key: cord-0982660-lmjaldcs authors: Li, Jianfeng; Zhang, Linyuan; Ren, Zhihua; Xing, Caihong; Qiao, Peihuan; Chang, Bing title: Meteorological factors correlate with transmission of 2019-nCoV: Proof of incidence of novel coronavirus pneumonia in Hubei Province, China date: 2020-04-03 journal: nan DOI: 10.1101/2020.04.01.20050526 sha: 30a471ddef6335b88d487168148d283c0676806c doc_id: 982660 cord_uid: lmjaldcs Objective: many potential factors contribute to the outbreak of COVID-19.It aims to explore the effects of various meteorological factors on the incidence of COVID-19. Methods: Taking Hubei province of China as an example, where COVID-19 was first reported and there were the most cases, we collected 53 days of confirmed cases (total 67773 cases) and ten meteorological parameters up to March 10. Correlation analysis and linear regression were used to judge the relationship of meteorological factors and increment of COVID-19 confirmed cases. Results: Under 95% CI, the increment of confirmed cases in Hubei were correlated with four meteorological parameters of average pressure, average temperature, minimum temperature and average water vapor pressure (equivalent to absolute humidity).The average pressure was positively correlated with the increment (r=+0.358).The negative correlations included average temperature (r=-0.306), minimum temperature (r=-0.347), and average water vapor pressure (r=-0.326). The linear regression results show if minimum temperature increases by 1℃, the incremental confirmed cases in Hubei decreases by 72.470 units on average. Conclusion: Statistically, the incidence of COVID-19 was correlated with average pressure, average temperature, minimum temperature and average water vapor pressure. It is positively correlated with the average pressure and negatively correlated with the other three parameters. Compared with relative humidity, 2019-nCov is more sensitive to water vapor pressure. The reason why the epidemic situation in Hubei expanded rapidly is significantly related to the climate characteristics of low temperature and dryness of Hubei in winter. , swine flu virus (Lowen et al., 2007) 、 SARS-Cov (Chan et al., 2011) , MERS-CoV (van Doremalen et al., 2013) , coronavirus (Casanova et al., 2010; Kim et al., 2007) . The effects of different meteorological parameters (temperature, RH, absolute humidity, atmospheric pressure) and other meteorological factors on viral transmission or viral activity, were studied and some relevant mechanisms were revealed. However, it still cannot be fully explained. For example, studies have suggested that the survival of influenza virus is affected by the amount of water vapor pressure (equal to absolute humidity, AH) in the surrounding air, however, it is unclear why the stability of an influenza virus encased within a droplet nucleus would be sensitive to atmospheric water vapor pressure conditions. It is presumed that high atmospheric humidity levels will lead to surface inactivation of lipid-containing viruses, such as influenza viruses (Shaman et al. & Kohn, 2009 ) . A variety of statistical methods were used for mathematical modeling to further reveal the potential laws. Kim et al. believed that the incidence and mortality of diseases followed the Poisson distribution and used Poisson function as the link function to establish a generalized linear model (GLM) (Kim et al., 2016) . Lim et al. used the cross design of Poisson GLM and temperature-matched case. Through case cross design, personal characteristics and long-term and seasonal trends can be controlled (Lim et al., 2012) . GLM was also used in the studies of Onozuka et al. and Souza et al. (Souza et al., 2012; Onozuka et al., 2009) . For seasonal viral infectious diseases, Gurgel et al. and Yusuf et al. used time series analysis to reduce model deviations (Gurgel et al., 2016; Yusury et al., 2007) . Chan et al., du Prel et al., Lin et al., Varela et al. use the autoregressive integral moving average (ARIMA) model to control the autocorrelation of time series data (Chan et al ., 2002; du Prel et al., 2009; Lin et al., 2009; Varela et al., 2004) . Carreras et al. used a generalized additive model (GAM) using a quasi-Poisson distribution as link function. In this study, the daily temperature range (DTR) was used as the main predictor, and other control variables included day of the week, RH and PM10. These variables are included in the model as linear terms. In the secondary analysis, the same model was used, but all variables were stratified by season, age group, seasonal pattern and socioeconomic status (Carreras et al., 2015) . In China, another study used GAM and controlled long-term trends. In addition, the average temperature was fitted into the model to explore the logarithmic risk of daily hospitalization (Bai YL, et al.2019 ). However, whether this relationship exists in respiratory infections caused by viruses other than influenza is unclear, furthermore, laboratory studies of novel coronavirus (2019-ncov) related to meteorological factors have not been reported. So far, the research on the COVID-2019 and the climatic characteristics of Hubei province in China has not been reported. Similarly, the relationship of the COVID-19 epidemic and meteorological factors has been unavailable in published professional journals. The purpose of this study is to unfold whether the meteorological factors have influence on the 2019-nCoV transmission and the occurrence and development of epidemic diseases, and to explore in detail the relationship between the explosion of confirmed cases and meteorological factors under subtropical monsoon climate conditions, this is of practical significance for predicting the extinction of the virus. Due to the different characteristics of virus transmission in different latitude regions, therefore, with appropriate spatial resolution, Hubei province is used instead of China or Wuhan City (i.e., at provincial level rather than national level, also not city level) analyzing surveillance data is very important for understanding the global persistence of viruses and formulating local prevention and control measures. This study designed the correlation analysis between the COVID-19 increment cases in Hubei province and 10 meteorological parameters and the regression analysis of four sensitive parameters, combined with the meteorological characteristics of Hubei province, namely cold and dry in winter, hot and rainy in summer with frequent meteorological disasters, in order to find the relevant clues of meteorological factors on the occurrence, development and extinction of COVID-19. altitude: 23.6 (the altitude is calculated from Qingdao tide inspection station in Shandong as the starting point )). The available data period is from December 1, 2019 to March 10, 2020 (100 daily meteorological data). The actual data selected starts on January 1, 2020 and ends on March 10, 2020. Meteorological observatory data include: average air pressure (hPa), average temperature (℃), maximum temperature (℃), minimum temperature (℃), average water vapor pressure (hPa) (equivalent to water vapor pressure), average relative humidity (%), average wind speed (m/s), precipitation (mm), total solar radiation (0.01 megajoules/square meter), maximum solar irradiance (Watt/square meter ). Afterwards, it uses Ave_Pre, Ave_Temp, Max_Temp, Min_Temp, Ave_WVP, Ave_RH, Ave_WS, Prep, Tot_SR and Max_SI to refer to them respectively. According to the suggestion of the Meteorological Data Center of China Meteorological Administration, the average water vapor pressure (hPa) should be introduced as a quantity representing the moisture content in the atmosphere. When there is much water vapor in the atmosphere, the water vapor pressure is large and vice versa. (2) Number of confirmed cases Wuhan Health Committee reported that the local epidemic situation in Wuhan was reported on December 27. After tracing to the source, it was confirmed that the first patient was diagnosed in December 12. For a long time, Wuhan Health Committee had been using "unexplained viral pneumonia" rather than COVID-2019 before this, so the cases could not guarantee its authenticity. After January 16 th , 2020, especially on 17 th and 18 th , the state has delegated the power of virus testing to the Hubei Center for Disease Control (HCDC), that is, the samples do not need to be sent to Beijing, and can be tested in the local organization, HCDC detection capability is about . 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 April 3, 2020. . confirmed cases on the 17 th and 18 th increased. This study uses January 17, 2020 as the starting point of time to retrieve data. Case data include Hubei's cumulative confirmed cases (67773 as of March 10), Hubei's cumulative death (3046 as of March 10), and Hubei's incremental cases, namely HB_CumN, HB_CumD, and HB_DeltaN respectively. As for number of confirmed cases, several issues to be explained: 1) From 0: 00 to 24: 00 on February 12, 2020, the number of newly incremental cases of COVID-19 in Hubei province surged explosively to 14840 (including 13332 clinically diagnosed cases), in which are 13436 cases in Wuhan. The reason was that the General Office of the State Health Commission and the Office of the State Administration of Traditional Chinese Medicine issued the new coronavirus infection pneumonia diagnosis and treatment plan (trial version 5). Type of "Clinical Diagnosis" has been added to the classification of case diagnosis in Hubei province, so that patients can receive standardized treatment as early as possible according to confirmed cases and further improve the success rate of treatment. According to this plan, Hubei province carried out investigation on the previous suspected cases and revised the diagnosis results, and diagnosed the new patients according to the new diagnosis classification. In order to be consistent with the classification of case diagnosis released by other provinces in the country, since February 12, 2020, the number of clinically diagnosed cases in Hubei province has been included in the number of confirmed cases for publication. 2) The incremental confirmed cases in January 19, 2020 were 0. Here, the above two days are outliers and are eliminated in correlation analysis and regression analysis. (1) Correlation analysis Pearson correlation coefficient, Spearman rank correlation coefficient and Kendall Rank determination correlation coefficient are commonly used in the correlation analysis for binary variables. The calculation method of Spearman rank correlation coefficient is as follows. The calculation formula is as follows: . 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 April 3, 2020. . The value range of correlation coefficient r is equal and greater than -1, equal and lower than 1. r > 0 is positive correlation, r <0 is negative correlation; 0.3 <|r|<0.5 is low degree linear correlation; 0.5 <|r|< 0.8 is significant linear correlation; (2) Linear regression On the basis of correlation analysis, we estimate the parameters in the regression using the Ordinary Least Square (OLS) method. Incremental cases and four meteorological parameters are further selected for unary linear regression. All regressions are performed with the code SPSS v15. The prediction model of linear regression is: In the formula, t X represents the period t independent variable; t Y represents the dependent variable of period t; a and b represent the parameters of a linear regression equation. Obtained by the following formula: For the regression analysis of quantitative data, four prerequisites must be met: the linear relationship between incremental cases (dependent variables) and certain meteorological parameters, and each parameter is independent of each other, the residual of incremental cases satisfies the normality, and the residual of incremental cases satisfies the homogeneity of variance. . 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 April 3, 2020. . https://doi.org/10. 1101 If linear regression results cannot be obtained, generalized linear regression is considered. Table 1 summarizes the basic statistical results of incremental cases and meteorological parameters. As of March 20, 2020, the minimum number of incremental cases in Hubei province was 13 and the maximum was 4810, which occurred on February 13, 2020. From January 1 st , 2020 to March 10, the average air pressure range is 1006.0-1031.2 hPa, the average is 1021.7hPa, the standard deviation is 5.1 hPa, and the fluctuation range is small. The average wind speed ranges from 0 to 5.0 m/s and the fluctuation range are small too. The maximum rainfall is 38.9mm, which occurs in January 9, 2020 and has no obvious regularity. The average temperature, the maximum temperature and the minimum temperature are 6.9 ℃ , 11.6 ℃ and 3.4 ℃ respectively, the standard deviation of the maximum temperature is 5.0 ℃ > that of the minimum temperature 4.3 ℃ > that of the average temperature 4.0 ℃ . The average water vapor pressure and the average RH are both related to the water loading in the air, and the mean ± standard deviations are 8.3 ± 2.4 (hPa) and 83.0 ± 8.2 (%), respectively. Ultraviolet intensity is not the routine monitoring items in the meteorological station. Therefore, the daily total solar radiation and the maximum solar irradiance are selected as two auxiliary parameters, and their mean values are 757.7 (0.01 MJ/m 2 ) respectively and 456.6 (W/m 2 ), the standard deviations are 587.3 (0.01 MJ/m 2 ) and 274.2 (W/m 2 ), respectively. Obviously, the volatility of the values is very strong. Figure 1 shows the frequency statistics of incremental cases and meteorological parameters, among which the normality of average air pressure, average temperature, minimum temperature, average relative humidity and average wind speed is better. The total solar radiation and the maximum solar irradiance present a saddle-shaped bimodal structure. Figure 2 shows the tendency of incremental cases, average temperature, average water pressure and the maximum solar irradiance changing with time. Obviously, incremental cases-time pair is a single-peak structure. As for average temperature-time pair, it can be seen that there is a fluctuating process after about January 10, 2020. As for average water vapor pressure-time pair, after January 10, 2020, also has a fluctuating process, but the trend is relatively gentle. However, . 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 April 3, 2020. . there are two parallel tracks for the maximum solar irradiance-time pair, the general trend is to rise, but the trend is gentle. Table 2 summarizes cross-relationship between Hubei delta number with weather parameters. Under the condition of confidence interval 95% (95% CI), incremental cases in Hubei province have significant correlation with four meteorological parameters: average air pressure, average temperature, minimum temperature and average water vapor pressure. among them, there is a positive correlation with the average air pressure (P = 0.010), and the correlation coefficient r = + 0.358, which belongs to low degree linear correlation. Negative correlation indexes include average temperature (P = 0.029), minimum temperature (P = 0.013), average water pressure (P = 0.020), correlation coefficient r =-0.306,-0.347, -0.326. From this, it can be judged that the increment of cases will be inhibited by the increase of temperature and water vapor loading. At the same time, the increase of average air pressure may increase the reproduction rate of the virus. In addition, the maximum temperature, RH and incremental cases are also negatively correlated, but the significance level is insufficient. Figure 3 shows the curve fitting of HB_DeltaN with average pressure, average temperature, minimum temperature and average water vapor pressure. . 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 April 3, 2020. . Figure 4a shows the normality of the residual of incremental cases satisfies. (2) Time series method, commonly used in seasonal and repeatable viral infectious diseases. Because the 2019-nCoV is a sudden infectious disease, time series analysis cannot be considered (Gurgel et al., 2016; Yusef et al., 2007) . (3) Unary linear regression prediction is a method to establish the linear regression equation of X and Y for prediction according to the correlation between independent variable X and dependent variable Y. If linear regression results cannot be obtained, generalized linear regression is considered. The extension of the linear model of the generalized linear model is to establish the relationship between the mathematical expectation value of the response variable and the prediction variable of the linear combination through the link function. Its feature is that it does not forcibly change the natural measurement of data, and the data can have nonlinear and non-constant variance structure, it is a development of linear model when studying non-normal distribution of response value and simple and direct linear transformation of nonlinear model. However, linear regression results are easy to explain natural processes of respiratory infectious diseases caused by viruses and are of great significance. (Kim et al., 2016; Lim et al., 2012; Souza et al., 2012; Onozuka et al., 2009) . In this study, the correlation analysis between 10 meteorological parameters and case . 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 April 3, 2020. . increment was introduced, and finally 4 parameters were found to have strong correlation with case increment. Considering the three-month statistical cycle, the actual valid data is only 53. In the case of limited observations, there can be 4 parameters with obvious correlation. We have every reason to believe that, first, with the increase of effective observations, the statistical correlation between parameters will gradually appear; secondly, case increment is very sensitive to meteorological parameters, and analyzing cases with meteorological parameters is an effective method; thirdly, in addition to the minimum temperature, the other three parameters cannot establish a linear regression equation. Similarly, this study also uses the method of multiple linear regression analysis, but the overall fitting degree is very poor, the ultimate reason, it is not the problem of statistical method itself, but the effective observations for sudden infectious diseases is too little and the law is not obvious. 2019-nCoV evolution to seasonal infectious disease such as influenza virus is possible, if this happens, then periodic and repetitive data can be accumulated; meanwhile, this study finds that meteorological parameters, such as the minimum temperature and the average water vapor pressure, are the specific influencing factors of 2019-nCoV, this will become very meaningful for predicting the 2019-nCoV epidemic trend in the future. In addition, if regression analysis is to be carried out between temperature and RH and the number of cases (whether cumulative or incremental), taking into account the correlation between independent variables, either the Durbin-Watson Test (0 100%. the magnitude of RH is not only related to the moisture content in the atmosphere, but also decreases with the increase of temperature. When the water vapor pressure remains unchanged, the temperature rises, the saturated water vapor pressure increases, and the RH decreases. Therefore, there is a correlation between temperature and RH, and the two are inter-dependent variables. There has always been opposition to the occurrence and epidemic of virus (influenza) by water vapor pressure and RH (Prussin et al., 2018) . Water vapor pressure describes the mass of water vapor per unit of air (i .e. AH), while RH describes the ratio of actual concentration of water vapor to the maximum possible concentration, this ratio varies with temperature. Studies have shown that the relationship between water vapor pressure and RH is significant (Shaman & Kohn. 2009 ). According to their analysis, water vapor pressure explained 50% and 90% of influenza virus transmission and survival variability respectively, while RH explained 12% and 36% of variability respectively. In the study of establishing influenza-related mortality model, it is found that the change of water vapor pressure is the root cause of seasonal trend, while RH prediction is relatively insufficient (Shaman et al., 2010) .It is also reported that AH(water vapor pressure) is better than RH in virus inactivation. Causal Analysis of Global Influenza incidence data shows that water vapor pressure is a stronger driver than RH (Deyle et al., 2016) . In epidemiological studies, seasonal and metering conditions of influenza were compared, and the results showed lower temperature and water vapor pressure can increase the survival and . 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 April 3, 2020 . . https://doi.org/10.1101 transmission of influenza virus in temperate regions (Shaman & Kohn, 2009; Shaman et al., 2010 Shaman et al., & 2011 Kolberg et al., 2019) . Studies from China also prove that influenza virus is negatively correlated with temperature and water vapor pressure (Cao et al., 2010; Sun et al., 2018; Yang et al., 2019; Su et al., 2020) . The average water vapor pressure in sub-cold zone climate is lower than 4 hPa, which may trigger the occurrence of influenza (Kolberg et al., 2019) . Studies in tropical/subtropical regions of China have found that, water vapor pressure is negatively correlated with the incidence of A/dnan1pdm09 (Influenza A) and Yamagata (Influenza B) (Pan et al., 2019) . This study argues that it is because RH is related to both the water vapor pressure and the temperature, and the annual changes are relatively complex so the changes of time in the past study included one year or many years, and meteorological parameters including different seasons (range is larger). However, in our study because the effective time period was for 51 days, and in the epidemic area, the range of average minimum temperature was 18.10 °C (-3.6 ~ 14.5°C), the regression coefficient with statistical significance cannot be obtained, but the correlation analysis shows that it has positive and negative correlation with the average temperature, water vapor pressure and atmospheric pressure. In other words, the increment of cases is less sensitive to relative humidity. According to the data analysis results of the epidemic situation in Hubei province, under the subtropical monsoon climate conditions, the RH cannot be selected, and the vapor pressure should be selected as the parameter to analyze the influence relationship between the moisture in the air and the activity of the case. We demonstrate for the first time the epidemiological correlation between water vapor pressure and respiratory tract infection virus-2019-nCoV other than influenza. In coronavirus(SARS-CoV, MERS-CoV) and RSV have not been reported to be associated with water vapor pressure. According to its related mechanism analysis, Sundell et al., believes that, as for the relationship between climatic factors and viral respiratory epidemiology, in temperate climate, non-coated viruses (HRV and HEV) when causing infection, may be transmitted through large drops or close contact rather than through small atomized particles, so the dependence of transmission factors on meteorological factors is relatively small (Sundell et al., 2016 ) . Previous studies have shown that the envelope (E) protein of coronavirus that causes Severe Acute Respiratory Syndrome is directly related to viral toxicity (DeDiego et al.,2014; 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 April 3, 2020 . . https://doi.org/10.1101 .04.01.20050526 doi: medRxiv preprint et al.,2018 Schoeman &Fielding,2019) , our speculation that the envelope proteins closely related to 2019-nCov pathogenicity are sensitive to the average minimum temperature and water vapor pressure (negative correlation), and they are more dependent on climatic factors, especially temperature (including average temperature and minimum temperature). This finding should have theoretical significance for viral respiratory infectious diseases with abundant meteorological factors, and of course need more evidence to support it in the future. Previous studies believe that atmospheric pressure is positively correlated with influenza virus (Li et al., 2015; Soebiyanto et al., 2010) . Atmospheric pressure may be the driver of influenza (Sundell et al.,2016) . RSV activity increases with the increase of atmospheric pressure (Hervás et al.,2012 ). An analysis of the relationship between the meteorological factors of 9 cities with different climate types in the United States and the activity of RSV shows that in Delhi, air pressure is the main related factor of the total amount of RSV, it was associated with 22% of RSV activity (Yusuf et al.,2007) . The cause of influenza may be that nasal mucosa is easy to rupture under dry conditions and high pressure in winter, so it is easy to be infected with virus. However, the detailed mechanism needs further study (Su et al.,2020) . This study has some limitations. First, studies have shown that COVID-19 has a certain incubation period [average incubation period is 5.2 days, 95% CI: 4.1 ~ 7.0], but this estimation is based on 10 cases, more information support must be provided. The COVID-19 in Hubei province belongs to sudden acute infectious disease, and the pathogen has not yet been determined. The relevant information we have obtained does not have the presumed incubation period parameter (lag effect). Before January 17, Wuhan released the number of intermittent cases, but it was not a daily statistical result, which did not meet the requirements of the basic data collection designed by our research. At the same time, our collection range of cases started from January 17, covering 53 days. By March 10, the incidence of cases in Hubei province decreased. The collection date of cases has covered most of the basic process of occurrence and development in Hubei province. This content should be added to the analysis of relevant factors for further summarizing and analyzing the incidence of epidemic cases in Hubei province in order to make the research more accurate and scientific. . 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 April 3, 2020. . Secondly, we have examined the correlation studies between 10 meteorological indicators and case increment respectively, average atmospheric pressure, Average temperature, average minimum temperature, average water pressure and case increment have significant correlation. We believe that atmospheric pressure, temperature and water vapor pressure are crucial to shaping conditions conducive to the diffusion of 2019-nCoV aerosol, but in the epidemiological model, whether they should be treated as individual factors or as an associated entity remains to be further studied. Unary linear regression analysis was carried out on average atmospheric pressure, average temperature, average minimum temperature, average water pressure and case increment respectively, and a statistically significant regression equation was obtained. However, there is no statistically significant binary linear regression equation describing two factors. We need further research, such as the correlation between the onset of viral respiratory tract infection under the combined action of minimum temperature and water vapor pressure. Third, we cannot exclude the possible effects of some mixed factors, such as population flow and air pollution, which may affect the number of diseases and further deviate our results. In addition, we did not consider host factors, such as immunity or vulnerability, which play a role in disease transmission. A large number of studies have shown that viral activity that causes respiratory infectious diseases is sensitive to climate. Climatic factors may affect the survival and transmission of the virus in the environment, host susceptibility and exposure possibility. We found that 2019-nCoV showed significant role in meteorological factors during the outbreak of the COVID-19 in Hubei province, but we are also very clear, as scholars have agreed, meteorological parameters can only explain no more than 30% changes in influenza activity (Monamele et al., 2017) , there are still many problems about 2019-nCoV to be confirmed. Social distancing measures are essential components of the public health response to COVID-19. The purpose of these mitigation measures is to reduce transmission, thereby delaying the peak of the epidemic, reducing the total number of infected people, and spreading new cases over a longer period of time to relieve the pressure on the health care system and achieve the purpose of controlling the epidemic (Fong et al., 2020) . The corresponding measures taken in Wuhan and Hubei Province have already seen practical results. . 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 April 3, 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 April 3, 2020. . *. Correlation is significant at the 0.05 level (2-tailed). . 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 April 3, 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 April 3, 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 April 3, 2020. 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 April 3, 2020. . https://doi.org/10.1101/2020.04.01.20050526 doi: medRxiv preprint (1) Meteorological Observatory data The available data period provided from the Meteorological Data Center of China Meteorological Administration is from December 1, 2019 to March 10, 2020 (100 daily meteorological data). The actual data selected starts on January 1, 2020 and ends on March 10, 2020. Meteorological observatory data include: average air pressure (hPa), average temperature ( ℃ ), maximum temperature (℃), minimum temperature (℃), average water vapor pressure (hPa) (equivalent to absolute humidity), average relative humidity (%), average wind speed (m/s), precipitation (mm), total solar radiation (0.01 megajoules/square meter), maximum solar irradiance (Watt/square meter ). Afterwards, it uses Ave_Pre, Ave_Temp, Max_Temp, Min_Temp, Ave_WVP, Ave_RH, Ave_WS, Prep, Tot_SR and Max_SI to refer to them respectively. (2) Number of confirmed cases This study uses January 17, 2020 as the starting point of time to retrieve data. Case data include Hubei's cumulative confirmed cases, Hubei's cumulative death, and Hubei's incremental cases, namely HB_CumN, HB_CumD, and HB_DeltaN respectively. February 12, 2020 and January 19, 2020 are outliers. . 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 April 3, 2020. . 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Supplementary information contains an Excel file with datasheet containing the meteorological observatory data and number of confirmed cases from January 1, 2020 to March 10, 2020 in Hubei province of China. The data produced in this study can be found in the Supplementary information. The datafile includes an Excel file and a Word file which describes the data.