Sensitivity to climate change is widespread across zoonotic diseases

Abstract

Climate change is expected to exacerbate infectious diseases, yet the climate sensitivity of zoonotic diseases (driven by spillover from animal reservoirs) is markedly understudied compared to vector-borne and water-borne infections. To address this gap, we conducted a global systematic review and quantitative synthesis to identify relationships between climatic indicators (temperature, precipitation, humidity) and zoonotic disease risk metrics worldwide. We identified 185 studies from 55 countries, describing 547 measures across 51 diseases, with most studies testing linear (n=166) rather than nonlinear (n=23) relationships. We found evidence of climate sensitivity across diverse zoonotic diseases (significant non-zero relationships in 64.3% of temperature effects, 49.8% of precipitation effects, and 48.9% of humidity effects), but with broad variation in direction and strength. Positive effects of temperature and rainfall on disease risk were more common than negative effects (39.1% vs. 25.2% and 30.5% vs. 19.2% of all records, respectively). These studies were predominantly located in areas expected to have substantial increases in annual mean temperature (>1.5oC in 93% of studies) and rainfall (>25 mm in 46% of studies) by 2041-2070. Notably, the most consistent relationship was between temperature and vector-borne zoonoses (50% of Positive effects, mean Hedge's g = 0.31). Overall, our analyses provide evidence that climate sensitivity is common across zoonoses, likely leading to substantial yet complex effects of future climate change on zoonotic burden. Finally, we highlight the need for future studies to use biologically appropriate models, rigorous space-time controls, consider causal perspectives and address taxonomic and geographic biases to allow a robust consensus of climate-risk relationships to emerge.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This research was supported by the UKRI Biotechnology and Biological Sciences Research Council (BBSRC) grants (Refs: BB/X005364/1 and BB/X005364/2), the Wellcome Trust grants (Refs: 220179/Z/20/Z, 220179/A/20/Z, and 226080/Z/22/Z), the National Science Foundation (NSF) grant (Ref: 2213854), and the Wolfson Foundation (via a UCL Excellence Fellowship; RG).

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