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      The role of environmental factors on transmission rates of the COVID-19 outbreak: an initial assessment in two spatial scales

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          Abstract

          First identified in Wuhan, China, in December 2019, a novel coronavirus (SARS-CoV-2) has affected over 16,800,000 people worldwide as of July 29, 2020 and was declared a pandemic by the World Health Organization on March 11, 2020. Influenza studies have shown that influenza viruses survive longer on surfaces or in droplets in cold and dry air, thus increasing the likelihood of subsequent transmission. A similar hypothesis has been postulated for the transmission of COVID-19, the disease caused by SARS-CoV-2. It is important to propose methodologies to understand the effects of environmental factors on this ongoing outbreak to support decision-making pertaining to disease control. Here, we examine the spatial variability of the basic reproductive numbers of COVID-19 across provinces and cities in China and show that environmental variables alone cannot explain this variability. Our findings suggest that changes in weather (i.e., increase of temperature and humidity as spring and summer months arrive in the Northern Hemisphere) will not necessarily lead to declines in case counts without the implementation of drastic public health interventions.

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          Absolute humidity and pandemic versus epidemic influenza.

          Experimental and epidemiologic evidence indicates that variations of absolute humidity account for the onset and seasonal cycle of epidemic influenza in temperate regions. A role for absolute humidity in the transmission of pandemic influenza, such as 2009 A/H1N1, has yet to be demonstrated and, indeed, outbreaks of pandemic influenza during more humid spring, summer, and autumn months might appear to constitute evidence against an effect of humidity. However, here the authors show that variations of the basic and effective reproductive numbers for influenza, caused by seasonal changes in absolute humidity, are consistent with the general timing of pandemic influenza outbreaks observed for 2009 A/H1N1 in temperate regions, as well as wintertime transmission of epidemic influenza. Indeed, absolute humidity conditions correctly identify the region of the United States vulnerable to a third, wintertime wave of pandemic influenza. These findings suggest that the timing of pandemic influenza outbreaks is controlled by a combination of absolute humidity conditions, levels of susceptibility, and changes in population-mixing and contact rates.
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            Temperature significant change COVID-19 Transmission in 429 cities

            Background There is no evidence supporting that temperature changes COVID-19 transmission. Methods We collected the cumulative number of confirmed cases of all cities and regions affected by COVID-19 in the world from January 20 to February 4, 2020, and calculated the daily means of the average, minimum and maximum temperatures in January. Then, restricted cubic spline function and generalized linear mixture model were used to analyze the relationships. Results There were in total 24,232 confirmed cases in China and 26 overseas countries. In total, 16,480 cases (68.01%) were from Hubei Province. The lgN rose as the average temperature went up to a peak of 8.72℃ and then slowly declined. The apexes of the minimum temperature and the maximum temperature were 6.70℃ and 12.42℃ respectively. The curves shared similar shapes. Under the circumstance of lower temperature, every 1℃ increase in average, minimum and maximum temperatures led to an increase of the cumulative number of cases by 0.83, 0.82 and 0.83 respectively. In the single-factor model of the higher-temperature group, every 1℃ increase in the minimum temperature led to a decrease of the cumulative number of cases by 0.86. Conclusion The study found that, to certain extent, temperature could significant change COVID-19 transmission, and there might be a best temperature for the viral transmission, which may partly explain why it first broke out in Wuhan. It is suggested that countries and regions with a lower temperature in the world adopt the strictest control measures to prevent future reversal.
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              Analysis of meteorological conditions and prediction of epidemic trend of 2019-nCoV infection in 2020

              Objective: To investigate the meteorological condition for incidence and spread of 2019-nCoV infection, to predict the epidemiology of the infectious disease, and to provide a scientific basis for prevention and control measures against the new disease. Methods: The meteorological factors during the outbreak period of the novel coronavirus pneumonia in Wuhan in 2019 were collected and analyzed, and were confirmed with those of Severe Acute Respiratory Syndrome (SARS) in China in 2003. Data of patients infected with 2019-nCoV and SARS coronavirus were collected from WHO website and other public sources. Results: This study found that the suitable temperature range for 2019-nCoV coronavirus survival is (13-24 degree Celsius), among which 19 degree Celsius lasting about 60 days is conducive to the spread between the vector and humans; the humidity range is 50%-80%, of which about 75% humidity is conducive to the survival of the coronavirus; the suitable precipitation range is below 30 mm/ month. Cold air and continuous low temperature over one week are helpful for the elimination of the virus. The prediction results show that with the approach of spring, the temperature in north China gradually rises, and the coronavirus spreads to middle and high latitudes along the temperature line of 13-18 degree Celsius. The population of new coronavirus infections is concentrated in Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai and other urban agglomerations. Starting from May 2020, the Beijing-Tianjin-Hebei urban agglomeration, the Central China Zhengzhou-Wuhan urban agglomeration, the eastern Jiangsu-Zhejiang-Shanghai urban agglomeration, and the southern Pearl River Delta urban agglomeration are all under a high temperature above 24 degree Celsius, which is not conducive to the survival and reproduction of coronaviruses, so the epidemic is expected to end. Conclusions: A wide range of continuous warm and dry weather is conducive to the survival of 2019-nCoV. The coming of spring, in addition to the original Wuhan-Zhengzhou urban agglomeration in central China, means that the prevention and control measures in big cities located in mid-latitude should be strengthened, especially the monitoring of transportation hubs. The Pearl River Delta urban agglomeration is a concentrated area of population in south China, with a faster temperature rise than those in mid-high latitudes, and thus the prevention in this area should be prioritized. From a global perspective, cities with a mean temperature below 24 degree Celsius are all high-risk cities for 2019-nCoV transmission before June.
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                Author and article information

                Contributors
                canelle.poirier@outlook.fr
                msantill@g.harvard.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 October 2020
                12 October 2020
                2020
                : 10
                Affiliations
                [1 ]GRID grid.2515.3, ISNI 0000 0004 0378 8438, Computational Health Informatics Program, Boston Children’s Hospital, ; Boston, MA 02215 USA
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Pediatrics, , Harvard Medical School, ; Boston, MA 02215 USA
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA 02215 USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Earth and Planetary Sciences, , Harvard University, ; Cambridge, MA 02138 USA
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard T.H. Chan School of Public Health, ; Boston, MA 02215 USA
                Article
                74089
                10.1038/s41598-020-74089-7
                7552413
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: National Institute Of General Medical Sciences of the National Institutes of Health
                Award ID: R01GM130668
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                © The Author(s) 2020

                Uncategorized

                diseases, statistical methods, infectious diseases

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