Effectiveness of the public health measures to prevent the spread of COVID-19

Pontoh, Resa Septiani; Toharudin, Toni; Zahroh, Solichatus; Supartini, Enny

doi:10.28919/cmbn/4711

Cited by 2 publications

(1 citation statement)

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“…Social alignment is a public health technique that people can use to slow the spread and spread of infectious diseases such as coronaviruses. These local measures had been done in Indonesia and worked effectively to control the pandemic's spread (Pontoh et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

Purwandari¹,

Zahroh²,

Hidayat³

et al. 2022

10.5267/j.dsl

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COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.

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Section: Introductionmentioning

confidence: 99%

Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

Purwandari¹,

Zahroh²,

Hidayat³

et al. 2022

10.5267/j.dsl

View full text Add to dashboard Cite

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A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

Tayarani-Najaran

2022

IEEE Trans. Syst. Man Cybern, Syst.

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The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this paper, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic's behaviour. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multi-objective optimisation algorithm is developed which uses the model to search through the policy space and finds a set of policies that minimise the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modelling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this paper to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a meta-model algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The meta-model is a set of Support Vector Machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimisation process which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimisation algorithms are performed.

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Effectiveness of the public health measures to prevent the spread of COVID-19

Cited by 2 publications

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Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

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