key: cord-0425548-8fry4z5a authors: Gupta, A.; Saha, L. title: Correlation between the environmental parameters with outbreak pattern of COVID-19: A district level investigation based on yearlong period in India date: 2021-07-01 journal: nan DOI: 10.1101/2021.06.28.21259631 sha: 09bacc8afbce7b3f9bfd1ba823d7d7bb5904fa2e doc_id: 425548 cord_uid: 8fry4z5a The present study has investigated the role of regional meteorology and air quality parameters in the outbreak pattern of COVID-19 pandemic in India. Using the remote sensing based dataset of 12 environmental variables we correlated infective case counts at a district level in India. Our investigation carried out on the circumstantial data from more than 300 major affected districts in India and found that air quality parameters are playing very crucial role in this outbreak. Among the air pollutants, O3 was better correlating with infection counts followed by AOD, CO, NO2, BC and SO2. We also observed that among the weather parameters air temperature, incoming shortwave radiation, wind speed are positively and significantly associate with outbreak pattern and precipitation and humidity are negatively correlated with confirmed cases; only cloud cover has no significant relation. We noted that coastal districts in the both coast of India and districts located in the plain and low-lying areas have experienced bitter situation during this pandemic. Our study suggests that improving air quality with proper strict regulations and complete lockdown during the peak of pandemic could reduce the misfortune in all over India. In the 21st century, the world has been battle-scarred by the pernicious ordeal of coronavirus 2 disease (COVID-19) since early 2020. By the end of March, 2021, a total of 152,006,071 3 confirmed cases with a death toll of 3,193,315 have been registered all over the world 4 (https://www.worldometers.info/coronavirus/). This pneumonia infection caused by Severe 5 Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) had initially broken out in 6 Wuhan The district wise spatial distribution of COVID-19 confirmed cases and recoveries during the 116 1st COVID-19 wave (up to March, 2021) are shown in Figure 1 . It shows that 35.03% 117 districts had noted cases within the range of 10,000 to 1,00,000; while only 3.05% districts in 118 India registered more than 1 lakh cases during this study period. Noteworthy, most of the 119 districts have registered counts of infective cases within the range of 2500-5000 following the 120 range of 10000-25000. Comprehensively, 21 states registered more than 1 lakh infected 121 cases; among them only Maharashtra and Kerala had registered more than 10 lakh cases. The 122 spatial distribution of cases also depict that the coastal districts were more anguished by 123 misfortune than the districts located in the interior part of the country whereas the districts 124 from the Himalayan Mountains and its foothills had lesser strife during this pandemic. The 125 temporal pattern of daily cases of infections, recoveries, deceased, and test positivity (%) 126 from all over the country were shown in Figure 2a . It depicts that the peak of pandemic was 127 during the early September, 2020 when the daily death counts and confirmed cases were 128 highest, however daily test positivity was highest during July. These trends were at their 129 trough during the winter followed by the post-monsoon seasons but again started to incline 130 since March, 2021. 131 132 Figure 1 Distribution of confirmed cases 133 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 1, 2021. ; https://doi.org/10.1101/2021.06.28.21259631 doi: medRxiv preprint Pearson's correlation technique was applied to explore the degree of association of 137 environmental parameters with infected and recovered cases at 95% confidence interval using 138 equation 1. A correlogram is also being prepared to better represent the interrelation of the 139 variables of the input dataset. 140 infection which also could be accorded through the present study. Ciencewicki and Jaspers, 2007). In the current study we found all the seven air quality 179 parameters to be significantly and positively correlated with confirmed cases. Figure . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July Foreseeably, the poor air quality was perceived to be significantly correlated with the 263 COVID-19 dissemination during the study period. The positive correlation between coal 264 blazed pollution and COVID-19 outbreak, as well as the negative association between the 265 recovery count from this disease and prevailing regional pollution levels stoutly suggest that 266 the exorbitant air pollution due to bounteous non-renewable energy generation and its 267 perpetual consumption might perturb the inbred immunity of residents in circumjacent area, 268 and hinder them also in agile recovery from this disease. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 1, 2021. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 1, 2021. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 1, 2021. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 1, 2021. Coker . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 1, 2021. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over 578 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 1, 2021. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., 621 Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 1, 2021. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 1, 2021. ; Kim, J.W., Park, S., Lim, C.W., Lee CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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