David L Smith, Daniel J Laydon, Kaustubh Chakradeo, Mark P Khurana, Jaffer Okiring, David A Duchene, Samir Bhatt
{"title":"Climate Change and Malaria: A Call for Robust Analytics","authors":"David L Smith, Daniel J Laydon, Kaustubh Chakradeo, Mark P Khurana, Jaffer Okiring, David A Duchene, Samir Bhatt","doi":"10.1101/2024.09.16.24313623","DOIUrl":null,"url":null,"abstract":"Mosquito ecology and malaria parasite development in mosquitoes display marked sensitivity to weather, in particular to temperature and precipitation. Therefore, climate change is expected to profoundly affect malaria epidemiology in its transmission, spatiotemporal distribution and consequent disease burden. However, malaria transmission is also complicated by other factors (e.g.\\ urbanisation, economics, genetics, drug resistance) which together constitute a highly complex, dynamical system, where the influence of any single factor is highly uncertain. In this study, we therefore aim to re-evaluate the evidence underlying the widespread belief that climate change will increase worldwide malaria transmission. We firstly review the different types of studies that have contributed to this evidence-base: i) studies that project changes in transmission due to inferred relationships between environmental and mosquito entomology; ii) studies that focus on either mechanistic transmission or time series models to estimate the effects of malaria control strategies on prevalence, and iii) regression-based studies that look for associations between environmental variables and malaria prevalence. We then employ a simple statistical model to show that environmental variables alone do not account for the observed spatiotemporal variation in malaria prevalence. Our review raises several concerns about the robustness of the analyses used for advocacy around climate change and malaria. We find that, while climate change's effect on malaria is highly plausible, empirical evidence is much less certain. Future research on climate change and malaria must become integrated into malaria control programs, and understood in context as one factor among many affecting malaria. Our work outlines gaps in modelling that we believe are priorities for future research.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.16.24313623","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Mosquito ecology and malaria parasite development in mosquitoes display marked sensitivity to weather, in particular to temperature and precipitation. Therefore, climate change is expected to profoundly affect malaria epidemiology in its transmission, spatiotemporal distribution and consequent disease burden. However, malaria transmission is also complicated by other factors (e.g.\ urbanisation, economics, genetics, drug resistance) which together constitute a highly complex, dynamical system, where the influence of any single factor is highly uncertain. In this study, we therefore aim to re-evaluate the evidence underlying the widespread belief that climate change will increase worldwide malaria transmission. We firstly review the different types of studies that have contributed to this evidence-base: i) studies that project changes in transmission due to inferred relationships between environmental and mosquito entomology; ii) studies that focus on either mechanistic transmission or time series models to estimate the effects of malaria control strategies on prevalence, and iii) regression-based studies that look for associations between environmental variables and malaria prevalence. We then employ a simple statistical model to show that environmental variables alone do not account for the observed spatiotemporal variation in malaria prevalence. Our review raises several concerns about the robustness of the analyses used for advocacy around climate change and malaria. We find that, while climate change's effect on malaria is highly plausible, empirical evidence is much less certain. Future research on climate change and malaria must become integrated into malaria control programs, and understood in context as one factor among many affecting malaria. Our work outlines gaps in modelling that we believe are priorities for future research.