Disease transition dynamics among infectious disease patients in Oyo State, Nigeria: a markov chain analysis

BACKGROUND: Recently, Government at all levels, are seeking for ways to reduce drastically the spread of infectious diseases. This research work investigated disease transition matrix among infectious disease patients in Oyo State, Nigeria. The study employed reviewing data of patients from sourced datasets contained information on the human population, exposed population, and reported cases of the infectious diseases in Oyo State, for both children and adult in 2021, 2022 and 2023, respectively using Markovian Approach. METHODS: Data on children from age 0-19, was estimated to be 78,050 comprised of 75,658(diarrhea), 63(hepatitis), 605(pertussis), 939 (measles) and 785(tuberculosis) cases. The second category was 5,685 adults' data from age 20 and above that consisted 2,297 cases of diarrhea, 223 (hepatitis), 180 (pertussis) 187 (measles) and 2,798 (tuberculosis) cases. Markov chain approach was used to predict the population under risk of infectious diseases and the expected number of visits to each state of the diseases before absorption occur was captured. A prediction of likelihood of infection with respect to diseases in the future was also apprehended. RESULTS: The study findings showed that the future states of the diseases were solely dependent on both initial and present states and that the infected population had high probability of recovery at 5% level of significance. The transition pattern, fundamental matrix and the future transient state of the diseases reviewed that as many people that are exposed and infected with the diseases in the study area got recovered which may be due to government policy and health care facilities put in place. Probability of absorption depicted that, as many people that are infected got recovered and only small percentage of death were recorded. CONCLUSION: This study concluded that as people move from one state of infectious diseases to another state, their recovered process is high.