IMPLEMENTASI APPROXIMATE BAYESIAN COMPUTATION-SEQUENTIAL MONTE CARLO MENGGUNAKAN DATA WABAH CAMPAK 2010 DI MALAWI - Sektiaruni, Arfiah Kania; Dwi Rahmi, Salsabila; Nurfatimah, Dinda Khamila; Hafizhoh, Zulfa; Pratiwi, Oktavia Galih; Sulistiyowati, Anis (Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika, 2024)
Infectious disease is an infectious condition caused by the proliferation of microorganisms that can be transmitted to other healthy individuals. One of the infectious diseases is measles. Measles is an infectious disease caused by a highly contagious and potentially fatal virus. Measles continues to be one of the leading causes of child death, particularly in Sub-Saharan Africa and South Asia. In 2010, there was a major measles outbreak that caused 134,000 cases and 304 deaths in Malawi. This study aims to estimate parameter values ​​in the 2010 measles outbreak model in Malawi using the ABC-SMC method. The purpose of estimating parameter values ​​is to validate and measure the predictive accuracy of the model. The ABC-SMC algorithm provides a computationally efficient estimation procedure compared to the ABC rejection algorithm. This method can be used for very complex models and does not require the requirement of a likelihood function. The results obtained from this study show that the ABC-SMC method can be used effectively to model the spread of infectious measles with age structure, the parameter estimation results show that this method can replicate the actual population distribution with high accuracy, and through five generation iterations it succeeded in producing more accurate parameter estimates