Understanding COVID-19 diffusion requires an interdisciplinary, multi-dimensional approach

Shedding light on the cellular mechanisms involved in the combined adverse effects of fine particulate matter and SARS-CoV-2 on human lung cells

Airborne pathogens represent a topic of scientific relevance, especially considering the recent COVID-19 pandemic. Air pollution, and particulate matter (PM) in particular, has been proposed as a possible risk factor for the onset and spread of pathogen-driven respiratory diseases. Regarding SARS-CoV-2 infection, exposure to fine PM (PM2.5, particles with an aerodynamic diameter < 2.5 μm) has been associated with increased incidence of the COVID-19 disease. To provide useful insights into the mechanisms through which PM might be involved in infection, we exposed human lung cells (A549) to PM2.5 and SARS-CoV-2, to evaluate the toxicological properties and the molecular pathways activated when airborne particles are combined with viral particles. Winter PM2.5 was collected in a metropolitan urban area and its physico-chemical composition was analyzed. A549 cells were exposed to SARS-CoV-2 concomitantly or after pre-treatment with PM2.5. Inflammation, oxidative stress and xenobiotic metabolism were the main pathways investigated. Results showed that after 72 h of exposure PM2.5 significantly increased the expression of the angiotensin-converting enzyme 2 (ACE2) receptor, which is one of the keys used by the virus to infect host cells. We also analyzed the endosomal route in the process of internalization, by studying the expression of RAB5 and RAB7. The results show that in cells pre-activated with PM and then exposed to SARS-CoV-2, RAB5 expression is significantly increased. The activation of the inflammatory process was then studied. Our findings show an increase of pro-inflammatory markers (NF-kB and IL-8) in cells pre-activated with PM for 72 h and subsequently exposed to the virus for a further 24 h, further demonstrating that the interaction between PM and SARS-CoV-2 determines the severity of the inflammatory responses in lung epithelial cells. In conclusion, the study provides mechanistic biological evidence of PM contribution to the onset and progression of viral respiratory diseases in exposed populations.

Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil

OBJECTIVES

In 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal.

STUDY DESIGN

Ecological retrospective study.

METHODS

We applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables.

RESULTS

We showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality.

CONCLUSIONS

The present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.

Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities

The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.

Geoepidemiological perspective on COVID-19 pandemic review, an insight into the global impact

The COVID-19 pandemic showed major impacts, on societies worldwide, challenging healthcare systems, economies, and daily life of people. Geoepidemiology, an emerging field that combines geography and epidemiology, has played a vital role in understanding and combatting the spread of the virus. This interdisciplinary approach has provided insights into the spatial patterns, risk factors, and transmission dynamics of the COVID-19 pandemic at different scales, from local communities to global populations. Spatial patterns have revealed variations in incidence rates, with urban-rural divides and regional hotspots playing significant roles. Cross-border transmission has highlighted the importance of travel restrictions and coordinated public health responses. Risk factors such as age, underlying health conditions, socioeconomic factors, occupation, demographics, and behavior have influenced vulnerability and outcomes. Geoepidemiology has also provided insights into the transmissibility and spread of COVID-19, emphasizing the importance of asymptomatic and pre-symptomatic transmission, super-spreading events, and the impact of variants. Geoepidemiology should be vital in understanding and responding to evolving new viral challenges of this and future pandemics.

Comparing the spreading characteristics of monkeypox (MPX) and COVID-19: Insights from a quantitative model

Climate change is acknowledged to directly affect not only the environment, economy, and society but also the transmission dynamics of infectious diseases, thereby impacting public health. The recent experiences with the spread of SARS-CoV-2 and Monkeypox have highlighted the complex and interconnected nature of infectious diseases, which are strongly linked to various determinants of health. Considering these challenges, adopting a new vision such as the trans-disciplinary approach appears to be imperative. This paper proposes a new theory about viruses' spread, based on a biological model, accounting for the optimisation of energy and material resources for organisms' survival and reproduction in the environment. The approach applies Kleiber's law scaling theory, originally developed in biology, to model community dynamics in cities. A simple equation can be used to model pathogen spread without accounting for each species' physiology by leveraging the superlinear scaling of variables with population size. This general theory offers several advantages, including the ability to explain the rapid and surprising spread of both SARS-CoV-2 and Monkeypox. The proposed model shows similarities in the spreading processes of both viruses, based on the resulting scaling factors, and opens new avenues for research. By fostering cooperation and integrating knowledge from different disciplines to effectively tackle the multifaceted dimensions of disease outbreaks, we can work towards preventing future health emergencies.

Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest

The long-distance spreading and transport of airborne particulate matter (PM) of biogenic or chemical compounds, which are thought to be possible carriers of SARS-CoV-2 virions, can have a negative impact on the incidence and severity of COVID-19 viral disease. Considering the total Aerosol Optical Depth at 550 nm (AOD) as an atmospheric aerosol loading variable, inhalable fine PM with a diameter ≤2.5 µm (PM2.5) or coarse PM with a diameter ≤10 µm (PM10) during 26 February 2020-31 March 2022, and COVID-19's five waves in Romania, the current study investigates the impact of outdoor PM on the COVID-19 pandemic in Bucharest city. Through descriptive statistics analysis applied to average daily time series in situ and satellite data of PM2.5, PM10, and climate parameters, this study found decreased trends of PM2.5 and PM10 concentrations of 24.58% and 18.9%, respectively compared to the pre-pandemic period (2015-2019). Exposure to high levels of PM2.5 and PM10 particles was positively correlated with COVID-19 incidence and mortality. The derived average PM2.5/PM10 ratios during the entire pandemic period are relatively low (<0.44), indicating a dominance of coarse traffic-related particles' fraction. Significant reductions of the averaged AOD levels over Bucharest were recorded during the first and third waves of COVID-19 pandemic and their associated lockdowns (~28.2% and ~16.4%, respectively) compared to pre-pandemic period (2015-2019) average AOD levels. The findings of this research are important for decision-makers implementing COVID-19 safety controls and health measures during viral infections.

Economic costs related to coronavirus disease 2019 pandemic: A systematic literature review

Due to the COVID-19 pandemic, the pressure on healthcare systems has been tremendous and it is having a huge economic impact. The objective of this paper is to carry out a systematic literature review of the publications that have analysed the costs derived from COVID-19 from the patient's perspective. Not only is this study aimed at measuring the impact of COVID-19 in economic terms, but also in qualitative terms, analysing, for instance, the types of costs (direct and indirect) and their variations depending on the geographical area under study. Searches were conducted in PubMed, Cochrane Library, Web of Science and Scopus. The time frame for the analysis was from the start of the pandemic until 9th December 2021. A total of 322 papers were found. The number of articles assessed for eligibility was 32, and after applying the exclusion criteria, 13 papers were included in the qualitative synthesis. Most of the studies analysed only direct costs (69.23%) and were focused on Asia (61.54%), where the larger indirect costs, as well as the greater total costs, were found. However, the higher costs per patient and year were shown for the United States. This investigation showed the importance of COVID-19 in national, regional and local budgets. More studies are to be developed in Europe due to both the existing differences in the health care systems and financing by country and the difference in the incidence of COVID-19 by country and wave.

Virus-laden droplet nuclei in vortical structures associated with recirculation zones in indoor environments: A possible airborne transmission of SARS-CoV-2

Droplet nuclei dispersion patterns in indoor environments are reviewed from a physics view to explore the possibility of airborne transmission of SARS-CoV-2. This review analyzes works on particle dispersion patterns and their concentration in vortical structures in different indoor environments. Numerical simulations and experiments reveal the formation of the buildings' recirculation zones and vortex flow regions by flow separation, airflow interaction around objects, internal dispersion of airflow, or thermal plume. These vortical structures showed high particle concentration because particles are trapped for long periods. Then a hypothesis is proposed to explain why some medical studies detect the presence of SARS-CoV-2 and others do not detect the virus. The hypothesis proposes that airborne transmission is possible if virus-laden droplet nuclei are trapped in vortical structures associated with recirculation zones. This hypothesis is reinforced by a numerical study in a restaurant that presented possible evidence of airborne transmission by a large recirculating air zone. Furthermore, a medical study in a hospital is discussed from a physical view for identifying the formation of recirculation zones and their relation with positive tests for viruses. The observations show air sampling site located in this vortical structure is positive for the SARS-CoV-2 RNA. Therefore, the formation of vortical structures associated with recirculation zones should be avoided to minimize the possibility of airborne transmission. This work tries to understand the complex phenomenon of airborne transmission as a way in the prevention of transmission of infectious diseases.

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

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

The spread of the omicron variant: Identification of knowledge gaps, virus diffusion modelling, and future research needs

The World Health Organization (WHO) recognised variant B.1.1.529 of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a variant of concern, termed "Omicron", on November 26, 2021. Its diffusion was attributed to its several mutations, which allow promoting its ability to diffuse worldwide and its capability in immune evasion. As a consequence, some additional serious threats to public health posed the risk to undermine the global efforts made in the last two years to control the pandemic. In the past, several works were devoted to discussing a possible contribution of air pollution to the SARS-CoV-2 spread. However, to the best of the authors' knowledge, there are still no works dealing with the Omicron variant diffusion mechanisms. This work represents a snapshot of what we know right now, in the frame of an analysis of the Omicron variant spread. The paper proposes the use of a single indicator, commercial trade data, to model the virus spread. It is proposed as a surrogate of the interactions occurring between humans (the virus transmission mechanism due to human-to-human contacts) and could be considered for other diseases. It allows also to explain the unexpected increase in infection cases in China, detected at beginning of 2023. The air quality data are also analyzed to evaluate for the first time the role of air particulate matter (PM) as a carrier of the Omicron variant diffusion. Due to emerging concerns associated with other viruses (such as smallpox-like virus diffusion in Europe and America), the proposed approach seems to be promising to model the virus spreading.

Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States

BACKGROUND

COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota.

METHODS

COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters.

RESULTS

County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed.

CONCLUSION

Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.

Combined Impact of Omicron Vaccination and Environmental Risk Exposure: A Thailand Case Study

This research aimed to determine the levels of COVID-19 booster dose vaccinations in Thai populations in areas with environmental risk exposure during the Omicron outbreak. Five of twenty provinces in Thailand were selected by assessing environmental risk exposure for study settings. A total of 1038 people were interviewed by a structured questionnaire. The predicting factors of COVID-19 booster dose vaccinations were analyzed by univariate and multivariate analysis. The results showed that 69.4% (95% CI 66.5-72.1) of the population was vaccinated with COVID-19 booster doses. Multiple logistics regression revealed that the female gender (AOR 1.49, 95% CI 1.11-2.00), all age groups from 38 to 60 years old, all education levels of at least secondary school, high income (AOR 1.16, 95% CI 1.15-2.24), populations having experience with COVID-19 infection (AOR 2.27, 95% CI 2.05-3.76), knowledge of vaccine (AOR 1.78, 95% CI 1.11-2.83), and trusting attitude (AOR 1.76, 95% CI 1.32-2.36) were factors among those more likely to take COVID-19 booster dose vaccinations in high-environmental-risk-exposure areas. Therefore, an effective booster dose campaign with education programs to increase attitudes toward booster vaccinations should be implemented for the resilience of COVID-19 prevention and control.

Are mega-events super spreaders of infectious diseases similar to COVID-19? A look into Tokyo 2020 Olympics and Paralympics to improve preparedness of next international events

Tokyo Summer Olympics and Paralympics have raised social issues regarding the potential rise in COVID-19 cases in Japan and risks associated with the safe organization of mega sporting events during the pandemic, such as the FIFA World Cup Qatar 2022. This study investigates the Tokyo Summer Olympics as a unique case study to clarify the drivers of infectivity and provide guidelines to host countries for the safe organization of subsequent international sporting events. The result here reveals that Tokyo and Japan did not experience a rise in confirmed cases of COVID-19 due to the hosting of the Summer Olympics. Still, transmission dynamics seems to be mainly driven by the high density of population (about 1.2%, p-value <0.001) like other larger cities in Japan (result confirmed with Mann-Whitney U test, significance at 0.05). Our study provided evidence that hosting mega sporting events during this COVID-19 pandemic is safe if strictly maintained the precautions with non-pharmaceutical (and pharmaceutical) measures of control of infections. The Tokyo Summer Olympics hosting will be exemplary for next international events due to the successful implementation of preventive measures during COVID-19 pandemic crisis.

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

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

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

Antiviral Peptides as Anti-Influenza Agents

Influenza viruses represent a leading cause of high morbidity and mortality worldwide. Approaches for fighting flu are seasonal vaccines and some antiviral drugs. The development of the seasonal flu vaccine requires a great deal of effort, as careful studies are needed to select the strains to be included in each year's vaccine. Antiviral drugs available against Influenza virus infections have certain limitations due to the increased resistance rate and negative side effects. The highly mutative nature of these viruses leads to the emergence of new antigenic variants, against which the urgent development of new approaches for antiviral therapy is needed. Among these approaches, one of the emerging new fields of "peptide-based therapies" against Influenza viruses is being explored and looks promising. This review describes the recent findings on the antiviral activity, mechanism of action and therapeutic capability of antiviral peptides that bind HA, NA, PB1, and M2 as a means of countering Influenza virus infection.

Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level

Respiratory infectious diseases (e.g., COVID-19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies focus on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of transmission trends. Firstly, the front two modules reproduce the movements of individuals and the droplets of infectors' expiratory activities, respectively. Then, the outputs are fed to the third module to estimate the personal exposure risk. Finally, the number of new cases is predicted in the final module. By predicting the new COVID- 19 cases in the United States, the performances of our model and 4 other existing macroscopic or microscopic models are compared. Specifically, the mean absolute error, root mean square error, and mean absolute percentage error provided by the proposed model are respectively 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models. The quantitative results reveal that our model can accurately predict the transmission trends from a microscopic perspective, and it can benefit the further investigation of many microscopic disease transmission factors (e.g., non-walkable areas and facility layouts).

"pySewage": a hybrid approach to predict the number of SARS-CoV-2-infected people from wastewater in Brazil

It is well known that the new coronavirus pandemic has global environmental, public health, and economic implications. In this sense, this study aims to monitor SARS-CoV-2 in the largest wastewater treatment plant of Goiânia, which processes wastewater from more than 700,000 inhabitants, and to correlate the molecular and clinical data collected. Influent and effluent samples were collected at Dr. Helio de Seixo Britto's wastewater treatment plant from January to August 2021. Viral concentration was performed with polyethylene glycol before viral RNA extraction. Real-time qPCR (N1 and N2 gene assays) was performed to detect and quantify the viral RNA present in the samples. The results showed that 43.63% of the samples were positive. There is no significant difference between the detection of primers N1 (mean 3.23 log10 genome copies/L, std 0.23) and N2 (mean 2.95 log10 genome copies/L, std 0.29); also, there is no significant difference between the detection of influent and effluent samples. Our molecular data revealed a positive correlation with clinical data, and infection prevalence was higher than clinical data. In addition, we developed a user-friendly web application to predict the number of infected people based on the detection of viral load present in wastewater samples and may be applied as a public policy strategy for monitoring ongoing outbreaks.

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

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

Assessing temporal correlation in environmental risk factors to design efficient area-specific COVID-19 regulations: Delhi based case study

Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value < 0.001) of COVID-19 cases and mortalities with PM2.5 (r > 0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.

Mapping the research landscape of Covid-19 from social sciences perspective: a bibliometric analysis

COVID-19 has emerged as a widely researched topic and the academia has taken interest in the effects of COVID-19 in various sectors of human life and society. Most of the bibliometric research addresses scientific contributions in medicine, health, and virology related topics, with very little emphasis on social sciences. Therefore, to address this gap, a bibliometric analysis of research related to COVID-19 in the subject area of social sciences was performed on selected publications from January 2020 to mid-2021. A total of 9289 articles were analysed to identify major emerging themes of Covid-19 and social sciences and how research collaborations between countries have helped in communicating critical issues to academia. The empirical results indicate the dominance of psychology and business economics subjects in the social sciences sphere, with the emerging themes as psychosocial problems among people, economics, the outbreak of SARS, and discussions on the quality of life in terms of surroundings and environment. The study also suggests that more collaborations between social scientists working in different nations is required to explore the less focussed themes addressing the local challenges of poor nations.

Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore

Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5-1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.

Health, Economic and Social Development Challenges of the COVID-19 Pandemic: Strategies for Multiple and Interconnected Issues

The COVID-19-pandemic-related economic and social crises are leading to huge challenges for all spheres of human life across the globe. Various challenges highlighted by this pandemic include, but are not limited to, the need for global health cooperation and security, better crisis management, coordinated funding in public health emergencies, and access to measures related to prevention, treatment and control. This systematic review explores health, economic and social development issues in a COVID-19 pandemic context and aftermath. Accordingly, a methodology that focuses on identifying relevant literature with a focus on meta-analysis is used. A protocol with inclusion and exclusion criteria was developed, with articles from 15 December 2019 to 15 March 2022 included in the study. This was followed by a review and data analysis. The research results reveal that non-pharmaceutical measures like social distancing, lockdown and quarantine have created long-term impacts on issues such as changes in production and consumption patterns, market crashes resulting in the closure of business operations, and the slowing down of the economy. COVID-19 has exposed huge health inequalities across most countries due to social stratification and unequal distribution of wealth and/or resources. People from lower socio-economic backgrounds lack access to essential healthcare services during this critical time for both COVID-19 and other non-COVID ailments. The review shows that there is minimal literature available with evidence and empirical backup; similarly, data/studies from all countries/regions are not available. We propose that there is a need to conduct empirical research employing a trans-disciplinary approach to develop the most effective and efficient strategies to combat the pandemic and its aftermath. There is a need to explore the social and ecological determinants of this contagious infection and develop strategies for the prevention and control of COVID-19 or similar infections in future.

Exposure to particulate matter: Direct and indirect role in the COVID-19 pandemic

Knowing the transmission factors and the natural environment that favor the spread of a viral infection is crucial to stop outbreaks and develop effective preventive strategies. This work aims to evaluate the role of Particulate Matter (PM) in the COVID-19 pandemic, focusing especially on that of PM as a vector for SARS-CoV-2. Exposure to PM has been related to new cases and to the clinical severity of people infected by SARS-CoV-2, which can be explained by the oxidative stress and the inflammatory response generated by these particles when entering the respiratory system, as well as by the role of PM in the expression of ACE-2 in respiratory cells in human hosts. In addition, different authors have detected SARS-CoV-2 RNA in PM sampled both in outdoor and indoor environments. The results of various studies lead to the hypothesis that the aerosols emitted by an infected person could be deposited in other suspended particles, sometimes of natural but especially of anthropogenic origin, that form the basal PM. However, the viability of the virus in PM has not yet been demonstrated. Should PM be confirmed as a vector of transmission, prevention strategies ought to be adapted, and PM sampling in outdoor environments could become an indicator of viral load in a specific area.

Epidemiologic characteristics and differential management strategies of seven case series with COVID-19 outbreaks caused by asymptomatic carriers from June 2020 to May 2021 in China

With the COVID-19 epidemic quickly under control in China in the early stage of 2020, global cooperation/communications may pose great challenges to epidemic control and prevention in the country. Large-scale spread by asymptomatic carriers was a concern. We obtained data on new cluster outbreak regions with COVID-19 caused by asymptomatic carriers from June 2020 to May 2021 in China, and reported the epidemiological characteristics, the possible routes of viral transmission and infection, and different control strategies. These results show the importance of regular screening for high-risk populations and differential management strategies for epidemic control, which provide an objective basis for suppressing the spread of the SARS-CoV-2 virus. These experiences can be used as a reference to minimize the subsequent spread of virus mutants in various places.

Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year

The present work aims to give an overview on the international scientific papers related to the territorial spreading of SARS-CoV-2, with a specific focus upon applied quantitative geography and territorial analysis, to define a general structure for epidemiological geography research. The target publications were based on GIS spatial analysis, both in the sense of topological analysis and descriptive statistics or lato sensu geographical approaches. The first basic purpose was to organize and enhance the vast knowledge developments generated hitherto by the first pandemic that was studied "on-the-fly" all over the world. The consequent target was to investigate to what extent researchers in geography were able to draw scientifically consistent conclusions about the pandemic evolution, as well as whether wider generalizations could be reasonably claimed. This implied an analysis and a comparison of their findings. Finally, we tested what geographic approaches can say about the pandemic and whether a reliable spatial analysis routine for mapping infectious diseases could be extrapolated. We selected papers proposed for publication during 2020 and 209 articles complied with our parameters of query. The articles were divided in seven categories to enhance existing commonalities. In some cases, converging conclusions were extracted, and generalizations were derived. In other cases, contrasting or inconsistent findings were found, and possible explanations were provided. From the results of our survey, we extrapolated a routine for the production of epidemiological geography analyses, we highlighted the different steps of investigation that were attained, and we underlined the most critical nodes of the methodology. Our findings may help to point out what are the most critical conceptual challenges of epidemiological mapping, and where it might improve to engender informed conclusions and aware outcomes.

SARS-CoV-2 Transmission Risk Model in an Urban Area of Mexico, Based on GIS Analysis and Viral Load

The COVID-19 pandemic highlighted health systems vulnerabilities, as well as thoughtlessness by governments and society. Due to the nature of this contingency, the use of geographic information systems (GIS) is essential to understand the SARS-CoV-2 distribution dynamics within a defined geographic area. This work was performed in Tepic, a medium-sized city in Mexico. The residence of 834 COVID-19 infected individuals was georeferenced and categorized by viral load (Ct). The analysis took place during the maximum contagion of the first four waves of COVID-19 in Mexico, analyzing 158, 254, 143, and 279 cases in each wave respectively. Then heatmaps were built and categorized into five areas ranging from very low to very high risk of contagion, finding that the second wave exhibited a greater number of cases with a high viral load. Additionally, a spatial analysis was performed to measure urban areas with a higher risk of contagion, during this wave this area had 19,203.08 km² (36.11% of the city). Therefore, a kernel density spatial model integrated by meaningful variables such as the number of infected subjects, viral load, and place of residence in cities, to establish geographic zones with different degrees of infection risk, could be useful for decision-making in future epidemic events.

"SARS-CoV-2 is transmitted by particulate air pollution": Misinterpretations of statistical data, skewed citation practices, and misuse of specific terminology spreading the misconception

In epidemiology, there are still outdated myths associated with the spread of respiratory infections. Recently, we have witnessed the origination of a new misconception, to the effect that SARS-CoV-2 is transmitted in the open air by way of particulate air pollution (atmospheric particulate matter (PM)). There is no evidence to support the idea behind this misconception. Nevertheless, more and more people are involved in animated debate and the number of studies concerning atmospheric PM as a carrier of SARS-CoV-2 is growing rapidly. In this work, the origin of the misconception was investigated, and the published papers which have contributed to the spread of this myth were analyzed. The results show that the following factors lie behind the origin and spread of the misconception: a) The specific terminology is not always clearly defined or consistently used by scientists. In particular, the terms 'particulate matter', 'atmospheric aerosol particles', 'air pollutants', and 'atmospheric aerosols' need to be clarified, and besides they are often equated to 'infectious aerosols', 'virus-bearing aerosols', 'bio-aerosols', 'virus-laden particles', 'respiratory aerosol/droplets', and 'droplet nuclei'. b) Authors misinterpret statistical data and information from other sources. Interpretation of the correlation between PM levels and the increasing incidence and severity of COVID-19 infection, is often changed from "PM may reflect the indirect action of certain atmospheric conditions that maintain infectious nuclei suspended for prolonged periods, parameters that also act on atmospheric pollutants" to "PM could cause an increase in infectious droplets/aerosols containing SARS-CoV-2." This is a dramatic change to the meaning. Moreover, it is often not taken into account that PM may reflect activities in areas with high population density and this population density at the same time contributes to the spread COVID-19. c) Skewed citation practices. Many authors cite a hypothetical conclusion from an original study, then other authors cite the papers of these authors as primary sources. This practice leads to the effect that there are many witnesses to a 'phenomenon' that did not ever occur. Thus, the terminology used in interdisciplinary communications should be more nuanced and defined precisely. Authors should be more careful when citing unconfirmed data (and hypotheses) as well as in interpreting statistical data so as to avoid confusion and spreading false information. This is especially important now in the era of the COVID-19 pandemic.

A global assessment of COVID-19 diffusion based on a single indicator: Some considerations about air pollution and COVID-19 spread

The extraordinariness of COVID-19 occurred in a world that was completely unprepared to face it. To justify this, sometimes literature proposes positive associations between concentrations of some air pollutants and SARS-CoV-2 mortality and infectivity. However, several of these studies are affected by incomplete data analysis and/or incorrect accounts of spread dynamics that can be attributed to respiratory viruses. Based on separate analyses involving all the USA states and globally all the world countries suffering from the pandemic, this communication shows that commercial trade seems to be a good indicator of virus spread, being proposed as a surrogate of human-to-human interactions. The results of this study strongly support the conclusion that this new indicator could result fundamental to model (and avoid) possible future pandemics, strongly suggesting dedicated studies devoted to better investigate its significance.

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (R_n) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), ozone (O₃)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and R_n, and 3) the interaction and non-linear effects of the different variables on R_n, based on GeoDetector and Boosted regression tree. The results showed that the global R_n had was spatially clustered, and the average R_n increased From March to November 2020. Global R_n was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO₂, SO₂, O₃) and socioeconomic aspects (GDP, GHE). The interaction of SO₂ and O₃, SO₂ and RH, and O₃ and T strongly affected R_n. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.

Airborne magnetic nanoparticles may contribute to COVID-19 outbreak: Relationships in Greece and Iran

This work attempts to shed light on whether the COVID-19 pandemic rides on airborne pollution. In particular, a two-city study provides evidence that PM_2.5 contributes to the timing and severity of the epidemic, without adjustment for confounders. The publicly available data of deaths between March and October 2020, updated it on May 30, 2021, and the average seasonal concentrations of PM_2.5 pollution over the previous years in Thessaloniki, the second-largest city of Greece, were investigated. It was found that changes in coronavirus-related deaths follow changes in air pollution and that the correlation between the two data sets is maximized at the lag time of one month. Similar data from Tehran were gathered for comparison. The results of this study underscore that it is possible, if not likely, that pollution nanoparticles are related to COVID-19 fatalities (Granger causality, p < 0.05), contributing to the understanding of the environmental impact on pandemics.

Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis

Coronavirus disease 2019 (COVID-19) continues to be a pandemic threat that is generating a constant state of alert in manifold countries. One of the strategies of defense against infectious diseases (e.g., COVID-19) is the vaccinations that decrease the numbers of infected individuals and deaths. In this context, the optimal level of vaccination for COVID-19 is a basic point to control this pandemic crisis in society. The study here,-using data of doses of vaccines administered per 100 inhabitants, confirmed cases and case fatality ratio of COVID-19 between countries (N=192) from March to May 2021,- clarifies the optimal levels of vaccination for reducing the number of infected individuals and, consequently, the numbers of deaths at global level. Findings reveal that the average level of administering about 80 doses of vaccines per 100 inhabitants between countries can sustain a reduction of confirmed cases and number of deaths. In addition, results suggest that an intensive vaccination campaign in the initial phase of pandemic wave leads to a lower optimal level of doses administered per 100 inhabitants (roughly 47 doses of vaccines administered) for reducing infected individuals; however, the growth of pandemic wave (in May, 2021) moves up the optimal level of vaccines to about 90 doses for reducing the numbers of COVID-19 related infected individuals. All these results here could aid policymakers to prepare optimal strategies directed to a rapid COVID-19 vaccination rollout, before the takeoff of pandemic wave, to lessen negative effects of pandemic crisis on environment and socioeconomic systems.

Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles

The outbreak of coronavirus disease 2019 (COVID-19) has had a significant repercussion on the health, economy, politics and environment, making coronavirus-related issues more complicated and difficult to adequately address by relying on a single field. Interdisciplinary research can provide an effective solution to complex issues in the related field of coronavirus. However, whether coronavirus-related research becomes more interdisciplinary still needs corroboration. In this study, we investigate interdisciplinary status of the coronavirus-related fields via the COVID-19 Open Research Dataset (CORD-19). To this end, we calculate bibliometric indicators of interdisciplinarity and apply a co-occurrence analysis method. The results show that co-occurrence relationships between cited disciplines have evolved dynamically over time. The two types of co-occurrence relationships, Immunology and Microbiology & Medicine and Chemical Engineering & Chemistry, last for a long time in this field during 1990-2020. Moreover, the number of disciplines cited by coronavirus-related research increases, whereas the distribution of disciplines is uneven, and this field tends to focus on several dominant disciplines such as Medicine, Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology. We also measure the disciplinary diversity of COVID-19 related papers published from January to December 2020; the disciplinary variety shows an upward trend, while the degree of disciplinary balance shows a downward trend. Meanwhile, the comprehensive index ²D^s demonstrates that the degree of interdisciplinarity in coronavirus field decreases between 1990 and 2019, but it increases in 2020. The results help to map the interdisciplinarity of coronavirus-related research, gaining insight into the degree and history of interdisciplinary cooperation.

SARS-CoV-2 and other pathogens in municipal wastewater, landfill leachate, and solid waste: A review about virus surveillance, infectivity, and inactivation

This review discusses the techniques available for detecting and inactivating of pathogens in municipal wastewater, landfill leachate, and solid waste. In view of the current COVID-19 pandemic, SARS-CoV-2 is being given special attention, with a thorough examination of all possible transmission pathways linked to the selected waste matrices. Despite the lack of works focused on landfill leachate, a systematic review method, based on cluster analysis, allows to analyze the available papers devoted to sewage sludge and wastewater, allowing to focalize the work on technologies able to detect and treat pathogens. In this work, great attention is also devoted to infectivity and transmission mechanisms of SARS-CoV-2. Moreover, the literature analysis shows that sewage sludge and landfill leachate seem to have a remote chance to act as a virus transmission route (pollution-to-human transmission) due to improper collection and treatment of municipal wastewater and solid waste. However due to the incertitude about virus infectivity, these possibilities cannot be excluded and need further investigation. As a conclusion, this paper shows that additional research is required not only on the coronavirus-specific disinfection, but also the regular surveillance or monitoring of viral loads in sewage sludge, wastewater, and landfill leachate. The disinfection strategies need to be optimized in terms of dosage and potential adverse impacts like antimicrobial resistance, among many other factors. Finally, the presence of SARS-CoV-2 and other pathogenic microorganisms in sewage sludge, wastewater, and landfill leachate can hamper the possibility to ensure safe water and public health in economically marginalized countries and hinder the realization of the United Nations' sustainable development goals (SDGs).

Evolution of the pandemic: Analysis of demographic characteristics of COVID-19-infected patients during its two waves in Gwalior district of central India

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Preparedness of countries to face COVID-19 pandemic crisis: Strategic positioning and factors supporting effective strategies of prevention of pandemic threats

The Coronavirus Disease 2019 (COVID-19) continues to generate a constant pandemic threat with new mutations of the viral agent (SARS-CoV-2) that create socioeconomic issues. One of the fundamental problems is the evaluation of the preparedness of countries to cope with COVID-19 pandemic crisis to detect and support factors associated with the reduction of mortality and the growth of vaccinations in society. The study here confronts this problem by developing two basic indexes, which measure the performance of countries to face pandemic threats. In particular, the Index r (as resilience) detects the countries having the best performance in the reduction of the negative impact of mortality related to COVID-19 pandemic and the Index p (as preparedness and prevention) assesses best-performer countries to support COVID-19 vaccinations in order to constrain future pandemic threats and support the recovery of socioeconomic systems. Index of resilience is a composite measure based on three indicators associated with COVID-19, given by average mortality, hospital occupancy and Intensive Care Units occupancy per 100 000 people, producing an overall score; Index of preparedness/prevention is a composite measure of two indicators related to COVID-19 vaccinations (i.e., doses of vaccines administered and total vaccinates per 100 000 people), producing also an overall score of performance. The application of these indexes on a case study of European countries, having a homogenous socioeconomic area, shows the strategic positioning of countries to cope with a major pandemic threat. Findings reveal that all countries have some weaknesses and no country has a high preparedness to cope with a major epidemic or pandemic. Moreover, results suggest that best-performer countries to cope with COVID-19 pandemic crisis have a smaller size of population and/or better public governance, associated with high expenditures in health system. These indexes can help policymakers for designing effective strategies to improve preparedness and prevention of countries to face future pandemic threats.

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

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

Exploring the effects of PM2.5 and temperature on COVID-19 transmission in Seoul, South Korea

With a recent surge of the new severe acute respiratory syndrome-coronavirus 2 (SARS-Cov-2, COVID-19) in South Korea, this study attempts to investigate the effects of environmental conditions such as air pollutants (PM_2.5) and meteorological covariate (Temperature) on COVID-19 transmission in Seoul. To account for unobserved heterogeneity in the daily confirmed cases of COVID-19 across 25 contiguous districts within Seoul, we adopt a full Bayesian hierarchical approach for the generalized linear mixed models. A formal statistical analysis suggests that there exists a positive correlation between a 7-day lagged effect of PM_2.5 concentration and the number of confirmed COVID-19 cases, which implies an elevated risk of the infectious disease. Conversely, temperature has shown a negative correlation with the number of COVID-19 cases, leading to reduction in relative risks. In addition, we clarify that the random fluctuation in the relative risks of COVID-19 mainly originates from temporal aspects, whereas no significant evidence of variability in relative risks is observed in terms of spatial alignment of the 25 districts. Nevertheless, this study provides empirical evidence using model-based formal assessments regarding COVID-19 infection risks in 25 districts of Seoul from a different perspective.

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.

A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases

Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2%. A significant number of epidemic diseases consisting of human Coronavirus display patterns. In this study, with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict the number of cases. First, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared, and we recommend further research.

Modeling of COVID-19 propagation with compartment models

The current pandemic is a great challenge for several research areas. In addition to virology research, mathematical models and simulations can be a valuable contribution to the understanding of the dynamics of the pandemic and can give recommendations to both physicians and politicians. In this paper we give an overview about mathematical models to describe the pandemic by differential equations. As a matter of principle the historic origin of the epidemic growth models will be remembered. Moreover we discuss models for the actual pandemic of 2020/2021. This will be done based on actual data of people infected with COVID-19 from the European Centre for Disease Prevention and Control (ECDC), input parameters of mathematical models will be determined and applied. These parameters will be estimated for the UK, Italy, Spain, and Germany and used in a SIR-type model. As a basis for the model's calibration, the initial exponential growth phase of the COVID-19 pandemic in the named countries is used. Strategies for the commencing and ending of social and economic shutdown measures are discussed. To respect heterogeneity of the people density in the different federal states of Germany diffusion effects are considered.

A post-pandemic sustainable scenario: What actions can be pursued to increase the raw materials availability?

On January 30, 2020, COVID-19 outbreak, detected for the first time in Wuhan (China), was declared by WHO a Public Health Emergency. In a strongly connected world, the consequent slowdown of the Chinese economy contributed to disrupt the global supply chains of several products. In a post-pandemic scenario, the expected rapid increase in demand of critical raw materials (associated with the transition to more green energy sources), coupled with the problems that some mining activities are relegated only in certain countries and regions, must be considered in a sustainable perspective. This work analyses the literature about (critical) raw materials and COVID-19, not only to present the impact of the pandemic on their supply, but also to propose some actions that should be pursued in a post-pandemic renaissance scenario, to increase raw materials availability, with great attention to most critical ones, in the frame of circular economy principles. The post-pandemic possibilities are evaluated and suitable actions are suggested to secure the raw materials availability for the foreseen increase of investments in crucial and strategic sectors, in accord with the UN Sustainable Development Goals (SDGs). The proposed actions can be summarized as policy, strategy, economy, and technology activities.

A critical review of heating, ventilation, and air conditioning (HVAC) systems within the context of a global SARS-CoV-2 epidemic

The Coronavirus disease (COVID-19) has spread over the world, resulting in more than 225 million patients, and 4.7 million deaths in September 2021. It also caused panic and terror, halted numerous activities, and resulted in the world economy deteriorates. It altered human behavior and compelled people to alter their lifestyles to avoid infection. Air conditioning systems are one of the most important sectors that must be considered because of the pandemic SARS-CoV-2 all over the world. Air is used as a heat transfer medium in heating, ventilation, and air conditioning (HVAC) systems. The air contains a variety of pollutants, viruses, and bacteria, all of which have an impact on and destroy human life. Significantly in summer, people spend more time in air conditioners which results in lower levels of vitamin D and melatonin which may affect the functioning of their immune system and are susceptible to receiving SARS-CoV-2 from other individuals. As an important component of air conditioning and ventilation systems, the air filter plays a significant role. As a result, researchers must work harder to improve its design to prevent the ultra-small particles loaded with COVID-19. This paper contributes to the design of existing HVAC systems in terms of their suitability and impact on the spread of the hybrid SARS-CoV-2 epidemic, as well as efforts to obtain a highly efficient air filter to remove super-sized particles for protection against epidemic infection. In addition, important guideline recommendations have been extracted to limit the spread of the SARS-CoV-2 throughout the world and to get the highest quality indoor air in air-conditioned places.

Exposure to air pollution and COVID-19 severity: A review of current insights, management, and challenges

Several epidemiological studies have suggested a link between air pollution and respiratory tract infections. The outbreak of coronavirus disease 2019 (COVID-19) poses a great threat to public health worldwide. However, some parts of the globe have been worse affected in terms of prevalence and deaths than others. The causes and conditions of such variations have yet to be explored. Although some studies indicated a possible correlation between air pollution and COVID-19 severity, there is yet insufficient data for a meaningful answer. This review summarizes the impact of air pollution on COVID-19 infections and severity and discusses the possible management strategies and challenges involved. The available literature investigating the correlation between air pollution and COVID-19 infections and mortality are included in the review. The studies reviewed here suggest that exposure to air pollution, particularly to PM_2.5 and NO₂ , is positively correlated with COVID-19 infections and mortality. Some data indicate that air pollution can play an important role in the airborne transmission of SARS-CoV-2. A high percentage of COVID-19 incidences has been reported in the most polluted areas, where patients needed hospital admission. The available data also show that both short-term and long-term air pollution may enhance COVID-19 severity. However, most of the studies that showed a link between air pollution and COVID-19 infections and mortality did not consider potential confounders during the correlation analysis. Therefore, more specific studies need to be performed focusing on some additional confounders such as individual age, population density, and pre-existing comorbidities to determine the impact of air pollution on COVID-19 infections and deaths. Integr Environ Assess Manag 2021;17:1114-1122. © 2021 SETAC.

International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the Coronavirus Disease 2019 (COVID-19), generating high numbers of COVID-19 related infected individuals and deaths, is still circulating in 2021 with new variants of the coronavirus, such that the state of emergency remains in manifold countries. Currently, there is still a lack of a full understanding of the factors determining the COVID-19 diffusion that clarify the causes of the variability of infections across different provinces and regions within countries. The main goal of this study is to explain new and main determinants underlying the diffusion of COVID-19 in society. This study focuses on international trade because this factor, in a globalized world, can synthetize different drivers of virus spread, such as mobility patterns, economic potentialities, and social interactions of an investigated areas. A case study research is performed on 107 provinces of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. Statistical analyses from March 2020 to February 2021 suggest that total import and export of provinces has a high association with confirmed cases over time (average r > 0.78, p-value <.001). Overall, then, this study suggests total import and export as complex indicator of COVID-19 transmission dynamics that outclasses other common parameters used to justify the COVID-19 spread, given by economic, demographic, environmental, and climate factors. In addition, this study proposes, for the first time, a time-dependent correlation analysis between trade data and COVID-19 infection cases to explain the relation between confirmed cases and social interactions that are a main source of the diffusion of SARS-CoV-2 and subsequent negative impact in society. These novel findings have main theoretical and practical implications directed to include a new parameter in modelling of the diffusion of COVID-19 pandemic to support effective policy responses of crisis management directed to constrain the impact of COVID-19 pandemic and similar infectious diseases in society.

Can commercial trade represent the main indicator of the COVID-19 diffusion due to human-to-human interactions? A comparative analysis between Italy, France, and Spain

The main goal of this study is to analyze the relation between commercial trade and pandemic severity in society, in order to support new hypotheses which can explain transmission dynamics of COVID-19, as well as promote policy responses to cope with future epidemics similar to COVID-19. This study considers the role of trade in the dynamics of pandemic diffusion, within and between countries, which has not been investigated yet in this emerging field of research. We focus on three large countries in Europe: Italy, France, and Spain. The analysis is performed at regional level (involving in total 52 European regions). Results suggest that the association between trade and pandemic severity seems to be supported by empirical evidence, making it possible to introduce new hypotheses for explaining transmission dynamics of COVID-19 within and between countries. In particular, international trade data is supposed to be used as a comprehensive indicator accounting for population density, economic dynamism, and human mobility. The statistical analyses, also in a multivariate context, strongly support this hypothesis and suggest that crisis management has to focus in the very first place on infections occurring outside the national boundaries, in order to cope with pandemic threat of new waves of COVID-19 and future similar epidemics/pandemics.

PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R₀) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM_2.5 is a major predictor of R₀ in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R₀, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.