ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019
Confronting models with data: the challenges of estimating disease spillover.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Are we overestimating risk of enteric pathogen spillover from wild birds to humans?Biological reviews of the Cambridge Philosophical Society · 2020Pooled it
- Habitat sharing and interspecies interactions in caves used by bats in the Republic of Congo.PeerJ · 2025Article
- Filling the gap: artificial intelligence-driven one health integration to strengthen pandemic preparedness in resource-limited settings.Frontiers in public health · 2025Review
- Modeling spillover dynamics: understanding emerging pathogens of public health concern.Scientific reports · 2024Article
- Human disturbance increases coronavirus prevalence in bats.Science advances · 2023Article
- The role of invasive alien species in the emergence and spread of zoonoses.Biological invasions · 2023Article
- Risk of African swine fever virus transmission among wild boar and domestic pigs in Poland.Frontiers in veterinary science · 2023Article
- Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species.Frontiers in veterinary science · 2023Article
- Adaptive risk-based targeted surveillance for foreign animal diseases at the wildlife-livestock interface.Transboundary and emerging diseases · 2022Article
- The importance of fine-scale predictors of wild boar habitat use in an isolated population.Ecology and evolution · 2022Article
- Estimation of the probability risks of African swine fever outbreaks using the maximum entropy method in North Sumatra Province, Indonesia.Veterinary world · 2022Article
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
- SARS-CoV-2 evolution in animals suggests mechanisms for rapid variant selection.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Understanding the origin of COVID-19 requires to change the paradigm on zoonotic emergence from the spillover to the circulation model.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2021Review
- Evolution of pathogen tolerance and emerging infections: A missing experimental paradigm.eLife · 2021Review
- From Protein to Pandemic: The Transdisciplinary Approach Needed to Prevent Spillover and the Next Pandemic.Viruses · 2021Review
- Elk migration influences the risk of disease spillover in the Greater Yellowstone Ecosystem.The Journal of animal ecology · 2021Article
- SARS-CoV-2 evolution in animals suggests mechanisms for rapid variant selection.bioRxiv : the preprint server for biology · 2021Article
- A framework for surveillance of emerging pathogens at the human-animal interface: Pigs and coronaviruses as a case study.Preventive veterinary medicine · 2021Article
- A Review of Pathogen Transmission at the Backyard Chicken-Wild Bird Interface.Frontiers in veterinary science · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For pathogens known to transmit across host species, strategic investment in disease control requires knowledge about where and when spillover transmission is likely. One approach to estimating spillover is to directly correlate observed spillover events with covariates. An alternative is to mechanistically combine information on host density, distribution and pathogen prevalence to predict where and when spillover events are expected to occur. We use several case studies at the wildlife-livestock disease interface to highlight the challenges, and potential solutions, to estimating spatio-temporal variation in spillover risk. Datasets on multiple host species often do not align in space, time or resolution, and may have no estimates of observation error. Linking these datasets requires they be related to a common spatial and temporal resolution and appropriately propagating errors in predictions can be difficult. Hierarchical models are one potential solution, but for fine-resolution predictions at broad spatial scales, many models become computationally challenging. Despite these limitations, the confrontation of mechanistic predictions with observed events is an important avenue for developing a better understanding of pathogen spillover. Systems where data have been collected at all levels in the spillover process are rare, or non-existent, and require investment and sustained effort across disciplines. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.
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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.