Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone. (6th May 2019)

• Record Type: Journal Article
• Authors: Getz, Wayne M.; Salter, Richard; Mgbara, Whitney
• Is Part Of: Philosophical transactions. Volume 374:Number 1775(2019)
• Publication Date: 2019-05-06
• Subjects: SIR models -- R0 -- latent period -- appropriate complexity models -- effective-population-at-risk -- time-dependent force-of-infection; Biology -- Periodicals; Science -- Periodicals; 570
• DOI: 10.1098/rstb.2018.0282
• Languages: English
• ISSNs: 0962-8436

Abstract

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 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-of-infection ( 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 − 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'.