Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Abstract

47Climate drives population dynamics, but when the underlying mechanisms are 48 unresolved, studies can lead to seemingly contradictory effects of climate on natural 49 populations. Climate-sensitive vector-borne diseases such as dengue, chikungunya, and 50Zika are one example where climate appears to have opposing effects in different 51 contexts. In this study, we use a mathematical model to directly connect climate-driven 52 mosquito physiology measured in laboratory studies to observed vector and disease 53 dynamics in the field across ecologically and culturally distinct settings in Ecuador and 54Kenya. We show that temperature, rainfall, and humidity predict Aedes aepgyti 55 abundances and laboratory-confirmed arboviral incidence across ecologically distinct 56 settings. Further, this trait-based approach resolves seemingly contradictory results from 57 prior field studies and highlights climate conditions where mechanisms remain 58 unresolved. Using this mechanistic model, we tested several intervention strategies and 59 found that reducing immature mosquito habitat or contact rate between mosquitoes and 60 humans are more effective interventions than killing adult mosquitoes. These results can 61 help guide intervention efforts and improve climate change predictions for vector-borne 62 diseases. 63 64 Introduction: 65Climate is a major driver of species interactions and population dynamics, but the 66 mechanisms underlying these relationships are often poorly understood and rarely tested 67 in the field [1]. One of the primary ways that climate impacts populations is through its 68 effects on species' vital rates [2]. However, these mechanistic effects can lead to 69 seemingly contradictory results in the field because multiple climate variables may act 70 synergistically, with each climate variable potentially affecting multiple vital rates, and 71 their impacts may be nonlinear, changing direction and relative importance across a 72 gradient of conditions. Vector-borne diseases provide an interesting case study to test 73 whether climate sensitive traits measured in controlled, laboratory settings can reconcile 74 seemingly contradictory results from field studies. For example, mosquito-borne 75 arboviral diseases such as dengue, chikungunya, and Zika are clearly climate-sensitive: a 76 body of field research has consistently identified temperature, rainfall, and humidity as 77 important predictors of disease, but sometimes with opposite conclusions about the 78 magnitude and direction of effects of climate on mosquito and disease dynamics [3][4][5][6][7][8]. 79For example, dengue incidence correlated with temperature positively in Mexico [9] but 80 negatively in Thailand [10]. We hypothesize that such opposing effects could be 81 simultaneously correct if disease dynamics are context-dependent or nonlinear, and each 82 model describes true disease dynamics but only within a small subset of conditions (e.g., 83 specific locations or seasons). 84 85 Understanding the mechanisms that drive disease dynamic...

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