ArticleEcology letters2020
Integrating data mining and transmission theory in the ecology of infectious diseases.
Article in Ecology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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
21 citing papers in PubMed.
- Climate-driven changes in zoonotic risk of arenaviral hemorrhagic fevers in South America.Npj viruses · 2026Article
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza.Bioinformatics advances · 2025Article
- Knowledge, risk perceptions and practices regarding rodents and their associated pathogens: environmental consultants in Chile.One health outlook · 2024Article
- Exploring Iguape Virus-A Lesser-Known Orthoflavivirus.Viruses · 2024Review
- Global Mpox spread due to increased air travel.Geospatial health · 2024Article
- A review of emerging health threats from zoonotic New World mammarenaviruses.BMC microbiology · 2024Review
- Beyond Borders: Investigating the Mysteries of Cacipacoré, a Lesser-Studied Arbovirus in Brazil.Viruses · 2024Article
- Association between anthropization and rodent reservoirs of zoonotic pathogens in Northwestern Mexico.PloS one · 2024Article
- Reservoir population ecology, viral evolution and the risk of emerging infectious disease.Proceedings. Biological sciences · 2022Article
- Article
- Live animal markets: Identifying the origins of emerging infectious diseases.Current opinion in environmental science & health · 2022Review
- Research and Innovation Opportunities to Improve Epidemiological Knowledge and Control of Environmentally Driven Zoonoses.Annals of global health · 2022Review
- Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.Proceedings. Biological sciences · 2021Article
- Rocio Virus: An Updated View on an Elusive Flavivirus.Viruses · 2021Review
- Land-use change and rodent-borne diseases: hazards on the shared socioeconomic pathways.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.bioRxiv : the preprint server for biology · 2021Article
- Impacts of biodiversity and biodiversity loss on zoonotic diseases.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Interventions can shift the thermal optimum for parasitic disease transmission.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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
Abstract
Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent-borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining-modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.
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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.