Comparison of Active COVID-19 Cases per Population Using Time-Series Models

This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the thirty-six different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered and death cases as of 4 November 2020 of COVID-19 and populace of each state. A 14-multi step ahead forecast system for active coronavirus cases was built, analyzed and compared for six (6) different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.

Comparison of Active COVID-19 Cases Per Population Using Time-Series Models

This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the thirty-six different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered and death cases as of 4 November 2020 of COVID-19 and populace of each state. A 14-multi step ahead forecast system for active coronavirus cases was built, analyzed and compared for six (6) different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.

Predicting the death rate around the world due to Covid-19 using regression analysis

Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of Infected Individuals, and the number of Recovered Individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on 4 different productive rates i.e. 2.45, 2.55, 2.65, and 2.75. The analysis is done on two different datasets; the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12: 59,450,123 new cases and 432,499,003 total cases and 10,928,383 deaths.

Pattern Of Infections In Custodial Deaths – A Forensic Autopsy Study

Background: In a country like India, the prisoner cells are not well structured. For instance prisons are known to be a high risk environment for infections like tuberculosis (TB), HIV, HCV, HBV etc. due to overcrowding, low levels of nutrition, poor infection control and lack of accessible healthcare services. This study is an effort to know about the pattern of infections in custodial death cases detected during post mortem examination. Aims and Objectives: To determine the common infections and to know the mortality caused by infections among prisoners in custody, people in police custody and mentally ill patients in mental health institute in Punjab.Methods:This cross sectional study of 100 cases of custodial deaths from 1st Jan 2019 to 4thMay 2021, was carried out in the Forensic Medicine department in collaboration with department of Microbiology, Govt. Medical College, Amritsar.Results:Klebsiella Pneumoniae (37.5%) is more common in age group of 10-30 years while Staphylococcus Aureus (35.9%) infection was more common in the age group of 31 to 50 years. Incidence of positive growth of infections was slightly more in female inmates (76.9%) as compared to the males (75.9%). Inmates from urban area background showed more growth of Staphylococcus Aureus (35.7%) than from rural area background(25%).Conclusions:Timely medical diagnosis and treatment of each prisoner with availability of good doctors are the important issues relating to the healthcare of the individuals in custody.

A Forensic Autopsy Study of Dead Foetuses and Newborns

Background: The term abandonment refers to babies or foetuses which are found abandoned at various unwanted places such as gutter, rubbish dumps, railway tracts and bushes. It does not refer to live born babies left in places, such as hospitals where care can be given by someone other than the mother. Aims and Objectives: To find out the distribution of death cases of newborns/feotuses and to trace its probable reason and its relationship with female foeticide.Methods:A retrospective study of all the medico-legal autopsies of foetuses and newborns was conducted in Forensic medicine and Toxicology department, Government Medical College, Amritsar (Punjab) from Jan 1, 2014 to Jul 31, 2021. During this period, 46 cases of fetal and newborn deaths had been studied.Results:The dead bodies of known foetuses/newborns is 43.5% cases while total unknown cases were 56.5% cases. 32.6% cases were non viable foetuses while 10.8% cases died as a result of prematurity. All the unknown cases (56.5%) were found from the abandoned places like street, bushes, canal side, water bodies that mainly includes pond and railway tract.Conclusion:Despite, the problem is present in every corner of the nation, there is dearth of research studies on this issue. Stringent measures and strict checks are required against antenatal sex determination. The motive behind the abandonment of foetuses can be any, but this grave issue needs urgent attention.

Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model

Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study’s selected countries.

The risk of fatal bleeding complications in jugular catheterization in patients with coagulopathy: A retrospective analysis of death cases in closed claims and the Medical Accident Investigating System in Japan

Background To prevent recurrence of medical accidents, the Medical Accident Investigating System was implemented in October 2015 by the Japan Medical Safety Research Organization (Medsafe Japan) to target deaths from medical care that were unforeseen by the administrator. Medsafe Japan analyzed the 10 cases of central venous catheterization-related deaths reported in the system and published recommendations in March 2017. However, the particular emphasis for the prevention of central venous catheterization-related deaths is unclear. Methods This study aimed to identify the recommendation points that should be emphasized to prevent recurrence of central venous catheterization-related deaths. We assessed central venous catheterization in 8530 closed-claim cases between January 2002 and December 2016 covered by the medical insurer Sompo-Japan. Moreover, we compared central venous catheterization-related death in closed-claim cases with death in reported cases. Results The background, error type, anatomic insertion site, and fatal complication data were evaluated for 37 closed-claim cases, of which 12 (32.4%) were death cases. Of the 12 closed-claim cases and 10 reported cases, 9 (75.0%) closed-claim cases and 9 (90.0%) reported cases were related to vascular access. Among these, 5 closed-claim cases (41.7%) and 7 reported cases (77.8%) were related to internal jugular vein catheterization (p = 0.28). Coagulopathy was observed in 3 (60.0%) of 5 closed-claim cases and 6 (85.7%) of 7 reported cases. Conclusions The risk of internal jugular catheterization in patients with coagulopathy must be carefully considered.

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

Delineation of public health risk factor associate with the mortality ratio in covid-19 pandemic situation during legislative assembly elections from Indian states

The coronavirus is an accurate disease and this virus-related pandemic is hammering human health and increased the public health emergency till now. The main objective of this study is to find out the death, mortality ratio, new cases, and recoveries case identification and correlation analysis between them using regression technique on legislative assembly elections from India. This study encompassed the present disorder of India throughout the elections time in India from 27th March 2021 to 29th April 2020. Statistical analysis was developed by the covid-19 database for monitoring and analyzing the health statutes during elections. Mortality ratio, the relation between active and death cases, active cases and recover cases in India are calculating corona affected data. The results show that death cases were high in the second wave of coronavirus in India. The correlation between daily death and new cases was strong positive (R2= 0.9306). The relationship between recoveries and death was stronger positive (R2=0.9832). The daily death and active cases collation indicated that strong positive (R2= 0.9703). The COVID-19 is dangerous to people's health. The virus is more life-threatening and if people will not follow the WHO guidelines, and it strength demonstration additional havoc very shortly.

Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.