key: cord-0958737-ycrrsr5c authors: pawar, shrikant; Stanam, Aditya; Chaudhari, Mamata; Rayudu, Durga title: Effects of temperature on COVID-19 transmission date: 2020-03-30 journal: nan DOI: 10.1101/2020.03.29.20044461 sha: 22f2920d4ce3a7382321f71a5f2268275fd7ec98 doc_id: 958737 cord_uid: ycrrsr5c Coronavirus disease 2019 (COVID19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV2), it was first identified in 2019 in Wuhan, China and has resulted in the 2019-20 coronavirus pandemic. As of March 1, 2020, 79,968 patients in China and 7169 outside of China had tested positive for COVID19 and a mortality rate of 3.6% has been observed amongst Chinese patients. Its primary mode of transmission is via respiratory droplets from coughs and sneezes. The virus can remain viable for up to three days on plastic and stainless steel or in aerosols for upto 3 hours and is relatively more stable than the known human coronaviruses. It is stable in faeces at room temperature for at least 1-2 days and can be stable in infected patients for up to 4 days. Heat at 56 degree Celsius kills the SARS coronavirus at around 10000 units per 15 minutes. Thus, temperature is an important factor in survival of COVID19 virus and this article focuses on understanding the relationship between temperature and COVID19 transmission from the data available between January-March 2020. Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) [1] . Ever since, the disease was first identified in 2019 in Wuhan, China, it has spread worldwide quickly. The World Health Organization (WHO) declared the 2019-2020 coronavirus outbreak a pandemic and a Public Health Emergency of International Concern (PHEIC). As of March 20, 2020, more than 240,000 cases have been reported, and more 10,000 people have lost their lives [2] . When the SARS outbreak happened in 2002-2003, SARS-CoV spread to approximately 30 countries only [3] . However, the current outbreak of COVID-19 by SARS-CoV-2 has spread to 176 countries or territories which suggests the enhanced ability of viruses to be aerosolized [2] . Environmental factors such as atmospheric temperature modulates the survival and spread of virus aerosols. It was shown that survival of influenza viral aerosols is reduced at higher temperatures [4] . In this study, we aimed to ascertain the relation between atmospheric temperatures and the COVID-19 disease recovery rates across the globe. The data used in this study comes from Chinese medical community website DXY reporting COVID-19 cases at the province level in China [5] . The data for countries and regions outside mainland China is collected from an interactive web-based dashboard to track COVID-19 in real time [6] . DXY updates case counts every 15 minutes in all provinces in China while other countries' case counts are manually updated in a web-based dashboard from centres for disease control and prevention (CDC) of Taiwan, Europe, the World Health Organization (WHO), the government of Canada, and the Australian government [6] . The location of confirmed, death and recovered cases was obtained from the coordinate points through NGS coordinate conversion and transformation tool (NCAT) [7] . The respective temperature information for each location was obtained from Raspisaniye Pogodi (RP) temperature channel, Petersburg, Russia having license for the activity in hydrometeorology and adjacent fields. The website provides six day temperature forecasts and information on the actual temperature, observed at ground stations where the forecasts are completely updated twice a day (05:30 and 17:30 UTC) [8] . Temperature reports were downloaded for each location for the day range of 01/22/2020-03/16/2020 which were correlated with the number of confirmed positive cases, deaths and recoveries. The ggpairs() function from the GGally package was used to create a plot matrix to see how the variables relate to each other [9] . The coefficient errors provide a variation of the estimated coefficients from the actual average while the t-value measures how many standard deviations the estimated coefficient is from zero. Residuals describe how well does the model fit the data and a bell curve distribution is an ideal for any linear fit [10] . Residuals, coefficients, degrees of freedom, R-squared and F-statistic values are provided in Table 1 . i. Countries including China, Iran, Italy, Germany and France showed maximum impact with COVID-19 transmission and morbidity: The data retrieved from 01/22/2020-03/16/2020 showed 81033 confirmed positive cases of COVID-19 infection in China, 27980 in Italy, 7272 in Germany, 6650 in France and 14991 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) 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 March 30, 2020. . ii. Changes in temperature shows no significant correlation with cases transmitted, deaths or recovered: Linear regression describes the relationship between a response variable of interest and one or more predictor variables by separating the signal from the noise [11] . Correlation analysis between temperatures, cases confirmed positive, dead and recovered was performed separately for overall countries in the month of January, February and March of 2020. For cases confirmed positive, dead and recovered, a total number of cases was used as a variable value while an average of temperature (degree celsius (°C)) for each of the months was used as a variable temperature value. Comparing overall cases for temperature, no significant correlation between temperatures and cases confirmed positive, dead or recovered was observed ( Figure 5A ), however, a strong correlation (~0.9) was observed between cases confirmed and deaths for each of the three months. To further investigate effects of temperature, country (Australia) with highest average temperature (January~18°C, February~17°C, March~16°C) was compared separately with the country (Canada) with lowest average temperature (January~-3°C, February~-5°C, March~-2°C) for the month of March 2020 ( Figure 5B : Top Left and Top Right). Again, no significant correlation between temperatures and cases confirmed positive, dead or recovered was observed, however, an interesting correlation pattern was seen where a drop in correlation (~0.5) was found between cases confirmed and recovered in Australia and a drop in correlation (~0.3) was found between cases confirmed and deaths in Canada. China being the earliest and highly affected country with COVID-19 transmission, separate correlation plots were generated between temperatures and cases confirmed positive, dead or recovered for the months January-March 2020 ( Figure 5B : Bottom Left, Bottom Right; Figure 5C : Left). While no significant correlation between temperatures and cases confirmed positive, dead or recovered was observed, a strong correlation between cases confirmed and recovered (~0.9), and cases confirmed and deaths (~1) was recorded. Finally, a correlation matrix ( Figure 5C : Right) for the month of March 2020 for the US was generated and again no significant correlations were observed between any of the selected variables. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. A multiple regression analysis was performed to create a model for predicting deaths from cases confirmed and an average temperature of certain locations. A strong correlation between cases confirmed with deaths and cases confirmed with recovered was observed, so a robust regression model was expected. Although, we found insignificant correlations between temperatures and cases confirmed positive, dead or recovered, considering it as one of the variables in multiple regression model won't make a significant difference. A significant P value and F score was obtained (Table 1 Data analyzed from 01/22/2020-03/16/2020 for COVID-19 confirmed positive, death and recovered cases identify an increasing number of cases being recovered (~83%) especially in China, while Iran and Italy showed a slow recovery with 30% and 37% recovery rates respectively. China reported the highest percent deaths (~3.96%) in the world. Although a strong correlation between cases confirmed with recovered, and cases confirmed with deaths was observed, changes in temperature showed no significant correlation with cases transmitted, deaths or recovered for the period 01/22/2020-03/16/2020. However, an interesting finding was observed wherein a drop in correlation was found between cases confirmed with recovered in Australia (average temperature ~16°C) and cases confirmed with deaths in Canada (average temperature ~-2°C). Finally, a multiple regression model was generated using predicted April 2020 temperatures with confirmed positive cases for March 2020 for Australia, Canada, China, and the US to predict the number of deaths in April 2020. The generated model predicted ~14, 16, 3972 and 223 more deaths for countries Australia, Canada, China, and the US respectively. This data and model signifies urgency of confining COVID-19's pandemic spread with the predicted rise in number of deaths. SP and AS conceived the concepts, planned and designed the article. SP and AS primarily wrote and edited the manuscript. DR and MC supported the data collection and analysis. The authors declare that they have no competing interests. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) 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 March 30, 2020. . No external funding has been utilized for this study. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) 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 March 30, 2020. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) 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 March 30, 2020. . All rights reserved. No reuse allowed without permission. (which was not certified by peer review) 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 March 30, 2020. . All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) 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 March 30, 2020. . https://doi.org/10.1101/2020.03.29.20044461 doi: medRxiv preprint File 2: Number of cases positive, dead and recovered with latitude, longitude and average temperatures from 01/22/2020-03/16/2020 divided within 3 months. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) 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 March 30, 2020. . https://doi.org/10.1101/2020.03.29.20044461 doi: medRxiv preprint COVID-19) and the virus that causes it. World Health Organization COVID-19) outbreak situation. World Health Organization Cumulative number of reported probable cases of Severe Acute Respiratory Syndrome (SARS) Using the systematic review methodology to evaluate factors that influence the persistence of influenza virus in environmental matrices Chinese Center for Disease Control and Prevention. Tracking the epidemic An interactive web-based dashboard to track COVID-19 in real time Coordinate Conversion and Transformation Tool (NCAT) The Generalized Pairs Plot Residuals and Influence in Regression (Repr