Improved inference of time-varying reproduction numbers during infectious disease outbreaks

By Robin N. Thompson, J. E. Stockwin, R. D. Gaalen, J. Polonsky, Zhian N. Kamvar, P. A. Demarsh, Elisabeth Dahlqwist, S. Li, E. Miguel, T. Jombart and 3 more. Epidemics, 2019

488 citations recorded by the source corpus.

Highlights • Real-time estimation of reproduction numbers during outbreaks can guide control.• Using up-to-date serial interval data and accounting for imported cases is vital.• We develop a framework for estimating pathogen transmissibility appropriately.• We demonstrate it using data from outbreaks of influenza, Ebola and MERS.• Our approach is implemented in R package EpiEstim and online application EpiEstim App.

Subfields: Infectious Diseases, Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/27582eaf6289f830b57b3c6dcfc5fd5a29510e1c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Modeling and Simulation

1. Alessandro Vespignani
2. Cécile Viboud
3. Vittoria Colizza

In Public Health, Environmental and Occupational Health

1. Alessandro Vespignani
2. Cécile Viboud
3. Mark Newman

Citations in the corpus

• Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study (2020, methodology): “8 We used a Bayesian approach 9,10 to estimate R t from the time series of symptom onset dates and the distribution of the serial interval, considering importations and local transmission. 11 For this analysis, the last 9 days of the dataset were excluded to account for the possible incompleteness o”