Editing IPCC:AR6/WGII/Chapter-7 (section)

==== 7.2.2.1 Observed Impacts on Vector-Borne Diseases ====

<div id="h3-6-siblings" class="h3-siblings"></div>

Climate-sensitive VBDs include mosquito-borne diseases, rodent-borne diseases and tick-borne diseases. Many infectious agents, vectors, non-human reservoir hosts, and pathogen replication rates can be sensitive to ambient climatic conditions. Elevated proliferation and reproduction rates at higher temperatures, longer transmission season, changes in ecology and climate-related migration of vectors, reservoir hosts or human populations contribute to this climate sensitivity (Rocklöv &amp; Dubrow, 2020; [[#Semenza--2021|Semenza and Paz, 2021]] ). Age-standardised DALY rates for many VBDs have decreased over the last decade due to factors unrelated to climate. Vulnerability to VBD is strongly determined by sociodemographic factors (e.g., children, the elderly and pregnant women are at greater risk) with exposure to vectors being strongly influenced by various factors including socioeconomic status, housing quality, healthcare access, susceptibility, occupational setting, recreational activity, conflicts and displacement ( Rocklöv &amp; Dubrow, 2020; [[#Semenza--2021|Semenza and Paz, 2021]] ). Figure 7.5 illustrates how climatic and non-climatic drivers and responses determine VBD outcomes.

<div id="_idContainer021" class="Figure"></div>

[[File:6e685e9b9a80d5ef88beb6de20e7e1dc IPCC_AR6_WGII_Figure_7_005.png]]

'''Figure 7.5 |''' '''Analysis of the underlying drivers of infectious disease threat events (IDTEs) detected in Europe from 2008 to 2013 by epidemic intelligence at the European Centre of Disease Prevention and Control.''' Seventeen drivers were identified and categorised into three groups: globalisation and environment (green), sociodemographic (red) and public health system (blue). The drivers are illustrated as diamond shapes and arranged in the top and bottom row; the sizes are proportional to the overall frequency of the driver. Here IDTEs (epidemics or first autochthonous cases) of VBDs are illustrated as a horizontal row of dots in the middle. These empirical data include the IDTEs of VBDs such as West Nile fever, malaria, dengue fever, chikungunya and Hantavirus infection. Source: [[#Semenza--2016|Semenza et al. (2016)]] .

''Evidence has increased since AR5 that the vectorial capacity has increased for dengue fever, malaria and other mosquito-borne diseases and that higher global average temperatures are making wider geographic areas more suitable for transmission (very high confidence).'' Transmission rates of malaria are directly influenced by climatic and weather variables such as temperature, with non-climatic socioeconomic factors and health system responses counteracting the climatic drivers ''(very high confidence).'' The burden of malaria is greatest in Africa, where more than 90% of all malaria-related deaths occur ( [[#M’Bra--2018|M’Bra et al., 2018]] ; Caminade et al., 2019). Between 2007 and 2017, DALYs for malaria have decreased by 39% globally. Malaria is mainly caused by five distinct species of plasmodium parasite ( ''Plasmodium falciparum'' , ''Plasmodium vivax'' , ''Plasmodium malariae'' , ''Plasmodium ovale'' and ''Plasmodium knowlesi'' ) and is transmitted by Anopheline mosquitoes. Evidence suggests that in highland areas of Colombia and Ethiopia, malaria has shifted in warmer years towards higher altitudes, indicating that, without intervention, malaria will increase at higher elevations as the climate warms ( [[#Siraj--2014|Siraj et al., 2014]] ; [[#Midekisa--2015|Midekisa et al., 2015]] ). Each year, local outbreaks of malaria occur due to importation in areas from which it was once eradicated, such as Europe, but the risk of re-establishment is considered low.

''The transmission of dengue fever is linked to climatic and weather variables such as temperature, relative humidity and rainfall (high confidence).'' The dengue virus is carried and spread by ''Aedes'' mosquitoes, primarily ''Aedes aegypti'' . Dengue has the second highest burden of VBDs, with the majority of deaths occurring in Asia ( [[#Bhatt--2013|Bhatt et al., 2013]] ). Since 1950, global dengue burden has grown and is attributable to a combination of climate-associated expansion in the geographic range of the vector species and non-climatic factors such as globalised air traffic, urbanisation and ineffective vector abatement measures. Temperature, relative humidity and rainfall variables are significantly and positively associated with increased dengue case incidence and/or transmission rates globally, including in Vietnam ( [[#Phung--2015|Phung et al., 2015]] ; Xuan le et al., 2014), Thailand ( [[#Xu--2019a|Xu et al., 2019a]] ), India ( [[#Mutheneni--2017|Mutheneni et al., 2017]] ; [[#Rao--2018|Rao et al., 2018]] ; [[#Mala--2019|Mala and Jat, 2019]] ), Indonesia ( [[#Kesetyaningsih--2018|Kesetyaningsih et al., 2018]] ), the Philippines ( [[#Carvajal--2018|Carvajal et al., 2018]] ), the USA ( [[#Lopez--2018|Lopez et al., 2018]] ; [[#Pena-Garcia--2017|Pena-Garcia et al., 2017]] ; [[#Duarte--2019|Duarte et al., 2019]] ; [[#Rivas--2018|Rivas et al., 2018]] ; [[#Silva--2016a|Silva et al., 2016a]] ), Jordan ( [[#Obaidat--2018|Obaidat and Roess, 2018]] ) and Timor-Leste ( [[#Wangdi--2018|Wangdi et al., 2018]] ). Variation in winds, sea surface temperatures and rain over the tropical eastern Pacific Ocean (El Niño-Southern Oscillation; ENSO) have been linked to increased dengue incidence in Colombia ( [[#Quintero-Herrera--2015|Quintero-Herrera et al., 2015]] ; [[#McGregor--2018|McGregor and Ebi, 2018]] ; [[#Pramanik--2020|Pramanik et al., 2020]] ) and its interannual variation successfully forecasted in Ecuador using ENSO indices as predictors ( [[#Petrova--2019|Petrova et al., 2019]] ). The observed time lag between climate exposures and increased dengue incidence is approximately 1–2 months ( [[#Chuang--2017|Chuang et al., 2017]] ; [[#Lai--2018|Lai, 2018]] ; [[#Chang--2018|Chang et al., 2018]] ).

''Changing climatic patterns are facilitating the spread of CHIKV, Zika, Japanese encephalitis and Rift Valley Fever in Asia, Latin America, North America and Europe'' ( ''high confidence).'' Climate change may have facilitated the emergence of CHIKV as a significant public health challenge in some Latin American and Caribbean countries ( [[#Yactayo--2016|Yactayo et al., 2016]] ; [[#Pineda--2016|Pineda et al., 2016]] ) and contributed to chikungunya outbreaks in Europe (Rocklöv et al., 2019; [[#Mascarenhas--2018|Mascarenhas et al., 2018]] ; [[#Morens--2014|Morens and Fauci, 2014]] ). The Zika virus outbreak in South America in 2016 was preceded by 2007 outbreaks on Pacific islands and followed a period of record high temperatures and severe drought conditions in 2015 ( [[#Paz--2016|Paz and Semenza, 2016]] ; [[#Tesla--2018|Tesla et al., 2018]] ). Increased use of household water storage containers during the drought is correlated with a range expansion of ''Aedes aegypti'' during this period, increasing household exposure to the vector ( [[#Paz--2016|Paz and Semenza, 2016]] ). Changing climate also appears to be a risk factor for the spread of Japanese encephalitis to higher altitudes in Nepal ( [[#Ghimire--2015|Ghimire and Dhakal, 2015]] ) and in southwest China ( [[#Zhao--2014|Zhao et al., 2014]] ). In eastern Africa, climate change may be a risk factor in the spread of Rift Valley Fever ( [[#Taylor--2016a|Taylor et al., 2016a]] ).

''Changes in temperature, precipitation, and relative humidity have been implicated as drivers of West Nile fever in southeastern Europe (medium confidence'' '').'' The average temperature and precipitation prior to the exceptional 2018 West Nile outbreak in Europe was above the 1981–2010 period average, which may have contributed to an early upsurge of the vector population ( [[#Marini--2020|Marini et al., 2020]] ; [[#Haussig--2018|Haussig et al., 2018]] ; [[#Semenza--2021|Semenza and Paz, 2021]] ). In 2019 and 2020, West Nile fever was first detected in birds and subsequently in humans in Germany and the Netherlands ( [[#Ziegler--2020|Ziegler et al., 2020]] ; [[#Vlaskamp--2020|Vlaskamp et al., 2020]] ).

''Climate change has contributed to the spread of the Lyme disease vector'' Ixodes scapularis '', a corresponding increase in cases of Lyme disease in North America (high confidence) and the spread of the Lyme disease and tick-borne encephalitis vector'' Ixodes ricinus ''in Europe (medium confidence).'' In Canada, there has been a geographic range expansion of the black-legged tick ''I. scapularis,'' the main vector of ''Borrelia burgdorferi'' , the agent of Lyme disease. Vector surveillance of ''I. scapularis'' has identified strong correlation between temperatures and the emergence of tick populations, their range and recent geographic spread, with recent climate warming coinciding with a rapid increase in human Lyme disease cases ( [[#Clow--2017|Clow et al., 2017]] ; [[#Cheng--2017|Cheng et al., 2017]] ; [[#Gasmi--2017|Gasmi et al., 2017]] ; [[#Ebi--2017|Ebi et al., 2017]] ). ''Ixodes ricinus'' , the primary vector in Europe for both Lyme borreliosis and tick-borne encephalitis is sensitive to humidity and temperature ( [[#Daniel--2018|Daniel et al., 2018]] ; [[#Estrada-Peña--2020|Estrada-Peña and Fernández-Ruiz, 2020]] ) ( ''high confidence'' ). There has been an observed range expansion to higher latitudes in Sweden and to higher elevations in Austria and the Czech Republic.

Rodent-borne disease outbreaks have been linked to weather and climate conditions in a small number of studies published since AR5, but more research is needed in this area ''.'' In Kenya, a positive association exists between precipitation patterns and ''Theileria'' -infected rodents, but for ''Anaplasma'' , ''Theileria'' and ''Hepatozoon'' , the association between rainfall and pathogen varies according to rural land use types ( [[#Young--2017|Young et al., 2017]] ). Weather variability plays a significant role in transmission rates of haemorrhagic fever with renal syndrome (HFRS) ( [[#Hansen--2015|Hansen et al., 2015]] ; [[#Xiang--2018|Xiang et al., 2018]] ; [[#Liang--2018|Liang et al., 2018]] ; [[#Fei--2015|Fei et al., 2015]] ; [[#Xiao--2014|Xiao et al., 2014]] ; [[#Vratnica--2017|Vratnica et al., 2017]] ; [[#Roda%20Gracia--2015|Roda Gracia et al., 2015]] ; [[#Monchatre-Leroy--2017|Monchatre-Leroy et al., 2017]] ; [[#Bai--2019|Bai et al., 2019]] ). In Chongqing, HFRS incidence has been positively associated with rodent density and rainfall ( [[#Bai--2015|Bai et al., 2015]] ).

<div id="7.2.2.2" class="h3-container"></div>

<span id="observed-impacts-on-waterborne-diseases"></span>