ArticleJournal of biological dynamics2018
Discrete stochastic analogs of Erlang epidemic models.
Article in Journal of biological dynamics, 2018. 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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Who cites it
14 citing papers in PubMed.
- Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation.BMC medical informatics and decision making · 2023Article
- A discrete model for the evaluation of public policies: The case of Colombia during the COVID-19 pandemic.PloS one · 2023Article
- Estimating the undetected emergence of COVID-19 in the US.PloS one · 2023Article
- Article
- An agent-based model of spread of a pandemic with validation using COVID-19 data from New York State.Physica A · 2022Article
- COVID-19 heterogeneity in islands chain environment.PloS one · 2022Article
- Model with transmission delays for COVID-19 control: Theory and empirical assessment.Journal of public economic theory · 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
- Dynamics of SARS-CoV-2 with waning immunity in the UK population.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Modeling Infectious Behaviors: The Need to Account for Behavioral Adaptation in COVID-19 Models.Policy and complex systems · 2021Article
- A non-central beta model to forecast and evaluate pandemics time series.Chaos, solitons, and fractals · 2020Article
- Hitchhiking, collapse, and contingency in phage infections of migrating bacterial populations.The ISME journal · 2020Article
- Survival dynamical systems: individual-level survival analysis from population-level epidemic models.Interface focus · 2020Article
- Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
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2 authors.
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Abstract
Erlang differential equation models of epidemic processes provide more realistic disease-class transition dynamics from susceptible (S) to exposed (E) to infectious (I) and removed (R) categories than the ubiquitous SEIR model. The latter is itself is at one end of the spectrum of Erlang SE[Formula: see text]I[Formula: see text]R models with [Formula: see text] concatenated E compartments and [Formula: see text] concatenated I compartments. Discrete-time models, however, are computationally much simpler to simulate and fit to epidemic outbreak data than continuous-time differential equations, and are also much more readily extended to include demographic and other types of stochasticity. Here we formulate discrete-time deterministic analogs of the Erlang models, and their stochastic extension, based on a time-to-go distributional principle. Depending on which distributions are used (e.g. discretized Erlang, Gamma, Beta, or Uniform distributions), we demonstrate that our formulation represents both a discretization of Erlang epidemic models and generalizations thereof. We consider the challenges of fitting SE[Formula: see text]I[Formula: see text]R models and our discrete-time analog to data (the recent outbreak of Ebola in Liberia). We demonstrate that the latter performs much better than the former; although confining fits to strict SEIR formulations reduces the numerical challenges, but sacrifices best-fit likelihood scores by at least 7%.
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