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The heightened contamination of urban rivers with psychiatric drugs poses significant environmental and public health risks due to their persistence and bioaccumulative nature. In this study, we assessed the concentrations of 10 psychiatric medicines in water and sediment samples from three rivers in Curitiba, Brazil, spanning the SARS-CoV-2 pandemic from September 2020 to November 2023. Our analysis revealed substantial variations in drug concentrations across rivers and sampling periods, with fluoxetine and sertraline being the most prevalent in waters (up to 1118 ng L -1 ) and sediments (up to 70 ng g -1 DW). Sediments emerged as primary repositories for psychiatric drugs. The COVID-19 pandemic notably impacted drug concentrations, with fluoxetine and alprazolam concentrations surging by up to 741 % and 524 %, respectively, compared to pre-pandemic levels. The current concentrations of drugs in rivers pose risks to aquatic life. Despite variations, overall drug concentrations increased by 20 % for fluoxetine and 15 % for alprazolam after the pandemic. These findings underscore the persistent environmental risks associated with heightened drug consumption, emphasizing the urgent need for continued monitoring and intervention strategies to mitigate ecological and public health impacts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study examines the spread of COVID-19 in São Paulo, Brazil, using a combination of cellular automata and geographic information systems to model the epidemic's spatial dynamics. By integrating epidemiological models with georeferenced data and social indicators, we analyse how the virus propagates in a complex urban setting, characterized by significant social and economic disparities. The research highlights the role of various factors, including mobility patterns, neighbourhood configurations, and local inequalities, in the spatial spreading of COVID-19 throughout São Paulo. We simulate disease transmission across the city's 96 districts, offering insights into the impact of network topology and district-specific variables on the spread of infections. The study seeks to fine-tune the model to extract epidemiological parameters for further use in a statistical analysis of social variables. Our findings underline the critical importance of spatial analysis in public health strategies and emphasize the necessity for targeted interventions in vulnerable communities. Additionally, the study explores the potential of mathematical modelling in understanding and mitigating the effects of pandemics in urban environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The widespread of chlorhexidine and antibiotics in the water bodies, which grew during the global COVID-19 pandemic, can increase the dispersion of antibiotic resistance. We assessed the occurrence of these pharmaceutical compounds as well as SARS-CoV-2 and analysed the bacterial community structure of hospital and urban wastewaters from Brazil, Cameroon, and Madagascar. Water and wastewater samples (n = 59) were collected between January-June 2022. Chlorhexidine, azithromycin, levofloxacin, ceftriaxone, gentamicin and meropenem were screened by Ultra-High-Performance Liquid Chromatography coupled with mass spectrometer. SARS-CoV-2 was detected based on the nucleocapsid gene (in Cameroon and Madagascar), and envelope and spike protein-encoding genes (in Brazil). The total community-DNA was extracted and used for bacterial community analysis based on the 16S rRNA gene. To unravel likely interaction between pharmaceutical compounds and/or SARS-CoV-2 with the water bacterial community, multivariate statistics were performed. Chlorhexidine was found in hospital wastewater effluent from Brazil with a maximum concentration value of 89.28 μg/L. Additionally, antibiotic residues such as azithromycin and levofloxacin were also present at concentrations between 0.32-7.37 μg/L and 0.11-118.91 μg/L, respectively. In Cameroon, azithromycin was the most found antibiotic present at concentrations from 1.14 to 1.21 μg/L. In Madagascar instead, ceftriaxone (0.68-11.53 μg/L) and levofloxacin (0.15-0.30 μg/L) were commonly found. The bacterial phyla statistically significant different (P < 0,05) among participating countries were Proteobacteria, Patescibacteria and Dependentiae which were mainly abundant in waters sampled in Africa and, other phyla such as Firmicutes, Campylobacterota and Fusobacteriota were more abundant in Brazil. The phylum Caldisericota was only found in raw hospital wastewater samples from Madagascar. The canonical correspondence analysis results suggest significant correlation of azithromycin, meropenem and levofloxacin with bacteria families such as Enterococcaceae, Flavobacteriaceae, Deinococcaceae, Thermacetogeniaceae and Desulfomonilaceae, Spirochaetaceae, Methanosaetaceae, Synergistaceae, respectively. Water samples were also positive for SARS-CoV-2 with the lowest number of hospitalized COVID-19 patients in Madagascar (n = 7) and Brazil (n = 30). Our work provides new data about the bacterial community profile and the presence of pharmaceutical compounds in the hospital effluents from Brazil, Cameroon, and Madagascar, whose limited information is available. These compounds can exacerbate the spreading of antibiotic resistance and therefore pose a risk to public health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The escalating global concern of antimicrobial resistance poses a significant challenge to public health. This study delved into the occurrence of resistant bacteria and antimicrobial resistance genes in the waters and sediments of urban rivers and correlated this emergence and the heightened use of antimicrobials during the COVID-19 pandemic. Isolating 45 antimicrobial-resistant bacteria across 11 different species, the study identifies prevalent resistance patterns, with ceftriaxone resistance observed in 18 isolates and ciprofloxacin resistance observed in 13 isolates. The detection of extended-spectrum β-lactamases, carbapenemases, and acquired quinolone resistance genes in all samples underscores the gravity of the situation. Comparison with a pre-pandemic study conducted in the same rivers in 2019 reveals the emergence of previously undetected new resistant species, and the noteworthy presence of new resistant species and alterations in resistance profiles among existing species. Notably, antimicrobial concentrations in rivers increased during the pandemic, contributing significantly to the scenario of antimicrobial resistance observed in these rivers. We underscore the substantial impact of heightened antimicrobial usage during epidemics, such as COVID-19, on resistance in urban rivers. It provides valuable insights into the complex dynamics of antimicrobial resistance in environmental settings and calls for comprehensive approaches to combat this pressing global health issue, safeguarding both public and environmental health., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The mpox epidemic had spread worldwide and become an epidemic of international concern. Before the emergence of targeted vaccines and specific drugs, it is necessary to numerically simulate and predict the epidemic. In order to better understand and grasp its transmission situation, and take some countermeasures accordingly when necessary, we predicted and simulated mpox transmission, vaccination and control scenarios using model developed for COVID-19 predictions. The results show that the prediction model can also achieve good results in predicting the mpox epidemic based on modified SEIR model. The total number of people infected with mpox on Dec 31, 2022 reached 83878, while the prediction of the model was 96456 with a relative error of 15%. The United States, Brazil, Spain, France, the United Kingdom and Germany are six countries with serve mpox epidemic. The predictions of their epidemic are 30543, 11191, 7447, 5945, 5606 and 4291 cases respectively, with an average relative error of 20%. If 30% of the population is vaccinated using a vaccine that is 78% effective, the number of infected people will drop by 29%. This shows that the system can be practically applied to the prediction of mpox epidemic and provide corresponding decision-making reference., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The analyses of genetic traits, dispersion patterns and phylogenomics are essential for understanding the evolutionary forces driving SARS-CoV-2 viruses in these three years of COVID-19 pandemics. Brazil is one of the most affected countries in the world and not sufficient genomic studies have been performed. The emergence of P.1 lineage led to one of the most serious public health crises on record. Our study presents the genomic sequencing and characterization of 412 samples from Rio Grande do Sul state, in the Brazilian Southern region, during Gamma and Delta epidemic waves, in 2021. Additionally, molecular evolution tests were performed to identify positively selected sites in Brazil between 2020 and 2022, as well as offer some evolutionary perspective about the maintenance of multiple spike mutations in Omicron lineages. Genomic epidemiology analysis has indicated an intense P.1 (Gamma) diversification followed by rapid Delta substitution in Southern Brazil., Competing Interests: Declaration of competing interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper., (Copyright © 2023 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.)

The overwhelming majority of SARS-CoV-2 epidemiological studies cover a narrow time period, making general knowledge about the COVID-19 pandemic difficult. To assess COVID-19-related host aspects in the overall pandemic, we analyzed COVID-19 cases during the first two years of SARS-CoV-2 circulation in southern Brazil. Herein, 390 patients admitted in 2020-2022 to a Brazilian public referral hospital were allocated into two groups according to the COVID-19 outcome: hospital discharge (n=237) or death (n=153). In the univariate analysis, several variables, including sociodemographic, clinical and laboratory aspects (primary data), were significantly different between the analyzed groups. In multivariate logistic regression, eight of these factors remained associated with the COVID-19 outcome. In particular, we report oxygen supplementation and the need for hemodialysis as predictors of hospital discharge and death from COVID-19, respectively. To the best of our knowledge, none of these findings have been previously reported in the Brazilian or world population. In conclusion, our results contribute to current knowledge by demonstrating that factors described at different times may remain associated with COVID-19 over the pandemic and by identifying novel predictors of COVID-19 outcome., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The main objective of this paper is to propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a setup under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate comprise the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly more straightforward and tractable than semi-Markov models with a similar level of generality. Based on stochastic stability, we derive a sufficient condition for a shrinking epidemic regarding the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil, and assess the effect of different stabilising strategies in the latter setting. Results suggest that the proposed approach can curb the epidemic with various occupation rate levels if the mitigation is timely., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Covid-19, caused by severe acute respiratory syndrome coronavirus 2, broke out as a pandemic during the beginning of 2020. The rapid spread of the disease prompted an unprecedented global response involving academic institutions, regulatory agencies, and industries. Vaccination and nonpharmaceutical interventions including social distancing have proven to be the most effective strategies to combat the pandemic. In this context, it is crucial to understand the dynamic behavior of the Covid-19 spread together with possible vaccination strategies. In this study, a susceptible-infected-removed-sick model with vaccination (SIRSi-vaccine) was proposed, accounting for the unreported yet infectious. The model considered the possibility of temporary immunity following infection or vaccination. Both situations contribute toward the spread of diseases. The transcritical bifurcation diagram of alternating and mutually exclusive stabilities for both disease-free and endemic equilibria were determined in the parameter space of vaccination rate and isolation index. The existing equilibrium conditions for both points were determined in terms of the epidemiological parameters of the model. The bifurcation diagram allowed us to estimate the maximum number of confirmed cases expected for each set of parameters. The model was fitted with data from São Paulo, the state capital of SP, Brazil, which describes the number of confirmed infected cases and the isolation index for the considered data window. Furthermore, simulation results demonstrate the possibility of periodic undamped oscillatory behavior of the susceptible population and the number of confirmed cases forced by the periodic small-amplitude oscillations in the isolation index. The main contributions of the proposed model are as follows: A minimum effort was required when vaccination was combined with social isolation, while additionally ensuring the existence of equilibrium points. The model could provide valuable information for policymakers, helping define disease prevention mitigation strategies that combine vaccination and non-pharmaceutical interventions, such as social distancing and the use of masks. In addition, the SIRSi-vaccine model facilitated the qualitative assessment of information regarding the unreported infected yet infectious cases, while considering temporary immunity, vaccination, and social isolation index., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic has led governments worldwide to impose extensive restrictions on citizens, some of which may have long-term impact after their removal. Education is arguably the policy domain where closure policies are anticipated to lead to greatest lasting loss, in this case learning loss. Currently, limited data exists from which researchers and practitioners can draw insightful conclusions about how to remedy the problem. In this paper, we outline the global pattern in pandemic school-closure periods and illustrate data needs through the examples drawn from Brazil and India, two large countries which experienced prolonged periods of school closures during the pandemic. We conclude with a series of recommendations for building an improved data environment at government, school and household levels, to serve the building back agenda in education, and to provide better opportunities for evidence-based policymaking thereafter., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Schistosomiasis remains a serious public health concern in Brazil and the Schistosomiasis Control Program (PCE) was elaborated to assist in the control of the disease. Nevertheless, the irruption of the COVID-19 pandemic may have impacted the program. Herein, we assessed the impact of the pandemic on PCE actions in an endemic area in the region with the highest positivity rate for schistosomiasis in Brazil. We conducted an ecological, population-based study using data from the PCE of the state of Alagoas, between 2015 and 2021, to calculate the percentage of change. The temporal trend analysis was performed using the segmented log-linear regression model. To evaluate the spatial distribution of the data, choropleth maps were made showing the values of the% of change. Moran maps was elaborated to indicate the critical areas. Our analysis showed a decrease in the population surveyed in 2020 (-41.00%) and 2021 (-18.42%). Likewise, there was a reduction in the number of Kato-Katz tests performed (2020 = -43.45%; and in 2021 = -19.63%) and, consequently, a drop in the rate of positive tests (-37.98% in 2020 and -26.14% in 2021). Importantly, treatment of positive cases was lower than 80% (77.44% in 2020 and 77.38% in 2021). Additionally, spatial clusters with negative percentage values of up to -100% of the PCE indicators were identified mostly in the municipalities of the coastal areas that are historically most affected by schistosomiasis. Taken together, our analyzes corroborate that PCE actions in endemic municipalities of Alagoas were impacted by the COVID-19 pandemic., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Current persistent outbreak of COVID-19 is triggering a series of collective responses to avoid infection. To further clarify the impact mechanism of adaptive protection behavior and vaccination, we developed a new transmission model via a delay differential system, which parameterized the roles of adaptive behaviors and vaccination, and allowed to simulate the dynamic infection process among people. By validating the model with surveillance data during March 2020 and October 2021 in America, India, South Africa, Philippines, Brazil, UK, Spain and Germany, we quantified the protection effect of adaptive behaviors by different forms of activity function. The modeling results indicated that (1) the adaptive activity function can be used as a good indicator for fitting the intervention outcome, which exhibited short-term awareness in these countries, and it could reduce the total human infections by 3.68, 26.16, 15.23, 4.23, 7.26, 1.65, 5.51 and 7.07 times, compared with the reporting; (2) for complete prevention, the average proportions of people with immunity should be larger than 90%, 92%, 86%, 71%, 92%, 84%, 82% and 76% with adaptive protection behaviors, or 91%, 97%, 94%, 77%, 92%, 88%, 85% and 90% without protection behaviors; and (3) the required proportion of humans being vaccinated is a sub-linear decreasing function of vaccine efficiency, with small heterogeneity in different countries. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics"., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic has caused a global health crisis, and wastewater-based epidemiology (WBE) has emerged as an important tool to assist public health decision-making. Recent studies have shown that the SARS-CoV-2 RNA concentration in wastewater samples is a reliable indicator of the severity of the pandemic for large populations. However, few studies have established a strong correlation between the number of infected people and the viral concentration in wastewater due to variations in viral shedding over time, viral decay, infiltration, and inflow. Herein we present the relationship between the number of COVID-19-positive patients and the viral concentration in wastewater samples from three different hospitals (A, B, and C) in the city of Belo Horizonte, Minas Gerais, Brazil. A positive and strong correlation between wastewater SARS-CoV-2 concentration and the number of confirmed cases was observed for Hospital B for both regions of the N gene (R = 0.89 and 0.77 for N1 and N2, respectively), while samples from Hospitals A and C showed low and moderate correlations, respectively. Even though the effects of viral decay and infiltration were minimized in our study, the variability of viral shedding throughout the infection period and feces dilution due to water usage for different activities in the hospitals could have affected the viral concentrations. These effects were prominent in Hospital A, which had the smallest sewershed population size, and where no correlation between the number of defecations from COVID-19 patients and viral concentration in wastewater was observed. Although we could not determine trends in the number of infected patients through SARS-CoV-2 concentrations in hospitals' wastewater samples, our results suggest that wastewater monitoring can be efficient for the detection of infected individuals at a local level, complementing clinical data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic required a wide range of adaptations to the way that water sector operated globally. This paper looks into the impact of the COVID-19 pandemic on Brazilian water sector and evaluates the water sector's organisational resilience from the lens of water professionals. This study uses British Standard (BS 65000:2014)'s Resilience Maturity Scale method to evaluate organisational resilience in water sector under two defined scenarios of before and during the pandemic. For this purpose, the self-assessment framework developed by Southern Water in the United Kingdom (based on BS 65000:2014), comprising of the core resilience elements of Direction, Awareness, Alignment, Learning, Strengthening, and Assurance, are used for evaluations. A qualitative-quantitative surveying method is used for data collection. A total of 14 responses to the whole questionnaire were received from May 2021 to August 2021, each representing one water company in Brazil (four local companies and ten state-owned ones). The analyses identified COVID-19 as a threat multiplier particularly to already existing financial challenges due to the pre-existing threats in water sector. Bad debt and the COVID-19 emergency measures are recognised as the main challenges by 21 % and 14 % of the survey respondents. The state-owned and local companies scored an almost similar maturity level 3, 35 % and 34 % respectively, while the local companies scored much lower at maturity level 4 i.e., 26 % as opposed to 47 % in state-owned sector. This indicates that COVID-19 has a greater impact on local companies and the needs to increase preparedness. This study replicates an international experience to raise awareness on water sector's resiliency in Brazil and how it can be improved to withstand future external shocks. It sheds light on how and what existing challenges can be exacerbated facing a global shock and proposes opportunities for improvement of resilience maturity in water sector in Brazil., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Introduction: Scientometrics is a discipline that allows the analysis of the characteristics of publications in each field of knowledge using different indicators. The aim of this research was to analyse world scientific publication as to COVID-19 related to dentistry., Methods: A specialised search strategy was used to obtain all the documents published in journals indexed in the Scopus database between December 2019 and February 2022. The study variables were exported and analysed in SciVal (Elsevier)., Results: In all, 2071 documents were retrieved; of those, Oral Diseases has the highest impact with an average of 12.3 citations per publication, and the British Medical Journal was identified as the journal with the highest scientific output (107). India (292) and the UK (287) were the countries with the most published papers. The Universidade de São Paulo in Brazil was the institution with the highest number of published papers (61) and with the highest impact., Conclusions: During the pandemic, scientific publications on dentistry and COVID-19 have been increasing considerably. International collaboration has the highest percentage of publications whilst India and the UK are the countries with the highest scientific production., Competing Interests: Conflict of interest None disclosed., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Different fields of knowledge have faced challenges due to the COVID-19 pandemic. Essential public services serving the population's needs were maintained. Previous studies have addressed the possibility of health hazards from handling solid waste (SW) among trash collection service workers, without introducing any data proving or dismissing those risks. Occupational health is stated in at least 2 of the 17 Sustainable Development Goals. Pearson Coefficient (r) was employed to analyze the correlation between the number of cases of COVID-19 in SW collection workers in the city of São Paulo, considering the district city halls where the COVID-19 patients live and the SW collection workers' route, from March 2020 to March 2021. Employees who collected wastes with COVID-19: 2% collected healthcare SD, 4% worked at the waste transshipment stations, 10% household variable route collection, and 83% in defined routes collecting household wastes. Positive and moderate correlation was confirmed (r = 0.59) between the total number of COVID-19 cases and the trash collection routes, and the number of contaminated COVID-19 workers from their trash collection routes. Considering concessionary companies separately, the correlation found in the Southeastern grouping is 0.77 (positive and high correlation), and the correlation found in the Northwestern grouping is 0.18 (positive but insignificant correlation). The Northwestern grouping's concessionary had implemented more effective occupational health and safety management policies and disclosed them among their stakeholders. Results suggest concern related to occupational health and safety for SW collection workers and substantiating the vulnerability of this essential activity during the pandemic., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

This work presents the first case of SARS-CoV-2 RNA detection in leachate collected from a transfer station in the city of São Paulo, Brazil. After calibration of the viral detection method already used for wastewater samples with a pilot leachate sample and virus fragments in laboratory, twelve polyethylene glycol concentrated leachates samples were tested by RT-qPCR. The results confirmed the presence of N1 gene in 9 of the 12 analyzed samples between epidemiological weeks 33 and 38 of the year 2021 (08/15/2021 to 09/19/2021). The occurrence of the N2 gene was only observed in 5 of the 12 samples. The concentration values for N1 and N2 genes varied between 3.1 and 4.6 log 10 .GC·L -1 , which are values close to those measured in sanitary wastewater. This method showed to be a promising procedure to verify the presence of viral RNA in municipal solid waste leachate, being especially useful where there is no treatment system and sanitation infrastructure, which makes the conventional wastewater surveillance unfeasible., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic, caused by the highly transmissible SARS-CoV-2 virus, has overloaded health systems in many contexts Conant and Wolfe (2008). Brazil has experienced more than 345,000 deaths, as of April/2021 Conant and Wolfe (2008), with dire consequences for the country's public and private health systems. This paper aims to estimate the synchronization graph between the cities' contagion waves from public COVID-19 data records. For this purpose, the Motif-Synchronization method Magwire et al. (2011) was applied to publicly available COVID-19 data records to determine the sequential relationship of occurrence of the waves among Bahia's cities. We find synchronization between waves of infection between cities, suggesting diffusion of the disease in Bahia and a potential role for inter-city transportation Saba et al. (2018), Saba et al. (2014), Araújo et al. (2018) in the dynamics of this phenomenon McKee and Stuckler (2020), Chinazzi et al. (2020), Tizzoni et al. (2014). Our main contribution lies in the use of the Motif-Synchronization method applied to COVID-19 data records, with the results revealing a pattern of disease spread that extends beyond city boundaries., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

The SARS-CoV-2 virus was first registered in Brazil by the end of February 2020. Since then, the country counts over 150000 deaths due to COVID-19 and faces a profound social and economic crisis; there is also an ongoing health catastrophe, with the majority of hospital beds in many Brazilian cities currently occupied with COVID-19 patients. Thus, a Nonlinear Model Predictive Control (NMPC) scheme used to plan appropriate social distancing measures (and relaxations) in order to mitigate the effects of this pandemic is formulated in this paper. The strategy is designed upon an adapted data-driven Susceptible-Infected-Recovered-Deceased (SIRD) model, which includes time-varying auto-regressive immunological parameters. A novel identification procedure is proposed, composed of analytical regressions, Least-Squares optimization and auto-regressive model fits. The adapted SIRD model is validated with real data and able to adequately represent the contagion curves over large forecast horizons. The NMPC strategy is designed to generate piecewise constant quarantine guidelines, which can be reassessed (relaxed/strengthened) each week. Simulation results show that the proposed NMPC technique is able to mitigate the number of infections and progressively loosen social distancing measures. With respect to a "no-control" condition, the number of deaths could be reduced in up to 30% if the proposed NMPC coordinated health policy measures are enacted., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

In this paper, Transfer Learning is used in LSTM networks to forecast new COVID cases and deaths. Models trained in data from early COVID infected countries like Italy and the United States are used to forecast the spread in other countries. Single and multistep forecasting is performed from these models. The results from these models are tested with data from Germany, France, Brazil, India, and Nepal to check the validity of the method. The obtained forecasts are promising and can be helpful for policymakers coping with the threats of COVID-19., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

Objectives: To describe the profile of hospital deaths in Brazil according to cause of admission during the pre-pandemic (2019) and pandemic periods (2020)., Methods: Descriptive study based on individual-level records of all hospital admissions with death outcomes reimbursed by the Brazilian National Health System in 2019 and 2020., Results: The number of hospital deaths increased by 16.7% in 2020 compared with 2019 (522,686 vs 609,755). Coronavirus disease 2019 (COVID-19) was associated with 19.5% (118,879) of all hospital deaths in 2020, surpassing diseases of the circulatory system (15.4%, 93,735) and diseases of the respiratory system (14.9%, 91,035)., Conclusions: COVID-19 was the main cause of death in public hospitals in Brazil in 2020., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: In 2020, the COVID-19 pandemic brought a work and stress overload to healthcare workers, increasing their vulnerability to mental health impairments. In response, the authors created the COMVC-19 program. The program offered preventive actions and mental health treatment for the 22,000 workers of The Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP). This paper aims to describe its implementation and share what we have learned from this experience., Methods: Workers were able to easily access the program through a 24/7 hotline. Additionally, a mobile phone app that screened for signs and symptoms of emotional distress and offered psychoeducation and/or referral to treatment was made available. Data from both these sources as well as any subsequent psychiatric evaluations were collected., Results: The first 20 weeks of our project revealed that most participants were female, and part of the nursing staff working directly with COVID-19 patients. The most frequently reported symptoms were: anxiety, depression and sleep disturbances. The most common diagnoses were Adjustment, Anxiety, and Mood disorders., Conclusions: Implementing a mental health program in a multimodal intervention was feasible in a major quaternary public hospital. Our data also suggests that preventive actions should primarily be aimed at anxiety and depression symptoms, with a particular focus on the nursing staff.

Currently most researchers consider humanity's extermination of biodiversity as the antecedent of ideal conditions for the emergence of new viruses and diseases. Animals lose their natural habitats due to extensive landscape changes, consequently crowding them together and increasing their interaction with humans. Additionally, it is also important to emphasise the increasing concern on climate change because climate can modify the distribution and intensity of other diseases such as vector-borne disease. Unfortunately, the global resources for biodiversity conservation were diluted by government support for activities harmful to the environment. A tragic example is from the Amazon rainforest, that experienced fast environmental depletion and high social inequalities. Extractive systems and extensive land use on a large scale have induced deforestation, great loss of biodiversity, carbon emission, and water contamination, leading to indigenous land dispossession, violence, and rural-urban migration. The deforested areas in the Amazon region increase considerably at an alarming speed each year. The COVID-19 pandemic is an evidence to show how viruses and pathogens move further and faster than before, which means we must also show a quick response. It requires financing and, mostly, changes in human behaviour. The message is simple: we need to rethink our current relationship with nature and with ourselves, which should lead to a social transformation towards the sustainable use of the available resources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The first Covid-19 patient in India was reported on January 30, 2020 at the state of Kerala. The patient number rose to three by February 3, 2020. In the month of March 2020, the transmissions started to increase when the people started to return back to India from the Covid-19 affected countries. On March 12, a 76-year-old man having a travel history to Saudi Arabia was the first reported fatality in India due to Covid 19. Then for the prevention of the propagation of Covid, the Indian government declared a state of health emergency and strict counter measures were taken, including locking down of cities, prohibiting almost all avoidable activities and restricting population's mobility. From March 24, 2020 due to the complete lockdown in the country, human activities were heavily restricted in the whole geographical regions of India. This pandemic lockdown eventually serves as an opportunity to observe the background concentrations of pollutants in the atmosphere. The PM 2.5 distribution can affect human health and to overcome this problem, setting up of regulation for PM is necessary. In the present study Probability density functions (PDF) method have been utilised for the investigation of PM 2.5 pollutant data distribution of five countries namely, India, China, France, Brazil and United States of America (USA) for their respective lockdown period of 2020 and corresponding same period of 2019. A detailed study has been done for India, and for that purpose India has been divided into three regions (Central India, Coastal India and Indo-Gangetic Plain (IGP)) on the basis of different meteorological conditions. PM 2.5 concentration for hourly basis has been analysed for the lockdown period 24th March to 15th June 2020 and compared with the PM 2.5 concentration of previous year 2019 for the same time period. To understand the effect of lockdown in PM 2.5 emission in India, which will give us an idea about the background concentration, PDFs (probability density functions) have also been generated for the whole year from 2015 to 2019. The "goodness-of-fit" of the probability density functions, to the data, was assessed, using various statistical indices (Chi-square test). Results show that the PM 2.5 reduction during the lockdown period of 2020 as compared to the same period of 2019 is sufficiently large. This study will give a certain degree of idea to the regulatory bodies on planning and implementation of strict air quality control plans., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Human sewage from Florianopolis (Santa Catarina, Brazil) was analyzed for severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) from October 2019 until March 2020. Twenty five ml of sewage samples were clarified and viruses concentrated using a glycine buffer method coupled with polyethylene glycol precipitation, and viral RNA extracted using a commercial kit. SARS-CoV-2 RNA was detected by RT-qPCR using oligonucleotides targeting N1, S and two RdRp regions. The results of all positive samples were further confirmed by a different RT-qPCR system in an independent laboratory. S and RdRp amplicons were sequenced to confirm identity with SARS-CoV-2. Genome sequencing was performed using two strategies; a sequence-independent single-primer amplification (SISPA) approach, and by direct metagenomics using Illumina's NGS. SARS-CoV-2 RNA was detected on 27th November 2019 (5.49 ± 0.02 log 10 SARS-CoV-2 genome copies (GC) L -1 ), detection being confirmed by an independent laboratory and genome sequencing analysis. The samples in the subsequent three events were positive by all RT-qPCR assays; these positive results were also confirmed by an independent laboratory. The average load was 5.83 ± 0.12 log 10 SARS-CoV-2 GC L -1 , ranging from 5.49 ± 0.02 log 10 GC L -1 (27th November 2019) to 6.68 ± 0.02 log 10 GC L -1 (4th March 2020). Our findings demonstrate that SARS-CoV-2 was likely circulating undetected in the community in Brazil since November 2019, earlier than the first reported case in the Americas (21st January 2020)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1-score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by-case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Visual analytics techniques are useful tools to support decision-making and cope with increasing data, particularly to monitor natural or artificial phenomena. When monitoring disease progression, visual analytics approaches help decision-makers to understand or even prevent dissemination paths. In this paper, we propose a new visual analytics tool for monitoring COVID-19 dissemination. We use k-nearest neighbors of cities to mimic neighboring cities and analyze COVID-19 dissemination based on comparing a city under consideration and its neighborhood. Moreover, such analysis is performed within periods, which facilitates the assessment of isolation policies. We validate our tool by analyzing the progression of COVID-19 in neighboring cities of São Paulo state, Brazil., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The world is experiencing the worst global health crisis in recent decades since December/2019 due to a new pandemic coronavirus. The COVID-19 disease, caused by SARS-CoV-2, has resulted in more than 30 million cases and 950 thousand deaths worldwide as of September 21, 2020. Determining the extent of the virus on public surfaces is critical for understanding the potential risk of infection in these areas. In this study, we investigated the presence of SARS-CoV-2 RNA on public surfaces in a densely populated urban area in Brazil. Forty-nine of 933 samples tested positive (5.25%) for SARS-CoV-2 RNA, including samples collected from distinct material surfaces, including metal and concrete, and distinct places, mainly around hospital care units and public squares. Our data indicated the contamination of public surfaces by SARS-CoV-2, suggesting the circulation of infected patients and the risk of infection for the population. Constant monitoring of the virus in urban areas is required as a strategy to fight the pandemic and prevent further infections., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic has been the greatest global public health threat of the 21st century. Additionally, it has been challenging for the Brazilian shores that were recently (2019/2020) affected by the most extensive oil spill in the tropical oceans. Monitoring programs and studies about the economic, social and ecological consequences of the oil disaster were being carried out when the COVID-19 (coronavirus disease 2019) pandemic was declared, which has heavily affected Brazil. For Brazilian seagrasses conservation, this scenario is especially challenging. An estimated area of +325 km 2 seagrass meadows was affected by the 2019 oil spill. However, this area is undoubtedly underestimated since seagrasses have not yet been adequately mapped along the 9000 km-long Brazilian coast. In addition to scientific budget cuts, the flexibilization of public and environmental policies in recent years and absence of systematic field surveys due to COVID-19 has increased the underestimation of affected seagrass areas and ecosystem service losses due to the oil spill. Efforts to understand and solve the oil spill crisis were forced to stop (or slow down) due to COVID-19 and the economic crisis, leaving ecosystems and society without answers or conditions to identify the source(s) that was/were responsible for this spill, mitigate the damage to poor communities, promote adequate impact assessment or restoration plans, or properly monitor the environment. Our results highlight that pandemic and large-scale environmental disasters may have had a synergistic effect on the economy (e.g., artisanal fisheries and tourism), public health and ecology, mainly due to government inaction, social inequality and poorly studied tropical ecosystems. The results of this study also demonstrate the need to analyze the short- and long-term impacts of the combined effects (oil spill + COVID-19) on the recovery of the economy and coastal ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Although an increasing body of data reports the detection of SARS-CoV-2 RNA in air, this does not correlate to the presence of infectious viruses, thus not evaluating the risk for airborne COVID-19. Hence there is a marked knowledge gap that requires urgent attention. Therefore, in this systematic review, viability/stability of airborne SARS-CoV-2, SARS-CoV and MERS-CoV viruses is discussed., Methods: A systematic literature review was performed on PubMed/MEDLINE, Web of Science and Scopus to assess the stability and viability of SARS-CoV, MERS-CoV and SARS-CoV-2 on air samples., Results and Discussion: The initial search identified 27 articles. Following screening of titles and abstracts and removing duplicates, 11 articles were considered relevant. Temperatures ranging from 20 °C to 25 °C and relative humidity ranging from 40% to 50% were reported to have a protective effect on viral viability for airborne SARS-CoV and MERS-CoV. As no data is yet available on the conditions influencing viability for airborne SARS-CoV-2, and given the genetic similarity to SARS-CoV and MERS-CoV, one could extrapolate that the same conditions would apply. Nonetheless, the effect of these conditions seems to be residual considering the increasing number of cases in the south of USA, Brazil and India, where high temperatures and humidities have been observed., Conclusion: Higher temperatures and high relative humidity can have a modest effect on SARS-CoV-2 viability in the environment, as reported in previous studies to this date. However, these studies are experimental, and do not support the fact that the virus has efficiently spread in the tropical regions of the globe, with other transmission routes such as the contact and droplet ones probably being responsible for the majority of cases reported in these regions, along with other factors such as human mobility patterns and contact rates. Further studies are needed to investigate the extent of aerosol transmission of SARS-CoV-2 as this would have important implications for public health and infection-control policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

At the beginning of the SARS-COV-2 outbreak in Brazil, there was a striking difference between the contamination rate in the Amazonian States and the South and the Southeast States. The regions near the Amazon rainforest presented much higher and faster contaminations. This paper attempts to explain this phenomenon through a global analysis of the COVID-19 epidemic in Brazil. It also investigates the relationship between climate conditions and airborne transmission with the evolution of contagion in the Amazonian states. The method of investigation of the spread of SARS-COV-2 in these different macro-environments was based on the analysis of three extensive daily official databases on the number of deaths, the percentage of adherence of the populations to the restriction policies, and the local climatic conditions. Besides, the social conditions in those States were also taken into account. Then, it was compared the epidemiologic results for States with very different climatic characteristics and that had adopted, almost simultaneously, similar social isolation measures. However, all these analyses were not able to explain the remarkable difference in the evolution of the pandemic among Brazilian regions. So, it was necessary to invoke airborne transmission, facilitated by the very high air humidity, as a decisive factor to explain the faster evolution of contagion in the rainforest region. Air humidity seems to be the most important climatic factor in viral spreading, while usual ambient temperatures do not have a strong influence. Another very important result of this analysis was the observation that the onset of collective immunity may have been achieved with a contamination rate of about 15% of the Amazonian population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The World Health Organization has recently declared South America the new epicenter of the COVID-19 pandemic, as Brazil has become one of the most affected countries. Besides public health and economic impacts, social isolation has also caused indirect environmental effects. The aim of this study was to assess environmental impacts caused by shifts on solid waste production and management due to the COVID-19 pandemic in Brazil. We have analyzed data from 30 cities, representing a population of more than 53.8 million people (25.4% of the Brazilian population). Unexpectedly, solid waste production in the main cities in Brazil has decreased during the social isolation period, possibly because of reduced activity in commercial areas. The latest data on solid waste in Brazil have revealed that more than 35% of medical waste has not been treated properly. Furthermore, improper disposal of facemasks has been reported in several cities and may increase the risk for COVID-19 spread. The suspension of recycling programs has hindered natural resources from being saved, with emphasis on 24,076 MWh of electric power and 185,929 m 3 of potable water - respectively enough to supply 152,475 households and 40,010 people, over a month. Furthermore, total sale price for recyclable materials during the suspension of recycling programs reaches more than 781 thousand dollars, being these materials disposed in landfills - demanding an extra volume of 19,000 m 3 - reducing landfill lifespan, and hence causing a double loss: economic and environmental., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Competing Interests: Declaration of Competing Interests The authors whose names are listed immediately below report the following details of affiliation or involvement in an organization or entity with a financial or non-financial interest in the subject matter or materials discussed in this manuscript. Please specify the nature of the conflict on a separate sheet of paper if the space below is inadequate. Author names: Felipe Branco Arcadepani; Vitor Soares Tardelli and Thiago Marques Fidalgo