2016/01/21 by Bradford P. Taylor, Jonathan Dushoff, Taylor, Bradford P +3
Mathematics · Medicine · #COVID-19 epidemiological studies #FOS: Biological sciences #Populations and Evolution (q-bio.PE) #Viral Infections and Outbreaks Research
paper · pdf · doi:10.48550/arxiv.1601.06829
openalex publication_date 2016/01/21 · openalex created_date 2022/08/15 · openalex updated_date 2026/07/28
Dynamic models - often deterministic in nature - were used to estimate the\nbasic reproductive number, R0, of the 2014-5 Ebola virus disease (EVD)\nepidemic outbreak in West Africa. Estimates of R0 were then used to project\nthe likelihood for large outbreak sizes, e.g., exceeding hundreds of thousands\nof cases. Yet fitting deterministic models can lead to over-confidence in the\nconfidence intervals of the fitted R0, and, in turn, the type and scope of\nnecessary interventions. In this manuscript we propose a hybrid\nstochastic-deterministic method to estimate R0 and associated confidence\nintervals (CIs). The core idea is that stochastic realizations of an underlying\ndeterministic model can be used to evaluate the compatibility of candidate\nvalues of R0 with observed epidemic curves. The compatibility is based on\ncomparing the distribution of expected epidemic growth rates with the observed\nepidemic growth rate given "process noise", i.e., arising due to stochastic\ntransmission, recovery and death events. By applying our method to reported EVD\ncase counts from Guinea, Liberia and Sierra Leone, we show that prior estimates\nof R0 based on deterministic fits appear to be more confident than analysis of\nstochastic trajectories suggests should be possible. Moving forward, we\nrecommend including a hybrid stochastic-deterministic fitting procedure when\nquantifying the full R0 CI at the onset of an epidemic due to multiple sources\nof noise.\n