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.
The most important quantity in infectious disease epidemiology is the basic reproduction number (R 0). R 0 is the expected value of the number of infections per unit time. This paper aims to model the spread of COVID-19 in Indonesia using the multi-state SIRD model and then determine the transition intensities to construct R 0. The estimation of the transition intensity uses the maximum likelihood approach with the assumption of a homogeneous time Markov chain with an exponential distribution of transition intensity and the number of transitions in a Poisson distribution. The results of the transition intensity estimation are used to construct R 0 with the next generation matrix method. From the multi-state SIRD model, the largest transition is shown in the individual healing process, namely the movement from an infected to susceptible state, while the smallest transition is the transition from susceptible to dead. The R 0 obtained is 1.079708 (> 1) meaning that the number of individuals infected with COVID-19 will increase until it reaches a stable point. Transition intensities is an effective way of determining R 0 where the dynamics of disease transmission depends on the number of individuals transition between states and the total waiting time in a certain state. R 0 > 1 states that the COVID-19 pandemic in Indonesia has not been over yet.
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