Predicting the local COVID-19 outbreak around the world with meteorological conditions: a model-based qualitative study

Transmission rate and control efficiency of COVID-19 was lower in warm and wet climate

COVID-19 has caused huge damage to public health around the world, revealing the influencing factors are essential to take effective control. By using a global dataset covering 617 time series over the world, we estimated the transmission parameters and modeled human and climate effects on COVID-19 transmission. We found that the average transmission rate was lower in warm climate over the world and in wet climate (more precipitation) in Europe. The maximum transmission rate was lower in warm climate in the world, China and USA, and in wet climate in China. The control efficiency in the world, China, and USA was lower in warm and wet condition. In general, our results indicate that warm and wet climate do not favor transmission and human intervention of COVID-19, and COVID-19 transmission rate would be lower in warm and wet seasons or regions than in dry and cold ones.

Weather indicators and improving air quality in association with COVID-19 pandemic in India

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM₁₀, PM_2.5, CO, NO₂, SO₂, NH₃, and O₃) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00500-021-06012-9.

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus's transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.

Statistical model for factors correlating with COVID-19 deaths

BACKGROUND

The COVID-19 pandemic has caused major disruption in societies globally. Our aim is to understand, what factors were associated with the impact of the pandemic on death rates. This will help countries to better prepare for and respond in future pandemics.

METHODS

We modeled with a linear mixed effect model the impact of COVID-19 with the dependent variable "Daily mortality change" (DMC) with country features variables and intervention (containment measurement) data. We tested both country characteristics consisting of demographic, societal, health related, healthcare system specific, environmental and cultural feature as well as COVID-19 specific response in the form of social distancing interventions.

RESULTS

A statistically significant country feature was Geert Hofstede's masculinity, i.e., the extent to which the use of force is endorsed socially, correlating positively with a higher DMC. The effects of different interventions were stronger that those of country features, particularly cancelling public events, controlling international travel and closing workplaces.

CONCLUSION

Social distancing interventions and the country feature: Geert Hofstede's masculinity dimension had a significant impact on COVID-19 mortality change. However other country features, such as development and population health did not show significance. Thus, the crises responders and scholars could revisit the concept and understanding of preparedness for and response to pandemics.

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate

Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

SARS-CoV-2 phase I transmission and mutability linked to the interplay of climatic variables: a global observation on the pandemic spread

The study aims to determine the impact of global meteorological parameters on SARS-COV-2, including population density and initiation of lockdown in twelve different countries. The daily trend of these parameters and COVID-19 variables from February 15th to April 25th, 2020, were considered. Asian countries show an increasing trend between infection rate and population density. A direct relationship between the time-lapse of the first infected case and the period of suspension of movement controls the transmissivity of COVID-19 in Asian countries. The increase in temperature has led to an increase in COVID-19 spread, while the decrease in humidity is consistent with the trend in daily deaths during the peak of the pandemic in European countries. Countries with 65°F temperature and 5 mm rainfall have a negative impact on COVID-19 spread. Lower oxygen availability in the atmosphere, fine droplets of submicron size together with infectious aerosols, and low wind speed have contributed to the increase in total cases and mortality in Germany and France. The onset of the D614G mutation and subsequent changes to D614 before March, later G614 in mid-March, and S943P, A831V, D839/Y/N/E in April were observed in Asian and European countries. The results of the correlation and factor analysis show that the COVID-19 cases and the climatic factors are significantly correlated with each other. The optimum meteorological conditions for the prevalence of G614 were identified. It was observed that the complex interaction of global meteorological factors and changes in the mutational form of CoV-2 phase I influenced the daily mortality rate along with other comorbid factors. The results of this study could help the public and policymakers to create awareness of the COVID-19 pandemic.

Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices

Most coronavirus disease 2019 (COVID-19) models use a combination of agent-based and equation-based models with only a few incorporating environmental factors in their prediction models. Many studies have shown that human and environmental factors play huge roles in disease transmission and spread, but few have combined the use of both factors, especially for SARS-CoV-2. In this study, both man-made policies (Stringency Index) and environment variables (Niño SST Index) were combined to predict the number of COVID-19 cases in South Korea. The performance indicators showed satisfactory results in modeling COVID-19 cases using the Non-linear Autoregressive Exogenous Model (NARX) as the modeling method, and Stringency Index (SI) and Niño Sea Surface Temperature (SST) as model variables. In this study, we showed that the accuracy of SARS-CoV-2 transmission forecasts may be further improved by incorporating both the Niño SST and SI variables and combining these variables with NARX may outperform other models. Future forecasting work by modelers should consider including climate or environmental variables (i.e., Niño SST) to enhance the prediction of transmission and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Prediction of COVID-19 cases using the weather integrated deep learning approach for India

Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non-linear problems. In the present study, the relationship between weather factor and COVID-19 cases was assessed, and also developed a forecasting model using long short-term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID-19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID-19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short-term (1 day lead) forecast of COVID-19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium-range forecast skill (1-7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.

Climatic factors influence the spread of COVID-19 in Russia

The study is the first attempt to assess the role of climatic predictors in the rise of COVID-19 intensity in the Russian climatic region. The study used the Random Forest algorithm to understand the underlying associations and monthly scenarios. The results show that temperature seasonality (29.2 ± 0.9%) has the highest contribution for COVID-19 transmission in the humid continental region. In comparison, the diurnal temperature range (26.8 ± 0.4%) and temperature seasonality (14.6 ± 0.8%) had the highest impacts in the sub-arctic region. Our results also show that September and October have favorable climatic conditions for the COVID-19 spread in the sub-arctic and humid continental regions, respectively. From June to August, the high favorable zone for the spread of the disease will shift towards the sub-arctic region from the humid continental region. The study suggests that the government should implement strict measures for these months to prevent the second wave of COVID-19 outbreak in Russia.

Improving Hemodynamic Status of COVID-19 Patients with Murottal Therapy

BACKGROUND: COVID-19 is a respiratory tract infection caused by the coronavirus. Some patients with severe symptoms require hospital treatment needing oxygen support. The COVID-19 condition affects the hemodynamic status of the patient. AIM: This study aimed to analyze the effect of murottal surah Ar-Rahman therapy on the hemodynamic status of COVID-19 patients in A. Yani Islamic Hospital Surabaya. METHODS: This research design was a quasi-experiment with pre-test–post-test one group design without a control group. The population was the confirmed positive patient for COVID-19 who were treated in the Mina Room of A. Yani Islamic Hospital Surabaya from November 2020 to January 2021 with a total of 63 patients. The sample was 55 patients which were taken by purposive sampling. The variables in this study were the hemodynamic status, including blood pressure, respiration rate, heart rate, and oxygen saturation that were given murottal therapy Surah Ar-Rahman. Data were analyzed using Paired t-test and those that were not normally distributed by sign-test. RESULTS: The results of this study found that there were differences in the pre-post variables of systolic blood pressure (p = 0.000), heart rate (p = 0.000), respiration rate (p = 0.000), and oxygen saturation (p = 0.000). There was no difference in the diastolic blood pressure variable (p = 0.263). CONCLUSIONS: This study concluded that the Surah Ar-Rahman therapy murottal can be used and effective to improve hemodynamic status in conjunction with therapy from the medical team. This therapy is also very easy to use anywhere.

Understanding the heterogeneity of COVID-19 deaths and contagions: The role of air pollution and lockdown decisions

The uneven geographical distribution of the novel coronavirus epidemic (COVID-19) in Italy is a puzzle given the intense flow of movements among the different geographical areas before lockdown decisions. To shed light on it, we test the effect of the quality of air (as measured by particulate matter and nitrogen dioxide) and lockdown restrictions on daily adverse COVID-19 outcomes during the first pandemic wave in the country. We find that air pollution is positively correlated with adverse outcomes of the pandemic, with lockdown being strongly significant and more effective in reducing deceases in more polluted areas. Results are robust to different methods including cross-section, pooled and fixed-effect panel regressions (controlling for spatial correlation), instrumental variable regressions, and difference-in-differences estimates of lockdown decisions through predicted counterfactual trends. They are consistent with the consolidated body of literature in previous medical studies suggesting that poor quality of air creates chronic exposure to adverse outcomes from respiratory diseases. The estimated correlation does not change when accounting for other factors such as temperature, commuting flows, quality of regional health systems, share of public transport users, population density, the presence of Chinese community, and proxies for industry breakdown such as the share of small (artisan) firms. Our findings provide suggestions for investigating uneven geographical distribution patterns in other countries, and have implications for environmental and lockdown policies.

Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19

Introduction

In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. Considering this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in each social, economic, and political scenario.

Methodology

We present a generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO.

Results

The main results were: (a) Our model was able to accurately fit the either deaths or active cases data of all tested countries using optimized coefficient values in agreement with recent reports; (b) when trying to fit both sets of data at the same time, fit was good for most countries, but not all. (c) Using our model, large ranges for each input, and optimization we predict death values for 15, 30, 45, and 60 days ahead with errors in the order of 5, 10, 20, and 80%, respectively; (d) sudden changes in the number of active cases cannot be predicted by the model unless data from outside sources are used.

Conclusion

The results suggest that the presented model may be used to predict 15 days ahead values of total deaths with errors in the order of 5%. These errors may be minimized if social distance data are inputted into the model.

Association between human coronaviruses' epidemic and environmental factors on a global scale

Environmental factors could influence the epidemic of virus in human; however, the association remains intricate, and the evidence is still not clear in human coronaviruses (HCoVs). We aimed to explore and compare the associations between HCoVs' epidemic and environmental factors globally. Four common HCoVs' data were collected by a systematic literature review, and data of MERS, SARS, and COVID-19 were collected from the World Health Organization's reports. Monthly positive rates of common HCoVs and incidence rates of MERS, SARS, and COVID-19 were calculated. Geographical coordinates were used to link virus data and environmental data. Generalized additive models (GAMs) were used to quantitatively estimate the association of environmental factors with HCoVs' epidemic. We found that there are wide associations between HCoVs and environmental factors on a global scale, and some of the associations were nonlinear. In addition, COVID-19 has the most similarities in associations' direction with common HCoVs, especially for HCoV-HKU1 in four environmental factors including the significantly negative associations with average temperature, precipitation, vegetation coverage (p<0.05), and the U-shaped association with temperature range. This study strengthened the relevant research evidences and provided significant insights into the epidemic rules of HCoVs in general. The similarities between COVID-19 and common HCoVs indicated that it is critically important to strengthen surveillance on common HCoVs and pay more attention to environmental factors' role in surveillance and early warning of HCoVs' epidemic.

The impact of weather condition and social activity on COVID-19 transmission in the United States

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.

Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.

Long-term statistical assessment of meteorological indicators and COVID-19 outbreak in hot and arid climate, Bahrain

The COVID-19 pandemic has significantly impacted the global lifestyle, and the spreading of the virus is unprecedented. This study is aimed at assessing the association between the meteorological indicators such as air temperature (°C), relative humidity (%), wind speed (w/s), solar radiation, and PM_2.5 with the COVID-19 infected cases in the hot, arid climate of Bahrain. Kendall and Spearman rank correlation coefficients and quantile on quantile regression were used as main econometric analysis to determine the degree of the relationship between related variables. The dataset analysis was performed from 05 April 2020, to 10 January 2021. The empirical findings indicate that the air temperature, humidity, solar radiation, wind speed indicators, and PM2.5 have a significant association with the COVID-19 newly infected cases. The current study findings allow us to suggest that Bahrain's relatively successful response to neighboring GULF economies can be attributed to the successful environmental reforms and significant upgrades to the health care facilities. We further report that a long-term empirical analysis between meteorological factors and respiratory illness threats will provide useful policy measures against future outbreaks.

Role of mobile health in the situation of COVID-19 pandemics: pros and cons

Mobile health (mHealth), an abbreviated term used for portable healthcare, is characterized by the World Health Organization (WHO) as an utilization of portable healthcare monitoring equipments by the health care delivery system. The review article is based on the utilization of the mobile phones in the form of text information, image sharing, video call, doctor appointment, and auto-generated schedule during the global pandemics situations. Initially the motivation behind the technology was the availability of physical and psychological care for unreached communities. But, in the situation of pandemics, there is a high momentum to the mHealth application in the healthcare delivery system. The differential utilization of cell phone has led to the multiplication of health-related personalized applications day by day for the human race; there are numerous expected roads for mHealth to be fill in as an aide instrument to general health care when most of the developed and developing nations are in a cyclic process of lockdown of the century. In the present times, people are advised by the WHO to make a physical distance from everyone; hence the mHealth promptly came into existence with its importance. However, there are both pros and cons of every technology; here mHealth helps to provide improved treatment availability due to its promptness, ease, accessibility but on the another hand, there is the limitation of mHealth applications as personal data sharing with the network providers is easier. There are concerns like moral, legitimate, and clinical issues identified with mHealth usage, incorporating issues with information security, protection issues with limits, and interjurisdictional practice concerns. The technology-centric model launching incorporates the traditional medical approaches and training through the emerging technology-centric model into medical and educational systems to support medical practitioners and the patients. The review article presents the proof for the focal points and impediments of rebuilding a medicinal service framework on essential considerations. It depends on a fast, however, orderly audit of critical sources of strewn writing. The results are unpredictable for various reasons, including varying meanings of administrations, staff and the limits among essential and auxiliary consideration, changing hierarchical structures, and expanding dependence on essential consideration groups for mHealth.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Is compulsory home quarantine less effective than centralized quarantine in controlling the COVID-19 outbreak? Evidence from Hong Kong

Faced with the global spread of COVID-19, the Hong Kong government imposed compulsory home quarantine on all overseas arrivals, while cities in mainland China and Macau adopted a more stringent centralized quarantine approach. This study evaluates the effectiveness of compulsory home quarantine as a means of pandemic control. Combining epidemiological data with traditional socioeconomic and meteorological data from over 250 cities, we employ the Synthetic Control Method (SCM) to construct a counterfactual "synthetic Hong Kong". This model simulates the infection trends for a hypothetical situation in which HK adopts centralized quarantine measures, and compares them to actual infection numbers. Results suggest that home quarantine would have been less effective than centralized quarantine initially. However, the infection rate under home quarantine later converges with the counterfactual estimate under centralized quarantine (0.136% vs. 0.174%), suggesting similar efficacy in the later phase of implementation. Considering its minimal reliance on public resources, home quarantine with heightened enforcement may therefore be preferable to centralized quarantine in countries with limited public health resources. Home quarantine as a quarantine alternative balances public protection and individual freedom, while conserving resources, making it a more sustainable option for many cities.

Global COVID-19 Transmission and Mortality-Influence of Human Development, Climate, and Climate Variability on Early Phase of the Pandemic

Many of the respiratory pathogens show seasonal patterns and association with environmental factors. In this article, we conducted a cross-sectional analysis of the influence of environmental factors, including climate variability, along with development indicators on the differential global spread and fatality of COVID-19 during its early phase. Global climate data we used are monthly averaged gridded data sets of temperature, humidity and temperature anomaly. We used Human Development Index (HDI) to account for all nation wise socioeconomic factors that can affect the reporting of cases and deaths and build a stepwise negative binomial regression model. In the absence of a development indicator, all environmental variables excluding the specific humidity have a significant association with the spread and mortality of COVID-19. Temperature has a weak negative association with COVID-19 mortality. However, HDI is shown to confound the effect of temperature on the reporting of the disease. Temperature anomaly, which is being regarded as a global warming indicator, is positively associated with the pandemic's spread and mortality. Viewing newer infectious diseases like SARS-CoV-2 from the perspective of climate variability has a lot of public health implications, and it necessitates further research.

COVID-19 and environmental concerns: A rapid review

COVID-19 has slowed global economic growth and consequently impacted the environment as well. Parallelly, the environment also influences the transmission of this novel coronavirus through various factors. Every nation deals with varied population density and size; air quality and pollutants; the nature of land and water, which significantly impact the transmission of coronavirus. The WHO (Ziaeepour et al., 2008) [1] has recommended rapid reviews to provide timely evidence to the policymakers to respond to the emergency. The present study follows a rapid review along with a brief bibliometric analysis of 328 research papers, which synthesizes the evidence regarding the environmental concerns of COVID-19. The novel contribution of this rapid review is threefold. One, we take stock of the diverse findings as regards the transmission of the novel coronavirus in different types of environments for providing conclusive directions to the ongoing debate regarding the transmission of the virus. Two, our findings provide topical insights as well as methodological guidance for future researchers in the field. Three, we inform the policymakers on the efficacy of environmental measures for controlling the spread of COVID-19.

The association between initial COVID-19 spread and meteorological factors in Indonesia

On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and a half months later (15/05/2020), the cumulative number of infection cases was 16,496, with a total of 1076 mortalities. This study investigates the possible role of weather in the early cases of COVID-19 in six selected cities in Indonesia. Daily temperature and relative humidity data from weather stations nearby in each city were collected from March 3 to April 30, 2020, corresponding with COVID-19 incidence. Correlation tests and regression analysis were performed to examine the association of those two data series. Moreover, we analyzed the distribution of COVID-19 referring the weather data to estimate the effective range of weather data supporting the COVID-19 incidence. Our result reveals that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) present significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak, with the strongest correlations found at the 5-day lag, i.e., 0.37 (- 0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that most COVID-19 cases in Indonesia occurred in the daily temperature range of 25-31 °C and relative humidity of 74-92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a more prominent role and should be given greater consideration in preventing the spread of COVID-19.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42398-021-00202-9.

Association between meteorological indicators and COVID-19 pandemic in Pakistan

This study was designed to investigate the impact of meteorological indicators (temperature, rainfall, and humidity) on total COVID-19 cases in Pakistan, its provinces, and administrative units from March 10, 2020, to August 25, 2020. The correlation analysis showed that COVID-19 cases and temperature showed a positive correlation. It implies that the increase in COVID-19 cases was reported due to an increase in the temperature in Pakistan, its provinces, and administrative units. The generalized Poisson regression showed that the rise in the expected log count of COVID-19 cases was 0.024 times for a 1 °C rise in the average temperature in Pakistan. Second, the correlation between rainfall and COVID-19 cases was negative in Pakistan. However, the regression coefficient between the expected log count of COVID-19 cases and rainfall was insignificant in Pakistan. Third, the correlation between humidity and the total COVID-19 cases was negative, which implies that the increase in humidity is beneficial to stop the transmission of COVID-19 in Pakistan, its provinces, and administrative units. The reduction in the expected log count of COVID-19 cases was 0.008 times for a 1% increase in the humidity per day in Pakistan. However, humidity and COVID-19 cases were positively correlated in Sindh province. It is required to create awareness among the general population, and the government should include the causes, symptoms, and precautions in the educational syllabus. Moreover, people should adopt the habit of hand wash, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to control the COVID-19.

Systematic review of the effects of environmental factors on virus inactivation: implications for coronavirus disease 2019

Environmental factors such as temperature and relative humidity can affect the inactivation and transmission of coronaviruses. By reviewing medical experiments on virus survival and virus transmission between infected and susceptible species in different temperature and humidity conditions, this study explores the influence of temperature and relative humidity on the survival and transmission of viruses, and provides suggestions, with experimental evidence, for the environmental control measures of Coronavirus Disease 2019. The results indicated that (1) virus viability and infectivity is increased at a low temperature of 5 ℃ and reduced at higher temperatures. (2) Virus survival and transmission is highly efficient in a dry environment with low relative humidity, and also in a wet environment with high relative humidity, and it is minimal at intermediate relative humidity. Therefore, in indoor environments, the lack of heating in winter or overventilation, leading to low indoor temperature, can help virus survival and help susceptible people being infected. On the contrary, modulating the indoor relative humidity at an intermediate level is conducive to curb epidemic outbreaks.

Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions

As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate (R). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention ("lockdown") and reductions in individuals' mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies.

Climate change, environment pollution, COVID-19 pandemic and mental health

Converging data would indicate the existence of possible relationships between climate change, environmental pollution and epidemics/pandemics, such as the current one due to SARS-CoV-2 virus. Each of these phenomena has been supposed to provoke detrimental effects on mental health. Therefore, the purpose of this paper was to review the available scientific literature on these variables in order to suggest and comment on their eventual synergistic effects on mental health. The available literature report that climate change, air pollution and COVID-19 pandemic might influence mental health, with disturbances ranging from mild negative emotional responses to full-blown psychiatric conditions, specifically, anxiety and depression, stress/trauma-related disorders, and substance abuse. The most vulnerable groups include elderly, children, women, people with pre-existing health problems especially mental illnesses, subjects taking some types of medication including psychotropic drugs, individuals with low socio-economic status, and immigrants. It is evident that COVID-19 pandemic uncovers all the fragility and weakness of our ecosystem, and inability to protect ourselves from pollutants. Again, it underlines our faults and neglect towards disasters deriving from climate change or pollution, or the consequences of human activities irrespective of natural habitats and constantly increasing the probability of spillover of viruses from animals to humans. In conclusion, the psychological/psychiatric consequences of COVID-19 pandemic, that currently seem unavoidable, represent a sharp cue of our misconception and indifference towards the links between our behaviour and their influence on the "health" of our planet and of ourselves. It is time to move towards a deeper understanding of these relationships, not only for our survival, but for the maintenance of that balance among man, animals and environment at the basis of life in earth, otherwise there will be no future.

Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide

Approximately a year into the COVID-19 pandemic caused by the SARS-CoV-2 virus, many countries have seen additional "waves" of infections, especially in the temperate northern hemisphere. Other vulnerable regions, such as South Africa and several parts of South America have also seen cases rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate observed for other common respiratory viruses such as seasonal influenza. Here, we look for empirical evidence of seasonality using a robust estimation framework. For 359 large cities across the world, we estimated the basic reproduction number (R₀) using logistic growth curves fitted to cumulative case data. We then assess evidence for association with climatic variables through ordinary least squares (OLS) regression. We find evidence of seasonality, with lower R₀ within cities experiencing greater surface radiation (coefficient = -0.005, p < 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R₀ and temperature during the early phase of the epidemic in China. However, climatic variables had much weaker explanatory power compared to socioeconomic and disease control factors. Rates of transmission and health burden of the continuing pandemic will be ultimately determined by population factors and disease control policies.

The relationship between mobility and COVID-19 pandemic: Daily evidence from an emerging country by causality analysis

This study examines the relationship between mobility (a proxy for transport) and the COVID-19 pandemic by focusing on Turkey as an example of an emerging country. In this context, eight types of mobility and two indicators of COVID-19 were analyzed using daily data from March 11, 2020 to December 7, 2020 by applying Toda-Yamamoto causality test. The findings revealed that (i) there is cointegration between the variables in the long term; (ii) there is an econometric causality between mobility indicators (mobility of grocery, park, residential, retail, and workplace) and pandemic indicators; (iii) various mobility indicators have an econometric causality with different pandemic indicators; (iv) neither driving mobility nor walking mobility has an econometric causality with the pandemic indicators whereas some of the other types of mobility, such as grocery, park, and retail do. These results generally show the effects of mobility and highlight the importance of appropriate mobility restrictions in terms of the pandemic.

Environmental determinants of COVID-19 transmission across a wide climatic gradient in Chile

Several studies have examined the transmission dynamics of the novel COVID-19 disease in different parts of the world. Some have reported relationships with various environmental variables, suggesting that spread of the disease is enhanced in colder and drier climates. However, evidence is still scarce and mostly limited to a few countries, particularly from Asia. We examined the potential role of multiple environmental variables in COVID-19 infection rate [measured as mean relative infection rate = (number of infected inhabitants per week / total population) × 100.000) from February 23 to August 16, 2020 across 360 cities of Chile. Chile has a large climatic gradient (≈ 40º of latitude, ≈ 4000 m of altitude and 5 climatic zones, from desert to tundra), but all cities share their social behaviour patterns and regulations. Our results indicated that COVID-19 transmission in Chile was mostly related to three main climatic factors (minimum temperature, atmospheric pressure and relative humidity). Transmission was greater in colder and drier cities and when atmospheric pressure was lower. The results of this study support some previous findings about the main climatic determinants of COVID-19 transmission, which may be useful for decision-making and management of the disease.

Mathematical modelling for decision making of lockdown during COVID-19

Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this scenario, people must be careful and cautious about the symptoms and should act accordingly. Accurate predictions of different factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the pandemic and precautions can be taken accordingly. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level. The novelty of the proposed work lies here. In this paper, first, authors have analysed spreading of the said disease using data collected from various platforms and then, have presented a predictive mathematical model for fifteen countries from first, second and third world for probable future projections of this pandemic. The prediction can be used by planning commission, healthcare organizations and the government agencies as well for creating suitable arrangements against this pandemic.

Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan

Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM2.5 concentration (μgm-3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM2.5, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM2.5 and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R2 value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen.

Seasonal variation and COVID-19 infection pattern: A gap from evidence to reality

In December 2019, the coronavirus disease (COVID-19) was discovered in China, causing many cases and deaths. Several studies have explored the role of environmental factors in the spread of COVID-19, emphasizing the effect of two weather parameters, humidity and temperature. Those parameters are evidently vital in affecting outbreaks of infectious respiratory diseases, like influenza; yet, such an effect on COVID-19 remains controversial. This review explores the relation between the change in weather-related factors and the transmission of the COVID-19. With seasonal variation from winter to summer and in the absence of adopting thorough public health measures, elevated temperature and humidity might not limit the COVID-19 cases. Hence, we need multidisciplinary strategies and interventions to limit the burden of this pandemic over the healthcare systems.

Impact of wind speed and air pollution on COVID-19 transmission in Pakistan

This study investigated the effect of wind speed and air pollution on COVID-19 from March 10, 2020, to October 04, 2020, in Pakistan. Wind speed and COVID-19 had positive correlation in Pakistan and its provinces. The inverted U-shaped dose-response curve was found for wind speed and COVID-19 in Punjab. Initially, the dose-response curve showed a positive link between wind speed and COVID-19 in Pakistan, Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. Later, it becomes downward sloped in Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. The expected log count of COVID-19 was increased by 0.113 times (Pakistan), 0.074 times (Punjab), 0.042 times (Sindh), and 0.082 times (Khyber Pakhtunkhwa) for a 1 km/h increase in the wind speed. The correlation between particulate matter and COVID-19 was positive (Pakistan, Punjab, and Islamabad Capital Territory) and negative (Sindh). The dose-response curve for particulate matter and COVID-19 had inverted U-shaped (Pakistan, Punjab, and Khyber Pakhtunkhwa) positively sloped (Islamabad Capital Territory), and negatively sloped (Sindh). The inverted U-shaped association shows that the COVID-19 initially increased due to a rise in the particulate matter but reduced when the particulate matter was above the threshold level. The particulate matter was also responsible to wear face masks and restricted mobility. The expected log count of COVID-19 cases was reduced by 0.005 times in Sindh for 1 unit increase in particulate matter. It is recommended to reduce particulate matter to control respiratory problems. The government should use media (print, electronic, social) and educational syllabus to create awareness about precautionary measures.

Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: A case study of New Delhi

Meteorological parameters show a strong influence on disease transmission in urban localities. The combined influence of factors such as daily mean temperature, absolute humidity and average wind speed on the attack rate and mortality rate of COVID-19 rise in Delhi, India has been investigated in this case study. A Random forest regression algorithm has been utilized to compare the epidemiological and meteorological parameters. The performance of the model has been evaluated using statistical performance metrics. The random forest model shows a strong positive correlation between the predictor parameters on the attack rate (96.09%) and mortality rate (93.85%). On both the response variables, absolute humidity has been noted to be the variable of highest influence. In addition, both temperature and wind speed have shown moderate positive influence on the transmission and survival of coronavirus during the study period. The synergistic effect of absolute humidity with temperature and wind speed contributing towards the increase in the attack and mortality rate has been addressed. The inhibition to respiratory droplet evaporation, increment in droplet size due to hygroscopic effect and the enhanced duration of survival of coronavirus borne in respiratory droplets are attributed to the increase in coronavirus infection under the observed weather conditions.

The spread of COVID-19 virus through population density and wind in Turkey cities

Beyond the contact and respiratory transmission of the COVID-19 virus, it has recently been reported in the literature that humidity, temperature, and air pollution may be effective in spreading the virus. However, taking the measurements regionally suspects the accuracy or validity of the data. In this research, climate values (temperature, humidity, number of sunny days, wind intensity) of 81 provinces in Turkey were collected in March 2020. Also, the population, population density of the provinces, and average air pollution data were taken. The findings of the study showed that population density and wind were effective in spreading the virus and both factors explained for 94% of the variance in virus spreading. Air temperature, humidity, the number of sunny days, and air pollution did not affect the number of cases. Besides, population density mediated the effect of wind speed (9%) on the number of COVID-19 cases. The finding that COVID-19 virus, invisible in the air, spreads more in windy weather indicates that the virus in the air is one threatening factor for humans with the wind speed that increases air circulation.

Global evidence for ultraviolet radiation decreasing COVID-19 growth rates

With nearly every country combating the 2019 novel coronavirus (COVID-19), there is a need to understand how local environmental conditions may modify transmission. To date, quantifying seasonality of the disease has been limited by scarce data and the difficulty of isolating climatological variables from other drivers of transmission in observational studies. We combine a spatially resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a statistical approach developed to quantify causal effects of environmental conditions in observational data settings. We find that ultraviolet (UV) radiation has a statistically significant effect on daily COVID-19 growth rates: a SD increase in UV lowers the daily growth rate of COVID-19 cases by ∼1 percentage point over the subsequent 2.5 wk, relative to an average in-sample growth rate of 13.2%. The time pattern of lagged effects peaks 9 to 11 d after UV exposure, consistent with the combined timescale of incubation, testing, and reporting. Cumulative effects of temperature and humidity are not statistically significant. Simulations illustrate how seasonal changes in UV have influenced regional patterns of COVID-19 growth rates from January to June, indicating that UV has a substantially smaller effect on the spread of the disease than social distancing policies. Furthermore, total COVID-19 seasonality has indeterminate sign for most regions during this period due to uncertain effects of other environmental variables. Our findings indicate UV exposure influences COVID-19 cases, but a comprehensive understanding of seasonality awaits further analysis.

Meteorological parameters and COVID-19 spread-Russia a case study

An attempt was made in this chaper to understand the meteorological controls on SARS-CoV-2 (COVID-19) spread in Russia. Russia is one of the most affected country for COVID-19 and significant death cases were recorded. A continuous seven-month data from 31 January to 23 August 2020 from different locations in the country was collected through the commonly available websites. COVID data (total cases (966189), daily new cases (11656), daily deaths (232), and total recovered (777960)) and meteorological parameters (temperature, dew, precipitation, humidity, and wind speed) were used for this analysis. The results show an increasing trend of daily new cases and daily deaths during lock down period, and it gradually decreased or stabilized in the post lock down period. It infers the effectiveness of movement control during the lock down period, that stops further spreading. The positive correlation between COVID cases and temperature indicate that the increase of temperature increases the spreading and vice versa. The negative relationship of humidity with death cases also facilitates the pandemic spread. Thus, the outcome of this study may help to address concerns about the COVID-19 pandemic among the public and policymakers.

COVID-19 in the environment

In recent months, the presence of an emerging disease of infectious etiology has paralyzed everyone, already being a public health problem due to its high rate of infection, a life-threatening disease. The WHO has named it COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV2). New studies provide information of the role of the environment in COVID-19 transmission process, mortality related to this infectious disease and the impact on human health. The following review aims to analyze information on the implications of COVID-19 infection on human health and the impact of its presence on the environment, from its transmission capacity and the role of air pollutants and climatological factors to reducing the air pollution during confinement. Likewise, it provides a vision of the impact on the environment and human health of exposure to disinfectants and the presence of COVID-19 in wastewater, among other actions.

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.

Global evidence for ultraviolet radiation decreasing COVID-19 growth rates

There is interest in whether COVID-19 cases respond to environmental conditions. If an effect is present, seasonal changes in local environmental conditions could alter the global spatial pattern of COVID-19 and inform local public health responses. Using a comprehensive global dataset of daily COVID-19 cases and local environmental conditions, we find that increased daily ultraviolet (UV) radiation lowers the cumulative daily growth rate of COVID-19 cases over the subsequent 2.5 wk. Although statistically significant, the implied influence of UV seasonality is modest relative to social distancing policies. Temperature and specific humidity cumulative effects are not statistically significant, and total COVID-19 seasonality remains to be established because of uncertainty in the net effects from seasonally varying environmental variables.

With nearly every country combating the 2019 novel coronavirus (COVID-19), there is a need to understand how local environmental conditions may modify transmission. To date, quantifying seasonality of the disease has been limited by scarce data and the difficulty of isolating climatological variables from other drivers of transmission in observational studies. We combine a spatially resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a statistical approach developed to quantify causal effects of environmental conditions in observational data settings. We find that ultraviolet (UV) radiation has a statistically significant effect on daily COVID-19 growth rates: a SD increase in UV lowers the daily growth rate of COVID-19 cases by ∼1 percentage point over the subsequent 2.5 wk, relative to an average in-sample growth rate of 13.2%. The time pattern of lagged effects peaks 9 to 11 d after UV exposure, consistent with the combined timescale of incubation, testing, and reporting. Cumulative effects of temperature and humidity are not statistically significant. Simulations illustrate how seasonal changes in UV have influenced regional patterns of COVID-19 growth rates from January to June, indicating that UV has a substantially smaller effect on the spread of the disease than social distancing policies. Furthermore, total COVID-19 seasonality has indeterminate sign for most regions during this period due to uncertain effects of other environmental variables. Our findings indicate UV exposure influences COVID-19 cases, but a comprehensive understanding of seasonality awaits further analysis.

Examining the correlation between the weather conditions and COVID-19 pandemic in India: A mathematical evidence

In this article, for the analysis of Covid-19 progression in India, we present new insights to formulate a data-driven epidemic model and approximation algorithm using the real data on infection, recovery and death cases with respect to weather in the view of mathematical variables.

A Retrospective Study on the Use of Chinese Patent Medicine in 24 Medical Institutions for COVID-19 in China

Objective: This research aims to analyze the application regularity of Chinese patent medicine during the COVID-19 epidemic by collecting the names of the top three Chinese patent medicines used by 24 hospitals in 14 provinces of China in four time periods (January 20-22, February 16-18, March 01-03, April 01-03, 2020), and explore its contribution to combating the disease. Methods: 1) We built a database of the top three Chinese patent medicines used by 24 hospitals. 2) The frequency and efficacy distribution of Chinese patent medicine were analyzed with risk areas, regions, and hospitals of different properties as three factors. 3) Finally, we analyzed the differences in the use of heat-clearing and non-heat-clearing medicines among the three factors (χ² test) and the correlation between the Chinese patent medicine and COVID-19 epidemic (correlation analysis) with SPSS 23.0 statistical software. Results: 1) The heat-clearing medicine was the main use category nationwide during January 20-22, 2020. Meanwhile, there was a significant difference in the utilization rate of heat-clearing and non-heat-clearing medicine in different risk areas (p < 0.01). 2) The variety of Chinese patent medicine was increased nationwide during February 16-18, 2020, mainly including tonics, blood-activating and resolving-stasis, and heat-clearing medicines. Meanwhile, there was a significant difference in the utilization rate of heat-clearing and non-heat-clearing medicine in the southern and northern regions (p < 0.05). 3) Tonics, and blood-activating and resolving-stasis medicines became the primary use categories nationwide during March 01-03, 2020. 4) The tonics class, and blood-activating and resolving-stasis medicine were still the primary categories nationwide during April 01-03, 2020. Meanwhile, there was a significant difference in the utilization rate of heat-clearing and non-heat-clearing medicine in different risk areas (p < 0.01). Conclusion: Chinese patent medicine has a certain degree of participation in fighting against the COVID-19. The efficacy distribution is related to the risk area, region, and hospital of different properties, among which the risk area is the main influencing factor. It is hoped that future research can further collect the application amount of Chinese patent medicine used in hospitals all over the country, so as to perfectly reflect the relationship between Chinese patent medicine and the epidemic situation.

Seasonality and uncertainty in global COVID-19 growth rates

The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives. It remains unknown, as of April 2020, whether summer weather will reduce its spread, thereby alleviating strains on hospitals and providing time for vaccine development. Early insights from laboratory studies and research on related viruses predicted that COVID-19 would decline with higher temperatures, humidity, and ultraviolet (UV) light. Using current, fine-scaled weather data and global reports of infections, we develop a model that explains 36% of the variation in maximum COVID-19 growth rates based on weather and demography (17%) and country-specific effects (19%). UV light is most strongly associated with lower COVID-19 growth. Projections suggest that, without intervention, COVID-19 will decrease temporarily during summer, rebound by autumn, and peak next winter. Validation based on data from May and June 2020 confirms the generality of the climate signal detected. However, uncertainty remains high, and the probability of weekly doubling rates remains >20% throughout summer in the absence of social interventions. Consequently, aggressive interventions will likely be needed despite seasonal trends.

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.

Potential link between compromised air quality and transmission of the novel corona virus (SARS-CoV-2) in affected areas

The emergence of a novel human corona virus disease (COVID-19) has been declared as a pandemic by the World Health Organization. One of the mechanisms of airborne transmission of the severe acute respiratory syndrome - corona virus (SARS-CoV-2) amid humans is through direct ejection of droplets via sneezing, coughing and vocalizing. Nevertheless, there are ample evidences of the persistence of infectious viruses on inanimate surfaces for several hours to a few days. Through a critical review of the current literature and a preliminary analysis of the link between SARS-CoV-2 transmission and air pollution in the affected regions, we offer a perspective that polluted environment could enhance the transmission rate of such deadly viruses under moderate-to-high humidity conditions. The aqueous atmospheric aerosols offer a conducive surface for adsorption/absorption of organic molecules and viruses onto them, facilitating a pathway for higher rate of transmission under favourable environmental conditions. This mechanism partially explains the role of polluted air besides the exacerbation of chronic respiratory diseases in the rapid transmission of the virus amongst the public. Hence, it is stressed that more ambitious policies towards a cleaner environment are required globally to nip in the bud what could be the seeds of a fatal outbreak such as COVID-19.

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.

Correlation between weather and COVID-19 pandemic in India: An empirical investigation

This study is an attempt to find and analyze the correlation between Covid-19 pandemic and weather conditions in Indian context. Secondary data analysis of surveillance data of COVID-19 is taken from Wikipedia (updating information from World Health Organization) & statista.com and weather data through Power Data Access Viewer (DAV) (power.Iarc.nasa.gov) from NASA after mentioning latitude and longitude of India. The minimum temperature (°C) at 2 metre, maximum temperature (°C) at 2 metre, temperature (°C) at 2 metre and relative humidity (%) are taken as component of weather. To find the association, Spearman's rank correlation test was applied. The minimum, maximum temperature (°C) at 2 m, temperatures (°C) at 2 m and humidity at 2 m are significantly correlated with COVID-19 pandemic cases (r = 0.93, 0.94, 0.83, and 0.30) at 99% two-tailed significance level. The findings serve as an initial evidence to reduce the incidence rate of COVID-19 in India and useful in policy making.

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.

Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.