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Beyond Predicting the Number of Infections: Predicting Who is Likely to Be COVID Negative or Positive

BACKGROUND: This study aims to identify individuals’ likelihood of being COVID negative or positive, enabling more targeted infectious disease prevention and control when there is a shortage of COVID-19 testing kits. METHODS: We conducted a primary survey of 521 adults on April 1–10, 2020 in Iran, w...

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Detalles Bibliográficos
Autores principales: Zhang, Stephen X, Sun, Shuhua, Afshar Jahanshahi, Asghar, Wang, Yifei, Nazarian Madavani, Abbas, Li, Jizhen, Mokhtari Dinani, Maryam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721298/
https://www.ncbi.nlm.nih.gov/pubmed/33299369
http://dx.doi.org/10.2147/RMHP.S273755
Descripción
Sumario:BACKGROUND: This study aims to identify individuals’ likelihood of being COVID negative or positive, enabling more targeted infectious disease prevention and control when there is a shortage of COVID-19 testing kits. METHODS: We conducted a primary survey of 521 adults on April 1–10, 2020 in Iran, where 3% reported being COVID-19 positive and 15% were unsure whether they were infected. This relatively high positive rate enabled us to conduct the analysis at the 5% significance level. RESULTS: Adults who exercised more were more likely to be COVID-19 negative. Each additional hour of exercise per day predicted a 78% increase in the likelihood of being COVID-19 negative. Adults with chronic health issues were 48% more likely to be COVID-19 negative. Those working from home were the most likely to be COVID-19 negative, and those who had stopped working due to the pandemic were the most likely to be COVID-19 positive. Adults employed in larger organizations were less likely to be COVID-19 positive. CONCLUSION: This study enables more targeted infectious disease prevention and control by identifying the risk factors of COVID-19 infections from a set of readily accessible information. We hope this research opens a new research avenue to predict the individual likelihood of COVID-19 infection by risk factors.