Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining

The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 records of patient, identified the most common symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively

STEM Undergraduate Research Symposium 2016 Full Program

Full Program of the 2016 LSSF STEM Undergraduate Research Conference

Bio-surveillance Capability Requirements for the Global Health Security: Study on Epidemiological Differences of COVID-19 Cases

Background: Just eleven months after the first reported COVID-19 infection, the global tally has surpassed 60 million cases with a global death toll standing at 1.4 million. Even though with the launch of the Global Health Security Agenda in 2014, only 67 countries came under the umbrella of this agenda and trying to exchange as well as integrate various strengths to fight against massive threats of multiple infectious diseases. The current Covid-19 pandemic basically exposed the paucity of capacities and capabilities of nations’ Bio-surveillance System, even the so-called developed ones. Method: Cross-sectional study was carried out within the time period of the 16th September - 30th November 2020, taking into account the secondary data of COVID-19 patients up to 22nd April 2020, in South Korea, Australia, & England as sample population. After the extensive analysis of the data-driven from the authorized websites of the three countries - the Incidence Rate (%) and Case Fatality Rate (%) according to age and sex groups were compared along with Crude Incidence Rate, Crude Mortality Rate, Age & Sex-Specific Incidence Rate, Age & Sex-Specific Mortality Rate, Age & Sex Adjusted Incidence rate, Age & Sex Adjusted Mortality rate, by plotting into charts and graphs. Results: In the case of all three countries, Incidence Rates are increasing with the increase in age of the population. Except for the female of South Korea, the incidence of COVID-19 in both the other two countries were high in case of the male population. the mortality rate of male patients was higher than female patients in all age groups in all three countries. In the case of England, the Incidence Rate (%) and Case Fatality Rate (%) according to age and sex groups along with Crude Incidence Rate, Crude Mortality Rate, Age & Sex-Specific Incidence Rate, Age & Sex-Specific Mortality Rate, Age & Sex Adjusted Incidence rate, Age & Sex Adjusted Mortality Rate all are 30 to more than 100 times higher than Australia and South Korea. Australia shows the lowest in COVID-19 infection and death rates among the countries in all aspects. Conclusion: This study shows the gaps of currently available bio-surveillance methods leading to an uncontrolled and unprecedented surge of the ongoing COVID-19 contagion and fatality world wide, driving mankind into an uncertain future. Ameliorating the currently available bio-hazard and disease surveillance system by filling those gaps up along with the help of continuous need-based research and innovations, imply tremendous importance to overcome the current situation and to predict upcoming “Disease-X” threats.open석