ArticleProceedings of the National Academy of Sciences of the United States of America2016
Climatic and evolutionary drivers of phase shifts in the plague epidemics of colonial India.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 20 citations in OpenAlex.
- Spatiotemporal analysis of plague risk in Tibet: Multi-source data-driven ensemble model development and validation.One health (Amsterdam, Netherlands) · 2026Article
- Seasonality of endemic COVID-19.mBio · 2023Article
- Genomic diversity of Yersinia pestis from Yunnan Province, China, implies a potential common ancestor as the source of two plague epidemics.Communications biology · 2023Article
- Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city.Nature communications · 2023Article
- Serological evidence of Yersinia pestis infection in rodents and carnivores in Northwestern Iran.PLoS neglected tropical diseases · 2023Article
- Epidemiological Characteristics of Human and Animal Plague in Yunnan Province, China, 1950 to 2020.Microbiology spectrum · 2022Article
- The influence of temperature on the seasonality of historical plague outbreaks.Proceedings. Biological sciences · 2021Article
- Yersinia pestis: the Natural History of Plague.Clinical microbiology reviews · 2020Review
- Acceleration of plague outbreaks in the second pandemic.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Human plague: An old scourge that needs new answers.PLoS neglected tropical diseases · 2020Review
- Article
- Heterogeneous susceptibility to rotavirus infection and gastroenteritis in two birth cohort studies: Parameter estimation and epidemiological implications.PLoS computational biology · 2019Article
- Human-environment interactions in population and ecosystem health.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Climate and society in long-term perspective: Opportunities and pitfalls in the use of historical datasets.Wiley interdisciplinary reviews. Climate changeReview
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2 authors at 1 institution in 1 country.
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Abstract
Immune heterogeneity in wild host populations indicates that disease-mediated selection is common in nature. However, the underlying dynamic feedbacks involving the ecology of disease transmission, evolutionary processes, and their interaction with environmental drivers have proven challenging to characterize. Plague presents an optimal system for interrogating such couplings: Yersinia pestis transmission exerts intense selective pressure driving the local persistence of disease resistance among its wildlife hosts in endemic areas. Investigations undertaken in colonial India after the introduction of plague in 1896 suggest that, only a decade after plague arrived, a heritable, plague-resistant phenotype had become prevalent among commensal rats of cities undergoing severe plague epidemics. To understand the possible evolutionary basis of these observations, we developed a mathematical model coupling environmentally forced plague dynamics with evolutionary selection of rats, capitalizing on extensive archival data from Indian Plague Commission investigations. Incorporating increased plague resistance among rats as a consequence of intense natural selection permits the model to reproduce observed changes in seasonal epidemic patterns in several cities and capture experimentally observed associations between climate and flea population dynamics in India. Our model results substantiate Victorian era claims of host evolution based on experimental observations of plague resistance and reveal the buffering effect of such evolution against environmental drivers of transmission. Our analysis shows that historical datasets can yield powerful insights into the transmission dynamics of reemerging disease agents with which we have limited contemporary experience to guide quantitative modeling and inference.
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