A Markov model to quantify the transitions in the psychological health of young adults in India during the COVID-19 pandemic - Goyal, Babita; Sabharwal, Alka; Joshi, Lalit Mohan (Brazilian Journal of Biometrics, 2026)
A longitudinal data set where the system is characterized by its states in place of the values of the underlying random variables taken over time, can be modeled using a Markov model. In case of psychological data, the Markov models are beneficial as problems may progress or regress over time thus exhibiting the shift in the states of the system. These models, when applied to cohort studies may indicate at a general shift in the psychological health of the cohort under study. In a study regarding the psychological health of young adults in higher education in India during COVID-19 pandemic period, three independent surveys were conducted using the Strength and Difficulty Questionnaire (SDQ). 162 respondents were found to have been participated in all three surveys. A Markov chain model was used to study the transition of the respondents’ psychological health over different phases of the pandemic duration in respect of the observed scores of the two components of SDQ viz, the ‘Difficulty’ score and the ‘Impact’ score; and the estimated ‘Impact’ scores obtained from the observed ‘Difficulty’ scores on application of the Quantile Regression and Quantile Regression Neural Network. For all the three data sets, the Markov model indicated at the prominent shift from a ‘Normal’ state to the ‘Borderline’ and the ‘Abnormal’ states of SDQ. Moreover, the stationary distributions showed significantly higher probabilities of being in the ‘Borderline’ and the ‘Abnormal’ states during the pandemic period than what is suggested by the psychological manuals in standard times.