Author: Zhang, S. X.; Sun, S.; Jahanshahi, A. A.; Wang, Y.; Madavani, A. N.; Dinani, M. M.
Title: Beyond predicting the number of infections: predicting who is likely to be COVID negative or positive Cord-id: 2jhrd9ex Document date: 2020_5_5
ID: 2jhrd9ex
Snippet: This study provides the first attempt to identify people at greater risk of COVID-19 infection, enabling more targeted infectious disease prevention and control, which are especially important in the ongoing shortage of COVID-19 testing. We conducted a primary survey of 521 adults on April 1-10, 2020 in Iran, where the official infection rate was 0.08%. In our sample, 3% reported being COVID-19 positive and 15% were unsure of their status. This relatively high positive rate enabled us to conduct
Document: This study provides the first attempt to identify people at greater risk of COVID-19 infection, enabling more targeted infectious disease prevention and control, which are especially important in the ongoing shortage of COVID-19 testing. We conducted a primary survey of 521 adults on April 1-10, 2020 in Iran, where the official infection rate was 0.08%. In our sample, 3% reported being COVID-19 positive and 15% were unsure of their status. This relatively high positive rate enabled us to conduct the analysis at the 5% significance level. At the time of the survey, 44% of the adults worked from home; 26% still went to work in their workplaces; 27% had stopped working due to the COVID-19 pandemic; and 3% were unemployed. 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 medical illnesses were 48% more likely to be COVID-19 negative. In terms of work situation, those who worked from home were the most likely to be COVID-19 negative, and those who had stopped working were the most likely to be COVID-19 positive. Individuals in larger organizations were less likely to be COVID-19 positive. Given the testing shortage in many countries, we identify a novel approach to predict the likelihood of COVID-19 infection by a set of personal and work situation characteristics, in order to help to identify individuals with more or less risk of contracting the virus. We hope this research opens a new research avenue to identify the individual risk factors of COVID-19 infection to enable more targeted infectious disease prevention, communication, testing, and control to complement the effort to expand testing capacity.
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