Beyond Predicting the Number of Infections: Predicting Who is Likely to Be COVID Negative or Positive
Autor: | Stephen X. Zhang, Jizhen Li, Asghar Afshar Jahanshahi, Shuhua Sun, Yifei Wang, Maryam Mokhtari Dinani, Abbas Nazarian Madavani |
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Rok vydání: | 2020 |
Předmět: |
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19) business.industry 030503 health policy & services Health Policy Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Public Health Environmental and Occupational Health Economic shortage 03 medical and health sciences 0302 clinical medicine Infectious disease (medical specialty) Statistical significance Environmental health Pandemic Medicine 030212 general & internal medicine 0305 other medical science business |
Zdroj: | Risk Management and Healthcare Policy. 13:2811-2818 |
ISSN: | 1179-1594 |
DOI: | 10.2147/rmhp.s273755 |
Popis: | 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. |
Databáze: | OpenAIRE |
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