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Climate sensitivity is widely but unevenly spread across zoonotic diseases

Infectious diseases

December 9, 2025

  • Date (DD-MM-YYYY)

    16-12-2025 to 16-12-2026

    Available on-demand until 16th December 2026

  • Cost

    Free

  • Education type

    Publication

  • CPD subtype

    On-demand

Description

Climate change is expected to exacerbate infectious diseases, yet the climate sensitivity of zoonotic diseases (driven by spillover from animal reservoirs) is understudied compared to vector-borne and water-borne infections. To address this gap, we conducted a scoping review and quantitative synthesis to identify relationships between climatic indicators (temperature, precipitation, humidity) and zoonotic disease risk metrics worldwide. We identified 218 studies from 65 countries describing 852 measures across 53 diseases, with most studies testing linear (n = 193) rather than nonlinear (n = 28) relationships. We found evidence of climate sensitivity across diverse zoonotic diseases (significant nonzero relationships in 69.1% of temperature effects, 63.5% of precipitation effects, and 53.6% of humidity effects), but with variation in direction and strength. Positive effects of temperature and rainfall on disease risk were more common than negative effects (46.5% vs. 22.6% and 37.8% vs. 25.7% of all records, respectively). These studies were predominantly located in areas expected to have substantial increases in annual mean temperature (>1.5 °C in 97% of studies) and rainfall (>25 mm in 53% of studies) by 2041 to 2070. Notably, the most consistent relationship was between temperature and vector-borne zoonoses (56% of positive effects, mean Hedges’ g = 0.36). Our analyses provide evidence that climate sensitivity is common across zoonoses, likely leading to substantial yet complex effects of climate change on zoonotic burden. We emphasize the need for future studies to utilize biologically relevant models, apply rigorous space-time controls, consider causal perspectives, and address taxonomic and geographic biases to allow robust consensus of climate–risk relationships to emerge.

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