SARS-CoV-2 and other pathogenic microorganisms in the environment

Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. Consequently, recent research has focused on identifying and analyzing factors that can effectively regulate human flow. However, among the multiple factors that are expected to have an effect, few studies have investigated those that are particularly associated with human flow during the COVID-19 pandemic. In addition, few studies have investigated how regional characteristics and the number of vaccinations for these factors affect human flow. Furthermore, increasing the number of verified cases in countries and regions with insufficient reports is important to generalize conclusions. Therefore, in this study, a group-level analysis was conducted for Narashino City, Chiba Prefecture, Japan, using a human flow prediction model based on machine learning. High-importance groups were subdivided by regional characteristics and the number of vaccinations, and visual and correlation analyses were conducted at the factor level. The findings indicated that tree-based models, especially LightGBM, performed better in terms of prediction. In addition, the cumulative number of vaccinated individuals and the number of newly infected individuals are likely explanatory factors for changes in human flow. The analyses suggested a tendency to move with respect to the number of newly infected individuals in Japan or Tokyo, rather than the number of new infections in the area where they lived when vaccination had not started. With the implementation of vaccination, attention to the number of newly infected individuals in their residential areas may increase. However, after the spread of vaccination, the perception of infection risk may decrease. These findings can contribute to the proposal of new measures for efficiently controlling human flows and determining when to mitigate or reinforce specific measures.

The Clinical Severity of COVID-19 Variants of Concern: Retrospective Population-Based Analysis

BACKGROUND

SARS-CoV-2 variants of concern (VOCs) emerged and rapidly replaced the original strain worldwide. The increased transmissibility of these new variants led to increases in infections, hospitalizations, and mortality. However, there is a scarcity of retrospective investigations examining the severity of all the main VOCs in presence of key public health measures and within various social determinants of health (SDOHs).

OBJECTIVE

This study aims to provide a retrospective assessment of the clinical severity of COVID-19 VOCs in the context of heterogenous SDOHs and vaccination rollout.

METHODS

We used a population-based retrospective cohort design with data from the British Columbia COVID-19 Cohort, a linked provincial surveillance platform. To assess the relative severity (hospitalizations, intensive care unit [ICU] admissions, and deaths) of Gamma, Delta, and Omicron infections during 2021 relative to Alpha, we used inverse probability treatment weighted Cox proportional hazard modeling. We also conducted a subanalysis among unvaccinated individuals, as assessed severity differed across VOCs and SDOHs.

RESULTS

We included 91,964 individuals infected with a SARS-CoV-2 VOC (Alpha: n=20,487, 22.28%; Gamma: n=15,223, 16.55%; Delta: n=49,161, 53.46%; and Omicron: n=7093, 7.71%). Delta was associated with the most severe disease in terms of hospitalization, ICU admissions, and deaths (hospitalization: adjusted hazard ratio [aHR] 2.00, 95% CI 1.92-2.08; ICU: aHR 2.05, 95% CI 1.91-2.20; death: aHR 3.70, 95% CI 3.23-4.25 relative to Alpha), followed generally by Gamma and then Omicron and Alpha. The relative severity by VOC remained similar in the unvaccinated individual subanalysis, although the proportion of individuals infected with Delta and Omicron who were hospitalized was 2 times higher in those unvaccinated than in those fully vaccinated. Regarding SDOHs, the proportion of hospitalized individuals was higher in areas with lower income across all VOCs, whereas among Alpha and Gamma infections, 2 VOCs that cocirculated, differential distributions of hospitalizations were found among racially minoritized groups.

CONCLUSIONS

Our study provides robust severity estimates for all VOCs during the COVID-19 pandemic in British Columbia, Canada. Relative to Alpha, we found Delta to be the most severe, followed by Gamma and Omicron. This study highlights the importance of targeted testing and sequencing to ensure timely detection and accurate estimation of severity in emerging variants. It further sheds light on the importance of vaccination coverage and SDOHs in the context of pandemic preparedness to support the prioritization of allocation for resource-constrained or minoritized groups.

Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in the Middle East and North Africa: Longitudinal Trend Analysis

BACKGROUND

This study updates the COVID-19 pandemic surveillance in the Middle East and North Africa (MENA) we first conducted in 2020 with 2 additional years of data for the region.

OBJECTIVE

The objective of this study is to determine whether the MENA region meets the criteria for moving from a pandemic to endemic. In doing so, this study considers pandemic trends, dynamic and genomic surveillance methods, and region-specific historical context for the pandemic. These considerations continue through the World Health Organization (WHO) declaration of the end of the public health emergency for the COVID-19 pandemic on May 5, 2023.

METHODS

In addition to updates to traditional surveillance data and dynamic panel estimates from the original study by Post et al, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional weekly speed of COVID-19 spread was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data from September 4, 2020, to May 12, 2023.

RESULTS

The speed of COVID-19 spread for the region had remained below the outbreak threshold for 7 continuous months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant and positive, the weekly shift parameters suggested the coefficients had most recently turned negative, meaning the clustering effect of new COVID-19 cases became even smaller in the 2 weeks around the WHO declaration. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of the speed of spread equal to 10 became entirely insignificant from October 2022 onward.

CONCLUSIONS

The COVID-19 pandemic had far-reaching effects on MENA, impacting health care systems, economies, and social well-being. Although COVID-19 continues to circulate in the MENA region, the rate of transmission remained well below the threshold of an outbreak for over 1 year ahead of the WHO declaration. COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. Both standard and enhanced surveillance metrics confirm that the pandemic had transitioned to endemic by the time of the WHO declaration.

Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in Latin America and the Caribbean: Longitudinal Trend Analysis

BACKGROUND

In May 2020, the World Health Organization (WHO) declared Latin America and the Caribbean (LAC) the epicenter of the COVID-19 pandemic, with over 40% of worldwide COVID-19-related deaths at the time. This high disease burden was a result of the unique circumstances in LAC.

OBJECTIVE

This study aimed to (1) measure whether the pandemic was expanding or contracting in LAC when the WHO declared the end of COVID-19 as a public health emergency of international concern on May 5, 2023; (2) use dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history; and (3) provide, with a focus on prevention policies, a historical context for the course of the pandemic in the region.

METHODS

In addition to updates of traditional surveillance data and dynamic panel estimates from the original study, we used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern (VOCs). We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Additionally, we conducted a 1-sided t test for whether the regional weekly speed (rate of novel COVID-19 transmission) was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the period from August 2020 to May 2023.

RESULTS

The speed of pandemic spread for the region had remained below the outbreak threshold for 6 months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant for the 120-day period ending on the week of May 5, 2023, the coefficients were relatively modest in magnitude (0.457 and 0.491, respectively). Furthermore, the shift parameters for either of the 2 most recent weeks around May 5, 2023, did not indicate any change in this clustering effect of cases on future cases. From December 2021 onward, Omicron was the predominant VOC in sequenced viral samples. The rolling t test of speed=10 became entirely insignificant from January 2023 onward.

CONCLUSIONS

Although COVID-19 continues to circulate in LAC, surveillance data suggest COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. However, the region experienced a high COVID-19 burden in the early stages of the pandemic, and prevention policies should be an immediate focus in future pandemics. Ahead of vaccination development, these policies can include widespread testing of individuals and an epidemiological task force with a contact-tracing system.

Decomposing mechanisms of COVID-19 mortality in empirical datasets: A modeling study

Our objective was to decompose mortality mechanisms during the coronavirus disease 2019 (COVID-19) pandemic to estimate direct, indirect, and associated deaths from COVID-19. Given the confirmatory diagnosis of COVID-19, a death event that was not necessarily caused by respiratory complications but stemmed from other complications was categorized as an indirect death from COVID-19. Associated deaths occurred in patients who did not have COVID-19 but died during the surge in COVID-19 cases when overwhelming pressure was exerted on the healthcare system. Analyzing the sixth wave (i.e., the first epidemic wave of the Omicron B.1.1.529 variant from January to May 2022), decomposition was achieved using the binomial and Poisson sampling process models fitted to two pieces of data (i.e., COVID-19 death certificate and excess data by major cause of death). The total numbers of direct, indirect, and associated deaths during the sixth wave in Osaka were estimated at 1,071; 948; and 2,157; respectively. The number of associated deaths was greater than the sum of direct and indirect deaths. We further observed that the composition of indirect and associated deaths differed by major cause of death, and deaths caused by circulatory disease included a greater proportion of indirect deaths compared with deaths by other causes. The goals of healthcare services for endemic COVID-19 include the sustainable provision of services to avoid preventable deaths. Therefore, gaining an in-depth understanding of mechanisms that lead to excess death is vital for improving future pandemic response efforts.

Negative effects of high public debt on health systems facing pandemic crisis: Lessons from COVID-19 in Europe to prepare for future emergencies

The investigation goal here was to analyze how the level of public debt affects preparedness of health systems to face emergencies. In particular, this study examined the negative effects of high public debt on health systems of European countries in the presence of the COVID-19 pandemic crisis. Empirical evidence revealed that European countries with a lower level of government debt as a percentage of GDP both in 2009 and 2019 (the period before the arrival of the pandemic) had lower COVID-19 fatality rates compared to countries with higher levels of public debt. The explanation is that high levels of public debt in countries trigger budget constraints that limit their ability to allocate resources to healthcare systems (e.g., health expenditures and investments), weakening health system performance and causing systemic vulnerability and lower preparedness during emergencies, such as with the COVID-19 pandemic. Implications of health policies are suggested to improve strategies of crisis management.

Unveiling the Role of PAR 1: A Crucial Link with Inflammation in Diabetic Subjects with COVID-19

Inflammation is a distinguished clinical manifestation of COVID-19 and type 2 diabetes mellitus (T2DM), often associated with inflammatory dysfunctions, insulin resistance, metabolic dysregulation, and other complications. The present study aims to test the hypothesis that serum concentrations of PAR-1 levels differ between COVID-19 diabetic patients (T2DM) and non-diabetic COVID-19 patients and determine their association with different biochemical parameters and inflammatory biomarkers. T2DM patients with COVID-19 (n = 50) with glycated hemoglobin (HbA1c) levels of (9.23 ± 1.66) and non-diabetic COVID-19 patients (n = 50) with HbA1c levels (4.39 ± 0.57) were recruited in this study. The serum PAR-1 levels (ELISA method) were determined in both groups and correlated with parameters such as age, BMI, inflammatory markers including CRP, interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α), D-dimer, homocysteine, and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Demographic variables such as BMI (29.21 ± 3.52 vs. controls 21.30 ± 2.11) and HbA1c (9.23 ± 1.66 vs. controls 4.39 ± 0.57) were found to be statistically elevated in COVID-19 T2DM patients compared to non-diabetic COVID-19 patients. The concentrations of several inflammatory biomarkers and PAR-1 were remarkably increased in the COVID-19 T2DM group when compared with the non-diabetic COVID-19 group. The univariate analysis revealed that increased serum PAR-1 estimations were positively correlated with enhanced HbA1c, BMI, inflammatory cytokines, D-dimer, homocysteine, and NT-proBNP. The findings in the current study suggest that increased levels of serum PAR-1 in the bloodstream could potentially serve as an independent biomarker of inflammation in COVID-19 patients with T2DM.

Progress of disinfection catalysts in advanced oxidation processes, mechanisms and synergistic antibiotic degradation

Human diseases caused by pathogenic microorganisms make people pay more attention to disinfection. Meanwhile, antibiotics can cause microbial resistance and increase the difficulty of disease treatment, resulting in risk of triggering a vicious circle. Advanced oxidation process (AOPs) has been widely studied in the field of synergistic treatment of the two contaminates. This paper reviews the application of catalytic materials and their modification strategies in the context of AOPs for disinfection and antibiotic degradation. It also delves into the mechanisms of disinfection such as the pathways for microbial inactivation and the related influencing factors, which are essential for understanding the pivotal role of catalytic materials in disinfection principles by AOPs. More importantly, the exploratory research on the combined use of AOPs for disinfection and antibiotic degradation is discussed, and the potential and prospects in this field is highlighted. Finally, the limitations and challenges associated with the application of AOPs in disinfection and antibiotic degradation are summarized. It aims to provide a starting point for future research efforts to facilitate the widespread use of advanced oxidation processes in the field of public health.

The changing contributory role to infections of work, public transport, shopping, hospitality and leisure activities throughout the SARS-CoV-2 pandemic in England and Wales

Background

Understanding how non-household activities contributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections under different levels of national health restrictions is vital.

Methods

Among adult Virus Watch participants in England and Wales, we used multivariable logistic regressions and adjusted-weighted population attributable fractions (aPAF) assessing the contribution of work, public transport, shopping, and hospitality and leisure activities to infections.

Results

Under restrictions, among 17,256 participants (502 infections), work [adjusted odds ratio (aOR) 2.01 (1.65-2.44), (aPAF) 30% (22-38%)] and transport [(aOR 1.15 (0.94-1.40), aPAF 5% (-3-12%)], were risk factors for SARS-CoV-2 but shopping, hospitality and leisure were not. Following the lifting of restrictions, among 11,413 participants (493 infections), work [(aOR 1.35 (1.11-1.64), aPAF 17% (6-26%)] and transport [(aOR 1.27 (1.04-1.57), aPAF 12% (2-22%)] contributed most, with indoor hospitality [(aOR 1.21 (0.98-1.48), aPAF 7% (-1-15%)] and leisure [(aOR 1.24 (1.02-1.51), aPAF 10% (1-18%)] increasing. During the Omicron variant, with individuals more socially engaged, among 11,964 participants (2335 infections), work [(aOR 1.28 (1.16-1.41), aPAF (11% (7-15%)] and transport [(aOR 1.16 (1.04-1.28), aPAF 6% (2-9%)] remained important but indoor hospitality [(aOR 1.43 (1.26-1.62), aPAF 20% (13-26%)] and leisure [(aOR 1.35 (1.22-1.48), aPAF 10% (7-14%)] dominated.

Conclusions

Work and public transport were important to transmissions throughout the pandemic with hospitality and leisure's contribution increasing as restrictions were lifted, highlighting the importance of restricting leisure and hospitality alongside advising working from home, when facing a highly infectious and virulent respiratory infection.

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.

Comparison of homemade mask designs based on calculated infection risk, using actual COVID-19 infection scenarios

During pandemics such as COVID-19, shortages of approved respirators necessitate the use of alternative masks, including homemade designs. The effectiveness of the masks is often quantified in terms of the ability to filter particles. However, to formulate public policy the efficacy of the mask in reducing the risk of infection for a given population is considerably more useful than its filtration efficiency (FE). The effect of the mask on the infection profile is complicated to estimate as it depends strongly upon the behavior of the affected population. A recently introduced tool known as the dynamic-spread model is well suited for performing population-specific risk assessment. The dynamic-spread model was used to simulate the performance of a variety of mask designs (all used for source control only) in different COVID-19 scenarios. The efficacy of different masks was found to be highly scenario dependent. Switching from a cotton T-shirt of 8% FE to a 3-layer cotton-gauze-cotton mask of 44% FE resulted in a decrease in number of new infections of about 30% in the New York State scenario and 60% in the Harris County, Texas scenario. The results are valuable to policy makers for quantifying the impact upon the infection rate for different intervention strategies, e.g., investing resources to provide the community with higher-filtration masks.

Investigation of the effects of temperature and relative humidity on the propagation of COVID-19 in different climatic zones

This study aims to evaluate the effects of temperature and relative humidity on the propagation of COVID-19 for indoor heating, ventilation, and air conditioning design and policy development in different climate zones. We proposed a cumulative lag model with two specific parameters of specific average temperature and specific relative humidity to evaluate the impact of temperature and relative humidity on COVID-19 transmission by calculating the relative risk of cumulative effect and the relative risk of lag effect. We considered the temperature and relative humidity corresponding to the relative risk of cumulative effect or the relative risk of lag effect equal to 1 as the thresholds of outbreak. In this paper, we took the overall relative risk of cumulative effect equal to 1 as the thresholds. Data on daily new confirmed cases of COVID-19 since January 1, 2021, to December 31, 2021, for three sites in each of four climate zones similar to cold, mild, hot summer and cold winter, and hot summer and warm winter were selected for this study. Temperature and relative humidity had a lagged effect on COVID-19 transmission, with peaking the relative risk of lag effect at a lag of 3-7 days for most regions. All regions had different parameters areas with the relative risk of cumulative effect greater than 1. The overall relative risk of cumulative effect was greater than 1 in all regions when specific relative humidity was higher than 0.4, and when specific average temperature was higher than 0.42. In areas similar to hot summer and cold winter, temperature and the overall relative risk of cumulative effect were highly monotonically positively correlated. In areas similar to hot summer and warm winter, there was a monotonically positive correlation between relative humidity and the overall relative risk of cumulative effect. This study provides targeted recommendations for indoor air and heating, ventilation, and air conditioning system control strategies and outbreak prevention strategies to reduce the risk of COVID-19 transmission. In addition, countries should combine vaccination and non-pharmaceutical control measures, and strict containment policies are beneficial to control another pandemic of COVID-19 and similar viruses.

20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil

The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.

Visual analysis of the prevention and control measures of COVID-19 in Chinese ports

In 2022, COVID-19 solutions in China have entered a normal stage, and the solutions imported from ports have been transformed from emergency prevention and control measures to investigative long-term prevention and control measures. Therefore, it is necessary to study solutions for COVID-19 at border ports. In this study, 170 research papers related to the prevention and control measures of COVID-19 at ports from 2020 to September 2022 were retrieved from Wanfang database, HowNet database, Wip database, and WoS core collection. Citespace 6.1.R2 software was used to research institutions visualize and analyze researchers and keywords to explore their research hotspots and trends. After analysis, the overall volume of documents issued in the past 3 years was stable. The major contributors are scientific research teams such as the Chinese Academy of Inspection and Quarantine Sciences (Han Hui et al.) and Beijing Customs (Sun Xiaodong et al.), with less cross-agency cooperation. The top five high-frequency keywords with cumulative frequency are as follows: COVID-19 (29 times), epidemic prevention and control (29 times), ports (28 times), health quarantine (16 times), and risk assessment (16 times). The research hotspots in the field of prevention and control measures for COVID-19 at ports are constantly changing with the progress of epidemic prevention and control. Cooperation between research institutions needs to be strengthened urgently. The research hotspots are the imported epidemic prevention and control, risk assessment, port health quarantine, and the normalized epidemic prevention and control mechanism, which is the trend of research and needs further exploration in the future.

Smoking and prevalence of COVID-19: Evidence from studies from January 2020 - May 2020

It is well-known that smoking tobacco harms the respiratory system and can lead to various health problems. Regarding COVID-19, a respiratory illness caused by the novel coronavirus SARS-CoV-2, smoking may have implications for both the risk of infection and the severity of the disease. Several studies have explored the association between smoking and COVID-19. However, findings have been somewhat inconsistent and vary from region to region for sample size. This article aims to study the prevalence of COVID-19 among those affected with their ongoing smoking history by computing pooled estimates of the published research. Fixed effect meta-analysis by following the guidelines of PRISMA has been carried out on 34 studies. The patients with confirmed RT-PCR and CT-scan were included, a total of 13,368; The studies' quality assessment was performed according to the Appraisal Checklist recommended by the Joanna Briggs Institute. The effect sizes of the published research are presented in the form of pooled estimates with their respective confidence intervals. Forest plots are used to represent the effect size graphically. Current smokers' effect sizes are 0.12 (CI = 0.11-0.12); for non-smokers, it is estimated to be 0.88 (CI = 0.88-0.89). The heterogeneity statistic I2 describes 0% of the total variation, meaning no heterogeneity among studies exists. A higher prevalence of COVID-19 among non-smokers is observed than the smokers.

Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain

Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 10³ and 10⁶ SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

SARS-CoV-2 airborne infection probability estimated by using indoor carbon dioxide

Airborne transmission is one of the main routes of SARS-CoV-2 spread. It is important to determine the circumstances under which the risk of airborne transmission is increased as well as the effective strategy to reduce such risk. This study aimed to develop a modified version of the Wells-Riley model with indoor CO₂ to estimate the probability of airborne transmission of SARS-CoV-2 Omicron strains with a CO₂ monitor and to evaluate the validity of this model in actual clinical practices. We used the model in three suspected cases of airborne transmission presented to our hospital to confirm its validity. Next, we estimated the required indoor CO₂ concentration at which R₀ does not exceed 1 based on the model. The estimated R₀ (R₀, basic reproduction number) based on the model in each case were 3.19 in three out of five infected patients in an outpatient room, 2.00 in two out of three infected patients in the ward, and 0.191 in none of the five infected patients in another outpatient room. This indicated that our model can estimate R₀ with an acceptable accuracy. In a typical outpatient setting, the required indoor CO₂ concentration at which R₀ does not exceed 1 is below 620 ppm with no mask, 1000 ppm with a surgical mask and 16000 ppm with an N95 mask. In a typical inpatient setting, on the other hand, the required indoor CO₂ concentration is below 540 ppm with no mask, 770 ppm with a surgical mask, and 8200 ppm with an N95 mask. These findings facilitate the establishment of a strategy for preventing airborne transmission in hospitals. This study is unique in that it suggests the development of an airborne transmission model with indoor CO₂ and application of the model to actual clinical practice. Organizations and individuals can efficiently recognize the risk of SARS-CoV-2 airborne transmission in a room and thus take preventive measures such as maintaining good ventilation, wearing masks, or shortening the exposure time to an infected individual by simply using a CO₂ monitor.

Recycling COVID-19 health care wastes in bitumen modification: a case of disposable medical gloves

Disposable medical gloves (DMGs) have long been used to mitigate the risk of direct exposure to diverse microorganisms and body fluids; hence, they are a critical weapon to protect patients and healthcare staff from infectious diseases. Measures to control the spread of COVID-19 have sparked the production of an excessive number of DMGs, most of which are eventually being disposed of in landfills. Untreated DMGs in landfills do not only pose a direct risk of transmitting coronavirus and other pathological germs but also pollute air, water, and soil dramatically. As a healthier alternative, recycling discarded polymer-rich DMGs into bitumen modification is considered to be a prospective waste management strategy applicable to the asphalt pavement industry. In this study, this conjecture is tested by examining two common DMGs - latex gloves and vinyl gloves - at four different percentages (1%, 2%, 3%, and 4% by weight). The morphological characteristics of DMG-modified specimens were inspected by using a high-definition scanning electron microscope (SEM) equipped with an energy dispersive X-ray analyzer (EDX). A wide range of laboratory tests including penetration, softening point temperature, ductility, and elastic recovery were undertaken to evaluate the impact of waste gloves on the conventional engineering properties of bitumen. Moreover, viscoelastic behavior and modification processing were studied by conducting the dynamic shear rheometer (DSR) test and the Fourier transform infrared spectroscopy (FTIR) analysis. Test results have revealed the outstanding potential of recycled DMG waste for modifying neat asphalt binder. More specifically, bitumens modified with 4% latex glove and 3% vinyl glove were seen as capable of superiorly withstanding permanent deformations caused by heavy axle loads at high service temperatures. Furthermore, it has been shown that 1.2 tons of modified binder would embed approximately 4000 pairs of recycled DMGs. This study shows that DMG waste can be used as a viable modifier, which would help open a new avenue for mitigating the environmental pollution arising from the COVID-19 pandemic.

SARS-CoV-2 surveillance-based on municipal solid waste leachate in Brazil

Municipal solid waste leachate-based epidemiology is an alternative viral tracking tool that applies fresh truck leachate as an early warning of public health emergencies. This study aimed to investigate the potential of SARS-CoV-2 surveillance based on solid waste fresh truck leachate. Twenty truck leachate samples were ultracentrifugated, nucleic acid extracted, and real-time RT-qPCR SARS-CoV-2 N1/N2 applied. Viral isolation, variant of concern (N1/N2) inference, and whole genome sequencing were also performed. SARS-CoV-2 was detected on 40% (8/20) of samples, with a concentration from 2.89 to 6.96 RNA Log10 100 mL-1. The attempt to isolate SARS-CoV-2 and recover the whole genome was not successful; however, positive samples were characterized as possible pre-variant of concern (pre-VOC), VOC Alpha (B.1.1.7) and variant of interest Zeta (P.2). This approach revealed an alternative tool to infer SARS-CoV-2 in the environment and may help the management of local surveillance, health, and social policies.

Association of the corona virus (Covid-19) epidemic with environmental risk factors

The current outbreak of the novel coronavirus SARS-CoV-2 (coronavirus disease 2019; previously 2019-nCoV), epicenter in Hubei Province (Wuhan), People's Republic of China, has spread too many other countries. The transmission of the corona virus occurs when people are in the incubation stage and do not have any symptoms. Therefore, the role of environmental factors such as temperature and wind speed becomes very important. The study of Acute Respiratory Syndrome (SARS) indicates that there is a significant relationship between temperature and virus transmission and three important factors, namely temperature, humidity and wind speed, cause SARS transmission. Daily data on the incidence and mortality of Covid-19 disease were collected from World Health Organization (WHO) website and World Meter website (WMW) for several major cities in Iran and the world. Data were collected from February 2020 to September 2021. Meteorological data including temperature, air pressure, wind speed, dew point and air quality index (AQI) index are extracted from the website of the World Meteorological Organization (WMO), The National Aeronautics and Space Administration (NASA) and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Statistical analysis carried out for significance relationships. The correlation coefficient between the number of infected people in one day and the environmental variables in the countries was different from each other. The relationship between AQI and number of infected was significant in all cities. In Canberra, Madrid and Paris, a significant inverse relationship was observed between the number of infected people in one day and wind speed. There is a significant positive relationship between the number of infected people in a day and the dew point in the cities of Canberra, Wellington and Washington. The relationship between the number of infected people in one day and Pressure was significantly reversed in Madrid and Washington, but positive in Canberra, Brasilia, Paris and Wuhan. There was significant relationship between Dew point and prevalence. Wind speed showed a significant relationship in USA, Madrid and Paris. AQI was strongly associated with the prevalence of covid19. The purpose of this study is to investigate some environmental factors in the transmission of the corona virus.

SARS-CoV-2 surveillance in medical and industrial wastewater-a global perspective: a narrative review

The novel coronavirus SARS-CoV-2 has spread at an unprecedented rate since late 2019, leading to the global COVID-19 pandemic. During the pandemic, being able to detect SARS-CoV-2 in human populations with high coverage quickly is a huge challenge. As SARS-CoV-2 is excreted in human excreta and thus exposed to the aqueous environment through sewers, the goal is to develop an ideal, non-invasive, cost-effective epidemiological method for detecting SARS-CoV-2. Wastewater surveillance has gained widespread interest and is increasingly being investigated as an effective early warning tool for monitoring the spread and evolution of the virus. This review emphasizes important findings on SARS-CoV-2 wastewater-based epidemiology (WBE) in different continents and techniques used to detect SARS-CoV-2 in wastewater during the period 2020-2022. The results show that WBE is a valuable population-level method for monitoring SARS-CoV-2 and is a valuable early warning alert. It can assist policymakers in formulating relevant policies to avoid the negative impacts of early or delayed action. Such strategy can also help avoid unnecessary wastage of medical resources, rationalize vaccine distribution, assist early detection, and contain large-scale outbreaks.

Animal experiments on respiratory viruses and analogous studies of infection factors for interpersonal transmission

Air pollution caused by SARS-CoV-2 and other viruses in human settlements will have a great impact on human health, but also a great risk of transmission. The transmission power of the virus can be represented by quanta number in the Wells-Riley model. In order to solve the problem of different dynamic transmission scenarios, only a single influencing factor is considered when predicting the infection rate, which leads to large differences in quanta calculated in the same space. In this paper, an analog model is established to define the indoor air cleaning index RL and the space ratio parameter. Based on infection data analysis and rule summary in animal experiments, factors affecting quanta in interpersonal communication were explored. Finally, by analogy, the factors affecting person-to-person transmission mainly include viral load of infected person, distance between individuals, etc., the more severe the symptoms, the closer the number of days of illness to the peak, and the closer the distance to the quanta. In summary, there are many factors that affect the infection rate of susceptible people in the human settlement environment. This study provides reference indicators for environmental governance under the COVID-19 epidemic, provides reference opinions for healthy interpersonal communication and human behavior, and provides some reference for accurately judging the trend of epidemic spread and responding to the epidemic.

New trajectories of technologies for the removal of pollutants and emerging contaminants in the environment

Modern society has increasingly a diffusion of pollutants and emerging contaminants (e.g., different types of chemicals and endocrine disruptors in pharmaceuticals, pesticides, household cleaning, and personal care products, etc.) that have detrimental effects on the environment (atmosphere, hydrosphere, biosphere and anthroposphere) and also generate diseases and disorders on the people health. Environmental science requires efforts in the detection and elimination of manifold pollutants and emerging pollutants with appropriate product and process technologies. This study aims to analyze different paths of treatment technologies to investigate their evolution and predict new directions of promising technological trajectories to support the removal of contaminants directed to reach, whenever possible, sustainable development objectives. The work is mainly devoted to wastewater treatment technologies. A proposed model analyzes the evolution of patents (proxy of innovation and new technology) on publications (proxy of science and knowledge advances) to quantify the relative growth rate of new trajectories of technologies to remove pollutants and emerging contaminants. Results reveal that new directions of treatment technologies having an accelerated rate of growth are (in decreasing order): biochar and reverse osmosis in physical-based technologies, coagulation, and disinfection water treatments in chemical-based technologies and anaerobic processes in biological-based technologies. Other main technologies, such as carbon nanotubes and advanced oxidation processes, seem to be in the initial phase of development and need learning by using processes and further science and technology advances to be implemented as effective treatments and cost-effective. The results here are in accord with global water and wastewater equipment treatment market revenues by technology, showing a similar trend. These findings bring us to the main information to extend the knowledge about new directions of technologies for the treatment and/or elimination of pollutants and microorganisms that can support decisions of policymakers towards goals of sustainable development by reducing environmental degradation and people health disorders.

The change in the relationship between temperature and respiratory diseases among children in Guangzhou, China

Temperature is closely associated with respiratory disease (RD) in children, but few studies have examined whether the relationship between ambient temperature and RD in children changed after the COVID-19 epidemic. The aim of this study was to assess the relationship between temperature and RD in children after the COVID-19 epidemic in Guangzhou, China. We used a distributed lag nonlinear model to compare the relationship between temperature and RD among children in Guangzhou from 2018 to 2022. The results showed an S-shaped relationship between temperature and RD in the post-COVID-19 period with a reference minimum risk at a temperature of 21 °C and an increasing relative risk (RR) at extremely low temperature (ELT) and extremely high temperature (EHT). The highest RR associated with EHT was 1.935 (95% confidence interval [CI]: 1.314-2.850) at a lag of 0-14 days. The on-the-day lag effects were found to be strongest at the lag 0 day of EHT with a RR of 1.167 (95% CI: 1.021-1.334). Furthermore, each 1 °C increase in post-COVID-19 temperature increased the risk of RD by 8.2% (95% CI: 1.044-1.121). Our study provides evidence that the relationship between temperature and RD in children in Guangzhou changed after the COVID-19 epidemic, and high temperature is more likely to cause RD in children. Relevant government departments and parents should understand the relationship between temperature and RD in children and develop new preventive measures.

What is the optimal country for minimum COVID-19 morbidity and mortality rates?

The SARS-CoV-2 is a deceptive virus. Despite the remarkable progress in genetic sequencing and subsequent vaccine development, the world continues to grapple with the ominous threats of rapidly appearing SARS-CoV-2 variants. The objective of this manuscript is to rank world countries based on the anticipated scope of COVID-19 morbidity and mortality, measured in terms of prevalence per 1 million persons, from the lowest to the highest. The ranking of 162 countries is based on predictions of empirical models, which include three explanatory variables: hospital beds per thousand persons, population density, and the median age of the country's population. Referring to the COVID-19 scope of morbidity, the lowest likelihood of infection is obtained in Niger and Mali, where the dominant characteristic is the young median age (15.1-16.4 years). Referring to the COVID-19 scope of mortality, the lowest likelihood is obtained in Singapore. For Singapore, the dominant feature is the high population density. The optimal solution is intensive vaccination campaigns in the initial phase of the pandemic, particularly among countries with low GDP per capita. Yet, vaccinations may work only where the personal immune system is healthy and thus respond by creating antibodies to the SARS-CoV2 virus. Referring to populations that lack the natural protection of the healthy immune system and thus cannot be vaccinated (e.g., old people, cancer patients undergoing chemotherapy treatments), a complementary solution might be coordination between countries and the establishment of field hospitals, testing laboratories, isolation of areas, humanitarian aid-in the same manner of treatment in other disasters like earthquakes.

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

Scholars and experts argue that future pandemics and/or epidemics are inevitable events, and the problem is not whether they will occur, but when a new health emergency will emerge. In this uncertain scenario, one of the most important questions is an accurate prevention, preparedness and prediction for the next pandemic. The main goal of this study is twofold: first, the clarification of sources and factors that may trigger pandemic threats; second, the examination of prediction models of on-going pandemics, showing pros and cons. Results, based on in-depth systematic review, show the vital role of environmental factors in the spread of Coronavirus Disease 2019 (COVID-19), and many limitations of the epidemiologic models of prediction because of the complex interactions between the new viral agent SARS-CoV-2, environment and society that have generated variants and sub-variants with rapid transmission. The insights here are, whenever possible, to clarify these aspects associated with public health in order to provide lessons learned of health policy that may reduce risks of emergence and diffusion of new pandemics having negative societal impact.

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.

Environment and COVID-19 incidence: A critical review

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

COVID-19 face masks attracted Cellulomonas and Acinetobacter bacteria and provided breeding haven for red cotton bug (Dysdercus suturellus) and house cricket (Acheta domesticus)

This study investigated the possibility of COVID-19 medical face masks to affect bacterial and macrofaunal communities in open soil environment. An estimated 1.24 trillion of face masks have been used and discarded as a result of the COVID-19 pandemic, with a significant part of this ending up in the soil environment, where they degrade gradually over time. Because bacteria and macrofauna are sensitive indicators of changes in soil ecosystem, we investigated possible impacts of face masks on population, distribution, and diversity of these soil species. Effect on soil bacterial community was studied by both culture-based and advanced molecular (metagenomics) approach, while impact on macrofauna was investigated by examining monoliths around heap of masks for soil insects. In both cases, control soil experiments without face masks were also set up and monitored over a period of 48 weeks. The study found that the presence of face masks led to a more diverse bacterial community, although no influence on overall bacterial population was evidenced. More importantly, bacteria belonging to the genera Cellulomonas and Acinetobacter were found prominently around face masks and are believed to be involved in biodegradation of the masks. The bacterial community around the masks was dominated by Proteobacteria (29.7-38.7%), but the diversity of species increased gradually with time. Tiny black ants (Monomorium invidium) were attracted to the face masks to take advantage of water retained by the masks during the period of little rainfall. The heaps of face masks also provided shelter and breeding "haven" for soil insects, notably the red cotton bug (Dysdercus suturellus) and house cricket (Acheta domesticus), thereby impacting positively on the population of insect species in the environment. This study provides insights into the actual impacts of face masks on soil organisms under normal outdoor environmental conditions.

COVID-19 cases with a contact history: A modeling study of contact history-stratified data in Japan

The purpose of the present study was to develop a transmission model of COVID-19 cases with and without a contact history to understand the meaning of the proportion of infected individuals with a contact history over time. We extracted epidemiological information regarding the proportion of coronavirus disease 2019 (COVID-19) cases with a contact history and analyzed incidence data stratified by the presence of a contact history in Osaka from January 15 to June 30, 2020. To clarify the relationship between transmission dynamics and cases with a contact history, we used a bivariate renewal process model to describe transmission among cases with and without a contact history. We quantified the next-generation matrix as a function of time; thus, the instantaneous (effective) reproduction number was calculated for different periods of the epidemic wave. We objectively interpreted the estimated next-generation matrix and replicated the proportion of cases with a contact p(t) over time, and we examined the relevance to the reproduction number. We found that p(t) does not take either the maximum or minimum value at a threshold level of transmission with R(t)=1.0. With R(t) < 1 (subcritical level), p(t) was a decreasing function of R(t). Qualitatively, the minimum p(t) was seen in the domain with R(t) > 1. An important future implication for use of the proposed model is to monitor the success of ongoing contact tracing practice. A decreasing signal of p(t) reflects the increasing difficulty of contact tracing. The present study findings indicate that monitoring p(t) would be a useful addition to surveillance.

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.

Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m²/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.

Protection against SARS-CoV-2 BA.4 and BA.5 subvariants via vaccination and natural infection: A modeling study

With continuing emergence of new SARS-CoV-2 variants, understanding the proportion of the population protected against infection is crucial for public health risk assessment and decision-making and so that the general public can take preventive measures. We aimed to estimate the protection against symptomatic illness caused by SARS-CoV-2 Omicron variants BA.4 and BA.5 elicited by vaccination against and natural infection with other SARS-CoV-2 Omicron subvariants. We used a logistic model to define the protection rate against symptomatic infection caused by BA.1 and BA.2 as a function of neutralizing antibody titer values. Applying the quantified relationships to BA.4 and BA.5 using two different methods, the estimated protection rate against BA.4 and BA.5 was 11.3% (95% confidence interval [CI]: 0.01-25.4) (method 1) and 12.9% (95% CI: 8.8-18.0) (method 2) at 6 months after a second dose of BNT162b2 vaccine, 44.3% (95% CI: 20.0-59.3) (method 1) and 47.3% (95% CI: 34.1-60.6) (method 2) at 2 weeks after a third BNT162b2 dose, and 52.3% (95% CI: 25.1-69.2) (method 1) and 54.9% (95% CI: 37.6-71.4) (method 2) during the convalescent phase after infection with BA.1 and BA.2, respectively. Our study indicates that the protection rate against BA.4 and BA.5 are significantly lower compared with those against previous variants and may lead to substantial morbidity, and overall estimates were consistent with empirical reports. Our simple yet practical models enable prompt assessment of public health impacts posed by new SARS-CoV-2 variants using small sample-size neutralization titer data to support public health decisions in urgent situations.

Effects of strict containment policies on COVID-19 pandemic crisis: lessons to cope with next pandemic impacts

The goal of the study here is to analyze and assess whether strict containment policies to cope with Coronavirus Disease 2019 (COVID-19) pandemic crisis are effective interventions to reduce high numbers of infections and deaths. A homogenous sample of 31 countries is categorized in two sets: countries with high or low strictness of public policy to cope with COVID-19 pandemic crisis. The findings here suggest that countries with a low intensity of strictness have average confirmed cases and fatality rates related to COVID-19 lower than countries with high strictness in containment policies (confirmed cases are 24.69% vs. 26.06% and fatality rates are 74.33% vs. 76.38%, respectively, in countries with low and high strictness of COVID-19 public policies of containment). What this study adds is that high levels of strict restriction policies may not be useful measures of control in containing the spread and negative impact of pandemics similar to COVID-19 and additionally a high strictness in containment policies generates substantial social and economic costs. These findings can be explained with manifold socioeconomic and environmental factors that support transmission dynamics and circulation of COVID-19 pandemic. Hence, high levels of strictness in public policy (and also a high share of administering new vaccines) seem to have low effectiveness to stop pandemics similar to COVID-19 driven by mutant viral agents. These results here suggest that the design of effective health policies for prevention and preparedness of future pandemics should be underpinned in a good governance of countries and adoption of new technology, rather than strict and generalized health polices having ambiguous effects of containment in society.

Time-series COVID-19 policymaker analysis of the UAE, Taiwan, New Zealand, Japan and Hungary

There are two types of policy analysis: socioeconomic analysis and public policy outcome analysis. The socioeconomic analysis is used for understanding the relationship between COVID-19 incident and mortality and building effective governance. There are two types of policy outcome analysis: general policy analysis and time series policy analysis. This paper is a policy outcome analysis of COVID-19, not a policy analysis. This paper examines COVID-19 policy outcome analysis of five countries such as the UAE, Taiwan, New Zealand, Japan and Hungary. Two policy outcome analysis tools are used in this paper such as scorecovid to generate a snapshot list of sorted scores and time-series hiscovid to identify when policymakers made mistakes for correcting mistakes in the near future policy update. Scores in both tools are based on the population mortality rate: dividing the number of COVID-19 deaths by the population in millions. The lower the score, the better the policy. The higher the score, the more deaths that make people unhappy. COVID-19 death is the most unfortunate event in life and is caused by policy. The introduced time-series policy analysis tool, hiscovid discovered ten facts of five countries. Discovered ten facts will be detailed in this paper. Visualization of policy outcomes over time will play an important role in mitigating the COVID-19 pandemic.

Integrated review of the nexus between toxic elements in the environment and human health

Emerging pollutants in the environment due to economic development have become a global challenge for environmental and human health management. Potentially toxic elements (PTEs), a major group of pollutants, have been detected in soil, air, water and food crops. Humans are exposed to PTEs through soil ingestion, consumption of water, uptake of food crop products originating from polluted fields, breathing of dust and fumes, and direct contact of the skin with contaminated soil and water. The dose absorbed by humans, the exposure route and the duration (i.e., acute or chronic) determine the toxicity of PTEs. Poisoning by PTEs can lead to excessive damage to health as a consequence of oxidative stress produced by the formation of free radicals and, as a consequence, to various disorders. The toxicity of certain organs includes neurotoxicity, nephrotoxicity, hepatotoxicity, skin toxicity, and cardiovascular toxicity. In the treatment of PTE toxicity, synthetic chelating agents and symptomatic supportive procedures have been conventionally used. In addition, there are new insights concerning natural products which may be a powerful option to treat several adverse consequences. Health policy implications need to include monitoring air, water, soil, food products, and individuals at risk, as well as environmental manipulation of soil, water, and sewage. The overall goal of this review is to present an integrated view of human exposure, risk assessment, clinical effects, as well as therapy, including new treatment options, related to highly toxic PTEs.

The environment, epidemics, and human health

In this editorial piece, the Editors of the Virtual Special Issue (VSI) "The environment, epidemics, and human health" comment on the papers accepted for publication, which were selected after peer-reviewing among all those manuscripts submitted to the Special Issue. In view of the title of the VSI, it is clear that its aim goes beyond the COVID-19 pandemic, trying to explore relations among environmental aspects, any kind of epidemics, and human health. However, COVID-19 is still hitting as a global and current main issue, causing that manuscripts dealing with this disease and the SARS-CoV-2 virus are of high relevance in the whole set of research papers published.

The effect of selective serotonin and norepinephrine reuptake inhibitors on clinical outcome of COVID-19 patients: A systematic review and meta-analysis

Background and Aim

Due to the high social and economic burden and also mortality and morbidity caused by coronavirus disease 2019 (COVID-19) in the past few years, researchers have aimed at finding solutions to suppressing the severity of infection. Recently, selective serotonin and serotonin-norepinephrine reuptake inhibitors (SSRI/SNRI) have been investigated as an adjuvant treatment for COVID-19. The aim of the current study was to investigate the impact of SSRI/SNRIs on outcomes of COVID-19 patients.

Methods

In this systematic review and meta-analysis, a comprehensive search strategy consisting of relevant words was performed by two researchers in PubMed, Scopus and EMBASE libraries. Studies reporting the effect of SSRI and/or SNRI use in COVID-19 patients' outcome were included. Hospitalization, mortality, hospitalization event, and length of hospital stay were considered as main outcomes of this study. Analysis was carried out using Comprehensive Meta-Analysis (CMA-version 2) and final data were reported as odds ratio (OR) and 95% confidence interval (CI).

Results

Our search led to the final selection of 9 articles including 15,287 patients. The effect of fluvoxamine, fluoxetine, and the overall effect of SSRI/SNRI use on mortality of COVID-19 patients were investigated in 3, 2, and 7 articles, respectively. The results of our analyses showed that these medications could significantly decrease mortality of COVID-19 patients (OR and 95% [CI]: 0.595 [0.467-0.758], 0.620 [0.469-0.821], and 0.596 [0.437-0.813]). The effect of SSRI/SNRIs on hospitalization events of COVID-19 patients was not significant (OR: 0.240% and 95% CI: 0.041-1.4). Also, length of hospital stay was longer in patients who administrated SSRIs.

Conclusion

According to this study's results, SSRI/SNRIs may be effective in reducing mortality of COVID-19 patients, suggesting the superiority of fluvoxamine to fluoxetine. The safety profile and affordable cost of SSRI/SNRIs for a short-term use may be other reasons to propose them as beneficial medications in preventing mortality in COVID-19.

COVID-19 Vaccination is not a Sufficient Public Policy to face Crisis Management of next Pandemic Threats

This study reveals that a vast vaccination campaign is a necessary but not sufficient public policy to reduce the negative impact of Coronavirus Disease 2019 (COVID-19) pandemic crisis because manifold factors guide the spread of this new infectious disease and related mortality in society. Statistical evidence here, based on a worldwide sample of countries, shows a positive correlation between people fully vaccinated and COVID-19 mortality (r = + 0.65, p-value < 0.01). Multivariate regression, controlling income per capita, confirms this finding. Results suggest that the increasing share of people vaccinated against COVID-19 seems to be a necessary but not sufficient health policy to reduce mortality of COVID-19. The findings here can be explained with the role of Peltzman effect, new variants, environmental and socioeconomic factors that affect the diffusion and negative impact of COVID-19 pandemic in society. This study extends the knowledge in this research field to design effective public policies of crisis management for facing next pandemic threats.

Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories

The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.

Improving preparedness for next pandemics: Max level of COVID-19 vaccinations without social impositions to design effective health policy and avoid flawed democracies

In the presence of pandemic threats, such as Coronavirus Disease 2019 (COVID-19) crisis, vaccination is one of the fundamental strategies to cope with negative effects of new viral agents in society. The rollout of vast vaccination campaigns also generates the main issue of hesitancy and resistance to vaccines in a share of people. Many studies have investigated how to reduce the social resistance to vaccinations, however the maximum level of vaccinable people against COVID-19 (and in general against pandemic diseases), without coercion in countries, is unknown. The goal of this study is to solve the problem here by developing an empirical analysis, based on global data, to estimate the max share of people vaccinable in relation to socioeconomic wellbeing of nations. Results, based on 150 countries, reveal that vaccinations increase with the income per capita, achieving the maximum share of about 70% of total population, without coercion. This information can provide new knowledge to establish the appropriate goal of vaccination campaigns and in general of health policies to cope with next pandemic impacts, without restrictions that create socioeconomic problems. Overall, then, nations have a natural level of max vaccinable people (70% of population), but strict policies and mandates to achieve 90% of vaccinated population can reduce the quality of democracy and generate socioeconomic issues higher than (pandemic) crisis.

Assessment and Impacts of Air Pollution from Brick Kilns on Public Health in Northern Pakistan

Brick kilns add enormous quantities of organic pollutants to the air that can cause serious health issues, especially in developing countries; poor air quality is associated with community health problems, yet receives no attention in Northern Pakistan. The present study, therefore, assessed the chemical composition and investigated the impacts of air pollution from brick kilns on public health. A field-based investigation of air pollutants, i.e., PM1, PM2.5 and PM10, CO2, CO, NO, NO2, H2S, and NH3 using mobile scientific instruments was conducted in selected study area locations. Social surveys were conducted to investigate the impacts of air pollution on community health. The results reveal the highest concentrations of PM1, PM2.5, and PM10, i.e., 3377, 2305, and 3567.67 µg/m3, respectively, in specific locations. Particulate matter concentrations in sampling points exceeded the permissible limits of the Pakistan National Environmental Quality Standard and, therefore, may risk the local population’s health. The highest mean value of CO2 was 529 mg/L, and other parameters, such as CO, NO, NO2, H2S, and NH3 were within the normal range. The social survey’s findings reveal that particulate matter was directly associated with respiratory diseases such as asthma, which was reported in all age groups selected for sampling. The study concluded by implementing air pollution reduction measures in brick kiln industries to protect the environment and community health. In addition, the region’s environmental protection agency needs to play an active role in proper checking and integrated management to improve air quality and protect the community from air hazards.

Photoluminescent inorganic nanoprobe-based pathogen detection

Pathogens are serious threats to human health, and traditional detection techniques suffer from various limitations. The unique optical properties of photoluminescent inorganic nanomaterials, such as high photoluminescence quantum yields, good photostability, and tunable spectrum, make them ideal tools for the detection of pathogens with high specificity and sensitivity. In this review, the design strategies, working mechanisms, and applications of photoluminescent inorganic nanomaterial-based probes in pathogen detection are introduced. In particular, the design and construction of stimuli-responsive nanoprobes and their potential in these fields are highlighted.