Actual Number Of Cases Research Articles

Foodborne disease outbreaks in flour and flour‐based food products from microbial pathogens in the United States, and their health economic burden

The most comprehensive and inclusive estimates for the economic burden of foodborne illness yield values as high as $97.4 billion USD annually. However, broad incidence and cost estimates have limited use if they cannot be attributed to specific foods, for the purposes of food safety control. In this study, we estimated the economic burden of foodborne illnesses resulting from flour and flour-based food products in the United States from the years 2001 to 2021. The outbreak, illness burden, and health economic data are combined to generate these estimates. Our model combined outbreak data with published Centers for Disease Control and Prevention multipliers to estimate the annual number of illnesses associated with flour-borne pathogens. We then integrated illness severity data with an updated economic model that accounts for costs related to medical care, productivity loss, loss of life, along with the quality of life loss that entails pain and suffering. In total, 752 cases and 223 hospitalizations from flour-related illnesses were reported from 2001 to 2021, with an average of 37.6 cases of reported cases annually. However, the actual number of cases, accounting for underreporting and underdiagnosis, can be as high as 19,440 annually. Pathogens involved in these outbreaks are Salmonella, E. coli O157:H7, and E. coli O121. Our estimates suggest average annual economic losses, including healthy years of life lost, of $108 and $258 million using two alternative models.

Newly diagnosed diseases and the frequency of their occurrence in patients after a new coronavirus infection. Results of an International Register "Dynamics Analysis of Comorbidities in SARS-CoV-2 Survivors (ACTIV SARS-CoV-2)" (12-month follow-up)

Aim. To analyze newly diagnosed diseases and features of the post-COVID course in patients after a coronavirus disease 2019 (COVID-19) within 12-month follow-up.Material and methods. A total of 9364 consecutively hospitalized patients were included in the ACTIV registry. Enrollment of patients began on June 29, 2020, and was completed on March 30, 2021, corresponding to the first and second waves of the pandemic. Demographic, clinical, and laboratory data, computed tomography (CT) results, information about inhospital clinical course and complications of COVID-19 during hospitalization were extracted from electronic health records using a standardized data collection form. The design included follow-up telephone interviews with a standard questionnaire at 3, 6, and 12 months to examine the course of post-COVID period.Results. According to the ACTIV registry, 18,1% of patients after COVID-19 had newly diagnosed diseases (NDDs) over the next 12 months. Hypertension (HTN), type 2 diabetes and coronary artery disease (CAD) prevailed in the NDD structure. Comparison of the age-standardized incidence of NDDs (HTN, CAD, diabetes) in the post-COVID period in the ACTIV registry with NDD incidence in 2019 according to Rosstat and the expected incidence of NDDs according to the EPOHA study revealed that HTN, diabetes, CAD in patients after COVID-19 were registered more often as follows: HTN by 7,0 and 4,4 times, diabetes by 7,3 and 8,8 times, CAD by 2,3 and 2,9 times, respectively. NDDs most often developed in patients aged 47 to 70 years. Comparison of the actual and expected number of cases of newly diagnosed HTN, CAD and diabetes depending on age showed that the actual number of cases in the population of patients in the ACTIV register is significantly higher than expected for patients aged 45-69 years and for patients with hypertension or diabetes and aged <45 years. Patients with NDDs, compared with patients without NDDs, had a more severe acute COVID-19 course. Statistically significant independent predictors of NDD occurrence (HTN and/or diabetes and/or CAD) within 1 year after hospital discharge were age (direct relationship), body mass index (direct relationship) and glucose levels upon admission to hospital (direct relationship).Conclusion. Available evidence suggests that a strategy for managing COVID-19 survivors should include mandatory screening for early detection of cardiovascular disease and diabetes, which will be key to reducing the risk of further COVID-19 consequences.

AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine.

The COVID-19 pandemic has created an emergency across the globe. The number of corona positive and death cases is still rising worldwide. All countries' governments are taking various steps to control the infection of COVID-19. One step to control the coronavirus's spreading is to quarantine. But the number of active cases at the quarantine center is increasing daily. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for monitoring people at the quarantine center in two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase proposed a geographic-based routing that involves components like Network-in-box, Roadside-unit, and vehicles. An effective route is determined using route value to transmit data from the quarantine center to the observation center. The route value depends on the factors such as density, shortest path, delay, vehicular data carrying delay, and attenuation. The performance metrics considered for this phase are E2E delay, number of network gaps, and packet delivery ratio, and the proposed work performs better than the existing routing like geographic source routing, anchor-based street traffic aware routing, Peripheral node based GEographic DIstance Routing . The analysis of health data is done at the observation center. In the health data analysis phase, the health data is classified into multi-class using a support vector machine. There are four categories of health data: normal, low-risk, medium-risk, and high-risk. The parameters used to measure the performance of this phase are precision, recall, accuracy, and F-1 score. The overall testing accuracy is found to be 96.8%, demonstrating strong potential for our technique to be adopted in practice.

Revisiting classical SIR modelling in light of the COVID-19 pandemic.

Classical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions, as well as how we think about classical infectious disease modelling paradigms. We aim to assess the appropriateness of model parameter estimates and prediction results in classical infectious disease compartmental modelling frameworks given available data types (infected, active, quarantined, and recovered cases) for situations where just one data type is available to fit the model. Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment. We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed (SIR) modelling framework. We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy: a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County, Montana, USA. We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data. We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification. Using a classical example of influenza epidemics in an England boarding school, we argue that the Susceptible-Infected-Quarantined-Recovered (SIQR) model is more appropriate than the commonly employed SIR model given the data collected (number of active cases). Similarly, we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected. We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data. For both a classical influenza data set and a COVID-19 case data set, we demonstrate the implications of using the "right" data in the "wrong" model. The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections, as well as minimal vaccination requirements.

Management of patients with presumed germline pathogenic variant from tumor-only genomic sequencing: A retrospective analysis at a single facility.

Cancer treatment is increasingly evolving toward personalized medicine, which sequences numerous cancer-related genes and identifies therapeutic targets. On the other hand, patients with germline pathogenic variants (GPV) have been identified as secondary findings (SF) and oncologists have been urged to handle them. All SF disclosure considerations for patients are addressed and decided at the molecular tumor boards (MTB) in the facility. In this study, we retrospectively summarized the results of all cases in which comprehensive genomic profiling (CGP) test was conducted at our hospital, and discussed the possibility of presumed germline pathogenic variants (PGPV) at MTB. MTB recommended confirmatory testing for 64 patients. Informed consent was obtained from attending physicians for 53 of them, 30 patients requested testing, and 17 patients tested positive for a confirmatory test. Together with already known variants, 4.5 % of the total confirmed in this cohort. Variants verified in this study were BRCA1 (n = 12), BRCA2 (n = 6), MSH2 (n = 2), MSH6 (n = 2), WT1 (n = 2), TP53, MEN1, CHEK2, MLH1, TSC2, PTEN, RB1, and SMARCB1. There was no difference in the tumor's VAF between confirmed positive and negative cases for variants determined as PGPV by MTB. Current results demonstrate the actual number of cases until confirmatory germline test for patients with PGPV from tumor-only CGP test through the discussion at the MTB. The practical results at this single facility will serve as a guide for the management of the selection and distribution of SF in the genome analysis.

The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model

With the spatial structure of urban agglomerations, well-developed transportation networks and close economic ties can increase the risk of intercity transmission of infectious diseases. To reveal the epidemic transmission mechanism in urban agglomerations and to explore the effectiveness of traffic control measures, this study proposes an Urban-Agglomeration-based Epidemic and Mobility Model (UAEMM) based on the reality of urban transportation networks and population mobility factors. Since the model considers the urban population inflow, along with the active intracity population, it can be used to estimate the composition of urban cases. The model was applied to the Chang-Zhu-Tan urban agglomeration, and the results show that the model can better simulate the transmission process of the urban agglomeration for a certain scale of epidemic. The number of cases within the urban agglomeration is higher than the number of cases imported into the urban agglomeration from external cities. The composition of cases in the core cities of the urban agglomeration changes with the adjustment of prevention and control measures. In contrast, the number of cases imported into the secondary cities is consistently greater than the number of cases transmitted within the cities. A traffic control measures discount factor is introduced to simulate the development of the epidemic in the urban agglomeration under the traffic control measures of the first-level response to major public health emergency, traffic blockades in infected areas, and public transportation shutdowns. If none of those traffic control measures had been taken after the outbreak of COVID-19, the number of cases in the urban agglomeration would theoretically have increased to 3879, which is 11.61 times the actual number of cases that occurred. If only one traffic control measure had been used alone, each of the three measures would have reduced the number of cases in the urban agglomeration to 30.19 %–57.44 % of the theoretical values of infection cases, with the best blocking effect coming from the first-level response to major public health emergency. Traffic control measures have a significant effect in interrupting the spread of COVID-19 in urban agglomerations. The methodology and main findings presented in this paper are of general interest and can also be used in studies in other countries for similar purposes to help understand the spread of COVID-19 in urban agglomerations.

Dengue outbreak after devastating floods: double trouble for Pakistan

Dengue outbreak after devastating floods: double trouble for Pakistan

Forecasting of Monkeypox Cases in the World Using the ARIMA Model

While the Covid-19 epidemic in the world was not over yet, the monkeypox epidemic started. The monkeypox virus spread to more than 59 countries in 4 months. Computer-aided forecasting models are needed to effectively control this spread. It has been seen in previous outbreaks that time-series models are effective in estimating the impact of the epidemic and taking necessary precautions. In this study, different Automatic Regressive Integrated Moving Average (ARIMA) models were developed to successfully forecast the number of monkeypox cases in the World. Daily confirmed monkeypox cases data from 07 May-12 July 2022 were used in the study. 07 May 2022-02 July data were used in the training of ARIMA models. The prediction performances of the models were tested with the data of 03 July-12 July 2022. According to the test results, the ARIMA(2,2,1) model with the lowest RMSE=483, MAE=410, and MAPE=4.82 was determined as the most successful model. It has been determined that the determined ARIMA model is in good agreement with the real values with an average error value of around 5%. The number of monkeypox cases for the next 7-day was forecasted using ARIMA(2,2,1) model. While the model predicts the number of monkeypox cases to be 15056 for 19 July 2022, the actual number of cases is 15032 proves the model's success. This is the first study to estimate the number of monkeypox cases using the ARIMA method, and the results show that the ARIMA model is a convenient method for estimating the number of monkeypox cases.

Backcasting COVID-19: a physics-informed estimate for early case incidence.

It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea and Brazil. We assume that the second wave was more accurately monitored, even though we acknowledge that behavioural changes occurred during the pandemic and region- (or country-) specific monitoring protocols evolved. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50%. On the other hand, in South Korea-which had a proactive mitigation approach-a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 18% predicted underestimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases.

A COVID-19 model incorporating variants, vaccination, waning immunity, and population behavior

Vaccines for COVID-19 have allowed countries to combat the spread of the disease. However, new variants have resulted in significant spikes in cases and raised severe health and economic concerns. We present a COVID-19 model to predict coupled effects of vaccine multiple-dose roll-out strategies, vaccine efficacy, waning immunity, population level of caution, sense of safety, under-reporting of cases, and highly prevalent variants such as the Delta (B.1.617.2) and Omicron (B.1.1.529). The modeling framework can incorporate new variants as they emerge to give critical insights into the new cases and guide public policy decision-making concerning vaccine roll-outs and reopening strategies. The model is shown to recreate the history of COVID-19 for five countries (Germany, India, Japan, South Africa, and the United States). Parameters for crucial aspects of the pandemic, such as population behavior, new variants, vaccination, and waning immunity, can be adjusted to predict pandemic scenarios. The model was used to conduct trend analysis to simulate pandemic dynamics taking into account the societal level of caution, societal sense of safety, and the proportions of individuals vaccinated with first, second, and booster doses. We used the results of serological testing studies to estimate the actual number of cases across countries. The model allows quantification of otherwise hard to quantify aspects such as the infectious power of variants and the effectiveness of government mandates and population behavior. Some example cases are presented by investigating the competitive nature of COVID variants and the effect of different vaccine distribution strategies between immunity groups.

Delta Variant

Nothing is harder than to realize when you are living through history. For most of us, each day is pretty much like another. There is nothing historically remarkable about that. Occasionally, however, our lives are punctuated by events, both natural and man-made, that are apocalyptic and often (but not always) beyond our control – natural disasters, war, pestilence, and famine. These are the events that the historian must recognize. At this time, it is the COVID-19 pandemic that demands to be recorded by “his-story” so that posterity will know what we did right, and what we did wrong. This author has taken up the challenge of producing accurate, unbiased, comprehensive, technical annals of the global coronavirus pandemic that began in 2019. “The Delta Variant” is the third publication in this series. We are now near the end of the third year of the pandemic (summer/fall 2021). As predicted by this author, it has been a draconian year. Last year’s peak in the number of active cases was not a global maximum for the pandemic in the U.S., since this year the number of active cases has already surpassed it. Without knowing where the global maximum lies, no accurate predictions can be made about the magnitude and duration of this modern plague. The “Delta Variant” (δ-variant) of COVID-19 has greatly complicated efforts to combat the virus. The “anti-vaxxer” movement, uncontrolled migration of people into and within the U.S, and the relaxation of safety measures during the late spring and early summer in the U.S. also contributed difficulties. All of these problems were foreseen by the author and were discussed in the second paper (“Vaccine Safety”) of this series on the COVID pandemic. However, our biggest problem in the U.S. was an over confidence born of a natural summertime trough in the daily infection rate. We wanted to believe the infection was past, so we ignored the experience of India, and our administrators fueled our hopes with their words and actions. We believed because we wanted to believe – except for this author. So, what went wrong? What is a δ-variant, and why is it so dangerous? That will be the topic of this publication.

THE CORRELATION OF SERUM ALBUMIN LEVELS AND STAGES OF DECUBITUS IN INPATIENTS AT DR. SOETOMO REGIONAL GENERAL HOSPITAL OF SURABAYA

Introduction : The prevalence of decubitus ulcers in Asia is amounted to 2.7%-16.8%, and in Indonesia, there was a 40% prevalence of decubitus ulcers in 2017. In 2017, the prevalence of decubitus in Bandung was found to be 10.8%. Consequently, the number of active cases and deaths of decubitus ulcers reached 48%. Therefore, efforts to reduce the number of decubitus ulcers are urgently needed, thereby preventing the risk of decubitus severity. Objective: To evaluate the correlation of serum albumin levels and stages of decubitus in inpatients at Dr. Soetomo Regional General Hospital of Surabaya Method: This research was successfully carried out by means of observational analytics, with a cross-sectional research design. Moreover, this research data were derived from medical record data of inpatients at Dr. Soetomo Regional General Hospital of Surabaya in the period of January 2018 – December 2020, with a total of 144,002 patients. 197 decubitus patients in Pandan I and Pandan Wangi rooms were involved in the sample selection process. Based on the inclusion and exclusion criteria, 57 patients were finally determined as research samples. Samples were selected by utilizing Simple Random Sampling. The number of samples in the case group and control group was amounted to 39 patients, respectively. The data of this research were then analyzed using the Spearman Correlation test. Result: The research results indicated that the least number of decubitus patients at stage I was amounted to 8 people (10.3%), and the highest number of decubitus patients at stage III was amounted to 12 people (15.4%). Furthermore, the results of the Spearman correlation test showed that there was a significant correlation of serum albumin levels and stages of decubitus (p = 0.002). Conclusion: There was a significant correlation of serum albumin levels and stages of decubitus in inpatients at Dr. Soetomo Regional General Hospital of Surabaya in the period of January 2018 – December 2020.

Dissipative Dynamic Libraries (DDLs) and Dissipative Dynamic Combinatorial Chemistry (DDCC)

AbstractThis Concept is focused on the key features of dissipative dynamic combinatorial chemistry (DDCC). DDCC deals with transient libraries of compounds, maintained out‐of‐equilibrium by the consumption of a fuel, whose composition changes upon the selection pressure of kinetic and/or thermodynamic processes. Concepts and definitions of kinetic and thermodynamic dissipative dynamic libraries (“KDDL” and “TDDL”), are introduced and illustrated by a number of actual cases, thus showing the consistency of the present approach. Such concepts and definitions can help establish a common language for this emerging field, which, in our view, has the potential to become highly relevant to supramolecular chemistry.

Implementation of the Prophet Model in COVID-19 Cases Forecast

One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.

Knowledge, attitudes, detection, and reporting practices of elder abuse among orthopedists.

The purpose of the present study is to explore orthopedists' level of knowledge regarding laws and directives related to the detection and reporting of elder abuse, their attitudes towards the detection and reporting of elder abuse, detection and reporting practices, and the associations between these variables. In this cross-sectional study, 145 orthopedists employed in various Israeli hospitals-senior orthopedists, orthopedic residents, and orthopedic interns-completed a questionnaire. The orthopedists had a medium level of knowledge and held positive attitudes on the detection and reporting of elder abuse, but the actual number of cases they detected and reported was low. Senior orthopedists had the highest level of knowledge and also the most positive attitudes, followed by orthopedic residents, and then orthopedic interns. Common reasons for unwillingness to report cases of elder abuse differed between levels of training. A minority of the orthopedists had received training on the detection and management of elder abuse. Higher knowledge and more positive attitudes were found associated with a higher number of detected and reported cases of elder abuse. The importance of training orthopedists on the detection and management of elder abuse cannot be overemphasized. It seems that orthopedists should receive such training early in their career. IV.

Evolutionary algorithm based approach for solving transportation problems in normal and pandemic scenario

In recent times, COVID-19 pandemic has posed certain challenges to transportation companies due to the restrictions imposed by different countries during the lockdown. These restrictions cause delay and/ or reduction in the number of trips of vehicles, especially, to the regions with higher restrictions. In a pandemic scenario, regions are categorized into different groups based on the levels of restrictions imposed on the movement of vehicles based on the number of active cases (i.e., number of people infected by COVID-19), number of deaths, population, number of COVID-19 hospitals, etc. The aim of this study is to formulate and solve a fixed-charge transportation problem (FCTP) during this pandemic scenario and to obtain transportation scheme with minimum transportation cost in minimum number of trips of vehicles moving between regions with higher levels of restrictions. For this, a penalty is imposed in the objective function based on the category of the region(s) where the origin and destination are situated. However, reduction in the number of trips of vehicles may increase the transportation cost to unrealistic bounds and so, to keep the transportation cost within limits, a constraint is imposed on the proposed model. To solve the problem, the Genetic Algorithm (GA) has been modified accordingly. For this purpose, we have designed a new crossover operator and a new mutation operator to handle multiple trips and capacity constraints of vehicles. For numerical illustration, in this study, we have solved five example problems considering three levels of restrictions, for which the datasets are generated artificially. To show the effectiveness of the constraint imposed for reducing the transportation cost, the same example problems are then solved without the constraint and the results are analyzed. A comparison of results with existing algorithms proves that our algorithm is effective. Finally, some future research directions are discussed.

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia's large-scale social distancing policies.

The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19’s spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

Ten‐year follow‐up of Nicolaides–Baraitser syndrome with a de novo mutation and analysis of 58 gene loci of SMARCA2‐associated NCBRS

As a clinical subtype of SWI/SNF‐related intellectual disability syndromes, Nicolaides–Baraitser syndrome (NCBRS, OMIM601358) has a unique genotype–phenotype. Due to the scarcity of the number of cases reported and the limitations of diagnosis methods, so far only more than 80 cases have been reported worldwide. In this article, a new patient with a de novo mutation was followed up for 10 years; it includes the epilepsy treatment process, the characteristics of NBCRS with seizures, typical faces, sparse hair, prominent interphalangeal joints, and intellectual disability, and we also summarized the genotype–phenotype of the 80 reported cases for comparison. Due to insufficient studies and lack of attention paid to the syndrome, it is believed that the actual number of cases should be far more than the reported number. The syndrome is phased and progressive. The genotype–phenotype correlation of the disease is related to the location of the gene locus, especially closely related to the SNF2 ATPase domain.ConclusionsThe understanding of NCBRS is lagging, we need to strengthen the screening process of the phenotypic disease with intellectual disability, and perfecting multiple types of diagnostic techniques will help the discovery of the disease; its clinical features are staged and are slowly progressive, and long‐term prognosis must be taken precautious with long‐term follow‐up required.

Vaccination and variants: Retrospective model for the evolution of Covid-19 in Italy

The last year of Covid-19 pandemic has been characterized by the continuous chase between the vaccination campaign and the appearance of new variants that puts further obstacles to the possibility of eradicating the virus and returning to normality in a short period. In the present paper we develop a deterministic compartmental model to describe the evolution of the Covid-19 in Italy as a combined effect of vaccination campaign, new variant spreading and mobility restrictions. Particular attention is given to the mechanism of waning immunity, appropriately timed with respect to the effective progress of the vaccination campaign in Italy. We perform a retrospective analysis in order to explore the role that different mechanisms, such as behavioral changes, variation of the population mobility, seasonal variability of the virus infectivity, and spreading of new variants have had in shaping the epidemiological curve. We find that, in the large time window considered, the most relevant mechanism is the seasonal variation in the stability of the virus, followed by the awareness mechanism, that induces individuals to increase/relax self-protective measures when the number of active cases increases/decreases. The appearance of the Delta variant and the mobility variations have had instead only marginal effects. In absence of vaccines the emerging scenario would have been dramatic with a percentage difference in the number of total infections and total deaths, in both cases, larger than fifty per cent. The model also predicts the appearance of a more contagious variant (the Omicron variant) and its becoming dominant in January 2022.

Treatment seeking for alcohol-related issues during the COVID-19 pandemic: An analysis of an addiction-specialized psychiatric treatment facility

The COVID-19 pandemic and its societal impact may cause long-term behavioral changes in alcohol use due to increased psychological distress, unemployment, and time spent home. The purpose of this study was to determine whether or not the COVID-19 pandemic had a significant impact on treatment seeking for alcohol use disorders and related problems in a Swedish psychiatric treatment facility. Using an interrupted-time-series design on data derived from an addiction-specific psychiatric treatment facility located in Malmö, Sweden, we hypothesized that treatment seeking would decrease during the pandemic based on previous research identifying limited alcohol availability and affordability, as well as accessibility to treatment centers as influential factors. In addition, we assessed the predictive power of alcohol sales and number of active cases in the region using simple linear regressions. Results indicated that the pandemic had little to no effect on the number of people needing care, however a significant step change was found in treatment seeking patterns for unique female patients during the second wave (October 2020). Regression analyses indicated that alcohol sales and the number of active cases in the region did not significantly predict treatment seeking. A causal relationship between the onset of the pandemic and variation in treatment seeking for alcohol use could not be established. More research is needed to fully understand the pandemic’s impact on alcohol use behavior change.