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Beyond predicting the number of infections: predicting who is likely to be COVID negative or positive
Preprint
in English
| medRxiv
| ID: ppmedrxiv-20086348
ABSTRACT
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{middle dot}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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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Observational study
/
Prognostic study
Language:
English
Year:
2020
Document type:
Preprint