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
Rok vydání: 2020
Předmět:
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