key: cord-0840890-53v1olqq authors: Shahzadi, I.; Shahzadi, A.; Haider, J.; Naz, S.; Aamir, R. M.; Haider, A.; Sharif, H. R.; Khan, I. M.; Ikram, M. title: Impact of Meteorological factors and population size on the transmission of Micro-size respiratory droplets based Coronavirus: A brief study of highly infected cities in Pakistan date: 2020-07-17 journal: nan DOI: 10.1101/2020.07.14.20153544 sha: 0ec55584f02892ba57a79195f40d4c04067bb721 doc_id: 840890 cord_uid: 53v1olqq Ongoing Coronavirus epidemic (COVID-19) identified first in Wuhan, China posed huge impact on public health and economy around the globe. Both cough and sneeze based droplets or aerosols encapsulated COVID-19 particles are responsible for air borne transmission of this virus and caused unexpected escalation and high mortality worldwide. Current study intends to investigate correlation of COVID-19 epidemic with meteorological parameters particularly, temperature, rainfall, humidity, and wind speed along with population size. Data set of COVID-19 for highly infected cities of Pakistan was collected from the official website of National Institute of health (NIH). Spearman rank (rs) correlation coefficient test employed for data analysis revealed significant correlation between temperature minimum (TM), temperature average (TA), wind speed (WS) and population size (PS) with COVID-19 pandemic. Furthermore, receiver operating characteristics (ROC) curve was used to analyze the sensitivity of TA, WS, and PS on transmission rate of COVID-19 in selected cities of Pakistan. The results obtained for sensitivity and specificity analysis for all selected parameters signifies sensitivity and direct correlation of COVID-19 transmission with temperature variation, WS and PS. Positive correlation and strong association of PS parameter with COVID-19 pandemic suggested need of more strict actions and control measures for highly populated cities. These findings will be helpful for health regulatory authorities and policymakers to take specific measures to combat COVID-19 epidemic in Pakistan. International Committee on Taxonomy of Viruses named this novel coronavirus as severe acute 57 respiratory syndrome coronavirus 2 (SARS-CoV-2) on 11 th February 2020 (Yang and Wang, 58 2020). The COVID-19 has more severe effects than Severe Acute Respiratory Syndrome (SARS) 59 . CC-BY-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 17, 2020. small sized particles (size < 5μm) that transmit to larger distance and may lead to higher rate of 89 . CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint 157 The collected data was analyzed by spearman's rank correlation coefficient (rs) or Spearman's rho 158 (ρ) to determine the appropriate relationship between climatic variables and COVID-19 cases of 159 targeted cities. It is similar to Pearson correlation coefficient and is non-parametric test that 160 analyzes how well the association between two variables can be defined using a monotonic Given that the data used in this study are not normally distributed, it is appropriate to use 165 correlation coefficients for the analyses that can be calculated via the following equation. where n represents the number of alternatives, and di is the difference between the ranks of two 169 parameters. All statistical analysis was performed using Microsoft excel 2010. . CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint A probability curve known as receiver operating characteristic (ROC) curve has been widely is employed for ROC analysis while AUC was determined with 95% confidence intervals (CI). The AUC value for a perfect model is 1, worthless model is 0.5 and imperfect model is 0. The ROC curve is also referred as 1-specificity and sensitivity curve. as Lahore Karachi and Peshawar of Pakistan until July 4, 2020 are also provided (Fig. 2B) . 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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint April 9, 2020 to June 9, 2020. . CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint Table 1 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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint . CC-BY-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 17, 2020. In this study, we explore the statistical relationship between climatic variables and COVID-19 241 cases by spearman's correlation coefficient method followed by ROC curves analysis to CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint populated cities to control devastating effects of COVID-19. Although our findings suggested 292 strong correlation of COVID-19 epidemic and various meteorological parameters involving 293 extensive data analysis from various cities of Pakistan. Still, current study has certain limitations 294 as we did not consider other factors like personal hygeine, people mobility and endurance that 295 need to be considered in further studies. CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint finalized formatting and supervised the study. All authors have given approval to the final version 321 of the manuscript. Influenza and humidity-Why a bit more damp may be good for you! Droplet fate in indoor environments, or can we prevent the spread of infection? Toward understanding the risk of secondary 386 airborne infection: emission of respirable pathogens Coronavirus 388 disease (COVID-19) Pandemic and Pakistan World Health Organization (WHO) 2020. Critical preparedness, readiness and response actions 390 for COVID-19: interim guidance COVID-19 and Italy: what next? The Lancet The epidemiology and pathogenesis of coronavirus disease 393 (COVID-19) outbreak Impact of weather on COVID-19 pandemic in Turkey Temperature and latitude analysis to predict potential spread and seasonality for 398 COVID-19 Is Pakistan prepared to 400 tackle the coronavirus epidemic? Absolute humidity modulates influenza survival, transmission, and 402 seasonality Emerging 2019 novel 404 coronavirus (2019-nCoV) pneumonia ROC analysis applied to the evaluation of medical imaging techniques 323 The authors declare no conflict of interest. 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 17, 2020. 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 17, 2020. 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 17, 2020. CC-BY-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 17, 2020. . https://doi.org/10.1101/2020.07.14.20153544 doi: medRxiv preprint