Syndromic surveillance of COVID-19 using crowdsourced data
As of August 20, 2020, COVID-19 has caused ~22.4 million co nfirmed cases and over 789,000 confirmed deaths, globally [1]. However, the global case and death counts are likely much higher due to a variety of factors, such as misdiagnoses during the early stages of the pandemic, testing disparities,...
- Autores:
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2020
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/14509
- Acceso en línea:
- https://doi.org/10.1016/j.lanwpc.2020.100024
http://hdl.handle.net/20.500.12010/14509
- Palabra clave:
- Syndromic surveillance
COVID-19
Using crowdsourced data
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
- Rights
- License
- Abierto (Texto Completo)
| Summary: | As of August 20, 2020, COVID-19 has caused ~22.4 million co nfirmed cases and over 789,000 confirmed deaths, globally [1]. However, the global case and death counts are likely much higher due to a variety of factors, such as misdiagnoses during the early stages of the pandemic, testing disparities, and high rates of asymptomatic carriers (up to 50%) of the SARS-CoV-2 virus [2]. Surveillance of COVID-19 has largely relied on confirmed case and death statistics, contact tracing, and projections via epidemiological modeling [3,4]. Furthermore, the timeliness of data availability often suffers from reporting delays due to the incubation period, testing lags, and others |
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