The fit and predict COVID-19 using an extended compartmental model in the context of Indonesia

Indrazno Siradjuddin¹, Bella Cahya Ningrum¹, Inta Nurkhaliza Agiska¹, Arwin Datumaya Wahyudi Sumari², Yan Watequlis Syaifudin¹, Rosa Andrie Asmara¹, Nobuo Funabiki³

1. ¹ Politeknik Negeri Malang
2. ² Faculty of Industrial Technology, Adisutjipto Institute of Aerospace Technology, Daerah Istimewa Yogyakarta, Indonesia
3. ³ Okayama University

AIP conference proceedings, 2024

In a pandemic outbreak such as COVID-19, that spreads widely around the world requires in depth research to predict the pattern of its spread and strategies to reduce the number of spreads. Predictive control is a mathematical approach to control systems where a model is used to make predictions about the future behavior of a system, and these predictions are then used to determine the control actions that should be taken. Based on these predictions, control measures such as lockdowns and social distancing could be optimized to reduce the spread of the virus and minimize its impact. A dynamic model is needed for quick and precise handling decisions. Mathematical models Epidemiology can be used in various fields, the implementation of which is COVID-19. The presentation of a dynamical system can use the basic compartment model, in which critical 𝐶 and death 𝒟 states are considered, addressing more possibilities of mutual transitions between compartment states. 𝒮ℰℐℛ𝒞𝒟 estimates and forecasts the COVID-19 spread under uncertainties and constraints. This paper focuses on This paper focuses on fit and predict the 𝒮ℰℐℛ𝒞𝒟 model with time dependent basic reproduction numbers and health resource-dependent death rates to real COVID-19 data in Indonesia. Data have been collected for a specific period of time, the best-fitting parameters have been obtained, and the changes in the basic reproduction number over time have been estimated. The presented model has included the transition probability of the infected state to the critical state and the transition probability of the critical state to the death state, based on different age groups, so that the number of hospital beds has also been cooperative in computing the compartment states. Using the best-fitting parameters of the 𝒮ℰℐℛ𝒞𝒟 model, the development of the compartment states in the near future was estimated. The simulation result shows that the obtained parameters of the 𝒮ℰℐℛ𝒞𝒟 model were reasonably satisfying where the simulated states from the model could fit the real data with a small degree of error so that it can be used to predict the path of further deployment.

DOI10.1063/5.0201201Copy