The Impact of Stay-at-Home Orders on Binge Drinking Patterns in the US

Objectives: This study aims to evaluate the impact of the stay-at-home orders, especially closing and reopening bars and other drinking establishments, on binge drinking patterns in US populations in Metropolitan Statistical Areas (MSAs). Method(s): Data on binge drinking and heavy binge drinking for this study was extracted from the 2018-2021 Behavioral Risk Factor Surveillance System (BRFSS). Data on regulations were collected by National Academy for State Health Policy. We used two staggered differences-in-differences strategies to account for monthly variations in bar regulations. We implemented a strategy that used never treated states as controls via the Stata package CSDID and a strategy that directly imputed counterfactuals for treated states via the Stata package FECT. The outcomes were measured by the number of binge drinkers or heavy binge drinkers per 1000 population. The treatment effect was estimated while controlling for age, income level, race, chronic conditions, gender, MSA fixed effects, and month fixed effects. Stay-at-home orders were coded as 1 in the first full month of implementation and were assumed to impact the entire state equally. Bars were assumed to reopen if the indoor service has been reactivated at any capacity. Result(s): For heavy binge drinking, the average treatment effect on the treated group was 4.86 per 1000 population (p=0.027) using FECT package and 6.74 per 1000 population (p = 0.025) using CSDID package. No significant effect was found for binge drinking. Conclusion(s): We provide suggestive evidence that stay-at-home orders may have increased heavy binge drinking in metropolitan areas. We estimated this led to a 3.38% (FECT) or 4.68% (CSDID) increase in heavy binge drinking during the pandemic. Future work will assess the characteristics of areas that saw the greatest increase in heavy binge drinking, and explore why heavy binge drinkers were more vulnerable than binge drinkers during the Covid.Copyright © 2023

Lung cancer screening use before & during the COVID-19 pandemic in the US: Population-based evidence from 2019-2021

Introduction: Lung cancer is the leading cause of cancer-related death in the US with an estimated 236,740 new cases and 130,180 deaths expected in 2022. While early detection with low-dose computed tomography reduces lung cancer mortality by at least 20%, there has been a low uptake of lung cancer screening (LCS) use in the US. The COVID-19 pandemic caused significant disruption in cancer screening. Yet, little is known about how COVID-19 impacted already low use of LCS. This study aims to estimate LCS use before (2019) and during (2020 and 2021) the COVID-19 pandemic among LCS-eligible population in the US. Method(s): We used population-based, nationally representative, cross-section data from the 2019 (n=4,484), 2020 (n=1,239) and 2021 (n=1,673) Behavioral Risk Factor Surveillance System, Lung Cancer Screening module. The outcome was self-reported LCS use among eligible adults in the past 12 months. For 2019 and 2020, the eligibility was defined based on US Preventive Services Task Force (USPSTF) initial criteria-adults aged 55 to 80 years old, who were current and former smokers (had quit within the past 15 years) with at least 30 pack years of smoking history. For 2021, we used the USPSTF updated criteria- adults aged 50 to 80 years, current and former smokers (who had quit within the past 15 years) with at least 20 pack years of smoking history. We applied sampling weights to account for the complex survey design to generate population estimates and conducted weighted descriptive statistics and logistic regression models. Result(s): Overall, there were an estimated 1,559,137 LCS-eligible respondents from 16 US states in 2019 (AZ, ID, KY, ME, MN, MS, MT, NC, ND, PA, RI, SC, UT, VT, WV, WI), 200,301 LCS-eligible respondents from five states in 2020 (DE, ME, NJ, ND, SD), and 668,359 LCS-eligible respondents from four states in 2021 (ME, MI, NJ, RI). Among 2,427,797 LCS-eligible adults, 254,890;38,875;and 122,240 individuals reported receiving LCS in 2019, 2020 and 2021, respectively. Overall, 16.4% (95% CI 14.4-18.5), 19.4% (95% CI 15.3-24.3), and 18.3% (95% CI 15.6-21.3) received LCS during 2019, 2020, and 2021, respectively. In all years, the proportion of LCS use was higher among adults aged 65-74, insured, those with fair and poor health, lung disease and history of cancer (other than lung cancer). In 2020, a higher proportion of adults living in urban areas reported receiving LCS compared to those living in rural areas (20.36% vs. 12.7%, p=0.01). Compared to non-Hispanic White adults, the odds of receiving LCS was lower among Hispanic adults and higher among Non-Hispanic American Indian/Alaskan Native adults in 2020 and 2021, respectively. Conclusion(s): LCS uptake remains low in the US. An estimated 2,011,792 adults at high-risk for developing lung cancer did not receive LCS during 2019, 2020 and 2021. Efforts should be focused to increase LCS awareness and uptake across the US to reduce lung cancer burden.

An interactive national digital surveillance system to fight against COVID-19 in Bangladesh.

Background: COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation. Objectives: This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh. Methods: The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes. Results: This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform. Conclusion: This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.

Incidence, hospitalization, and mortality in children aged 5 years and younger with respiratory syncytial virus-related diseases: A systematic review and meta-analysis.

Objectives: Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infection in young children. We aimed to analyze the factors affecting the estimation of RSV-related disease burden, and to provide evidence to help establish a surveillance system. Methods: We searched the English- and Chinese-language databases for articles published between January 1, 2010 and June 2, 2022. The quality of the included articles was assessed using the Agency for Healthcare Research and Quality scale. Random-effects models were used for data synthesis and subgroup analyses. This review was registered in the Prospective Register of Systematic Reviews (PROSPERO: CRD42022372972). Results: We included 44 studies (149,321,171 participants), all of which were of medium or high quality. The pooled RSV-related disease incidence, hospitalization rate, in-hospital mortality, and overall mortality rates in children aged 5 years and younger were 9.0 per 100 children per year (95% confidence interval [CI]: 7.0-11.0), 1.7 per 100 children per year (95% CI: 1.3-2.1), 0.5 per 100 children per year (95% CI: 0.4-0.5), and 0.05 per 100 children per year (95% CI: 0.04-0.06), respectively. Age, economics, surveillance types, case definition, and data source were all recognized as influencing factors. Conclusions: A standardized and unified RSV surveillance system is required. Case definition and surveillance types should be fully considered for surveillance of different age groups.

Principles for evaluation of one health surveillance: The EVA book

This book outlines essential elements of the evaluation of health surveillance within the One Health concept. It provides an introduction to basic theoretical notions of evaluation and vividly discusses related challenges. Expert authors cover the entire spectrum of available, innovative methods, from those for system process evaluations to methods for the economic evaluation of the surveillance strategies. Each chapter provides a detailed description of the methodology required and the tools available as illustrated by practical examples of animal health or One Health surveillance evaluations in both developed and developing countries. Targeting not only scientists, including epidemiologists, but also technical advisers of decision-makers, the present work is suitable for the evaluation of any type of health surveillance system - animal, human or combined - regardless of the socio-economic context. The volume is richly equipped with practical tools and examples, which enables the reader to apply the methods described. Increasing importance of health surveillance, and threats from disease outbreaks such as the coronavirus pandemic, underline the practical relevance of this work, which will fill an important gap in the literature. © Springer Nature Switzerland AG 2022. All rights reserved.

The Relationship between Prostate Cancer and Colorectal Cancer Screening: How Important Is Shared Decision-Making?

INTRODUCTION AND OBJECTIVE: While guidelines require shared decision-making (SDM) prior to PSA screening, there are more categorical recommendations for colorectal cancer (CRC) screening in persons aged 50-75.Historically, though, patients have been more likely to pursue PSA vs CRC, despite more equivocal recommendations for the former and stronger evidence for the latter.Recently, COVID-19 altered access to and patients' perceptions of healthcare services. This study investigates the relationship between PSA screening, CRC screening, and SDM, with the hypothesis that SDM is associated with higher rates of both screening practices. METHOD(S): The 2020 Behavioral Risk Factor Surveillance System (BRFSS) annual survey report was assessed for male patients between the ages of 50 and 75. The identified patients were further eliminated based on response availability: history of PSA and CRC screening must have been provided. Univariate logistic regression was used for predictor selection and multivariate logistic regression models were generated to assess associations between socioeconomic factors that influenced SDM for PSA screening as well as screening practices themselves. RESULT(S): 38,617 men between the ages of 50 and 75 met the inclusion criteria. 44% of respondents had PSA screening, 72.2% CRC screening, 38.83% had both, and 22.67% had neither. 45.2% had discussions about PSA advantages and 22.1% had discussions about PSA disadvantages. Odds of CRC screening increased if the patients had PSA screening (OR 2.52, CI 2.43 - 2.6), and if they had discussion on the advantages (OR 1.23, CI 1.09 - 1.38) and the disadvantages (OR 1.23, CI 1.13 - 1.34). Odds of PSA screening increased if the patient had CRC screening (OR 1.48, CI 1.37 - 1.58), had a discussion of the advantages of PSA testing (OR 2.06, CI 1.91 - 2.21), and when the PSA test was recommended (OR 3.95, CI 3.84 - 4.06). Odds of PSA screening decreased when the disadvantages of PSA were discussed (OR 0.84, CI 0.74-0.95). The Northeast had higher odds of PSA screening (OR 1.29, 1.16-1.41) but lower odds of CRC screening (0.68, CI 0.58-0.77) compared to the West. There were no significant associations with being from the Midwest or South and history of PSA or CRC screening. CONCLUSION(S): There are higher rates of CRC vs PSA screening in a contemporary cohort of patients in men aged 50-75. PSA screening has decreased amid fluctuating guidelines in an era of SDM, and in the post-COVID environment. SDM is associated with both screening practices, and may serve as a metric for quality of care across cancer types.

The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic.

This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

Promoting the Safety of Pregnant Women: Information Management System Strategy for the COVID-19 Vaccination

Background: Promoting the immunity of pregnant women during the Covid-19 pandemic through vaccination against SARS-CoV-2 infection is one of the main challenges. It is important to manage the information related to receiving the vaccine and its possible complications for surveillance of its safety and to deal with the challenges. Based on this, it is necessary to design a national information management system for the COVID-19 vaccination. Objective(s): To promote the safety of pregnant women by providing a national model of an information management system for pregnant women's COVID-19 vaccination in Iran. Method(s): The present research was of applied descriptive type. Based on the review of articles and information sources and a com-parative study of the information management and surveillance system for the vaccination of pregnant women in developed coun-tries, and according to the country's organizational structure, the national model of the information management system for pregnant women's COVID-19 vaccination was designed for Iran. Then the validation of the model was examined in two steps using the Delphi technique. Finally, after analyzing the data, the final model was presented. Result(s): The findings were categorized into two main groups, including the structural components (responsible organization and databases, surveillance center, participating organizations, and data sources) and informational process (data set, data collection, quality control, data exchanges, data processing, reporting) that reached 100% consensus of experts. Conclusion(s): For developing IMS for the COVID-19 vaccination of pregnant women, it is necessary to specify the responsible organization and the participating centers, create surveillance centers and databases, and define the information management system process.Copyright © 2023, Author(s).

DNN and Model Combined Passive Localization and Social Distancing with Partial Inertial Aiding

At the outbreak of COVID-19, researchers worldwide are seeking approaches to containing this disease. It is necessary to monitor social distance in enclosed public areas, such as subways or shopping malls. Passive localization, such as surveillance cameras, is a natural candidate for this issue, which is meaningful for rapid response to finding the infected suspect. However, the latest surveillance camera system is rotatable, even movable. And it is impossible for professionals to regularly calibrate the extrinsic parameters in a large-scale application, like COVID-19 suspect monitoring. We propose an inertial-aided passive localization method using surveillance camera for social distance measurement without the necessity to obtain extrinsic parameters. Moreover, the hardware modification cost of the off-the-shelf commercial camera is low, which suits the immediate application. The method uses SGBM (Semi-Global Block Matching) for 3D reconstruction and combines YOLOv3 and Gaussian Mixture Model (GMM) clustering algorithm to extract pedestrian point clouds in real time. Combining the 2D DNN-based and model-based methods makes a better balance between the computational load and the detection accuracy than end-to-end 3D DNN-based method. The inertial sensor provides an extra observation for the coordinate transformation from the camera frame into the world ground frame. Results show we can get a decimeter-level social distancing accuracy under noisy background and foreground environments at a low cost, which is promising for urgent COVID-19 public area monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The lessons of COVID-19 pandemic for communicable diseases surveillance system in Kurdistan Region of Iraq.

Objectives: This study aimed to determine the opportunities of and barriers to communicable diseases surveillance system (CDSS) during the COVID-19 pandemic and the extent to which the disease integrated into the CDSS in the Kurdistan region of Iraq. Study design: A descriptive qualitative approach was applied. Methods: We conducted seven semi-structured interviews and seven interviewee in a focus group discussion (FGD) with purposefully identified Key Informants (KI) from June to December 2020. All interviews were digitally recorded and transcribed verbatim. We adopted a mixed deductive-inductive approach for thematic data analysis, facilitated by using MAXQDA20 software for data management. Results: Although the CDSS was considered appropriate and flexible, the COVID-19 was interpreted not to be integrated into the system due to political influence. The main concerns regarding core and support activities were the lack of epidemic preparedness, timeliness, and partial cessation of training and supervision during the pandemic. The existence of reasonable surveillance infrastructure, i.e., trained staff, was identified as an opportunity for improvement. The main challenges include staff deficiency, absence of motivation and financial support for present staff, scarce logistics, managerial and administrative issues, and lack of cooperation, particularly among stakeholders and surveillance staff. Conclusion: Our findings revealed that the CDSS in the Kurdistan region requires substantial enhancement in epidemic preparedness, strengthening human resources, and logistics. the system can be developed by fostering meaningful intersectoral collaboration. We advocate that the health authorities and policy-makers prioritise the surveillance and effective management of communicable diseases.

COVID-19 surveillance: Large decrease in clinical notifications and epidemiological investigation questionnaires for laboratory-confirmed cases after the 2nd epidemic wave, Portugal March 2020-July 2021.

Introduction: In Portugal, COVID-19 laboratory notifications, clinical notifications (CNs), and epidemiological investigation questionnaires (EI) were electronically submitted by laboratories, clinicians, and public health professionals, respectively, to the Portuguese National Epidemiological Surveillance System (SINAVE), as mandated by law. We described CN and EI completeness in SINAVE to inform pandemic surveillance efforts. Methods: We calculated the proportion of COVID-19 laboratory-notified cases without CN nor EI, and without EI by region and age group, in each month, from March 2020 to July 2021. We tested the correlation between those proportions and monthly case counts in two epidemic periods and used Poisson regression to identify factors associated with the outcomes. Results: The analysis included 909,720 laboratory-notified cases. After October 2020, an increase in the number of COVID-19 cases was associated with a decrease in the submissions of CN and EI. By July 2021, 68.57% of cases had no associated CN nor EI, and 96.26% had no EI. Until January 2021, there was a positive correlation between monthly case counts and the monthly proportion of cases without CN nor EI and without EI, but not afterward. Cases aged 75 years or older had a lower proportion without CN nor EI (aRR: 0.842 CI95% 0.839-0.845). When compared to the Norte region, cases from Alentejo, Algarve, and Madeira had a lower probability of having no EI (aRR;0.659 CI 95%0.654-0.664; aRR 0.705 CI 95% 0.7-0.711; and aRR 0.363 CI 95% 0.354-0.373, respectively). Discussion: After January 2021, CN and EI were submitted in a small proportion of laboratory-confirmed cases, varying by age and region. Facing the large number of COVID-19 cases, public health services may have adopted other registry strategies including new surveillance and management tools to respond to operational needs. This may have contributed to the abandonment of official CN and EI submission. Useful knowledge on the context of infection, symptom profile, and other knowledge gaps was no longer adequately supported by SINAVE. Regular evaluation of pandemic surveillance systems' completeness is necessary to inform surveillance improvements and procedures considering dynamic objectives, usefulness, acceptability, and simplicity.

US Adults Practicing Healthy Lifestyles Before and During COVID-19: Comparative Analysis of National Surveys.

BACKGROUND: Practicing healthy lifestyles can reduce the risk to develop noncommunicable diseases and the related mortality. Studies showed that practicing healthy lifestyles could enhance disease-free life expectancy and preserve bodily functions. However, engagement in healthy lifestyle behavior was suboptimal. OBJECTIVE: This study aimed to define individuals' lifestyle characteristics before and during COVID-19 and determine the factors associated with practicing a healthy lifestyle. This cross-sectional study was conducted using data from the 2019 and 2021 Behavioral Risk Factor Surveillance System surveys. METHODS: US individuals aged ≥18 years were interviewed via phone call. Healthy lifestyles were assessed through corresponding questions regarding the maintenance of optimal body weight, physical activity, daily consumption of at least five portions of fruits and vegetables, current smoking status, and alcohol consumption. Missing data were imputed using a package in the R statistical software. The effects of practicing a healthy lifestyle on cases without missing data and those with imputation were reported. RESULTS: There were 550,607 respondents (272,543 and 278,064 from 2019 and 2021, respectively) included in this analysis. The rates of practicing a healthy lifestyle were 4% (10,955/272,543) and 3.6% (10,139/278,064) in 2019 and 2021, respectively. Although 36.6% (160,629/438,693) of all 2021 respondents had missing data, the results of the logistic regression analysis for cases without missing data and those with imputation were similar. Of the cases with imputation, women (odds ratio [OR] 1.87) residing in urban areas (OR 1.24) with high education levels (OR 1.73) and good or better health status (OR 1.59) were more likely to practice healthier lifestyles than young individuals (OR 0.51-0.67) with a low household income (OR 0.74-0.78) and chronic health conditions (OR 0.48-0.74). CONCLUSIONS: A healthy lifestyle should be strongly promoted at the community level. In particular, factors associated with a low rate of practice of healthy lifestyles should be targeted.

Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using YOLOv3 algorithm.

The eruption of COVID-19 pandemic has led to the blossoming usage of face masks among individuals in the communal settings. To prevent the transmission of the virus, a mandatory mask-wearing rule in public areas has been enforced. Owing to the use of face masks in communities at different workplaces, an effective surveillance seems essential because several security analyses indicate that face masks may be used as a tool to hide the identity. Therefore, this work proposes a framework for the development of a smart surveillance system as an aftereffect of COVID-19 for recognition of individuals behind the face mask. For this purpose, transfer learning approach has been employed to train the custom dataset by YOLOv3 algorithm in the Darknet neural network framework. Moreover, to demonstrate the competence of YOLOv3 algorithm, a comparative analysis with YOLOv3-tiny has been presented. The simulated results verify the robustness of YOLOv3 algorithm in the recognition of individuals behind the face mask. Also, YOLOv3 algorithm achieves a mAP of 98.73% on custom dataset, outperforming YOLOv3-tiny by approximately 62%. Moreover, YOLOv3 algorithm provides adequate speed and accuracy on small faces.

[The preparedness of the Italian obstetric surveillance system in the response to the emergency of the SARS-CoV-2 pandemic: methodological aspects of a population-based study]. / La preparedness dell'Italian obstetric surveillance system in occasione della pandemia da SARS-CoV-2: aspetti metodologici di uno studio di popolazione.

This paper aims to describe the Italian obstetric surveillance system (ItOSS) preparedness as an element for a timely response to the new Coronavirus pandemic. ItOSS is a surveillance network that has been collecting data on maternal mortality and conducting population studies on obstetric near misses since 2013. At the beginning of the pandemic, ItOSS launched a new population-based project to monitor SARS-CoV-2 infection during pregnancy and post-partum and promptly give back information useful to clinicians and decision-makers. All the regions and autonomous provinces, for a total of 289 birth units (PN), joined the study. Data relating to pregnant or post-partum women with a confirmed SARS-CoV-2 infection diagnosis addressing the maternities for outpatient visits or hospitalization were collected. The project methodology entails that each participating maternity reports the cases to ItOSS uploading data through an open-source platform. The on-line form includes sociodemographic and clinical data and maternal-neonatal outcomes. Biological samples to detect possible vertical transmission are also collected voluntarily. A total of 534 incident cases were reported from February 25th to July 10th 2020; 7 regions also collected biological samples for 227 cases; data collection is still ongoing.A preliminary analysis of the first 146 SARS-CoV-2 positive women who gave birth between February 25th to April 22nd shows an incidence rate of the infection equal to 2.1/1,000 in Italy and 6.9/1,000 in the Lombardy Region (Northern Italy). The brief time needed to setting up and operating the project, the national coverage, the adoption of shared tools for data collection, the quality and completeness of the information collected show how the availability of active networks like ItOSS represents a crucial element to hold a high level of preparedness in case of a health emergency.

Real-Time Surveillance System for Detection of Social Distancing

As the corona virus can mutate and due to other scientific factor associated to it, experts believe that COVID-19 will remain with us for decades. Therefore, one has to keep social distancing measures. Accepting the pandemic situation, the paper presents a mechanism for detecting violations of social distancing using deep learning to estimate the distance between individuals to diminish the influence of COVID-19. The focus of this paper is to understand the effect of social distancing on the spread of COVID-19 by using YOLOv3 and Faster-RCNN and proposes IFRCNN (improved faster region convolution neural network). The proposed method IFRCNN is checked on a live streaming video of pedestrians walking on the street. This paper keeps the live updates of the recorded video along with social distancing violation records on a location, so how many people in a location are maintaining social distancing. Updates will be stored in a cloud-based storage system and any organization or firm can get live updates of that location in their digital devices.

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus's possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early "warning signal"; however, a lot of limitations of wastewater surveillance have also been identified.

Risk management and surveillance systems: A Model proposed for the provincial public hospital (CHP)

During the Covid-19 pandemic, the provincial hospitals (CHP) of Morocco have demonstrated a remarkable capacity for reactivity. They were able to reorganize themselves to ensure the response to the pandemic and maintain basic functions. However, this reactivity alone does not prove their resilience in the face of new infections and possible disasters. The CHP must plan its responsiveness well in advance by building an intra-hospital health vigilance system that meets basic provincial requirements and that communicates with its internal and external environments. A qualitative analysis, using the focus group method, on Ifrane hospital risk management system revealed that, the systems for notification and declaration of adverse events are set up under the responsibility of multiple actors and using multiple notification forms. The processes and procedures are not clear with an overlap between the activities of quality assurance, risk management and complaints treatment system. A lot of information does not arrive at its destination on time, which impacts decision-making. This work proposes a hospital risk management model with two systems, one for safety of care and the other for risk management, taking into account the missions of the CHP and the different sources of information. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

Rapid and Flexible RT-qPCR Surveillance Platforms To Detect SARS-CoV-2 Mutations.

Multiple mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) increase transmission, disease severity, and immune evasion and facilitate zoonotic or anthropozoonotic infections. Four such mutations, ΔH69/V70, L452R, E484K, and N501Y, occurred in the SARS-CoV-2 spike glycoprotein in combinations that allow the simultaneous detection of VOCs. Here, we present two flexible reverse transcription-quantitative PCR (RT-qPCR) platforms for small- and large-scale screening (also known as variant PCR) to detect these mutations and schemes for adapting the platforms to future mutations. The large-scale RT-qPCR platform was validated by pairwise matching of RT-qPCR results with whole-genome sequencing (WGS) consensus genomes, showing high specificity and sensitivity. Both platforms are valuable examples of complementing WGS to support the rapid detection of VOCs. Our mutational signature approach served as an important intervention measure for the Danish public health system to detect and delay the emergence of new VOCs. IMPORTANCE Denmark weathered the SARS-CoV-2 crisis with relatively low rates of infection and death. Intensive testing strategies with the aim of detecting SARS-CoV-2 in symptomatic and nonsymptomatic individuals were available by establishing a national test system called TestCenter Denmark. This testing regime included the detection of SARS-CoV-2 signature mutations, with referral to the national health system, thereby delaying outbreaks of variants of concern. Our study describes the design of the large-scale RT-qPCR platform established at TestCenter Denmark in conjunction with whole-genome sequencing to report mutations of concern to the national health system. Validation of the large-scale RT-qPCR platform using paired WGS consensus genomes showed high sensitivity and specificity. For smaller laboratories with limited infrastructure, we developed a flexible small-scale RT-qPCR platform to detect three signature mutations in a single run. The RT-qPCR platforms are important tools to support the control of the SARS-CoV-2 endemic in Denmark.

REAL-TIME MONITORING FOR SOCIAL DISTANCING VIA YOLOV5 ALGORITHM

The deadly COVID-19 outbreak has wreaked havoc around the globe. As of 2022, there have been around 42 million cases worldwide, with 1.14 million deaths. A fuller knowledge of the epidemic demonstrates that the irresponsibility of a single person can have far-reaching, irrevocable consequences. COVID-19 must be contained by social isolation. Consequently, a system to monitor and identify the human-endangering distance is required. The proposed technique uses Euclidean distance-derived bounding boxes and distance metrics to leverage the YOLOv5 object identification model to track known individuals. Experimentation demonstrated that the YOLO v5-based Euclidean distance method outperformed other deep learning algorithms such as YOLOv3 and YOLOv4. Our model achieved less inference time and a high process frame score with balanced mAP. © 2022, International Organization on 'Technical and Physical Problems of Engineering'. All rights reserved.