How causal inference concepts can guide research into the effects of climate on infectious diseases

TL;DR

This paper explores how causal inference methods can improve understanding of how climate change impacts infectious disease dynamics, emphasizing interdisciplinary approaches for reliable effect estimation and prediction.

Contribution

It demonstrates the application of causal inference concepts to epidemiological research on climate effects, guiding study design, bias reduction, and interpretation.

Findings

Causal inference helps assess study design and location.

Strategies to evaluate and reduce bias in observational data.

Improved interpretation of meteorological effects on disease transmission.

Abstract

A pressing question resulting from global warming is how infectious diseases will be affected by climate change. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proven difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here, we show how concepts from causal inference -- the sub-field of statistics aiming at inferring causality from data -- can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study's location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.