Class prediction of the prevalent transmission mode of COVID-19 within a geographic area
International Journal of Medical Engineering and Informatics
; 15(2):120-130, 2022.
Artículo
en Inglés
| EMBASE | ID: covidwho-2312716
ABSTRACT
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.
community transmission; covid-19; multi-class prediction; predictive model; transmission mode; article; coronavirus disease 2019; geographic distribution; k fold cross validation; learning algorithm; mathematical model; multinomial logistic regression; prediction; prevalence; sporadic disease; support vector machine; validation process; virus transmission
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
EMBASE
Tipo de estudio:
Estudio pronóstico
Idioma:
Inglés
Revista:
International Journal of Medical Engineering and Informatics
Año:
2022
Tipo del documento:
Artículo
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