key: cord-0785490-6227zuqz authors: Mozumder, Salatul Islam; Amin, Mohammad Shaiful Alam; Uddin, Mohammad Rakib; Talukder, Musabbir Jahan title: Coronavirus COVID-19 outbreak and control: effect of temperature, relative humidity, and lockdown implementation date: 2020-12-23 journal: Arch Pediatr DOI: 10.1016/j.arcped.2020.12.006 sha: 72da8f316c24b12b5ae4a80d9ec5e463024aa238 doc_id: 785490 cord_uid: 6227zuqz Meteorological parameters are important factors that have an influence on infectious diseases. The present study aimed to explore the correlation between the spread of COVID-19, temperature, and relative humidity. The effect of human-imposed control parameters in the form of lockdown on the dissipation of COVID-19 was also analyzed. Data were collected on the three study variables – temperature, relative humidity, and lockdown period – from nine of the most infected cities worldwide as well as information on changes in the number of COVID-19 patients from the beginning to a specific point in the lockdown period. A generalized regression model was applied to explore the effect of temperature and relative humidity on the change in daily new cases of COVID-19. The regression analysis did not find any significant correlation between temperature, humidity, and change in number of COVID-19 cases. Analysis of the cities with wide-ranging temperature variations showed a negative correlation of COVID-19 transmission (p=0.079) with temperature but a relatively nonsignificant correlation with relative humidity (p=0.198). The number of total deaths was also higher in low-temperature countries compared with high-temperature countries. The specific growth rate in COVID-19 cases was decreased by more than 66% after implementation of a lockdown. This growth rate was exponentially decreased over time through the proper implementation of lockdown. Analysis of the real-case scenario and application of predictive models showed that for New York, Lombardy, and Madrid more than 120 days of strict lockdown was required for complete control of the transmission of COVID-19. The outbreak of coronavirus has led to a global health emergency. In late December 2019, a new type of coronavirus was discovered in Wuhan, China, later named "SARS-CoV-2" [1] and the infectious disease caused by this virus was termed "COVID-19" [2] . The virus is transmitted through human touch and/or respiratory droplets usually produced by coughing, sneezing, or talking [3] . As a result, the outbreak has spread from Wuhan, China to the rest of the world affecting almost 205 countries. To date, more than 64 million (64,194,674 on December 2, 2020) people have been infected by COVID-19, of whom nearly 1,486,829 have died and 44,441,249 have recovered [4] . These numbers increasing exponentially daily, with the worst scenario seen in the United States, Brazil, Italy, Spain, France, Germany, the United Kingdom, and Iran. Researchers are trying to understand the metabolic behavior of the novel coronavirus as well as the characteristics of transmission. People with severe COVID-19 illness are treated in specialized and intensive care units. Maintaining social distance, practicing basic hygiene habits, and wearing masks can be used as a protective measure [5] . Besides preventing the spread of coronavirus infection, it is necessary to predict the future behavior of COVID-19 disease; for example, how it will be spread, which regions would be most at risk, and how long it takes for complete recovery. It is very important to predict the transmission scenario of the COVID-19 epidemic based on experimental or field data. This will help policymakers take the necessary actions to reduce the severity of this epidemic. A few COVID-19 epidemiological studies have been conducted based on relevant environmental parameters such as temperature, pressure, and relative humidity [6, 7] . Most of this work was focused on the city of Wuhan. To date, and to the best of our knowledge, there has been no scenario analysis of coronavirus infection to predict the recovery period through the application of lockdowns. This study draws a relationship of the percentage change (% change) in daily new COVID-19 cases as a function of temperature and relative humidity in different cities and countries Lagos, Nigeria; and Khartoum, Sudan). A number of cities with wide-ranging temperature variations were used to assess the effect of temperature on COVID-19 transmission and mortality. The effect of lockdown, restricting the movement of people from place to place to avoid contact, on the rate of transmission of COVID-19 was also examine. Based on the analysis of real-time Page 4 of 14 J o u r n a l P r e -p r o o f 4 data from New York City, Lombardy, and Madrid, a model was proposed to predict the complete recovery period possible with a strict maintenance of lockdown. Data on temperature were collected from the Time and Date AS website [8] and data on humidity were from the AccuWeather website [9] . The source of the data on coronavirus-related cases for The specific growth rate of COVID-19 cases (μ) refers to the steepness of the growth of COVID-19 cases. It was defined as the increase in number of COVID-19 cases per unit of total COVID-19 cases per unit time: where, N is the number of COVID-19 cases, N0 is the initial number of COVID-19 cases, and t is time (days). μ (COVID-19 cases/COVID-19 cases/day; simple noted by 1/day) was calculated from the slope of ln N vs. t curve. The % change in COVID-19 cases can be calculated according to Eq. 3: Most of the biological system follows an exponential relationship either in terms of the COVID-19 transmission rate [6] or in the reduction of the rate. Implementation of lockdown decreased the value of μ exponentially. The decrease in μ over time can be expressed as Eq. 4, which was also used as a predictive model to calculate the time needed for complete control of COVID-19 transmission. Here, η (1/days) is the specific decreasing rate of μ and μ0 is μ at the beginning of lockdown. η (1/days) was estimated from the slope of ln (μ) vs. t (days) curve and μ0 from the intercept. The regression coefficient (R 2 ) was used to evaluate the fitting ability of the predictive model. The closer the fitting coefficient is to 1, the more accurate the prediction. Temperature and relative humidity were selected as independent variables for the daily new cases of COVID-19. Each dataset was divided based on the different cities, such as New York, Lombardy, Madrid, and Wuhan, to eliminate the chance of any error. The following polynomial equation (Eq. 5) was employed to examine the data: where, X is the % change in daily new cases, A0 is the constant coefficient, A1, A2, and A3 are regression coefficients, and T and H are independent variables for temperature and humidity, respectively. Regression analysis with ANOVA was conducted to evaluate the significance of the independent variables temperature and relative humidity on the % change in daily new cases of COVID-19. Moreover, the significance of the effect of temperature, considering the average temperature of April 2020, on the total number of COVID-19 cases in different cities and countries was also evaluated using Eq. 5. The cities and countries used in this study were: New York, USA; In New York City, the outbreak of COVID-19 started in the first week of March 2020. Figure 1a . The figure shows that there was some relationship between the % change in daily new cases with temperature and relative humidity. The % change in daily new cases increased with increasing temperature and relative humidity. The days with increased temperature and relative humidity led 19, since the major transmission route is human-to-human transmission [3] . However, since the % change in daily new cases was still positive it indicated that the scenario was still getting worse. From the experience in Wuhan, China, it was observed that it took approximately 40 days to control the overall situation after implementation of a citywide shutdown (Figure 1b) . Therefore, we could assume that the COVID-19 situation would be under control in New York City after April 20, 2020, which in reality was not true. A very similar type of response in the relationship between % change in COVID-19 cases, temperature, and relative humidity was observed for the cities of Lombardy, Italy (Figure 1c) and Madrid, Spain (Figure 1d) . The % change in COVID-19 cases dramatically decreased after implementation of lockdown. A similar behavior was observed in Victoria, Australia ( Figure A1a in supplementary material) were lockdown was properly implemented. However, in the cities/countries of, e.g., Delhi, Sao Paulo, Lagos, and Sudan where the implementation of lockdown was less respected there was no significant reduction in % change in COVID-19 cases ( Figure A1 transmission with temperature [13, 14] . Wang et al. [3] found that, to a certain extent, temperature could significantly change the transmission of COVID-19. They found the average temperature of Wuhan (around 2.4-12.5 o C) to be the optimal temperature for viral transmission, and suggested adopting the strictest control measures to prevent future outbreaks in countries and regions with a lower temperature. Sajadi et al. (2020) [15] also reported that average temperatures of 5-11 o C with low specific humidity were favorable for COVID-19 transmission. As preventive measures for the control of COVID-19 transmission, almost all affected countries have implemented lockdown of the entire country or of cities. This practice has significantly reduced the transmission of COVID-19 expressed here as the specific growth rate of COVID-19 cases. In this study we also examined the decreasing pattern of the specific growth rate of COVID-19 cases, comparing it before and after the implementation of lockdown (Figure 3) . Figure 3d) , respectively. As a result of lockdown, the specific growth rate of COVID-19 cases was significantly reduced. The predictive model used in this study is described in Eq. 4. The model parametersinitial specific growth rate of COVID-19 cases (μ0) and specific decreasing rate of μ (η)were estimated from the data available after lockdown and are listed in (Figure 4) of cities or countries. The model validation was confirmed by the regression coefficient (R 2 ), which was 0.8915 for New York City 0.9389 for Lombardy, and 0.8896 was for Madrid, and also by a visual comparison of the data in Figure 4 . The validated model was applied for the scenario analysis; i.e., to predict the time require to achieve almost complete recovery after applying the lockdown. If the lockdown procedure continues in the same way, together with other supporting measure, as in the beginning of the lockdown period, a period of more than 120 days is required for New York City, Lombardy, and Madrid, to establish almost complete control of COVID-19 transmission (Figure 4) . Although Lombardy had the lowest specific growth rate at the beginning of lockdown, it took almost the same time as New York and Madrid to establish control due to the lowest decreasing rate of specific growth rate (η) for Lombardy. The predictive model developed here can also be applied in scenario analysis for other cities where lockdown was potentially applied. 1. The cities and countries with an average temperature higher than 25 o C had a significantly reduced rate of COVID-19 transmission and number of deaths due to COVID-19. 2. Countries like India, Bangladesh, and others where the average daily temperature in winter is around 10 o C are at a high risk of severe effects of COVID-19 during the winter season (November, December, and January). 3. Proper implementation of lockdown led to a two-to threefold reduction in the specific growth rate of COVID-19 cases. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2 Naming the coronavirus disease (COVID-19) and the virus that causes it Temperature significant change COVID-19 Transmission in 429 cities Coronavirus disease (COVID-19) advice for the public Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan World Temperatures -Weather Around The World Local, National, & Global Daily Weather Forecast High Temperature and High Humidity Reduce the Transmission of COVID-19 The Effects of Temperature and Relative Humidity on the Viability of the SARS Coronavirus Humidity and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19 2020 A predictive model was proposed to predict the time for recovery through the proper implementation of lockdown. According to this model, New York city, USA, Lombardy, Italy, and Madrid, Spain need more than 120 days for complete control of COVID-19 transmission after applying potential lockdown.