Using early detection data to estimate the date of emergence of an epidemic outbreak

• Published in: PLoS computational biology Vol. 20; no. 3; p. e1011934
• Main Authors: Jijón, Sofía, Czuppon, Peter, Blanquart, François, Débarre, Florence
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.03.2024; PLOS; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and life sciences; Control; COVID-19; Development and progression; Disease transmission; Epidemics; Epidemiology; Estimates; France; Genomics; Germany; Health aspects; Infection; Infections; Infectious diseases; Life Sciences; Medical research; Medicine and Health Sciences; Medicine, Experimental; Ordinary differential equations; Outbreaks; Pandemics; Pathogens; Population biology; Population dynamics; Research and Analysis Methods; Severe acute respiratory syndrome coronavirus 2; Simulation; Simulation methods; Stochastic analysis; Stochastic models; Stochasticity; Time series; Viral diseases; United Kingdom; China; France; Germany; United Kingdom > UK; SARS-COV-2
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011934

While the first infection of an emerging disease is often unknown, information on early cases can be used to date it. In the context of the COVID-19 pandemic, previous studies have estimated dates of emergence (e.g., first human SARS-CoV-2 infection, emergence of the Alpha SARS-CoV-2 variant) using mainly genomic data. Another dating attempt used a stochastic population dynamics approach and the date of the first reported case. Here, we extend this approach to use a larger set of early reported cases to estimate the delay from first infection to the Nth case. We first validate our framework by running our model on simulated data. We then apply our model using data on Alpha variant infections in the UK, dating the first Alpha infection at (median) August 21, 2020 (95% interpercentile range across retained simulations (IPR): July 23-September 5, 2020). Next, we apply our model to data on COVID-19 cases with symptom onset before mid-January 2020. We date the first SARS-CoV-2 infection in Wuhan at (median) November 28, 2019 (95% IPR: November 2-December 9, 2019). Our results fall within ranges previously estimated by studies relying on genomic data. Our population dynamics-based modelling framework is generic and flexible, and thus can be applied to estimate the starting time of outbreaks in contexts other than COVID-19.