A Supervised Multi-tree XGBoost Model for an Earlier COVID-19 Diagnosis Based on Clinical Symptoms

Efficient screening of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) enables quick and efficient diagnosis of SARS-CoV-2 and can mitigate the burden on healthcare systems. The aim was to assist the medical team globally in triaging incoming patients, especially in countries with limited healthcare infrastructure. In this context, the features with imminent infection risk (Test Indication, Fever, and Headache) were obtained using a multi-tree XGBoost algorithm. Based on their feature importance, the top three clinically relevant earlier clinical symptoms (attributes) were employed to create a Multi-tree XGBoost-based model for an earlier prediction of SARS-CoV-2. Overall, our Multi-tree XGBoost model predicted SARS-CoV-2 infection status with a high F1-score (0.9920 +/- 0.008) and AUC value (0.9974 +/- 0.0026) only by assessing the primary three clinical symptoms related to COVID-19 infection. Thus our multi-tree XGBoost - based model suggests a simple and accurate method for earlier detection of SARS-CoV-2 cases and initiating proper treatment protocol for SARS-CoV-2 positive patients. Therefore, we can conclude that our model will allow the health organizations to potentially reduce the infection rate and mortality in masses with COVID-19 infection and fatality due to SARS-CoV-2.

COVID-19 vaccine dosages and government factors role on the global variation in COVID-19 mortality: A statistical and regression analysis

The objective of our study was to explore the influence of the current vaccination program and other relevant government factors to explain the variation in COVID-19 mortality in the world. The study involves a cross-sectional survey of COVID-19 related and government factors from 161 countries. We retrieved and processed publically available coronavirus pandemic data (July 17, 2021) from several online databases, excluding countries' data violating correlation and regression analysis assumptions. In addition, partial correlations studies and multivariate analysis were performed to explore the influence current vaccination program and other relevant government factors on the relationship between the explanatory variable and the total deaths due to COVID-19. The partial-correlation studies revealed that controlling for a complete dosage of COVID-19 vaccine per 100 people in the population had a significant (P<0.001) impact on the strength of the relationship between some explanatory variables and the response variable (total COVID-19 mortality). Furthermore, the Stepwise Linear Regression (SLR) model shows that the covariates, namely total_cases, hospital patients per million, hospital beds per thousand, male smokers, and people fully vaccinated per hundred, added significantly (P<0.001) to the prediction of the response variable. Our SLR model validation study revealed that the observed total COVID-19 mortality was highly correlated with the predicted total COVID-19 mortality in various countries (r = 0.977, P<0.001). Our Stepwise Linear Regression model performs significantly better with an R-squared value of 0.958 and adjusted R-squared value of 0.956 than other related regression models built to predict COVID-19 mortality. Based on our current findings, we conclude that governments with better hospital infrastructure and people with complete dosages of the COVID-19 vaccine will have minimal COVID-19 fatalities. © 2022 The Authors.

COVID-19 Pandemic and Maternal Perspectives

Aim Coronavirus (COVID-19) pandemic has affected perinatal women worldwide. Our study aimed to describe the opinions of perinatal women about COVID-19 related knowledge, attitude, and practices. Methods Pregnant and Postnatal women (n=223) were included and those who did not consent, and less than 16 weeks' gestation, were excluded. SPSS version 26 was used for descriptive statistics. Results Most of the women had good knowledge about COVID 19 regarding its nature, transmission, & symptoms. Their information sources were news (139/206=67.5%) and the internet (85/206=41%). Women understood the uncertainty around its effect on pregnancy;as it is a novel infection. A substantial number of women were concerned (130/206=63%), upset by social isolation (86/206=42%), negatively impacted by the visitor restrictions in hospital (154/206=75%), and faced COVID-19 related reduced household finances (97/206=47%). Most of them used hand washing (201/206=98%) & social distancing (191/206=93%) as preventive measures. They reported compromised contact with General Physician (GP) service as compared to the hospital service (85/206=41% Vs 31/206=15% respectively) during the pandemic. Conclusions The main challenges of the COVID-19 pandemic for perinatal women are the jeopardized GP & hospital services & psychological distress. It is imperative to incorporate telemedicine & virtual visits to tackle the burden of the COVID-19 pandemic. Perinatal women, are particularly vulnerable to the psychological impacts of the COVID-19 pandemic & societal lockdown, thus necessitating holistic interventions.