Number Of New Cases Research Articles

Community Diagnosis Activities in Efforts to Reduce Hypertension Cases in the Working Area of Legok Health Center, Legok District, Tangerang District, Banten Province

Background: Hypertension is one of the most common causes of heart disease, stroke, and kidney failure. Hypertension cases in Indonesia until 2018 were recorded at around 34.1%. There was an increase compared to 2013 which was 25.8%. The number of new cases of hypertension during July to September 2023 at the Legok Health Center is known to increase every month, from 63 to 77 cases with the highest cases in Babakan Village. From the mini-survey, it was found that knowledge about hypertension was still lacking. Objective: To increase public knowledge about hypertension as a whole in the working area of the Legok Health Center. Methods: A community diagnosis approach was used to identify problems using Blum's Paradigm. Problem prioritization was determined using non-scoring Delphi technique and the root cause of the problem was determined through fish-bone diagram. Data on intervention outcomes were obtained through pre and post test questionnaires. Monitoring was conducted using the plan-do-check-action (PDCA) cycle. Results: Based on the results of the analysis using the fishbone diagram, the main problem is lifestyle. The results of the activity obtained all participants scored ≥70 points on the post-test and blood pressure screening ≥140 and / or ≥90 was obtained in 10 people (35.71%). The nutritional status of under-weight was found in 2 people (7.14%), normal in 5 (17.86%), overweight in 3 (10.71%), obesity level 1 in 12 people (42.86%), and obese level 2 in 6 people (21.43%) and abdominal circumference ≥80 cm in 21 people (75%). Conclusion: Based on our intervention, it can be stated that our intervention is successful so that it is expected to reduce hypertension cases at the Legok Health Center

Early detection of new pandemic waves. Control chart and a new surveillance index.

The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.

A Proposed Framework of Vulnerability Assessment and Penetration Testing (VAPT) in Cloud Computing Environments from Penetration Tester Perspective

Penetration testing is a process that focuses on finding security vulnerabilities in a target environment that could let an attacker penetrate the network or computer system or steal information. Due to the COVID-19 endemic, most employees still implement working from home or a hybrid approach, even though the number of new cases of COVID-19 is decreasing. However, working from home depends mainly on cloud computing applications that help employees efficiently accomplish their daily work. This situation also increased the number of data generated from various sources, so they may be exposed to different security risks. This research will propose a framework to conduct vulnerability assessment and penetration testing (VAPT) in cloud service models such as SaaS, PaaS, and IaaS from the perspective of penetration testers. This proposed framework is developed through the integration and mapping of existing frameworks and guidelines to conduct VAPT on testing components such as web applications, APIs, network testing, etc. In this proposed framework, the method of conducting VAPT for each cloud service model will be discussed in detail, from the planning and reconnaissance stage until the report is delivered to the cloud subscriber or cloud provider. An advantage of this proposed framework for the penetration tester is that there is still a lack of methods or guidelines for conducting VAPT that cover all the cloud service models in one comprehensive document

An Analysis Comparing the Predictions of Predictive Models with the Actual Number of New Cases during the COVID-19 Pandemic

On January 3, 2020, Chinese health officials discovered a pneumonia outbreak in the Chinese city of Wuhan. The virus swiftly spread to most countries, infecting a substantial portion of the population. On September 28, 2020, about a million deaths were reported worldwide. The virus is typically transmitted through coughing or sneezing on others. Because there is no effective vaccine, the majority of governments have enforced lockdowns to slow the virus's spread. Other preventive measures, such as travel bans, social isolation, hand hygiene, and the use of face masks, were critical in restricting the virus's spread. However, it proved difficult to contain the virus, which had spread to over 200 nations with a population of over 7 billion people. Large data sets were acquired on a daily basis, and data analytics became critical for uncovering trends and establishing how the infection spread. Several studies have been conducted to develop a mathematical forecast for the pandemic since the disease's first cases in India. First examined with reference to India, these models differed significantly in their scope, underlying assumptions, and numerical forecasts. The objective of this research is to evaluate the predictive models' efficacy by comparing their forecasts with the actual number of new cases reported every day in India during the COVID-19 outbreak.

Effets médiateurs de la symptomatologie de stress post-traumatique et des symptômes de dissociation somatoforme sur la relation entre l’attachement et la qualité de vie liée à la santé après un infarctus du myocarde

Myocardial infarction (MI) is a particularly frequent coronary disease: the number of new cases of MI recorded each year amounts to nearly 90,000 in France and nearly 790,000 in the United States. Over the past 15 years, the number of people who can survive this major cardiac event has substantially increased, as a result of advances in medical treatment and a reduction in delay to intervention. However, patients often report a decrease in their health-related quality of life following myocardial infarction. In other words, some patients perceive that the disease negatively affects various aspects of their daily lives, including physical functioning, emotional state, social relationships, and overall well-being. Furthermore, studies have shown that health-related quality of life is a prognostic indicator of the evolution of patients’ health status: the more health-related quality of life is reduced, the greater the risk of all-cause mortality, recurrence of cardiovascular events, and re-hospitalization for heart disease. In view of these elements, the identification of potentially modifiable factors, and in particular psychological factors, having an impact on health-related quality of life is particularly relevant. More specifically, insecure attachment, post-traumatic stress symptomatology and somatoform dissociation symptoms could have an impact on the health related-quality of life of people who have experienced a major cardiac event. ObjectivesThe objective of this study was to investigate the mediating role of posttraumatic stress symptoms and somatoform dissociative symptomatology in the relationship between insecure attachment and health related-quality of life after a myocardial infarction. Materials and methodsA total of 73 participants, having suffered a myocardial infarction and participating in a cardiovascular rehabilitation program, completed questionnaires to assess the degree of insecurity of their attachment (Experiences in Close Relationship Scale-12; ECR-12), the severity of post-traumatic stress symptoms (Post-Traumatic Stress Disorder Checklist Scale; PCL-5) and somatoform dissociation symptoms (Somatoform Dissociation Questionnaire; SDQ-20), and their health related-quality of life (MacNew Heart Disease-Health Related Quality of Life; MacNew). Mediation analyses were conducted according to the recommendations formulated by Baron and Kenny. ResultsOur results showed that only anxious attachment was positively and significantly associated with the severity of posttraumatic stress symptomatology, whereas such a link was not observed for avoidant attachment. The importance of posttraumatic symptomatology fully explained why individuals with high levels of fear of abandonment also have the poorest emotional, physical, and social quality of life after myocardial infarction. The importance of dissociative somatoform symptomatology partially explained why individuals with an insecure attachment style characterized by a high level of abandonmen were also those with the poorest health-related quality of life following myocardial infarction. ConclusionsThe results of this study tend to demonstrate the importance of taking into account the specificities of attachment, posttraumatic stress symptoms and somatoform dissociation phenomena in the context of interventions aimed at improving the health related-quality of life of people after a myocardial infarction. Insofar as secure attachment appears to be a protective factor against the risk of developing posttraumatic stress disorder, a psychotherapeutic approach aimed at promoting this form of attachment seems to be well justified. Specifically, it may be helpful to provide patients with a psychotherapeutic framework that takes into account their attachment experiences to allow them to change their beliefs about themselves and others, while helping them develop coping strategies appropriate to the stress. Psychotherapeutic interventions aimed jointly at increasing of the degree of attachment security and reducing the symptoms of posttraumatic stress and somatoform dissociation could be relevant to improve the health-related quality of life after a myocardial infarction.

Age-period-cohort analysis of incidence, mortality and disability-adjusted life years of esophageal cancer in global, regional and national regions from 1990 to 2019

ObjectiveIn view of the high incidence and mortality of esophageal cancer, the latest statistical data on the disease burden of esophageal cancer can provide strategies for cancer screening, early detection and treatment, and help to rationally allocate health resources. This study provides an analysis of the global disease burden and risk factors of esophageal cancer from 1990 to 2019.MethodsUsing the 2019 Global Burden of Disease, Injury and Risk Factor (GBD) data, we present the incidence, mortality and disability-adjusted life years (DALY) of esophageal cancer in 21 regions and 204 countries and different sociodemographic index (SDI) regions from 1990 to 2019. The age-period-cohort model was used to estimate the age, period, and cohort trend of esophageal cancer in different SDI regions. The estimated proportion of DALY attributable to each risk factor from 1990 to 2019.ResultsFrom 1990 to 2019, the number of new cases of esophageal cancer, the number of deaths and DALY increased by 67.07%, 55.97% and 42.13%, respectively, but age standardized incidence rate (ASIR), age standardized mortality rate (ASMR) and age standardized DALY rate (ASDR) decreased by 19.28%, 25.32% and 88.22%, respectively. Overall, the results of the age-period-cohort model showed that the incidence, mortality, and DALY rates in countries and regions with higher SDI levels showed a downward trend over time and with the passage of time. Conversely, there were no significant changes in incidence and mortality in countries and regions with low SDI levels. In the past 30 years, the incidence and death of esophageal cancer in the world has gradually changed to people over 80 years old, but the population aged 60–79 still accounts for the largest proportion. The global DALY in esophageal cancer is mainly attributable to smoking, followed by alcohol consumption and occupational exposure.ConclusionsAlthough ASIR, ASMR and ASDR have decreased significantly, esophageal cancer is still the main factor causing the disease burden worldwide. Public health administrators in low SDI and low-middle SDI countries are high-risk areas for esophageal cancer, and preventive control measures should be implemented to raise awareness, screening, and treatment of esophageal cancer in these areas. Tobacco and alcohol control and reduction of occupational hazards are key steps in reducing the burden of esophageal cancer.

Access to oncology care in Mali: a qualitative study on breast cancer

BackgroundBreast cancer is the most common cancer in terms of incidence and mortality among women worldwide, including in Africa, and a rapid increase in the number of new cases of breast cancer has recently been observed in sub-Saharan Africa. Oncology is a relatively new discipline in many West African countries, particularly Mali; thus, little is known about the current state of cancer care infrastructure and oncology practices in these countries.MethodsTo describe the challenges related to access to oncology care in Mali, we used a qualitative approach, following the Consolidated Criteria for Reporting Qualitative Research (COREQ). Thirty-eight semistructured interviews were conducted with health professionals treating cancer in Mali (n = 10), women with breast cancer (n = 25), and representatives of associations (n = 3), and 40 participant observations were conducted in an oncology unit in Bamako. We used the theoretical framework on access to health care developed by Levesque et al. a posteriori to organise and analyse the data collected.ResultsAccess to oncology care is partly limited by the current state of Mali's health infrastructure (technical platform failures, repeated strikes in university hospitals, incomplete free health care and the unavailability of medicines) and exacerbated by the security crisis that has been occurring the country since 2012. The lack of specialist doctors, combined with limited screening campaigns and a centralised and fragmented technical platform in Bamako, is particularly detrimental to breast cancer treatment. Women's lack of awareness, lack of information throughout the treatment process, stereotypes and opposition to amputations all play a significant role in their ability to seek and access quality care, leading some women to therapeutically wander and others to want to leave Mali. It also leaves them in debt and jeopardises the future of their children. However, the high level of trust in doctors, the involvement of international actors, the level of social support and the growing influence of civil society on the issue of cancer also represent great current opportunities to fight cancer in Mali.ConclusionDespite the efforts of successive Malian governments and the commitment of international actors, the provision of health care is still limited in the country, entrenching global inequalities in women's bodies.

A statistical fuzzy clustering approach to analyze the characteristics of the number of new cancer cases in Asia for Health

Cancer is a disease characterized by the uncontrolled growth and spread of abnormal cells in an organ of the human body. Asia is the continent that has the most significant number of new cases of cancer, with a percentage of 49.3% of the number of cancer patients in the world. Preventive action to deal with the spread of cancer is the responsibility of the government to improve the quality of health in the country, so it is necessary to take action to prevent the spread of cancer and help archieve the Sustainable Development Goals (SDGs) at the third point in the field of health. One of them is by determining the characteristics of the cancer and clustering countries in Asia based on their characteristics. This article will discuss the clustering of countries in Asia using fuzzy clustering in the form of fuzzy k-means, fuzzy Gustafson-Kessel babushka and fuzzy k-medoids. the results obtained from the analysis show that using fuzzy k-means will have a more excellent fuzzy silhouette index value compared to fuzzy Gustafson-Kessel babushka and fuzzy k-medoids, which is 0.6313.

Spatial-temporal analysis of hepatitis E in Hainan Province, China (2013-2022): insights from four major hospitals.

Exploring the Incidence, Epidemic Trends, and Spatial Distribution Characteristics of Sporadic Hepatitis E in Hainan Province from 2013 to 2022 through four major tertiary hospitals in the Province. We collected data on confirmed cases of hepatitis E in Hainan residents admitted to the four major tertiary hospitals in Haikou City from January 2013 to December 2022. We used SPSS software to analyze the correlation between incidence rate and economy, population density and geographical location, and origin software to draw a scatter chart and SAS 9.4 software to conduct a descriptive analysis of the time trend. The distribution was analyzed using ArcMap 10.8 software (spatial autocorrelation analysis, hotspot identification, concentration, and dispersion trend analysis). SAS software was used to build an autoregressive integrated moving average model (ARIMA) to predict the monthly number of cases in 2023 and 2024. From 2013 to 2022, 1,922 patients with sporadic hepatitis E were treated in the four hospitals of Hainan Province. The highest proportion of patients (n = 555, 28.88%) were aged 50-59 years. The annual incidence of hepatitis E increased from 2013 to 2019, with a slight decrease in 2020 and 2021 and an increase in 2022. The highest number of cases was reported in Haikou, followed by Dongfang and Danzhou. We found that there was a correlation between the economy, population density, latitude, and the number of cases, with the correlation coefficient |r| value fluctuating between 0.403 and 0.421, indicating a linear correlation. At the same time, a scatter plot shows the correlation between population density and incidence from 2013 to 2022, with r2 values fluctuating between 0.5405 and 0.7116, indicating a linear correlation. Global Moran's I, calculated through spatial autocorrelation analysis, showed that each year from 2013 to 2022 all had a Moran's I value >0, indicating positive spatial autocorrelation (p < 0.01). Local Moran's I analysis revealed that from 2013 to 2022, local hotspots were mainly concentrated in the northern part of Hainan Province, with Haikou, Wenchang, Ding'an, and Chengmai being frequent hotspot regions, whereas Baoting, Qiongzhong, and Ledong were frequent cold-spot regions. Concentration and dispersion analysis indicated a clear directional pattern in the average density distribution, moving from northeast to southwest. Time-series forecast modeling showed that the forecast number of newly reported cases per month remained relatively stable in 2023 and 2024, fluctuating between 17 and 19. The overall incidence of hepatitis E in Hainan Province remains relatively stable. The incidence of hepatitis E in Hainan Province increased from 2013 to 2019, with a higher clustering of cases in the northeast region and a gradual spread toward the southwest over time. The ARIMA model predicted a relatively stable number of new cases each month in 2023 and 2024.

The Interplay of Stress and Salivary Cortisol Saliva in Multibacillary Leprosy: A Cross-Sectional Analysis in South Sulawesi, Indonesia

Background: Leprosy remains a significant health concern in Indonesia, particularly in South Sulawesi, where the prevalence rate has reached 0,78 per 10,000 population. The increasing number of new cases, mainly of the multibacillary type, poses a serious epidemiological and clinical challenge. Leprosy patients often face social stigma, leading them to live in isolation, causing additional stress for the patients. Stress triggers various physiological responses, including the activation of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic-adrenal-medullary (SAM) system. Cortisol, the primary glucocorticoid secreted by the adrenal cortex, is the end product of the HPA axis. In the salivary glands, cortisol exists only in its active form, different from its presence in the serum. Objective: To analyze salivary cortisol levels and correlate them with the stress scale in multibacillary leprosy patients Methods: This study used a descriptive-analytic approach with a cross-sectional method. Twenty-five samples in Makassar from November 2022 to January 2023, with the criteria of those newly diagnosed with multi-bacillary leprosy based on clinical and laboratory examination, participated. The stress scale was assessed with the Perceived Stress Scale-10 (PSS-10). Using enzyme-linked immunosorbent assay (ELISA) to measure saliva cortisol levels. Result: The correlation for the stress scale with the cortisol saliva levels shows a negative correlation with salivary cortisol levels in this study showing a mean value of 67,76 ng/ml and a significance value of 0.000. Conclusion: There is a negative correlation between stress scale and salivary cortisol levels. The more severe the stress, the lowest cortisol saliva level

African colorectal cancer burden in 2022 and projections to 2050.

The burden of colorectal cancer (CRC) is on a rapid increase on the African continent, yet grossly under reported. Herein, we provide and updated estimates of CRC burden (incidence and mortality) across Africa as of 2022, and make crucial predictions to 2050. We gathered information on CRC incidence and mortality from the GLOBOCAN 2022 database, which covers 185 countries. The age-standardised incidence and mortality rates (ASRs) per 100,000 person-years were determined. Cases and deaths up to 2050 were estimated using 2022 incidence and mortality rates. In 2022, an estimated 70,428 cases and 46,087 mortalities due to CRC were recorded across the African continent. Africa's ASRs for CRC incidence and mortality were 8.2 and 5.6 per 100,000 population, respectively, and were highest in North Africa followed by East Africa. At national levels, CRC ranked in the top four of the most commonly diagnosed cancers in more than half (56%) of African countries. ASRs of both incidence and mortality were higher among males than females. New cases are predicted to increase by 139.7% (from 70,428 in 2022 to 168,683 in 2050) at the current incidence rate. Similarly, mortalities will increase by 155.2% (from 46,061 in 2022 to 117,568 in 2050). CRC remains a major cause of morbidity and mortality in many African countries, and the number of new cases and deaths is predicted to rise significantly by 2050. Efforts to reduce the incidence of preventable CRC cases should be prioritised.

Leprosy in Children with a Focus on the Asian Region: A Narrative Review

Abstract Children are often more susceptible to contracting Mycobacterium leprae infection because of their immature immunity as well as their exposure to intrafamily interactions. The percentage of children among newly identified leprosy cases serves as a robust indicator of the ongoing transmission of the disease. Globally, the number of new cases fell by 19.3% between 2013 and 2022. A reduction of over 50% in the incidence of leprosy among children was seen, with rates declining from 10.4 cases per million children in 2013 to 5.1 per million in 2022. Children may exhibit atypical clinical symptoms, and the clinical presentation of leprosy in children can often be misdiagnosed as differential diagnoses such as pityriasis alba, nevus depigmentosus, pityriasis versicolor, among others, contributing to diagnostic difficulties. The cultural stigma surrounding leprosy may also discourage parents from seeking medical help promptly, prolonging the time to diagnosis. Furthermore, health-care systems in some regions may lack adequate training or resources for diagnosing pediatric leprosy. These challenges underscore the importance of increased awareness, early detection, and improved health-care infrastructure to effectively diagnose and treat leprosy in children. Stakeholders must give top priority in policy making and interventions to lower the prevalence of leprosy and its consequences in children and adolescents. The present article is an attempt to review the epidemiological, clinical, and therapeutic dimensions of childhood leprosy cases in Asia.

Generalized logistic model with time-varying parameters to analyze COVID-19 outbreak data

&lt;abstract&gt;&lt;p&gt;Accurately estimating the number of infections that actually occur in the earliest phases of an outbreak and predicting the number of new cases per day in various countries is crucial for real-time monitoring of COVID-19 transmission. Numerous studies have used mathematical models to predict the progression of infection rates in several countries following the appearance of epidemiological outbreaks. In this study, we analyze the data reported and then study several logistical-type phenomenological models and their application in practice for forecasting infection evolution. When several epidemic waves follow one another, it is important to stress that a traditional logistic model cannot necessarily be fully adapted to the data made available. New models are being introduced to simultaneously take account of human behavior, measures taken by the government, and epidemiological conditions. This research used a generalized logistic model based on parameters that vary over time to describe trends in COVID-19-infected cases in countries that have undergone several waves. In two-wave scenarios, the parameters of the model evolve dynamically over time following a logistic function, where the first and second waves are characterized by two extreme values for the early period and the late one, respectively.&lt;/p&gt;&lt;/abstract&gt;

A Multioutput Classifier Model for Breast Cancer Treatment Prediction

A growing number of new cases and fatalities occur each year due to breast cancer, making it the most frequent malignancy globally. Utilizing a multioutput classifier technique with algorithms such as CatBoost, XGBoost, NN, and NN Binary, this work presents a new model for predicting breast cancer treatments: surgery, radiotherapy, and chemotherapy. We tackle the pressing need for accurate medical treatments by developing a model to enhance the predicted accuracy of breast cancer treatment outcomes. The model accomplishes impressive results in predicting surgical outcomes; in particular, Neural Networks (NN and NN Binary) perform exceptionally well in terms of recall and precision, reaching 97% accuracy and 98% F1-scores. While the model's accuracy is only about 63% for radiotherapy, it shows a promising recall of up to 84%. Accuracy and precision in chemotherapy predictions remain stable at 82%, with AUC-ROC values of up to 89%, suggesting excellent discrimination ability. By combining multioutput classifiers with sophisticated algorithms, we hope to make treatment prediction models more tailored to individual breast cancer patient profiles, which might usher in a new era of tailored treatment plans and meet the rising demand for precision medicine in cancer care.

Türkiye'de CoronaVac ile Kovid-19 Aşılama Başlangıcında Sars-Cov-2 Yayılımının Matematiksel Modellenmesi

The Sars-CoV-2 virus, first detected in Wuhan, China, became a global crisis that affected the entire world and was declared a pandemic by the World Health Organization (WHO) in March 2020. The most basic protective measure in the fight against pandemics facing humanity is vaccination. From this point of view, data is collected between January 13 and February 11, 2021 by taking the number of daily cases, deaths and recovered patients in Türkiye. During this period, vaccination against Covid-19 with Sinovac's CoronaVac vaccine is started in Türkiye. Mathematical predictive models of the observed values are constructed and compared using polynomial regression (up to the 3rd degree) and nonlinear regression, i.e., curve fitting methods, and SIR (Susceptible-Infected-Removed), which is a system of ordinary differential equations (ODEs). The efficiencies of these prediction models are tested, validated, and the most effective mathematical prediction models are proposed. The values of root mean square error (RMSE) and mean absolute percentage error (MAPE) are used as performance measures to compare the methods. The proposed prediction models are also used for forecasting. The number of new cases occurring each day is predicted using the time-dependent equations of the SIR method, which are solved using the Euler method. It is found that the SIR method is quite successful in predicting the observed values compared to the other methods, but the QR method are given more successful results in predicting the total number of deaths

Optimization of correction factors for WBS in SARS-COV-2 uncertainty factors

Effluent-based epidemiological methods based on effluent analysis are a new method to study drug consumption. In this study, CF was reestimated and improved by establishing a reasonable model to improve the accuracy of the WBE method. Before analyzing the obtained data, we need to eliminate errors and their blank data. Then, after downloading the exported data, knowing that there were 1099 sewers in the data downloaded on CDC, the data table was filtered, the blank data was excluded, and the data from April 24, 2022 to May 8, 2022 was selected as the percentage change in SARS-CoV-2RNA levels at 15-day intervals; Publication of SARS-CoV-2 concentration data sheets in wastewater in nuclear-weapon States. The data on CoV-2 concentration in wastewater were screened and eliminated, deleted from the blank data of SARS-CoV-2 virus concentration, and classified according to the measured serial number of sewers, and the SARS-CoV-2 virus concentration data of a total of 451 sewers were summarized. Then select based on the heatmap of the number of new cases in each state in the last 30 days, and based on the heatmap of the number of new cases in each state in the last 30 days, select the abbreviated state name of CA, CT, ID, IL, IL, KS, KY, MA, MI, MO, NE, NV, New York, Ohio, Oregon, TX, UT, VA, WA, WV, WI. Analyze the data. Finally, based on K-means clustering, linear regression and bootstrap algorithms, combined with relevant parameters, normal distribution and bias distribution, a new CF is re-established to improve the accuracy of WBE.

Investigation on The Epidemic Dynamics of Esophageal Cancer and Changes in Dietary Structure of Residents in China

Write a summary and analysis of the pattern of changes in dietary structure and esophageal cancer risk factors in the Chinese population during the previous ten years. Analyze the association between the trend of esophageal cancer changes and dietary structure in the Chinese population by compiling statistics on the consumption of different foods by Chinese residents over the course of two time periods in the previous ten years. Residents' dietary patterns have changed significantly, with a rise in the consumption of beef, lamb, chicken, salt, and edible oil, while a decrease in the use of grains. The incidence rate and mortality of esophageal cancer have decreased in China over the past few decades, but the number of new cases and fatalities is gradually increasing. In order to reduce the occurrence of esophageal cancer, active intervention is needed to change residents' eating patterns. eating structure is intimately linked to the development of tube cancer.

Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN

Esophageal cancer is a common malignant tumor in daily life, which seriously affects human health. Esophageal cancer in China. The incidence rate is among the highest in the world, and there are a large number of new cases of esophageal cancer every year. At present, the diagnosis of esophageal cancer is mainly based on the use of electronic gastroscopy, which reflects the observed situation on the fluorescent screen and conducts detection through the fluorescent screen. With the increasing number of patients, the pressure and intensity of the doctor's work are getting greater and greater[1]. From the perspective of molecular level, the occurrence and development of esophageal cancer, like other cancers, are related to the activation of proto-oncogenes and the inhibition of anti-apoptosis genes. CBAM Faster R-CNN is proposed to solve the problems such as the esophageal region is not obvious and the background region occupies a large proportion in the feature map obtained by the backbone network of Faster R-CNN in barium meal imaging. CBAM is added to the convolutional attention module CBAM on the basis of the original Faster R-CNN model. To enhance the saliency of the features of the esophageal region in the feature map. CBAM Faster R-CNN model was used to train the training set after data enhancement, and Recall, Precision and AP values were used for evaluation and analysis.

A Review on the Role of Rodents in the Transmission of Emerging Zoonotic Bacterial Diseases

Emerging infectious diseases (EIDs) are those that are either newly discovered in a community or those that are already there but are rapidly spreading geographically or becoming more frequent. Bacteria are considered to be the source of 54% of newly EIDs, and 175 pathogenic species are connected to diseases that are suspected of emerging. Emergent infectious diseases are those that have either not yet been identified and are impacting a population, or those that are currently present but are rapidly expanding to new regions or generating a high number of new cases within an existing population. Emerging diseases also include infectious diseases that once affected a region, declined, or were under control but are returning more frequently. Moreover, EIDs are infections that were formerly limited to a particular area, declined, or were under control but are now manifesting more frequently.

Application of GIS and spatiotemporal analyses in viral infection modelling using multiple datasets – A case study on the SARS-CoV-2 epidemic

Introduction/objectiveViral and infectious diseases such as COVID-19 continue to pose a significant public health threat. In order to create an early warning system for new pandemics or emerging versions of the virus, it is imperative to study its epidemiology. In this study, we created a geospatial model to predict the weekly contagion and lethality rates of COVID-19 in Ireland. MethodsMore than forty parameters including atmospheric pollutants, metrological variables, sociodemographic factors, and lockdown phases were introduced as input variables to the model. The significant parameters in predicting the number of new cases and the death toll were identified. QGIS software was employed to process input data, and a principal component regression (PCR) model was developed using the statistical add-on XLSTAT. Results and conclusionsThe developed models were able to predict more than half of the variations in contagion and lethality rates. This indicates that the proposed model can serve to help prediction systems for the identification of future high-risk conditions. Nevertheless, there are additional parameters that could be included in future models, such as the number of deaths in care homes, the percentage of contagion and mortality among health workers, and the degree of compliance with social distancing.