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Year : 2020  |  Volume : 16  |  Issue : 3  |  Page : 115-119

Thinking beyond lockdowns and social distancing: Identifying the predictors of spread for COVID-19 pandemic using ecological correlation study

1 Department of Community Medicine, Dr. RP Government Medical College, Tanda, Himachal Pradesh, India
2 Department of Community Medicine, ESIC Medical College and Hospital, Faridabad, Haryana, India

Correspondence Address:
Dr. Sunil Kumar Raina
Department of Community Medicine, Dr. RP Government Medical College, Tanda - 176 002, Himachal Pradesh
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/AMJM.AMJM_39_20

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Background: COVID-19 (SARSCoV2 or 2019 novel coronavirus [nCov]) pandemic has spread to every continent except Antarctica, and cases have been rising daily globally. However, COVID-19 is not just a health crisis. Aim: The present study was aimed to identify the predictors defining spread of the pandemic. Methodology: An ecological correlation study was conducted to identify the factors predicting the spread of COVID-19 (SARSCoV2 or 2019 nCov) pandemic worldwide. For this purpose, countries affected with Covid-19 worldwide from both the northern and southern hemispheres were included using a predefined inclusion criteria. Data from the selected countries were retrieved for the duration extending from January 1, 2020, to March 31, 2020. Results: A significant moderate positive correlation between cumulative Covid-19 cases and number of motor vehicles registered per 100 persons was observed in January 2020 (r = 0.623, P = 0.01) and March 2020 (r = 0.620, P = 0.01). Population density remained positively correlated with the cases of Covid-19. A strong significant correlation was observed in February (r = 0.746, P = 0.001). Increase in the length of highway road in countries was statistically significantly correlated with the increase of cases in the month of March 2020 (r = 0.644, P = 0.007). However, after accounting for all the variables, none of the variables could be identified as an independent predictor of cumulative cases except for time (in form of months). The R2 of the model was 41.4%. Conclusions: Urbanization, urban clustering, and population density may be the important contributors in spread; however, their role as independent predictor may be questionable. Therefore, it is time to think beyond social distancing and lockdowns and strengthen primary care and public health.

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