Prediction of Survival COVID-19 Patients by Using Backpropagation Neural Network Algorithm

COVID-19 is a disease caused by a virus and increasing in cases every day. This is because the large number of patients makes it difficult to be treated at the hospital. This is behind the need for survival prediction of COVID-19 patients within 48 days so that the medical team can prioritize patients who are predicted to not survive on that period. In this research, the firefly algorithm is used which aims to select attributes and will perform comparisons for data that is balance or imbalance and combined with data that do feature selection and does not feature selection. The data that will be used are age, asthma, diabetes, gender, COPD, pregnancy, hypertension, obesity, ICU, chronic kidney disease, smoking, heart disease, immune deficiency, pneumonia, and other medical history. In this research, the selected attributes were gender, type of patient, intubation, pneumonia, age, pregnancy, diabetes, COPD (Chronic Obstructive Pulmonary Disease), asthma, hypertension, other diseases, obesity, chronic kidney disease, smokers, contact with COVID patients, and ICU. The prediction model with the highest level of performance is a model with balanced data with a recall value of 0.79, then a precision value of 0.93, then an f score of 0.85, then an accuracy value of 0.86, then a specificity 0,93, then a NPV 0,82 and a geometric mean value of 0.87 © 2022 IEEE.

Comparison of Naïve Bayes, C4.5 and K-Nearest Neighbor for Covid-19 Data Classification

Covid-19 is a new virus that appeared in the city of Wuhan in 2019. This virus spreads very quickly even to Indonesia. One effort that can be done to detect the presence of this virus is the PCR and antigen test. Increasing this case resulted in a medical team having difficulty detecting suspects exposed to viruses. This research was conducted to find the best classification algorithm in predicting and classifying status on the suspected Covid-19 both exposed or not exposed. The method used in this study is Naïve Bayes, C4.5 and K-Nearest Neighbor which have very high accuracy using secondary data from the Dumai City Health Agency. From this study it was found that the algorithm C4.5 as the best algorithm in predicting the status of COVID-19 patients, especially in the city of Dumai with an accuracy of 86.54%, recall 71.51%and precision 85.14%. This study has implications for further researchers in choosing an algorithm to predict the COVID-19 case. © 2022 IEEE.

Use of mobile medical teams to fill critical gaps in health service delivery in complex humanitarian settings, 2017-2020: a case study of South Sudan.

The vulnerable populations in the protracted humanitarian crisis in South Sudan are faced with constrained access to health services and frequent disease outbreaks. Here, we describe the experiences of emergency mobile medical teams (eMMT) assembled by the World Health Organization (WHO) South Sudan to respond to public health emergencies. Interventions: the eMMTs, multidisciplinary teams based at national, state and county levels, are rapidly deployed to conduct rapid assessments, outbreak investigations, and initiate public health response during acute emergencies. The eMMTs were deployed to locations affected by flooding, conflicts, famine, and disease outbreaks. We reviewed records of deployment reports, outreach and campaign registers, and analyzed the key achievements of the eMMTs for 2017 through 2020. Achievements: the eMMTs investigated disease outbreaks including cholera, measles, Rift Valley fever and coronavirus disease (COVID-19) in 13 counties, conducted mobile outreaches in emergency locations in 38 counties (320,988 consultations conducted), trained 550 healthcare workers including rapid response teams, and supported reactive measles vaccination campaigns in seven counties [148,726, (72-125%) under-5-year-old children vaccinated] and reactive oral cholera vaccination campaigns in four counties (355,790 vaccinated). The eMMT is relevant in humanitarian settings and can reduce excess morbidity and mortality and fill gaps that routine health facilities and health partners could not bridge. However, the scope of the services offered needs to be broadened to include mental and psychosocial care and a strategy for ensuring continuity of vaccination services and management of chronic conditions after the mobile outreach is instituted.

The effect of dispatch of national medical teams to Wuhan on its control and prevention of COVID-19.

OBJECTIVES: As a unique prevention and control measure, the dispatch of national medical teams to Wuhan has played a key role in protecting Wuhan against COVID-19. This study aimed to quantitatively evaluate the effect of this key measure in reducing infections and fatalities. STUDY DESIGN: A scenario analysis is used in this study, where the forming of scenarios is on the basis of the stages of medical to Wuhan. We divided the evaluation into 4 scenarios: Scenario Ⅰ-no dispatch, Scenario Ⅱ-dispatch of 4599 medical staff, Scenario Ⅲ-dispatch of 16,000 staff, and Scenario Ⅳ-dispatch of 32,000 staff. METHODS: The extended Susceptible-Exposed-Infectious-Recovered-Death model was adopted to quantify the effect of the dispatch of national medical teams to Wuhan on COVID-19 prevention and control. RESULTS: The dispatch dramatically cuts the channels for the transmission of the virus and succeeds in raising the cure rates while reducing the fatality rates. If there were no dispatch at all, a cumulative total of 158,881 confirmed cases, 18,700 fatalities and a fatality rate of 11.77% would have occurred in Wuhan, which are 3.2 times, 4.8 times and 1.5 times the real figures respectively. The dispatch has avoided 108,541 confirmed cases and 14,831 fatalities in this city. CONCLUSIONS: The proven successful measure provides valuable experience and enlightenment to international cooperation on prevention and control of COVID-19, as well as a similar outbreak of new emerging infectious diseases.

RETROSPECTIVE ANALYSIS OF INTERVENTIONS PERFORMED BY EMERGENCY MEDICAL TEAMS IN POLAND BEFORE AND DURING THE SARS COV-2 PANDEMIC.

OBJECTIVE: Introduction: The SARS-CoV-2 virus was recognized in December 2019 in China. From that moment it has quickly spread around the whole world. It causes COVID-19 disease manifested by breathlessness, coughing and high temperature. The COVID-19 pandemic has become a great challenge for humanity. The aim: To analyze interventions of emergency medical teams during the SAR-CoV-2 pandemic, and to compare obtained data with the same periods in 2018-2019. PATIENTS AND METHODS: Material and methods: The study retrospectively analyzed interventions of emergency medical teams in the period from 15.03 to 15.05 in 2018 - 2020. 1,479,530 interventions of emergency medical teams were included in the study. The number of interventions, reasons for calls, and diagnoses made by heads of the emergency medical teams during the SARS-CoV-2 pandemic were compared to the same period in 2018-2019. RESULTS: Results: Authors observed the decline in the number of interventions performed by emergency medical teams during the pandemic in relation to earlier years by approximately 25%. The big decline concerned interventions that were the reason for calls to public places, such as "traffic accident" and "collapse". In the case of diagnoses made by the head of the emergency medical team, the diagnoses regarding stroke or sudden cardiac arrest remained at the similar level. Others showed a marked decline. CONCLUSION: Conclusions: Reduced social activity contributed to a reduced number of interventions by emergency medical teams in public places. The societal fear of the unknown also contributed to the decrease in the number of interventions performed by emergency medical teams. People began to avoid contact with other people.