Spatiotemporal Evolution of Ebola Virus Disease at Sub-National Level during the 2014 West Africa Epidemic: Model Scrutiny and Data Meagreness

Abstract

BackgroundThe Ebola outbreak in West Africa has infected at least 27,443 individuals and killed 11,207, based on data until 24 June, 2015, released by the World Health Organization (WHO). This outbreak has been characterised by extensive geographic spread across the affected countries Guinea, Liberia and Sierra Leone, and by localized hotspots within these countries. The rapid recognition and quantitative assessment of localised areas of higher transmission can inform the optimal deployment of public health resources.MethodsA variety of mathematical models have been used to estimate the evolution of this epidemic, and some have pointed out the importance of the spatial heterogeneity apparent from incidence maps. However, little is known about the district-level transmission. Given that many response decisions are taken at sub-national level, the current study aimed to investigate the spatial heterogeneity by using a different modelling framework, built on publicly available data at district level. Furthermore, we assessed whether this model could quantify the effect of intervention measures and provide predictions at a local level to guide public health action. We used a two-stage modelling approach: a) a flexible spatiotemporal growth model across all affected districts and b) a deterministic SEIR compartmental model per district whenever deemed appropriate.FindingsOur estimates show substantial differences in the evolution of the outbreak in the various regions of Guinea, Liberia and Sierra Leone, illustrating the importance of monitoring the outbreak at district level. We also provide an estimate of the time-dependent district-specific effective reproduction number, as a quantitative measure to compare transmission between different districts and give input for informed decisions on control measures and resource allocation. Prediction and assessing the impact of control measures proved to be difficult without more accurate data. In conclusion, this study provides us a useful tool at district level for public health, and illustrates the importance of collecting and sharing data.

Mathematical models to characterize early epidemic growth: A review

“…For instance, prior work has highlighted that the initial, seemingly exponential spread of the 2014-15 Ebola epidemic in West Africa at the national level was in fact a composition of local asynchronous and sub-exponential outbreaks [5,8]. That is, local district or county-level outbreaks of Ebola show spatial asynchrony and follow a slower growth pattern that can be best approximated by polynomial rather than exponential functions during at least 3 consecutive disease generations (Figure 1).…”

Effect of vaccinating health care workers to control Ebola virus disease: a modelling analysis of outbreak data

“…Contemporaneous analyses of the West African outbreak suggested a pattern of initially sustained transmission in the community, followed by a slow decline in transmission [22][23][24][25].…”

Modeling spatial invasion of Ebola in West Africa

“…For instance, Merler et al [21] developed a model of Ebola within Liberia using an agent-based spatial model in which the country is represented by a grid with varying population densities. In addition, Santermans and coauthors demonstrated that the outbreak is heterogeneous, with transmission rates differing between locales [26]. This lends credence to the approach of modelling the outbreak as a set of connected local-level outbreaks, as opposed to one homogeneous outbreak.…”