Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data

Journal article

When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated "backward" reporting delay conditional distribution, and 2) adjustment for right censoring using the NobBS software package to nowcast cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (Rt) in real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations.PMD was supported by the fellowship Ramón Areces Foundation. JAH was funded by the National Institute of General Medical Sciences, Award U54GM088558, and the National Institutes of Health Director’s Early Independence, Award DP5-OD028145. ML was supported by the Morris-Singer Fund and by a subcontract from the Carnegie Mellon University under an award from the US Centers for Disease Control and Prevention, Award U01IP001121). MS was supported by the National Institute Of General Medical Sciences, Award R01GM130668-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.S

Authors: De Salazar, PM; Lu, F; Hay, JA; Gómez-Barroso, D; Fernández-Navarro, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Computational Biology
• Volume: 18
• Issue: 3
• Pages: e1009964-e1009964
• Publication date: 2022-03-31
• DOI: 10.1371/journal.pcbi.1009964
• EISSN: 1553-7358
• ISSN: 1553-734X
• Language: English
• Keywords: Epidemic model; Econometrics; Mathematics; Statistics; Medicine; Computer science; Censoring (clinical trials); Environmental health; Population