Editing IPCC:AR6/WGII/Chapter-9 (section)

===== 9.10.3.4.1 Adaptation to prevent malaria =====

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Increasing distribution and coverage of long-lasting insecticide-treated bed nets, improved diagnostic tests and increasing health service access could mitigate the impacts of climate change on malaria if aligned with the predicted or actual burden of malaria ( ''medium confidence'' ) ( [[#Kienberger--2014|Kienberger and Hagenlocher, 2014]] ; [[#Thwing--2017|Thwing et al., 2017]] ). Understanding seasonal shifts in malaria transmission suitability as a result of climate change can guide more targeted seasonal public health responses and better planning for different types of management and control interventions based on the impact. For example, an increase in the number of months where climate conditions are suitable for mosquito survival will require public health responses for an extended period of time ( [[#Ryan--2020|Ryan et al., 2020]] ).

In malaria-endemic areas, repeated malaria infections can provide temporary immunity, which reduces new clinical cases ( [[#Laneri--2015|Laneri et al., 2015]] ; [[#Yamana--2016|Yamana et al., 2016]] ). Conversely, where people have little or no immunity, exposure to malaria can lead to epidemics ( [[#Semakula--2017a|Semakula et al., 2017a]] ; [[#Ryan--2020|Ryan et al., 2020]] ). Pregnant women and infants remain at risk of severe malaria, regardless of immunity status. Vector control and case management capacity should be rapidly scaled up in newly affected areas where risks for epidemics are high and populations are especially vulnerable. Poverty-alleviation initiatives underpin malaria control as the malaria burden is strongly tied to socioeconomic status ( [[#Huldén--2014|Huldén et al., 2014]] ; [[#Degarege--2019|Degarege et al., 2019]] ).

Contextualised risk studies on local drivers of transmission are still lacking and present a major gap in developing appropriate adaptation strategies ( ''high confidence'' ). Progress has been made identifying and ranking vulnerability and exposure indicators ( [[#Protopopoff--2009|Protopopoff et al., 2009]] ; [[#Onyango--2016a|Onyango et al., 2016a]] ), however, better linking of biophysical and socioeconomic determinants of risk in integrated assessment models is needed ( [[#Caminade--2019|Caminade et al., 2019]] ; [[#Zermoglio--2019|Zermoglio et al., 2019]] ), as are applied approaches to develop adaptation strategies for risk management ( [[#Leedale--2016|Leedale et al., 2016]] ; [[#Onyango--2016b|Onyango et al., 2016b]] ; [[#Sadoine--2018|Sadoine et al., 2018]] ).

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