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

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

Abstract


COVID-19 which has become a global pandemic, recently, has spread to hundreds of countries and territories. This pandemic spreads rapidly through human transmission. In order to reduce the spread of COVID-19, the government emerged several policies. Numerous public health measures can be implemented to counter the risk of an emerging outbreak with pandemic potential. Meanwhile, Jakarta and West Java are the regions with the most confirmed cases in Indonesia, the government announced Large-Scale Social Restrictions (PSBB) policy in both provinces. Many researchers conducted forecasting methods for modeling or predict the further number of cases of this pandemic. Forecasting is slightly hard because of those interventions. In this study, we involved some of neural network forecasting methods, including Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine meanwhile neural networks become well-known at this time for forecasting the number of active, confirmed, recovered, death, and daily new cases in Jakarta and West Java. These methods are undertaking automatically without considering any factors that will be impacted the result as the reason that we assumed those factors have pursued the pattern of each case. The best model for all of the cases is the MLP (10,10) model. This intervention carried out by the government, namely PSBB, proved effective in reducing the spread of this pandemic in Jakarta and West Java. This can be seen from the results of the daily new cases which show a downward trend for both although still fluctuating.

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Published: 2020-06-18

How to Cite this Article:

Resa Septiani Pontoh, Toni Toharudin, Solichatus Zahroh, Enny Supartini, Effectiveness of the public health measures to prevent the spread of COVID-19, Commun. Math. Biol. Neurosci., 2020 (2020), Article ID 31

Copyright © 2020 Resa Septiani Pontoh, Toni Toharudin, Solichatus Zahroh, Enny Supartini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

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