Spatial and temporal dynamics of superspreading events in the 2014–2015 West Africa Ebola epidemic

Abstract

The unprecedented scale of the Ebola outbreak in Western Africa (2014)(2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion (∼61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.Ebola | superspreading | offspring distribution | Bayesian inference T he outbreak size of the 2014 Ebola virus (EBOV) epidemic in Western Africa was unprecedented, and control measures failed to contain the epidemic at its early rapidly growing stage (1, 2). Mathematical models played a key role in inferring the transmission dynamics of EBOV (3). Modeling work succeeded in inferring, in particular, the basic reproductive number R0 (and the time-varying reproductive number, Rt ), which represents the average number of secondary cases that may be generated by a given infectious case (e.g., refs. 4-6). Although these parameters encapsulate knowledge about the average transmission potential of the epidemic at the population level, they fail to reflect individual variation in transmission, which may be more informative for devising targeted control measures.An important phenomenon in disease transmission is so-called superspreading, in which certain individuals (i.e., superspreaders) disproportionately infect a large number of secondary cases relative to an "average" infectious individual (whose infectivity may be well-represented by Rt ). Mathematically, the distribution of secondary cases is given ...

Extreme Competence: Keystone Hosts of Infections

“…This lack of inclusion of heterogeneity might explain why simple epidemiological models have been unable to explain or predict the behavior of some outbreaks [9]. Recently, researchers have demonstrated the value of using individuallevel variants of R 0 [10], such as n [6], to model disease dynamics. One particular advantage of n is that it can be decomposed into three elements: infectiousness, contact rate, and infectious period [4].…”

“…When the distribution of n is described in a population, it is often non-normal with only a few individuals having very high n [6,10]. This distribution (k) is not altogether surprising; in many host-parasite systems, we have long known that 20% of hosts are responsible for 80% of new infections [5].…”

A Retrospective Analysis of the Spatial and Temporal Patterns of the West African Ebola Epidemic, 2014–2015

“…The analysis by Lau et al justifies our assumption that the epidemic largely remained within a given district, so that the urban and rural waves may be considered separately; in addition, the differences in timing mean that information from the earlier wave is valuable in understanding later developments. The results in Lau et al () also serve to highlight the potential benefits that could accrue from tracking the movements of infected persons as occurred with later isolation policies. Koch () describes how greater use of portable GPS devices in rural areas might assist in early identification, although the nature of this Ebola epidemic is such that such an approach would not have been effective on this occasion.…”

“…In retrospect, it was determined (see Lau et al ) that most of the transmissions were local and the spread to new areas could be linked to a small number of index cases. They state that “Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion (∼61%) of the infections.” That is, one or a few infected individuals traveled to the urban areas and spread the virus to those with whom they came into contact.…”

Red Cross volunteers’ experience with a mobile community event- based surveillance (CEBS) system in Sierra Leone during-and after the Ebola outbreak- A qualitative study

“…In addition to this, fear and distrust in the health facilities increased during the outbreak [14], and an increasing number of infected persons remained in their communities until they died [15], as so-called "superspreaders" causing a high number of secondary cases [16].…”