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

Pablo M De Salazar, Fred Lu, James A Hay, Diana Gómez-Barroso, Pablo Fernández-Navarro, Elena V Martínez, Jenaro Astray-Mochales, Rocío Amillategui, Ana García-Fulgueiras, Maria D Chirlaque, Alonso Sánchez-Migallón, Amparo Larrauri, María J Sierra, Marc Lipsitch, Fernando Simón, Mauricio Santillana and Miguel A Hernán

PLOS Computational Biology, 2022, vol. 18, issue 3, 1-14

Abstract: 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.Author summary: When surveillance systems cannot be repurposed quickly enough for novel infectious agents, missing information becomes a major challenge in monitoring the outbreak in real time. We propose a statistical approach that uses available data to construct the epidemic curves, which describe the number of individuals infected over time. We apply our 3-step approach to estimate these real-time epidemic curves during the early SARS-CoV-2 outbreak in Spain. We demonstrate that our approach, combined with the understanding of its limitations, can (a) provide useful information earlier and more reliably than conventional surveillance approaches, and (b) aid in the decision-making process towards outbreak mitigation in real-time.

Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009964 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 09964&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1009964

DOI: 10.1371/journal.pcbi.1009964

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().