Search

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts. [ABSTRACT FROM AUTHOR]

This paper simulates a hypothetical pan-coronavirus vaccine that confers immediate sterilizing immunity against all SARS-CoV-2 variants. Simulations used a SEIIS (susceptible, exposed, infective, immune, susceptible) spreadsheet model that ran two parallel subpopulations: one that accepted vaccination and another that refused it. The two subpopulations could transmit infections to one another. Using data from the United States (US), the simulated vaccine was tested against limiting factors such as vaccine hesitancy, slow vaccination distribution, and the development of high-transmission variants. The vaccine was often successful at reducing cases, but high-transmission variants and discontinuation of non-pharmaceutical interventions (NPIs) such as masking greatly elevated cases. A puzzling outcome was that if NPIs were discontinued and high-transmission variants became common, the model predicted consistently higher rates of disease than are actually observed in the US in 2024. However, if cumulative exposure to virus antigens increased the duration of immunity or decreased the infectivity of the virus, the model predictions were brought back into a more realistic range. The major finding was that even when a COVID-19 vaccine always produces sterilizing immunity against every SARS-CoV-2 variant, its ability to control the epidemic can be compromised by multiple common conditions. [ABSTRACT FROM AUTHOR]

This multidisciplinary composition of the contributors to this Special Issue provides the readers with multiple and original views and approaches, enriching the scientific debate on SARS-CoV-2 epidemiology and pathogenesis. Concerning the topics, the majority of the papers ( I n i = 11) dealt with the epidemiology of SARS-CoV-2 infection in various contexts, while 10 articles focused on specific aspects characterizing the pathogenesis of the disease. Since its emergence in 2019 in Wuhan City, Hubei Province, China, SARS-CoV-2 has spread across hundreds of countries and all continents. 10.3390/microorganisms9040800 4 Jo S., Kim S., Yoo J., Kim M.-S., Shin D.H. A Study of 3CLpros as Promising Targets against SARS-CoV and SARS-CoV-2. [Extracted from the article]

The COVID-19 pandemic has prompted an overwhelming surge in research investigating different aspects of the disease and its causative agent. In this study, we aim to discern research themes and trends in the field of wastewater-based epidemiology (WBE) of SARS-CoV-2 and other respiratory viruses over the past decade. We examined 904 papers in the field authored by researchers from 87 countries. Despite the low reported incidence of COVID-19 in 2023, researchers are still interested in the application of WBE to SARS-CoV-2. Based on network visualization mapping of 189 keyword co-occurrences, method optimization, source, transmission, survival, surveillance or early-warning detection systems, and variants of concern in wastewater were found to be the topics of greatest interest among WBE researchers. A trend toward evaluations of the utility of new technologies such as digital PCR and WBE for other respiratory viruses, particularly influenza, was observed. The USA emerged as the leading country in terms of research publications, citations, and international collaborations. Additionally, Science of the Total Environment stood out as the journal with the highest number of publications and citations. The study highlighted areas for further research, including data normalization and biosensor-based data collection, and emphasized the need for international collaboration and standardized methodology for WBE in future research directions. [ABSTRACT FROM AUTHOR]

Since the onset of the coronavirus disease, COVID-19 pandemic, concern arose for those who might be at higher risk of a worse COVID-19 prognosis, such as those with cystic fibrosis (CF). In this context, we evaluated the features of hospitalized patients with CF due to severe acute respiratory infection (SARI) in Brazil and we also performed a systematic review including all the studies published from the beginning of the first case of COVID-19 (17 November 2019) to the date of this search (23 May 2022) which included, concomitantly, patients with CF and COVID-19 in the worldwide population. In our Brazilian data, we evaluated the period from December 2019 to March 2022, and we included 33 demographical and clinical patients' features. We classified the patients into groups: (G1) SARI due to another viral infection than severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (23; 5.4%), (G2) SARI due to an unknown etiological agent (286; 67.1%), and (G3) SARI due to SARS-CoV-2 infection (117; 27.5%). The individuals in G3 tended to be older, especially over 50 years old, and presented a higher prevalence of dyspnea, peripheral capillary oxygen saturation (SpO2) <95%, and cardiopathy. The highest prevalence for intensive care unit (ICU) treatment (52; 44.4%) and invasive mechanical ventilation (29; 24.8%) was for patients in G3. Almost half of the patients in G3 died (51; 43.6%); in contrast, none in G1 died. However, we observed 43 (15.0%) deaths in G2. In addition, 12 (4.2%) and one (0.9%) death not associated with SARI occurred, respectively, in the G2 and G3. The patients who died due to SARS-CoV-2 infection had a higher frequency of SpO2 <95% (46; 90.2%), ICU treatment (34; 66.7%), and invasive mechanical ventilation (27; 52.9%) when compared to those who recovered. The systematic review comprised a total of 31 papers published as observational studies. These studies comprised 661,386 patients in total, including children, adults, and elderly age groups. However, only 19,150 (2.9%) patients were diagnosed with CF and, from these patients, 2523 (0.4%) were diagnosed with both CF and COVID-19. It was observed that the most common outcome was the need for hospitalization (n = 322 patients with CF), and the need for oxygen support (n = 139 patients with CF). One hundred patients with CF needed intensive care units, fifty patients needed non-invasive mechanical ventilation support, and only three patients were described as receiving invasive mechanical ventilation support. Deaths were described in 38 patients with CF. Importantly, lung-transplanted patients with CF represented an increased risk of death in one publication; in accordance, another study described that lung transplantation and moderate to severe lung disease were independent risk factors for severe outcomes after SARS-CoV-2 infection. In contrast with the literature, in conclusion, Brazilian patients in G3 presented a severe phenotype, even though most of the other studies did not observe worse outcomes in patients with CF and COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has proven to be a social and economic disruptor on a global scale, severely curtailing people’s ability to travel and engage in many recreation activities. Periodic lockdown and stay-at-home orders have exacerbated the situation. In this social climate, urban green spaces have attained a high significance for the maintenance of the physical and mental health of the population. Given the presence of similar coronaviruses in animal populations, it can be predicted that future epidemics and even pandemics will occur. Urban planning needs to incorporate the lessons learnt during COVID-19 in order to future-proof our communities. This paper reviews the role that urban green spaces played during the COVID-19 pandemic. Based on these observations, the paper discusses a range of options for the provision of pandemic-sensitive spaces for physical exercise and mental recreation. Design concepts for long-term planning adjustments as well as for future ad-hoc solutions are provided. These include the provision of social distancing ‘pods’ embedded in design and landscaping of permanent parks, the design of ad-hoc, socially distanced ‘parklets’ on a quietened street and a rethink of the design of curb-to-boundary setbacks (nature strip) in residential streets. [ABSTRACT FROM AUTHOR]

COVID-19 represents the greatest health crisis in recent human history. To date, it is still difficult to estimate its impact on mortality. This paper investigates the excess crude mortality in 27 European countries. The differences between the values observed in 2020 and 2021 with those predicted by a joinpoint regression model were evaluated. A multi-regression analysis was implemented to assess the relationship between health variables and excess mortality. Europe experienced a marked and surprising (S-value > 52) increase in crude mortality during 2020 (Δ% = +10.0%, 95% CI: [2.5; 18.7]) and 2021 (Δ% = +12.1%, 95% CI: [4.3; 21.2]). The difference between average excesses of Eastern and Western countries was not surprising (S < 2) and had little relevance (ΔE-W = −2.4, 95% CI: [−2; 7]) during 2020 but was more pronounced (S = 15, ΔE-W = +17.2, 95% CI: [11.0; 23.5]) during 2021. Excess crude mortality increased in 2021 (Δ% = +65%, 95% CI: [12.6; 118], S = 5.9). Evidence has been found for a surprising and marked negative linear relationship between COVID-19 vaccinations and excess mortality ("2021 excess mortality = A + BX4", with "A = 58 ± 7, S = 28" and "B = −0.65 ± 0.10, S = 22, Radj2 = 0.65, 95% CI: [0.38; 0.82]). In light of the current literature, these findings provide solid evidence of the substantial role of COVID-19 in the unexpected and marked excess mortality recorded in Europe. COVID-19 vaccinations have appeared to be one of the main determinants for reducing mortality. Future research should explore these aspects in more detail. [ABSTRACT FROM AUTHOR]

Despite the stunning speed with which highly effective and safe vaccines have been developed, the emergence of new variants of SARS-CoV-2 causes high rates of (re)infection, a major impact on health care services, and a slowdown to the socio-economic system. For COVID-19, accurate and timely forecasts are therefore essential to provide the opportunity to rapidly identify risk areas affected by the pandemic, reallocate the use of health resources, design countermeasures, and increase public awareness. This paper presents the design and implementation of an approach based on autoregressive models to reliably forecast the spread of COVID-19 in Italian regions. Starting from the database of the Italian Civil Protection Department (DPC), the experimental evaluation was performed on real-world data collected from February 2020 to March 2022, focusing on Calabria, a region of Southern Italy. This evaluation shows that the proposed approach achieves a good predictive power for out-of-sample predictions within one week (R-squared > 0.9 at 1 day, R-squared > 0.7 at 7 days), although it decreases with increasing forecasted days (R-squared > 0.5 at 14 days). [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected many aspects of human life around the world, due to its tremendous outcomes on public health and socio-economic activities. Policy makers have tried to develop efficient responses based on technologies and advanced pandemic control methodologies, to limit the wide spreading of the virus in urban areas. However, techniques such as social isolation and lockdown are short-term solutions that minimize the spread of the pandemic in cities and do not invert long-term issues that derive from climate change, air pollution and urban planning challenges that enhance the spreading ability. Thus, it seems crucial to understand what kind of factors assist or prevent the wide spreading of the virus. Although AI frameworks have a very efficient predictive ability as data-driven procedures, they often struggle to identify strong correlations among multidimensional data and provide robust explanations. In this paper, we propose the fusion of a heterogeneous, spatio-temporal dataset that combine data from eight European cities spanning from 1 January 2020 to 31 December 2021 and describe atmospheric, socio-economic, health, mobility and environmental factors all related to potential links with COVID-19. Remote sensing data are the key solution to monitor the availability on public green spaces between cities in the study period. So, we evaluate the benefits of NIR and RED bands of satellite images to calculate the NDVI and locate the percentage in vegetation cover on each city for each week of our 2-year study. This novel dataset is evaluated by a tree-based machine learning algorithm that utilizes ensemble learning and is trained to make robust predictions on daily cases and deaths. Comparisons with other machine learning techniques justify its robustness on the regression metrics RMSE and MAE. Furthermore, the explainable frameworks SHAP and LIME are utilized to locate potential positive or negative influence of the factors on global and local level, with respect to our model's predictive ability. A variation of SHAP, namely treeSHAP, is utilized for our tree-based algorithm to make fast and accurate explanations. [ABSTRACT FROM AUTHOR]

Prior to the COVID-19 pandemic, the analysis of urological wastewater had been a matter of academic curiosity and community-wide big-picture studies looking at drug use or the presence of select viruses such as Hepatitis. The COVID-19 pandemic saw systematic testing of urological wastewater emerge as a significant early detection tool for the presence of SARS-CoV-2 in a community. Even though the pandemic still rages in all continents, it is time to consider the post-pandemic world. This paper posits that urban planners should treat urological wastewater as a communal public health asset and that future sewer design should allow for stratified multi-order sampling. [ABSTRACT FROM AUTHOR]

(1) Background: An online survey-based observational cross-sectional study aimed at elucidating the experience and attitudes of an unstructured population regarding diagnostic imaging. (2) Methods: Invitations to participate were distributed using mixed-mode design to deidentified residents aged 18 years and older. Main outcome measures included morbidity structure and incidence of diagnostic imaging administrations. (3) Results: Respondents (n = 1069) aged 44.3 ± 14.4 years; 32.8% suffered from cardiovascular diseases (CVD); 9.5% had chronic respiratory pathology; 28.9% considered themselves healthy. Respondents with COVID-19 history (49.7%) reported higher rates of computed tomography (CT) (p < 0.0001), magnetic resonance imaging (MRI) (p < 0.001), and ultrasound (p < 0.05). COVID-19 history in CVD respondents shifted imaging administrations towards CT and MRI (p < 0.05). Every tenth respondent received MRI, CT, and ultrasound on a paid basis; 29.0% could not pay for diagnostic procedures; 13.1% reported unavailable MRI. Professional status significantly affected the pattern of diagnostic modalities (p < 0.05). MRI and CT availability differed between respondents in urban and rural areas (p < 0.0001). History of technogenic events predisposed responders to overestimate diagnostic value of fluorography (p < 0.05). (4) Conclusions: Preparedness to future pandemics requires the development of community-based outreach programs focusing on people's awareness regarding medical imaging safety and diagnostic value. [ABSTRACT FROM AUTHOR]

Simple Summary: ASF remains a significant concern for the Philippines as it has impacted the swine industry and food security. It causes high mortality rates, leading to decreased pork production, high prices of pork and pork products, and financial losses for farmers and stakeholders, especially for the livelihoods of many small-scale pig farmers in the Philippines. Since its first appearance in 2019, the government has implemented strict measures to control the spread of the disease that include enhanced surveillance, strict biosecurity and quarantine protocols, restricted movement of live pigs and pork products, and culling of infected and at-risk pigs. Ongoing surveillance and cooperation between industry stakeholders, government agencies, and research institutions and collaboration with international organizations and experts to develop enhanced strategies for ASF control and prevention are necessary to mitigate ASF's impacts on the swine industry. Moreover, public awareness campaigns and support for affected farmers are continuously in place to combat ASF in the Philippines. African swine fever (ASF), a highly contagious disease of swine, has posed a significant global threat to the swine industry. As an archipelago, the Philippines has a geographic advantage when it comes to the risk of ASF transmission. However, since its introduction to the Philippines in 2019, it has proliferated not only in backyard and commercial farms but also in wild pig populations. While certain parts of the country were more affected than others, the epidemiologic features of ASF necessitate that all affected areas must be closely monitored and that confirmed cases be treated with the utmost care. With the very limited data on ASF epidemiology and surveillance in the Philippines, future efforts to combat ASF must place even greater emphasis on improved prevention and control strategies. It is worth mentioning that the government's efforts toward comprehensive ASF surveillance and epidemiological investigation into the possible ASFV sources or transmission pathways are the most important measures in the prevention and control of ASF outbreaks. This review article provides a comprehensive overview of the current swine industry and ASF situation in the Philippines, which includes its epidemiology, surveillance, prevention, and control strategies. [ABSTRACT FROM AUTHOR]

Background: Suboptimal influenza and pneumococcal vaccination rates have been reported before the COVID-19 pandemics in certain populations at risk for severe infection. The aim of this longitudinal cohort study was to investigate changes in influenza and pneumococcal vaccination rates and patient perceptions in patients with psoriasis (PsO) before and during the pandemic. Methods: Data on vaccination, patient and disease characteristics, comorbidity, and patient perceptions were collected with questionnaires before and during the pandemic approximately one year later. Results: Over the whole cohort who participated in the follow-up visit (n = 287; 59.2% male; mean age: 56.3 years), both influenza and pneumococcal lifetime vaccination prevalences increased significantly from 50.5% to 66.2% and from 16.0% to 41.5%, respectively. A total of 88.5% of PsO patients were interested in a COVID-19 vaccination or had already received it. The reasons for and against vaccinations changed significantly before and during the pandemic. Conclusions: Despite a promising increase in the vaccination prevalence in our PsO cohort, it remains important that awareness for vaccinations is encouraged and closely monitored in future research, particularly in populations at risk. [ABSTRACT FROM AUTHOR]

Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area. [ABSTRACT FROM AUTHOR]

Background: Wastewater-based epidemiology (WBE) has become crucial for early microbial outbreak detection and public health surveillance globally, underscored by the COVID-19 pandemic. However, despite advancements in sampling and analyses, interpreting results and estimating infection rates pose challenges. Enhancements in sewer system engineering, understanding the wastewater environment, and addressing the impact of the environment on the accuracy of results are needed. Objective: This scoping review aims to identify engineering knowledge gaps in WBE to guide future study designs. Design: Research on "wastewater-based epidemiology" involving "engineering", published between 2015 and 2023, was extracted from the Scopus database. Results: This scoping review examines elements influencing WBE's precision and reliability, especially in identifying and measuring SARS-CoV-2 RNA. It identifies significant effects of engineering, analytical practices, and the wastewater's composition on WBE performance. Conclusions: This review calls for further investigation into economical evaluation methods of these factors to enhance WBE data normalization and interpretation, utilizing existing wastewater treatment plant data used for treatment control, which could be a cost-effective approach over more expensive population biomarkers. This approach, aside from SARS-CoV-2, holds potential for application to a broader number and types of diseases, as well as population consumption habits. [ABSTRACT FROM AUTHOR]

Wastewater-based epidemiology has garnered considerable research interest, concerning the COVID-19 pandemic. Restrictive public health interventions and mobility limitations are measures to avert a rising case prevalence. The current study integrates WBE monitoring strategies, Google mobility data, and restriction information to assess the epidemiological development of COVID-19. Various SARIMAX models were employed to predict SARS-CoV-2 cases in Liechtenstein and two Austrian regions. This study analyzes four primary strategies for examining the progression of the pandemic waves, described as follows: 1—a univariate model based on active cases; 2—a multivariate model incorporating active cases and WBE data; 3—a multivariate model considering active cases and mobility data; and 4—a sensitivity analysis of WBE and mobility data incorporating restriction policies. Our key discovery reveals that, while WBE for SARS-CoV-2 holds immense potential for monitoring COVID-19 on a societal level, incorporating the analysis of mobility data and restriction policies enhances the precision of the trained models in predicting the state of public health during the pandemic. [ABSTRACT FROM AUTHOR]

The study of mortality is an ever-active field of research, and new methods or combinations of methods are constantly being developed. In the actuarial domain, the study of phenomena disrupting mortality and leading to excess mortality, as in the case of COVID-19, is of great interest. Therefore, it is relevant to investigate the extent to which an epidemiological model can be integrated into an actuarial approach in the context of mortality. The aim of this project is to establish a method for the study of excess mortality due to an epidemic and to quantify these effects in the context of the insurance world to anticipate certain possible financial instabilities. We consider a case study caused by SARS-CoV-2 in Belgium during the year 2020. We propose an approach that develops an epidemiological model simulating excess mortality, and we incorporate this model into a classical approach to pricing life insurance products. [ABSTRACT FROM AUTHOR]

Contact tracing is a method used to control the spread of a pandemic. The objectives of this research are to conduct an empirical review and content analysis to identify the environmental factors causing the spread of the pandemic and to propose an ontology-based big data architecture to collect these factors for prediction. No research studies these factors as a whole in pandemic prediction. The research method used was an empirical study and content analysis. The keywords contact tracking, pandemic spread, fear, hygiene measures, government policy, prevention programs, pandemic programs, information disclosure, pandemic economics, and COVID-19 were used to archive studies on the pandemic spread from 2019 to 2022 in the EBSCOHost databases (e.g., Medline, ERIC, Library Information Science & Technology, etc.). The results showed that only 84 of the 588 archived studies were relevant. The risk perception of the pandemic (n = 14), hygiene behavior (n = 7), culture (n = 12), and attitudes of government policies on pandemic prevention (n = 25), education programs (n = 2), business restrictions (n = 2), technology infrastructure, and multimedia usage (n = 24) were the major environmental factors influencing public behavior of pandemic prevention. An ontology-based big data architecture is proposed to collect these factors for building the spread prediction model. The new method overcomes the limitation of traditional pandemic prediction model such as Susceptible-Exposed-Infected-Recovered (SEIR) that only uses time series to predict epidemic trend. The big data architecture allows multi-dimension data and modern AI methods to be used to train the contagion scenarios for spread prediction. It helps policymakers to plan pandemic prevention programs. [ABSTRACT FROM AUTHOR]

Candidemia is one of the healthcare-associated infections that has high mortality. The risk factors that predispose a patient to develop this infection are mostly found in patients of greater severity and COVID-19 contributes to the risk of death. The aim of this study is to evaluate epidemiological characteristics and risk factors for mortality in patients with candidemia before and during the COVID-19 pandemic era. This is a retrospective study conducted at Instituto Central from 2016 to 2020 of patients with candidemia that were evaluated for demographic data, medical history, risk factors, microbiological data, therapeutic measures, complementary exams, device management, and outcome defined by 30-day mortality. A total of 170 episodes were included (58.2% males; mean age of 56 years). The overall incidence density of candidemia per 1000 admissions and per 1000 patient-days was 1.17 and 0.17, respectively, with an increase of 38% in the year 2020. The use of a central venous catheter was the most prevalent (93.5%) condition, followed by the previous use of antibiotics (91.1%). Corticosteroid use ranked seventh (56.4%). C. albicans was responsible for 71 (41.7%) of the isolates, followed by C. tropicalis and C. glabrata, with 34 (20%) isolates each. Echinocandin was prescribed in 60.1% of cases and fluconazole in 37%. Echocardiography resulted in six (5.08%) cases of endocarditis and fundoscopy resulting in two (2.4%) endophthalmitis. The 30-day mortality was 93/170 (54.7%). The risk factors associated with mortality were age (OR 1.03, CI 95% 1.01–1.06), heart disease (OR 7.51, CI 95% 1.48–37.9), hemodialysis (OR 3.68, CI 95% 1.28–10.57), and use of corticosteroids (OR 2.83, CI 95% 1.01–7.92). The COVID-19 pandemic had an impact on the increase incidence of candidemia. The persistently high mortality highlights the need for better management strategies, control of risk factors, and guarantee of adequate treatment. [ABSTRACT FROM AUTHOR]

Wastewater-based epidemiology (WBE) is currently used to monitor not only the spread of the viral SARS-CoV-2 pandemic but also that of other viruses in endemic conditions, particularly in the absence of syndromic surveillance. The continuous monitoring of sewage requires high expenditure and significant time investments, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Tuscany (Italy) and targeted human adenovirus (HAdV), norovirus genogroup II (NoVggII), enterovirus (EV), and SARS-CoV-2. Samples were collected at the entrances of treatment plants and concentrated using PEG/NaCl precipitation, and viral nucleic acids were extracted and detected through real-time reverse transcription qPCR. NoVggII was the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. Compared with data that were previously collected in the same study area (from February 2021 to September 2021), the results for SARS-CoV-2 revealed a shift from an epidemic to an endemic pattern, at least in the region under investigation, which was likely due to viral mutations that led to the spreading of new variants with increased resistance to summer environmental conditions. In conclusion, using standardized methods and an efficient monitoring strategy, WBE proves valuable for viral surveillance in pandemic and epidemic scenarios, enabling the identification of temporal–local distribution patterns that are useful for making informed public health decisions. [ABSTRACT FROM AUTHOR]

Background: The appearance of COVID-19 had a major impact on healthcare and the epidemiology of other diseases. Following the cessation of non-pharmacologic interventions destined to limit the spread of COVID-19, influenza reemerged. The aim of this study was to compare the pre-pandemic influenza seasons with the influenza seasons after the emergence of the COVID-19 pandemic, and to identify differences in terms of clinical characteristics, risk factors, complications, outcomes, and antiviral and antibiotic treatments. Methods: We conducted a retrospective cohort study from the Teaching Hospital of Infectious Diseases database in Cluj-Napoca, Romania. We analyzed four pre-pandemic seasons and the seasons after the onset of COVID-19. We included adult patients hospitalized with confirmed influenza between October 2016 and August 2023. Variables such as age, sex, duration of hospitalization, severity, clinical manifestations, comorbidities, and Charlson comorbidity index were assessed. Results: A total of 941 patients were included in the analysis. The percentage of severe influenza was similar in both groups, but mortality from influenza was significantly lower after 2022. Virtually all patients were prescribed antivirals; antibiotic prescriptions decreased in the post-COVID-19 influenza seasons. Conclusion: The present study suggests that influenza seasons after 2022 had lower mortality and attenuated clinical presentation. [ABSTRACT FROM AUTHOR]

Background: The impact of the COVID-19 pandemic on the incidence rate of childhood type 1 diabetes (T1D) is controversial. Our aim was to analyze the incidence of new-onset T1D among children aged 0–17 before and during the COVID-19 pandemic in Israel. Methods: Data obtained from the national T1D registry for children aged 0–17 were analyzed for the pre-pandemic (1997–2019) and pandemic (2020–2022) periods. In the pre-pandemic period, 7246 children with newly diagnosed T1D were compared with 1490 children diagnosed during the pandemic period. Results: T1D incidence significantly increased in the 0–17 age group from a mean of 12.9/105 (pre-pandemic) to 17.7/105 and 16.7/105 during the first two years of the pandemic (2020 and 2021, respectively) (p = 0.0001). Stratifying by age group (0–4, 5–9, 10–14, and 15–17) revealed a significant increase in the 5–9, 10–14, and 15–17 groups, both in 2020 (p = 0.0001) and in 2021 (p = 0.0001). The incidence rate in the 0–4 age group showed no change in the first year of the pandemic (2020) (p = 0.4). However, in the second year of the pandemic (2021), there was a significant increase from 6.3/105 in the pre-pandemic period to 9.1/105 (p = 0.001). Anti-COVID-19 vaccination in 2022 led to a significant decrease in the incidence rates in the 10–14 and 15–17 age groups (p = 0.03 and p = 0.02, respectively). Conclusion: The COVID-19 pandemic was associated with a significant increase in the incidence of new-onset T1D in prepubertal and pubertal children. Anti-COVID-19 vaccination decreased the incidence rate significantly only in pubertal children. [ABSTRACT FROM AUTHOR]

Background: Public health measures for COVID-19 mitigation influenced the circulation of Respiratory Syncytial Virus (RSV) during the 2020–2021 winter season. In the following autumn, an unprecedented resurgence of RSV occurred. Our study monitored RSV pediatric infections one and two years after the relaxation of containment measures for the COVID-19 pandemic. Methods: We analyzed diagnostic molecular data for SARS-CoV-2, flu, and RSV infections and clinical data from children with respiratory symptoms referring to our hospital during the 2021–2022 and 2022–2023 seasons. Results: In the 2021–2022 season, the number of RSV-affected children was very high, especially for babies <1 year. The outbreak appeared in a shorter interval of time, with a high clinical severity. In the 2022–23 season, a reduced number of infected pediatric patients were detected, with a similar hospitalization rate (46% vs. 40%), and RSV accounted for 12% of the infections. Coinfections were observed in age <2 years. In RSV patients, symptoms were similar across the two seasons. Conclusions: The clinical presentation of RSV in the two post-COVID seasons suggests that the pathophysiology of the virus did not change across these two years. Further studies are needed to continuously monitor RSV to support an effective prevention strategy. [ABSTRACT FROM AUTHOR]

Background: Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. Materials and Methods: We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. Results: Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26–59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%. Conclusion: The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner. [ABSTRACT FROM AUTHOR]

Vaccination is the most cost-effective method of preventing COVID-19; however, data on its effect on patients with multimorbidity is limited. The aim was to evaluate the effect of vaccination against new coronavirus infection (NCI) in patients with multimorbid pathology in hospital treatment on the outcome of COVID-19 disease. An analysis was carried out of 1832 records of patients in one of the COVID-19 hospitals in Moscow for 2020–2022. Statistical analysis was carried out using the StatTech v. 3.1.3 software, and the binary logistic regression (BLR) method was used to obtain prognostic models. The median age of patients was 69 years, and 76% of them had received two vaccine doses. To assess the outcome of the disease, two prognostic models were obtained depending on the presence of a multimorbidity in patients: cardiovascular pathology and/or atherosclerosis and/or type 2 diabetes mellitus (Model 1) or atherosclerosis and/or type 2 diabetes mellitus and/or encephalopathy (Model 2), against the background of the presence or absence of vaccination against NCI. When assessing the outcome of NCI in Model 1, the odds of death decreased by 3.228 times with two doses of Sputnik V in patients with multimorbidity. According to Model 2, for patients with multimorbidity, the chances of death decreased by 3.281 times with two doses of Sputnik V. The presence of two doses of Sputnik V increased the likelihood of recovery in patients with multimorbidity by more than three times. [ABSTRACT FROM AUTHOR]

Wastewater-based epidemiology (WBE) has become an effective tool in the surveillance of infectious diseases such as COVID-19. In this work, we performed a brief study of monitoring the SARS-CoV-2 viral load in wastewater from six nursing homes located in the metropolitan area of A Coruña (Spain) between December 2020 and March 2021. The main objective was to detect SARS-CoV-2 outbreaks among residents and study the efficacy of the vaccination campaign. SARS-CoV-2 viral load (RNA copies per L of wastewater) was determined by reverse-transcription quantitative PCR (RT-qPCR) using the quantification cycle (Cq) values for the nucleocapsid (N) gene. Our results showed that the increase in viral load preceded the increase in clinical cases, favoring an early warning system that detects COVID-19 outbreaks in advance, making it possible to contain and stop the transmission of the virus among residents. In addition, the efficacy of the new COVID-19 vaccines was evidenced, since after the vaccination campaign in nursing homes in A Coruña, it was observed that many residents did not present any symptoms of the disease, although they excreted high amounts of virus in their feces. WBE is a cost-effective strategy that should be implemented in all cities to prevent new emerging diseases or future pandemic threats. [ABSTRACT FROM AUTHOR]

The comparison of the development of the SARS-CoV-2 epidemic in several neighboring regions can help researchers to assess the risks and develop more effective strategies and approaches in the field of preventive medicine. We analyzed the infection and mortality statistics for the 2020–2022 period in ten individual regions of the Siberian Federal District of Russia. We also sequenced complete genomes, which allowed us to analyze the genetic diversity of SARS-CoV-2 circulated in each of the ten regions and to build a phylogenetic dendrogram for the virus variants. The ParSeq v.1.0 software was developed to automate and speed up the processing and analysis of viral genomes. At the beginning of the pandemic, in the first two waves, the B.1.1 variant (20B) dominated in all regions of the Siberian Federal District. The third and fourth waves were caused by the Delta variant. Mortality during this period was at a maximum; the incidence was quite high, but the number of deposited genomes with GISAID during this period was extremely low. The maximum incidence was at the beginning of 2022, which corresponds to the arrival of the Omicron variant in the region. The BA.5.2 variant became the dominant one. In addition, by using NextClade, we identified three recombinants in the most densely populated areas. [ABSTRACT FROM AUTHOR]

Bloodstream infections (BSIs) are associated with high mortality and inappropriate or delayed antimicrobial therapy. The purpose of this study was to investigate the impact of the COVID-19 pandemic on the epidemiology of BSIs in hospitalized patients. The research aimed to compare the incidence of BSIs and blood culture results in patients hospitalized before and during the COVID-19 pandemic. Methods: Retrospective and prospective data were collected from blood cultures obtained from 4289 patients hospitalized between June 2018 and July 2022. Two groups of patients were distinguished: those with BSIs admitted during the pre-COVID-19 period and those admitted during the COVID-19 surge. Demographic and clinical data, blood cytology, and biochemistry results were analyzed, and the usefulness of PCT was assessed in patients with COVID-19. Results: The study showed a significant increase in the incidence of BSIs during the pandemic compared to the pre-COVID-19 period. Positive blood cultures were obtained in 20% of patients hospitalized during the pandemic (vs. 16% in the pre-COVID-19 period). The incidence of BSIs increased from 1.13 to 2.05 cases per 1000 patient days during COVID-19, and blood culture contamination was more frequently observed. The mortality rate was higher for patients hospitalized during the COVID-19 pandemic. An increased frequency of MDRO isolation was observed in the COVID-19 period. Conclusions: The incidence of BSIs increased and the mortality rate was higher in the COVID-19 period compared to the pre-COVID-19 period. The study showed limited usefulness of procalcitonin in patients with COVID-19, likely due to the administered immunosuppressive therapy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic demanded changes in healthcare systems worldwide. The lockdown brought about difficulties in healthcare access. However, trauma still required further attention considering its modifications. The presented study aims to investigate the variances in epidemiological patterns of trauma during the lockdown and the previous year, with a view to better understand the modifications in healthcare provision. The authors analyzed data from the first lockdown in 2020 (12 March–30 May) and the same period in 2019 from 35 hospitals in Lublin Province. A total of 10,806 patients in 2019 and 5212 patients in 2020 were included in the research. The uncovered changes adhered to the total admissions and mortality rate, the frequency of injuries in particular body regions, and injury mechanisms. The lockdown period resulted in a reduction in trauma, requiring an altered approach to healthcare provision. Our research indicates that the altered approach facilitated during such periods is essential for delivering tailored help to trauma patients. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) is the most common pathogen causing respiratory tract infections in infants, affecting over 90% of children within the first two years of life. It may cause lower respiratory tract infections, which constitute a significant healthcare burden both in the primary and secondary care settings. Meanwhile, the data regarding RSV disease in Poland is scarce, and published data significantly differs from the numbers reported for other countries with longstanding surveillance and reporting systems. A literature review and an expert panel were conducted to (1) understand the healthcare burden of RSV infections in Poland; (2) collect data on infection seasonality, patient pathway, and management patterns; and (3) evaluate RSV infection surveillance in Poland. According to the literature, RSV is the major agent responsible for non-influenza respiratory diseases in Poland. The reported rates of hospitalization for RSV infections are 267.5/100,000 for children under 5 years of age and 1132.1/100,000 for those under 1 year of age. Comparisons with data from other countries suggest that these values may be underestimated, possibly due to insufficient access to microbiological testing and a low awareness of RSV. Infections occur mainly between December and April, however, this pattern has changed following the implementation of preventive measures for coronavirus disease 2019 in the past few years. According to available reports, bronchodilators, antibiotics, corticosteroids, and X-ray imaging have been frequently used. The surveillance system in Poland has limitations, but these may be overcome due to recent changes in healthcare law as well as the availability and reimbursement of diagnostic tests. [ABSTRACT FROM AUTHOR]

The study aimed to assess clinical pharmacology patterns of prescribed and taken medications in older cardiovascular patients using electronic health records (EHRs) (n = 704) (2019–2022). Medscape Drug Interaction Checker was used to identify pairwise drug–drug interactions (DDIs). Prevalence rates of DDIs were 73.5% and 68.5% among taken and prescribed drugs, respectively. However, the total number of DDIs was significantly higher among the prescribed medications (p < 0.05). Serious DDIs comprised 16% and 7% of all DDIs among the prescribed and taken medications, respectively (p < 0.05). Median numbers of DDIs between the prescribed vs. taken medications were Me = 2, IQR 0–7 vs. Me = 3, IQR 0–7 per record, respectively. Prevalence of polypharmacy was significantly higher among the prescribed medications compared with that among the taken drugs (p < 0.05). Women were taking significantly more drugs and had higher prevalence of polypharmacy and DDIs (p < 0.05). No sex-related differences were observed in the list of prescribed medications. ICD code U07.1 (COVID-19, virus identified) was associated with the highest median DDI number per record. Further research is warranted to improve EHR structure, implement patient engagement in reporting adverse drug reactions, and provide genetic profiling of patients to avoid potentially serious DDIs. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic poses an unprecedented public health challenge in Malaysia. The impact of COVID-19 varies between countries, including geographically divided states within a country. The deadly transmission of COVID-19 has taken a heavy toll in Sabah, Malaysia's third most populous state, contributing nearly 10% to the recorded national death toll as of 31 December 2022. Although several SARS-CoV-2 genome sequences have been analysed in Malaysia, molecular epidemiology data from Sabah focusing on the diversity and evolution of SARS-CoV-2 variants are still lacking. This study examines the major SARS-CoV-2 variants and emerging mutations from Sabah, the Malaysian Borneo, which is geographically divided from West Malaysia by the South China Sea. Methods: A total of 583 COVID-19 samples were subjected to whole genome sequencing and analysed with an additional 1123 Sabah COVID-19 sequences retrieved from the GISAID EpiCoV consortium. Nextclade and Pangolin were used to classify these sequences according to the clades and lineages. To determine the molecular evolutionary characteristics, Bayesian time-scaled phylogenetic analysis employing the maximum likelihood algorithm was performed on selected SARS-CoV-2 genome sequences, using the Wuhan-Hu-1 sequence as a reference. Results: Sabah was affected starting from the second COVID-19 wave in Malaysia, and the early sequences were classified under the O clade. The clade was gradually replaced during subsequent waves by G, GH, GK and GRA, with the latter being dominant as of December 2022. Phylogenetically, the Delta isolates in this study belong to the three main subclades 21A, 21J and 21I, while Omicron isolates belong to 21M, 21L and 22B. The time-scaled phylogeny suggested that SARS-CoV-2 introduced into Sabah originated from Peninsular Malaysia in early March 2020, and phylodynamic analysis indicated that increased viral spread was observed in early March and declined in late April, followed by an evolutionary stationary phase in June 2020. Conclusion: Continuous molecular epidemiology of SARS-CoV-2 in Sabah will provide a deeper understanding of the emergence and dominance of each variant in the locality, thus facilitating public health intervention measures. [ABSTRACT FROM AUTHOR]

The first cases of COVID-19 were reported in Wuhan Province, in China, in December 2019, spreading rapidly around the world. The World Health Organization (WHO) declared this pandemic at the beginning of March 2020 and, at the same time, the first patient in Galați County was confirmed. Both the global and the regional epidemiological evolutions have taken place with variations in incidence, which have been graphically recorded in several "waves". We conducted a retrospective study on cases of COVID-19 infection, hospitalized between March and June 2020 in an infectious diseases clinic from Galati, in the south-east of Romania. The present paper describes the "first-wave" regional epidemiological and clinical-biological features and the evolution of the COVID-19 pandemic. A poor outcome was related to late presentation to hospital, old age, and over six comorbid conditions including Alzheimer's disease. The high death rate among people from long-term care institutions is the consequence of the cumulative risk factors associated with immune senescence and inflammation, while COVID-19 is more likely a contributing factor to lower life expectancy. [ABSTRACT FROM AUTHOR]

On the 30 January 2020, the WHO declared a public health emergency of international concern due to the coronavirus disease 2019 (COVID-19). Social restrictions with different efficiencies were put in place to avoid transmission. Students living in student accommodation constitute an interesting group to test restrictions because they share living places, workplaces and daily routines, which are key factors in the transmission. In this paper, we present a new geospatial agent-based simulation model to explore the transmission of COVID-19 between students living in Newcastle University accommodation and the efficiency of simulated restrictions (e.g., facemask, lockdown, self-isolation). Results showed that facemasks could reduce infection peak by 30% if worn by all students; an early lockdown could keep 65% of the students safe in the best case; self-isolation could keep 86% of the students safe; while the combination of these measures could prevent disease in 95% of students in the best case-scenario. Spatial analyses showed that the most dangerous places were those where many students interact for a long time, such as faculties and accommodation. The developed ABM could help university managers to respond to current and future epidemics and plan effective responses to keep safe as many students as possible. [ABSTRACT FROM AUTHOR]

The objective of this paper is to describe the surveillance system MIDaS and to show how this system has been used for evaluating the consequences of the French COVID-19 lockdown on the bacterial mix of AP-HM and the antibiotic resistance. MIDas is a kind of surveillance activity hub, allowing the automatic construction of surveillance control boards. We investigated the diversity and resistance of bacterial agents from respiratory, blood, and urine samples during the lockdown period (from week 12 to 35 of 2020), using the same period of years from 2017 to 2019 as control. Taking into account the drop in patient recruitment, several species have exhibited significant changes in their relative abundance (either increasing or decreasing) with changes up to 9%. The changes were more important for respiratory and urine samples than for blood samples. The relative abundance in respiratory samples for the whole studied period was higher during the lockdown. A significant increase in the percentage of wild phenotypes during the lockdown was observed for several species. The use of the MIDaS syndromic collection and surveillance system made it possible to efficiently detect, analyze, and follow changes of the microbiological population as during the lockdown period. [ABSTRACT FROM AUTHOR]

All four serotypes of the dengue virus (DENV1–4) cause a phenotypically similar illness, but serial infections from different serotypes increase the risk of severe disease. Thus, genomic surveillance of circulating viruses is important to detect serotype switches that precede community outbreaks of disproportionate magnitude. A phylogenetic analysis was conducted on near full length DENV genomes sequenced from serum collected from a prospective cohort study from the Colombo district, Sri Lanka during a 28-month period using Oxford nanopore technology, and the consensus sequences were analyzed using maximum likelihood and Bayesian evolutionary analysis. From 523 patients, 328 DENV sequences were successfully generated (DENV1: 43, DENV2: 219, DENV3:66). Most circulating sequences originated from a common ancestor that was estimated to have existed from around 2010 for DENV2 and around 2015/2016 for DENV1 and DENV3. Four distinct outbreaks coinciding with monsoon rain seasons were identified during the observation period mostly driven by DENV2 cosmopolitan genotype, except for a large outbreak in 2019 contributed by DENV3 genotype I. This serotype switch did not result in a more clinically severe illness. Phylogeographic analyses showed that all outbreaks started within Colombo city and then spread to the rest of the district. In 2019, DENV3 genotype I, previously, rarely reported in Sri Lanka, is likely to have contributed to a disease outbreak. However, this did not result in more severe disease in those infected, probably due to pre-existing DENV3 immunity in the community. Targeted vector control within Colombo city before anticipated seasonal outbreaks may help to limit the geographic spread of outbreaks. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is among the major global public health issues. According to recent projections, a continued rise in DM prevalence is expected in the following decades. The research has shown that DM is associated with poorer outcomes of coronavirus disease 2019 (COVID-19). However, there is growing evidence suggesting that COVID-19 is associated with new-onset DM type 1 and type 2. This review aims to summarize the current knowledge about the new onset of DM following COVID-19. All identified studies were longitudinal, and they have predominantly shown a significantly increased risk for new-onset DM (both type 1 and type 2) following a SARS-CoV-2 infection. Increased risk of poorer COVID-19 outcomes (mechanical ventilation, death) was noted in persons with new-onset DM following SARS-CoV-2 infection. Studies investigating risk factors for new-onset DM in COVID-19 patients showed that severe disease, age, ethnicity, ventilation, and smoking habits were associated with DM occurrence. The information summarized in this review presents a valuable source of evidence for healthcare policymakers and healthcare workers in the effort of planning prevention measures for new-onset DM after SARS-CoV-2 infection and the timely identification and appropriate treatment of patients with COVID-19 who could be at greater risk for new-onset DM. [ABSTRACT FROM AUTHOR]

To face the COVID-19 outbreak, a wide range of non-pharmaceutical interventions (NPIs) aimed at limiting the spread of the virus in communities, such as mask-wearing, hand hygiene, social distancing, travel restrictions, and school closures, were introduced in most countries. Thereafter, a significant reduction of new asymptomatic and symptomatic COVID-19 cases occurred, although there were differences between countries according to the type and duration of the NPIs. In addition, the COVID-19 pandemic has been accompanied by significant variations in the global incidence of diseases due to the most common non-SARS-CoV-2 respiratory viruses and some bacteria. In this narrative review, the epidemiology of the most common non-SARS-CoV-2 respiratory infections during the COVID-19 pandemic is detailed. Moreover, factors that could have had a role in modifying the traditional circulation of respiratory pathogens are discussed. A literature analysis shows that NPIs were the most important cause of the general reduction in the incidence of influenza and respiratory syncytial virus infection in the first year of the pandemic, although the different sensitivity of each virus to NPIs, the type and duration of measures used, as well as the interference among viruses may have played a role in modulating viral circulation. Reasons for the increase in the incidences of Streptococcus pneumoniae and group A Streptococcus infections seem strictly linked to immunity debt and the role played by NPIs in reducing viral infections and limiting bacterial superimposed infections. These results highlight the importance of NPIs during pandemics, the need to monitor the circulation of infectious agents that cause diseases similar to those caused by pandemic agents, and the need to make efforts to improve coverage with available vaccines. [ABSTRACT FROM AUTHOR]

The ESKAPE group constitute a threat to public health, since these microorganisms are associated with severe infections in hospitals and have a direct relationship with high mortality rates. The presence of these bacteria in hospitals had a direct impact on the incidence of healthcare-associated coinfections in the SARS-CoV-2 pandemic. In recent years, these pathogens have shown resistance to multiple antibiotic families. The presence of high-risk clones within this group of bacteria contributes to the spread of resistance mechanisms worldwide. In the pandemic, these pathogens were implicated in coinfections in severely ill COVID-19 patients. The aim of this review is to describe the main microorganisms of the ESKAPE group involved in coinfections in COVID-19 patients, addressing mainly antimicrobial resistance mechanisms, epidemiology, and high-risk clones. [ABSTRACT FROM AUTHOR]

Multisystem inflammatory syndrome in children (MIS-C) is a pediatric hyperinflammatory syndrome related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection whose epidemiology is not very well known at present. The objective of the study was to better understand the incidence of MIS-C in the Apulia region in southern Italy. Our primary goal was to estimate the incidence of newly identified cases of MIS-C in children aged 0–18 years, during a period of six months, encompassing the second pandemic wave. We also analyzed the characteristics of our cohort in terms of clinical features, treatment, and outcomes. The cumulative incidence of MIS-C was 3.27 per 100,000 residents between 0 and 18 years of age. In our cohort, gastrointestinal, mucocutaneous, and cardiac involvement were the most common clinical features. With our step-up approach to therapy, no patients required intensive care unit (ICU) admission and no cardiac sequelae after 6 months of onset were found in echocardiograms. Conclusion: Our epidemiological study of MIS-C in southern Italy showed unexpectedly overlapping figures with other US studies. [ABSTRACT FROM AUTHOR]

Introduction: The Coronavirus disease of 2019 (COVID-19) is a catastrophic emerging global health threat caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has a wide range of complications and sequelae. It is devastating in developing countries, causing serious health and socioeconomic crises as a result of the increasingly overburdened healthcare system. This study was conducted to determine the prevalence of SARS-CoV-2 infection in Ethiopia. Methods: Electronic databases, such as PubMed, Google Scholar, Web of Science, Research Gate, Embase, and Scopus were thoroughly searched from March to April 2022 to identify relevant studies. The quality of the included studies was evaluated using the Newcastle-Ottawa Quality scale for cross-sectional studies. STATA-12 was used for analysis. A random-effects model was used to compute the pooled prevalence of SARS-CoV-2 infection. The heterogeneity was quantified by using the I2 value. Subgroup analysis was done for sex, age of study subjects, population type, diagnostic methods, and publication year. Publication bias was assessed using a funnel plot and Egger's test. A sensitivity analysis was also done. Result: 11 studies consisting of 35,376 study participants (15,759 male and 19,838 female) were included in this systematic review and meta-analysis. The pooled prevalence of SARS-CoV-2 was 8.83%. There was substantial heterogeneity, with an I2 value of 99.3%. The pooled prevalence of SARS-CoV-2 was higher in males (9.27%) than in females (8.8%). According to the publication year, a higher prevalence was obtained in 2021 (12.69%). Similarly, it was higher in the population of specific groups (16.65%) than in the general population (5.75%). Conclusion: the national pooled prevalence of SARS-CoV-2 infection in the Ethiopian population was 8.83%. This indicates that the burden of COVID-19 is still high, which urges routine screening and appropriate treatment. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious disease caused by SARS-CoV-2. This virus presents high levels of mutation and transmissibility, which contributed to the emergence of the pandemic. Our study aimed to analyze, in silico, the genomic diversity of SARS-CoV-2 strains in Bahia State by comparing patterns in variability of strains circulating in Brazil with the first isolated strain NC_045512 (reference sequence). Genomes were collected using GISAID, and subsequently aligned and compared using structural and functional genomic annotation. A total of 744 genomes were selected, and 20,773 mutations were found, most of which were of the SNP type. Most of the samples presented low mutational impact, and of the samples, the P.1 (360) lineage possessed the highest prevalence. The most prevalent epitopes were associated with the ORF1ab protein, and in addition to P.1, twenty-one other lineages were also detected during the study period, notably B.1.1.33 (78). The phylogenetic tree revealed that SARS-CoV-2 variants isolated from Bahia were clustered closely together. It is expected that the data collected will help provide a better epidemiological understanding of the COVID-19 pandemic (especially in Bahia), as well as helping to develop more effective vaccines that allow less immunogenic escape. [ABSTRACT FROM AUTHOR]

Many of the models used nowadays in mathematical epidemiology, in particular in COVID-19 research, belong to a certain subclass of compartmental models whose classes may be divided into three " (x , y , z) " groups, which we will call respectively "susceptible/entrance, diseased, and output" (in the classic SIR case, there is only one class of each type). Roughly, the ODE dynamics of these models contains only linear terms, with the exception of products between x and y terms. It has long been noticed that the reproduction number R has a very simple Formula in terms of the matrices which define the model, and an explicit first integral Formula is also available. These results can be traced back at least to Arino, Brauer, van den Driessche, Watmough, and Wu (2007) and to Feng (2007), respectively, and may be viewed as the "basic laws of SIR-type epidemics". However, many papers continue to reprove them in particular instances. This motivated us to redraw attention to these basic laws and provide a self-contained reference of related formulas for (x , y , z) models. For the case of one susceptible class, we propose to use the name SIR-PH, due to a simple probabilistic interpretation as SIR models where the exponential infection time has been replaced by a PH-type distribution. Note that to each SIR-PH model, one may associate a scalar quantity Y (t) which satisfies "classic SIR relations",which may be useful to obtain approximate control policies. [ABSTRACT FROM AUTHOR]

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R 0 , influence the simulated outbreaks, and find that our model behaves as expected: the higher the R 0 , the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Given the present pandemic caused by the severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 virus, the authors tried fitting existing models for the daily loss of lives. Based on data reported by Worldometers on the initial stages (first wave) of the pandemic for countries acquiring the disease, the authors observed that the logarithmic rendering of their data hinted the response of a first-order process to a step function input, which may be modeled by a three-parameters function, as described in this paper. This model was compared against other similar, log(N)-class of models that are non-compartmental type (such as the susceptible, infected, and removed, or SIR models), obtaining good fit and statistical comparison results, where N denotes the cumulative number of daily presumed deaths. This simple first-order response model can also be applied to bacterial and other biological growth phenomena. Here we describe the model, the numerical methods utilized for its application to actual pandemic data, and the statistical comparisons with other models which shows that our simple model is comparatively outstanding, given its simplicity. While researching the models available, the authors found other functions that can also be applied, with extra parameters, to be described in follow-on articles. [ABSTRACT FROM AUTHOR]

Since COVID-19 was declared a pandemic, Brazil has become one of the countries most affected by this disease. A year into the pandemic, a second wave of COVID-19 emerged, with a rapid spread of a new SARS-CoV-2 lineage of concern. Several vaccines have been granted emergency-use authorization, leading to a decrease in mortality and severe cases in many countries. However, the emergence of SARS-CoV-2 variants raises the alert for potential new waves of transmission and an increase in pathogenicity. We compared the demographic and clinical data of critically ill patients infected with COVID-19 hospitalized in Rio de Janeiro during the first and second waves between July 2020 and October 2021. In total, 106 participants were included in this study; among them, 88% had at least one comorbidity, and 37% developed severe disease. Disease severity was associated with older age, pre-existing neurological comorbidities, higher viral load, and dyspnea. Laboratory biomarkers related to white blood cells, coagulation, cellular injury, inflammation, renal, and liver injuries were significantly associated with severe COVID-19. During the second wave of the pandemic, the necessity of invasive respiratory support was higher, and more individuals with COVID-19 developed acute hepatitis, suggesting that the progression of the second wave resulted in an increase in severe cases. These results can contribute to understanding the behavior of the COVID-19 pandemic in Brazil and may be helpful in predicting disease severity, which is a pivotal for guiding clinical care, improving patient outcomes, and defining public policies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic rapidly evolved in December 2019 and to prevent its spread, effective vaccines were produced and made available to the population. Despite their availability so far in Cameroon, the vaccination coverage remains low. This study aimed at describing the epidemiology of the acceptance of vaccines against COVID-19 in some urban and rural areas of Cameroon. A cross-sectional, descriptive and analytical survey was conducted from March 2021 to August 2021 targeting unvaccinated individuals from urban and rural area. After receiving appropriate administrative authorizations and an ethical clearance from the Institutional Review Board (or Ethics Committee) of Douala University (N° 3070CEI-Udo/05/2022/M), a cluster sampling at many degrees was performed and a language-adapted questionnaire was completed by each consenting participant. Data were analyzed using Epi info version 7.2.2.6 software and for p-values < 0.05, the difference was considered as statistically significant. Out of 1053 individuals, 58.02% (611/1053) participants were residing in urban and 41.98% (442/1053) in rural areas. Good knowledge relative to COVID-19 was significantly higher in urban areas as compared to rural areas (97.55% vs. 85.07, p < 0.000). The proportion of respondents who intended to accept the anti COVID-19 vaccine was significantly higher in urban areas than rural areas (42.55% vs. 33.26, p = 0.0047). Conversely, the proportion of anti COVID-19 reluctant respondents thinking that the vaccine can induce a disease was significantly higher in rural areas than urban areas (54 (35.07 vs. 8.84, p < 0.0001). The significant determinants of anti-COVID-19 acceptance were the level of education (p = 0.0001) and profession in the rural areas (p ≤ 0.0001), and only the profession (p = 0.0046) in the urban areas. This study globally showed that anti-COVID-19 vaccination remains a major challenge in urban as well as rural areas in Cameroon. We should continue sensitizing and educating the population about vaccine importance in preventing the COVID-19 spread. [ABSTRACT FROM AUTHOR]

The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline and Scopus was searched by combining text, words, and titles on medical topics. At the end of the search, this systematic review contained 75 records. The studies analyzed in this systematic review demonstrate that it is possible to predict the incidence and trends of some infectious diseases; by combining several techniques and types of machine learning, it is possible to obtain accurate and plausible results. [ABSTRACT FROM AUTHOR]

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to be overdispersed, meaning that only a fraction of infected cases contributes to super-spreading. While cluster interventions are an effective measure for controlling pandemics due to the viruses' overdispersed nature, a quantitative assessment of the risk of clustering has yet to be sufficiently presented. Using systematically collected cluster surveillance data for coronavirus disease 2019 (COVID-19) from June 2020 to June 2021 in Japan, we estimated the activity-dependent risk of clustering in 23 establishment types. The analysis indicated that elderly care facilities, welfare facilities for people with disabilities, and hospitals had the highest risk of clustering, with 4.65 (95% confidence interval [CI]: 4.43–4.87), 2.99 (2.59–3.46), and 2.00 (1.88–2.12) cluster reports per million event users, respectively. Risks in educational settings were higher overall among older age groups, potentially being affected by activities with close and uncontrollable contact during extracurricular hours. In dining settings, drinking and singing increased the risk by 10- to 70-fold compared with regular eating settings. The comprehensive analysis of the COVID-19 cluster records provides an additional scientific basis for the design of customized interventions. [ABSTRACT FROM AUTHOR]

Simple Summary: Dogs are considered man's best friend, with many owners sharing a close relationship with their dogs. This affection increased during the COVID-19 pandemic, as owners spent more time with their canines during the lockdowns and quarantines. During this time, owners noted how their dogs helped reduce their stress and anxiety. Yet, of concern is the known natural infection/transmission of SARS-CoV-2 reported in numerous animals (e.g., cats, minks, deer), necessitating the need to determine if dogs can contract or spread SARS-CoV-2. In this review, we focused on the human–canine interface of COVID-19 and the positives and negatives of this interface. We found that dogs can get COVID-19 from their owners but are asymptomatic when infected—it is unclear if it affects the health of dogs. Research is inconclusive on if dogs can transmit SARS-CoV-2 to their owners. Although dogs could be hosts, they are beneficial beyond just stress relief. Sniffer dogs can rapidly detect COVID-19-positive individuals in crowds, with almost 80% success in distinguishing between positive and negative people. Overall, we emphasize that more research is needed to understand the role of dogs in the interspecies spread and even contraction of SARS-CoV-2. COVID-19 is one of the deadliest epidemics. This pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the role of dogs in spreading the disease in human society is poorly understood. This review sheds light on the limited susceptibility of dogs to COVID-19 infections which is likely attributed to the relatively low levels of angiotensin-converting enzyme 2 (ACE2) in the respiratory tract and the phylogenetic distance of ACE2 in dogs from the human ACE2 receptor. The low levels of ACE2 affect the binding affinity between spike and ACE2 proteins resulting in it being uncommon for dogs to spread the disease. To demonstrate the role of dogs in spreading COVID-19, we reviewed the epidemiological studies and prevalence of SARS-CoV-2 in dogs. Additionally, we discussed the use of detection dogs as a rapid and reliable method for effectively discriminating between SARS-CoV-2 infected and non-infected individuals using different types of samples (secretions, saliva, and sweat). We considered the available information on COVID-19 in the human–dog interfaces involving the possibility of transmission of COVID-19 to dogs by infected individuals and vice versa, the human–dog behavior changes, and the importance of preventive measures because the risk of transmission by domestic dogs remains a concern. [ABSTRACT FROM AUTHOR]