Environmental Medicine and Toxicology

Environmental Medicine and Toxicology

Markov switching modeling of covid-19 case counts and associated clinical and epidemiological characteristics in Kermanshah province, western Iran

Document Type : Original Article

Authors
Maryam Mohammadian-Khoshnoud 1
Fatemeh Khosravi Shadmani 2, 3
Zohreh Manoochehri 4
1 Modeling of Noncommunicable Diseases Research Center, Institute of Health Sciences and Technology, Hamadan University of Medical Sciences, Hamadan, Iran
2 Department of Social Welfare Management, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
3 Social Determinants of Health Research Center, Social Health Research Institute, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
4 Substance Abuse Prevention Research Center, Research Institute for Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
10.22034/emt.2026.601765.1006
Abstract
Background and Objectives: Understanding the epidemiological patterns and transmission of COVID-19 is essential for effective disease control. This study used a two-state Markov Switching model to analyze daily confirmed COVID-19 case counts, identify outbreak and non-outbreak periods, and examine clinical and epidemiological characteristics associated with daily case counts across these periods.
Methods: This time-series study included individuals who presented to hospitals in Kermanshah province with COVID-19-related symptoms from January 31, 2020, to January 5, 2021. Data were extracted from the MCMC system, which is affiliated with the Ministry of Health's emergency services. We used a two-state Markov Switching model to model daily COVID-19 case counts and examine their temporal association with daily aggregate clinical and epidemiological characteristics.
Results: We recorded 9,255 confirmed COVID-19 cases, including 4,912 males and 4,343 females. The mean age was 54.65 years (median: 55.27 years). Among patients, 14.6% had hypertension, 8.8% had diabetes, and 8% had cardiovascular disease. In the outbreak regime (State 2), the daily proportion of confirmed cases with asthma was positively associated with daily case counts, whereas chronic neurological disorders (State 2) and chronic liver disease (State 1) showed negative associations. In the non-outbreak regime (State 1), contact history with an infected individual was positively associated with daily case counts.
Conclusion: The Markov Switching approach provides a complementary framework for characterizing temporal variation in COVID-19 case counts and identifying outbreak and non-outbreak regimes. Further studies are needed to evaluate its applicability to infectious disease surveillance.

Graphical Abstract

Markov switching modeling of covid-19 case counts and associated clinical and epidemiological characteristics in Kermanshah province, western Iran

Highlights

  • A two-state Markov-switching model captured temporal variation in COVID-19 cases.
  • Distinct outbreak and non-outbreak regimes were identified.
  • Asthma and contact history were positively associated with case counts.
  • Neurological disorders and liver disease were negatively associated with case counts.

Keywords
COVID-19
Coronavirus
Markov switching
Time series
Modeling
Subjects
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Environmental Medicine and Toxicology
Volume 1, Issue 1
Summer 2026
Pages 22-30

  • Receive Date 09 September 2026
  • Revise Date 25 September 2026
  • Accept Date 26 September 2026
  • First Publish Date 26 September 2026
  • Publish Date 27 September 2026