2024-03-28T20:52:42Zhttp://repisalud.isciii.es/oai/requestoai:repisalud.isciii.es:20.500.12105/145072023-08-31T07:15:42Zcom_20.500.12105_15322com_20.500.12105_2051com_20.500.12105_2053com_20.500.12105_2052col_20.500.12105_16981col_20.500.12105_2054
00925njm 22002777a 4500
dc
De Salazar, Pablo M
author
Lu, Fred
author
Hay, James A
author
Gomez-Barroso, Diana
author
Fernandez-Navarro, Pablo L
author
Martínez, Elena V
author
Astray-Mochales, Jenaro
author
Amillategui Dos Santos, Rocío
author
García-Fulgueiras, Ana
author
Chirlaque, Maria D
author
Sánchez-Migallón, Alonso
author
Larrauri, Amparo
author
Sierra, María J
author
Lipsitch, Marc
author
Simón, Fernando
author
Santillana, Mauricio
author
Hernán, Miguel A
author
2022-03-31
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.
PLoS Comput Biol. 2022 Mar 31;18(3):e1009964.
http://hdl.handle.net/20.500.12105/14507
35358171
10.1371/journal.pcbi.1009964
1553-7358
PLoS Computational Biology
Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data