Your browser doesn't support javascript.
Estimating unobserved SARS-CoV-2 infections in the United States.
Perkins, T Alex; Cavany, Sean M; Moore, Sean M; Oidtman, Rachel J; Lerch, Anita; Poterek, Marya.
  • Perkins TA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556; taperkins@nd.edu.
  • Cavany SM; Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556.
  • Moore SM; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556.
  • Oidtman RJ; Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556.
  • Lerch A; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556.
  • Poterek M; Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556.
Proc Natl Acad Sci U S A ; 117(36): 22597-22602, 2020 09 08.
Article Dans Anglais | MEDLINE | ID: covidwho-725308
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during its initial invasion of the United States remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, we estimated that 108,689 (95% posterior predictive interval [95% PPI] 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model's predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median 0.98; 95% PPI 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States.
Sujets)
Mots clés

Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Sujet Principal: Pneumopathie virale / Infections à coronavirus / Maladies transmissibles émergentes / Modèles théoriques Type d'étude: Étude diagnostique / Étude observationnelle / Étude pronostique Limites du sujet: Humains Pays comme sujet: Amérique du Nord langue: Anglais Revue: Proc Natl Acad Sci U S A Année: 2020 Type de document: Article

Documents relatifs à ce sujet

MEDLINE

...
LILACS

LIS


Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Sujet Principal: Pneumopathie virale / Infections à coronavirus / Maladies transmissibles émergentes / Modèles théoriques Type d'étude: Étude diagnostique / Étude observationnelle / Étude pronostique Limites du sujet: Humains Pays comme sujet: Amérique du Nord langue: Anglais Revue: Proc Natl Acad Sci U S A Année: 2020 Type de document: Article