Markov switching modeling of covid-۱۹ case counts and associated clinical and epidemiological characteristics in Kermanshah province, western Iran

Background and Objectives: Understanding the epidemiological patterns and transmission of COVID-۱۹ is essential for effective disease control. This study used a two-state Markov Switching model to analyze daily confirmed COVID-۱۹ 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-۱۹-related symptoms from January ۳۱, ۲۰۲۰, to January ۵, ۲۰۲۱. 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-۱۹ case counts and examine their temporal association with daily aggregate clinical and epidemiological characteristics.Results: We recorded ۹,۲۵۵ confirmed COVID-۱۹ cases, including ۴,۹۱۲ males and ۴,۳۴۳ females. The mean age was ۵۴.۶۵ years (median: ۵۵.۲۷ years). Among patients, ۱۴.۶% had hypertension, ۸.۸% had diabetes, and ۸% had cardiovascular disease. In the outbreak regime (State ۲), the daily proportion of confirmed cases with asthma was positively associated with daily case counts, whereas chronic neurological disorders (State ۲) and chronic liver disease (State ۱) showed negative associations. In the non-outbreak regime (State ۱), 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-۱۹ case counts and identifying outbreak and non-outbreak regimes. Further studies are needed to evaluate its applicability to infectious disease surveillance.

کلیدواژه ها:

COVID-۱۹ ، Coronavirus ، Markov Switching ، time series ، Modeling