ArticleNature ecology & evolution2025
Causal inference concepts can guide research into the effects of climate on infectious diseases.
Article in Nature ecology & evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The impact of climate change on airborne and droplet-borne diseases and hospital response measures.Frontiers in public health · 2026Pooled it
- Reporting of structural population assumptions in prevalence-based studies of animal feeding operations and community health.Frontiers in public health · 2026Pooled it
- Climate-Sensitive Redistribution of Veterinary Parasites: An Attribution Framework for One Health Surveillance and Control.Biology · 2026Review
- The pitfalls of incidence-based time series regression for inferring the effects of weather on infectious diseases.PLoS computational biology · 2026Article
- Vulnerability to climate-driven pathogen-specific diarrheal diseases: secondary analysis from the Global Enteric Multicenter Study and Vaccine Impact on Diarrhea in Africa follow-up.Nature communications · 2026Article
- The limitations of non-mechanistic methods for characterizing pathogen-pathogen interactions: A simulation study.PLoS computational biology · 2026Article
- Evaluating the ability of a thermal biology-informed reproduction number to explain patterns of West Nile incidence in Europe.One health (Amsterdam, Netherlands) · 2026Article
- Unobserved confounders cannot explain over-crediting in avoided deforestation carbon projects.Nature ecology & evolution · 2026Article
- Understanding Climate Change Threats to Vertebrate Wildlife by Studying Ecoimmunology Across Biological Scales.Integrative and comparative biology · 2025Review
- Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.Current environmental health reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A pressing question resulting from global warming is how climate change will affect infectious diseases. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proved 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.