Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020
COVID-19; disease control; epidemic; epidemiology; fuzzy mathematics; genetic algorithm; infectivity; mortality; prediction; viral disease; Article; calibration; coronavirus disease 2019; epidemic; forecasting; fuzzy system; genetic algorithm; human; Malaysia; model; mortality; prediction; uncertain...
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my.uniten.dspace-254642023-05-29T16:09:44Z Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020 Alsayed A. Sadir H. Kamil R. Sari H. 57211634600 57215962794 26654785100 57203980452 COVID-19; disease control; epidemic; epidemiology; fuzzy mathematics; genetic algorithm; infectivity; mortality; prediction; viral disease; Article; calibration; coronavirus disease 2019; epidemic; forecasting; fuzzy system; genetic algorithm; human; Malaysia; model; mortality; prediction; uncertainty; validation process; Betacoronavirus; Coronavirus infection; health survey; pandemic; pathogenicity; public health service; quarantine; virus pneumonia; Malaysia; Coronavirus; Betacoronavirus; Coronavirus Infections; Forecasting; Health Surveys; Humans; Malaysia; Pandemics; Pneumonia, Viral; Public Health Administration; Quarantine The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible�Exposed�Infectious�Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 � 0.013, while the basic reproductive number is 2.28 � 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July�11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic. � 2020 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T08:09:44Z 2023-05-29T08:09:44Z 2020 Article 10.3390/ijerph17114076 2-s2.0-85086356262 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086356262&doi=10.3390%2fijerph17114076&partnerID=40&md5=856dcecf42be227e1bf30e3b5395563b https://irepository.uniten.edu.my/handle/123456789/25464 17 11 4076 1 15 All Open Access, Gold, Green MDPI AG Scopus |
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COVID-19; disease control; epidemic; epidemiology; fuzzy mathematics; genetic algorithm; infectivity; mortality; prediction; viral disease; Article; calibration; coronavirus disease 2019; epidemic; forecasting; fuzzy system; genetic algorithm; human; Malaysia; model; mortality; prediction; uncertainty; validation process; Betacoronavirus; Coronavirus infection; health survey; pandemic; pathogenicity; public health service; quarantine; virus pneumonia; Malaysia; Coronavirus; Betacoronavirus; Coronavirus Infections; Forecasting; Health Surveys; Humans; Malaysia; Pandemics; Pneumonia, Viral; Public Health Administration; Quarantine |
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57211634600 |
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57211634600 Alsayed A. Sadir H. Kamil R. Sari H. |
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Article |
| author |
Alsayed A. Sadir H. Kamil R. Sari H. |
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Alsayed A. Sadir H. Kamil R. Sari H. Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020 |
| author_sort |
Alsayed A. |
| title |
Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020 |
| title_short |
Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020 |
| title_full |
Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020 |
| title_fullStr |
Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020 |
| title_full_unstemmed |
Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020 |
| title_sort |
prediction of epidemic peak and infected cases for covid-19 disease in malaysia, 2020 |
| publisher |
MDPI AG |
| publishDate |
2023 |
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1806426633077260288 |
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13.214268 |