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. 2021 Jul;5(7):e404-e414.
doi: 10.1016/S2542-5196(21)00132-7.

Projecting the risk of mosquito-borne diseases in a warmer and more populated world: a multi-model, multi-scenario intercomparison modelling study

Affiliations

Projecting the risk of mosquito-borne diseases in a warmer and more populated world: a multi-model, multi-scenario intercomparison modelling study

Felipe J Colón-González et al. Lancet Planet Health. 2021 Jul.

Erratum in

Abstract

Background: Mosquito-borne diseases are expanding their range, and re-emerging in areas where they had subsided for decades. The extent to which climate change influences the transmission suitability and population at risk of mosquito-borne diseases across different altitudes and population densities has not been investigated. The aim of this study was to quantify the extent to which climate change will influence the length of the transmission season and estimate the population at risk of mosquito-borne diseases in the future, given different population densities across an altitudinal gradient.

Methods: Using a multi-model multi-scenario framework, we estimated changes in the length of the transmission season and global population at risk of malaria and dengue for different altitudes and population densities for the period 1951-99. We generated projections from six mosquito-borne disease models, driven by four global circulation models, using four representative concentration pathways, and three shared socioeconomic pathways.

Findings: We show that malaria suitability will increase by 1·6 additional months (mean 0·5, SE 0·03) in tropical highlands in the African region, the Eastern Mediterranean region, and the region of the Americas. Dengue suitability will increase in lowlands in the Western Pacific region and the Eastern Mediterranean region by 4·0 additional months (mean 1·7, SE 0·2). Increases in the climatic suitability of both diseases will be greater in rural areas than in urban areas. The epidemic belt for both diseases will expand towards temperate areas. The population at risk of both diseases might increase by up to 4·7 additional billion people by 2070 relative to 1970-99, particularly in lowlands and urban areas.

Interpretation: Rising global mean temperature will increase the climatic suitability of both diseases particularly in already endemic areas. The predicted expansion towards higher altitudes and temperate regions suggests that outbreaks can occur in areas where people might be immunologically naive and public health systems unprepared. The population at risk of malaria and dengue will be higher in densely populated urban areas in the WHO African region, South-East Asia region, and the region of the Americas, although we did not account for urban-heat island effects, which can further alter the risk of disease transmission.

Funding: UK Space Agency, Royal Society, UK National Institute for Health Research, and Swedish Research Council.

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Conflict of interest statement

Declaration of interests We declare no competing interests.

Figures

Figure 1
Figure 1
Relationship between LTS (months), temperature, and precipitation LTS relationship with mean annual temperature and total annual precipitation for the VECTRI (A), LMM_R0 (B), LCMI (C), DGM (D), UMEÅ-albopictus (E), and UMEÅ-aegypti (F) models. Values were binned linearly (n=100 bins). The computation was based on all spatial points and annual time steps for each disease model. For future scenarios only SSP2 (medium challenges to mitigation and adaptation) was considered. DGM=dengue model. LCMI=The Lancet Countdown malaria indicator. LMM_R0=The Liverpool Malaria Model. LTS=length of the transmission season. SSP=shared socioeconomic pathway.
Figure 2
Figure 2
Simulated changes in LTS Ensemble mean of the simulated changes in LTS for malaria (A) and dengue (B) across six emission and socioeconomic scenario combinations averaged over the period 2070–99 relative to the period 1970–99. SSPs include SSP1 (low challenges to mitigation and adaptation), SSP2 (medium challenges to mitigation and adaptation), and SSP5 (high challenges to mitigation with low challenges to adaptation). Blue colours indicate decreases in LTS and orange or pink colours indicate increases in LTS (months). LTS=length of the transmission season. RCP=representative concentration pathways. SSP=shared socioeconomic pathways.
Figure 3
Figure 3
Changes in LTS by altitude Ensemble mean of the simulated changes in LTS for malaria (A) and dengue (B) stratified by altitude, emissions, and socioeconomic scenario combination. The bars indicate the multi-model (three models per disease) ensemble mean for each scenario. Error bars indicate the spread of the predictions. SSPs include SSP1 (low challenges to mitigation and adaptation), SSP2 (medium challenges to mitigation and adaptation), and SSP5 (high challenges to mitigation with low challenges to adaptation). LTS=length of the transmission season. RCP=representative concentration pathways. SSP=shared socioeconomic pathways.
Figure 4
Figure 4
Changes in LTS by population density Ensemble mean of the simulated changes in LTS for malaria (A) and dengue (B) stratified by population density, emissions, and socioeconomic scenario combination. The bars indicate the multi-model ensemble mean for each scenario. Error bars indicate the spread of the predictions. SSPs include SSP1 (low challenges to mitigation and adaptation), SSP2 (medium challenges to mitigation and adaptation), and SSP5 (high challenges to mitigation with low challenges to adaptation). LTS=length of the transmission season. RCP=representative concentration pathways. SSP=shared socioeconomic pathways.
Figure 5
Figure 5
Additional PAR by altitude Ensemble mean of the additional PAR of malaria (A) and dengue (B) stratified by altitude, emissions, and socioeconomic scenario combination. The bars indicate the multi-model ensemble mean for each scenario. Error bars indicate the spread of the predictions. SSPs include SSP1 (low challenges to mitigation and adaptation), SSP2 (medium challenges to mitigation and adaptation), and SSP5 (high challenges to mitigation with low challenges to adaptation). PAR=population at risk. RCP=representative concentration pathways. SSP=shared socioeconomic pathways.
Figure 6
Figure 6
Additional PAR by population density Ensemble mean of the additional PAR for malaria (A) and dengue (B) stratified by population density, emissions, and socioeconomic scenario combination. The bars indicate the multi-model ensemble mean for each scenario. Error bars indicate the spread of the predictions. SSPs include SSP1 (low challenges to mitigation and adaptation), SSP2 (medium challenges to mitigation and adaptation), and SSP5 (high challenges to mitigation with low challenges to adaptation). PAR=population at risk. RCP=representative concentration pathways. SSP=shared socioeconomic pathways.

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