Estimation of Transmission Rate and Recovery Rate of SIR Pandemic Model Using Kalman Filter

COVID-19 is a global pandemic that significantly impacts all aspects. The number of victims who died makes this disease so terrible. Various policies continue to be pursued to reduce the spread and impact of COVID-19. The spread of a disease can be modeled in differential equation modeling. This differential equation modeling is known as the SIR Model. A differential equation can be expressed in a state-space model. The state-space model is a model that is widely used to design a modern control system. This research carried out the transmission rate and recovery rate estimates in the SIR pandemic model. Estimation of the transmission rate and recovery rate in this study poses a challenge to the value of the number of people confirmed as infected. The experimental result shows that the transmission and recovery rates can be estimated using the data for the infected and recovered persons. Estimates of infected and recovered people were conducted using the Kalman Filter.

@article{Putra2022,
  title     = {Estimation of Transmission Rate and Recovery Rate of SIR Pandemic Model Using Kalman Filter},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Wahyu Sukestyastama Putra and Afrig Aminuddin and Ibnu Hadi Purwanto and Rakhma Shafrida Kurnia and Ika Asti Astuti},
  doi       = {10.14569/IJACSA.2022.0131239},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131239}
}