English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Dynamic models - often deterministic in nature - were used to estimate thebasic reproductive number, R_0, of the 2014-5 Ebola virus disease (EVD)epidemic outbreak in West Africa. Estimates of R_0 were then used to projectthe likelihood for large outbreak sizes, e.g., exceeding hundreds of thousandsof cases. Yet fitting deterministic models can lead to over-confidence in theconfidence intervals of the fitted R_0, and, in turn, the type and scope ofnecessary interventions. In this manuscript we propose a hybridstochastic-deterministic method to estimate R_0 and associated confidenceintervals (CIs). The core idea is that stochastic realizations of an underlyingdeterministic model can be used to evaluate the compatibility of candidatevalues of R_0 with observed epidemic curves. The compatibility is based oncomparing the distribution of expected epidemic growth rates with the observedepidemic growth rate given "process noise", i.e., arising due to stochastictransmission, recovery and death events. By applying our method to reported EVDcase counts from Guinea, Liberia and Sierra Leone, we show that prior estimatesof R_0 based on deterministic fits appear to be more confident than analysis ofstochastic trajectories suggests should be possible. Moving forward, werecommend including a hybrid stochastic-deterministic fitting procedure whenquantifying the full R_0 CI at the onset of an epidemic due to multiple sourcesof noise.

Stochasticity and the limits to confidence when estimating R_0 of Ebola and other emerging infectious diseases