Dataset Information

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

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.

SUBMITTER: De Salazar PM

PROVIDER: S-EPMC9004750 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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

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

PLoS computational biology 20220331 3

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 o ...[more]

PMID: 35358171

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Project description:IntroductionYemen was one of the last countries in the world to declare the first case of the pandemic, on 10 April 2020. Fear and concerns of catastrophic outcomes of the epidemic in Yemen were immediately raised, as the country is facing a complex humanitarian crisis. The purpose of this report is to describe the epidemiological situation in Yemen during the first 2 months of the SARS-CoV-2 epidemic.MethodsWe analyzed the epidemiological data from 18 February to 05 June 2020, including the 2 months before the confirmation of the first case. We included in our analysis the data from 10 out of 23 governorates of Yemen, located in southern and eastern part of the country.ResultsA total of 469 laboratory confirmed, 552 probable and 55 suspected cases with onset of symptoms between 18 February and 5 June 2020 were reported through the surveillance system. The median age among confirmed cases was 46 years (range: 1-90 years), and 75% of the confirmed cases were male. A total of 111 deaths were reported among those with confirmed infection. The mean age among those who died was 53 years (range: 14-88 years), with 63% of deaths (n = 70) occurring in individuals under the age 60 years. A total of 268 individuals with confirmed SARS-CoV-2 infection were hospitalized (57%), among whom there were 95 in-hospital deaths.ConclusionsThe surveillance strategy implemented in the first 2 months of the SARS CoV 2 in the southern and eastern governorates of Yemen, captured mainly severe cases. The mild and moderate cases were not self-reported to the health facilities and surveillance system due to limited resources, stigma, and other barriers. The mortality appeared to be higher in individuals aged under 60 years, and most fatalities occurred in individuals who were in critical condition when they reached the health facilities. It is unclear whether the presence of other acute comorbidities contributed to the high death rate among SARS-CoV-2 cases. The findings only include the southern and eastern part of the country, which is home to 31% of the total population of Yemen, as the data from the northern part of the country was inaccessible for analysis. This makes our results not generalizable to the rest of the country.

Project description:BackgroundData from poison centers have the potential to be valuable for public health surveillance of long-term trends, short-term aberrations from those trends, and poisonings occurring in near-real-time. This information can enable long-term prevention via programs and policies and short-term control via immediate public health response. Over the past decade, there has been an increasing use of poison control data for surveillance in the United States, Europe, and New Zealand, but this resource still remains widely underused.ObjectiveThe British Columbia (BC) Drug and Poison Information Centre (DPIC) is one of five such services in Canada, and it is the only one nested within a public health agency. This study aimed to demonstrate how DPIC data are used for routine public health surveillance in near-real-time using the case study of its alerting system for illness related to consumption of shellfish (ASIRCS).MethodsEvery hour, a connection is opened between the WBM software Visual Dotlab Enterprise, which holds the DPIC database, and the R statistical computing environment. This platform is used to extract, clean, and merge all necessary raw data tables into a single data file. ASIRCS automatically and retrospectively scans a 24-hour window within the data file for new cases related to illnesses from shellfish consumption. Detected cases are queried using a list of attributes: the caller location, exposure type, reasons for the exposure, and a list of keywords searched in the clinical notes. The alert generates a report that is tailored to the needs of food safety specialists, who then assess and respond to detected cases.ResultsThe ASIRCS system alerted on 79 cases between January 2015 and December 2016, and retrospective analysis found 11 cases that were missed. All cases were reviewed by food safety specialists, and 58% (46/79) were referred to designated regional health authority contacts for follow-up. Of the 42% (33/79) cases that were not referred to health authorities, some were missing follow-up information, some were triggered by allergies to shellfish, and some were triggered by shellfish-related keywords appearing in the case notes for nonshellfish-related cases. Improvements were made between 2015 and 2016 to reduce the number of cases with missing follow-up information.ConclusionsThe surveillance capacity is evident within poison control data as shown from the novel use of DPIC data for identifying illnesses related to shellfish consumption in BC. The further development of surveillance programs could improve and enhance response to public health emergencies related to acute illnesses, chronic diseases, and environmental exposures.

Dataset Information

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

Publications

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

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