Association of COVID-19 pandemic with meteorological parameters over Singapore

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.

Impact of Lockdown Measures and Meteorological Parameters on the COVID-19 Incidence and Mortality Rate in Bangladesh

The coronavirus disease 2019 (COVID-19) pandemic has become a public health crisis and a global catastrophe for human societies. In the absence of a vaccine, non-pharmaceutical interventions have been implemented across the world to reduce COVID-19 transmission. Recently, several studies have articulated the influence of meteorological parameters on COVID-19 infections in several countries. The purpose of this study was to investigate the effect of lockdown measures and meteorological parameters on COVID-19 daily confirmed cases and deaths in Bangladesh. Different parameters, such as case fatality rate, recovery rate, number of polymerase chain reaction tests, and percentages of confirmed cases were calculated for data covering March to September 2020. The meteorological data include daily average temperature, humidity, and wind speed, and their effects on COVID-19 data were analyzed after 0, 3, 7, and 14 days. A linear regression analysis revealed that all the studied meteorological parameters were positively correlated with the daily new cases and deaths in Bangladesh, while the highest correlations were observed for the 14 days incubation period. These results provide useful implications for the healthcare authorities to contain the pandemic in Bangladesh and beyond.

Assessing the COVID-19 Impact on Air Quality: A Machine Learning Approach

The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.

COVID-19 Crisis Creates Opportunity towards Global Monitoring & Surveillance

The spectrum of emerging new diseases as well as re-emerging old diseases is broadening as infectious agents evolve, adapt, and spread at enormous speeds in response to changing ecosystems. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recent phenomenon and may take a while to understand its transmission routes from less traveled territories, ranging from fomite exposure routes to wastewater transmission. The critical challenge is how to negotiate with such catastrophic pandemics in high-income countries (HICs ~20% of the global population) and low-and middle-income countries (LMICs ~ 80% of the global population) with a total global population size of approximately eight billion, where practical mass testing and tracing is only a remote possibility, particularly in low-and middle-income countries (LMICs). Keeping in mind the population distribution disparities of high-income countries (HICs) and LMICs and urbanisation trends over recent years, traditional wastewater-based surveillance such as that used to combat polio may help in addressing this challenge. The COVID-19 era differs from any previous pandemics or global health challenges in the sense that there is a great deal of curiosity within the global community to find out everything about this virus, ranging from diagnostics, potential vaccines/therapeutics, and possible routes of transmission. In this regard, the fact that the gut is the common niche for both poliovirus and SARS-CoV-2, and due to the shedding of the virus through faecal material into sewerage systems, the need for long-term wastewater surveillance and developing early warning systems for better preparedness at local and global levels is increasingly apparent. This paper aims to provide an insight into the ongoing COVID-19 crisis, how it can be managed, and what measures are required to deal with a current global international public health concern. Additionally, it shed light on the importance of using wastewater surveillance strategy as an early warning practical tool suitable for massive passive screening, as well as the urgent need for microfluidic technology as a rapid and cost-effective approach tracking SARS-CoV-2 in wastewater.

Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the COVID-19 cases - a study from a worst-hit state of India

The present study aims to examine the changes in air quality during different phases of the COVID-19 pandemic, including the lockdown (LD1-4) and unlock period (UL1-2) (post-lockdown) as compared to pre-lockdown (PL1-3) and to establish the relationships of the environmental and demographic variables with COVID-19 cases in the state of Maharashtra, the worst-hit state in India. Atmospheric pollutants such as PM2.5, PM10, NOx, and CO were substantially reduced during the lockdown and unlock phases with the greatest reduction in cities having larger traffic volumes. Compared with the immediate pre-lockdown period (PL3), the averaged PM2.5 and PM10 reduced by up to 51% and 47% respectively during the lockdown periods, which resulted in 'satisfactory' level of air quality index (AQI) as a result of reduced vehicular traffic and industrial closing. These parameters continued to reduce as much as 80% during the unlock periods due to the additive impact of weather (rainfall and temperature) combined with the lockdown conditions. Kendall's correlation matrix showed a significant negative correlation between temperature and air pollutants (r= - 0.35 to - 057). Conversely, SO2 and O3 did not improve, and in some cases, they increased during the lockdown and unlocking. COVID-19 spreading incidences were strongly and positively correlated with temperature (r < 0.62) and dew point (r < 0.73). Thus, this indicates that the increase in temperature and dew point cannot weaken the transmission of this virus. The number of COVID-19 cases relative to air pollutants was negatively correlated (r = - 0.33 to - 0.74), which may be a mere coincidence as a result of lockdown. However, based on pre-lockdown air quality data and demographic factors, it was found that particulate matter (PM2.5 and PM10) and population density are closely linked with higher morbidity and mortality although a more in-depth research is required in this direction to validate this finding. The onset of COVID-19 has allowed us to determine that 'immediate' changes in air quality within densely populated/industrialized areas can improve livelihood based on pollution mitigation. These findings could be used by policymakers to set new benchmarks for air pollution that would improve the quality of life for major sectors of the World's population. COVID-19 has shown us that we can make changes when necessary, and findings may pave the way for future research to inform policy on the tough choices we will have to make between quality of life and survival. Also, our results will enrich the ongoing discussion on the role of environmental factors on the transmission of COVID-19 and will help to take necessary steps for its control.

Non-linear correlation between daily new cases of COVID-19 and meteorological factors in 127 countries

Meteorological parameters are the critical factors of affecting respiratory infectious disease such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and influenza, however, the effect of meteorological parameters on coronavirus disease 2019 (COVID-19) remains controversial. This study investigated the effects of meteorological factors on daily new cases of COVID-19 in 127 countries, as of August 31 2020. The log-linear generalized additive model (GAM) was used to analyze the effect of meteorological variables on daily new cases of COVID-19. Our findings revealed that temperature, relative humidity, and wind speed are nonlinearly correlated with daily new cases, and they may be negatively correlated with the daily new cases of COVID-19 over 127 countries when temperature, relative humidity and wind speed were below 20°C, 70% and 7 m/s respectively. Temperature(>20°C) was positively correlated with daily new cases. Wind speed (when>7 m/s) and relative humidity (>70%) was not statistically associated with transmission of COVID-19. The results of this research will be a useful supplement to help healthcare policymakers in the Belt and Road countries, the Centers for Disease Control (CDC) and the World Health Organization (WHO) to develop strategies to combat COVID-19.

The Bayesian Susceptible-Exposed-Infected-Recovered model for the outbreak of COVID-19 on the Diamond Princess Cruise Ship

The outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R₀) and the effectiveness of containment measures. We developed an ordinary differential equation-based Susceptible-Exposed-Infected-Recovery (SEIR) model with Bayesian underpinning to estimate the main parameter of R₀ determined by transmission coefficients, incubation period, and the recovery rate. Bayesian Markov Chain Monte Carlo (MCMC) estimation method was used to tackle the parameters of uncertainty resulting from the outbreak of COVID-19 given a small cohort of the cruise ship. The extended stratified SEIR model was also proposed to elucidate the heterogeneity of transmission route by the level of deck with passengers and crews. With the application of the overall model, R₀ was estimated as high as 5.70 (95% credible interval: 4.23-7.79). The entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 63% (95% credible interval: 60-66%) infected passengers. With the deck-specific SEIR model, the heterogeneity of R₀ estimates by each deck was noted. The estimated R₀ figures were 5.18 for passengers (5-14 deck), mainly from the within-deck transmission, and 2.46 for crews (2-4 deck), mainly from the between-deck transmission. Modelling the dynamic of COVID-19 on the cruise ship not only provides an insight into timely evacuation and early isolation and quarantine but also elucidates the relative contributions of different transmission modes on the cruise ship though the deck-stratified SEIR model.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at (10.1007/s00477-020-01968-w).

Warmer weather unlikely to reduce the COVID-19 transmission: An ecological study in 202 locations in 8 countries

PURPOSE

To examine the association between meteorological factors (temperature, relative humidity, wind speed, and UV radiation) and transmission capacity of COVID-19.

METHODS

We collected daily numbers of COVID-19 cases in 202 locations in 8 countries. We matched meteorological data from the NOAA National Centers for Environmental Information. We used a time-frequency approach to examine the possible association between meteorological conditions and basic reproductive number (R0) of COVID-19. We determined the correlations between meteorological factors and R0 of COVID-19 using multiple linear regression models and meta-analysis. We further validated our results using a susceptible-exposed-infectious-recovered (SEIR) metapopulation model to simulate the changes of daily cases of COVID-19 in China under different temperatures and relative humidity conditions.

PRINCIPAL RESULTS

Temperature did not exhibit significant association with R0 of COVID-19 (meta p = 0.446). Also, relative humidity (meta p = 0.215), wind speed (meta p = 0.986), and ultraviolet (UV) radiation (meta p = 0.491) were not significantly associated with R0 either. The SEIR model in China showed that with a wide range of meteorological conditions, the number of COVID-19 confirmed cases would not change substantially.

CONCLUSIONS

Meteorological conditions did not have statistically significant associations with the R0 of COVID-19. Warmer weather alone seems unlikely to reduce the COVID-19 transmission.

Statistical analysis of COVID-19 infection caused by environmental factors: Evidence from Pakistan

Background

Coronavirus disease 2019 (COVID-19) has become a severe public health problem around the globe. Various epidemiological, statistical, and laboratory-based studies have shown that the role of temperature and other environmental factors has important influence in the transmission of coronaviruses. Scientific research is needed to answer the questions about the spread and transmission of the infection, whether people could be avoided from being infected with COVID-19 in next summer.

Aim

We aim to investigate the association of daily average temperature, daily average dew point, daily average humidity, daily average wind speed, and daily average pressure with the infection caused by this novel coronavirus in Pakistan.

Key findings

First, we check the correlation between environmental factors and daily infected cases of COVID-19; among them, temperature and dew point have positive linear relationship with daily infected cases of COVID-19. The thought-provoking findings of the present study suggested that higher temperature and dew point can contribute to a rise in COVID-19 disease in four provinces of Pakistan, possible to genome modifications and viral resistance to harsh environment. Moreover, it is also observed that humidity in Punjab and Sindh, and wind speed in Balochistan and Khyber Pakhtunkhwa have influenced the spreading of daily infected COVID-19 cases.

Significance

Current study will serve as a guideline to develop understanding of environmental factors that influence COVID-19 spread, helping policymakers to prepare and handle a catastrophe resulting from this pandemic.

Sunlight ultraviolet radiation dose is negatively correlated with the percent positive of SARS-CoV-2 and four other common human coronaviruses in the U.S

Human coronaviruses are RNA viruses that are sensitive to ultraviolet (UV) radiation. Sunlight contains UVA (320-400 nm), UVB (260-320 nm) and UVC (200-260 nm) action spectra. UVC can inactivate coronaviruses, including severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The incidence and mortality of coronavirus disease 2019 (COVID-19) are considered to be correlated with vitamin D levels. Vitamin D synthesis in human skin is closely related to exposure to UVB radiation. Therefore, the incidence and mortality of COVID-19 are also considered to be correlated with Vitamin D levels. In this study, Spearman and Kendall rank correlation analysis tests were used to analyze the correlation between the average percent positive of five human coronaviruses (SARS-CoV-2, CoVHKU1, CoVNL63, CoVOC43, and CoV229E) in the U.S. and the corresponding sunlight UV radiation dose The results indicated that the monthly average percent positive of four common coronaviruses was significantly negatively correlated with the sunlight UV radiation dose. The weekly percent positive of SARS-CoV-2 during April 17, 2020 to July 10, 2020 showed a significant negative correlation with the sunlight UV radiation dose in census regions 1 and 2 of the U.S. while no statistical significance in the other regions. Additionally, sunlight UV radiation also showed some negative effects with respect to the early SARS-CoV-2 transmission.

Influence of Meteorological Factors on the COVID-19 Transmission with Season and Geographic Location

The purpose of this study is to investigate whether the relationship between meteorological factors (i.e., daily maximum temperature, minimum temperature, average temperature, temperature range, relative humidity, average wind speed and total precipitation) and COVID-19 transmission is affected by season and geographical location during the period of community-based pandemic prevention and control. COVID-19 infected case records and meteorological data in four cities (Wuhan, Beijing, Urumqi and Dalian) in China were collected. Then, the best-fitting model of COVID-19 infected cases was selected from four statistic models (Gaussian, logistic, lognormal distribution and allometric models), and the relationship between meteorological factors and COVID-19 infected cases was analyzed using multiple stepwise regression and Pearson correlation. The results showed that the lognormal distribution model was well adapted to describing the change of COVID-19 infected cases compared with other models (R2 > 0.78; p-values < 0.001). Under the condition of implementing community-based pandemic prevention and control, relationship between COVID-19 infected cases and meteorological factors differed among the four cities. Temperature and relative humidity were mainly the driving factors on COVID-19 transmission, but their relations obviously varied with season and geographical location. In summer, the increase in relative humidity and the decrease in maximum temperature facilitate COVID-19 transmission in arid inland cities, while at this point the decrease in relative humidity is good for the spread of COVID-19 in coastal cities. For the humid cities, the reduction of relative humidity and the lowest temperature in the winter promote COVID-19 transmission.

Weather Variability and COVID-19 Transmission: A Review of Recent Research

Weather and climate play a significant role in infectious disease transmission, through changes to transmission dynamics, host susceptibility and virus survival in the environment. Exploring the association of weather variables and COVID-19 transmission is vital in understanding the potential for seasonality and future outbreaks and developing early warning systems. Previous research examined the effects of weather on COVID-19, but the findings appeared inconsistent. This review aims to summarize the currently available literature on the association between weather and COVID-19 incidence and provide possible suggestions for developing weather-based early warning system for COVID-19 transmission. Studies eligible for inclusion used ecological methods to evaluate associations between weather (i.e., temperature, humidity, wind speed and rainfall) and COVID-19 transmission. The review showed that temperature was reported as significant in the greatest number of studies, with COVID-19 incidence increasing as temperature decreased and the highest incidence reported in the temperature range of 0–17 °C. Humidity was also significantly associated with COVID-19 incidence, though the reported results were mixed, with studies reporting positive and negative correlation. A significant interaction between humidity and temperature was also reported. Wind speed and rainfall results were not consistent across studies. Weather variables including temperature and humidity can contribute to increased transmission of COVID-19, particularly in winter conditions through increased host susceptibility and viability of the virus. While there is less indication of an association with wind speed and rainfall, these may contribute to behavioral changes that decrease exposure and risk of infection. Understanding the implications of associations with weather variables and seasonal variations for monitoring and control of future outbreaks is essential for early warning systems.

Impact of Weather Parameters and Population Density on the COVID-19 Transmission: Evidence from 81 Provinces of Turkey

Weather factors are effective to transmission of various diseases. Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and kinds of influenza can be given as example these diseases. The novel corona virus which is called COVID-19 is the most dangerous problem all around the world in these days. Early studies have revealed that COVID-19 cases are affected by environmental factors. Therefore, the purpose of this paper is to examine the relationship between the number of novel coronavirus cases and several weather parameters in 81 provinces of Turkey. Mean incubation period of COVID-19 is in question. Thus, this paper also aims to provide better understanding of the exact incubation period in Turkey by employing four different timeframe which are on the day (lag 0), 3 days ago (lag 3), 7 days ago (lag 7) and 14 days ago (lag 14). We have considered population density as a control variable. The dataset cover COVID-19 cases, population density, average temperature, humidity, pressure, dew point, wind speed, and sunshine duration for 81 provinces of Turkey. We find that population density has a positive correlation with COVID-19 cases. We also find that in lag 3, all parameters except for sunshine duration are negatively correlated with COVID-19 cases and significant. However, only 3 parameters, temperature, air pressure and dew point are negatively correlated with COVID-19 cases and significant for lag 0, lag 7 and lag 14. In addition, temperature, air pressure and dew point parameters are negative and significant in all timeframes.

COVID-19 lockdown induced air pollution reduction over India: A lesson for future air pollution mitigation strategies

Air pollution is one of the biggest problems worldwide and needs to be addressed potentially with the implementation of updated stringent policies and legislative laws. The nationwide lockdown imposed to prevent the COVID-19 outbreak, has given us a unique opportunity to understand the contribution of anthropogenic emissions to the total atmospheric pollutant burden on a global as well as regional scale. Thus, in the present study, we try to investigate the impact of COVID-19 induced lockdown on common ambient air pollutants (i.e., PM_2.5, NO₂, and SO₂) concentration over 22 cities in India using in-situ measurement under a network of Centre Pollution and Control Board (CPCB). A significant reduction in the mean mass concentration of all the studied air pollutants (i.e., PM_2.5, NO₂, and SO₂) (nearly 10–70%) is found during different phases of lockdown which reached within the National Ambient Air Quality Standard (i.e., NAAQS). The reduction in studied air pollutants is more prominent during the first phase of lockdown (mainly NO₂) which could be due to the complete shutdown of industrial activities. The outcome of the present study will be helpful for policymakers to design cost-effective and accurate air pollution mitigation strategies for the development of a sustainable environment. The study also suggests that well-planned short-term and periodical lockdown could be an alternative effective tool of air pollution mitigation.

GIS-Based Analysis Framework to Identify the Determinants of COVID-19 Incidence and Fatality in Africa

Corona virus diseases 2019 (COVID-19) pandemic is an extraordinary threat with significant implications in all aspects of human life; therefore, it represents the most immediate challenge for the countries all over the world. This study, hence, is intended to identify the best GIS-based model that can explore, quantify, and model the determinants of COVID-19 incidence and fatality. For this purpose, geospatial models were developed to estimate COVID-19 incidence and fatality rates in Africa, up to 16th of August 2020 at the national level. The models involved Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analysis using ArcGIS. Spatial autocorrelation analysis recorded a positive spatial autocorrelation in COVID-19 incidence (Moran index 0.16, P = 0.1) and fatality (Moran index 0.26, P = 0.01) rates within different African countries. GWR model had higher R2 than OLS for prediction of incidence and mortality (58% vs 45% and 55% vs 53%). The main predictors of COVID-19 incidence rate were overcrowding, health expenditure, HIV infections, air pollution, and BCG vaccination (mean β = 3.10, 1.66, 0.01, 3.79, and -66.60 respectively, P < 0.05). The main determinants of COVID-19 fatality were prevalence of bronchial asthma, tobacco use, poverty, aging, and cardiovascular diseases fatality (mean β = 0.00162, 0.00004, -0.00025, -0.00144, and -0.00027 respectively, P < 0.05). Application of the suggested model can assist in guiding intervention strategies, particularly at the local and community level whenever the data on COVID-19 cases and predictors variables are available.

Strong link between coronavirus count and bad air: a case study of India

The present study aims to highlight the contrast relationship between COVID-19 (Coronavirus Disease-2019) infections and air pollutants for the Indian region. The COVID-19 data (cumulative, confirmed cases and deaths), air pollutants (PM₁₀, PM_2.5, NO₂ and SO₂) and meteorological data (temperature and relative humidity) were collected from January 2020 to August 2020 for all 28 states and the union territory of India during the pandemic. Now, to understand the relationship between air pollutant concentration, meteorological factor, and COVID-19 cases, the nonparametric Spearman's and Kendall's rank correlation were used. The COVID-19 shows a favourable temperature (0.55-0.79) and humidity (0.14-0.52) over the Indian region. The PM_2.5 and PM₁₀ gave a strong and negative correlation with COVID-19 cases in the range of 0.64-0.98. Similarly, the NO₂ shows a strong and negative correlation in the range of 0.64-0.98. Before the lockdown, the concentration of pollution parameters is high due to the shallow boundary layer height. But after lockdown, the overall reduction was reported up to 33.67% in air quality index (AQI). The background metrological parameters showed a crucial role in the variation of pollutant parameters (SO₂, NO₂, PM₁₀ and PM_2.5) and the COVID-19 infection with the economic aspects. The European Centre for Medium-Range Weather Forecasts derived monthly average wind speed was also plotted. It can see that January and February of 2020 show the least variation of air mass in the range of 1-2 m/s. The highest wind speed was reported during July and August 2020. India's western and southern parts experienced an air mass in the range of 4-8 m/s. The precipitation/wet deposition of atmospheric aerosols further improves the AQI over India. According to a study, the impact of relative humidity among all other metrological parameters is positively correlated with Cases and death. Outcomes of the proposed work had the aim of supporting national and state governance for healthcare policymakers.

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.

A correlation study between weather and atmosphere with COVID-19 pandemic in Islamabad, Pakistan

The present research aims to investigate the association amid weather and the most recent pandemic of COVID-19 in Islamabad, the capital of Pakistan. The source of COVID-19 surveillance data for the secondary data analysis was the Pakistan’s Ministry of National Health Services Regulations and Coordination. The weather data obtained from the Pakistan Metrological Department (PMD) was exercised in this research. The components of weather include wind speed (m/s), precipitation level (mm), normal, mean, maximum, and minimum temperature (°C). For data analysis, a non-parametric correlation test was used due to the reason that normality was not satisfied. Precipitation level (r =  − 0.285; p  =0 .022), normal temperature (r = 0.293; p  = 0.019) as well as the maximum temperature (r = 0.347; p  = 0.005) were very much associated with COVID-19 virus. Pollution data (showing the concentration of NO2) of the specific region comprising the study area extracted from the Sentinel-5P satellite was also compared for the two years (2019 and 2020). Since the country will be entering to a new weather season, the conclusions may well assist the strategy and decision-makers in the deterrence of COVID-19.

To avoid the noncausal association between environmental factor and COVID-19 when using aggregated data: Simulation-based counterexamples for demonstration

In the infectious disease epidemiology, the association between an independent factor and disease incidence (or death) counts may fail to infer the association with disease transmission (or mortality risk). To explore the underlying role of environmental factors in the course of COVID-19 epidemic, the importance of following the epidemiological metric's definition and systematic analytical procedures are highlighted. Cautiousness needs to be taken when understanding the outcome association based on the aggregated data, and overinterpretation should be avoided. The existing analytical approaches to address the inferential failure mentioned in this study are also discussed.

Environment and COVID-19: Pollutants, impacts, dissemination, management and recommendations for facing future epidemic threats

Coronavirus disease 2019 (COVID-19) has become a global pandemic. Its relationship with environmental factors is an issue that has attracted the attention of scientists and governments. This article aims to deal with a possible association between COVID-19 and environmental factors and provide some recommendations for adequately controlling future epidemic threats. Environmental management through ecosystem services has a relevant role in exposing and spreading infectious diseases, reduction of pollutants, and control of climatic factors. Pollutants and viruses (such as COVID-19) produce negative immunological responses and share similar mechanisms of action. Therefore, they can have an additive and enhancing role in viral diseases. Significant associations between air pollution and COVID-19 have been reported. Particulate matter (PM2.5, PM10) can obstruct the airway, exacerbating cases of COVID-19. Some climatic factors have been shown to affect SARS-CoV-2 transmission. Yet, it is not well established if climatic factors might have a cause-effect relationship to the spreading of SARS-CoV-2. So far, positive as well as negative indirect environmental impacts have been reported, with negative impacts greater and more persistent. Too little is known about the current pandemic to evaluate whether there is an association between environment and positive COVID-19 cases. We recommend smart technology to collect data remotely, the implementation of "one health" approach between public health physicians and veterinarians, and the use of biodegradable medical supplies in future epidemic threats.

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.

A correlation study between meteorological parameters and COVID-19 pandemic in Mumbai, India

BACKGROUND AND AIMS

Meteorological parameters play a major role in the transmission of infectious diseases such as COVID-19. In this study, we aim to analyze the correlation between meteorological parameters and COVID-19 pandemic in the financial capital of India, Mumbai.

METHODS

In this research, we collected data from April 27 till July 25, 2020 (90 days). A Spearman rank correlation test along with two-tailed p test and an Artificial Neural Network (ANN) technique have been used to predict the associations of COVID-19 with meteorological parameters.

RESULTS

A significant correlation of COVID-19 was found with temperature (T_min), dew point (DP_max), relative humidity (RH_max, RH_avg, RH_min) and surface pressure (P_max, P_avg, P_min). The parameters which showed significant correlation were then taken for the modeling and prediction of COVID-19 infections using Artificial Neural Network technique.

CONCLUSIONS

It was found that the relative humidity and pressure parameters had the most influencing effect out of all other significant parameters (obtained from Spearman's method) on the active number of COVID-19 cases. The finding in this study might be useful for the public, local authorities, and the Ministry of Health, Govt. of India to combat COVID-19.

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.

Relationship between Influenza Vaccination Coverage Rate and COVID-19 Outbreak: An Italian Ecological Study

Background: The lack of specific vaccines or drugs against coronavirus disease 2019 (COVID-19) warrants studies focusing on alternative clinical approaches to reduce the spread of this pandemic disease. In this study, we investigated whether anti-influenza vaccination plays a role in minimizing the diffusion of COVID-19 in the Italian population aged 65 and over. Methods: Four COVID-19 outcomes were used: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence, hospitalizations for COVID-19 symptoms, admissions to intensive care units for reasons related to SARS-CoV-2, and deaths attributable to COVID-19. Results: At univariate analyses, the influenza vaccination coverage rates correlated negatively with all COVID-19 outcomes (Beta ranging from -134 to -0.61; all p < 0.01). At multivariable analyses, influenza vaccination coverage rates correlated independently with SARS-CoV-2 seroprevalence (Beta (95% C.I.): -130 (-198, -62); p = 0.001), hospitalizations for COVID-19 symptoms (Beta (95% C.I.): -4.16 (-6.27, -2.05); p = 0.001), admission to intensive care units for reasons related to SARS-CoV-2 (Beta (95% C.I.): -0.58 (-1.05, -0.12); p = 0.017), and number of deaths attributable to COVID-19 (Beta (95% C.I.): -3.29 (-5.66, -0.93); p = 0.010). The R2 observed in the unadjusted analysis increased from 82% to 159% for all the considered outcomes after multivariable analyses. Conclusions: In the Italian population, the coverage rate of the influenza vaccination in people aged 65 and over is associated with a reduced spread and a less severe clinical expression of COVID-19. This finding warrants ad hoc studies to investigate the role of influenza vaccination in preventing the spread of COVID-19.

Impact of meteorological parameters on COVID-19 pandemic: A comprehensive study from Saudi Arabia

BACKGROUND

The Coronavirus disease 2019 (COVID-19) has now been declared a global public health disaster with no currently available vaccine. This study was undertaken to analyse the effect of meteorological parameters such as temperature, humidity, and wind speed on the spread of ongoing COVID-19 in Saudi Arabia.

METHODS

The COVID-19 dashboard for five major cities of Saudi Arabia - Riyadh, Makah, Jeddah, Medina and Dammam was used for data collection. The data on weather were collected from the Weather Underground Company (IBM business GA, USA, 2020). The data were analysed by Spearman's rank correlations using JASP statistical software in two main sections. In the first section the data on COVID-19 from cities were combined to analyse the overall picture of COVID-19 and in the second section, different meteorological parameters such as temperature, humidity and wind speed were analysed.

RESULTS

Novel data revealed interesting facts on the spreading of COVID-19 in Saudi Arabia, the data showed that the number of COVID-19 positive cases increases due to the decrease of temperature or humidity, whereas an average decrease in the wind speed was also found to be associated with an elevation of the number of positive cases.

CONCLUSIONS

This study determined the impact meteorological factors on the infectivity rate of COVID-19. An inverse association was found between the meteorological parameters with the spreading of COIVD-19. Therefore, this study directs the health authorities to implement specific measures against the spreading of this global pandemic based on weather patterns.

Relationship between temporal anomalies in PM2.5 concentrations and reported influenza/influenza-like illness activity

A small number of studies suggest atmospheric particulate matter with diameters 2.5 micron and smaller (PM2.5) may possibly play a role in the transmission of influenza and influenza-like illness (ILI) symptoms. Those studies were predominantly conducted under moderately to highly polluted outdoor atmospheres. The purpose of this study was to extend the data set to include a less polluted atmospheric environment. A relationship between PM2.5 and ILI activity extended to include lightly to moderately polluted atmospheres could imply a more complicated mechanism than that suggested by existing studies. We obtained concurrent PM2.5 mass concentration data, meteorological data and reported Influenza and influenza-like illness (ILI) activity for the light to moderately polluted atmospheres over the Tucson, AZ region. We found no relation between PM2.5 mass concentration and ILI activity. There was an expected relation between ILI, activity, temperature, and relative humidity. There was a possible relation between PM2.5 mass concentration anomalies and ILI activity. These results might be due to the small dataset size and to the technological limitations of the PM measurements. Further study is recommended since it would improve the understanding of ILI transmission and thereby improve ILI activity/outbreak forecasts and transmission model accuracies.