How causal inference concepts can guide research into the effects of climate on infectious diseases
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909111951556608 |
|---|---|
| author | Guevara, Laura Andrea Barrero Kramer, Sarah C Kurth, Tobias de Cellès, Matthieu Domenech |
| author_facet | Guevara, Laura Andrea Barrero Kramer, Sarah C Kurth, Tobias de Cellès, Matthieu Domenech |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How causal inference concepts can guide research into the effects of climate on infectious diseases Guevara, Laura Andrea Barrero Kramer, Sarah C Kurth, Tobias de Cellès, Matthieu Domenech Populations and Evolution Quantitative Methods 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. |
| title | How causal inference concepts can guide research into the effects of climate on infectious diseases |
| topic | Populations and Evolution Quantitative Methods |
| url | https://arxiv.org/abs/2402.12507 |