International Day Of Women in Statistics and Data Science

Neonatal mortality remains a major global public health challenge, with an estimated 6,200 newborns dying daily, mostly in settings where patient records are scarce. For data-scarce neonatal units, a key question arises: when transition data is limited, does it matter whether Analytical Estimation or Monte Carlo Simulation is used to model patient outcomes?

This study addresses that question using a neonatal dataset of 6,000 daily state transitions across 2,000 patients in Ghana. An absorbing Markov chain with four states: Hospital Admission, Neonatal Intensive Care Unit (NICU), Recovered, and Death was used. Both methods were evaluated across six data levels with 500 independent replications using bias, variance, standard deviation, and mean squared error.

Both methods perform similarly with adequate data and degrade equivalently under data scarcity because they share the same estimated transition matrix. Expected time to absorption is more sensitive to limited data than absorption probabilities, and 500 observed transitions emerge as the minimum reliable threshold. These findings provide an evidence-based data standard for resource-constrained healthcare systems.

Keywords: Absorbing Markov Chain, Monte Carlo Simulation, Analytical Estimation, Absorption Probability, Expected Time to Absorption, Sensitivity Analysis.

Authors: Emmanuella Frimpong, Dr. Irene Kafui Vorsah Amponsah, PhD (Visiting Lecturer at Ohio University)