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Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics.
Liu, Andrew Bo; Lee, Daniel; Jalihal, Amogh Prabhav; Hanage, William P; Springer, Michael.
Afiliação
  • Liu AB; Department of Systems Biology, Harvard Medical School; Boston, MA, USA.
  • Lee D; Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA.
  • Jalihal AP; Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA.
  • Hanage WP; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, MA, USA.
  • Springer M; Department of Systems Biology, Harvard Medical School; Boston, MA, USA.
medRxiv ; 2023 Oct 06.
Article em En | MEDLINE | ID: mdl-37398047
Researchers and policymakers have proposed systems to detect novel pathogens earlier than existing surveillance systems by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases (standard error: 76 cases) compared to 3,400 (standard error: 161 cases). Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Monitoring of air travel provides little benefit in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Screening_studies Idioma: En Revista: MedRxiv Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Screening_studies Idioma: En Revista: MedRxiv Ano de publicação: 2023 Tipo de documento: Article