Optimal temperature zone for the dispersal of COVID-19

Immunomics in one health: understanding the human, animal, and environmental aspects of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic underscores the critical need to integrate immunomics within the One Health framework to effectively address zoonotic diseases across humans, animals, and environments. Employing advanced high-throughput technologies, this interdisciplinary approach reveals the complex immunological interactions among these systems, enhancing our understanding of immune responses and yielding vital insights into the mechanisms that influence viral spread and host susceptibility. Significant advancements in immunomics have accelerated vaccine development, improved viral mutation tracking, and broadened our comprehension of immune pathways in zoonotic transmissions. This review highlights the role of animals, not merely as carriers or reservoirs, but as essential elements of ecological networks that profoundly influence viral epidemiology. Furthermore, we explore how environmental factors shape immune response patterns across species, influencing viral persistence and spillover risks. Moreover, case studies demonstrating the integration of immunogenomic data within the One Health framework for COVID-19 are discussed, outlining its implications for future research. However, linking humans, animals, and the environment through immunogenomics remains challenging, including the complex management of vast amounts of data and issues of scalability. Despite challenges, integrating immunomics data within the One Health framework significantly enhances our strategies and responses to zoonotic diseases and pandemic threats, marking a crucial direction for future public health breakthroughs.

Temperature-dependent Spike-ACE2 interaction of Omicron subvariants is associated with viral transmission

The continued evolution of severe acute respiratory syndrome 2 (SARS-CoV-2) requires persistent monitoring of its subvariants. Omicron subvariants are responsible for the vast majority of SARS-CoV-2 infections worldwide, with XBB and BA.2.86 sublineages representing more than 90% of circulating strains as of January 2024. To better understand parameters involved in viral transmission, we characterized the functional properties of Spike glycoproteins from BA.2.75, CH.1.1, DV.7.1, BA.4/5, BQ.1.1, XBB, XBB.1, XBB.1.16, XBB.1.5, FD.1.1, EG.5.1, HK.3, BA.2.86 and JN.1. We tested their capacity to evade plasma-mediated recognition and neutralization, binding to angiotensin-converting enzyme 2 (ACE2), their susceptibility to cold inactivation, Spike processing, as well as the impact of temperature on Spike-ACE2 interaction. We found that compared to the early wild-type (D614G) strain, most Omicron subvariants' Spike glycoproteins evolved to escape recognition and neutralization by plasma from individuals who received a fifth dose of bivalent (BA.1 or BA.4/5) mRNA vaccine and improve ACE2 binding, particularly at low temperatures. Moreover, BA.2.86 had the best affinity for ACE2 at all temperatures tested. We found that Omicron subvariants' Spike processing is associated with their susceptibility to cold inactivation. Intriguingly, we found that Spike-ACE2 binding at low temperature was significantly associated with growth rates of Omicron subvariants in humans. Overall, we report that Spikes from newly emerged Omicron subvariants are relatively more stable and resistant to plasma-mediated neutralization, present improved affinity for ACE2 which is associated, particularly at low temperatures, with their growth rates.IMPORTANCEThe persistent evolution of SARS-CoV-2 gave rise to a wide range of variants harboring new mutations in their Spike glycoproteins. Several factors have been associated with viral transmission and fitness such as plasma-neutralization escape and ACE2 interaction. To better understand whether additional factors could be of importance in SARS-CoV-2 variants' transmission, we characterize the functional properties of Spike glycoproteins from several Omicron subvariants. We found that the Spike glycoprotein of Omicron subvariants presents an improved escape from plasma-mediated recognition and neutralization, Spike processing, and ACE2 binding which was further improved at low temperature. Intriguingly, Spike-ACE2 interaction at low temperature is strongly associated with viral growth rate, as such, low temperatures could represent another parameter affecting viral transmission.

Natural and socio-environmental factors in the transmission of COVID-19: a comprehensive analysis of epidemiology and mechanisms

PURPOSE OF REVIEW

There are significant differences in the transmission rate and mortality rate of COVID-19 under environmental conditions such as seasons and climates. However, the impact of environmental factors on the role of the COVID-19 pandemic and the transmission mechanism of the SARS-CoV-2 is unclear. Therefore, a comprehensive understanding of the impact of environmental factors on COVID-19 can provide innovative insights for global epidemic prevention and control policies and COVID-19 related research. This review summarizes the evidence of the impact of different natural and social environmental factors on the transmission of COVID-19 through a comprehensive analysis of epidemiology and mechanism research. This will provide innovative inspiration for global epidemic prevention and control policies and provide reference for similar infectious diseases that may emerge in the future.

RECENT FINDINGS

Evidence reveals mechanisms by which natural environmental factors influence the transmission of COVID-19, including (i) virus survival and transport, (ii) immune system damage, (iii) inflammation, oxidative stress, and cell death, and (iiii) increasing risk of complications. All of these measures appear to be effective in controlling the spread or mortality of COVID-19: (1) reducing air pollution levels, (2) rational use of ozone disinfection and medical ozone therapy, (3) rational exposure to sunlight, (4) scientific ventilation and maintenance of indoor temperature and humidity, (5) control of population density, and (6) control of population movement. Our review indicates that with the continuous mutation of SARS-CoV-2, high temperature, high humidity, low air pollution levels, and low population density more likely to slow down the spread of the virus.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Development of the second version of Global Prediction System for Epidemiological Pandemic

Coronavirus disease 2019 (COVID-19) is a severe global public health emergency that has caused a major crisis in the safety of human life, health, global economy, and social order. Moreover, COVID-19 poses significant challenges to healthcare systems worldwide. The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics. However, because of the complexity of epidemics, predicting infectious diseases on a global scale faces significant challenges. In this study, we developed the second version of Global Prediction System for Epidemiological Pandemic (GPEP-2), which combines statistical methods with a modified epidemiological model. The GPEP-2 introduces various parameterization schemes for both impacts of natural factors (seasonal variations in weather and environmental impacts) and human social behaviors (government control and isolation, personnel gathered, indoor propagation, virus mutation, and vaccination). The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%. It also provided prediction and decision-making bases for several regional-scale COVID-19 pandemic outbreaks in China, with an average accuracy rate of 89.3%. Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread. The predicted results could serve as a reference for public health planning and policymaking.

Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors

In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.

Impact of human mobility on the epidemic spread during holidays

COVID-19 has posed formidable challenges as a significant global health crisis. Its complexity stems from factors like viral contagiousness, population density, social behaviors, governmental regulations, and environmental conditions, with interpersonal interactions and large-scale activities being particularly pivotal. To unravel these complexities, we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season, incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme. In addition, evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases. The findings suggested that intra-city mobility led to an average surge of 57.35% in confirmed cases of China, while inter-city mobility contributed to an average increase of 15.18%. In the simulation for Tianjin, China, a one-week delay in human mobility attenuated the peak number of cases by 34.47% and postponed the peak time by 6 days. The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak, with a notable disparity in peak cases when mobility was considered. This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread, the diffusion effect of intra-regional mobility was primarily responsible for the outbreak. We have a better understanding on how human mobility and infectious disease epidemics interact, and provide empirical evidence that could contribute to disease prevention and control measures.

Effect of temperature on fast transmission of COVID-19 in low per capita GDP Asian countries

An abrupt outbreak of COVID-19 caused enormous global concerns. Although all countries around the world are severely affected, developing Asian countries faced more difficulties due to their low per capita GDP. The temperature was considered a leading variable in spreading viral diseases, including COVID-19. The present study aimed to assess the relationship between temperature and the spread of COVID-19, with a focus on developing Asian countries. In a few Asian countries, COVID-19 spread rapidly in the summer, while in some countries, there is an increase in winter. A linear correlation was developed between COVID-19 cases/deaths and temperature for the selected countries, which were very weak. A coefficient of determination of 0.334 and 0.365 was observed between cases and average monthly max/min temperatures. A correlation of R2 = 0.307 and 0.382 was found between deaths and average max/min monthly temperatures, respectively. There is no scientific reason to assume that COVID-19 is more dominant at low than high temperatures. Therefore, it is believed that the results may be helpful for the health department and decision-makers to understand the fast spread of COVID-19.

Heat waves accelerate the spread of infectious diseases

COVID-19 pandemic appeared summer surge in 2022 worldwide and this contradicts its seasonal fluctuations. Even as high temperature and intense ultraviolet radiation can inhibit viral activity, the number of new cases worldwide has increased to >78% in only 1 month since the summer of 2022 under unchanged virus mutation influence and control policies. Using the attribution analysis based on the theoretical infectious diseases model simulation, we found the mechanism of the severe COVID-19 outbreak in the summer of 2022 and identified the amplification effect of heat wave events on its magnitude. The results suggest that approximately 69.3% of COVID-19 cases this summer could have been avoided if there is no heat waves. The collision between the pandemic and the heatwave is not an accident. Climate change is leading to more frequent extreme climate events and an increasing number of infectious diseases, posing an urgent threat to human health and life. Therefore, public health authorities must quickly develop coordinated management plans to deal with the simultaneous occurrence of extreme climate events and infectious diseases.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Environment and COVID-19 incidence: A critical review

The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented worldwide health crisis. Many previous research studies have found and investigated its links with one or some natural or human environmental factors. However, a review on the relationship between COVID-19 incidence and both the natural and human environment is still lacking. This review summarizes the inter-correlation between COVID-19 incidence and environmental factors. Based on keyword searching, we reviewed 100 relevant peer-reviewed articles and other research literature published since January 2020. This review is focused on three main findings. One, we found that individual environmental factors have impacts on COVID-19 incidence, but with spatial heterogeneity and uncertainty. Two, environmental factors exert interactive effects on COVID-19 incidence. In particular, the interactions of natural factors can affect COVID-19 transmission in micro- and macro- ways by impacting SARS-CoV-2 survival, as well as human mobility and behaviors. Three, the impact of COVID-19 incidence on the environment lies in the fact that COVID-19-induced lockdowns caused air quality improvement, wildlife shifts and socio-economic depression. The additional value of this review is that we recommend future research perspectives and adaptation strategies regarding the interactions of the environment and COVID-19. Future research should be extended to cover both the effects of the environment on the COVID-19 pandemic and COVID-19-induced impacts on the environment. Future adaptation strategies should focus on sustainable environmental and public policy responses.

The dynamics of early-stage transmission of COVID-19: A novel quantification of the role of global temperature

The global outbreak of COVID-19 has emerged as one of the most devastating and challenging threats to humanity. As many frontline workers are fighting against this disease, researchers are struggling to obtain a better understanding of the pathways and challenges of this pandemic. This paper evaluates the concept that the transmission of COVID-19 is intrinsically linked to temperature. Some complex nonlinear functional forms, such as the cubic function, are introduced to the empirical models to understand the interaction between temperature and the "growth" in the number of infected cases. An accurate quantitative interaction between temperature and the confirmed COVID-19 cases is obtained as log(Y) = -0.000146(temp_H)3 + 0.007410(temp_H)2 -0.063332 temp_H + 7.793842, where Y is the periodic growth in confirmed COVID-19 cases, and temp_H is the maximum daily temperature. This equation alone may be the first confirmed way to measure the quantitative interaction between temperature and human transmission of COVID-19. In addition, four important regions are identified in terms of maximum daily temperature (in Celsius) to understand the dynamics in the transmission of COVID-19 related to temperature. First, the transmission decreases within the range of -50 °C to 5.02 °C. Second, the transmission accelerates in the range of 5.02 °C to 16.92 °C. Essentially, this is the temperature range for an outbreak. Third, the transmission increases more slowly in the range of 16.92 °C to 28.82 °C. Within this range, the number of infections continues to grow, but at a slower pace. Finally, the transmission decreases in the range of 28.82 °C to 50 °C. Thus, according to this hypothesis, the threshold of 16.92 °C is the most critical, as the point at which the infection rate is the greatest. This result sheds light on the mechanism in the cyclicity of the ongoing COVID-19 pandemic worldwide. The implications of these results on policy issues are also discussed concerning a possible cyclical fluctuation pattern between the Northern and Southern Hemispheres.

What are the Effects of Climate Variables on COVID-19 Pandemic? A Systematic Review and Current Update

The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using ("Climate" OR "Climate Change" OR "Global Warming" OR "Global Climate Change" OR "Meteorological Parameters" OR "Temperature" OR "Precipitation" OR "Relative Humidity" OR "Wind Speed" OR "Sunshine" OR "Climate Extremes" OR "Weather Extremes") AND ("COVID" OR "Coronavirus disease 2019" OR "COVID-19" OR "SARS-CoV-2" OR "Novel Coronavirus") keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.

A Pre-Generation of Emergency Reference Plan Model of Public Health Emergencies with Case-Based Reasoning

Background and Purpose

In the early 21st century, the coronavirus alone has ravaged the world three times. Public health emergencies have caused a tremendous negative impact on public health, daily life, and global economic development, for having the characteristics of complexity and great harm. To tackle these problems, a pre-generation of emergency reference plan model of public health emergencies is proposed to better deal with the outbreak and spread of public health events.

Methods

The method is divided into three stages. First, the modified SEIR model is used to predict the attribute values of the target case. Then, the similar case sets are extracted and filtered by calculating the similarity through the cross-efficiency evaluation method with the parallel system. Finally, the multi-stage emergency effect evaluation model is conducted so that the emergency plan with the best response effect at this stage can be made for reference.

Results

We collected 25 typical events of COVID-19 that occurred in 11 cities in China as historical case bases and target cases, respectively. The result of the experiment verified the feasibility and effectiveness of the proposed method.

Conclusion

This paper presents a new perspective on making a public health emergency plan, which could improve the decision-making accuracy and efficiency, maximize the emergency effect and save precious time for emergency response. This model can provide rapid decision supports for decision-making for public services such as government departments, centers for disease control, medical emergency centers and transport authorities, etc.

The Effect of Face Mask, Air Temperature, and Humidity on COVID-19 Transmission: A Systematic Review and Meta-analysis

Context: At the beginning of the COVID-19 pandemic, the effects of personal protective equipment (PPE) such as face masks, as well as environmental conditions, including temperature and humidity changes, were discussed due to the lack of effective medicine. Methods: The preferred reporting items for systematic reviews and meta-analysis (PRISMA) were implemented to conduct the present systematic review. The articles were selected from papers published by May 2020 in PubMed, Web of Science, Science Direct, Scopus, and Google Scholar databases. This meta-analysis estimated relative risk (RR) and pooled mean depicted as effect size (ES) using the random or fixed effects methods. Results: Ten studies met inclusion criteria, four of which addressed the effect of face masks and six of which dealt with temperature and humidity changes. This eta-analysis study showed that wearing face masks against the COVID-19 virus had a remarkable safety impact with RR (%95 CI) 8.56 (2.10 - 34.90), (I2 = %0.0 P = 0.999), and the pooled mean changes in temperature and humidity were estimated to be with ES (%95 CI) 9.03 (4.32 - 13.74), (I2 = %99.7, P = 0.0001) and with ES (%95 CI) 56.82 (46.12 - 67.51), ( I2 = %99.3, P = 0.0001) during the outbreak of the COVID-19. Conclusions: The findings of this systematic review and meta-analysis illustrate the effectiveness of face masks, in general, in preventing the transmission of the COVID-19 virus. According to the findings, temperature and humidity changes do not increase the outbreak of the COVID-19 virus.

Effects of climatic factors on COVID-19 transmission in Ethiopia

Climatic conditions play a key role in the transmission and pathophysiology of respiratory tract infections, either directly or indirectly. However, their impact on the COVID-19 pandemic propagation is yet to be studied. This study aimed to evaluate the effects of climatic factors such as temperature, rainfall, relative humidity, sunshine duration, and wind speed on the number of daily COVID-19 cases in Addis Ababa, Ethiopia. Data on confirmed COVID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health Institute for the period 10th March 2020 to 31st October 2021. Data for climatic factors were obtained from the Ethiopia National Meteorology Agency. The correlation between daily confirmed COVID-19 cases and climatic factors was measured using the Spearman rank correlation test. The log-link negative binomial regression model was used to fit the effect of climatic factors on COVID-19 transmission, from lag 0 to lag 14 days. During the study period, a total of 245,101 COVID-19 cases were recorded in Addis Ababa, with a median of 337 new cases per day and a maximum of 1903 instances per day. A significant correlation between COVID-19 cases and humidity was observed with a 1% increase in relative humidity associated with a 1.1% [IRRs (95%CI) 0.989, 95% (0.97-0.99)] and 1.2% [IRRs (95%CI) 0.988, (0.97-0.99)] decrease in COVID-19 cases for 4 and 5 lag days prior to detection, respectively. The highest increase in the effect of wind speed and rainfall on COVID-19 was observed at 14 lag days prior to detection with IRRs of 1.85 (95%CI 1.26-2.74) and 1.078 (95%CI 1.04-1.12), respectively. The lowest IRR was 1.109 (95%CI 0.93-1.31) and 1.007 (95%CI 0.99-1.02) both in lag 0, respectively. The findings revealed that none of the climatic variables influenced the number of COVID-19 cases on the day of case detection (lag 0), and that daily average temperature and sunshine duration were not significantly linked with COVID-19 risk across the full lag period (p > 0.05). Climatic factors such as humidity, rainfall, and wind speed influence the transmission of COVID-19 in Addis Ababa, Ethiopia. COVID-19 cases have shown seasonal variations with the highest number of cases reported during the rainy season and the lowest number of cases reported during the dry season. These findings suggest the need to design strategies for the prevention and control of COVID-19 before the rainy seasons.

Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly

Air is a diverse mixture of gaseous and suspended solid particles. Several new substances are being added to the air daily, polluting it and causing human health effects. Particulate matter (PM) is the primary health concern among these air toxins. The World Health Organization (WHO) addressed the fact that particulate pollution affects human health more severely than other air pollutants. The spread of air pollution and viruses, two of our millennium's most serious concerns, have been linked closely. Coronavirus disease 2019 (COVID-19) can spread through the air, and PM could act as a host to spread the virus beyond those in close contact. Studies on COVID-19 cover diverse environmental segments and become complicated with time. As PM pollution is related to everyday life, an essential awareness regarding PM-impacted COVID-19 among the masses is required, which can help researchers understand the various features of ambient particulate pollution, particularly in the era of COVID-19. Given this, the present work provides an overview of the recent developments in COVID-19 research linked to ambient particulate studies. This review summarizes the effect of the lockdown on the characteristics of ambient particulate matter pollution, the transmission mechanism of COVID-19, and the combined health repercussions of PM pollution. In addition to a comprehensive evaluation of the implementation of the lockdown, its rationales-based on topographic and socioeconomic dynamics-are also discussed in detail. The current review is expected to encourage and motivate academics to concentrate on improving air quality management and COVID-19 control.

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (222Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020-15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021-1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO2, SO2, CO and 222Rn, and significant negative correlations, especially for spring-summer periods between ground O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020-1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.

A meta-analysis result: Uneven influences of season, geo-spatial scale and latitude on relationship between meteorological factors and the COVID-19 transmission

Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Correlation between COVID-19 and weather variables: A meta-analysis

Background

COVID-19 has significantly impacted humans worldwide in recent times. Weather variables have a remarkable effect on COVID-19 spread all over the universe.

Objectives

The aim of this study was to find the correlation between weather variables with COVID-19 cases and COVID-19 deaths.

Methods

Five electronic databases such as PubMed, Science Direct, Scopus, Ovid (Medline), and Ovid (Embase) were searched to conduct the literature survey from January 01, 2020, to February 03, 2022. Both fixed-effects and random-effects models were used to calculate pooled correlation and 95% confidence interval (CI) for both effect measures. Included studies heterogeneity was measured by Cochrane chi-square test statistic Q,I 2 andτ 2 . Funnel plot was used to measure publication bias. A Sensitivity analysis was also carried out.

Results

Total 38 studies were analyzed in this study. The result of this analysis showed a significantly negative impact on COVID-19 fixed effect incidence and weather variables such as temperature (r = -0.113∗∗∗), relative humidity (r = -0.019∗∗∗), precipitation (r = -0.143∗∗∗), air pressure (r = -0.073∗), and sunlight (r = -0.277∗∗∗) and also found positive impact on wind speed (r = 0.076∗∗∗) and dew point (r = 0.115∗∗∗). From this analysis, significant negative impact was also found for COVID-19 fixed effect death and weather variables such as temperature (r = -0.094∗∗∗), wind speed (r = -0.048∗∗), rainfall (r = -0.158∗∗∗), sunlight (r = -0.271∗∗∗) and positive impact for relative humidity (r = 0.059∗∗∗).

Conclusion

This meta-analysis disclosed significant correlations between weather and COVID-19 cases and deaths. The findings of this analysis would help policymakers and the health professionals to reduce the cases and fatality rate depending on weather forecast techniques and fight this pandemic using restricted assets.

A Linear Regression Prediction Model of Infectious Disease Spread Based on Baidu Migration and Effective Distance

Objective

To analyze the relationship between effective distance and epidemic spread trajectory and between arrival time and scale based on the COVID-19 data outbreak in Wuhan and thus to improve the prediction ability of the spread of infectious disease.

Methods

Up to January 28, 2020, the reporting date, the onset date, and the cumulative number of confirmed cases of COVID-19 in each province and city were collected. Baidu migration data was used to calculate the effective distance from Wuhan city to other regions. The reporting date and onset date of the first diagnosed patient were taken as the arrival time, respectively, to establish a linear regression model of effective distance and arrival time. In different provinces and cities, the logarithm of the cumulative number of confirmed cases with a base of 5 was taken as the criteria to determine the level of the cumulative confirmed cases. Based on this, the linear regression model of effective distance and the level of cumulative confirmed cases in the provincial and municipal units was established.

Results

The linear correlation between the reporting date of the first confirmed patient and the effective distance was not strong. The coefficients of determination (R²) for cities with and without the cities of Hubei Province were 0.36 and 0.44, respectively. And the linear correlation between the onset date of the first confirmed patient and the effective distance was strong. And the coefficients of determination (R²) for cities with and without the cities of Hubei Province were 0.67 and 0.83, respectively. And the linear correlation between the level of cumulative confirmed cases in the provincial and municipal units and the effective distance was strong, with an R² of 0.87 and 0.84, respectively. The regression coefficients of each linear model were statistically significant (P < 0.001).

Conclusion

The effective distance has a good fit with the model of the onset date of the first confirmed patient and the level of cumulative confirmed cases, which can predict the trajectory, time, and transmission range of the epidemic. It can be taken as the reference for the early warning, prevention, and control of sudden acute infectious diseases from a macro perspective.

Spatial correlates of COVID-19 first wave across continental Portugal

The first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the 'percentage of employment in services'; 'average time of commuting using individual transportation'; 'percentage of employment in the agricultural sector'; and 'average family size'. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies.

Artemisia Extracts and Artemisinin-Based Antimalarials for COVID-19 Management: Could These Be Effective Antivirals for COVID-19 Treatment?

As the world desperately searches for ways to treat the coronavirus disease 2019 (COVID-19) pandemic, a growing number of people are turning to herbal remedies. The Artemisia species, such as A. annua and A. afra, in particular, exhibit positive effects against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and COVID-19 related symptoms. A. annua is a source of artemisinin, which is active against malaria, and also exhibits potential for other diseases. This has increased interest in artemisinin's potential for drug repurposing. Artemisinin-based combination therapies, so-called ACTs, have already been recognized as first-line treatments against malaria. Artemisia extract, as well as ACTs, have demonstrated inhibition of SARS-CoV-2. Artemisinin and its derivatives have also shown anti-inflammatory effects, including inhibition of interleukin-6 (IL-6) that plays a key role in the development of severe COVID-19. There is now sufficient evidence in the literature to suggest the effectiveness of Artemisia, its constituents and/or artemisinin derivatives, to fight against the SARS-CoV-2 infection by inhibiting its invasion, and replication, as well as reducing oxidative stress and inflammation, and mitigating lung damage.

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review

COVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the 'One-Health' concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements.

SARS-CoV-2 and other airborne respiratory viruses in outdoor aerosols in three Swiss cities before and during the first wave of the COVID-19 pandemic

Caused by the SARS-CoV-2 virus, Coronavirus disease 2019 (COVID-19) has been affecting the world since the end of 2019. While virus-laden particles have been commonly detected and studied in the aerosol samples from indoor healthcare settings, studies are scarce on air surveillance of the virus in outdoor non-healthcare environments, including the correlations between SARS-CoV-2 and other respiratory viruses, between viruses and environmental factors, and between viruses and human behavior changes due to the public health measures against COVID-19. Therefore, in this study, we collected airborne particulate matter (PM) samples from November 2019 to April 2020 in Bern, Lugano, and Zurich. Among 14 detected viruses, influenza A, HCoV-NL63, HCoV-HKU1, and HCoV-229E were abundant in air. SARS-CoV-2 and enterovirus were moderately common, while the remaining viruses occurred only in low concentrations. SARS-CoV-2 was detected in PM10 (PM below 10 µm) samples of Bern and Zurich, and PM2.5 (PM below 2.5 µm) samples of Bern which exhibited a concentration positively correlated with the local COVID-19 case number. The concentration was also correlated with the concentration of enterovirus which raised the concern of coinfection. The estimated COVID-19 infection risks of an hour exposure at these two sites were generally low but still cannot be neglected. Our study demonstrated the potential functionality of outdoor air surveillance of airborne respiratory viruses, especially at transportation hubs and traffic arteries.

Several major issues concerning the environmental transmission and risk prevention of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is the most serious infectious disease pandemic in the world in a century, and has had a serious impact on the health, safety, and social and economic development of all mankind. Since the earth entered the “Anthropocene”, human activities have become the most important driving force of the evolution of the earth system. At the same time, the epidemic frequency of major human infectious diseases worldwide has been increasing, with more than 70% of novel diseases having zoonotic origins. The review of several major epidemics in human history shows that there is a common rule, i.e., changes in the natural environment have an important and profound impact on the occurrence and development of epidemics. Therefore, the impact of the natural environment on the current COVID-19 pandemic and its mechanisms have become scientific issues that need to be resolved urgently. From the perspective of the natural environment, this study systematically investigated several major issues concerning the environmental transmission and risk prevention of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). From a macroscopic temporal and spatial scale, the research focus on understand the impact of the destruction of the natural environment and global changes on the outbreak of infectious diseases; the threat of zoonotic diseases to human health; the regularity for virus diffusion, migration and mutation in environmental media; the mechanisms of virus transmission from animals and environmental media to humans; and environmental safety, secondary risk prevention and control of major epidemics. Suggestions were made for future key research directions and issues that need attention, with a view to providing a reference for the prevention and control of the global coronavirus disease 2019, and to improving the ability of response to major public health emergencies.

How Seasonality and Control Measures Jointly Determine the Multistage Waves of the COVID-19 Epidemic: A Modelling Study and Implications

The current novel Coronavirus Disease 2019 (COVID-19) is a multistage epidemic consisting of multiple rounds of alternating outbreak and containment periods that cannot be modeled with a conventional single-stage Suspected-Exposed-Infectious-Removed (SEIR) model. Seasonality and control measures could be the two most important driving factors of the multistage epidemic. Our goal is to formulate and incorporate the influences of seasonality and control measures into an epidemic model and interpret how these two factors interact to shape the multistage epidemic curves. New confirmed cases will be collected daily from seven Northern Hemisphere countries and five Southern Hemisphere countries from March 2020 to March 2021 to fit and validate the modified model. Results show that COVID-19 is a seasonal epidemic and that epidemic curves can be clearly distinguished in the two hemispheres. Different levels of control measures between different countries during different seasonal periods have different influences on epidemic transmission. Seasonality alone cannot cause the baseline reproduction number R0 to fall below one and control measures must be taken. A superposition of a high level of seasonality and a low level of control measures can lead to a dramatically rapid increase in reported cases.

Meteorological factors' effects on COVID-19 show seasonality and spatiality in Brazil

The meteorological conditions may affect COVID-19 transmission. However, the roles of seasonality and macro-climate are still contentious due to the limited time series for early-stage studies. We studied meteorological factors' effects on COVID-19 transmission in Brazil from February 25 to November 15, 2020. We aimed to explore whether this impact showed seasonal characteristics and spatial variations related to the macro-climate. We applied two-way fixed-effect models to identify the effects of meteorological factors on COVID-19 transmission and used spatial analysis to explore their spatial-temporal characteristics with a relatively long-time span. The results showed that cold, dry and windless conditions aggravated COVID-19 transmission. The daily average temperature, humidity, and wind speed negatively affected the daily new cases. Humidity and temperature played a dominant role in this process. For the time series, the influences of meteorological conditions on COVID-19 had a periodic fluctuation of 3-4 months (in line with the seasons in Brazil). The turning points of this fluctuation occurred at the turn of seasons. Spatially, the negative effects of temperature and humidity on COVID-19 transmission clustered in the northeastern and central parts of Brazil. This is consistent with the range of arid climate types. Overall, the seasonality and similar climate types should be considered to estimate the spatial-temporal COVID-19 patterns. Winter is a critical time to be alert for COVID-19, especially in the northern part of Brazil.

The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

The global transmission of new coronavirus variants

Coronavirus Disease 2019 (COVID-19) has caused tremendous losses to the world. This study addresses the impact and diffusion of the five major new coronavirus variants namely Alpha, Beta, Gamma, Eta, and Delta lineage. The results of this study indicate that Africa and Europe will be affected by new coronavirus variants the most compared with other continents. The comparative analysis indicates that vaccination can contain the spread of the virus in most of the continent, and non-pharmaceutical interventions (NPIs), such as restriction on gatherings and close public transport, will effectively curb the pandemic, especially in densely populated continents. According to our Global Prediction System of COVID-19 Pandemic, the diffusion of delta lineage in the US shows seasonal oscillation characteristics, and the first wave will occur in October 2021, with the record of 323,360, and followed by a small resurgence in April 2022, with the record of 184,196, while the second wave will reach to 232,622 cases in October 2022. Our study will raise the awareness of new coronavirus variants among the public, and will help the governments make appropriate directives to cope with the new coronavirus variants.

Study of climatology parameters on COVID-19 outbreak in Jordan

To control the spread of COVID-19 disease and reduce its mortality, an early and precise diagnose of this disease is of significant importance. Emerging research data show that the current COVID-19 pandemic may be affected by environmental conditions. Therefore, the impact of weather parameters on COVID-19 distribution should be explored to predict its development in the next few months. This research aims to study the association between the daily confirmed COVID-19 cases in the three major cities of Jordan; Amman, Zarqa, and Irbid and climate indicators to include the average daily temperature (°C), wind speed (m/s), relative humidity (%), pressure (kPa), and the concentration of four pollutants (CO, NO2, PM10, and SO2). The data were obtained from the World Air Quality Project website and the Jordanian Ministry of Environment. A total of 305 samples for each city was used to conduct the data analysis using multiple linear regression and a feedforward artificial neural network. It was concluded that the multiple linear regression and feedforward artificial neural network could forecast the COVID-19 confirmed cases in the case studies; Amman, Irbid, and Zarqa. Finally, global sensitivity analysis using Sobol analysis indicated that pressure in Amman and Zarqa and the concentration of NO2 in Irbid has a high rate of positive cases that supports the virus's spread.

Phylogenetic and genome-wide mutational analysis of SARS-CoV-2 strains circulating in Nigeria: no implications for attenuated COVID-19 outcomes

OBJECTIVES

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). The COVID-19 incidence and mortality rates are low in Nigeria compared to global trends. This research mapped the evolution of SARS-CoV-2 circulating in Nigeria and globally to determine whether the Nigerian isolates are genetically distinct from strains circulating in regions of the world with a high disease burden.

METHODS

Bayesian phylogenetics using BEAST 2.0, genetic similarity analyses, and genomewide mutational analyses were used to characterize the strains of SARS-CoV-2 isolated in Nigeria.

RESULTS

SARS-CoV-2 strains isolated in Nigeria showed multiple lineages and possible introductions from Europe and Asia. Phylogenetic clustering and sequence similarity analyses demonstrated that Nigerian isolates were not genetically distinct from strains isolated in other parts of the globe. Mutational analysis demonstrated that the D614G mutation in the spike protein, the P323L mutation in open reading frame 1b (and more specifically in NSP12), and the R203K/ G204R mutation pair in the nucleocapsid protein were most prevalent in the Nigerian isolates.

CONCLUSION

The SARS-CoV-2 strains in Nigeria were neither phylogenetically nor genetically distinct from virus strains circulating in other countries of the world. Thus, differences in SARS-CoV-2 genomes are not a plausible explanation for the attenuated COVID-19 outcomes in Nigeria.

Government Intervention, Human Mobility, and COVID-19: A Causal Pathway Analysis from 121 Countries

Based on data from 121 countries, the study assesses the dynamic effect and causality path of the government epidemic prevention policies and human mobility behaviors on the growth rates of COVID-19 new cases and deaths. Our results find that both policies and behaviors influenced COVID-19 cases and deaths. The direct effect of policies on COVID-19 was more than the indirect effect. Policies influence behaviors, and behaviors react spontaneously to information. Further, masks give people a false sense of security and increase mobility. The close public transport policy increased COVID-19 new cases. We also conducted sensitivity analysis and found that some policies hold robustly, such as the policies of school closing, restrictions on gatherings, stay-at-home requirements, international travel controls, facial coverings, and vaccination. The counterfactual tests suggest that, as of early March 2021, if governments had mandated masking policies early in the epidemic, the cases and deaths would have been reduced by 18% and 14% separately. If governments had implemented vaccination policies early in the pandemic, the cases and deaths would have been reduced by 93% and 62%, respectively. Without public transportation closures, cases and deaths would have been reduced by 40% and 10%, respectively.

Novel Evidence Showing the Possible Effect of Environmental Variables on COVID-19 Spread

Coronavirus disease (COVID-19) remains a serious issue, and the role played by meteorological indicators in the process of virus spread has been a topic of academic discussion. Previous studies reached different conclusions due to inconsistent methods, disparate meteorological indicators, and specific time periods or regions. This manuscript is based on seven daily meteorological indicators in the NCEP reanalysis data set and COVID-19 data repository of Johns Hopkins University from 22 January 2020 to 1 June 2021. Results showed that worldwide average temperature and precipitable water (PW) had the strongest correlation (ρ > 0.9, p < 0.001) with the confirmed COVID-19 cases per day from 22 January to 31 August 2020. From 22 January to 31 August 2020, positive correlations were observed between the temperature/PW and confirmed COVID-19 cases/deaths in the northern hemisphere, whereas negative correlations were recorded in the southern hemisphere. From 1 September to 31 December 2020, the opposite results were observed. Correlations were weak throughout the near full year, and weak negative correlations were detected worldwide (|ρ| < 0.4, p ≤ 0.05); the lag time had no obvious effect. As the latitude increased, the temperature and PW of the maximum confirmed COVID-19 cases/deaths per day generally showed a decreasing trend; the 2020-year fitting functions of the response latitude pattern were verified by the 2021 data. Meteorological indicators, although not a decisive factor, may influence the virus spread by affecting the virus survival rates and enthusiasm of human activities. The temperature or PW threshold suitable for the spread of COVID-19 may increase as the latitude decreases.

Latitude impact on Pandemic Sars-Cov-2 2020 outbreaks and possible utility of UV indexes in predictions of regional daily infections and deaths

The importance of two related factors ^_ latitude and solar ultraviolet radiation ^_ has been insufficiently recognized as determining the spread of pandemic Sars-CoV-2 outbreaks across the globe. In this study we provide evidence of the impact of latitude and investigate how daily RT-PCR diagnosed infections and deaths are quantitively correlated with the UV component of Solar light. Here, we present regression analyses using daily national numbers from Austria and from Portugal with daily ultraviolet indexes of two selected locations in these territories, obtained from a Satellite source. These countries, have similar surfaces areas and population size but Austria's mean latitude is 9° up-north. The equations derived from regression analyses of those two variables are comparable for both countries, fit best the fall (2^nd) pandemic wave and can be a useful non-R(t) (rate of transmission) dependent predictive tool. Similar equations were derived for deaths that follow infections within a few weeks delay. Strong correlations depend on the size of the region/country from which infections are collected, the robustness of screening practices, ideally kept through weekends and holidays. Besides the forecasting usefulness of such correlations, these findings also suggest that covid-19 transmission co-exists with a Sars-Cov-2 specific UV-induced immunosuppression response. While in 2020, intensity of pandemic spring and fall waves reflect a Solar UV-light modulation, we relate exceptional low temperature and humidity with additional waves, as the winter 2020/2021 3^rd wave, felt in the western European countries. This work may help understanding this Pandemic phenomenon and dealing with similar catastrophes in the future.

Assessment of Meteorological Variables and Air Pollution Affecting COVID-19 Cases in Urban Agglomerations: Evidence from China

The 2019 novel coronavirus disease (COVID-19) has become a severe public health and social problem worldwide. A limitation of the existing literature is that multiple environmental variables have not been frequently elaborated, which is why the overall effect of the environment on COVID-19 has not been conclusive. In this study, we used generalized additive model (GAM) to detect the relationship between meteorological and air pollution variables and COVID-19 in four urban agglomerations in China and made comparisons among the urban agglomerations. The four urban agglomerations are Beijing-Tianjin-Hebei (BTH), middle reaches of the Yangtze River (MYR), Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The daily rates of average precipitation, temperature, relative humidity, sunshine duration, and atmospheric pressure were selected as meteorological variables. The PM_2.5, PM₁₀, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO) contents were selected as air pollution variables. The results indicated that meteorological and air pollution variables tended to be significantly correlated. Moreover, the nature of the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and meteorological and air pollution variables (i.e., linear or nonlinear) varied with urban agglomerations. Among the variance explained by GAMs, BTH had the highest value (75.4%), while MYR had the lowest value (35.2%). The values of the YRD and PRD were between the above two, namely 45.6% and 62.2%, respectively. The findings showed that the association between SARS-CoV-2 and meteorological and air pollution variables varied in regions, making it difficult to obtain a relationship that is applicable to every region. Moreover, this study enriches our understanding of SARS-CoV-2. It is required to create awareness within the government that anti-COVID-19 measures should be adapted to the local meteorological and air pollution conditions.

A new measure of hygiene inequality applied to urban-rural comparison

Access to hygiene services remains one of the most urgent challenges facing countries, especially low-income ones. This has become much more critical in the current context of the COVID-19 pandemic. The WHO/UNICEF Joint Monitoring Program globally monitors access to hygiene service levels. As data are in three parts with a constant sum and a positive value, they are compositional data. Inequality is monitored in disaggregated data; in the urban-rural case, this is done through a simple difference between the urban and rural service levels. However, this simple form of calculation does not take into account the characteristics of the data, which can lead to erroneous interpretations of the results. Therefore, we propose an alternative measure of inequality that uses a ternary diagram and does not infringe on the data properties. The results of the new urban-rural inequality measure show spatial heterogeneity. The highest inequality occurs in Colombia, with a value of 37.1 percentage points, and the lowest in Turkmenistan, with a value of zero. Our results also show that 73 of the 76 countries evaluated have higher basic hygiene services in urban areas than in rural areas. This means that urban households have more availability of a handwashing facility on-premises with soap and water than rural households. Likewise, by subdividing the ternary diagram into ternary parcels, we could group and rank the countries based on hygiene service conditions in a hierarchical order using tripartite information. Finally, our study finds that a multivariate measure of inequality can be important for the public policies of the sector with a general vision, which underscores the value of making evidence-based decisions.

Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.

Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis

We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of an epidemic by detecting signs of changes in the data. We propose a novel methodology to address this issue. The key idea is to employ a new information-theoretic notion, which we call the differential minimum description length change statistics (D-MDL), for measuring the scores of change sign. We first give a fundamental theory for D-MDL. We then demonstrate its effectiveness using synthetic datasets. We apply it to detecting early warning signals of the COVID-19 epidemic using time series of the cases for individual countries. We empirically demonstrate that D-MDL is able to raise early warning signals of events such as significant increase/decrease of cases. Remarkably, for about [Formula: see text] of the events of significant increase of cases in studied countries, our method can detect warning signals as early as nearly six days on average before the events, buying considerably long time for making responses. We further relate the warning signals to the dynamics of the basic reproduction number R0 and the timing of social distancing. The results show that our method is a promising approach to the epidemic analysis from a data science viewpoint.

Impact of temperature on the affinity of SARS-CoV-2 Spike glycoprotein for host ACE2

The seasonal nature of outbreaks of respiratory viral infections with increased transmission during low temperatures has been well established. Accordingly, temperature has been suggested to play a role on the viability and transmissibility of SARS-CoV-2, the virus responsible for the COVID-19 pandemic. The receptor-binding domain (RBD) of the Spike glycoprotein is known to bind to its host receptor angiotensin-converting enzyme 2 (ACE2) to initiate viral fusion. Using biochemical, biophysical, and functional assays to dissect the effect of temperature on the receptor-Spike interaction, we observed a significant and stepwise increase in RBD-ACE2 affinity at low temperatures, resulting in slower dissociation kinetics. This translated into enhanced interaction of the full Spike glycoprotein with the ACE2 receptor and higher viral attachment at low temperatures. Interestingly, the RBD N501Y mutation, present in emerging variants of concern (VOCs) that are fueling the pandemic worldwide (including the B.1.1.7 (α) lineage), bypassed this requirement. This data suggests that the acquisition of N501Y reflects an adaptation to warmer climates, a hypothesis that remains to be tested.

Low temperatures or high isolation delay increases the average COVID-19 infections in India : A Mathematical modeling approach

The dynamics of COVID-19 in India are captured using a set of delay differential equations by dividing a population into five compartments. The Positivity and Boundedness of the system is shown. The Existence and Uniqueness condition for the solution of system of equations is presented. The equilibrium points are calculated and stability analysis is performed. Sensitivity analysis is performed on the parameters of the model. Bifurcation analysis is performed and the critical delay is calculated. By formulating the spread parameter as a function of temperature, the impact of temperature on the population is studied. We concluded that with the decrease in temperature, the average infections in the population increases. In view of the coming winter season in India, there will be an increase in new infections. This model falls in line with the characteristics that increase in isolation delay increases average infections in the population.

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.

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.

"Mass gathering events and COVID-19 transmission in Borriana (Spain): A retrospective cohort study"

OBJECTIVE

Mass gathering events (MGEs) are associated with the transmission of COVID-19. Between 6 and 10 March 2020, several MGEs related to the Falles festival took place in Borriana, a municipality in the province of Castellon (Spain). The aim of this study was to estimate the incidence of COVID-19 and its association with these MGEs, and to quantify the potential risk factors of its occurrence.

METHODS

During May and June 2020, a population-based retrospective cohort study was carried out by the Public Health Center of Castelló and the Hospital de la Plana in Vila-real. Participants were obtained from a representative sample of 1663 people with potential exposure at six MGEs. A questionnaire survey was carried out to obtain information about attendance at MGEs and COVID-19 disease. In addition, a serologic survey of antibodies against SARS-Cov-2 was implemented. Inverse probability weighted regression was used in the statistical analysis.

RESULTS

A total of 1338 subjects participated in the questionnaire survey (80.5%), 997 of whom undertook the serologic survey. Five hundred and seventy cases were observed with an attack rate (AR) of 42.6%; average age was 36 years, 62.3% were female, 536 cases were confirmed by laboratory tests, and 514 cases were found with SARS-CoV-2 total antibodies. Considering MGE exposure, AR was 39.2% (496/1264). A dose-response relationship was found between MGE attendance and the disease, (adjusted relative risk [aRR] = 4.11 95% confidence interval [CI]3.25-5.19). Two MGEs with a dinner and dance in the same building had higher risks. Associated risk factors with the incidence were older age, obesity, and upper and middle class versus lower class; current smoking was protective.

CONCLUSIONS

The study suggests the significance of MGEs in the COVID-19 transmission that could explain the subsequent outbreak in Borriana.

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.

Marginal warming associated with a COVID-19 quarantine and the implications for disease transmission

During January-February 2020, parts of China faced restricted mobility under COVID-19 quarantines, which have been associated with improved air quality. Because particulate pollutants scatter, diffuse, and absorb incoming solar radiation, a net negative radiative forcing, decreased air pollution can yield surface warming. As such, this study (1) documents the evolution of China's January-February 2020 air temperature and concurrent particulate changes; (2) determines the temperature response related to reduced particulates during the COVID-19 quarantine (C19Q); and (3) discusses the conceptual implications for temperature-dependent disease transmission. C19Q particulate evolution is monitored using satellite analyses, and concurrent temperature anomalies are diagnosed using surface stations and Aqua AIRS imagery. Meanwhile, two WRF-Chem simulations are forced by normal emissions and the satellite-based urban aerosol changes, respectively. Urban aerosols decreased from 27.1% of pre-C19Q aerosols to only 17.5% during C19Q. WRF-Chem resolved ~0.2 °C warming across east-central China, that represented a minor, though statistically significant contribution to C19Q temperature anomalies. The largest area of warming is concentrated south of Chengdu and Wuhan where temperatures increased between +0.2-0.3 °C. The results of this study are important for understanding the anthropogenic forcing on regional meteorology. Epidemiologically, the marginal, yet persistent, warming during C19Q may retard temperature-dependent disease transmission, possibly including SARS-CoV-2.

Spread of COVID-19 in Zambia: An assessment of environmental and socioeconomic factors using a classification tree approach

The global pandemic emergent from SARS-COV-2 (COVID-19) has continued to cause both health and socio-economic challenges worldwide. However, there is limited information on the factors affecting the dynamics of COVID-19, especially in developing countries, including African countries. In this study, we have focused on understanding the association of COVID-19 cases with environmental and socioeconomic factors in Zambia - a sub-Saharan African country. We used Zambia's district-level COVID-19 data, covering 18 March 2020 (i.e., from first reported cases) to 17 July 2020. Geospatial approaches were used to organize, extract and establish the dataset, while a classification tree (CT) technique was employed to analyze the factors associated with the COVID-19 cases. The analyses were conducted in two stages: (1) the binary analysis of occurrences of COVID-19 (i.e., COVID-19 or No COVID-19), and (2) a risk level analysis which grouped the number of cases into four risk levels (high, moderate, low and very low). The results showed that the distribution of COVID-19 cases in Zambia was significantly influenced by the socioeconomic factors compared to environmental factors. More specifically, the binary model showed that distance to the airport, population density and distance to the town centres were the most combination influential factors, while the risk level analysis indicated that areas with high rates of human immuno-deficient virus (HIV) infection had relatively high chances of having many COVID-19 cases compared to areas with low HIV rates. The districts that are far from major urban establishments and that experience higher temperatures have lower chances of having COVID-19 cases. This study makes two major contributions towards the understanding of COVID-19 dynamics: (1) the methodology presented here can be effectively applied in other areas to understand the association of environmental and socioeconomic factors with COVID-19 cases, and (2), the findings from this study present the empirical evidence of the relationship between COVID-19 cases and their associated environmental and socioeconomic factors. Further studies are needed to understand the relationship of this disease and the associated factors in different cultural settings, seasons and age groups, especially as the COVID-19 cases increase and spread in many countries.

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.