Evidence map›Paper›PMID 42204506›Full record

ArticleBMC public health2026

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

Adigun K Abimbola, Adigun O Abiodun, Adarabioyo M Idowu, Adewumi F D

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Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Adigun K AbimbolaDepartment of Mathematical and Physical Sciences, Afe Babalola University, Ado-Ekiti, Nigeria. adigunka@abuad.edu.ng.ORCID http://orcid.org/0000-0002-0824-7337
Adigun O AbiodunDepartment of Securities and Investments, Federal University of Technology, Akure, Nigeria.
Adarabioyo M IdowuDepartment of Mathematical and Physical Sciences, Afe Babalola University, Ado-Ekiti, Nigeria.
Adewumi F DDepartment of Mathematical and Physical Sciences, Afe Babalola University, Ado-Ekiti, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently, 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.

methodsData 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.

resultsThe 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.

conclusionThis study concluded that as people move from one state of infectious diseases to another state, their recovered process is high.

Indexed as

Communicable DiseasesAdolescentAdultChildChild, PreschoolFemaleHumansInfantInfant, NewbornMaleMarkov ChainsMiddle AgedNigeriaYoung AdultAbsorptionDiseasesFundamental MatrixMarkov ChainProbabilityTransient state

Identifiers

PMID42204506
PMCPMC13397657

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