ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019
Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone.
Article 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 21 papers.
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Who cites it
21 citing papers in PubMed.
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- The effect of governance structures on optimal control of two-patch epidemic models.Journal of mathematical biology · 2023Article
- A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.International journal of data science and analytics · 2023Article
- Simulation applications to support teaching and research in epidemiological dynamics.BMC medical education · 2022Article
- Transmission models indicate Ebola virus persistence in non-human primate populations is unlikely.Journal of the Royal Society, Interface · 2022Article
- Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina.PloS one · 2022Article
- Artificial intelligence knacks-based stochastic paradigm to study the dynamics of plant virus propagation model with impact of seasonality and delays.European physical journal plus · 2022Article
- Network models to evaluate vaccine strategies towards herd immunity in COVID-19.Journal of theoretical biology · 2021Article
- A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations.Journal of the Royal Society, Interface · 2021Article
- A versatile web app for identifying the drivers of COVID-19 epidemics.Journal of translational medicine · 2021Article
- COVID-19 pandemic and chaos theory.Mathematics and computers in simulation · 2021Article
- Multi-population stochastic modeling of Ebola in Sierra Leone: Investigation of spatial heterogeneity.PloS one · 2021Article
- Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases.PloS one · 2021Article
- Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios.Frontiers of medicine · 2020Article
- Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic.Entropy (Basel, Switzerland) · 2020Article
- Spatially explicit models for exploring COVID-19 lockdown strategies.Transactions in GIS : TG · 2020Article
- Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis.Journal of medical virology · 2020Article
- Phase- and epidemic region-adjusted estimation of the number of coronavirus disease 2019 cases in China.Frontiers of medicine · 2020Article
- A predictive model for healthcare coverage in Yemen.Conflict and health · 2020Article
- Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China.Cell discovery · 2020Article
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3 authors.
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Abstract
Dynamic SEIR (Susceptible, Exposed, Infectious, Removed) compartmental models provide a tool for predicting the size and duration of both unfettered and managed outbreaks-the latter in the context of interventions such as case detection, patient isolation, vaccination and treatment. The reliability of this tool depends on the validity of key assumptions that include homogeneity of individuals and spatio-temporal homogeneity. Although the SEIR compartmental framework can easily be extended to include demographic (e.g. age) and additional disease (e.g. healthcare workers) classes, dependence of transmission rates on time, and metapopulation structure, fitting such extended models is hampered by both a proliferation of free parameters and insufficient or inappropriate data. This raises the question of how effective a tool the basic SEIR framework may actually be. We go some way here to answering this question in the context of the 2014-2015 outbreak of Ebola in West Africa by comparing fits of an SEIR time-dependent transmission model to both country- and district-level weekly incidence data. Our novel approach in estimating the effective-size-of-the-populations-at-risk ( N
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