RESUMEN
This research developed a multinomial classification model that predicts the prevalent mode of transmission of the coronavirus from person to person within a geographic area, using data from the World Health Organization (WHO). The WHO defines four transmission modes of the coronavirus disease 2019 (COVID-19);namely, community transmission, pending (unknown), sporadic cases, and clusters of cases. The logistic regression was deployed on the COVID-19 dataset to construct a multinomial model that can predict the prevalent transmission mode of coronavirus within a geographic area. The k-fold cross validation was employed to test predictive accuracy of the model, which yielded 73% accuracy. This model can be adopted by local authorities such as regional, state, local government, and cities, to predict the prevalent transmission mode of the virus within their territories. The outcome of the prediction will determine the appropriate strategies to put in place or re-enforced to curtail further transmission.Copyright © 2023 Inderscience Enterprises Ltd.
RESUMEN
This research was aimed to extract association rules on the morbidity and mortality of corona virus disease 2019 (COVID-19). The dataset has four attributes that determine morbidity and mortality;including Confirmed Cases, New Cases, Deaths, and New Deaths. The dataset was obtained as of 2nd April, 2020 from the WHO website and converted to transaction format. The Apriori algorithm was then deployed to extract association rules on these attributes. Six rules were extracted: Rule 1. {Deaths, NewDeaths}=>{NewCases}, Rule 2. {ConfCases, NewDeaths}=>{NewCases}, Rule 3. {ConfCases, Deaths}=>{NewCases}, Rule 4. {Deaths, NewCases}=>{NewDeaths}, Rule 5. {ConfCases, Deaths}=>{NewDeaths}, Rule 6. {ConfCases, NewCases}=>{NewDeaths}, with confidence 0.96, 0.96, 0.86, 0.66, 0.59, 0.51 respectively. These rules provide useful information that is vital on how to curtail further spread and deaths from the virus, both in areas where the pandemic is already ravaging and in areas yet to experience the outbreak. © 2022 IGI Global. All rights reserved.
RESUMEN
The coronavirus disease-2019 (COVID-19) pandemic is an ongoing concern that requires research in all disciplines to tame its spread. Nine classification algorithms were selected for evaluating the most appropriate in predicting the prevalent COVID-19 transmission mode in a geographic area. These include multinomial logistic regression, k-nearest neighbour, support vector machines, linear discriminant analysis, naïve Bayes, C5.0, bagged classification and regression trees, random forest, and stochastic gradient boosting. Five COVID-19 datasets were employed for classification. Predictive accuracy was determined using 10-fold cross validation with three repeats. The Friedman's test was conducted, and the outcome showed the performance of each algorithm is significantly different. The stochastic gradient boosting yielded the highest predictive accuracy, 81%. This finding should be valuable to health informaticians, health analysts, and others regarding which machine learning tool to adopt in the efforts to detect dominant transmission mode of the virus within localities. © This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License
RESUMEN
This study was aimed to extract association rules on the pattern of incidents of the COVID-19 variants of concern (VOC) within geographic areas. The association rules mining technique was deployed on the World Health Organization's data on COVID-19. Five rules were extracted that show which VOC is likely present within a territory given that a particular VOC has already been confirmed present. The extracted rules give an idea on which VOC tourists should expect in a territory, even when not formally confirmed by authorities. The rules will guide tourists on the choices of tourism destinations to ensure their safety.