Association of COVID-19 global distribution and environmental and demographic factors: An updated three-month study

COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States

The devastating impact of COVID-19 on the United States has been profound since its onset in January 2020. Predicting the trajectory of epidemics accurately and devising strategies to curb their progression are currently formidable challenges. In response to this crisis, we propose COVINet, which combines the architecture of Long Short-Term Memory and Gated Recurrent Unit, incorporating actionable covariates to offer high-accuracy prediction and explainable response. First, we train COVINet models for confirmed cases and total deaths with five input features, and compare Mean Absolute Errors (MAEs) and Mean Relative Errors (MREs) of COVINet against ten competing models from the United States CDC in the last four weeks before April 26, 2021. The results show COVINet outperforms all competing models for MAEs and MREs when predicting total deaths. Then, we focus on prediction for the most severe county in each of the top 10 hot-spot states using COVINet. The MREs are small for all predictions made in the last 7 or 30 days before March 23, 2023. Beyond predictive accuracy, COVINet offers high interpretability, enhancing the understanding of pandemic dynamics. This dual capability positions COVINet as a powerful tool for informing effective strategies in pandemic prevention and governmental decision-making.

Spatial Analysis of Health System Factors in Infectious Disease Management: Lessons Learned from the COVID-19 Pandemic in Korea

Infectious disease outbreaks present ongoing and substantial challenges to health systems at local, national, and global levels, testing their preparedness, response capabilities, and resilience. This study aimed to identify and analyze critical health system-level factors that influence infection outbreaks, focusing on the experience of the COVID-19 pandemic in Korea. Conducted as a secondary data analysis, this study utilized national datasets from Korea. Given the inherent spatial dependencies in the spread of infectious diseases, we employed a spatial lag model to analyze data. While city-specific characteristics did not emerge as significant factors, health system variables, particularly the number of community health centers and health budgets, showed significant influence on the course of the COVID-19 outbreak, along with spatial autocorrelation coefficients. Our findings underscore the importance of enhancing public healthcare infrastructure, considering regional specificities, and promoting collaboration among local governments to bolster preparedness for future outbreaks. These insights are crucial for policymakers and healthcare professionals in formulating effective strategies to prevent, manage, and mitigate the impact of infectious disease outbreaks.

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study

BACKGROUND AND PURPOSE

The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses.

METHODS

A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values.

RESULTS

Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance.

CONCLUSION

This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

Health system characteristics and COVID-19 performance in high-income countries

BACKGROUND

The COVID-19 pandemic has shaken everyday life causing morbidity and mortality across the globe. While each country has been hit by the pandemic, individual countries have had different infection and health trajectories. Of all welfare state institutions, healthcare has faced the most immense pressure due to the pandemic and hence, we take a comparative perspective to study COVID-19 related health system performance. We study the way in which health system characteristics were associated with COVID-19 excess mortality and case fatality rates before Omicron variant.

METHODS

This study analyses the health system performance during the pandemic in 43 OECD countries and selected non-member economies through three healthcare systems dimensions: (1) healthcare finance, (2) healthcare provision, (3) healthcare performance and health outcomes. Health system characteristics-related data is collected from the Global Health Observatory data repository, the COVID-19 related health outcome indicators from the Our World in Data statistics database, and the country characteristics from the World Bank Open Data and the OECD statistics databases.

RESULTS

We find that the COVID-19 excess mortality and case fatality rates were systematically associated with healthcare system financing and organizational structures, as well as performance regarding other health outcomes besides COVID-19 health outcomes.

CONCLUSION

Investments in public health systems in terms of overall financing, health workforce and facilities are instrumental in reducing COVID-19 related mortality. Countries aiming at improving their pandemic preparedness may develop health systems by strengthening their public health systems.

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.

Relationship between meteorological and environmental factors and acute exacerbation for pediatric bronchial asthma: Comparative study before and after COVID-19 in Suzhou

Objective

Climate and environmental change is a well-known factor causing bronchial asthma in children. After the outbreak of coronavirus disease (COVID-19), climate and environmental changes have occurred. The present study investigated the relationship between climate changes (meteorological and environmental factors) and the number of hospitalizations for pediatric bronchial asthma in Suzhou before and after the COVID-19 pandemic.

Methods

From 2017 to 2021, data on daily inpatients diagnosed with bronchial asthma at Children's Hospital of Soochow University were collected. Suzhou Meteorological and Environmental Protection Bureau provided daily meteorological and environmental data. To assess the relationship between bronchial asthma-related hospitalizations and meteorological and environmental factors, partial correlation and multiple stepwise regression analyses were used. To estimate the effects of meteorological and environmental variables on the development of bronchial asthma in children, the autoregressive integrated moving average (ARIMA) model was used.

Results

After the COVID-19 outbreak, both the rate of acute exacerbation of bronchial asthma and the infection rate of pathogenic respiratory syncytial virus decreased, whereas the proportion of school-aged children and the infection rate of human rhinovirus increased. After the pandemic, the incidence of an acute asthma attack was negatively correlated with monthly mean temperature and positively correlated with PM2.5. Stepwise regression analysis showed that monthly mean temperature and O3 were independent covariates (risk factors) for the rate of acute asthma exacerbations. The ARIMA (1, 0, 0) (0, 0, 0) 12 model can be used to predict temperature changes associated with bronchial asthma.

Conclusion

Meteorological and environmental factors are related to bronchial asthma development in children. The influence of meteorological and environmental factors on bronchial asthma may be helpful in predicting the incidence and attack rates.

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.

Human Responses in Public Health Emergencies for Infectious Disease Control: An Overview of Controlled Topologies for Biomedical Applications

COVID-19 originated in Wuhan city of Hubei Province in China in December three years ago. Since then, it has spread to more than 210 countries and territories. This disease is caused by Severe Acute Respiratory Syndrome Coronavirus 2. The virus has a size of one to two nanometers and a single-stranded positive RNA. Droplets spread the virus from coughing and sneezing. This condition causes coughing, fever, acute respiratory problems, and even death. According to the WHO, the virus can survive outside the body for several hours. This research aimed to determine how environmental factors influenced the COVID-19 virus's survival and behavior, as well as its transmission, in a complex environment. Based on the results, virus transmissions are influenced by various human and environmental factors such as population distribution, travel, social behavior, and climate change. Environmental factors have not been adequately examined concerning the transmission of this epidemic. Thus, it is necessary to examine various aspects of prevention and control of this disease, including its effects on climate and other environmental factors.

The Relative Contribution of Climatic, Demographic Factors, Disease Control Measures and Spatiotemporal Heterogeneity to Variation of Global COVID-19 Transmission

Despite a substantial number of COVID-19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID-19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country-specific mobility-transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID-19 pandemic.

The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology

Studies on Coronavirus Disease 2019 (COVID-19) transmission indicate that geo-environmental factors have played a significant role in the global pandemic. However, there has not been a systematic review on the impact of geo-environmental factors on global COVID-19 transmission in the context of geography. As such, we reviewed 49 well-chosen studies to reveal the impact of geo-environmental factors (including the natural environment and human activity) on global COVID-19 transmission, and to inform critical intervention strategies that could mitigate the worldwide effects of the pandemic. Existing studies frequently mention the impact of climate factors (e.g., temperature and humidity); in contrast, a more decisive influence can be achieved by human activity, including human mobility, health factors, and non-pharmaceutical interventions (NPIs). The above results exhibit distinct spatiotemporal heterogeneity. The related analytical methodology consists of sensitivity analysis, mathematical modeling, and risk analysis. For future studies, we recommend highlighting geo-environmental interactions, developing geographically statistical models for multiple waves of the pandemic, and investigating NPIs and care patterns. We also propose four implications for practice to combat global COVID-19 transmission.

A narrative review on the role of temperature and humidity in COVID-19: Transmission, persistence, and epidemiological evidence

Since December 2019, the 2019 coronavirus disease (COVID-19) outbreak has become a global pandemic. Understanding the role of environmental conditions is important in impeding the spread of COVID-19. Given that airborne spread and contact transmission are considered the main pathways for the spread of COVID-19, this narrative review first summarized the role of temperature and humidity in the airborne trajectory of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Meanwhile, we reviewed the persistence of the virus in aerosols and on inert surfaces and summarized how the persistence of SARS-CoV-2 is affected by temperature and humidity. We also examined the existing epidemiological evidence and addressed the limitations of these epidemiological studies. Although uncertainty remains, more evidence may support the idea that high temperature is slightly and negatively associated with COVID-19 growth, while the conclusion for humidity is still conflicting. Nonetheless, the spread of COVID-19 appears to have been controlled primarily by government interventions rather than environmental factors.

COVID-19 pandemic over 2020 (withlockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors

How is the dynamics of Coronavirus Disease 2019 (COVID-19) in 2020 with an health policy of full lockdowns and in 2021 with a vast campaign of vaccinations? The present study confronts this question here by developing a comparative analysis of the effects of COVID-19 pandemic between April-September 2020 (based upon strong control measures) and April-September 2021 (focused on health policy of vaccinations) in Italy, which was one of the first European countries to experience in 2020 high numbers of COVID-19 related infected individuals and deaths and in 2021 Italy has a high share of people fully vaccinated against COVID-19 (>89% of population aged over 12 years in January 2022). Results suggest that over the period under study, the arithmetic mean of confirmed cases, hospitalizations of people and admissions to Intensive Care Units (ICUs) in 2020 and 2021 is significantly equal (p-value<0.01), except fatality rate. Results suggest in December 2021 lower hospitalizations, admissions to ICUs, and fatality rate of COVID-19 than December 2020, though confirmed cases and mortality rates are in 2021 higher than 2020, and likely converging trends in the first quarter of 2022. These findings reveal that COVID-19 pandemic is driven by seasonality and environmental factors that reduce the negative effects in summer period, regardless control measures and/or vaccination campaigns. These findings here can be of benefit to design health policy responses of crisis management considering the growth of COVID-19 pandemic in winter months having reduced temperatures and low solar radiations ( COVID-19 has a behaviour of influenza-like illness). Hence, findings here suggest that strategies of prevention and control of infectious diseases similar to COVID-19 should be set up in summer months and fully implemented during low-solar-irradiation periods (autumn and winter period).

The determinants of the changing speed of spread of COVID-19 across Italy

The COVID-19 epidemic showed inter-regional differences in Italy. We used an ecological study design and publicly available data to compare the basic reproduction number (R 0), the doubling time of the infection (DT) and the COVID-19 cumulative incidence (CI), death rate, case fatality rate (CFR) and time lag to slow down up to a 50-days doubling time in the first and the second 2020 epidemic waves (δ DT50) by region. We also explored socio-economic, environmental and lifestyle variables with multiple regression analysis. COVID-19 CI and CFR changed in opposite directions in the second vs . the first wave: the CI increased sixfold with no evidence of a relationship with the testing rate; the CFR decreased in the regions where it was initially higher but increased where it was lower. The R 0 did not change; the initially mildly affected regions, but not those where the first wave had most severely hit, showed a greater δ DT50 amplitude. Vehicular traffic, average temperature, population density, average income, education and household size showed a correlation with COVID-19 outcomes. The deadly experience in the first epidemic wave and the varying preparedness of the local health systems might have contributed to the inter-regional differences in the second COVID-19 epidemic wave.

The Geographical Distribution and Influencing Factors of COVID-19 in China

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person's perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

Incidence of COVID-19 infection and its variation with demographic and clinical profile: lessons learned at a COVID-19 RT-PCR laboratory in Nagpur, India

Introduction. The coronavirus disease 2019 (COVID-19) pandemic emerged as a global health crisis in 2020. The first case in India was reported on 30 January 2020 and the disease spread throughout the country within months. Old persons, immunocompromised patients and persons with co-morbidities, especially of the respiratory system, have a more severe and often fatal outcome to the disease. In this study we have analysed the socio-demographic trend of the COVID-19 outbreak in Nagpur and adjoining districts. Methods. The study was conducted from April to December 2020. Nasopharyngeal and oropharyngeal swabs collected from suspected cases of COVID-19 were tested using reverse-transcription polymerase chain reaction (RT-PCR) at a diagnostic molecular laboratory at a tertiary care hospital in central India. Patient-related data on demographic profile and indication for testing were obtained from laboratory requisition forms. The results of the inconclusive repeat samples were also noted. The data were analysed using SPSS v24.0. Results. A total of 46 898 samples were received from April to December 2020, of which 41 410 were included in the study; 90.6 % of samples belonged to adults and 9.4 % belonged to children. The overall positivity rate in the samples was 19.3 %, although it varied over the period. The yield was significantly high in the elderly age group (25.5 %) and symptomatic patients (22.6 %). On repeat testing of patients whose first test was inconclusive, 17.1% were positive. There was a steady increase of both the number of tests and the rate of positivity in the initial period of the study, followed by a sharp decline. Conclusion. We can conclude that rigorous contact tracing and COVID-appropriate behaviour (wearing a mask, social distancing and hand hygiene) are required to break the chain of transmission. Elderly people are more susceptible to infection and should follow stringent precautions. It is also important to perform repeat testing of those individuals whose tests are inconclusive with fresh samples so that no positive cases are missed. Understanding of demographics is crucial for better management of this crisis and proper allocation of resources.

Air quality index variation before and after the onset of COVID-19 pandemic: a comprehensive study on 87 capital, industrial and polluted cities of the world

BACKGROUND

Coronavirus disease 2019 (COVID-19) pandemic provided an opportunity for the environment to reduce ambient pollution despite the economic, social and health disruption to the world. The purpose of this study was to investigate the changes in the air quality indexes (AQI) in industrial, densely populated and capital cities in different countries of the world before and after 2020. In this ecological study, we used AQI obtained from the free available databases such as the World Air Quality Index (WAQI). Bivariate correlation analysis was used to explore the correlations between meteorological and AQI variables. Mean differences (standard deviation: SD) of AQI parameters of different years were tested using paired-sample t-test or Wilcoxon signed-rank test as appropriate. Multivariable linear regression analysis was conducted to recognize meteorological variables affecting the AQI parameters.

RESULTS

AQI-PM2.5, AQI-PM10 and AQI-NO2 changes were significantly higher before and after 2020, simultaneously with COVID-19 restrictions in different cities of the world. The overall changes of AQI-PM2.5, AQI-PM10 and AQI-NO2 in 2020 were - 7.36%, - 17.52% and - 20.54% compared to 2019. On the other hand, these results became reversed in 2021 (+ 4.25%, + 9.08% and + 7.48%). In general, the temperature and relative humidity were inversely correlated with AQI-PM2.5, AQI-PM10 and AQI-NO2. Also, after adjusting for other meteorological factors, the relative humidity was inversely associated with AQI-PM2.5, AQI-PM10 and AQI-NO2 (β = - 1.55, β = - 0.88 and β = - 0.10, P < 0.01, respectively).

CONCLUSIONS

The results indicated that air quality generally improved for all pollutants except carbon monoxide and ozone in 2020; however, changes in 2021 have been reversed, which may be due to the reduction of some countries' restrictions. Although this quality improvement was temporary, it is an important result for planning to control environmental pollutants.

Factors that most expose countries to COVID-19: a composite indicators-based approach

Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries' exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R  > 0.60) with composite indicator scores and moderately concordant ( K  > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population's life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population's life expectancy and the country's exposure to COVID-19.

A multiplicity of environmental, economic and social factor analyses to understand COVID-19 diffusion

Research on the impact of the environment on COVID-19 diffusion lacks a full-comprehensive perspective, and neglecting the multiplicity of the human-environment system can lead to misleading conclusions. We attempted to reveal all pre-existing environmental-to-human and human-to-human determinants that influence the transmission of COVID-19. As such, We estimated the daily case incidence ratios (CIR) of COVID-19 for prefectures across mainland China, and used a mixed-effects mixed-distribution model to study the association between the CIR and 114 factors related to climate, atmospheric environmental quality, terrain, population, economic, human mobility as well as non-pharmaceutical interventions (NPIs). Not only the changes in determinants over time as the pandemic progresses but also their lag and interaction effects were examined. CO, O3, PM10 and PM2.5 were found positively linked with CIR, but the effect of NO2 was negative. The temperature had no significant association with CIR, and the daily minimum humidity was a significant negatively predictor. NPIs' level was negatively associated with CIR until with a lag of 15 days. Higher accumulated destination migration scale flow from the epicenter and lower distance to the epicenter (DisWH) were associated with a higher CIR, however, the interaction between DisWH and the time was positive. The more economically developed and more densely populated cities have a higher probability of CIR occurrence, but they may not have a higher CIR intensity.The COVID-19 diffusion are caused by a multiplicity of environmental, economic, social factors as well as NPIs. First, multiple pollutants carried simultaneously on particulate matter affect COVID-19 transmission. Second, the temperature has a limited impact on the spread of the epidemic. Third, NPIs must last for at least 15 days or longer before the effect has been apparent. Fourth, the impact of population movement from the epicenter on COVID-19 gradually diminished over time and intraregional migration deserves more attention.

Factors associated with the difference between the incidence and case-fatality ratio of coronavirus disease 2019 by country

Coronavirus disease (COVID-19) has been spreading all over the world; however, its incidence and case-fatality ratio differ greatly between countries and between continents. We investigated factors associated with international variation in COVID-19 incidence and case-fatality ratio (CFR) across 107 northern hemisphere countries, using publicly available COVID-19 outcome data as of 14 September 2020. We included country-specific geographic, demographic, socio-economic features, global health security index (GHSI), healthcare capacity, and major health behavior indexes in multivariate models to explain this variation. Multiple linear regression highlighted that incidence was associated with ethnic region (p < 0.05), global health security index 4 (GHSI4) (beta coefficient [β] 0.50, 95% Confidence Interval [CI] 0.14-0.87), population density (β 0.35, 95% CI 0.10-0.60), and water safety level (β 0.51, 95% CI 0.19-0.84). The CFR was associated with ethnic region (p < 0.05), GHSI4 (β 0.53, 95% CI 0.14-0.92), proportion of population over 65 (β 0.71, 95% CI 0.19-1.24), international tourism receipt level (β - 0.23, 95% CI - 0.43 to - 0.03), and the number of physicians (β - 0.37, 95% CI - 0.69 to - 0.06). Ethnic region was the most influential factor for both COVID-19 incidence (partial [Formula: see text] = 0.545) and CFR (partial [Formula: see text] = 0.372), even after adjusting for various confounding factors.

COVID-19, body mass index and cholesterol: an ecological study using global data

Background

Coronavirus disease 2019 (COVID-19) is now globally considered a serious economic, social and health threat. A wide range of health related factors including Body Mass Index (BMI) is reported to be associated with the disease. In the present study, we analyzed global databases to assess the correlation of BMI and cholesterol with the risk of COVID-19.

Methods

In this ecological study, we used age-standardized BMI and cholesterol levels as well as the incidence and mortality ratio of COVID-19 at the national-levels obtained from the publicly available databases such as the World Health Organization (WHO) and NCD Risk Factor Collaboration (NCD-RisC). Bivariate correlation analysis was applied to assess the correlations between the study variables. Mean differences (standard deviation: SD) of BMI and cholesterol levels of different groups were tested using independent sample t-test or Mann–Whitney rank test as appropriate. Multivariable linear regression analysis was performed to identify variables affecting the incidence and mortality ratio of COVID-19.

Results

Incidence and mortality ratio of COVID-19 were significantly higher in developed (29,639.85 ± 20,210.79 for cases and 503.24 ± 414.65 for deaths) rather than developing (8153.76 ± 11,626.36 for cases and 169.95 ± 265.78 for deaths) countries (P < 0.01). Results indicated that the correlations of BMI and cholesterol level with COVID-19 are stronger in countries with younger population. In general, the BMI and cholesterol level were positively correlated with COVID-19 incidence ratio (β = 2396.81 and β = 30,932.80, p < 0.01,‌ respectively) and mortality ratio (β = 38.18 and β = 417.52, p < 0.05,‌ respectively) after adjusting for socioeconomic and demographic factors.

Conclusion

Countries with higher BMI or cholesterol at aggregate levels had a higher ratios of COVID-19 incidence and mortality. The aggregated level of cholesterol and BMI are important risk factors for COVID-19 major outcomes, especially in developing countries with younger populations. We recommend monitoring and promotion of health indicices to better prevent morbidity and mortality of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11715-7.

Association mining based approach to analyze COVID-19 response and case growth in the United States

Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.

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.

Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis

BACKGROUND

Coronavirus disease 2019 (COVID-19) originated in the People's Republic of China in December 2019. Thereafter, a global logarithmic expansion of cases occurred. Some countries have a higher rate of infections despite the early implementation of quarantine. Air pollution might be related to high susceptibility to the virus and associated case fatality rates (deaths/cases*100). Lima, Peru, has the second highest incidence of COVID-19 in Latin America and also has one the highest levels of air pollution in the region.

METHODS

This study investigated the association of levels of PM2.5 exposure in previous years (2010-2016) in 24 districts of Lima with cases, deaths and case fatality rates for COVID-19. Multiple linear regression was used to evaluate this association controlled by age, sex, population density and number of food markets per district. The study period was from March 6 to June 12, 2020.

RESULTS

There were 128,700 cases in Lima and 2382 deaths due to COVID-19. The case fatality rate was 1.93%. Previous exposure to PM2.5 (2010-2016) was associated with the number of COVID-19- cases (β = 0.07; 95% CI: 0.034-0.107) and deaths (β = 0.0014; 95% CI: 0.0006-0.0.0023) but not with the case fatality rate.

CONCLUSIONS

After adjusting for age, sex and number of food markets, the higher rates of COVID-19 in Metropolitan Lima are attributable to the increased PM2.5 exposure in the previous years, among other reasons. Reduction in air pollution from a long-term perspective and social distancing are needed to prevent the spread of virus outbreaks.

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.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.

Is coronavirus disease (COVID-19) seasonal? A critical analysis of empirical and epidemiological studies at global and local scales

Coronavirus disease (COVID-19) has infected more than 50 million people and killed more than one million, worldwide, during less than a year course. COVID-19, which has already become the worst pandemic in the last 100 years, is still spreading worldwide. Since the beginning of the outbreak, it has been of particular interest to understand whether COVID-19 is seasonal; the finding might help for better planning and preparation for the fight against the disease. Over the past 12 months, numerous empirical and epidemiological studies have been performed to define the distinct diffusion patterns of COVID-19. Thereby, a wealth of data has accumulated on the relationship between various seasonal meteorological factors and COVID-19 transmissibility at global and local scales. In this review, we aimed to discuss whether COVID-19 exhibits any seasonal features in a global and local perspective by collecting and providing summaries of the findings from empirical and epidemiological studies on the COVID-19 pandemic during its first seasonal cycle.

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis

Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.

Online dental teaching practices during the COVID-19 pandemic: a cross-sectional online survey from China

Background

Coronavirus disease 2019 (COVID-19) emerged in China in December 2019. The COVID-19 pandemic hindered dental education, as school buildings were closed. Online dental teaching provided an alternative teaching tool for dental education. However, the efficiency of online dental teaching and student preferences for online dental teaching are unclear.

Aim

To investigate the satisfaction with online dental teaching practices among undergraduate dental students and standardized resident physician training students during the COVID-19 pandemic in China.

Methods

A total of 104 undergraduate dental students and 57 standardized resident physician training students from Zhejiang University participated in the study. A 12-item survey was conducted. This investigation included the teaching methods received, frequency of classes, degree of satisfaction, preferred teaching method, whether to participate in a course regarding COVID-19 prevention, and the effects of teaching. The percentages were then calculated and evaluated for each item.

Results

A total of 161 students (104 undergraduate dental students and 57 standardized resident physician training students) participated in this survey. All students had online dental classes during the COVID-19 pandemic. Lecture-based learning (LBL), case-based learning (CBL), problem-based learning (PBL), team-based learning (TBL), and research-based learning (RBL) were selected as teaching methods. Students were more satisfied with LBL and CBL than PBL, RBL, and TBL. The majority of students had more than four classes per week. The most selected protective measures were hand washing, wearing masks, and wearing gloves. A total of 46.6% of students participated in courses on COVID-19. After training, the students consciously chose to wear face shields and protective clothing.

Conclusions

Dental students accepted online dental learning during the COVID-19 pandemic. Students preferred LBL and CBL and were satisfied with the classes. Courses on COVID-19 helped students understand how to prevent COVID-19 transmission in the dental clinic.

Time to address the spatiotemporal uncertainties in COVID-19 research: Concerns and challenges

In this correspondence, we emphasize methodological caveats of ecological studies assessing associations between COVID-19 and its physical and social environmental determinants. First, we stress that inference is error-prone due to the modifiable areal unit problem and the modifiable temporal unit problem. The possibility of confounding from using aggregated data is substantial due to the neglect of person-level factors. Second, studying the viral transmission of COVID-19 solely on people's residential neighborhoods is problematic because people are also exposed to nonhome locations and environments en-route along their daily mobility path. We caution against an uncritical application of aggregated data and reiterate the importance of stronger research designs (e.g., case-control studies) on an individual level. To address environmental contextual uncertainties due to people's day-to-day mobility, we call for people-centered studies with mobile phone data.

Temperature dependence of COVID-19 transmission

The recent COVID-19 pandemic follows in its early stages an almost exponential expansion, with the number of cases as a function of time reasonably well fit by N(t) ∝ eαt, in many countries. We analyze the rate α in different countries, starting in each country from a threshold of 30 total cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way. We look for a link between the rate α and the average temperature T of each country, in the month of the initial epidemic growth. We analyze a base set of 42 countries, which developed the epidemic at an earlier stage, an intermediate set of 88 countries and an extended set of 125 countries, which developed the epidemic more recently. Fitting with a linear behavior α(T), we find increasing evidence in the three datasets for a slower spread at high T, at 99.66% C.L., 99.86% C.L. and 99.99995% C.L. (p-value 5⋅10-7, or 5σ detection) in the base, intermediate and extended dataset, respectively. The doubling time at 25 °C is 40% ~ 50% longer than at 5 °C. Moreover we analyzed the possible existence of a bias: poor countries, typically located in warm regions, might have less intense testing. By excluding countries below a given GDP per capita from the dataset, we find that this affects our conclusions only slightly and only for the extended dataset. The significance always remains high, with a p-value of about 10-3 - 10-4 or less. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general, policy measures should be taken to prevent a second wave, such as safe ventilation in public buildings, social distancing, use of masks, testing and tracking policies, before the arrival of the next cold season.

Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis

Background

Corona virus disease (COVID-19) originated in China in December 2019. Thereafter, a global logarithmic expansion of the cases has occurred. Some countries have a higher rate of infections despite of early implementation of quarantine. Air pollution could be related to the high susceptibility to SARS-CoV-2 and the associated case-fatality rates (deaths/cases*100). Lima, Peru has the second highest incidence of COVID-19 in Latin America, and it is also one of the cities with highest levels of air pollution in the Region.

Methods

This study investigated the association of the levels of PM_2.5 exposure in the previous years (2010-2016) in 24 districts of Lima with the positive-cases, deaths and case-fatality rates of COVID-19. Multiple Linear regression was used to evaluate this association controlled by age, sex, population density and number of food markets per district. The study period was from March 6 to June 12, 2020.

Results

There were in Lima 128,700 SARS-CoV-2 positive cases, and 2,382 deaths due to COVID-19. The case-fatality rate was 1.93%. Previous exposure to PM_2.5 (years 2010-2016) was associated with number of Covid-19 positive-cases (β = 0.07; 95% CI: 0.034-0.107) and deaths (β = 0.0014; 95% CI: 0.0006-0.0.0023), but not with case-fatality rate.

Conclusions

the higher rates of COVID-19 in Metropolitan Lima is attributable, among others, to the increased PM_2.5 exposure in the previous years after adjusting for age, sex and number of food markets. Reduction of air pollution since a long-term perspective, and social distancing are needed to prevent spreads of virus outbreak.

"Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis."

Background Corona virus disease (COVID-19) originated in China in December 2019. Thereafter, a global logarithmic expansion of the cases has occurred. Some countries have a higher rate of infections despite of early implementation of quarantine. Air pollution could be related to the high susceptibility to SARS-CoV-2 and the associated case-fatality rates (deaths/cases*100). Lima, Peru has the second highest incidence of COVID-19 in Latin America, and it is also one of the cities with highest levels of air pollution in the Region. Methods This study investigated the association of the levels of PM _2.5 exposure in the previous years (2010-2016) in 24 districts of Lima with the positive-cases, deaths and case-fatality rates of COVID-19. Multiple Linear regression was used to evaluate this association controlled by age, sex, population density and number of food markets per district. The study period was from March 6 to June 12, 2020. Results There were in Lima 128,700 SARS-CoV-2 positive cases, and 2,382 deaths due to COVID-19. The case-fatality rate was 1.93%. Previous exposure to PM _2.5 (years 2010-2016) was associated with number of Covid-19 positive-cases ( β  = 0.07; 95% CI: 0.034-0.107) and deaths ( β  = 0.0014; 95% CI: 0.0006-0.0.0023), but not with case-fatality rate. Conclusions the higher rates of COVID-19 in Metropolitan Lima is attributable, among others, to the increased PM _2.5 exposure in the previous years after adjusting for age, sex and number of food markets. Reduction of air pollution since a long-term perspective, and social distancing are needed to prevent spreads of virus outbreak.

Intraregional propagation of Covid-19 cases in Pará, Brazil: assessment of isolation regime to lockdown

Due to the high incidence of COVID-19 case numbers internationally, the World Health Organization (WHO) declared a Public Health Emergency of global relevance, advising countries to follow protocols to combat pandemic advance through actions that can reduce spread and consequently avoid a collapse in the local health system. This study aimed to evaluate the dynamics of the evolution of new community cases, and mortality records of COVID-19 in the State of Pará, which has a subtropical climate with temperatures between 20 and 35 °C, after the implementation of social distancing by quarantine and adoption of lockdown. The follow-up was carried out by the daily data from the technical bulletins provided by the State of Pará Public Health Secretary (SESPA). On 18 March 2020, Pará notified the first case of COVID-19. After 7 weeks, the number of confirmed cases reached 4756 with 375 deaths. The results show it took 49 days for 81% of the 144 states municipalities, distributed over an area of approximately 1 248 000 km² to register COVID-19 cases. Temperature variations between 24.5 and 33.1 °C did not promote the decline in the new infections curve. The association between social isolation, quarantine and lockdown as an action to contain the infection was effective in reducing the region's new cases registration of COVID-19 in the short-term. However, short periods of lockdown may have promoted the virus spread among peripheral municipalities of the capital, as well as to inland regions.

Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India

The present study investigates the air pollution pattern over India during the COVID-19 lockdown period (24 March-31 May 2020), pre-lockdown (1-23 March 2020) and the same periods from 2019 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra aerosol optical depth (AOD) with level 2 (10 km × 10 km) and level 3 (1° × 1° gridded) collection 6.1 Dark Target Deep Blue (DT-DB) aerosol product the Tropospheric Monitoring Instrument (TROPOMI) NO₂ and SO₂ data with a spatial resolution of 7 km × 3.5 km. We also use long-term average (2000-2017) of AOD for March-May to identify existing hotspot regions and to compare the variations observed in 2019 and 2020. The aim of the present work is to identify the pollution hotspot regions in India that existed during the lockdown and understanding the future projection scenarios reported by previous studies in light of the present findings. We have incorporated Menn-Kendall trend analysis to understand the AOD trends over India and percentage change in AOD, NO₂ and SO₂ to identify air pollution pattern changes during the lockdown. The results indicate higher air pollution levels over eastern India over the coal-fired power plants clusters. By considering the earlier projected studies, our results suggest that eastern India will have higher levels of air pollution, making it a new hotspot region for air pollution with highest magnitudes.

Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario

India, the second-most populous country in the world is witnessing a daily surge in the COVID-19 infected cases. India is currently among the worst-hit nations worldwide due to the COVID-19 pandemic and ranks just behind Brazil and the USA. The prediction of the future course of the pandemic is thus of utmost importance in order to prevent further worsening of the situation. In this paper, we develop models for the past trajectory (March 01, 2020-July 25, 2020) and also make a month-long (July 26, 2020-August 24, 2020) forecast of the future evolution of the COVID-19 pandemic in India by using an autoregressive integrated moving average (ARIMA) model. We determine the most optimal ARIMA model (ARIMA(7,2,2)) based on the statistical parameters viz. root-mean-squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination ( R 2 ). Subsequently, the developed model is used to obtain a one month-long forecast for the cumulative cases, active cases, recoveries, and the number of fatalities. According to our forecasting results, India is likely to have 3800,989 cumulative infected cases, 1634,142 cumulative active cases, 2110,697 cumulative recoveries, and 56,150 cumulative deaths by August 24, 2020, if the current trend of the pandemic continues to prevail. The implications of these forecasts are that in the upcoming month, the infection rate of COVID-19 in India is going to escalate, while the rate of recovery and the case-fatality rate is likely to reduce. In order to avert these possible scenarios, the administration and health-care personnel need to formulate and implement robust control measures, while the general public needs to be more responsible and strictly adhere to the established and newly formulated guidelines in order to slow down the spread of the pandemic and prevent it from transforming into a catastrophe.

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

BACKGROUND

University students are increasingly recognized as a vulnerable population, suffering from higher levels of anxiety, depression, substance abuse, and disordered eating compared to the general population. Therefore, when the nature of their educational experience radically changes-such as sheltering in place during the COVID-19 pandemic-the burden on the mental health of this vulnerable population is amplified. The objectives of this study are to 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts.

METHODS

Cross-sectional data were collected through web-based questionnaires from seven U.S. universities. Representative and convenience sampling was used to invite students to complete the questionnaires in mid-March to early-May 2020, when most coronavirus-related sheltering in place orders were in effect. We received 2,534 completed responses, of which 61% were from women, 79% from non-Hispanic Whites, and 20% from graduate students.

RESULTS

Exploratory factor analysis on close-ended responses resulted in two latent constructs, which we used to identify profiles of students with latent profile analysis, including high (45% of sample), moderate (40%), and low (14%) levels of psychological impact. Bivariate associations showed students who were women, were non-Hispanic Asian, in fair/poor health, of below-average relative family income, or who knew someone infected with COVID-19 experienced higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside, or less than eight hours on electronic screens were likely to experience lower levels of psychological impact. Multivariate modeling (mixed-effects logistic regression) showed that being a woman, having fair/poor general health status, being 18 to 24 years old, spending 8 or more hours on screens daily, and knowing someone infected predicted higher levels of psychological impact when risk factors were considered simultaneously.

CONCLUSION

Inadequate efforts to recognize and address college students' mental health challenges, especially during a pandemic, could have long-term consequences on their health and education.

Associations between air pollution and COVID-19 epidemic during quarantine period in China

The coronavirus disease (COVID-19) has become a global public health threaten. A series of strict prevention and control measures were implemented in China, contributing to the improvement of air quality. In this study, we described the trend of air pollutant concentrations and the incidence of COVID-19 during the epidemic and applied generalized additive models (GAMs) to assess the association between short-term exposure to air pollution and daily confirmed cases of COVID-19 in 235 Chinese cities. Disease progression based on both onset and report dates as well as control measures as potential confounding were considered in the analyses. We found that stringent prevention and control measures intending to mitigate the spread of COVID-19, contributed to a significant decline in the concentrations of air pollutants except ozone (O3). Significant positive associations of short-term exposure to air pollutants, including particulate matter with diameters ≤2.5 μm (PM2.5), particulate matter with diameters ≤10 μm (PM10), and nitrogen dioxide (NO2) with daily new confirmed cases were observed during the epidemic. Per interquartile range (IQR) increase in PM2.5 (lag0-15), PM10 (lag0-15), and NO2 (lag0-20) were associated with a 7% [95% confidence interval (CI): (4-9)], 6% [95% CI: (3-8)], and 19% [95% CI: (13-24)] increase in the counts of daily onset cases, respectively. Our results suggest that there is a statistically significant association between ambient air pollution and the spread of COVID-19. Thus, the quarantine measures can not only cut off the transmission of virus, but also retard the spread by improving ambient air quality, which might provide implications for the prevention and control of COVID-19.

How do low wind speeds and high levels of air pollution support the spread of COVID-19?

The pandemic of coronavirus disease 2019 (COVID-19) is generating a high number of infected individuals and deaths. One of the current questions is how climatological factors and environmental pollution can affect the diffusion of COVID-19 in human society. This study endeavours to explain the relation between wind speed, air pollution and the diffusion of COVID-19 to provide insights to constrain and/or prevent future pandemics and epidemics. The statistical analysis here focuses on case study of Italy and reveals two main findings: 1) cities with high wind speed have lower numbers of COVID-19 related infected individuals; 2) cities located in hinterland zones (mostly those bordering large urban conurbations) with little wind speed and frequently high levels of air pollution had higher numbers of COVID-19 related infected individuals. Results here suggest that high concentrations of air pollutants, associated with low wind speeds, may promote a longer permanence of viral particles in polluted air of cities, thus favouring an indirect means of diffusion of the novel coronavirus (SARS-CoV-2), in addition to the direct diffusion with human-to-human transmission dynamics.

Association of COVID-19 distribution with air quality, sociodemographic factors, and comorbidities: an ecological study of US states

This ecological study investigated the association between COVID-19 distribution and air quality index (AQI), comorbidities and sociodemographic factors in the USA. The AQI factors included in the study are total AQI, ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide (NO₂). Other demographic, socioeconomic, and geographic variables were included as covariates. The correlations of COVID-19 variables-proportion of cases and deaths in each population, as well as case fatality rate with independent variables were determined by Pearson and Spearman correlation and multiple linear regression analyses. The results revealed that AQI-NO₂, population density, longitude, gross domestic product per capita, median age, total death of disease, and pneumonia per population were significantly associated with the COVID-19 variables (P < 0.05). Air pollutants, especially NO₂ in the US case, could be addressed as an important factor linked with COVID-19 susceptibility and mortality.

Investigating the Potential for Ultraviolet Light to Modulate Morbidity and Mortality From COVID-19: A Narrative Review and Update

During the COVID-19 (coronavirus disease of 2019) pandemic, researchers have been seeking low-cost and accessible means of providing protection from its harms, particularly for at-risk individuals such as those with cardiovascular disease, diabetes and obesity. One possible way is via safe sun exposure, and/or dietary supplementation with induced beneficial mediators (e.g., vitamin D). In this narrative review, we provide rationale and updated evidence on the potential benefits and harms of sun exposure and ultraviolet (UV) light that may impact COVID-19. We review recent studies that provide new evidence for any benefits (or otherwise) of UV light, sun exposure, and the induced mediators, vitamin D and nitric oxide, and their potential to modulate morbidity and mortality induced by infection with SARS-CoV-2 (severe acute respiratory disease coronavirus-2). We identified substantial interest in this research area, with many commentaries and reviews already published; however, most of these have focused on vitamin D, with less consideration of UV light (or sun exposure) or other mediators such as nitric oxide. Data collected to-date suggest that ambient levels of both UVA and UVB may be beneficial for reducing severity or mortality due to COVID-19, with some inconsistent findings. Currently unresolved are the nature of the associations between blood 25-hydroxyvitamin D and COVID-19 measures, with more prospective data needed that better consider lifestyle factors, such as physical activity and personal sun exposure levels. Another short-coming has been a lack of measurement of sun exposure, and its potential to influence COVID-19 outcomes. We also discuss possible mechanisms by which sun exposure, UV light and induced mediators could affect COVID-19 morbidity and mortality, by focusing on likely effects on viral pathogenesis, immunity and inflammation, and potential cardiometabolic protective mechanisms. Finally, we explore potential issues including the impacts of exposure to high dose UV radiation on COVID-19 and vaccination, and effective and safe doses for vitamin D supplementation.

Spread of SARS-CoV-2 through Latin America and the Caribbean region: A look from its economic conditions, climate and air pollution indicators

We have evaluated the spread of SARS-CoV-2 through Latin America and the Caribbean (LAC) region by means of a correlation between climate and air pollution indicators, namely, average temperature, minimum temperature, maximum temperature, rainfall, average relative humidity, wind speed, and air pollution indicators PM10, PM2.5, and NO2 with the COVID-19 daily new cases and deaths. The study focuses in the following LAC cities: Mexico City (Mexico), Santo Domingo (Dominican Republic), San Juan (Puerto Rico), Bogotá (Colombia), Guayaquil (Ecuador), Manaus (Brazil), Lima (Perú), Santiago (Chile), São Paulo (Brazil) and Buenos Aires (Argentina). The results show that average temperature, minimum temperature, and air quality were significantly associated with the spread of COVID-19 in LAC. Additionally, humidity, wind speed and rainfall showed a significant relationship with daily cases, total cases and mortality for various cities. Income inequality and poverty levels were also considered as a variable for qualitative analysis. Our findings suggest that and income inequality and poverty levels in the cities analyzed were related to the spread of COVID-19 positive and negative, respectively. These results might help decision-makers to design future strategies to tackle the spread of COVID-19 in LAC and around the world.

How socio-economic and atmospheric variables impact COVID-19 and influenza outbreaks in tropical and subtropical regions of Brazil

COVID-19 has been disturbing human society with an intensity never seen since the Influenza epidemic (Spanish flu). COVID-19 and Influenza are both respiratory viruses and, in this study, we explore the relations of COVID-19 and Influenza with atmospheric variables and socio-economic conditions for tropical and subtropical climates in Brazil. Atmospheric variables, mobility, socio-economic conditions and population information were analyzed using a generalized additive model for daily COVID-19 cases from March 1st to May 15th, 2020, and for daily Influenza hospitalizations (2017-2019) in Brazilian states representing tropical and subtropical climates. Our results indicate that temperature combined with humidity are risk factors for COVID-19 and Influenza in both climate regimes, and the minimum temperature was also a risk factor for subtropical climate. Social distancing is a risk factor for COVID-19 in all regions. For Influenza and COVID-19, the highest Relative Risks (RR) generally occurred in 3 days (lag = 3). Altogether among the studied regions, the most important risk factor is the Human Development Index (HDI), with a mean RR of 1.2492 (95% CI: 1.0926-1.6706) for COVID-19, followed by the elderly fraction for both diseases. The risk factor associated with socio-economic inequalities for Influenza is probably smoothed by Influenza vaccination, which is offered free of charge to the entire Brazilian population. Finally, the findings of this study call attention to the influence of socio-economic inequalities on human health.

Hand Hygiene Behaviors in a Representative Sample of Polish Adolescents in Regions Stratified by COVID-19 Morbidity and by Confounding Variables (PLACE-19 Study): Is There Any Association?

The hand hygiene may possibly influence the course of the COVID-19 pandemic, but the multifactorial influence on hand hygiene knowledge and behaviors is proven. The aim of the study was to analyze hand hygiene behaviors in a national representative sample of Polish adolescents in regions stratified by COVID-19 morbidity, while taking socioeconomic status of the region, as well rural or urban environment, into account as possible interfering factors. The study was conducted Polish Adolescents’ COVID-19 Experience (PLACE-19) Study population (n = 2323) that was recruited based on a random sampling of schools, while the pair-matching procedure was applied within schools and age, in order to obtain adequate number of boys and girls, representative for the general Polish population (n = 1222). The participants were asked about their handwashing habits while using Handwashing Habits Questionnaire (HHQ) and about applied procedure of washing hands. The results were compared in subgroups that were stratified by region for COVID-19 morbidity, socioeconomic status of the region, and rural/urban environment. In regions of low COVID-19 morbidity, a higher share of adolescents, than in regions of high morbidity, declared washing their hands before meals (p = 0.0196), after meals (p = 0.0041), after preparing meals (p = 0.0297), before using the restroom (p = 0.0068), after using the restroom (p = 0.0014), after combing their hair (p = 0.0298), after handshaking (p = 0.0373), after touching animals (p = 0.0007), after contacting babies (p = 0.0278), after blowing nose (p = 0.0435), after touching sick people (p = 0.0351), and after cleaning home (p = 0.0234). For the assessed steps of the handwashing procedure, in regions of low COVID-19 morbidity, a higher share of adolescents included them to their daily handwashing, than in regions of high morbidity, that was stated for removing watch and bracelets (p = 0.0052), removing rings (p = 0.0318), and drying hands with towel (p = 0.0031). For the comparison in regions stratified by Gross Domestic Product, the differences were only minor and inconsistent. For the comparison in place of residence stratified by number of residents in city, there were some minor differences indicating better hand hygiene behaviors in the case of villages and small towns when compared with medium and large cities (p < 0.05). It may be concluded that, in a population-based sample of Polish adolescents, individuals from regions of low COVID-19 morbidity presented more beneficial hand hygiene habits than those from regions of high COVID-19 morbidity.