Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone

Abstract

One contribution of 15 to a theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.Subject Areas: computational biology, health and disease and epidemiology 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 eff ) and initial number of exposed individuals (E 0 ) at both district and country levels, as well as the transmission function parameters, including a time-to-halving-the-force-ofinfection (t f/2 ) parameter, provides new insights into this Ebola outbreak. It reveals that the estimate R 0 1.7 from country-level data appears to seriously underestimate R 0 3.3 2 4.3 obtained from more spatially homogeneous district-level data. Country-level data also overestimate t f/2 22 weeks, compared with 8-10 weeks from district-level data. Additionally, estimates for the duration of individual infectiousness is around two weeks from spatially inhomogeneous country-level data compared with 2.4-4.5 weeks from spatially more homogeneous district-level data, which estimates are rather high compared with most values reported in the literature.This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis

“…This study was mainly a data‐driven analysis, with the data coming from epidemiological results of published studies (preprint included) and current case information, programed, simulated, and fitted by Python based on the SEIR model. The SEIR differs from the SIR model in the addition of a latency period and can provide a tool for predicting the size and duration of both unconstrained and managed outbreaks—the latter in the context of interventions such as case detection, quarantine, and treatment 26 …”

Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China

“…We employed an infectious disease dynamics model (SEIR model) for the purpose of modeling and predicting the number of COVID-19 cases in Wuhan, China. The model is a classic epidemic method to analyze the infectious disease, which has a definite latent period, and has proved to be predictive for a variety of acute infectious diseases in the past such as Ebola and SARS 22,[26][27][28][29][30][31] . Application of the mathematical model is of great guiding significance to assess the impact of isolation of symptomatic cases as well as observation of asymptomatic contact cases and to promote evidence-based decisions and policy.…”

Phase- and epidemic region-adjusted estimation of the number of coronavirus disease 2019 cases in China

“…Firstly, the SEIR model was based on a few essential assumptions (e.g., no multiple zoonotic sources of the virus, no infectivity during incubation period, and no super-spreaders), which lacked supportive evidence currently. Specifically, due to the inherent limitation of the SEIR model [25], which assumes diseases spread evenly across homogeneous population, the number of cases in the current study might be underestimated constrained by the possible existence of super-spreaders and asymptomatic infectors. We could do essential modification to the estimation model in future research.…”

“…We employed an infectious disease dynamics model (Susceptible, Exposed, Infectious, and Removed model (SEIR model)) for modeling and predicting the number of COVID-19 cases in Wuhan, Hubei Province and regions outside Hubei Province in China. The model is classically applied to analyze the infectious disease which has a definite latent period, and has been proved to be predictive for a variety of acute infectious diseases in the past such as Ebola and SARS [11,[22][23][24][25][26][27]. And utilization of this mathematical model is significantly instructive in assessing the impact of isolation of symptomatic cases as well as medical observation of asymptomatic contact cases and promoting evidence-based decisions and policies.…”