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Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

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 month...

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Autores principales: Alsayed, Abdallah, Sadir, Hayder, Kamil, Raja, Sari, Hasan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7312594/
https://www.ncbi.nlm.nih.gov/pubmed/32521641
http://dx.doi.org/10.3390/ijerph17114076
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author Alsayed, Abdallah
Sadir, Hayder
Kamil, Raja
Sari, Hasan
author_facet Alsayed, Abdallah
Sadir, Hayder
Kamil, Raja
Sari, Hasan
author_sort Alsayed, Abdallah
collection PubMed
description 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 (R(2)) 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.
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spelling pubmed-73125942020-06-29 Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020 Alsayed, Abdallah Sadir, Hayder Kamil, Raja Sari, Hasan Int J Environ Res Public Health Article 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 (R(2)) 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. MDPI 2020-06-08 2020-06 /pmc/articles/PMC7312594/ /pubmed/32521641 http://dx.doi.org/10.3390/ijerph17114076 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alsayed, Abdallah
Sadir, Hayder
Kamil, Raja
Sari, Hasan
Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7312594/
https://www.ncbi.nlm.nih.gov/pubmed/32521641
http://dx.doi.org/10.3390/ijerph17114076
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