Association between ambient temperature and COVID-19 infection in 122 cities from China

A Warm Summer is Unlikely to Stop Transmission of COVID-19 Naturally

The outbreak of coronavirus disease 2019 (COVID-19) showed various transmission rate (R t ) across different regions. The determination of the factors affecting transmission rate is urgent and crucial to combat COVID-19. Here we explored variation of R t between 277 regions across the globe and the associated potential socioeconomic, demographic, and environmental factors. At global scale, the R t started to decrease approximately 2 weeks after policy interventions initiated. This lag from the date of policy interventions initiation to the date when R t started to decrease ranges from 9 to 19 days, largest in Europe and North America. We find that proportion of elderly people or life expectancy can explain ~50% of variation in transmission rate across the 277 regions. The transmission rate at the point of inflection (R I ) increases by 29.4% (25.2-34.0%) for 1% uptick in the proportion of people aged above 65, indicating that elderly people face ~2.5 times higher infection risk than younger people. Air temperature is negatively correlated with transmission rate, which is mainly attributed to collinearities between air temperature and demographic factors. Our model predicted that temperature sensitivity of R I is only -2.7% (-5.2-0%) per degree Celsius after excluding collinearities between air temperature and demographic factors. This low temperature sensitivity of R I suggests that a warm summer is unlikely to impede the spread of COVID-19 naturally.

COVID-19 and environmental -weather markers: Unfolding baseline levels and veracity of linkages in tropical India

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe due to its contagion nature. We hereby report the baseline permanent levels of two most toxic air pollutants in top ranked mega cities of India. This could be made possible for the first time due to the unprecedented COVID-19 lockdown emission scenario. The study also unfolds the association of COVID-19 with different environmental and weather markers. Although there are numerous confounding factors for the pandemic, we find a strong association of COVID-19 mortality with baseline PM2.5 levels (80% correlation) to which the population is chronically exposed and may be considered as one of the critical factors. The COVID-19 morbidity is found to be moderately anti-correlated with maximum temperature during the pandemic period (-56%). Findings although preliminary but provide a first line of information for epidemiologists and may be useful for the development of effective health risk management policies.

Offsite Construction for Emergencies: A focus on Isolation Space Creation (ISC) measures for the COVID-19 pandemic

The outbreak of a pandemic of global concern, the Corona Virus Disease 2019 (COVID-19) has tested the capacity of healthcare facilities to the brim in many developed countries. In a minacious fashion of rapid spread and extreme transmission rate, COVID-19 has triggered a shortage of healthcare facilities such as hospital bed spaces and ventilators. Various strategies have been adopted by the worst-hit countries to slacken or halt the spread of the virus. Common Isolation Space Creation (ISC) measures for the COVID-19 pandemic containment includes self-isolation at home, isolation at regular hospitals, isolation at existing epidemic hospitals, isolation at retrofitted buildings for an emergency, isolation at Temporary Mobile Cabins (TMCs), isolation at newly constructed temporary hospitals for COVID-19. This study evaluates the ISC measures and proposes offsite and modular solutions for the construction industry and built environment to respond to emergencies. While this study has proposed a solution for creating emergency isolation spaces for effective containment of such pandemic, other critical COVID-19 challenges such as the shortage of healthcare staff and other facilities are not addressed in this study.

Impact of Extreme Hot Climate on COVID-19 Outbreak in India

Coronavirus Disease 2019 (COVID-19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID-19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID-19 epicenters. Daily COVID-19 count has strong covariability with local temperature, which accounts approximately 65-85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID-19 cases are clustered at temperature and humidity ranging within 27-32°C and 25-45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID-19 growth at the initial phase. The results are highly significant for predicting future COVID-19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO2 emission is alarmingly high in South Asia (India) and entails high risk of climate change and extreme hot summer. Zoonotic viruses are sensitive to warming induced climate change; COVID-19 epicenters are collocated on CO2 emission hotspots. The COVID-19 count distribution peaks at 31.0°C, which is 1.0°C higher than current (2020) and historical (1961-1990) mean, value. Approximately, 72% of the COVID-19 cases are clustered at severe to record-breaking hot extremes of historical temperature distribution spectrum. Therefore, extreme climate change has important role in the spread of COVID-19 pandemic. Hence, a strenuous mitigation measure to abate greenhouse gas (GHG) emission is essential to avoid such pandemics in future.

Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China

The outbreak of COVID-19 pandemic has a high spreading rate and a high fatality rate. To control the rapid spreading of COVID-19 virus, Chinese government ordered lockdown policies since late January 2020. The aims of this study are to quantify the relationship between geographic information (i.e., latitude, longitude and altitude) and cumulative infected population, and to unveil the importance of the population density in the spreading speed during the lockdown. COVID-19 data during the period from December 8, 2019 to April 8, 2020 were collected before and after lockdown. After discovering two important geographic factors (i.e., latitude and altitude) by estimating the correlation coefficients between each of them and cumulative infected population, two linear models of cumulative infected population and COVID-19 spreading speed were constructed based on these two factors. Overall, our findings from the models showed a negative correlation between the provincial daily cumulative COVID-19 infected number and latitude/altitude. In addition, population density is not an important factor in COVID-19 spreading under strict lockdown policies. Our study suggests that lockdown policies of China can effectively restrict COVID-19 spreading speed.

COVID-19 and the environment: A critical review and research agenda

The current Coronavirus infection (COVID-19) outbreak has had a substantial impact on many aspects of general life. Although a number of studies have been published on the topic already, there has not been a critical review of studies on the impacts of COVID-19 by and on environmental factors. The current study fills this gap by presenting a critical analysis of 57 studies on the nexus between COVID-19 and the environment, published in nine journals up to May 2020. Majority of the studies in our sample are published in Science of the Total Environment (74%), and studies used mostly descriptive statistics and regression as research methods. We identified four underlying research clusters based on a systematic content analysis of the studies. The clusters are: (1) COVID-19 and environmental degradation, (2) COVID-19 and air pollution, (3) COVID-19 and climate/metrological factors and (4) COVID-19 and temperature. Besides a critical analysis of the studies in each cluster, we propose research questions to guide future research on the relationship between COVID-19 and the environment.

Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.

The spread of 2019-nCoV in China was primarily driven by population density. Comment on "Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China" by Zhu et al

Recently, an article published in the journal Science of the Total Environment and authored by Zhu et al. has claimed the "Association between short-term exposure to air pollution and COVID-19 infection" (doi: https://doi.org/10.1016/j.scitotenv.2020.138704). This note shows that the stated dependence between the diffusion of the infection and air pollution may be the result of spurious correlation due to the omission of a common factor, namely, population density. To this end, the relationship between demographic, socio-economic, and environmental conditions and the spread of the novel coronavirus in China is analyzed with spatial regression models on variables deflated by population size. The infection rate - as measured by the number of cases per 100 thousand inhabitants - is found to be strongly related to the population density. At the same time, the association with air pollution is detected with a negative sign, which is difficult to interpret.

Meteorological impact on the COVID-19 pandemic: A study across eight severely affected regions in South America

The role of meteorological factors in the transmission of the COVID-19 still needs to be determined. In this study, the daily new cases of the eight severely affected regions in four countries of South America and their corresponding meteorological data (average temperature, maximum temperature, minimum temperature, average wind speed, visibility, absolute humidity) were collected. Daily number of confirmed and incubative cases, as well as time-dependent reproductive number (R_t) was calculated to indicate the transmission of the diseases in the population. Spearman's correlation coefficients were assessed to show the correlation between meteorological factors and daily confirmed cases, daily incubative cases, as well as Rt. In particular, the results showed that there was a highly significant correlation between daily incubative cases and absolute humidity throughout the selected regions. Multiple linear regression model further confirmed the negative correlation between absolute humidity and incubative cases. The absolute humidity is predicted to show a decreasing trend in the coming months from the meteorological data of recent three years. Our results suggest the necessity of continuous controlling policy in these areas and some other complementary strategies to mitigate the contagious rate of the COVID-19.

Role of air temperature and humidity in the transmission of SARS-CoV-2 in the United States

Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is urgently needed to inform mitigation efforts. Here, we estimated the relationship between air temperature or specific humidity (SH) and SARS-CoV-2 transmission in 913 U.S. counties with abundant reported infections from March 15 to August 31, 2020. Specifically, we quantified the associations of daily mean temperature and SH with daily estimates of the SARS-CoV-2 reproduction number ( Rt ) and calculated the fraction of Rt attributable to these meteorological conditions. Both lower temperature and lower SH were significantly associated with increased Rt . The fraction of Rt attributable to temperature was 5.10% (95% eCI: 5.00 - 5.18%), and the fraction of Rt attributable to SH was 14.47% (95% eCI: 14.37 - 14.54%). These fractions generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing a larger role than temperature.

Transmission of SARS-CoV-2 via fecal-oral and aerosols-borne routes: Environmental dynamics and implications for wastewater management in underprivileged societies

The advent of novel human coronavirus (SARS-CoV-2) and its potential transmission via fecal-oral and aerosols-borne routes are upcoming challenges to understand the fate of the virus in the environment. In this short communication, we specifically looked at the possibilities of these transmission routes based on the available literature directly related to the SARS-CoV-2 as well as on the closer phylogenetic relatives such as SARS-CoV-1. The available data suggest that, in addition to human-to-human contact, the virus may spread via fecal-oral and aerosols-borne routes. Existing knowledge states that coronaviruses have low stability in the environment due to the natural action of oxidants that disrupt the viral envelope. Previous recommended dosage of chlorination has been found to be not sufficient to inactivate SARS-CoV-2 in places where viral load is high such as hospitals and airports. Although there is no current evidence showing that coronaviruses can be transmitted through contaminated drinking water, there is a growing concern on the impact of the current pandemic wave on underprivileged societies because of their poor wastewater treatment infrastructures, overpopulation, and outbreak management strategies. More research is encouraged to trace the actual fate of SARS-CoV-2 in the environment and to develop/revise the disinfection strategies accordingly.

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study

BACKGROUND

The novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China.

OBJECTIVE

This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China.

METHODS

Daily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020).

RESULTS

Many factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors.

CONCLUSIONS

Many factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.

Association of climatic factors with COVID-19 in Pakistan

INTRODUCTION

Environmental factors such as wind, temperature, humidity, and sun exposure are known to affect influenza and viruses such as severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions. COVID-19 is a new pandemic with very little information available about its transmission and association with environmental factors. The goal of this paper is to explore the association of environmental factors on daily incidence rate, mortality rate, and recoveries of COVID-19.

METHODS

The environmental data for humidity, temperature, wind, and sun exposure were recorded from metrological websites and COVID-19 data such as the daily incidence rate, death rate, and daily recovery were extracted from the government's official website available to the general public. The analysis for each outcome was adjusted for factors such as lock down status, nationwide events, and the number of daily tests performed. Analysis was completed with negative binominal regression log link using generalised linear modelling.

RESULTS

Daily temperature, sun exposure, wind, and humidity were not significantly associated with daily incidence rate. Temperature and nationwide social gatherings, although non-significant, showed trends towards a higher chance of incidence. An increase in the number of daily testing was significantly associated with higher COVID-19 incidences (effect size ranged from 2.17-9.96). No factors were significantly associated with daily death rates. Except for the province of Balochistan, a lower daily temperature was associated with a significantly higher daily recovery rate.

DISCUSSION

Environmental factors such as temperature, humidity, wind, and daily sun exposure were not consistently associated with COVID-19 incidence, death rates, or recovery. More policing about precautionary measures and ensuring diagnostic testing and accuracy are needed.

Beyond the peak: A deterministic compartment model for exploring the Covid-19 evolution in Italy

Novel Covid-19 has had a huge impact on the world's population since December 2019. The very rapid spreading of the virus worldwide, with its heavy toll of death and overload of the healthcare systems, induced the scientific community to focus on understanding, monitoring and foreseeing the epidemic evolution, weighing up the impact of different containment measures. An immense literature was produced in few months. Many papers were focused on predicting the peak features through a variety of different models. In the present paper, combining the surveillance data-set with data on mobility and testing, we develop a deterministic compartment model aimed at performing a retrospective analysis to understand the main modifications occurred to the characteristic parameters that regulate the epidemic spreading. We find that, besides self-protective behaviors, a reduction of susceptibility should have occurred in order to explain the fast descent of the epidemic after the peak. A sensitivity analysis of the basic reproduction number, in response to variations of the epidemiological parameters that can be influenced by policy-makers, shows the primary importance of a rigid isolation procedure for the diagnosed cases, combined with an intensive effort in performing extended testing campaigns. Future scenarios depend on the ability to protect the population from the injection of new cases from abroad, and to pursue in applying rigid self-protective measures.

A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India

Meteorological parameters were crucial and effective factors in past infectious diseases, like influenza and severe acute respiratory syndrome (SARS), etc. The present study targets to explore the association between the coronavirus disease 2019 (COVID-19) transmission rates and meteorological parameters. For this purpose, the meteorological parameters and COVID-19 infection data from 28th March 2020 to 22nd April 2020 of different states of India have been compiled and used in the analysis. The gradient boosting model (GBM) has been implemented to explore the effect of the minimum temperature, maximum temperature, minimum humidity, and maximum humidity on the infection count of COVID-19. The optimal performance of the GBM model has been achieved after tuning its parameters. The GBM results in the best accuracy of R² = 0.95 for prediction of active cases in Maharashtra, and R² = 0.98 for prediction of recovered cases of COVID-19 in Kerala and Rajasthan, India.

Study of COVID-19 pandemic in London (UK) from urban context

COVID-19 transmission in London city was discussed in this work from an urban context. The association between COVID-19 cases and climate indicators in London, UK were analysed statistically employing published data from national health services, UK and Time and Date AS based weather data. The climatic indicators included in the study were the daily averages of maximum and minimum temperatures, humidity, and wind speed. Pearson, Kendall, and Spearman rank correlation tests were selected for data analysis. The data was considered up to two different dates to study the climatic effect (10th May in the first study and then updated up to 16th of July in the next study when the rest of the data was available). The results were contradictory in the two studies and it can be concluded that climatic parameters cannot solely determine the changes in the number of cases in the pandemic. Distance from London to four other cities (Birmingham, Leeds, Manchester, and Sheffield) showed that as the distance from the epicentre of the UK (London) increases, the number of COVID-19 cases decrease. What should be the necessary measure to be taken to control the transmission in cities have been discussed.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM_2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.

Effects of meteorological conditions and air pollution on COVID-19 transmission: Evidence from 219 Chinese cities

The spatial distribution of the COVID-19 infection in China cannot be explained solely by geographical distance and regulatory stringency. In this research we investigate how meteorological conditions and air pollution, as concurring factors, impact COVID-19 transmission, using data on new confirmed cases from 219 prefecture cities from January 24 to February 29, 2020. Results revealed a kind of nonlinear dose-response relationship between temperature and coronavirus transmission. We also found that air pollution indicators are positively correlated with new confirmed cases, and the coronavirus further spreads by 5-7% as the AQI increases by 10 units. Further analysis based on regional divisions revealed that in northern China the negative effects of rising temperature on COVID-19 is counteracted by aggravated air pollution. In the southern cities, the ambient temperature and air pollution have a negative interactive effect on COVID-19 transmission, implying that rising temperature restrains the facilitating effects of air pollution and that they jointly lead to a decrease in new confirmed cases. These results provide implications for the control and prevention of this disease and for the anticipation of another possible pandemic.

How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study

Starting from December 2019 the world has faced an unprecedented health crisis caused by the new Coronavirus (COVID-19) due to the SARS-CoV-2 pathogen. Within this topic, the aim of the paper was to quantify the effect of mobility habits in the spread of the Coronavirus in Italy through a multiple linear regression model. Estimation results showed that mobility habits represent one of the variables that explains the number of COVID-19 infections jointly with the number of tests/day and some environmental variables (i.e. PM pollution and temperature). Nevertheless, a proximity variable to the first outbreak was also significant, meaning that the areas close to the outbreak had a higher risk of contagion, especially in the initial stage of infection (time-decay phenomena). Furthermore, the number of daily new cases was related to the trips performed three weeks before. This threshold of 21 days could be considered as a sort of positivity detection time, meaning that the mobility restrictions quarantine commonly set at 14 days, defined only according to incubation-based epidemiological considerations, is underestimated (possible delays between contagion and detection) as a containment policy and may not always contribute to effectively slowing down the spread of virus worldwide. This result is original and, if confirmed in other studies, will lay the groundwork for more effective containment of COVID-19 in countries that are still in the health emergency, as well as for possible future returns of the virus.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM_2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.

The effect of latitude and PM2.5 on spreading of SARS-CoV-2 in tropical and temperate zone countries

The present work describes spreading of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) at the tropical and temperate zones which are explained based on insolation energy, Particulate Matter (PM_2.5), latitude, temperature, humidity, Population Density (PD), Human Development Index (HDI) and Global Health Security Index (GHSI) parameters. In order to analyze the spreading of SARS-CoV-2 by statistical data based on the confirmed positive cases which are collected between December 31, 2019 to April 25, 2020. The present analysis reveals that the outbreak of SARS-CoV-2 in the major countries lie on the Equator is 78,509 cases, the countries lie on the Tropic of Cancer is 62,930 cases (excluding China) and the countries lie on the Tropic of Capricorn is 22,842 cases. The tropical countries, which comes between the Tropic of Cancer and Tropic of Capricorn is reported to be 1,77,877 cases. The temperate zone countries, which are above and below the tropical countries are reported to be 25,66,171 cases so, the pandemic analysis describes the correlation between latitude, temperate zones, PM_2.5 and local environmental factors. Hence, the temperature plays a pivotal role in the spreading of coronavirus at below 20 °C. The spreading of SARS-CoV-2 cases in Northern and Southern Hemispheres has inverse order against absorption of insolated energy. In temperate zone countries, the concentration of PM_2.5 at below 20 μg/m³ has higher spreading rate of SARS-CoV-2 cases. The effect of insolation energy and PM_2.5, it is confirmed that the spreading of SARS-CoV-2 is explained by dumb-bell model and solid/liquid interface formation mechanism. The present meta-analysis also focuses on the impact of GHSI, HDI, PD and PM_2.5 on spreading of SARS-CoV-2 cases.

Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China

Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R₀) was calculated with the maximum likelihood "removal" method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R₀ was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R₀ and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R₀, while nitrogen dioxide (NO₂), 24-h average concentration of nitrogen dioxide (NO₂_24 h) and daily maximum temperature were inversely correlated to R₀, with coefficients of 0.644, 0.661, -0.636, -0.657, -0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R₀, including mean/maximum/minimum air pressure and mean wind speed, with coefficients of -0.697, -0.697, -0.697 and -0.841, respectively. There was no correlation with R₀ and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.

Use of Weather Variables in SARS-CoV-2 Transmission Studies

The persistence and intensity of the current severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic, and the advanced planning required to balance competing concerns of saving lives and avoiding economic collapse, may depend in part on whether the virus is sensitive to seasonal changes in environmental variables, such as temperature and humidity. Although multiple studies have sought to address possible effects of these variables on SARS-CoV-2 transmission, results of these studies have been varied. It is possible that at least some of the differing results are due to insufficient understanding of atmospheric science, including certain physical and chemical principles underlying selected meteorological variables, and how global seasons differ between tropical and temperate zones. The objective of this brief perspective is to provide information that may help explain some of the differing results of studies regarding the influence of environmental variables on transmissibility of SARS-CoV-2. This information may promote better variable selection and results interpretation in future studies of coronavirus disease 2019 (COVID-19) and other infectious diseases.

Impact of climate and ambient air pollution on the epidemic growth during COVID-19 outbreak in Japan

Coronavirus disease 2019 (COVID-19) rapidly spread worldwide in the first quarter of 2020 and resulted in a global crisis. Investigation of the potential association of the spread of the COVID-19 infection with climate or ambient air pollution could lead to the development of preventive strategies for disease control. To examine this association, we conducted a longitudinal cohort study of 28 geographical areas of Japan with documented outbreaks of COVID-19. We analyzed data obtained from March 13 to April 6, 2020, before the Japanese government declared a state of emergency. The results revealed that the epidemic growth of COVID-19 was significantly associated with increase in daily temperature or sunshine hours. This suggests that an increase in person-to-person contact due to increased outing activities on a warm and/or sunny day might promote the transmission of COVID-19. Our results also suggested that short-term exposure to suspended particles might influence respiratory infections caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Further research by well-designed or well-controlled study models is required to ascertain this effect. Our findings suggest that weather has an indirect role in the transmission of COVID-19 and that daily adequate preventive behavior decreases the transmission.

Global to USA County Scale Analysis of Weather, Urban Density, Mobility, Homestay, and Mask Use on COVID-19

Prior evaluations of the relationship between COVID-19 and weather indicate an inconsistent role of meteorology (weather) in the transmission rate. While some effects due to weather may exist, we found possible misconceptions and biases in the analysis that only consider the impact of meteorological variables alone without considering the urban metabolism and environment. This study highlights that COVID-19 assessments can notably benefit by incorporating factors that account for urban dynamics and environmental exposure. We evaluated the role of weather (considering equivalent temperature that combines the effect of humidity and air temperature) with particular consideration of urban density, mobility, homestay, demographic information, and mask use within communities. Our findings highlighted the importance of considering spatial and temporal scales for interpreting the weather/climate impact on the COVID-19 spread and spatiotemporal lags between the causal processes and effects. On global to regional scales, we found contradictory relationships between weather and the transmission rate, confounded by decentralized policies, weather variability, and the onset of screening for COVID-19, highlighting an unlikely impact of weather alone. At a finer spatial scale, the mobility index (with the relative importance of 34.32%) was found to be the highest contributing factor to the COVID-19 pandemic growth, followed by homestay (26.14%), population (23.86%), and urban density (13.03%). The weather by itself was identified as a noninfluential factor (relative importance < 3%). The findings highlight that the relation between COVID-19 and meteorology needs to consider scale, urban density and mobility areas to improve predictions.

Investigation of the Importance of Climatic Factors in COVID-19 Worldwide Intensity

The transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the severity of the related disease (COVID-19) are influenced by a large number of factors. This study aimed to investigate the correlation of COVID-19 case and death rates with possible causal climatological and sociodemographic factors for the March to May 2020 (first wave) period in a worldwide scale by statistically processing data for over one hundred countries. The weather parameters considered herein were air temperature, relative humidity, cumulative precipitation, and cloud cover, while sociodemographic factors included population density, median age, and government measures in response to the pandemic. The results of this study indicate that there is a statistically significant correlation between average atmospheric temperature and the COVID-19 case and death rates, with chi-square test p-values in the 0.001-0.02 range. Regarding sociodemographic factors, there is an even stronger dependence of the case and death rates on the population median age (p = 0.0006-0.0012). Multivariate linear regression analysis using Lasso and the forward stepwise approach revealed that the median age ranks first in importance among the examined variables, followed by the temperature and the delays in taking first governmental measures or issuing stay-at-home orders.

Association of COVID-19 pandemic with meteorological parameters over Singapore

Meteorological parameters are the critical factors affecting the transmission of infectious diseases such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and influenza. Consequently, infectious disease incidence rates are likely to be influenced by the weather change. This study investigates the role of Singapore's hot tropical weather in COVID-19 transmission by exploring the association between meteorological parameters and the COVID-19 pandemic cases in Singapore. This study uses the secondary data of COVID-19 daily cases from the webpage of Ministry of Health (MOH), Singapore. Spearman and Kendall rank correlation tests were used to investigate the correlation between COVID-19 and meteorological parameters. Temperature, dew point, relative humidity, absolute humidity, and water vapor showed positive significant correlation with COVID-19 pandemic. These results will help the epidemiologists to understand the behavior of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus against meteorological variables. This study finding would be also a useful supplement to help the local healthcare policymakers, Center for Disease Control (CDC), and the World Health Organization (WHO) in the process of strategy making to combat COVID-19 in Singapore.

Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy

This paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy. For January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion. Exhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution. The results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates. Viral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants. At this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein "spike" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is. Also, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator. Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.

Modifiable areal unit problem and environmental factors of COVID-19 outbreak

Several recent studies have explored the association between environmental factors, such as temperature, humidity, and air pollution, and the severity of the COVID-19 outbreak by analyzing the statistical association at the district level. However, we argue that the modifiable areal unit problem (MAUP) arises when aggregating disease and environmental data into districts, leading to bias in such studies. Therefore, in this study, we analyzed the association between environmental factors and the number of COVID-19 death cases under different aggregation strategies to illustrate the presence of MAUP. We used real-world COVID-19 outbreak data from the Hubei and Henan Provinces and studied their association with atmospheric NO2 levels. By fitting linear regression models with penalized splines on NO2, we found that the association between COVID-19 mortality and NO2 varies when data were aggregated (1) at the city level, (2) under two different aggregation strategies, and (3) at the provincial level, indicating the presence of MAUP. Therefore, this study reminds researchers of the presence of MAUP and the necessity to minimize this problem while exploring the environmental determinants of the COVID-19 outbreak.

Impact of population density on Covid-19 infected and mortality rate in India

The Covid-19 is a highly contagious disease which becomes a serious global health concern. The residents living in areas with high population density, such as big or metropolitan cities, have a higher probability to come into close contact with others and consequently any contagious disease is expected to spread rapidly in dense areas. However, recently, after analyzing Covid-19 cases in the USA researchers at the Johns Hopkins Bloomberg School of Public Health, London school of economics, and IZA—Institute of Labour Economics conclude that the spread of Covid-19 is not linked with population density. Here, we investigate the influence of population density on Covid-19 spread and related mortality in the context of India. After a detailed correlation and regression analysis of infection and mortality rates due to Covid-19 at the district level, we find moderate association between Covid-19 spread and population density.

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

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.

A Non-Contact Integrated Body-Ambient Temperature Sensors Platform to Contrast COVID-19

An integrated sensors platform for non-contact temperature monitoring is proposed in this work. The adopted solution, based on the combined integration of an infrared thermometer and a capacitive humidity sensor, is able to provide a fast and accurate tool for remotely sensing both ambient and body temperature in the framework of pandemic situations, such as COVID-19, thus avoiding any direct contact with people. The information relative to the ambient temperature is successfully exploited to derive a correction formula for the accurate extraction of body temperature from the measurement provided by the standard infrared sensor. Full details on the design of the proposed platform are provided in the work, by reporting relevant simulation results on the variations of ambient temperature, relative humidity, and body temperature. Experimental validations are also discussed to provide a full assessment of the proposed approach.

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

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.

Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy

The novel coronavirus disease (COVID-19) is a highly pathogenic, transmittable and invasive pneumococcal disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which emerged in December 2019 and January 2020 in Wuhan city, Hubei province, China and fast spread later on the middle of February 2020 in the Northern part of Italy and Europe. This study investigates the correlation between the degree of accelerated diffusion and lethality of COVID-19 and the surface air pollution in Milan metropolitan area, Lombardy region, Italy. Daily average concentrations of inhalable particulate matter (PM) in two size fractions PM2.5, PM10 and maxima PM10 ground level atmospheric pollutants together air quality and climate variables (daily average temperature, relative humidity, wind speed, atmospheric pressure field and Planetary Boundary Layer-PBL height) collected during 1 January-30 April 2020 were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces, or direct human-to-human personal contacts, it seems that high levels of urban air pollution, weather and specific climate conditions have a significant impact on the increased rates of confirmed COVID-19 Total number, Daily New and Total Deaths cases, possible attributed not only to indoor but also to outdoor airborne bioaerosols distribution. Our analysis demonstrates the strong influence of daily averaged ground levels of particulate matter concentrations, positively associated with average surface air temperature and inversely related to air relative humidity on COVID-19 cases outbreak in Milan. Being a novel pandemic coronavirus (SARS-CoV-2) version, COVID-19 might be ongoing during summer conditions associated with higher temperatures and low humidity levels. Presently is not clear if this protein "spike" of the new coronavirus COVID-19 is involved through attachment mechanisms on indoor or outdoor airborne aerosols in the infectious agent transmission from a reservoir to a susceptible host in some agglomerated urban areas like Milan is.

Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy

The novel coronavirus disease (COVID-19) is a highly pathogenic, transmittable and invasive pneumococcal disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which emerged in December 2019 and January 2020 in Wuhan city, Hubei province, China and fast spread later on the middle of February 2020 in the Northern part of Italy and Europe. This study investigates the correlation between the degree of accelerated diffusion and lethality of COVID-19 and the surface air pollution in Milan metropolitan area, Lombardy region, Italy. Daily average concentrations of inhalable particulate matter (PM) in two size fractions PM2.5, PM10 and maxima PM10 ground level atmospheric pollutants together air quality and climate variables (daily average temperature, relative humidity, wind speed, atmospheric pressure field and Planetary Boundary Layer-PBL height) collected during 1 January-30 April 2020 were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces, or direct human-to-human personal contacts, it seems that high levels of urban air pollution, weather and specific climate conditions have a significant impact on the increased rates of confirmed COVID-19 Total number, Daily New and Total Deaths cases, possible attributed not only to indoor but also to outdoor airborne bioaerosols distribution. Our analysis demonstrates the strong influence of daily averaged ground levels of particulate matter concentrations, positively associated with average surface air temperature and inversely related to air relative humidity on COVID-19 cases outbreak in Milan. Being a novel pandemic coronavirus (SARS-CoV-2) version, COVID-19 might be ongoing during summer conditions associated with higher temperatures and low humidity levels. Presently is not clear if this protein "spike" of the new coronavirus COVID-19 is involved through attachment mechanisms on indoor or outdoor airborne aerosols in the infectious agent transmission from a reservoir to a susceptible host in some agglomerated urban areas like Milan is.

WITHDRAWN: Can Traditional Chinese Medicine provide insights into controlling the COVID-19 pandemic: Serpentinization-induced lithospheric long-wavelength magnetic anomalies in Proterozoic bedrocks in a weakened geomagnetic field mediate the aberrant transformation of biogenic molecules in COVID-19 via magnetic catalysis

This article has been withdrawn at the request of the authors and the editors. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

Effect of meteorological factors on COVID-19 cases in Bangladesh

This work is intended to examine the effects of Bangladesh's subtropical climate on coronavirus diseases 2019 (COVID-19) transmission. Secondary data for daily meteorological variables and COVID-19 cases from March 8 to May 31, 2020, were collected from the Bangladesh Meteorological Department (BMD) and Institute of Epidemiology, Disease Control and Research (IEDCR). Distributed lag nonlinear models, Pearson's correlation coefficient and wavelet transform coherence were employed to appraise the relationship between meteorological factors and COVID-19 cases. Significant coherence between meteorological variables and COVID-19 at various time-frequency bands has been identified in this work. The results showed that the minimum (MinT) and mean temperature, wind speed (WS), relative humidity (RH) and absolute humidity (AH) had a significant positive correlation while contact transmission had no direct association with the number of COVID-19 confirmed cases. When the MinT was 18 °C, the relative risk (RR) was the highest as 1.04 (95%CI 1.01-1.06) at lag day 11. For the WS, the highest RR was 1.03 (95% CI 1.00-1.07) at lag day 0, when the WS was 21 km/h. When RH was 46%, the highest RR was 1.00 (95% CI 0.98-1.01) at lag day 14. When AH was 23 g/m3, the highest RR was 1.05 (95% CI 1.01-1.09) at lag day 14. We found a profound effect of meteorological factors on SARS-CoV-2 transmission. These results will assist policymakers to know the behavioral pattern of the SARS-CoV-2 virus against meteorological indicators and thus assist to devise an effective policy to fight against COVID-19 in Bangladesh.

Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India

Globally, since the end of December 2019, coronavirus disease (COVID-19) has been recognized as a severe infectious disease. Therefore, this study has been attempted to examine the linkage between climatic variables and COVID-19 particularly in National Capital Territory of Delhi (NCT of Delhi), India. For this, daily data of COVID-19 has been used for the period March 14 to June 11, 2020, (90 days). Eight climatic variables such as maximum, minimum and mean temperature (°C), relative humidity (%), bright sunshine hours, wind speed (km/h), evaporation (mm), and rainfall (mm) have been analyzed in relation to COVID-19. To study the relationship among different climatic variables and COVID-19 spread, Karl Pearson's correlation analysis has been performed. The Mann-Kendall method and Sen's slope estimator have been used to detect the direction and magnitude of COVID-19 trends, respectively. The results have shown that out of eight selected climatic variables, six variables, viz. maximum temperature, minimum temperature, mean temperature, relative humidity, evaporation, and wind speed are positively associated with coronavirus disease cases (statistically significant at 95 and 99% confidence levels). No association of coronavirus disease has been found with bright sunshine hours and rainfall. Besides, COVID-19 cases and deaths have shown increasing trends, significant at 99% confidence level. The results of this study suggest that climatic conditions in NCT of Delhi are favorable for COVID-19 and the disease may spread further with the increasing temperature, relative humidity, evaporation and wind speed. This is the only study which has presented the analysis of COVID-19 spread in relation to several climatic variables for the most densely populated and rapidly growing city of India. Thus, considering the results obtained, effective policies and actions are necessary especially by identifying the areas where the spread rate is increasing rapidly in this megacity. The prevention and protection measures should be adopted aiming at to reduce the further transmission of disease in the city.

Impact of meteorological conditions and air pollution on COVID-19 pandemic transmission in Italy

Italy was the first, among all the European countries, to be strongly hit by the COVID-19 pandemic outbreak caused by the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2). The virus, proven to be very contagious, infected more than 9 million people worldwide (in June 2020). Nevertheless, it is not clear the role of air pollution and meteorological conditions on virus transmission. In this study, we quantitatively assessed how the meteorological and air quality parameters are correlated to the COVID-19 transmission in two large metropolitan areas in Northern Italy as Milan and Florence and in the autonomous province of Trento. Milan, capital of Lombardy region, it is considered the epicenter of the virus outbreak in Italy. Our main findings highlight that temperature and humidity related variables are negatively correlated to the virus transmission, whereas air pollution (PM2.5) shows a positive correlation (at lesser degree). In other words, COVID-19 pandemic transmission prefers dry and cool environmental conditions, as well as polluted air. For those reasons, the virus might easier spread in unfiltered air-conditioned indoor environments. Those results will be supporting decision makers to contain new possible outbreaks.

Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)

Recently, anovel coronavirus disease (COVID-19) has become a serious concern for global public health. Infectious disease outbreaks such as COVID-19 can also significantly affect the sustainable development of urban areas. Several factors such as population density and climatology parameters could potentially affect the spread of the COVID-19. In this study, a combination of the virus optimization algorithm (VOA) and adaptive network-based fuzzy inference system (ANFIS) was used to investigate the effects of various climate-related factors and population density on the spread of the COVID-19. For this purpose, data on the climate-related factors and the confirmed infected cases by the COVID-19 across the U.S counties was used. The results show that the variable defined for the population density had the most significant impact on the performance of the developed models, which is an indication of the importance of social distancing in reducing the infection rate and spread rate of the COVID-19. Among the climatology parameters, an increase in the maximum temperature was found to slightly reduce the infection rate. Average temperature, minimum temperature, precipitation, and average wind speed were not found to significantly affect the spread of the COVID-19 while an increase in the relative humidity was found to slightly increase the infection rate. The findings of this research show that it could be expected to have slightly reduced infection rate over the summer season. However, it should be noted that the models developed in this study were based on limited one-month data. Future investigation can benefit from using more comprehensive data covering a wider range for the input variables.

A novel CFD analysis to minimize the spread of COVID-19 virus in hospital isolation room

The COVID-19 is a severe respiratory disease caused by a devastating coronavirus family (2019-nCoV) has become a pandemic across the globe. It is an infectious virus and transmits by inhalation or contact with droplet nuclei produced during sneezing, coughing, and speaking by infected people. Airborne transmission of COVID-19 is also possible in a confined place in the immediate environment of the infected person. Present study investigates the effectiveness of conditioned air released from air-conditioning machines to mix with aerosol sanitizer to reach every point of the space of the isolation room so as to kill the COVID-19 virus which will help to protect the lives of doctors, nurses and health care workers. In order to numerically model the laminar-transitional flows, transition SST k-ε model, which involves four transport equations are employed in the current study. It is found from the analysis that high turbulent fields generated inside the isolation room may be an effective way of distributing sanitizer in entire volume of isolation room to kill the COVID-19 virus.

A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China

The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, -0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between -9.41 °C and -13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (<0 °C) was exponentially associated with an increased transmission rate of COVID-19. These findings suggest that the mitigation of COVID-19 spread in densely populated and/or cold regions will be a great challenge.

Environmental concern regarding the effect of humidity and temperature on 2019-nCoV survival: fact or fiction

The new coronavirus, called 2019-nCoV, is a new type of virus that was first identified in Wuhan, China, in December 2019. Environmental conditions necessary for survival and spread of 2019-nCoV are somewhat transparent but unlike animal coronaviruses. We are poorly aware of their survival in environment and precise factors of their transmission. Countries located in east and west of globe did not have a significant impact on prevalence of disease among communities, and on the other hand, north and south have provided a model for relative prediction of disease outbreaks. The 2019-nCoV can survive for up to 9 days at 25 °C, and if this temperature rises to 30 °C, its lifespan will be shorter. The 2019-nCoV is sensitive to humidity, and lifespan of viruses in 50% humidity is longer than that of 30%. Also, temperature and humidity are important factors influencing the COVID-19 mortality rate and may facilitate 2019-nCoV transmission. Thus, considering the available and recent evidence, it seems that low temperatures, as well as dry and unventilated air, may affect stability and transmissibility of 2019-nCoV.

COVID-19: Environment concern and impact of Indian medicinal system

The COVID-19 outbreak has came in existence in late December 2019 at Wuhan, China. It is declared as an epidemic by WHO. The rationale of this study is to provide the details regarding prevention, environment concern, social economic consequences, and medicines for COVID-19. Social distancing, screening, lockdown, use of mask and application of sanitizer or soap at regular time interval is the best prevention against COVID-19. The "oral-feces" transmission of COVID-19 is threat to environment. Improper disposal of medical/biomedical and human waste may harm the total environment. Nitrifying-enriched activated sludge i.e. NAS approach can play important role to clean the environment compartments like sludge and waste. COVID-19 has shown impact on social and economic life, but there is no alternate until the drug discovery. In medicine or treatment of COVID-19 point of views, an integrated approach between modern and traditional medicine system may ensure an early prevention of further viral spread. Based on the symptoms of COVID-19, list of herbs and drugs of Indian Medicine System has been searched and reported. To develop the potential drug against COVID-19, the detailed experimentation and clinical trials to be performed for future prospective.

The mediating effect of air quality on the association between human mobility and COVID-19 infection in China

BACKGROUND

Previous studies have found that human mobility restrictions could not only prevent the spread of COVID-19, but also improve the air quality because of the reduction of industrial production, transportation and traffic. It is noteworthy that air quality is also closely related to the risk of COVID-19 infection. Therefore, we aimed to assess the mediating role of air quality on the association between human mobility and the infection caused by this novel coronavirus.

METHODS

We collected daily confirmed cases, human mobility data, air quality data and meteorological variables in 120 cities from China between January 23, 2020 and February 29, 2020. We applied the generalized additive model to examine the association of human mobility index with COVID-19 confirmed cases, and to assess the mediating effects of air quality index and each pollutant.

RESULTS

We observed a significant positive relationship between human mobility index and the daily counts of COVID-19 confirmed cases. A unit increase in human mobility index (lag0-14) was associated with a 6.45% increase in daily COVID-19 confirmed cases, and air quality index significantly mediated 19.47% of this association. We also observed a positive relationship between human mobility index and air quality index. In the pollutant level analyses, we found significant mediating effects of PM2.5, PM10, and NO2.

CONCLUSIONS

Our study suggests that limiting human movements could reduce COVID-19 cases by improving air quality besides decreasing social contact.

Real-time estimation and prediction of the mortality caused due to COVID-19 using particle swarm optimization and finding the most influential parameter

On March 11, 2020, the World Health Organization has declared the outbreak of COVID-19 as Pandemic, which is the massive challenges faced globally. Previous studies have indicated that the meteorological parameters can play a vital role in transmissibility and Mortality. In the present work, the influence of Comorbidity and meteorological parameters are investigated for Mortality caused due to COVID. For this, the most affected city by COVID-19 is considered, i.e., Mumbai, India, as a case study. It was found that Comorbidity is the most influential parameter on the Mortality of COVID-19. The Spearman correlation coefficient for meteorological parameters lies between 0.386 and 0.553, whereas for Comorbidity was found as 0.964. A regression model is developed using particle swarm optimization to predict the mortality cases for Mumbai, India. Further, the developed model is validated for the COVID-19 cases of Delhi, India, to emphasize the utility of the developed model for other cities. The measured and predicted curve shows a good fit with a mean percentage error of 0.00957% and a coefficient of determination of 0.9828. Thus, particle swarm optimization techniques demonstrate very high potential for the prediction of Mortality caused due to COVID-19. It is insisted that by providing constant health monitoring and adequate care for the comorbidity patients, the Mortality can be suppressed drastically. The present work can serve as an input to the policymakers to overcome the COVID-19 pandemic in India as well as other parts of the world.

Optimal temperature zone for the dispersal of COVID-19

It is essential to know the environmental parameters within which the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can survive to understand its global dispersal pattern. We found that 60.0% of the confirmed cases of coronavirus disease 2019 (COVID-19) occurred in places where the air temperature ranged from 5 °C to 15 °C, with a peak in cases at 11.54 °C. Moreover, approximately 73.8% of the confirmed cases were concentrated in regions with absolute humidity of 3 g/m3 to 10 g/m3. SARS-CoV-2 appears to be spreading toward higher latitudes. Our findings suggest that there is an optimal climatic zone in which the concentration of SARS-CoV-2 markedly increases in the ambient environment (including the surfaces of objects). These results strongly imply that the COVID-19 pandemic may spread cyclically and outbreaks may recur in large cities in the mid-latitudes in autumn 2020.