How causal inference concepts can guide research into the effects of climate on infectious diseases
arxiv(2024)
摘要
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.
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