Real-time Forecasting of the COVID-19 Epidemic using the Richards Model in South Sulawesi, Indonesia

Faihatuz Zuhairoh, Dedi Rosadi


This paper discussed Real-time Forecasting of the COVID-19 Epidemic using daily cumulative cases of COVID-19 in South Sulawesi. Our aim is to make model for the growth of COVID-19 cases in South Sulawesi in the top 5 provinces with the largest COVID-19 cases in Indonesia and predict when this pandemic reaches the peak of spread and when it ends. This paper used the Richards model, which is an extension of a simple logistic growth model with additional scaling parameters. Data used in the paper as of June 24, 2020 were taken from the official website of the Indonesian government. Our results are that the maximum cumulative number of COVID-19 cases has reached 10,000 to 12,000 cases. The peak of the pandemic is estimated to occur from June to July 2020 while continuing to impose social restrictions. The condition in South Sulawesi shows a sloping curve around October 2020, which means that there are still additional cases but not significant. When entering November, the curve starts to flat which indicates the addition of very small cases until the pandemic ends. The results of the pandemic peak prediction are the same as the Indonesian data; what is different is the prediction of when the pandemic will end. In the best-case scenario, the current data will tend to slow down, with the COVID-19 pandemic in South Sulawesi expected to end in November 2020. Our modeling procedure can provide information about the ongoing COVID-19 pandemic in South Sulawesi that may facilitate real-time public health responses about future disease outbreaks.


COVID-19; real-time forecast; Richards model; South Sulawesi; Indonesia.

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Arino, J., & Portet, S. (2020). A simple model for COVID-19. Infectious Disease Modelling, 5, 309-315.

Chen, Y. C., Lu, P. E., & Chang, C. S. (2020). A Time-dependent SIR model for COVID-19. arXiv preprint arXiv:2003.00122.

Gao, J., Wang, J. W., & Li, L. N. (2013). A Combined Model of Richards Model and BP Neural Network to Predict Transportation Carbon Emission. Journal of Chang’an University (Natural Science Edition), 33(4), 99-104.

Hsieh, Y. H. (2009). Richards model: a simple procedure for real-time prediction of outbreak severity. In Modeling and dynamics of infectious diseases (pp. 216-236).

Hsieh, Y. H. (2010). Pandemic Influenza A (H1N1) during Winter Influenza Season in the Southern Hemisphere. Influenza and Other Respiratory Viruses, 4, 187-197., retrieved on June 24, 2020., retrieved on June 24, 2020.

Lee, S., Lei, B., & Mallick, B. (2020). Estimation of COVID-19 Spread Curves Integrating Global Data and Borrowing Information. ArXiv.Org, 2005.00662.

Mulyanti, B., Purnama, W., & Pawinanto, R. E. (2020). Distance learning in vocational high schools during the COVID-19 pandemic in West Java province, Indonesia. Indonesian Journal of Science and Technology, 5(2), 271-282.

Nishiura, H. (2011). Real-Time Forecasting of an Epidemic using a Discrete Time Stochastic Model: A Case Study of Pandemic Influenza (H1N1-2009). BioMedical Engineering OnLine, 10, 15.

Paranjay, O. A., & Rajeshkumar, V. (2020). A Neural Network Aided Real-Time Hospital Recommendation System. Indonesian Journal of Science and Technology, 5(2), 42-60.

Pearl, R., & Reed, L.J. (1930). The Logistic Curve and the Census Count of 1930. Science, 72(1868), 399-401.

Putra, Z. A., & Abidin, S. A. Z. (2020). Application of SEIR Model in COVID-19 and the Effect of Lockdown on Reducing the Number of Active Cases. Indonesian Journal of Science and Technology, 5(2), 10-17.

Richards, F.J. (1959). A Flexible Growth Function for Empirical Use. Journal of Experimental Botany, 10, 290-301.

Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., & Chowell, G. (2020). Short-term forecasts of the COVID-19 epidemic in Guangdong and Zhejiang, China: February 13–23, 2020. Journal of Clinical Medicine, 9(2), 596.

Wang, X., Liu, S., & Huang, Y. (2016). A Study on the Rapid Parameter Estimation and the Grey Prediction in Richards Model. Journal of Systems Science and Information, 4(3), 223-234.



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