Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases

Pandemics, Income Inequality, and Refugees: The Case of COVID-19

Refugees are more vulnerable to COVID-19 due to factors such as low standard of living, accommodation in crowded households, difficulty in receiving health care due to high treatment costs in some countries, and inability to access public health and social services. The increasing income inequalities, anxiety about providing minimum living conditions, and fear of being unemployed compel refugees to continue their jobs, and this affects the number of cases and case-related deaths. The aim of the study is to analyze the impact of refugees and income inequality on COVID-19 cases and deaths in 95 countries for the year 2021 using Poisson regression, Negative Binomial Regression, and Machine Learning methods. According to the estimation results, refugees and income inequalities increase both COVID-19 cases and deaths. On the other hand, the impact of income inequality on COVID-19 cases and deaths is stronger than on refugees.

Impact of ICU strain on outcomes

PURPOSE OF REVIEW

Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes.

RECENT FINDINGS

This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions.

SUMMARY

An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.

Optimising health system capacity: A case study of community care staff's role transition in response to the coronavirus pandemic

The coronavirus disease (COVID-19) increased the demand for critical care spaces and the task for individual countries was to optimise the capacity of their health systems. Correlating governance and health system capacity to respond to global crises has subsequently garnered the pace in reviewing normalised forms of identifying health priorities. Aligning global health security and universal health security enhances the capacity and resilience of a health system. However, weak methods of governance hinder the alignment necessary for controlling infection spread and coping with the increase in demand for hospital critical care. A range of qualitative studies has explored staff experiences of providing care in hospitals amidst the COVID-19 pandemic. Nonetheless, limited understanding of the influence of governance on health and social care staff experiences in response to the COVID-19 pandemic exists. This case study aimed to explore the influence of health system governance on community care staff experiences of role transition in response to the COVID-19 pandemic in England. We used criterion sampling to include community care staff initially recruited to deliver a community integrated model of dementia care at two facilities repurposed in March 2020 to optimise hospital critical care space. Six community care staff participated in the narrative correspondence inquiry. A lack of control over resources, limitations in collective action in decision making and lack of a voice underpinned staff experiences of role transition in contexts of current crisis preparedness, transition shock and moral dilemmas. Health system governance influenced the disposition of community care staff's role transition in response to the COVID-19 pandemic. Staff's mere coping clouds the glass of wider issues in health system governance and capacity. The normative dominance that the control over resources and centrally determined health system priorities ordain require reviewing to enable optimal health and social care cross systems' capacity and resilience.

The Impact of Refugees on Income Inequality in Developing Countries by Using Quantile Regression, ANN, Fixed and Random Effect

Refugees affect the hosting countries both politically and economically, but the size of impact differs among these societies. While this effect emerges mostly in the form of cultural cohesion, security, and racist discourses in developed societies, it mostly stands out with its economic dimension such as unemployment, growth, and inflation in developing countries. Although different reflections exist in different societies, the reaction is expected to be higher if it affects social welfare negatively. Accordingly, one of the parameters that should be addressed is the effect of refugees on income distribution since the socio-economic impact is multifaceted. In this study, the effect of refugees on income inequality is analyzed by using quantile regression with fixed effects and Driscoll–Kraay Fixed Effect (FE)/Random Effect (RE) methods for the period of 1991 to 2020 in the 25 largest refugee-hosting developing countries. According to the findings of the study, the functional form of the relationship between refugees and income inequality in the countries is N-shaped. Accordingly, refugees first increase income inequality, decrease it after reaching a certain level, and then start increasing it, albeit at a low level.

Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning-A Scoping Review

The SARS-CoV-2 pandemic has put unprecedented pressure on the hospital sector around the world. It has shown the importance of preparing and planning in the future for an outbreak that overwhelms every aspect of a hospital on a rapidly expanding scale. We conducted a scoping review to identify, map, and systemize existing knowledge about the relationships between COVID-19 and hospital infrastructure adaptation and capacity planning worldwide. We searched the Web of Science, Scopus, and PubMed and hand-searched gray papers published in English between December 2019 and December 2021. A total of 106 papers were included: 102 empirical studies and four technical reports. Empirical studies entailed five reviews, 40 studies focusing on hospital infrastructure adaptation and planning during the pandemics, and 57 studies on modeling the hospital capacity needed, measured mostly by the number of beds. The majority of studies were conducted in high-income countries and published within the first year of the pandemic. The strategies adopted by hospitals can be classified into short-term (repurposing medical and non-medical buildings, remote adjustments, and establishment of de novo structures) and long-term (architectural and engineering modifications, hospital networks, and digital approaches). More research is needed, focusing on specific strategies and the quality assessment of the evidence.

Analyzing of Alzheimer’s Disease Based on Biomedical and Socio-Economic Approach Using Molecular Communication, Artificial Neural Network, and Random Forest Models

Alzheimer’s disease will affect more people with increases in the elderly population, as the elderly population of countries everywhere generally rises significantly. However, other factors such as regional climates, environmental conditions and even eating and drinking habits may trigger Alzheimer’s disease or affect the life quality of individuals already suffering from this disease. Today, the subject of biomedical engineering is being studied intensively by many researchers considering that it has the potential to produce solutions to various diseases such as Alzheimer’s caused by problems in molecule or cell communication. In this study, firstly, a molecular communication model with the potential to be used in the treatment and/or diagnosis of Alzheimer’s disease was proposed, and its results were analyzed with an artificial neural network model. Secondly, the ratio of people suffering from Alzheimer’s disease to the total population, along with data of educational status, income inequality, poverty threshold, and the number of the poor in Turkey were subjected to detailed distribution analysis by using the random forest model statistically. As a result of the study, it was determined that a higher income level was causally associated with a lower risk of Alzheimer’s disease.

Thermoluminescence Characteristic of Calcite with Gaussian Process Regression Model of Machine Learning

Thermoluminescence (TL) is defined as a luminescent phenomenon that can be detected when an insulator or semiconductor is thermally stimulated. Defective crystals store radiation until they are stimulated. Thermoluminescence is a method of monitoring the absorbed dose of dosimeters. The irradiation crystal is heated to 500 °C to display the absorbed dose as a luminescent light. The thermoluminescence dosimetric properties of calcite obtained from nature were investigated in this study. Machine learning (ML) was also examined utilizing Gaussian process regression (GPR) for stimulated TL characteristics. According to the experimental output, the TL glow curve has two main peaks located at 90 ^o C and 240 ^o C with good dosimetric properties. In the four regression models of GPR, the data of heating rate of 3 ^o C/sec has the lowest residual.

Investigation of Statistical Machine Learning Models for COVID-19 Epidemic Process Simulation: Random Forest, K-Nearest Neighbors, Gradient Boosting

COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results of their forecasting. The models developed are based on Random Forest, K-Nearest Neighbors, and Gradient Boosting methods. The models were studied for the adequacy and accuracy of predictive incidence for 3, 7, 10, 14, 21, and 30 days. The study used data on new cases of COVID-19 in Germany, Japan, South Korea, and Ukraine. These countries are selected because they have different dynamics of the COVID-19 epidemic process, and their governments have applied various control measures to contain the pandemic. The simulation results showed sufficient accuracy for practical use in the K-Nearest Neighbors and Gradient Boosting models. Public health agencies can use the models and their predictions to address various pandemic containment challenges. Such challenges are investigated depending on the duration of the constructed forecast.

Socio-Economic, Demographic and Health Determinants of the COVID-19 Outbreak

OBJECTIVE

In this study, the effects of social and health indicators affecting the number of cases and deaths of the COVID-19 pandemic were examined. For the determinants of the number of cases and deaths, four models consisting of social and health indicators were created.

METHODS

In this quantitative research, 93 countries in the model were used to obtain determinants of the confirmed cases and determinants of the COVID-19 fatalities.

RESULTS

The results obtained from Model I, in which the number of cases was examined with social indicators, showed that the number of tourists, the population between the ages of 15 and 64, and institutionalization had a positive effect on the number of cases. The results obtained from the health indicators of the number of cases show that cigarette consumption affects the number of cases positively in the 50th quantile, the death rate under the age of five affects the number of cases negatively in all quantiles, and vaccination positively affects the number of cases in 25th and 75th quantile values. Findings from social indicators of the number of COVID-19 deaths show that life expectancy negatively affects the number of deaths in the 25th and 50th quantiles. The population over the age of 65 and CO2 positively affect the number of deaths at the 25th, 50th, and 75th quantiles. There is a non-linear relationship between the number of cases and the number of deaths at the 50th and 75th quantile values. An increase in the number of cases increases the number of deaths to the turning point; after the turning point, an increase in the number of cases decreases the death rate. Herd immunity has an important role in obtaining this finding. As a health indicator, it was seen that the number of cases positively affected the number of deaths in the 50th and 75th quantile values and the vaccination rate in the 25th and 75th quantile values. Diabetes affects the number of deaths positively in the 75th quantile.

CONCLUSION

The population aged 15-64 has a strong impact on COVID-19 cases, but in COVID-19 deaths, life expectancy is a strong variable. On the other hand, it has been found that vaccination and the number of cases interaction term has an effect on the mortality rate. The number of cases has a non-linear effect on the number of deaths.

Classification of thermoluminescence features of CaCO3 with long short-term memory model

Calcium carbonate (CaCO3 ), a mineral commonly found in the Earth's crust, is mainly in the forms of calcite and aragonite. Calcite has the most stable type of thermodynamics at room temperature and ambient pressure. It has wide band gap structure and is of great interest for a wide-range technical and industrial applications due to its physical properties and suitability. In this study, a new method based on the long short-term memory (LSTM) model of deep learning is proposed to classify the thermoluminescence properties such as fading, cycle of measurement, heating rate, and dose-response of CaCO3 . Therefore the thermoluminescence properties of calcite was investigated as a suitable band structure and its coherent data were used to classify the features using a deep learning LSTM model. Experiments were carried out on a dataset consisting of four classes. The accuracy, precision, and sensitivity values of the proposed model obtained were 98.34, 97.90, and 98.56%, respectively. The classification process of the results obtained from the designed model showed good performance.