ACADSTAFF UGM
| Title | : | Multi-state Discrete-time Markov Chain SVIRS Model on the Spread of COVID-19 |
| Author | : |
FAIHATUZ ZUHAIROH (1) Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (2) Dr. Adhitya Ronnie Effendie, S.Si., M.Si., M.Sc. (3) |
| Date | : | 0 2022 |
| Keyword | : | COVID-19,discrete-time ,Markov chain,multi-state model,SVIRS COVID-19,discrete-time ,Markov chain,multi-state model,SVIRS |
| Abstract | : | This article discusses the spread of infectious diseases using the multi-state SVIRS model with the assumption that a discrete-time Markov chain (DTMC) occurs in a closed population that is regularly examined. This article aims to generate transition probabilities, which are then used to predict the number of confirmed cases in the next period. The multi-state SVIRS model uses four states, namely susceptible, vaccinated, infected, and recovered, followed by calculating the probabilities of each transition between states that are different from the compartment model. The model was applied to the COVID-19 data in Indonesia, which was analyzed using the statistical software R. The result showed that the transition probability of a person being infected according to the multi-state model with the assumption of DTMC SVIRS on the COVID-19 data was around 25.38% including those with and without vaccination. In comparison, the probability of being recovered was about 92.34%. Then this transition probability was used to predict the confirmed cases of COVID-19 in the next few days. The prediction results were highly accurate with a MAPE value less than 10%. The main contribution of this research is the use of the DTMC assumption, which is a stochastic model in determining the parameters of the differential equation formed by the compartment model and adding the vaccinated state in the model. The vaccinated cases in this article used the proportion of the efficacy of each vaccine used by several susceptible individuals, which, according to WHO recommendations, should be given in two doses. The multi-state model with the assumption of DTMC can model chronic diseases and infectious diseases. This can be seen from the results of the analysis of the COVID-19 data in Indonesia, in which the short-term prediction results had a high level of accuracy. |
| Group of Knowledge | : | |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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EL_30_2_24.pdf
Document Type : [PAK] Full Dokumen
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