Number Of New Cases Research Articles

Long-term monitoring of COVID-19 prevalence in raw and treated wastewater in Salvador, the largest capital of the Brazilian Northeast

Wastewater-based epidemiology (WBE) becomes an interesting epidemiological approach to monitoring the prevalence of SARS-CoV-2 broadly and non-invasively. Herein, we employ for the first time WBE, associated or not with the PEG 8000 precipitation method, for the detection of SARS-CoV-2 in samples of raw or treated wastewater from 22 municipal wastewater treatment stations (WWTPs) located in Salvador, the fourth most populous city in Brazil. Our results demonstrate the success of the application of WBE for detecting SARS-CoV-2 in both types of evaluated samples, regardless of the usage of PEG 8000 concentration procedure. Further, an increase in SARS-CoV-2 positivity rate was observed in samples collected in months that presented the highest number of confirmed COVID-19 cases (May/2021, June/2021 and January/2022). While PEG 8000 concentration step was found to significantly increase the positivity rate in treated wastewater samples (p < 0.005), a strong positive correlation (r: 0.84; p < 0.002) between non-concentrated raw wastewater samples with the number of new cases of COVID-19 (April/2021–February/2022) was observed. In general, the present results reinforce the efficiency of WBE approach to monitoring the presence of SARS-CoV-2 in either low- or high-capacity WWTPs. The successful usage of WBE even in raw wastewater samples makes it an interesting low-cost tool for epidemiological surveillance.

Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface.

The challenges humanity is facing due to the Covid-19 pandemic require timely and accurate forecasting of the dynamics of various epidemics to minimize the negative consequences for public health and the economy. One can use a variety of well-known and new mathematical models, taking into account a huge number of factors. However, complex models contain a large number of unknown parameters, the values of which must be determined using a limited number of observations, e.g., the daily datasets for the accumulated number of cases. Successful experience in modeling the COVID-19 pandemic has shown that it is possible to apply the simplest SIR model, which contains 4 unknown parameters. Application of the original algorithm of the model parameter identification for the first waves of the COVID-19 pandemic in China, South Korea, Austria, Italy, Germany, France, Spain has shown its high accuracy in predicting their duration and number of diseases. To simulate different epidemic waves and take into account the incompleteness of statistical data, the generalized SIR model and algorithms for determining the values of its parameters were proposed. The interference of the previous waves, changes in testing levels, quarantine or social behavior require constant monitoring of the epidemic dynamics and performing SIR simulations as often as possible with the use of a user-friendly interface. Such tool will allow predicting the dynamics of any epidemic using the data on the number of diseases over a limited period (e.g., 14 days). It will be possible to predict the daily number of new cases for the country as a whole or for its separate region, to estimate the number of carriers of the infection and the probability of facing such a carrier, as well as to estimate the number of deaths. Results of three SIR simulations of the COVID-19 epidemic wave in Japan in the summer of 2022 are presented and discussed. The predicted accumulated and daily numbers of cases agree with the results of observations, especially for the simulation based on the datasets corresponding to the period from July 3 to July 16, 2022. A user-friendly interface also has to ensure an opportunity to compare the epidemic dynamics in different countries/regions and in different years in order to estimate the impact of vaccination levels, quarantine restrictions, social behavior, etc. on the numbers of new infections, death, and mortality rates. As example, the comparison of the COVID-19 pandemic dynamics in Japan in the summer of 2020, 2021 and 2022 is presented. The high level of vaccinations achieved in the summer of 2022 did not save Japan from a powerful pandemic wave. The daily numbers of cases were about ten times higher than in the corresponding period of 2021. Nevertheless, the death per case ratio in 2022 was much lower than in 2020.

Body weight changes and diabetes mellitus incident: A cohort study from the Middle East.

Obesity is a known risk factor for diabetes, but the effect of weight changes on the incidence of diabetes is not yet determined. This study aims to evaluate the long-term effects of weight change [based on body mass index (BMI)] on the incidence of diabetes mellitus (DM) in a middle eastern population. In the Isfahan Cohort Study (ICS) 6504 adults equal or greater than 35 years of age were recruited at 2001 and were followed until 2013. Absolute BMI changes (ΔBMI) were calculated by subtracting the baseline BMI from the BMI measured at follow-ups. To compare participants with different baseline BMI easier, relative changes in BMI were quantified as the percentage of changes from baseline. DM was assessed based on standard definitions. Multivariable Cox regression was used to determine the association between ΔBMI and the incidence of diabetes. During follow-ups, 261 new cases of diabetes were recorded, with an IR of 3401.29 per 100,000 P-Y. The highest number of new cases of type 2 DM belongs to participants with overweight and obesity who had minimal BMI changes (less than 5% of their baseline BMI limits; 42 and 38 new cases, respectively). Participants who were obese at baseline and had lost more than 10% or gained 5-10% of baseline BMI were in the groups with the highest IR [360.05-95% CI (239.3-541.8) and 322.39-95% CI (178.5-582.1) respectively]. There was no significant association between BMI changes and the incidence of DM in the participants with normal BMI, overweight, and obesity at baseline in cure and adjusted models. This study showed there was no significant association between diabetes mellitus incidence and BMI changes.

Poor regulation, desperation, and misinformation, a countrywide analysis of self-medication and prescription patterns in Ecuador during the COVID-19 pandemic

BackgroundThe rapid spread of the SARS-CoV-2 virus during the early phase of the pandemic led to an unprecedented global health crisis. Various factors have influenced self-medication practices among the general population and unsubstantiated prescribing practices among healthcare professionals. ObjectiveThis study aimed to describe trends in the purchase and sale of medicines during the COVID-19 pandemic period (2020–2022) in Ecuador, by comparing them with pre-pandemic periods. MethodsIn this study, a cross-sectional design was employed to conduct a comprehensive analysis of 28 pharmacological groups, categorized according to the Anatomical Therapeutic Chemical Classification (ATC). Utilizing an integrated drug consumption database, the study examined physician prescribing data, medicine usage, and spending levels in Ecuador during the COVID-19 pandemic. The analysis involved computing absolute differences in monthly resolution, calculating excessive expenditure in comparison to previous yearly averages, and using Defined Daily Dose (DDD) methodology for internationally comparable results. Furthermore, a correlation analysis was performed to investigate potential associations between prescribed and consumed medicines and the number of new cases and deaths. ResultsIn Ecuador, the average yearly expenditure among these groups prior to the pandemic (2017–2019) amounted to $150,646,206 USD, whereas during 2020 and 2021, the same groups represented a total expenditure of $228,327,210, reflecting a significant increase. The excess expenditure during this period reached 51.4%, equivalent to $77,681,004 USD. Notably, 13% of this expenditure consisted of Over the Counter (OTC) Medicines. The study also identified a remarkable surge in sales of ivermectin, which increased by 2,057%, and hydroxychloroquine, which increased by 171%, as measured by DDD. ConclusionsThis study highlights the substantial consumption of medicines by the population in Ecuador during the pandemic. It is concerning that many medications were sold without proven therapeutic indications, indicating that misinformation and desperation may have led to improper prescribing by physicians and patients resorting to ineffective drugs. Moreover, since the sale of these therapeutic drugs requires a prescription, poor regulation, and a lack of control within pharmacies likely contributed to such practices.

Measuring leprosy case detection delay and associated factors in Indonesia: a community-based study

BackgroundLeprosy is a public health burden in Indonesia with a high number of new cases every year and a high proportion of disability among new cases. Case detection delay (CDD) can contribute to ongoing transmission and increased disability chances among leprosy patients. This study aimed to establish the CDD of leprosy and the factors associated with detection delay in Indonesia.MethodCommunity-based study with a cross-sectional design. Data were collected through interviews about sociodemographic and behavioral factors, anticipated stigma, and duration of CDD. Leprosy classification and case detection methods were obtained from health service records. A random sample was taken of 126 leprosy patients registered between 1st October 2020 and 31st March 2022 in the Tegal regency in the Central Java Province. Data were analysed by descriptive and analytical statistics using multiple linear regression.ResultsThe mean CDD, patient delay, and health system delay were 13.0 months, 9.7 months, and 3.2 months, respectively. Factors associated with longer CDD are younger age (below 35 years), male, found through passive case detection, and not having a family member with leprosy. Factors associated with longer patient delay were being younger (below 35 years), being male, not having a family member with leprosy, and anticipated stigma of leprosy. It was not possible to reliably identify factors associated with health system delay.ConclusionCDD in leprosy should be reduced in Indonesia. The Indonesian National Leprosy Control Program (NLCP) is advised to adopt an integrated intervention programme combining active case detection with targeted health education to reduce CDD and thereby preventing disabilities in people affected by leprosy.

Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models.

The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.

Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study.

The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field. This study's objective was to evaluate a Bayesian search algorithm to target hotspots of SARS-CoV-2 transmission in the community with the goal of detecting the most cases over time across multiple locations in Columbus, Ohio, from August to October 2021. The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling, in which the aim is to maximize the average number of new cases of SARS-CoV-2 diagnosed among a set of testing locations based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University, Wake Forest University, the Ohio Department of Health, the Ohio National Guard, and the Columbus Metropolitan Libraries conducted a study of bandit algorithms to maximize the detection of new cases of SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations, including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community event sites. Our team conducted between 0 and 56 tests at the 16 testing events, with an overall average of 25.3 tests conducted per event and a moving average that increased over time. Small incentives-including gift cards and take-home rapid antigen tests-were offered to those who approached the pop-up sites to encourage their participation. Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of underserved communities, particularly African Americans, with the pool of participants overrepresenting these people relative to the demographic profile of the local zip code in which testing sites were located. This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV.

Tuberculosis as an infectious disease and its prevalence in society current status

Since the beginning of recorded history, tuberculosis has been and continues to be a major health concern all over the world. Tuberculosis (TB) is a disease that is caused by bacteria that are passed from person to person through the air. In the past 15 years, the number of new cases of TB as well as the number of deaths has been declining. However, TB is still a very serious condition. The lungs are the most common target of tuberculosis infection; nevertheless, the disease can attack and harm any region of the body, including the brain, kidneys, or spine. Some of the general signs of tuberculosis disease include feelings of sickness or weakness, loss of weight, fever, and sweating at night. In addition to coughing, chest pain, and blood in the sputum, other symptoms of tuberculosis lung illness may include: Signs and symptoms of tuberculosis, The treatment of tuberculosis disease includes the use of various medications, each of which must be taken for a period ranging from six months to nine months (or even longer in the case of drug-resistant TB), depending on the chosen regimen.

Prediction and Impact Analysis of Passenger Flow in Urban Rail Transit in the Postpandemic Era

In the postpandemic era, exploring the relationship between the daily new COVID-19 cases and passenger flow in urban rail transit can help effectively predict the impact of future pandemic situations on rail transit. In this study, based on a gated recurrent unit (GRU) neural network model, the daily passenger flow in urban rail transit in the postpandemic era was predicted, and the results were compared with those obtained using the long short-term memory (LSTM) neural network and other conventional time series analysis models such as SARIMA (seasonal autoregressive integrated moving average). Based on the trained GRU model, a partial dependence plot (PDP) was adopted to explore the quantitative relationship between the daily passenger flow and the daily new cases or weather attribute. The results showed that (1) the prediction accuracy of the GRU neural network model was 95.25%, which was the highest among the prediction models studied, indicating that the GRU could achieve the best performance. (2) The GRU model did not fluctuate significantly in the initial training stage, and its convergence rate was higher than that of the LSTM. (3) The number of daily new cases was negatively correlated with the daily passenger flow. For every new case on the previous day, the daily passenger flow fell by an average of 54,600 person-times. (4) Compared with no rain condition, the daily passenger flow decreased by 207,600 person-times on an average on rainy days. In summary, the neural network could achieve accurate prediction, while the PDP could compensate for the “black box” disadvantage of nonparametric models, owing to which the quantitative relationship between the number of new cases and daily passenger flow could be successfully explored. Our study can serve as a basis for demand prediction, operational organization, and policy implementation related to urban rail transit.

Lipid metabolism-related gene signatures for predicting the prognosis of lung adenocarcinoma.

Lung cancer is one of the most common epithelial malignancies worldwide, accounting for the highest number of new cases and deaths. Metabolism is the sum of chemical reactions that produce energy to keep an organism alive. Several studies have shown that glucose and lipid metabolic disorders are common phenomena related to cancer cell genesis and progression. We screened the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD) samples of The Cancer Genome Atlas (TCGA) database, the Gene Set Enrichment Analysis (GSEA), and Gene Card database metabolism-related data, the metabolism-related DEGs of LUAD, as well as the univariate Cox regression analysis genes, for identifying significant outcome-related genes. The least absolute shrinkage and gene selection operator (LASSO) analysis was performed to establish the best risk model. Our study aimed to establish a lipid metabolism-related model for predicting LUAD prognosis. Furthermore, our model's prognosis prediction power was evaluated by survival analysis. This study finally identified 11 DEGs related to lipid metabolism that were significantly associated with the prognosis of lung adenocarcinoma. It provided a new idea for the treatment of high-risk lung adenocarcinoma patients. The constructed clinical prognosis model of lung adenocarcinoma related to lipid metabolism provides a new idea for clinical treatment of lung adenocarcinoma.

Atypical early presentation of subacute sclerosing panencephalitis in children: A case series

Subacute sclerosing panencephalitis (SSPE) is a progressive neurodegenerative disease of children and young adults that may occur as an uncommon complication 7–10 years after a measles infection. In measles – endemic countries like India, the incidence of this fatal disease is high and more number of new cases are detected in recent times. In this case series, we have presented three cases of SSPE between the 4 and 5 years age group, presented with abnormal movement and neuroregression and diagnosed by characteristic clinical features, raised anti-measles antibody titer in CSF, and typical Electroencephalography findings. All of them had a history of measles infection before the vaccination and very short latency period between measles infection and the development of SSPE. They were discharged in stable condition after the treatment with oral Isoprinosine and intrathecal Interferon alfa 2b. Early age of presentation, short latency period, and apparently good response to treatment for the time being prompted us to present these cases.

Chinese Protocol of Diagnosis and Treatment of Colorectal Cancer (2023 edition)

Colorectal cancer is one of the most common malignant tumors in China. In recent years, the incidence and mortality of colorectal cancer in China has been on the rise. According to the China Cancer Statistics Report in 2020, the incidence and mortality of colorectal cancer in China ranked second and fifth among all malignant tumors, with 555, 000 new cases and 286, 000 deaths. China has become the country with the highest number of new cases and deaths of colorectal cancer every year in the world, which seriously threatens the health of Chinese residents. In 2010, the National Ministry of Health organized the colorectal cancer expertise of the Chinese Medical Association to write the Chinese Protocol of Diagnosis and Treatment of Colorectal Cancer (2010 edition) and publish it publicly. Since 2010, the National Health and Family Planning Commission has organized experts to revise the protocol in 2015 and 2017, while the National Health Commission revised it in 2020 and 2023. The revised part of the Chinese Protocol of Diagnosis and Treatment of Colorectal Cancer (2023 edition) involves new progress in the field of imaging examination, pathological evaluation, surgery, chemotherapy and radiotherapy. The 2023 edition of the protocol not only referred to the contents of the international guidelines, but also combined with specific national conditions and clinical practice in China, and also included many evidence-based clinical data recently in China. The 2023 edition of the protocol would further promote the standardization of the diagnosis and treatment of colorectal cancer in China, improve the survival and prognosis of patients, and benefit millions of patients with colorectal cancer and their families.

Factors Influencing the Level of Knowledge of Reproductive Age Women about Visual Inspection with Acetic Acid (VIA Test) at PMB S. April - May Period in 2023

Background: Cervical cancer is a disease caused by sexually acquired infection with Human Papiloma Virus (HPV) sub-types, especially sub-types 16 and 18 (WHO, 2016). IVA is an examination of the cervix (cervix) by looking directly (naked) at the cervix after wiping the cervix with a 3-5% acetic acid solution. Based on data from the Indonesian cancer foundation, in Indonesia 1 hour 1 person dies from cervical cancer and 34.4 per cent of cancers in women. The number of new cases of cervical cancer is 40 to 45 per day.&#x0D; Purpose: To find out what factors influence the level of knowledge of WUS on knowledge about VIA test in PMB S period April-May 2023?&#x0D; Methods: The design used was a survey with a "Cross Sectional" approach, namely research on several populations observed at the same time, The population taken in this study were all Women of Fertile Age (WUS) who conducted family planning at PMB S on 18 April - 08 May Tangerang Banten Year 2023&#x0D; Results: Based on statistical tests, it was found that there was no significant relationship between age and the level of knowledge of women of childbearing age about visual inspection of acetic acid (VIA) test where the p value = 0.171 (p&gt; 0.050), there was no significant relationship between education and the level of knowledge of women of childbearing age about visual inspection of acetic acid (VIA) where the p value = 0.196 (p&gt; 0.050), it was found that there was no significant relationship between education and the level of knowledge of women of childbearing age about visual inspection of acetic acid (VIA) where the p value = 0.196 (p&gt; 0.050).0.050), it was found that there was a significant relationship between work and the level of knowledge of women of childbearing age about visual inspection of acetic acid (VIA) test at PMB Siti Rahayu Kreo Tangerang Banten in 2023 where the p value = 0.012 (p&gt; 0.050).&#x0D; Conclusion: There is no significant relationship between the level of knowledge of WUS and work, level of knowledge, age of WUS about visual inspection of acetic acid (VIA).

What is wrong with Chinese COVID-19 statistics?

The media is reporting tens of millions of new daily Covid-19 cases in China in the final days of 2022. However, official statistics have recorded in mainland China only 1.9 million cases since the start of the pandemic and stopped providing data after December 21, 2022. Results of SIR simulations showed that daily numbers of new cases stated to decline in December 2022. The contradictions in statistics and estimations are discussed. Millions of new daily cases in China look very unlikely.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

Trends in incidence and mortality for ovarian cancer in China from 1990 to 2019 and its forecasted levels in 30 years

BackgroundThe specific long-term trend in ovarian cancer (OC) rates in China has been rarely investigated. We aimed to estimate the temporal trends in incidence and mortality rates from 1990 to 2019 in OC and predict the next 30-year levels. Data on the incidence, mortality rates, and the number of new cases and deaths cases due to OC in the China cohort from 1990 to 2019 were retrieved from the Global Burden of Disease Study 2019. Temporal trends in incidence and mortality rates were evaluated by joinpoint regression models. The incidence and mortality rates and the estimated number of cases from 2020 to 2049 were predicted using the Bayesian age–period–cohort model.ResultsConsecutive increasing trends in age-standardized incidence (average annual percent change [AAPC] = 2.03; 95% confidence interval [CI], 1.90–2.16; p < 0.001) and mortality (AAPC = 1.58; 95% CI, 1.38−1.78; p < 0.001) rates in OC were observed from 1990–2019 in China. Theoretically, both the estimated age-standardized (per 100,000 women) incidence (from 4.77 in 2019 to 8.95 in 2049) and mortality (from 2.88 in 2019 to 4.03 in 2049) rates will continue to increase substantially in the coming 30 years. And the estimated number of new cases of, and deaths from OC will increase by more than 3 times between 2019 and 2049.ConclusionsThe disease burden of OC in incidence and mortality has been increasing in China over the past 30 years and will be predicted to increase continuously in the coming three decades.

Statistical Analysis of Covid-19 Outbreak with Benford’s Law

The coronavirus disease first identified in mid-December 2019 in Wuhan, China is an ongoing pandemic and the virus has spread around the world. As of 13 March 2020, the number of new cases started to increase significantly in Europe, and Europe was considered as the new center of the Covid-19 pandemic as announced by the WHO. Confirmed case rate (CCR), computed from the numbers of confirmed cases over numbers of tests of the countries can be used to confirm the quality of the numbers, and to detect the manipulation for health surveillance systems of the countries for managing the situation by testing whether or not follow Benford’s Law (BL). The main aim of this study is to test CCRs of the countries in Europe by BL to detect the data qualities and to monitor the manipulations, which can help to take precautions for the health surveillance systems of the countries.

Solution for the Mathematical Modeling and Future Prediction of the COVID-19 Pandemic Dynamics

The COVID-19 infectious disease spread in the world represents, by far, one of the most significant moments in humankind’s recent history, affecting daily activities for a long period of time. The data available now allow important modelling developments for the simulation and prediction of the process of an infectious disease spread. The current work provides strong insight for estimation and prediction mathematical model development with emphasis on differentiation between three distinct methods, based on data gathering for Romanian territory. An essential aspect of the research is the quantification and filtering of the collected data. The current work identified five main categories considered as the model’s inputs: inside temperatures (°C), outside temperatures (°C), humidity (%), the number of tests and the quantified value of COVID-19 measures (%) and, as the model’s outputs: the number of new cases, the number of new deaths, the total number of cases or the total number of deaths. Three mathematical models were tested to find the optimal solution: transfer vector models using transfer functions as elements, autoregressive-exogenous (ARX) models, and autoregressive-moving-average (ARMAX) models. The optimal solution was selected by comparing the fit values obtained after the simulation of all proposed models. Moreover, the manuscript includes a study of the complexity of the proposed models. Based on the gathered information, the structure parameters of the proposed models are determined and the validity and the efficiency of the obtained models are proven through simulation.

Longitudinal impact of the COVID-19 pandemic on the development of mental disorders in preadolescents and adolescents

BackgroundSchool closures and social distancing may have affected mental health among preadolescent and adolescent children, who are in a social developmental stage. Rates of anxiety, depression, and stress have been reported to have increased during the COVID-19 pandemic among teenagers worldwide. However, most studies have measured children's mental health in cross-sectional studies or short-term comparisons before and after lockdowns and school closures, and few studies have tracked the long-term effects on mental health among children and adolescents, despite the pandemic lasting more than 2 years.MethodsAn interrupted time-series analysis was performed for longitudinal changes in the monthly number of new mental disorders (eating disorders, schizophrenia, mood disorders, and somatoform disorders). Using a nationwide multicenter electronic health records database in Japan, we analyzed data of patients aged 9 to 18 years from 45 facilities that provided complete data throughout the study period. The study period covered January 2017 to May 2021, defining a national school closure as an intervention event. We modeled the monthly new diagnoses of each mental disorder using a segmented Poisson regression model.ResultsThe number of new diagnoses throughout the study period was 362 for eating disorders, 1104 for schizophrenia, 926 for mood disorders, and 1836 for somatoform disorders. The slope of the regression line in monthly number of new diagnoses increased in the post-pandemic period for all targeted mental disorders (change in slope for eating disorders 1.05, 95% confidence interval [CI] 1.00–1.11; schizophrenia 1.04, 95% CI 1.01–1.07; mood disorders 1.04, 95% CI 1.01–1.07; and somatoform disorders 1.04 95% CI 1.02–1.07). The number of new diagnoses for schizophrenia and mood disorders increased early after school closure; while eating disorders showed an increasing trend several months later. Somatoform disorders showed a decreasing trend followed by an increasing trend. Time trends by sex and age also differed for each mental disorder.ConclusionsIn the post-pandemic period, the number of new cases increased over time for eating disorders, schizophrenia, mood disorders, and somatoform disorders. The timing of increase and trends by sex and age differed for each mental disorder.

Which Factors Were Related to the Number of COVID-19 Cases in the 2022/2023 Season Compared to the 2021/2022 Season in Europe?

The number of COVID-19 cases was greater in early autumn 2022 in contrast to in autumn 2021. Therefore, we decided to examine the factors that may have affected differences in the number of COVID-19 cases between the time periods 2021/2022 and 2022/2023 with consideration of the occurrence of influenza. In this cross-sectional study, we conducted a multiple factor analysis using data from publicly available databases for weeks 35-14 in 2022/2023 and 2021/2022 for Austria, Germany, Greece, Italy, and Slovenia. In the 2021/2022 season, the analyzed countries had similar profiles and were characterized by restrictions, health system policies, and SARS-CoV-2 variants, such as Alpha, Beta, Delta, Kappa, Eta, as well as Omicron sublineages (BA.1, BA.2), which were positively correlated with the number of new cases of COVID-19 per million people. However, in the 2022/2023 season, the analyzed countries were described by groups of variables corresponding to vaccination, influenza, the number of flights, and the Omicron SARS-CoV-2 subvariant. In summary, crucial factors correlated with the increasing of number of COVID-19 cases in the 2021/2022 season were the presence of dominant SARS-CoV-2 variants as well as the lifting of restrictions and strict health system policies.