et al., 2015]] ; [[#Watts--2019|Watts et al., 2019]] ); vulnerabilities based on Sigudu et al. (2015) [[#Agtini--2005|Agtini et al. (2005)]] and [[#Sack--2003|Sack et al. (2003)]] . (2) Dengue: endemicity based on [[#Guzman--2015|Guzman and Harris (2015)]] . (3) Malaria: endemicity based on [[#Phillips--2017|Phillips et al. (2017)]] and the WHO Global Malaria Programme. Impacts of climate change on diseases and their vectors are most evident at the margins of current distributions. However, it is difficult to implicate climate change in areas with extensive existing transmission and vector/pathogen abundance, and it is particularly difficult to distinguish from concurrent directional trends in disease control, changes in land use, water access, socioeconomic and public health conditions. As a result, while many studies indicate increasing climate suitability of some areas for cholera, and changes in disease incidence for dengue and malaria, the degree to which these changes can be attributed to climate change remains challenging. Uncertainty statements for malaria and dengue reflect the degree to which observed trends in disease incidence can be related to observed climate change in the given region. For cholera, confidence statements reflect the degree to which observed trends in disease or pathogen incidence and coastal area suitability for outbreaks can be linked to observed climate change drivers in the given region. Acronyms: ONI (Oceanic Niño Index), Tmin (minimum temperature), SPI (Standardised Precipitation Index), LST (land surface temperature). Full references for this table can be found in Table SM2.6.

{| class="wikitable"
|-
!
! '''Cholera'''

! '''Dengue'''

! '''Malaria'''

|-
| colspan="4"| ''Africa''

|-
| Endemicity

| Endemic

| Endemic in sub-Saharan Africa but not South Africa

| Endemic

|-
| Climate drivers

| Disease incidence: northeast Africa, Central Africa and Madagascar: rainfall ( ''medium confidence'' )

Southeast Africa: rainfall, LST, SST, Plankton ( ''medium confidence'' )

eastern South Africa: SST, CHL

( ''low confidence'' due to ''limited evidence'' )

West Africa: rainfall (floods), LST, SST ( ''medium confidence'' )

|
| West Africa: temperature ( ''medium confidence'' )

East Africa: temperature ( ''medium confidence'' )

|-
| Direction of Change

| Area of coastline suitable for outbreak: northandwest Africa: increase ( ''low confidence'' )

Central and East Africa: no change ( ''low confidence'' )

South Africa: decrease ( ''low confidence'' )

| Potentially expanding

( ''low confidence'' )

Dengue and ''A. aegypti'' present but underdetected in climatically suitable areas

| East Africa: upward shift and increase in malaria and ''Anopheles'' spp. in highland areas ( ''medium confidence'' )

Widespread decreases due to malaria control ( ''medium confidence'' ) and warming climate ( ''low confidence'' )

|-
| Vulnerabilities

| Eastern South Africa: women of all ages more affected than men by outbreaks

|
|-
| colspan="4"| ''Asia''

|-
| Endemicity

| Endemic

| Endemic in South Asia, Southeast Asia and East Asia

| Endemic in South Asia, Southeast Asia, partially endemic in East Asia

|-
| Climate drivers

| Disease incidence: East Asia: SST, CHL, SLR ( ''medium confidence'' )

South Asia: SST, CHL, LST, rainfall (floods) ( ''high confidence'' )

| South Asia: rainfall, temperature, Humidity ''(medium confidence)''

Southeast Asia: rainfall, temperature ( ''medium confidence)''

East Asia: rainfall, temperature, Typhoons ( ''low confidence'' )

| South Asia: rainfall, temperature ( ''medium confidence'' )

Southeast Asia: rainfall, temperature ( ''medium confidence'' )

|-
| Direction of Change

| Area of coastline suitable for outbreak: increase ( ''low confidence'' )

| Southeast Asia: increase ( ''low confidence'' )

South Asia: increase ( ''medium confidence'' )

East Asia: increase ( ''low confidence'' )

| South Asia: increase ( ''medium confidence'' )

|-
| Vulnerabilities

| Southeast Asia: infants (&lt;9 years) with highest incidences of cholera

South Asia: older children and young adults (aged 16–20 years) more frequently reported with cholera than non-cholera diarrhoea

|
|-
| colspan="4"| ''Australasia''

|-
| Endemicity

| Not endemic

| Partially endemic in northern Australia

| Not endemic

|-
| Climate drivers

| No evidence for disease incidence

| Rainfall, temperature ( ''low confidence'' )

|
|-
| Direction of Change

| Area of coastline suitable for outbreak: no change ( ''low confidence'' )

| Increase in sporadic outbreaks due to climate change ( ''low confidence'' )

| No change

|-
| colspan="4"| ''Central America''

|-
| Endemicity

| Not endemic

| Endemic

| Partially endemic

|-
| Climate drivers

| No evidence for disease incidence

| ONI, SST, Tmin, temperature, rainfall, drought ( ''low confidence'' )

|
|-
| Direction of Change

| Areas of coastline suitable for outbreak: decrease ( ''low confidence)''

| Increasing due to climate ( ''low confidence'' )

Upward expansion of ''A. aegypti'' ( ''low confidence'' )

| Overall decrease not linked to climate change. Focal increases due to human activities.

|-
| colspan="4"| ''South America''

|-
| Endemicity

| Epidemic

| Endemic in all regions except southern South America

| Endemic

|-
| Climate drivers

| Abundance of coastal ''V. cholerae'' : northwestern South America:

SST, Plankton ( ''low confidence'' )

| Temperature, precipitation, drought

| Northern South America: temperature ( ''low confidence'' )

northern and southeastern South America: Tmax, Tmin, humidity ( ''low confidence'' )

|-
| Direction of Change

| Area of coastline suitable for outbreak: no change ( ''low confidence'' )

| Increasing due to urbanisation and decreased vector control programmes, not strongly linked to climate

| Higher elevation regions: Increase ( ''low confidence'' )

|-
| colspan="4"| ''Europe''

|-
| Endemicity

| Not endemic

| Southern Europe: focal outbreaks

| Not endemic

|-
| Climate drivers

| No evidence for disease incidence

Abundance of coastal ''V. cholerae'' : northern Europe: SST, Plankton ( ''medium confidence'' )

|
|-
| Direction of Change

| Area of coastline suitable for outbreak: increase ( ''low confidence'' )

| Mediterranean regions of southern Europe: outbreaks ( ''low confidence'' )

| No change

|-
| colspan="4"| ''North America''

|-
| Endemicity

| Not endemic

| Partially endemic in southern North America

| Not endemic

|-
| Climate drivers

| No evidence for disease incidence

Abundance of coastal V. cholerae: eastern North America: SST ( ''low confidence'' due to ''limited evidence'' )

| Winter Tmin ( ''low confidence'' )

|
|-
| Direction of Change

| Area of coastline suitable for outbreak: increase ( ''low confidence'' )

| Declining

| No change

|-
| colspan="4"| ''Small Islands''

|-
| Endemicity

| Epidemic

| Endemic on many small islands in the Tropics

| Endemic on many small islands in the Tropics

|-
| Climate drivers

| Disease incidence: Caribbean: SST, LST, rainfall ( ''low'' to ''medium confidence'' )

| Caribbean: SPI, Tmin ( ''low confidence'' )

|
|-
| Direction of Change

| Area of coastline suitable for outbreak: Caribbean and Pacific small islands: Decrease ( ''low confidence'' )

| Increasing ( ''low confidence'' )

| Decrease in Caribbean not linked to climate

|}

'''Observed and projected changes'''
In aquatic systems, at least 30 human pathogens with water infection routes (freshwater and marine) are affected by climate change ( [[IPCC:Wg2:Chapter:Chapter-3#3.5.3|Section 3.5.3]] , Table SM3.G) ( [[#Nichols--2018|Nichols et al., 2018]] ) . Warming, acidification, hypoxia, SLR and increases in extreme weather and climate events (e.g., MHWs, storm surges, flooding and drought), which are projected to intensify in the 21st century ( ''high confidence'' ) ( [[#IPCC--2021b|IPCC, 2021b]] ), are driving species’ geographic range shifts and global rearrangements in the location and extent of areas with suitable conditions for many harmful pathogens, including viruses, bacteria, algae, protozoa and helminths ( ''high confidence'' ) (Sections 2.3, 2.4.2.7, 3.5.5.3) ( [[#Trtanj--2016|Trtanj et al., 2016]] ; [[#Ebi--2017|Ebi et al., 2017]] ; [[#Manning--2017|Manning and Nobles, 2017]] ; [[#Pecl--2017|Pecl et al., 2017]] ; [[#Mantzouki--2018|Mantzouki et al., 2018]] ; [[#Nichols--2018|Nichols et al., 2018]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ; [[#IPCC--2019b|IPCC, 2019b]] ; [[#Kubickova--2019|Kubickova et al., 2019]] ; [[#Watts--2019|Watts et al., 2019]] ; [[#Watts--2020|Watts et al., 2020]] ; [[#Watts--2021|Watts et al., 2021]] ).

The incidence of cholera and ''Vibrio'' -related disease outbreaks have been shown to originate primarily in coastal regions, and then spread inland via human transportation. Our understanding of the impacts of climate-change drivers on the dynamics of ''Vibrio'' pathogens and related infections has been strengthened through improved observations from long-term monitoring programmes ( [[#Vezzulli--2016|Vezzulli et al., 2016]] ) and statistical modelling supported by large-scale and high-resolution satellite observations ( ''high confidence'' ) (( [[#Baker-Austin--2013|Baker-Austin et al., 2013]] ; [[#Escobar--2015|Escobar et al., 2015]] ; [[#Jutla--2015|Jutla et al., 2015]] ; [[#Martinez--2017|Martinez et al., 2017]] ; [[#Semenza--2017|Semenza et al., 2017]] ; [[#Racault--2019|Racault et al., 2019]] ; [[#Campbell--2020|Campbell et al., 2020]] ).

The poleward expansion of the distribution of ''Vibrio'' spp. has increased the risk of vibriosis outbreaks from multiple species in northern latitudes. Specifically, the coastal area suitable for ''Vibrio'' infections in the past 5 years has increased by 50.6% compared with a 1980s baseline at latitudes of 40°N–70°N; in the Baltic region, the highest-risk season has been extended by 6.5 weeks over the same periods ( [[#Watts--2021|Watts et al., 2021]] ). Already, studies have noted greater numbers of ''Vibrio'' -related human infections and, most notably, disease outbreaks linked to extreme weather events such as heat waves in temperate regions such as Northern Europe ( [[#Baker-Austin--2013|Baker-Austin et al., 2013]] ; [[#Baker-Austin--2017|Baker-Austin et al., 2017]] ; [[#Baker-Austin--2018|Baker-Austin et al., 2018]] ) ( ''high confidence'' ). By the end of the 21st century, under RCP6.0, the number of months of risk of ''Vibrio'' illness is projected to increase in Chesapeake Bay by 10.4 ± 2.4%, with largest increases during May and September, which are the months of strong recreational and occupational use, compared to a 1985–2000 baseline ( [[#Jacobs--2015|Jacobs et al., 2015]] ; [[#Davis--2019a|Davis et al., 2019a]] ). In the Gulf of Alaska, the coastal area suitable for ''Vibrio'' spp. is projected to increase on average by 58 ± 17.2% in summer under RCP6.0 by the 2090s, compared to a 1971–2000 baseline ( ''low to medium confidence'' ) ( [[#Jacobs--2015|Jacobs et al., 2015]] ).

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Cross-Chapter Box ILLNESS

The coastal area suitable for ''V. cholerae'' (the causative agent for cholera) has increased by 9.9% globally compared to a 2000s baseline ( [[#Escobar--2015|Escobar et al., 2015]] ; [[#Watts--2019|Watts et al., 2019]] ). However, in the case of ''V. cholerae'' and cholera disease incidence, climate change is more difficult to implicate because outbreaks require independent drivers to coincide (i.e., introduction of pathogenic strains of ''V. cholerae'' in the waters via mobility of human-infected populations) and observed trends are difficult to separate from concurrent directional trends in disease control, sanitation and water access, socioeconomic and public health conditions.

On land, increased global connectivity and mobility, unsustainable exploitation of wild areas and species and land conversion (agricultural expansion, intensification of farming, deforestation and infrastructure development), together with climate change-driven range shifts of species and human migration (Cross-Chapter Box MOVING PLATE in Chapter 5), have modified the interfaces between people and natural systems ( [[#IPBES--2018a|IPBES, 2018a]] ). Climate-driven increase in temperature, the frequency and intensity of extreme events as well as changes in precipitation and relative humidity have provided opportunities for rearrangements of disease geography and seasonality, and emergence into new areas ( ''high confidence'' ) ( [[#2.4.2.7|Section 2.4.2.7]] ). In particular, malaria has expanded into higher elevations in recent decades and, although attributing this to climate change remains challenging ( [[#Hay--2002|Hay et al., 2002]] ; [[#Pascual--2006|Pascual et al., 2006]] ; [[#Alonso--2011|Alonso et al., 2011]] ; [[#Campbell--2019c|Campbell et al., 2019c]] ), evidence that the elevational distribution of malaria has tracked warmer temperatures is compelling for some regions ( [[#Siraj--2014|Siraj et al., 2014]] ). Models based on both empirical relationships between temperature and the ''Anopheles'' mosquito and ''Plasmodium'' parasite traits that drive transmission ( [[#Mordecai--2013|Mordecai et al., 2013]] ; [[#Yamana--2013|Yamana and Eltahir, 2013]] ; [[#Johnson--2015|Johnson et al., 2015]] ) and existing mosquito distributions ( [[#Peterson--2009|Peterson, 2009]] ) predict that warming will increase the risk of malaria in highland East Africa and Southern Africa, while decreasing the risk in some lowland areas of Africa, as temperatures exceed the thermal optimum and upper thermal limit for transmission ( [[#Peterson--2009|Peterson, 2009]] ; [[#Yamana--2013|Yamana and Eltahir, 2013]] ; [[#Ryan--2015|Ryan et al., 2015]] ; [[#Watts--2021|Watts et al., 2021]] ).

In contrast to malaria, dengue has expanded globally since 1990, particularly in Latin America and the Caribbean, South Asia and sub-Saharan Africa ( [[#Stanaway--2016|Stanaway et al., 2016]] ). While urbanisation, changes in vector control and human mobility play roles in this expansion ( [[#Gubler--2002|Gubler, 2002]] ; [[#Åström--2012|Åström et al., 2012]] ; [[#Wesolowski--2015|Wesolowski et al., 2015]] ), the physiological suitability of temperatures for dengue transmission is also expected to have increased as climates have warmed ( [[#Colón-González--2013|Colón-González et al., 2013]] ; [[#Liu-Helmersson--2014|Liu-Helmersson et al., 2014]] ; [[#Mordecai--2017|Mordecai et al., 2017]] ; [[#Rocklöv--2019|Rocklöv and Tozan, 2019]] ). Models predict that dengue transmission risk will expand across many tropical, subtropical and seasonal temperate environments with future warming ( [[#Åström--2012|Åström et al., 2012]] ; [[#Colón-González--2013|Colón-González et al., 2013]] ; [[#Ryan--2019|Ryan et al., 2019]] ; [[#Iwamura--2020|Iwamura et al., 2020]] ; [[#Watts--2021|Watts et al., 2021]] )).

'''Adaptation options'''
During the 21st century, public health adaptation measures (Figure Cross-Chapter Box ILLNESS.2) have been put in place in attempts to control or eradicate a variety of infectious diseases by improving surveillance and early detection systems; constraining pathogen, vector, and/or reservoir host distributions and abundances; reducing the likelihood of transmission to humans; and improving treatment and vaccination programmes and strategies ( ''robust evidence'' , ''high agreement'' ) ( [[#Chinain--2014|Chinain et al., 2014]] ; [[#Adrian--2016|Adrian et al., 2016]] ; [[#Friedman--2017|Friedman et al., 2017]] ; [[#Konrad--2017|Konrad et al., 2017]] ; [[#Semenza--2017|Semenza et al., 2017]] ; [[#Borbor-Córdova--2018|Borbor-Córdova et al., 2018]] ; [[#Rocklöv--2020|Rocklöv and Dubrow, 2020]] ). In addition, the effective management and treatment of domestic and waste-water effluent, through better infrastructure and preservation of aquatic systems acting as natural water purifiers, have been key to securing the integrity of the surrounding water bodies, such as groundwater, reservoirs and lakes, and agricultural watersheds as well as protecting public health ( ''high confidence'' ) ( [[#Okeyo--2018|Okeyo et al., 2018]] ; [[#Guerrero-Latorre--2020|Guerrero-Latorre et al., 2020]] ; [[#Kitajima--2020|Kitajima et al., 2020]] ; [[#Sunkari--2021|Sunkari et al., 2021]] ). The preservation and restoration of natural ecosystems, with their associated higher levels of biodiversity, have been reported as significant buffers against epidemics and large-scale pathogen transmission ( ''medium confidence'' ) ( [[#Johnson--2010|Johnson and Thieltges, 2010]] ; [[#Ostfeld--2017|Ostfeld and Keesing, 2017]] ; [[#Keesing--2021|Keesing and Ostfeld, 2021]] ). Furthermore, the timely allocation of financial resources and sufficient political will in support of a ‘One Health’ scientific research approach, recognising the health of humans, animals and ecosystems as interconnected ( [[#Rubin--2014|Rubin et al., 2014]] ; [[#Whitmee--2015|Whitmee et al., 2015]] ; [[#Zinsstag--2018|Zinsstag et al., 2018]] ), holds potential for improving surveillance and prevention strategies that may help to reduce the risks of further spread and new emergence of pathogens and vectors ( ''medium confidence'' ) ( [[#Destoumieux-Garzón--2018|Destoumieux-Garzón et al., 2018]] ; [[#Hockings--2020|Hockings et al., 2020]] ; [[#Volpato--2020|Volpato et al., 2020]] ; [[#Hopkins--2021|Hopkins et al., 2021]] ; [[#Pörtner--2021|Pörtner et al., 2021]] ).

[[File:68b022be607d6a88f2a3699aef68fdb3 IPCC_AR6_WGII_Figure_2_Cross-Chapter-Box-Illness-1.png]]

'''Figure Cross-Chapter Box ILLNESS.1 |''' '''Adaptation measures to reduce risks of climate change impact on water- and vector-borne diseases.''' Impacts are identified at three levels: (1) on pathogen, host/vector distributions and abundance; (2) on pathogen-host transmission cycle occurrence and efficiency; and (3) on the likelihood of transmission to humans. Adaptation typology is based on ( [[#Biagini--2014|Biagini et al., 2014]] ; [[#Pecl--2019|Pecl et al., 2019]] ). For each type of adaptation, examples are provided with their level of evidence and agreement.

Cross-Chapter Box ILLNESS

Cross-Chapter Box ILLNESS

Cross-Chapter Box ILLNESS

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