Analysis of Individual-Level Epidemic Data: Study of 2018-2020 Ebola Outbreak in Democratic Republic of the Congo

Abstract

The 2018-2020 Ebola virus disease epidemic in Democratic Republic of the Congo (DRC) resulted in 3481 cases (probable and confirmed) and 2299 deaths. In this paper, we use a novel statistical method to analyze the individual-level incidence and hospitalization data on DRC Ebola victims. Our analysis suggests that an increase in the rate of quarantine and isolation by approximately 12% during the epidemic’s third and final wave was likely responsible for the eventual containment of the outbreak. The analysis further reveals that the total effective population size or the average number of individuals at risk for the disease exposure in three epidemic waves over the period of 24 months was around 19,000—a much smaller number than previously estimated and likely an evidence of at least partial protection of the population at risk through ring vaccination and contact tracing as well as adherence to strict quarantine and isolation policies.

Dynamic survival analysis for non-Markovian epidemic models

“…The analysis further helped quantify the indirect effect of vaccination on the unvaccinated young population in Israel. In [19], the DSA method was used to analyse the individual-level epidemic data from the Ebola pandemic in the Democratic Republic of Congo, suggesting success of the ring vaccination and contact tracing efforts evident from much lower estimates of the effective population size than previous analyses.…”

“…The DSA method combines dynamical systems theory and survival analysis. For a given dynamical system, typically described by ODEs or PDEs for population counts/proportions, the DSA method provides an alternative interpretation that characterizes probability laws of transfer times [9,17,19,21]. The mathematical underpinning is provided by a novel application of the Sellke construction.…”

“…Suppose we have a random sample of infection times t 1 , t 2 , …, t K from an epidemic trajectory observed partially until time T , for some finite, positive number T . Then, following the mean-field independence, the contribution of the infection times to the DSA likelihood function is given byℓIfalse(θfalse) :=∏i=1KfT,θfalse(tifalse).The contribution ℓ I can be modified in a straightforward fashion if the infection times are censored and/or truncated [19].…”

Dynamic Survival Analysis for non-Markovian Epidemic Models

“…The analysis further helped quantify the indirect effect of vaccination on the unvaccinated young population in Israel. In [36], the DSA method was used to analyse the individual-level epidemic data from the Ebola pandemic in the Democratic Republic of Congo suggesting success of the ring vaccination and contact tracing efforts evident from much lower estimate of the effective population size than previous analyses.…”