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

COVID-19 and environment: a poignant reminder of sustainability in the new normal

The nexus of COVID-19 and environment is conspicuously deep-rooted. The roles of environmental factors in the origin, transmission and spread of COVID-19 and the mutual impact of the pandemic on the global environment have been the two perspectives to view this nexus. The present paper attempts to systematically review the existing literature to understand and explore the linkages of COVID-19 with environment and proposes conceptual frameworks to underline this nexus. Our study indicates a critical role of meteorological factors, ambient air pollutants and wastewater in severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmission-spread dynamics. The study also focuses on the direct and indirect impacts of COVID-19 on the regional and global environment. Most of the indirect environmental effects of COVID-19 were attributed to global human confinement that resulted from the implementation of the pandemic containment measures. This worldwide anthropogenic 'pause' sent ripples to all environmental compartments and presented a unique test bed to identify anthropogenic impacts on the earth's natural systems. The review further addresses emerging sustainability challenges in the new normal and their potential solutions. The situation warrants critical attention to the environment-COVID-19 nexus and innovative sustainable practices to address the ramifications of short- and long-term environmental impacts of the COVID-19 pandemic.

Graphical abstract

Climate indicators and COVID-19 recovery: A case of Wuhan during the lockdown

The world needs to get out of the COVID-19 pandemic smoothly through a thorough socio-economic recovery. The first and the foremost step forward in this direction is the health recovery of the people infected. Our empirical study addresses this neglected point in the recent research on COVID-19 and specifically aims at exploring the impact of the environment on health recovery from COVID-19. The sample data are taken during the lockdown period in Wuhan, i.e., from 23rd January 2020 to 8th April 2020. The recently developed econometric technique of Quantile-on-Quantile regression, proposed by Shin and Zhu (2016) is employed to capture the asymmetric association between environmental factors (TEMP, HUM, PM2.5, PM10, CO, SO2, NO2, and O3) and the number of recovered patients from COVID-19. We observe significant heterogeneity in the association among variables across various quantiles. The findings suggest that TEMP, PM2.5, PM10, CO, NO2, and O3 are negatively related to the COVID-19 recovery, while HUM and SO2 show a positive association at most quantiles. The study recommends that maintaining a safe and comfortable environment for the patients may increase the chances of recovery from COVID-19. The success story of Wuhan, the initial epicenter of the novel coronavirus in China, can serve as an important case study for other countries to bring the outbreak under control. The current study could be conducive for the policymakers of those countries where the COVID-19 pandemic is still unrestrained.

Environmental perspectives of COVID-19 outbreaks: A review

The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began in December 2019 in China and has led to a global public health emergency. Previously, it was known as 2019-nCoV and caused disease mainly through respiratory pathways. The COVID-19 outbreak is ranked third globally as the most highly pathogenic disease of the twenty-first century, after the outbreak of SARS-CoV and Middle East respiratory syndrome in 2002 and 2012, respectively. Clinical, laboratory, and diagnostic methodology have been demonstrated in some observational studies. No systematic reviews on COVID-19 have been published regarding the integration of COVID-19 outbreaks (monitoring, fate and treatment) with environmental and human health perspectives. Accordingly, this review systematically addresses environmental aspects of COVID-19 outbreak such as the origin of SARS-CoV-2, epidemiological characteristics, diagnostic methodology, treatment options and technological advancement for the prevention of COVID-19 outbreaks. Finally, we integrate COVID-19 outbreaks (monitoring, fate and treatment) with environmental and human health perspectives. We believe that this review will help to understand the SARS-CoV-2 outbreak as a multipurpose document, not only for the scientific community but also for global citizens. Countries should adopt emergency preparedness such as prepare human resources, infrastructure and facilities to treat severe COVID-19 as the virus spreads rapidly globally.

Does Climate Variability Impact COVID-19 Outbreak? An Enhanced Semantics-Driven Theory-Guided Model

COVID-19, a life-threatening infection by novel coronavirus, has broken out as a pandemic since December 2019. Eventually, with the aim of helping the World Health Organization and other health regulators to combat COVID-19, significant research effort has been exerted during last several months to analyze how the various factors, especially the climatic aspects, impact on the spread of this infection. However, due to insufficient test and lack of data transparency, these research findings, at times, are found to be inconsistent as well as conflicting. In our work, we aim to employ a semantics-driven probabilistic framework for analyzing the causal influence as well as the impact of climate variability on the COVID-19 outbreak. The idea here is to tackle the data inadequacy and uncertainty issues using probabilistic graphical analysis along with embedded technology of incorporating semantics from climatological domain. Furthermore, the theoretical guidance from epidemiological model additionally helps the framework to better capture the pandemic characteristics. More significantly, we further enhance the impact analysis framework with an auxiliary module of measuring semantic relatedness on regional basis, so as to realistically account for the existence of multiple climate types within a single spatial region. This added notion of regional semantic relatedness further helps us to attain improved probabilistic analysis for modeling the climatological impact on this disease outbreak. Experimentation with COVID-19 datasets over 15 states (or provinces) belonging to varying climate regions in India, demonstrates the effectiveness of our semantically-enhanced theory-guided data-driven approach. It is worth noting that our proposed framework and the relevant semantic analyses are generic enough for intelligent as well as explainable impact analysis in many other application domains, by introducing minimal augmentation.

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.

Investigating the roles of meteorological factors in COVID-19 transmission in Northern Italy

The novel COVID-19 is a highly invasive, pathogenic, and transmittable disease that has stressed the health care sector and hampered global development. Information of other viral respiratory diseases indicates that COVID-19 transmission could be affected by varying weather conditions; however, the impact of meteorological factors on the COVID-19 death counts remains unexplored. By investigating the impact of meteorological factors (absolute humidity, relative humidity, and temperature), this study will contribute both theoretically and practically to the concerned domain of pandemic management to be better prepared to control the spread of the disease. For this study, data is collected from 23 February to 31 March 2020 for Milan, Northern Italy, one of the badly hit regions by COVID-19. The generalized additive model (GAM) is applied, and a nonlinear relationship is examined with penalized spline methods. A sensitivity analysis is conducted for the verification of model results. The results reveal that temperature, relative humidity, and absolute humidity have a significant but negative relationship with the COVID-19 mortality rate. Therefore, it is possible to postulate that cool and dry environmental conditions promote virus transmission, leading to an increase in COVID-19 death counts. The results may facilitate health care policymakers in developing and implementing effective control measures in a timely and efficient way.

Common factors of COVID-19 cases and deaths among the most affected 50 countries

AIMS

The Coronavirus (COVID-19) is a global pandemic requiring global responses. The objective of this paper is to identify the common factors of COVID-19 cases and deaths among the 50 most affected countries.

METHODS

We performed Ordinary least squares among a wide range of socio-economic, environmental, climatic and health indicators to explain the number of cases and deaths.

RESULTS

The findings are: (i) obesity is the only significant global denominator for the number of COVID-19 cases and deaths; (ii) the percentage of the population over the age of 65 and number of hospital beds per 1000 population inversely correlated to mortality from COVID-19.

CONCLUSIONS

Obesity increases vulnerability to COVID-19 infections and mortality. Global awareness of obesity and social investment in health infrastructure are pre-requisite for a pandemic adaptive future. However, the study is limited to cross-sectional data of April 17, 2020.

The role of airborne particles and environmental considerations in the transmission of SARS-CoV-2

Corona Virus Disease 2019 (COVID-19) caused by the novel coronavirus, results in an acute respiratory condition coronavirus 2 (SARS-CoV-2) and is highly infectious. The recent spread of this virus has caused a global pandemic. Currently, the transmission routes of SARS-CoV-2 are being established, especially the role of environmental transmission. Here we review the environmental transmission routes and persistence of SARS-CoV-2. Recent studies have established that the transmission of this virus may occur, amongst others, in the air, water, soil, cold-chain, biota, and surface contact. It has also been found that the survival potential of the SARS-CoV-2 virus is dependent on different environmental conditions and pollution. Potentially important pathways include aerosol and fecal matter. Particulate matter may also be a carrier for SARS-CoV-2. Since microscopic particles can be easily absorbed by humans, more attention must be focused on the dissemination of these particles. These considerations are required to evolve a theoretical platform for epidemic control and to minimize the global threat from future epidemics.

Association between environmental factors and COVID-19 in Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) continues to spread worldwide and has led to recession, rising unemployment, and the collapse of the health-care system. The aim of this study was to explore the exposure-response relationship between daily confirmed COVID-19 cases and environmental factors. We used a time-series generalized additive model (GAM) to investigate the short-term association between COVID-19 and environmental factors by using daily meteorological elements, air pollutant concentration, and daily confirmed COVID-19 cases from January 21, 2020, to February 29, 2020, in Shanghai, China. We observed significant negative associations between daily confirmed COVID-19 cases and mean temperature (Tave), temperature humidity index (THI), and index of wind effect (K), whereas air quality index (AQI), PM2.5, PM10 NO2, and SO2 were significantly associated with the increase in daily confirmed COVID-19 cases. A 1 °C increase in Tave, one-unit increase in THI, and 10-unit increase in K (lag 0-7 days) were associated with 4.7, 1.8, and 1.6% decrease in daily confirmed cases, respectively. Daily Tave, THI, K, PM10, and SO2 had significant lag and persistence (lag 0-7 days), whereas the lag and persistence of AQI, PM2.5, and NO2 were significant at both lag 0-7 and 0-14 days. A 10-μg/m3 increase in PM10 and 1-μg/m3 increase in SO2 was associated with 13.9 and 5.7% increase in daily confirmed cases at lag 0-7 days, respectively, whereas a 10-unit increase in AQI and a 10-μg/m3 increase in PM2.5 and NO2 were associated with 7.9, 7.8, and 10.1% increase in daily confirmed cases at lag 0-14 days, respectively. Our findings have important implications for public health in the city of Shanghai.

Effects of COVID-19 pandemic on the air quality of three megacities in India

COVID-19 pandemic compelled many countries in the world to go for a nationwide lockdown to prevent the spread of the coronavirus. India started the lockdown on 24 March 2020. We analyzed the air quality of three megacities of India, namely Mumbai, Delhi, and Kolkata, during the lockdown phase and compared it with the pre-lockdown and post-lockdown scenarios. We considered seven major air pollutants: PM_2.5, PM₁₀, NO₂, NH₃, SO₂, CO, and O₃. We analyzed the data acquired from 56 automatic air-monitoring stations (AAMS) under the Central Pollution Control Board (CPCB) spread across the megacities. The air pollution level in the eastern part of Mumbai and the western part of Delhi and Kolkata usually remains high. Delhi was the worst polluted megacity, followed by Kolkata and Mumbai. The stop of vehicular movements and industrial lockdown across the nation has substantial effects on the environment, especially in the atmosphere near the Earth's surface. Our analysis showed significant improvements in air quality during the period of lockdown (25 March to 14 April 2020) compared to the pre-lockdown phase (3 March to 23 March 2020) and the same time window of the previous year (25 March to 14 April 2019). The post-lockdown (15 April to 5 May) phase exhibited mixed results. We mapped the spatial pattern of these pollutants and the air quality index (AQI). According to CPCB, PM_2.5, PM₁₀, and CO are the major air pollutants in India that reduced by 47%, 41%, and 27% in Mumbai; 52%, 39%, and 13% in Delhi; and 49%, 37%, and 21% in Kolkata, respectively, in the lockdown phase. PM_2.5, PM₁₀, and NO₂ exhibited significant correlations across the three megacities. This study shows that occasional short-term lockdowns can effectively refresh the air in these megacities.

Inferring the Main Drivers of SARS-CoV-2 Global Transmissibility by Feature Selection Methods

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number R 0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence R 0, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R 0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R 0 predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R 0 or affect it differently than suggested by naïve analysis.

Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi

Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.

Temperature-mortality association during and before the COVID-19 pandemic in Italy: A nationwide time-stratified case-crossover study

OBJECTIVES

To identify the associations of temperature with non-COVID-19 mortality and all-cause mortality in the pandemic 2020 in comparison with the non-COVID-19 period in Italy.

METHODS

The data on 3,189,790 all-cause deaths (including 3,134,137 non-COVID-19 deaths) and meteorological conditions in 107 Italian provinces between February 1st and November 30th in each year of 2015-2020 were collected. We employed a time-stratified case-crossover study design combined with the distributed lag non-linear model to investigate the relationships of temperature with all-cause and non-COVID-19 mortality in the pandemic and non-pandemic periods.

RESULTS

Cold temperature exposure contributed higher risks for both all-cause and non-COVID-19 mortality in the pandemic period in 2020 than in 2015-2019. However, no different change was found for the impacts of heat. The relative risk (RR) of non-COVID-19 deaths and all-cause mortality at extremely cold (2 °C) in comparison with the estimated minimum mortality temperature (19 °C) in 2020 were 1.63 (95% CI: 1.55-1.72) and 1.45 (95%CI: 1.31-1.61) respectively, which were higher than all-cause mortality risk in 2015-2019 with RR of 1.19 (95%CI: 1.17-1.21).

CONCLUSION

Cold exposure indicated stronger impacts than high temperatures on all-cause and non-COVID-19 mortality in the pandemic year 2020 compared to its counterpart period in 2015-2019 in Italy.

Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia

The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.

Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach

The coronavirus disease 2019 (COVID-19) is still spreading fast in several tropical countries after more than one year of pandemic. In this scenario, the effects of weather conditions that can influence the spread of the virus are not clearly understood. This study aimed to analyse the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021. Rank correlation tests and a neural network named self-organising map (SOM) were used to investigate similarities in the dynamics of the disease in the cities and check possible relationships among the variables. Two periods were analysed (quarantine and post-quarantine) for all cities together and individually. The data were classified in seven groups based on city, date and biome using SOM. The virus transmission was most affected by mobility variables, especially in the post-quarantine. The meteorological variables presented different behaviours on the virus transmission in different biogeographical regions. The wind speed was one of the factors connected with the highest contamination rate recorded in Leticia. The highest new daily cases were recorded in Bogota where cold/dry conditions (average temperature <14 °C and absolute humidity >9 g/m³) favoured the contagions. In contrast, Barranquilla, Cartagena and Leticia presented an opposite trend, especially with the absolute humidity >22 g/m³. The results support the implementation of better local control measures based on the particularities of tropical regions.

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.

Impact of COVID-19 pandemic on socio-economic, energy-environment and transport sector globally and sustainable development goal (SDG)

The United Nation's Sustainable Development Goals (SDGs) want to have a peaceful world where human life will be in a safe, healthy, sustainable environment without any inequalities. However, the year 2020 experienced a global pandemic due to COVID-19. This COVID-19 created an adverse impact on human life, economic, environment, and energy and transport sector compared to the pre-COVID-19 scenario. These above-mentioned sectors are interrelated and thus lockdown strategy and stay at home rules to reduce the COVID-19 transmission had a drastic effect on them. With lockdown, all industry and transport sectors were closed, energy demand reduced greatly but the time shift of energy demand had a critical impact on grid and energy generation. Decreased energy demand caused a silver lining with an improved environment. However, drowned economy creating a negative impact on the human mind and financial condition, which at times led to life-ending decisions. Transport sector which faced a financial dip last year trying to coming out from the losses which are not feasible without government aid and a new customer-friendly policy. Sustainable transport and the electric vehicle should take high gear. While people are staying at home or using work from home scheme, building indoor environment must specially be taken care of as a compromised indoor environment affects and increases the risk of many diseases. Also, the energy-efficient building will play a key role to abate the enhanced building energy demand and more generation from renewable sources should be in priority. It is still too early to predict any forecast about the regain period of all those sectors but with vaccination now being introduced and implemented but still, it can be considered as an ongoing process as its final results are yet to be seen. As of now, COVID-19 still continue to grow in certain areas causing anxiety and destruction. With all these causes, effects, and restoration plans, still SDGs will be suffered in great order to attain their target by 2030 and collaborative support from all countries can only help in this time.

Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data

This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70-80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10668-021-01719-z.

Impact of Environmental Indicators on the COVID-19 Pandemic in Delhi, India

Currently, there is a massive debate on whether meteorological and air quality parameters play a crucial role in the transmission of COVID-19 across the globe. With this background, this study aims to evaluate the impact of air pollutants (PM2.5, PM10, CO, NO, NO2, and O3) and meteorological parameters (temperature, humidity, wind speed, and rainfall) on the spread and mortality due to the COVID-19 outbreak in Delhi from 14 Mar 2020 to 3 May 2021. The Spearman's rank correlation method employed on secondary data shows a significant correlation between the COVID-19 incidences and the PM2.5, PM10, CO, NO, NO2, and O3 concentrations. Amongst the four meteorological parameters, temperature is strongly correlated with COVID-19 infections and deaths during the three phases, i.e., pre-lockdown (14 March 2020 to 24 March 2020) (r = 0.79), lockdown (25 March 2020 to 31 May 2020) (r = 0.87), and unlock (1 June 2020 to 3 May 2021) (r = -0.75), explaining the variability of about 20-30% in the lockdown period and 18-19% in the unlock period. NO2 explained the maximum variability of 10% and 7% in the total confirmed cases and deaths among the air pollutants, respectively. A generalized linear model could explain 80% and 71% of the variability in confirmed cases and deaths during the lockdown and 82% and 81% variability in the unlock phase, respectively. These findings suggest that these factors may contribute to the transmission of the COVID-19 and its associated deaths. The study results would enhance the ongoing research related to the influence of environmental factors. They would be helpful for policymakers in managing the outbreak of COVID-19 in Delhi, India.

The dynamics of COVID-19 outbreak in Nigeria: A sub-national analysis

The African health crisis feared at the beginning of the COVID-19 pandemic has not materialized, and there is interest globally in understanding possible peculiarities in COVID-19 outbreak dynamics in the tropics and sub-tropics that have led to a much milder African outbreak than initial projections. Towards this, Susceptible-Infected-Recovered-Dead compartmental models were fitted to COVID-19 data from all Nigerian states in this study, from which four parameters were estimated per state. A density-based clustering method was used to identify states with similar outbreak dynamics, and the stage of the outbreak determined per state. Subsequently, outbreak dynamics were correlated with absolute humidity, temperature, population density and distance to the international passenger travel gateways in the country. The models revealed that while the outbreak is still increasing nationally, outbreaks in at least 12 states have peaked. A total of at least 519,672 confirmed cases were predicted by January 2021, with a worst case scenario of at least 14,785,457. Weak positive correlations were found between COVID-19 spread and absolute humidity (Pearson’s Coefficient = 0.136, p< 0.05) and temperature (Pearson’s Coefficient = 0.021, p< 0.05). While many studies have established links between temperature and humidity and COVID-19 spread, the correlation has most usually been negative where it exists. The findings in this study of possible positive correlation is in line with a number of previous studies showing such unexpected correlations in the tropics or subtropics. This highlights even more the importance of additional studies on COVID-19 dynamics in Africa.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Exploring the linkage between seasonality of environmental factors and COVID-19 waves in Madrid, Spain

Like several countries, Spain experienced a multi wave pattern of COVID-19 pandemic over more than one year period, between spring 2020 and spring 2021. The transmission of SARS-CoV-2 pandemics is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation.This study aims to quantify the impact of climate and air pollution factors seasonality on incidence and severity of COVID-19 disease waves in Madrid metropolitan region in Spain. We employed descriptive statistics and Spearman rank correlation tests for analysis of daily in-situ and geospatial time-series of air quality and climate data to investigate the associations with COVID-19 incidence and lethality in Madrid under different synoptic meteorological patterns. During the analyzed period (1 January 2020-28 February 2021), with one month before each of three COVID-19 waves were recorded anomalous anticyclonic circulations in the mid-troposphere, with positive anomalies of geopotential heights at 500 mb and favorable stability conditions for SARS-CoV-2 fast diffusion. In addition, the results reveal that air temperature, Planetary Boundary Layer height, ground level ozone have a significant negative relationship with daily new COVID-19 confirmed cases and deaths. The findings of this study provide useful information to the public health authorities and policymakers for optimizing interventions during pandemics.

Association between meteorological factors and daily new cases of COVID-19 in 188 countries: A time series analysis

By 31 December 2020, Coronavirus disease 2019 (COVID-19) had been prevalent worldwide for one year, and most countries had experienced a complete seasonal cycle. The role of the climate and environment are essential factors to consider in transmission. We explored the association between global meteorological conditions (including mean temperature, wind speed, relative humidity and diurnal temperature range) and new cases of COVID-19 in the whole past year. We assessed the relative risk of meteorological factors to the onset of COVID-19 by using generalized additive models (GAM) and further analyzed the hysteresis effects of meteorological factors using the Distributed Lag Nonlinear Model (DLNM). Our findings revealed that the mean temperature, wind speed and relative humidity were negatively correlated with daily new cases of COVID-19, and the diurnal temperature range was positively correlated with daily new cases of COVID-19. These relationships were more apparent when the temperature and relative humidity were lower than their average value (21.07°Cand 66.83%). The wind speed and diurnal temperature range were higher than the average value(3.07 m/s and 9.53 °C). The maximum RR of mean temperature was 1.30 under -23°C at lag ten days, the minimum RR of wind speed was 0.29 under 12m/s at lag 24 days, the maximum RR of range of temperature was 2.21 under 28 °C at lag 24 days, the maximum RR of relative humidity was 1.35 under 4% at lag 0 days. After a subgroup analysis of the countries included in the study, the results were still robust. As the Northern Hemisphere enters winter, the risk of global covid-19 remains high. Some countries have ushered in a new round of COVID-19 epidemic. Thus, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19 in winter.

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.

Environmental perspective of COVID-19: Atmospheric and wastewater environment in relation to pandemic

The pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major challenge to health systems worldwide. Recently, numbers of epidemiological studies have illustrated that climate conditions and air pollutants are associated with the COVID-19 confirmed cases worldwide. Researches also suggested that the SARS-CoV-2 could be detected in fecal and wastewater samples. These findings provided the possibility of preventing and controlling the COVID-19 pandemic from an environmental perspective. With this review, the main purpose is to summarize the relationship between the atmospheric and wastewater environment and COVID-19. In terms of the atmospheric environment, the evidence of the relationship between atmospheric environment (climate factors and air pollution) and COVID-19 is growing, but currently available data and results are various. It is necessary to comprehensively analyze their associations to provide constructive suggestions in responding to the pandemic. Recently, large numbers of studies have shown the widespread presence of this virus in wastewater and the feasibility of wastewater surveillance when the pandemic is ongoing. Therefore, there is an urgent need to clarify the occurrence and implication of viruses in wastewater and to understand the potential of wastewater-based epidemiology of pandemic. Overall, environmental perspective-based COVID-19 studies can provide new insight into pandemic prevention and control, and minimizes the economic cost for COVID-19 in areas with a large outbreak or a low economic level.

The Influence of Potential Infection on the Relationship between Temperature and Confirmed Cases of COVID-19 in China

Considering the impact of the number of potential new coronavirus infections in each city, this paper explores the relationship between temperature and cumulative confirmed cases of COVID-19 in mainland China through the non-parametric method. In this paper, the floating population of each city in Wuhan is taken as a proxy variable for the number of potential new coronavirus infections. Firstly, to use the non-parametric method correctly, the symmetric Gauss kernel and asymmetric Gamma kernel are applied to estimate the density of cumulative confirmed cases of COVID-19 in China. The result confirms that the Gamma kernel provides a more reasonable density estimation of bounded data than the Gauss kernel. Then, through the non-parametric method based on the Gamma kernel estimation, this paper finds a positive relationship between Wuhan’s mobile population and cumulative confirmed cases, while the relationship between temperature and cumulative confirmed cases is inconclusive in China when the impact of the number of potential new coronavirus infections in each city is considered. Compared with the weather, the potentially infected population plays a more critical role in spreading the virus. Therefore, the role of prevention and control measures is more important than weather factors. Even in summer, we should also pay attention to the prevention and control of the epidemic.

Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis

COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.

Exposure-lag-response associations between weather conditions and ankylosing spondylitis: a time series study

Background

Patients with ankylosing spondylitis (AS) have reported that their pain becomes worse when the local weather changes. However, there is limited evidence verifying the short-term associations between meteorological factors and outpatient visits for patients with AS. Therefore, this study evaluates this possible association.

Methods

Meteorological data and data on daily AS outpatient visits to a general hospital in Hefei, China, from 2014 to 2019 were collected and analysed. Distributed lag nonlinear models and Poisson regression models were employed to determine the association between weather conditions and outpatient visits; the results were also stratified by gender and age.

Results

High relative humidity is significantly associated with all patient visits in lag 1 (RR = 1.113, 95% CI 1.021 to 1.213) and lag 7 days (RR = 1.115, 95% CI 1.014 to 1.227). A low relative risk to the nadir is observed in lag 4 days (RR = 0.920, 95% CI 0.862 to 0.983). Male and young patients (< 65 years) are more vulnerable to damp weather, and elderly people (≥ 65 years) are significantly affected by high temperatures in lag 7 days (RR = 3.004, 95% CI 1.201 to 7.510).

Conclusions

Our findings suggest a potential relationship between exposure to weather conditions and increased risk of AS outpatient visits. These results can aid hospitals in preparing for and managing hospital visits by AS patients when the local weather conditions change.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04523-y.

Do Temperature and Humidity Affect the Transmission of SARS-CoV-2?-A Flexible Regression Analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus that causes Coronavirus disease 2019 (COVID-19). Temperature and humidity are two essential factors in the transmission of SARS-CoV-2 affect the respiratory system of human. This study aimed to investigate the effects of temperature and humidity on the transmission of SARS-CoV-2 and the Spread Covid-19. The daily number of SARS-CoV-2 infected new cases, and the number of death due to Covid-19 are considered the response variables. Data are collected from March 08, 2020 to January 31, 2021. A flexible regression model under the Generalized Additive Models for Location Scale and Shape framework is used to analyze data. The temperature and humidity have a significant impact on the transmission of SARS-CoV-2. The temperature is highly significant in the number of SARS-CoV-2 infected new cases and number of death due to COVID-19. In contrast, the humidity is significant on the number of SARS-CoV-2 infected new cases, but it is insignificant on the number of death due to COVID-19 at a 5% level of significance. The analysis revealed that both the temperature and humidity inversely affected the daily number of deaths and new cases of COVID-19.

Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling

The significant health and economic effects of COVID-19 emphasize the requirement for reliable forecasting models to avoid the sudden collapse of healthcare facilities with overloaded hospitals. Several forecasting models have been developed based on the data acquired within the early stages of the virus spread. However, with the recent emergence of new virus variants, it is unclear how the new strains could influence the efficiency of forecasting using models adopted using earlier data. In this study, we analyzed daily positive cases (DPC) data using a machine learning model to understand the effect of new viral variants on morbidity rates. A deep learning model that considers several environmental and mobility factors was used to forecast DPC in six districts of Japan. From machine learning predictions with training data since the early days of COVID-19, high-quality estimation has been achieved for data obtained earlier than March 2021. However, a significant upsurge was observed in some districts after the discovery of the new COVID-19 variant B.1.1.7 (Alpha). An average increase of 20–40% in DPC was observed after the emergence of the Alpha variant and an increase of up to 20% has been recognized in the effective reproduction number. Approximately four weeks was needed for the machine learning model to adjust the forecasting error caused by the new variants. The comparison between machine-learning predictions and reported values demonstrated that the emergence of new virus variants should be considered within COVID-19 forecasting models. This study presents an easy yet efficient way to quantify the change caused by new viral variants with potential usefulness for global data analysis.

Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities

Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries.

Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations.

Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed.

Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.

Global effect of city-to-city air pollution, health conditions, climatic & socio-economic factors on COVID-19 pandemic

The rate of spread of the global pandemic calls for much attention from the empirical literature. The limitation of extant literature in assessing a comprehensive COVID-19 portfolio that accounts for complexities in the spread and containment of the virus underscores this study. We investigate the effect of city-to-city air pollutant species, meteorological conditions, underlying health conditions, socio-economic and demographic factors on COVID-19 health outcomes. We utilize a panel estimation of 615 cities in 6 continents from January 1 to June 11, 2020. While social distancing measures, movement restrictions and lockdown are reported to have improved environmental quality, we show that ambient PM_2.5 remains unhealthy and above the acceptable threshold in several countries. Our empirical assessment shows that while ambient PM_2.5, nitrogen dioxide, ozone, pressure, dew, Windgust, and windspeed increase the spread of COVID-19, high relative humidity and ambient temperature have mitigation effect on COVID-19, hence, decreases the number of confirmed cases. We report 66.3% of countries projected to experience a second wave of COVID-19 if government stringency and safety protocols are not enhanced. By extension, our assessments demonstrate that several factors namely underlying health conditions, meteorological, air pollution, health system quality, socio-economic and demographics spur the reproduction effect of COVID-19 across countries. Our study highlights the importance of government stringency in containing the spread of COVID-19 and its impacts.

Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology

The current pandemic disease coronavirus (COVID-19) has not only become a worldwide health emergency, but also devoured the global economy. Despite appreciable research, identification of targeted populations for testing and tracking the spread of COVID-19 at a larger scale is an intimidating challenge. There is a need to quickly identify the infected individual or community to check the spread. The diagnostic testing done at large-scale for individuals has limitations as it cannot provide information at a swift pace in large populations, which is pivotal to contain the spread at the early stage of its breakouts. Recently, scientists are exploring the presence of SARS-CoV-2 RNA in the faeces discharged in municipal wastewater. Wastewater sampling could be a potential tool to expedite the early identification of infected communities by detecting the biomarkers from the virus. However, it needs a targeted approach to choose optimized locations for wastewater sampling. The present study proposes a novel fuzzy based Bayesian model to identify targeted populations and optimized locations with a maximum probability of detecting SARS-CoV-2 RNA in wastewater networks. Consequently, real time monitoring of SARS-CoV-2 RNA in wastewater using autosamplers or biosensors could be deployed efficiently. Fourteen criteria such as population density, patients with comorbidity, quarantine and hospital facilities, etc. are analysed using the data of 14 lac individuals infected by COVID-19 in the USA. The uniqueness of the proposed model is its ability to deal with the uncertainty associated with the data and decision maker's opinions using fuzzy logic, which is fused with Bayesian approach. The evidence-based virus detection in wastewater not only facilitates focused testing, but also provides potential communities for vaccine distribution. Consequently, governments can reduce lockdown periods, thereby relieving human stress and boosting economic growth.

The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level

Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comprehensive analysis. We collected data on COVID-19 cases and the factors of interest in 340 prefectures of mainland China from 1 December 2019 to 30 April 2020. Moran's I statistic, Getis-Ord Gi^⁎ statistic and Kulldorff's space-time scan statistics were used to identify spatial clusters of COVID-19, and the geographically weighted regression (GWR) model was applied to investigate the effects of the associated factors on COVID-19 incidence. A total of 67,449 laboratory-confirmed cases were reported during the study period. Wuhan city as well as its surrounding areas were the cluster areas, and January 25 to February 21, 2020, was the clustering time of COVID-19. The population outflow from Wuhan played a significant role in COVID-19 transmission, with the local coefficients varying from 14.87 to 15.02 in the 340 prefectures. Among the meteorological parameters, relative humidity and precipitation were positively associated with COVID-19 incidence, while the average wind speed showed a negative correlation, but the relationship of average temperature with COVID-19 incidence inconsistent between northern and southern China. NO₂ was positively associated, and O₃ was negatively associated, with COVID-19 incidence. Environment with high levels of inbound migration or travel, poor ventilation, high humidity or heavy rainfall, low temperature, and high air pollution may be favorable for the growth, reproduction and spread of SARS-CoV-2. Therefore, applying appropriate lockdown measures and travel restrictions, strengthening the ventilation of living and working environments, controlling air pollution and making sufficient preparations for a possible second wave in the relatively cold autumn and winter months may be helpful for the control and prevention of COVID-19.

COVID-19 pandemic in the Middle East countries: coronavirus-seeking behavior versus coronavirus-related publications

The spread of COVID-19 has created a fundamental need for coordinated mechanisms responding to outbreaks in different sectors. One of the main sectors relates to information supply and demand in the middle of this pandemic in the digital environment. It could be called an infodemiology. It is known as a promising approach to solving the challenge in the present age. At this level, the purpose of this article is to investigate the COVID-19 related search process by field research. Data were retrieved from Google Trends in Middle Eastern countries alongside scientific research output of Middle Eastern scientists towards COVID-19 in Web of Science, Scopus, and PubMed. Daily COVID-19 cases and deaths were retrieved from the World Health Organization. We searched for descriptive statistical analyses to detect coronavirus-seeking behavior versus coronavirus releases in the Middle East in 2020. Findings show that people in the Middle East use various keyword solutions to search for COVID-19 in Google. There is a significant correlation between coronavirus confirmed cases and scientific productivity (January 2020-December 2020). Also, there is a positive association between the number of deaths and the number of scientific publications (except Jordan). It was a positive and significant association between online coronavirus-seeking behavior on Google (RSVs) and the confirmed cases (except Syria and Yemen). Furthermore, it was a positive relationship between RSVs and scientific productivity in the Middle East (except Bahrain and Qatar). From an infodemiological viewpoint, there is a significant correlation between coronavirus information demand and its information provision.

The Association Between COVID-19, Air Pollution, and Climate Change

This mini-review aims to highlight both the positive and negative relationship between COVID-19 and air pollution and climate change based on current studies. Since, COVID-19 opened a bibliographic door to scientific production, so there was a limit to research at the moment. There were two sides to the relationship between COVID-19 and both air pollution and climate change. The associated with climate change, in particular, defines the relationship very loosely. Many studies have revealed a positive correlation between COVID-19 and each air pollutants, while some studies shown a negative correlation. There were a few studies that focused on the relationship between COVID-19 in terms of climate. Meanwhile, there were many studies explained the relationship with meteorological factors instead.

COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data

Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D.

Meteorological conditions are heterogeneous factors for COVID-19 risk in China

Whether meteorological factors influence COVID-19 transmission is an issue of major public health concern, but available evidence remains unclear and limited for several reasons, including the use of report date which can lag date of symptom onset by a considerable period. We aimed to generate reliable and robust evidence of this relationship based on date of onset of symptoms. We evaluated important meteorological factors associated with daily COVID-19 counts and effective reproduction number (R_t) in China using a two-stage approach with overdispersed generalized additive models and random-effects meta-analysis. Spatial heterogeneity and stratified analyses by sex and age groups were quantified and potential effect modification was analyzed. Nationwide, there was no evidence that temperature and relative humidity affected COVID-19 incidence and R_t. However, there were heterogeneous impacts on COVID-19 risk across different regions. Importantly, there was a negative association between relative humidity and COVID-19 incidence in Central China: a 1% increase in relative humidity was associated with a 3.92% (95% CI, 1.98%-5.82%) decrease in daily counts. Older population appeared to be more sensitive to meteorological conditions, but there was no obvious difference between sexes. Linear relationships were found between meteorological variables and COVID-19 incidence. Sensitivity analysis confirmed the robustness of the association and the results based on report date were biased. Meteorological factors play heterogenous roles on COVID-19 transmission, increasing the possibility of seasonality and suggesting the epidemic is far from over. Considering potential climatic associations, we should maintain, not ease, current control measures and surveillance.

Variation of population density on a beach: A simple analytical formulation

Since the novel coronavirus was reported (December 2019), the virus spread rapidly breaking through frontiers and impacting almost all countries of the world. Tourism is one of the sectors that has been heavily affected by the crisis. Even though there are several protocols for tourism activities that were written for the austral summer season, at the present, physical distancing is considered an effective way to reduce the spread of the virus. Clear and simple public actions should be rapidly implemented by the authorities to minimize the number of people on the beach. For instance, increasing the number of accesses to the beaches, building parking lots adjacent to the farthest beaches, or opening coastal roads on the outskirts of town to expand usable beaches, would be some simple and direct measures to reduce the beach population density. A simple analytical formulation for assessing the percentage decrease of the static population density respect to the absolute maximum population density on a beach is described in this paper. The variables of this simple analytical tool are the instantaneous sea-level (tide), air temperature, and beach expansion. Beach expansion is the single and manageable variable of the formulation, and refers to the inclusion of some near or adjacent extension of beach. It is suggested that the expansion of beaches would be very useful not only for pandemic time but also for the new normality. An application of this methodology is presented in the municipality of La Costa, Argentina.

Using urban landscape pattern to understand and evaluate infectious disease risk

COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82∼63.56 ‱ and 3.00∼15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951∼181.261. The adjusted R2 in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide.

A Descriptive Analysis of the Scientific Literature on Meteorological and Air Quality Factors and COVID-19

The role of meteorological and air quality factors in moderating the transmission of SARS-CoV-2 and severity of COVID-19 is a critical topic as an opportunity for targeted intervention and relevant public health messaging. Studies conducted in early 2020 suggested that temperature, humidity, ultraviolet radiation, and other meteorological factors have an influence on the transmissibility and viral dynamics of COVID-19. Previous reviews of the literature have found significant heterogeneity in associations but did not examine many factors relating to epidemiological quality of the analyses such as rigor of data collection and statistical analysis, or consideration of potential confounding factors. To provide greater insight into the current state of the literature from an epidemiological standpoint, the authors conducted a rapid descriptive analysis with a strong focus on the characterization of COVID-19 health outcomes and use of controls for confounding social and demographic variables such as population movement and age. We have found that few studies adequately considered the challenges posed by the use of governmental reporting of laboratory testing as a proxy for disease transmission, including timeliness and consistency. In addition, very few studies attempted to control for confounding factors, including timing and implementation of public health interventions and metrics of population compliance with those interventions. Ongoing research should give greater consideration to the measures used to quantify COVID-19 transmission and health outcomes as well as how to control for the confounding influences of public health measures and personal behaviors.

Influences of weather-related parameters on the spread of Covid-19 pandemic - The scenario of Bangladesh

OBJECTIVE

Weather parameters such as temperature, humidity, air quality index and wind speed are the important factors influencing the infectious diseases like Covid-19. Therefore, this study aims to discuss and analyse the relation between weather parameters and the spread of Coronavirus disease (Covid-19) from the perspective of Bangladesh.

METHODS

Correlation among weather parameters and infection and death rate were established using several graphical plots and wind rose diagrams, Kendall and Spearman correlation and appropriate discussion with relevancy and reference. Information presented in this study has been extracted from 1st April 2020 to 30th December 2020.

RESULTS

Analyses show that with the decrease in temperature, infection rate increased significantly. Also, the number of infection increases as wind speed increases. As the absolute humidity rate of Bangladesh is almost constant; therefore, the authors are unable to predict any relation of absolute humidity with the number of infection. Further, the prediction for the number of infections based on the wind direction for the several regions of seven divisions in Bangladesh is vulnerable for the upcoming several months.

CONCLUSION

This study has analysed the dependency of weather parameters on a number of infections along with predicting the upcoming danger zones.

Uncurtaining the effect of COVID-19 in diabetes mellitus: a complex clinical management approach

The aim of the present review is to overview the common properties of corona virus and hence proofs well beginning of corona virus in persons with diabetes, and its treatment. Globally, it has been observed that according to the statistics, India has the second largest number of people with diabetes. Literature review has been implemented within the databases using suitable keywords. For persons suffering from diabetic disorder, the COVID-19 infection becomes a dual challenge. Diabetes is a severe metabolic situation which causes the sugar levels in the blood to increase than the normal level. Normally, communicable disease like COVID-19 is more prevailing in patients with diabetes. Diabetic patient has poor immune response to infections. The different bacterial, viral, parasitic, and mycotic infections showed increased probability in diabetic patients as compared to non-diabetic patient. All these conclusions clear out the intention that the diabetic patients are more susceptible to enhanced inflammatory response that may lead to rapid spreading of COVID-19 infection with high rate of mortality. In the present situation of pandemic, managing diabetes seems to be quite challenging and diabetic patient having COVID-19 infection should follow normal course of antihypertensive and antidiabetic drugs prescribed with the exception of sodium glucose co-transpoters-2 inhibitors which would increase the risk of dehydration and ketoacidosis. In view of above discussion, this article highlights the proposed mechanism of COVID-19 infection linking it with diabetes, antidiabetic drugs to be used in COVID-19 infection along with their advantages, and disadvantages and management of COVID-19 infection diabetic patient.

Association between air pollution and COVID-19 infection: evidence from data at national and municipal levels

The impact of high concentrations of air pollution on COVID-19 has been a major air quality and life safety issue in recent studies. This study aimed to assess the contribution of different air pollution indicators in different spaces on the newly confirmed cases of coronavirus. According to causality's results between air pollution (AP) and COVID-19 infection in 9 countries, first, we examined the non-linear relationship from AP to COVID-19 with PM2.5 as the rating variable (the cut point is 35 μg/m3) at the national level. It is concluded that the effects of PM2.5 and PM10 on COVID-19 are more sensitive in Russia, England, Germany, and France, while O3 and PM2.5 are more sensitive in America and Canada from 21 Jan to 20 May. Second, we examined the threshold effects from AP to COVID-19 with PM2.5, PM10, SO2, CO, NO2, and O3 as the threshold variables, respectively, at the municipal level in China during the period 28 Jan to 31 May. It is concluded that except CO, the remaining 5 indicators are very sensitive to the increase of newly confirmed cases, and the spread of COVID-19 can be prevented and controlled by the determination of thresholds. In addition, the 9 countries and 27 provinces show that PM2.5 in high concentrations is the more sensitive pollutant on the spread of COVID-19 infection.

Are population size and diverse climatic conditions the driving factors for next COVID-19 pandemic epicenter in India?

Although a nationwide lockdown was imposed in India amid COVID-19 outbreak since March 24, 2020, the COVID-19 infection is increasing day-by-day. Till June 10, 2021 India has recorded 29,182,072 COVID cases and 359,695 deaths. A number of factors help to influence COVID-19 transmission rate and prevalence. Accordingly, the present study intended to integrate the climatic parameters, namely ambient air temperature (AT) and relative humidity (H) with population mass (PM) to determine their influence for rapid transmission of COVID-19 in India. The sensibility of AT, H and PM parameters on COVID-19 transmission was investigated based on receiver operating characteristics (ROC) classification model. The results depicted that AT and H models have very low sensibility (i.e., lower area under curve value 0.26 and 0.37, respectively compared with AUC value 0.5) to induce virus transmission and discrimination between infected people and healthy ones. Contrarily, PM model is highly sensitive (AUC value is 0.912, greater than AUC value 0.5) towards COVID-19 transmission and discrimination between infected people and healthy ones and approximate population of 2.25 million must impose like social distancing, personal hygiene, etc. as strategic management policy. Therefore, it is predicted, India could be the next epicenter of COVID-19 outbreak because of its over population.

Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study's findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.

Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19

The COVID-19 pandemic affected the whole world, but not all countries were impacted equally. This opens the question of what factors can explain the initial faster spread in some countries compared to others. Many such factors are overshadowed by the effect of the countermeasures, so we studied the early phases of the infection when countermeasures had not yet taken place. We collected the most diverse dataset of potentially relevant factors and infection metrics to date for this task. Using it, we show the importance of different factors and factor categories as determined by both statistical methods and machine learning (ML) feature selection (FS) approaches. Factors related to culture (e.g., individualism, openness), development, and travel proved the most important. A more thorough factor analysis was then made using a novel rule discovery algorithm. We also show how interconnected these factors are and caution against relying on ML analysis in isolation. Importantly, we explore potential pitfalls found in the methodology of similar work and demonstrate their impact on COVID-19 data analysis. Our best models using the decision tree classifier can predict the infection class with roughly 80% accuracy.

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.

The association between opioids, environmental, demographic, and socioeconomic indicators and COVID-19 mortality rates in the United States: an ecological study at the county level

Background

The spread of the COVID-19 pandemic throughout the world presents an unprecedented challenge to public health inequities. People who use opioids may be a vulnerable group disproportionately impacted by the current pandemic, however, the limited prior research in this area makes it unclear whether COVID-19 and opioid use outcomes may be related, and whether other environmental and socioeconomic factors might play a role in explaining COVID-19 mortality. The objective of this study is to evaluate the association between opioid-related mortality and COVID-19 mortality across U.S. counties.

Methods

Data from 3142 counties across the U.S. were used to model the cumulative count of deaths due to COVID-19 up to June 2, 2020. A multivariable negative-binomial regression model was employed to evaluate the adjusted COVID-19 mortality rate ratios (aMRR).

Results

After controlling for covariates, counties with higher rates of opioid-related mortality per 100,000 persons were found to be significantly associated with higher rates of COVID-19 mortality (aMRR: 1.0134; 95% CI [1.0054, 1.0214]; P = 0.001). Counties with higher average daily Particulate Matter (PM2.5) exposure also saw significantly higher rates of COVID-19 mortality. Analyses revealed rural counties, counties with higher percentages of non-Hispanic whites, and counties with increased average maximum temperatures are significantly associated with lower mortality rates from COVID-19.

Conclusions

This study indicates need for public health efforts in hard hit COVID-19 regions to also focus prevention efforts on overdose risk among people who use opioids. Future studies using individual-level data are needed to allow for detailed inferences.

Impact of outdoor and indoor meteorological conditions on the COVID-19 transmission in the western region of Saudi Arabia

Meteorological conditions may influence the incidence of many infectious diseases. Coronavirus disease-2019 (COVID-19) is a highly contagious, air-borne, emerging, viral disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In 2020, the COVID-19 global pandemic affected more than 210 countries and territories worldwide including Saudi Arabia. There are contradictory research papers about the correlation between meteorological parameters and incidence of COVID-19 in some countries worldwide. The current study investigates the impact of outdoor and indoor meteorological conditions on the daily recorded COVID-19 cases in western region (Makkah and Madinah cities) of Saudi Arabia over a period of 8 months from March to October 2020. Reports of the daily confirmed COVID-19 cases from the webpage of Saudi Ministry of Health (MOH) were used. Considering, the incubation period of COVID-19 which ranged from 2 to 14 days, the relationships between daily COVID-19 cases and outdoor meteorological factors (temperature, relative humidity, and wind speed) using a lag time of 10 days are investigated. The results showed that the highest daily COVID-19 cases in Makkah and Madinah were reported during the hottest months of the year (April-July 2020) when outdoor temperature ranged from 26.51 to 40.71 °C in Makkah and of 23.89-41.20 °C in Madinah, respectively. Partial negative correlation was detected between outdoor relative humidity and daily recorded COVID-19 cases. No obvious correlation could be demonstrated between wind speed and daily COVID-19 cases. This indicated that most of SARS-CoV-2 infection occurred in the cool, air-conditioned, dry, and bad-ventilated indoor environment in the investigated cities. These results will help the epidemiologists to understand the correlation between both outdoor and indoor meteorological conditions and SARS-CoV-2 transmissibility. These findings would be also a useful supplement to assist the local healthcare policymakers to implement and apply a specific preventive measures and education programs for controlling of COVID-19 transmission.