Eco-epidemiological assessment of the COVID-19 epidemic in China, January-February 2020

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

Spatial relationship between COVID-19 and previous infectious disease outbreaks: Evidence from South Korea

As the novel coronavirus disease (COVID-19) has been rapidly spreading across the world, scholars have started paying attention to risk factors that affect the occurrence of the infectious disease. While various urban characteristics have been shown to influence the outbreak, less is known about whether COVID-19 is more likely to be transmitted in areas with a greater number of incidents of previous infectious diseases. This study examines a spatial relationship between COVID-19 and previous infectious diseases from a spatial perspective. Using the confirmed cases of COVID-19 and other types of infectious diseases across South Korea, we identified spatial clusters through regression and spatial econometric models. We found that COVID-19-confirmed case rates tended to be clustered despite no similarity with the spatial patterns of previous infectious diseases. Existing infectious diseases from abroad were associated with the occurrence of COVID-19, while the effect diminished after controlling for the spatial effect. Our findings highlight the importance of regional-level infectious disease surveillance for the effective prevention and control of COVID-19.

Association Between Air Pollution, Climate Change, and COVID-19 Pandemic: A Review of the Recent Scientific Evidence

Background: Recent studies indicated the possible relationship between climate change, environmental pollution, and Coronavirus Disease 2019 (COVID-19) pandemic. This study reviewed the effects of air pollution, climate parameters, and lockdown on the number of cases and deaths related to COVID-19. Methods: The present review was performed to determine the effects of weather and air pollution on the number of cases and deaths related to COVID-19 during the lockdown. Articles were collected by searching the existing online databases, such as PubMed, Science Direct, and Google Scholar, with no limitations on publication dates. Afterwards, this review focused on outdoor air pollution, including PM2.5, PM10, NO2, SO2, and O3, and weather conditions affecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19. Results: Most reviewed investigations in the present study showed that exposure to air pollutants, particularly PM2.5 and NO2, is positively related to COVID-19 patients and mortality. Moreover, these studies showed that air pollution could be essential in transmitting COVID-19. Local meteorology plays a vital role in coronavirus spread and mortality. Temperature and humidity variables are negatively correlated with virus transmission. The evidence demonstrated that air pollution could lead to COVID-19 transmission. These results support decision-makers in curbing potential new outbreaks. Conclusions: Overall, in environmental perspective-based COVID-19 studies, efforts should be accelerated regarding effective policies for reducing human emissions, bringing about air pollution and weather change. Therefore, using clean and renewable energy sources will increase public health and environmental quality by improving global air quality.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis

BACKGROUND

The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory.

OBJECTIVE

The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States.

METHODS

This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation.

RESULTS

Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States.

SIGNIFICANCE

The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.

COVID-19 pandemic, global advisories and the imperatives of strengthening the public healthcare system: Nigeria in context

Purpose

Given the efficacy of a robust public healthcare system in handling public health emergencies, the rapid rate of COVID-19 pandemic infection in early-hit (advanced) countries with competent healthcare system is intriguing. The popular public health argument supports the strengthening of the healthcare system as a significant response strategy to minimize infection. Hence, this paper examines the catalysts that exacerbated the pandemic’s rapid spread in these countries despite the sound state of their healthcare system. Also, it assesses the condition of Nigerian public healthcare system in the lights of the novel COVID-19 pandemic and suggests the need for improvement and effective functioning.

Design/methodology/approach

This paper uses a documentary approach to establish the authors’ opinion on the subject matter under investigation.

Findings

Factors such as climate, temperature, and humidity levels played a key role in infection in the winter of 2020. These factors facilitated for the pandemic’s rapid spread in advanced countries. In peripheral countries like Nigeria, the public healthcare system is burdened by a lack of funding, an insufficiency in welfare and training for healthcare staff and facilities and other operational challenges. Hence, the effective management of COVID-19 outbreak in Nigeria relative to advanced countries was hindered by the inadequacies mentioned above.

Originality/value

This paper provides an understanding on the condition of public healthcare system in peripheral nations in relation to the healthcare system advisories from the World Health Organization (WHO) in the context of handling the pandemic outbreak. Also, it explains the catalysts that heightened the pandemic’s rapid spread in advanced countries despite the higher capacity of their healthcare system to manage health emergencies.

Study of the Effect of Urban Densification and Micrometeorology on the Sustainability of a Coronavirus-Type Pandemic

This research examines the persistence of a pandemic in urban environments subjected to intensive densification processes, applying chaotic analysis tools to hourly time series constructed by relating accumulated patients with meteorological and pollutant variables (measured at ground level). To investigate this objective, seven communes of the metropolitan region of Santiago de Chile that present intensive urbanization processes that affect urban micrometeorology, favoring the concentration of pollutants, were considered. Quotients were constructed between the number of hourly patients with SARS-CoV-2 that accumulated in each commune over a period of two years and the hourly variables of urban micrometeorology (temperature, magnitude of wind speed, relative humidity) and pollutant concentration (tropospheric ozone, particulate material of 2.5 and 10 μm) constituting a new family of time series. Chaos theory was applied to these new time series, obtaining the chaotic parameters Lyapunov coefficient, correlation entropy, Lempel–Ziv complexity, Hurst coefficient and the fractal dimension in each measurement commune. The results showed that the accumulated patients (2020–2022), of the order of 400,000, belonged to the five communes (with a built area of approximately 300,000 m2 in recent years) that had the highest urban densification, which affected urban meteorology, favored the concentration of pollutants and made the SARS-Cov-2 pandemic more persistent. The “ideal” density of built housing should balance a pandemic and nullify its expansion.

Effects of environmental parameters and their interactions on the spreading of SARS-CoV-2 in North Italy under different social restrictions. A new approach based on multivariate analysis

In 2020 North Italy suffered the SARS-CoV-2-related pandemic with a high number of deaths and hospitalization. The effect of atmospheric parameters on the amount of hospital admissions (temperature, solar radiation, particulate matter, relative humidity and wind speed) is studied through about 8 months (May-December). Two periods are considered depending on different conditions: a) low incidence of COVID-19 and very few regulations concerning personal mobility and protection ("free/summer period"); b) increasing incidence of disease, social restrictions and use of personal protections ("confined/autumn period"). The "hospitalized people in medical area wards/100000 residents" was used as a reliable measure of COVID-19 spreading and load on the sanitary system. We developed a chemometric approach (multiple linear regression analysis) using the daily incidence of hospitalizations as a function of the single independent variables and of their products (interactions). Eight administrative domains were considered (altogether 26 million inhabitants) to account for relatively homogeneous territorial and social conditions. The obtained models very significantly match the daily variation of hospitalizations, during the two periods. Under the confined/autumn period, the effect of non-pharmacologic measures (social distances, personal protection, etc.) possibly attenuates the virus diffusion despite environmental factors. On the contrary, in the free/summer conditions the effects of atmospheric parameters are very significant through all the areas. Particulate matter matches the growth of hospitalizations in areas with low chronic particulate pollution. Fewer hospitalizations strongly correspond to higher temperature and solar radiation. Relative humidity plays the same role, but with a lesser extent. The interaction between solar radiation and high temperature is also highly significant and represents surprising evidence. The solar radiation alone and combined with high temperature exert an anti-SARS-CoV-2 effect, via both the direct inactivation of virions and the stimulation of vitamin D synthesis, improving immune system function.

Measuring the mobility impact on the COVID-19 pandemic

This assessment aims at measuring the impact of different location mobility on the COVID-19 pandemic. Data over time and over the 27 Brazilian federations in 5 regions provided by Google's COVID-19 community mobility reports and classified by place categories (retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences) are autoregressed on the COVID-19 incidence in Brazil using generalized linear regressions to measure the aggregate dynamic impact of mobility on each socioeconomic category. The work provides a novel multicriteria approach for selecting the most appropriate estimation model in the context of this application. Estimations for the time gap between contagion and data disclosure for public authorities' decision-making, estimations regarding the propagation rate, and the marginal mobility contribution for each place category are also provided. We report the pandemic evolution on the dimensions of cases and a geostatistical analysis evaluating the most critical cities in Brazil based on optimized hotspots with a brief discussion on the effects of population density and the carnival.

Bayesian network-based spatial predictive modelling reveals COVID-19 transmission dynamics in Eswatini

The first case of COVID-19 in Eswatini was first reported in March 2020, posing an unprecedented challenge to the country’s health and socio-economic systems. Using geographic information system (GIS) data comprising 15 socioeconomic, demographic and environmental variables, we model the spatial variability of COVID-19 transmission risk based on case data for the period under strict lockdown (up to 8th May 2020) and after the lockdown regulations were gradually eased (up to 30th June 2020). We implemented and tested 13 spatial data-driven Bayesian network (BN) learning algorithms to examine the factors that determine the spatial distribution of COVID-19 transmission risk. All the BN models performed very well in predicting the COVID-19 cases as evidenced by low log loss (0.705–0.683) and high recall values (0.821–0.836). The tree-augmented naïve (TAN) model outperformed all other BN learning algorithms. The proximity to major health facilities, churches, shopping centres and supermarkets as well as average annual traffic density were the strongest predictors of transmission risk during strict lockdown. After gradual relaxation of the lockdown, the proportion of the youth (15–40 years old) in an area became the strongest predictor of COVID-19 transmission in addition to the proximity to areas where people congregate, excluding churches. The study provides useful insights on the spatio-temporal dynamics of COVID-19 transmission drivers thereby aiding the design of geographically-targeted interventions. The findings also point to the robustness of BN models in spatial predictive modelling and graphically explaining spatial phenomena under uncertainty and with limited data.

The Association of Opioid Use Disorder and COVID-19, a Longitudinal Study

Background

Coronavirus disease 2019 (COVID-19) quickly spread to the world, causing a pandemic. While some studies have found no link between opioid use disorder (OUD) and COVID-19, the role of opioid on COVID-19 is challenging. The present study aimed to determine the relationship between OUD and COVID-19.

Methods

This was a prospective cohort study. We used data from the third phase of the Shahroud Eye Cohort Study on 4394 participants which started in September 2019 and ended before the COVID-19 epidemic in Shahroud in February 2020. The participants were followed for about 13 months till March 26, 2021. COVID-19 was detected by RT-PCR on swap samples from the oropharynx and nasopharynx. The incidence of COVID-19 compared in OUD and non-OUD participants, and relative risk was calculated in log-binomial regression models.

Results

Among the 4394 participants with a mean age of 61.1 years, 120 people had OUD. The incidence of COVID-19 in participants with OUD and non-OUD was 4.17% and 6.22%, respectively (P-value: 0356). The relative risk of OUD for COVID-19 was 0.60 (95% confidence intervals: 0.25-1.44; P value: 0.251).

Conclusions

OUD was not associated with COVID-19. The claim that people with OUD are less likely to develop COVID-19 is not supported by these data.

Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19

There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor - temperature - was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries.

Impacts of the COVID-19 epidemic on merchant ship activity and pollution emissions in Shanghai port waters

This study aims to evaluate impacts of the COVID-19 epidemic on merchant ship activities and corresponding atmospheric pollutant emissions in Shanghai port waters. Comparing AIS data from February 2019 and from February 2020, it is found that the merchant ship count and utilization frequency are reduced during the epidemic period. The epidemic could result in longer ship turnaround times because of more operation time for berthing and anchoring activities. Ship emission comparison results reveal that the cargo ship emissions are significantly reduced while container ships and tankers produce a slightly decreased emissions resulting by strict COVID-19 quarantine measures. In addition, the unit ship emission intensity is greatly reduced for ships which are under the normal cruising status while berthing and anchoring operations are associated with increased ship emissions. This implies that it is urgent to promote the use of shore power equipment for merchant ships during the epidemic period.

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies

The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the most serious global public health crises. A collection and analysis of studies related to the association between COVID-19 (coronavirus disease 2019) transmission and meteorological factors, such as humidity, is vital and indispensable for disease prevention and control. A comprehensive literature search using various databases, including Web of Science, PubMed, and Chinese National Knowledge Infrastructure, was systematically performed to identify eligible studies from Dec 2019 to Feb 1, 2021. We also established six criteria to screen the literature to obtain high-quality literature with consistent research purposes. This systematic review included a total of 62 publications. The study period ranged from 1 to 8 months, with 6 papers considering incubation, and the lag effect of climate factors on COVID-19 activity being taken into account in 22 studies. After quality assessment, no study was found to have a high risk of bias, 30 studies were scored as having moderate risks of bias, and 32 studies were classified as having low risks of bias. The certainty of evidence was also graded as being low. When considering the existing scientific evidence, higher temperatures may slow the progression of the COVID-19 epidemic. However, during the course of the epidemic, these climate variables alone could not account for most of the variability. Therefore, countries should focus more on health policies while also taking into account the influence of weather.

SARS-CoV-2: lessons from both the history of medicine and from the biological behavior of other well-known viruses

SARS-CoV-2 is the etiological agent of the current pandemic worldwide and its associated disease COVID-19. In this review, we have analyzed SARS-CoV-2 characteristics and those ones of other well-known RNA viruses viz. HIV, HCV and Influenza viruses, collecting their historical data, clinical manifestations and pathogenetic mechanisms. The aim of the work is obtaining useful insights and lessons for a better understanding of SARS-CoV-2. These pathogens present a distinct mode of transmission, as SARS-CoV-2 and Influenza viruses are airborne, whereas HIV and HCV are bloodborne. However, these viruses exhibit some potential similar clinical manifestations and pathogenetic mechanisms and their understanding may contribute to establishing preventive measures and new therapies against SARS-CoV-2.

"Distance-Driven" Versus "Density-Driven": Understanding the Role of "Source-Case" Distance and Gathering Places in the Localized Spatial Clustering of COVID-19-A Case Study of the Xinfadi Market, Beijing (China)

The frequent occurrence of local COVID-19 today gives a strong necessity to better understand the effects of "source-case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID-19 cases, which originated in the Xinfadi market in Beijing from June-July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero-inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID-19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID-19 was identified in both global and local modeling estimation. The dominant role of the "source-case" distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance-driven" to "density-driven" of the localized spatial clustering of COVID-19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID-19.

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.

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

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

The effect of human settlement temperature and humidity on the growth rules of infected and recovered cases of COVID-19

This study investigated the impact of humidity and temperature on the spread of COVID-19 (SARS-CoV-2) by statistically comparing modelled pandemic dynamics (daily infection and recovery cases) with daily temperature and humidity of three climate zones (Mainland China, South America and Africa) from January to August 2020. We modelled the pandemic growth using a simple logistic function to derive information of the viral infection and describe the growth of infected and recovered cases. The results indicate that the infected and recovered cases of the first wave were controlled in China and managed in both South America and Africa. There is a negative correlation between both humidity (r = - 0.21; p = 0.27) and temperature (r = -0.22; p = 0.24) with spread of the virus. Though this study did not fully encompass socio-cultural factors, we recognise that local government responses, general health policies, population density and transportation could also affect the spread of the virus. The pandemic can be managed better in the second wave if stricter safety protocols are implemented. We urge various units to collaborate strongly and call on countries to adhere to stronger safety protocols in the second wave.

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

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

Associations between meteorology and COVID-19 in early studies: Inconsistencies, uncertainties, and recommendations

Meteorological variables, such as the ambient temperature and humidity, play a well-established role in the seasonal transmission of respiratory viruses and influenza in temperate climates. Since the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, a growing body of literature has attempted to characterize the sensitivity of COVID-19 to meteorological factors and thus understand how changes in the weather and seasonality may impede COVID-19 transmission. Here we select a subset of this literature, summarize the diversity in these studies' scopes and methodologies, and show the lack of consensus in their conclusions on the roles of temperature, humidity, and other meteorological factors on COVID-19 transmission dynamics. We discuss how several aspects of studies' methodologies may challenge direct comparisons across studies and inflate the importance of meteorological factors on COVID-19 transmission. We further comment on outstanding challenges for this area of research and how future studies might overcome them by carefully considering robust modeling approaches, adjusting for mediating and covariate effects, and choosing appropriate scales of analysis.

Serum biomarkers for prediction of mortality in patients with COVID-19

BACKGROUND

There is limited information regarding the role of biomarker levels at predicting mortality in patients with the coronavirus disease (COVID-19) pandemic. The purpose of this study is to determine the differences in serum biomarker levels in adults with COVID-19 who survived hospitalization from those who did not.

METHODS

A comprehensive search was completed on PubMed, EMBASE and Cochrane libraries to identify studies of interest. Endpoints of interest were blood counts, hepatic function test, acute phase reactants, cytokines and cardiac biomarkers.

RESULTS

A total of 10 studies with 1584 patients were included in the pooled analyses. Biomarkers that were noted to be significantly higher in those who died from coronavirus disease included: white blood cell count, neutrophil count, C-reactive protein, high sensitivity C-reactive protein, procalcitonin, ferritin, D-dimer, interleukin-6, lactate dehydrogenase, creatine kinase, prothrombin time, aspartate aminotransferase, alanine aminotransferase, total bilirubin and creatinine. Lymphocyte count, platelet count and albumin were significantly lower in patients who died.

CONCLUSION

This pooled analysis of 10 studies including 1584 patients identified significant differences in biomarkers on admission in patients who survived from those who did not. Further research is needed to develop risk stratification models to help with judicious use of limited health-care resources.

Visible and Real Sizes of New COVID-19 Pandemic Waves in Ukraine

Background. To simulate the COVID-19 pandemic dynamics, various data sets and different mathematical models can be used. In particular, previous simulations for Ukraine were based on smoothing of the dependence of the number of cases on time, classical and the generalized SIR (susceptible-infected-removed) models. Different simulation and comparison methods were based on official accumulated number of laboratory confirmed cases and the data reported by Johns Hopkins University. Since both datasets are incomplete (a very large percentage of infected persons are asymptomatic), the accuracy of calculations and predictions is limited. In this paper we will try to assess the degree of data incompleteness and correct the relevant forecasts. Objective. We aimed to estimate the real sizes of two new epidemic waves in Ukraine and compare them with visible dynamics based on the official number of laboratory confirmed cases. We also aimed to estimate the epidemic durations and final numbers of cases. Methods. In this study we use the generalized SIR model for the epidemic dynamics and its known exact solution. The known statistical approach is adopted in order to identify both the degree of data incompleteness and parameters of SIR model. Results. We have improved the method of estimating the unknown parameters of the generalized SIR model and calculated the optimal values ​​of the parameters. In particular, the visibility coefficients and the optimal values of the model parameters were estimated for two pandemic waves in Ukraine occurred in December 2020–March 2021. The real number of cases and the real number of patients spreading the infection versus time were calculated. Predictions of the real final sizes and durations of the pandemic in Ukraine are presented. If current trends continue, the end of the pandemic should be expected no earlier than in August 2022. Conclusions. New method of the unknown parameters identification for the generalized SIR model was proposed, which allows estimating the coefficients of data incompleteness as well. Its application for two pandemic waves in Ukraine has demonstrated that the real number of COVID-19 cases is approximately four times higher than those shown in official statistics. Probably, this situation is typical for other countries. The reassessments of the COVID-19 pandemic dynamics in other countries and clarification of world forecasts are necessary.

Economic Role of Population Density during Pandemics-A Comparative Analysis of Saudi Arabia and China

As a novel infection with relatively high contagiousness, the coronavirus disease emerged as the most pertinent threat to the global community in the twenty-first century. Due to Covid-19's severe economic impacts, the establishment of reliable determining factors can help to alleviate future pandemics. While a population density is often cited as a major determinant of infectious cases and mortality rates, there are both proponents and opponents to this claim. In this framework, the study seeks to assess the role of population density as a predictor of Covid-19 cases and deaths in Saudi Arabia and China during the Covid-19 pandemic. With high infectivity and mortality being a definitive characteristic of overpopulated regions, the authors propose that Henry Kissinger's population reduction theory can be applied as a control measure to control future pandemics and alleviate social concerns. If high-density Chinese regions are more susceptible to Covid-19 than low-density Saudi cities, the authors argue that Neo-Malthusian models can be used as a basis for reducing the impacts of the coronavirus disease on the economic growth in countries with low population density. However, the performed correlation analysis and simple linear regression produced controversial results with no clear connection between the three studied variables. By assessing population density as a determinant of health crises associated with multiple socio-economic threats and epidemiological concerns, the authors seek to reinvigorate the scholarly interest in Neo-Malthusian models as a long-term solution intended to mitigate future disasters. The authors recommend that future studies should explore additional confounding factors influencing the course and severity of infectious diseases in states with different population densities.

Twitter as a Tool for Citizen Education and Sustainable Cities after COVID-19

The social confinement resulting from the COVID-19 crisis temporarily reduced greenhouse gas emissions. Although experts contend that the decrease in pollution rates was not drastic, some surveys detect growth in social concern about the climate. In this new climate-conscious environment, municipalities and local governments are promoting a new way of living and caring for cities, even before they can regain national and international freedom of movement. This work analyzes the connections between new climate awareness arising from the COVID-19 crisis, proposals of sustainable citizenship around the world, and its communication on Twitter to educate the new eco-conscious audience. The methodology mixes quantitative and qualitative analysis, using the Twitonomy Premium tool and the Twitter research tool with data extracted at the end of December 2020. Among the top ten most influential and active accounts, the results show educational institutions, local institutions, companies, neighborhoods, associations, and influencers. The impossibility of living in the city has not prevented citizen education and commitment to make real change for when that city and its citizens return to normality. However, this new normality must be different: more ecological, more responsible, more sustainable, and practiced from early childhood.

Exploratory study on the spatial relationship between emerging infectious diseases and urban characteristics: Cases from Korea

In the modern global context of interconnected populations, the recent emergence of infectious diseases involves complex interactions. The purpose of this study is to investigate the spatial correlations between urban characteristics, taking into account the socio-ecological aspects, and the emergence of infectious diseases. Using exploratory spatial data analysis and spatial regression between the infectious disease emergence data and 14 urban characteristics, we analyzed 225 spatial units in South Korea, where there was a re-emergence of measles and a 2015 outbreak of Middle East Respiratory Syndrome. As results of exploratory spatial data analysis, the emerging infectious diseases had spatial dependence and showed spatial clusters. Spatial regression models showed that urban characteristic factors had different effects according to the type of infectious disease. Common factors were characteristics related to low socioeconomic status in water or food-borne diseases and manageable infectious diseases. Intermittent infections disease epidemics are related to high-quality residential environments and the response capacity of the local government. New infectious diseases are different than other infectious diseases, which are related to the ecological environment. This study suggests spatial policies for preventing infectious diseases considering the spatial relationships between urban characteristics and infectious diseases as well as the management of public health.

Mutagenic and teratogenic toxicity evaluation of Forsythia suspensa leaves aqueous extract

Forsythia suspensa leaves (FSL), rich in phillyrin, forsythiaside A, phillygenin, rutin, and other compounds, is a known traditional Chinese medicine (TCM). It has been effective in heat retreat and detoxification. In this study, we performed the mutagenic and teratogenic toxicity evaluation of FSL aqueous extract (FSLAE) using the bacterial reverse mutation assay (Ames test), mouse bone marrow micronucleus assay, spermatocyte chromosomal aberration assay in mice. Kunming mice and SD rats were used were for the mutagenic and the teratogenic studies, respectively. We found that FSLAE was not mutagenic and did not induce unfavorable chromosomal events. Additionally, the Ames test revealed FSLAE was not genotoxic and showed no mutagenic activity in histidine dependent strains of Salmonella typhimurium at concentrations up to 5000 μg/plate. Likewise, in vivo test revealed no induced micronucleus of mouse bone marrow or chromosome aberration in spermatocytes up to the dose of 10.00 g/kg BW. For the teratogenic evaluations, pregnant rats were treated with 1.04, 2.08, and 4.17 g/kg FSL, and fetuses were examined on the 6-15 day of pregnancy. We observed no maternal toxicity and embryotoxicity related to the treatment. Based on these in vitro and in vivo studies, we concluded the genotoxic and teratogenic safety of FSL.

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

PURPOSE

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

METHODS

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

PRINCIPAL RESULTS

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

CONCLUSIONS

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

Correlation of ambient temperature and COVID-19 incidence in Canada

The SARS-CoV-2 is a novel coronavirus identified as the cause of COVID-19 and, as the pandemic evolves, many have made parallels to previous epidemics such as SARS-CoV (the cause of an outbreak of severe acute respiratory syndrome [SARS]) in 2003. Many have speculated that, like SARS, the activity of SARS-CoV-2 will subside when the climate becomes warmer. We sought to determine the relationship between ambient temperature and COVID-19 incidence in Canada. We analyzed over 77,700 COVID-19 cases from four Canadian provinces (Alberta, British Columbia, Ontario, and Quebec) from January to May 2020. After adjusting for precipitation, wind gust speed, and province in multiple linear regression models, we found a positive, but not statistically significant, association between cumulative incidence and ambient temperature (14.2 per 100,000 people; 95%CI: -0.60-29.0). We also did not find a statistically significant association between total cases or effective reproductive number of COVID-19 and ambient temperature. Our findings do not support the hypothesis that higher temperatures will reduce transmission of COVID-19 and warns the public not to lose vigilance and to continue practicing safety measures such as hand washing, social distancing, and use of facial masks despite the warming climates.

Epidemic Dynamics Kinetic Model and Its Testing on the Covid-19 Epidemic Spread Data

A kinetic model has been proposed for the spread of epidemics, describing the dynamics of the variation in the number of disease-free, infected, and recovered (SIR) cases, based on a lag logistic equation. It has been established that this model predicts the possibility of existence of a quasi-steady-state mode of an epidemic in which the number of infected cases is constant due to the balance of the daily increment of infections and recoveries. Conditions have been identified under which such a mode can be a source of the advance of the second epidemic wave. The COVID-19 pandemic data were used to show the possibility of reliable forecasts based on this model of the spread of an epidemic for a period of up to two months.

SARS-CoV-2 viability under different meteorological conditions, surfaces, fluids and transmission between animals

Since the COVID-19 outbreak, researchers have tried to characterise the novel coronavirus SARS-CoV-2 to better understand the pathogenic mechanisms of the virus and prevent further dissemination. As a consequence, there has been a bloom in scientific research papers focused on the behaviour of the virus in different environmental contexts. Nevertheless, despite these efforts and due to its novelty, available information about this coronavirus is limited, as several research studies are still ongoing. This review aims to shed light on this issue. To that end, we have examined the scientific literature to date regarding the viability of SARS-CoV-2 on surfaces and fluids or under different environmental conditions (temperature, precipitation and UV radiation). We have also addressed the role of animals in the transmission of this coronavirus.

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.

Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China

The outbreak of COVID-19 pandemic has a high spreading rate and a high fatality rate. To control the rapid spreading of COVID-19 virus, Chinese government ordered lockdown policies since late January 2020. The aims of this study are to quantify the relationship between geographic information (i.e., latitude, longitude and altitude) and cumulative infected population, and to unveil the importance of the population density in the spreading speed during the lockdown. COVID-19 data during the period from December 8, 2019 to April 8, 2020 were collected before and after lockdown. After discovering two important geographic factors (i.e., latitude and altitude) by estimating the correlation coefficients between each of them and cumulative infected population, two linear models of cumulative infected population and COVID-19 spreading speed were constructed based on these two factors. Overall, our findings from the models showed a negative correlation between the provincial daily cumulative COVID-19 infected number and latitude/altitude. In addition, population density is not an important factor in COVID-19 spreading under strict lockdown policies. Our study suggests that lockdown policies of China can effectively restrict COVID-19 spreading speed.

Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.

The spread of 2019-nCoV in China was primarily driven by population density. Comment on "Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China" by Zhu et al

Recently, an article published in the journal Science of the Total Environment and authored by Zhu et al. has claimed the "Association between short-term exposure to air pollution and COVID-19 infection" (doi: https://doi.org/10.1016/j.scitotenv.2020.138704). This note shows that the stated dependence between the diffusion of the infection and air pollution may be the result of spurious correlation due to the omission of a common factor, namely, population density. To this end, the relationship between demographic, socio-economic, and environmental conditions and the spread of the novel coronavirus in China is analyzed with spatial regression models on variables deflated by population size. The infection rate - as measured by the number of cases per 100 thousand inhabitants - is found to be strongly related to the population density. At the same time, the association with air pollution is detected with a negative sign, which is difficult to interpret.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM_2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM_2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.

A flow virometry process proposed for detection of SARS-CoV-2 and large-scale screening of COVID-19 cases

The viral pneumonia COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly over 210 countries and declared as pandemic by WHO. WHO has emphasized on the scale-up of testing capacity, followed by isolation of infected individuals, and contact tracing, as the ‘backbone’ of managing the pandemic. Globally, the detection of SARS-CoV-2 in patients is done by real-time PCR (RT-PCR) and blood antibody-based testing. Here, a flow cytometry-based high-throughput screening system is proposed for testing of COVID-19 cases where the virus particle binds to specific primary antibodies and the resultant virus–antibody complex then binds to fluorescent-tagged secondary antibodies. The fluorescence signal could be measured in a flow channel for qualitative detection of virus in the test sample.

Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world's regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country's response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.

Studying the trend of the novel coronavirus series in Mauritius and its implications

Mauritius stands as one of the few countries in the world to have controlled the current pandemic, the novel coronavirus 2019 (COVID-19) to a significant extent in a relatively short lapse of time. Owing to uncertainties and crisis amid the pandemic, as an emergency announcement, the World Health Organization (WHO) solicits the help of health authorities, especially, researchers to conduct in-depth research on the evolution and treatment of COVID-19. This paper proposes an integer-valued time series model to analyze the series of COVID-19 cases in Mauritius wherein the corresponding innovation term accommodates for covariate specification. In this set-up, sanitary curfew followed by sanitization and sensitization campaigns, time factor and safe shopping guidelines have been tested as the most significant variables, unlike climatic conditions. The over-dispersion estimates and the serial auto-correlation parameter are also statistically significant. This study also confirms the presence of some unobservable effects like the pathological genesis of the novel coronavirus and environmental factors which contribute to rapid propagation of the zoonotic virus in the community. Based on the proposed COM-Poisson mixture models, we could predict the number of COVID-19 cases in Mauritius. The forecasting results provide satisfactory mean squared errors. Such findings will subsequently encourage the policymakers to implement strict precautionary measures in terms of constant upgrading of the current health care and wellness system and re-enforcement of sanitary obligations.