A simple approach to measure transmissibility and forecast incidence

Nouvellet, Pierre, Cori, Anne, Garske, Tini, Blake, Isobel M, Dorigatti, Ilaria, Hinsley, Wes, Jombart, Thibaut, Mills, Harriet L, Nedjati-Gilani, Gemma, Van Kerkhove, Maria D, Fraser, Christophe, Donnelly, Christl A, Ferguson, Neil M and Riley, Steven (2018) A simple approach to measure transmissibility and forecast incidence. Epidemics, 22. pp. 29-35. ISSN 1755-4365

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Abstract

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen "future" simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes - other than the widespread depletion of susceptible individuals - that produce non-exponential patterns of incidence.

Item Type: Article
Schools and Departments: School of Life Sciences > Evolution, Behaviour and Environment
Depositing User: Pierre Nouvellet
Date Deposited: 17 Aug 2018 10:49
Last Modified: 17 Aug 2018 10:50
URI: http://srodev.sussex.ac.uk/id/eprint/77898

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