International Day Of Women in Statistics and Data Science: Full Schedule

Registration opens August 15th at www.idwsds.org.

You will be able to validate your attendance (Check-In to sessions) using your personal schedule.

07:00 UTC

S107 - Absorbing Markov Chain Parameter Estimation Under Data Scarcity: A Comparative Study of Analytical and Monte Carlo Methods in Neonatal Care

Tuesday October 6, 2026 07:00 - 07:30 UTC

Zoom Room #1

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)

Speakers

Emmanuella Frimpong

Miami University, Oxford, Ohio

Ms. Emmanuella Frimpong is a graduate of the African Institute for Mathematical Sciences (AIMS) Ghana, where she completed her Master of Science in Mathematics, undertaking research on the topic “Comparing Monte Carlo Simulation and Analytical Estimation Methods for Absorbing Markov... Read More →

Organizers

Emmanuella Frimpong

Miami University, Oxford, Ohio

Tuesday October 6, 2026 07:00 - 07:30 UTC
Zoom Room #1

Plenary Talk, Technical Statistical Session

Country Ghana
Session ID 107

10:00 UTC

S208 - Optimal Model selection for incidence of Birth Asphyxia: NICU Centers in Greater Accra Region.

Tuesday October 6, 2026 10:00 - 10:30 UTC

Zoom Room #2

Abstract
Birth asphyxia remains a major contributor to neonatal morbidity and mortality in low- and middle-income countries, particularly in sub-Saharan Africa. This study investigated the determinants of birth asphyxia among newborns using a Quasi-Poisson regression model to account for overdispersion in the count data. Secondary data comprising neonatal and maternal records were analysed using descriptive statistics, correlation analysis, and inferential modelling. The Quasi-Poisson model selected after diagnostic assessment confirmed overdispersion in the response variable, making it more appropriate than the standard Poisson model. The Quasi-Poisson regression model for predicting birth asphyxia is given as : Birth Asphyxia = 1.742 +0.385(Birth Weight) + 0.012(Mothers Age)− 0.088(Gestational Age) + 0.0401(Sex)+ 0.301(Mode of Delivery) + 0.158(Parity)+ 0.067(SURVIVE)
The results showed that birth weight, gestational age, mode of delivery, maternal age, parity, and fetal presentation were significant predictors of birth asphyxia. Specifically, lower birth weight and shorter gestational age were associated with a higher incidence of birth asphyxia, while caesarean delivery and abnormal fetal presentation increased the likelihood of adverse birth outcomes.
Keywords: Birth asphyxia, Quasi-Poisson regression, Gestational age, Neonatal outcomes, Maternal health, Ghana.
Authors:
1. Selina Dadzie (Mphil)
2. Irene Kafui Vorsah Amponsah (PhD)

Speakers

Selina Dadzie

Student

Bio
Miss Selina Dadzie is a graduate student in Statistics at the University of Cape Coast, Ghana, where she is pursuing a Master of Philosophy (MPhil) in Statistics. Her research focuses on “Optimal Model Selection for Incidence of Birth Asphyxia: NICU Centers in Accra”, with particular... Read More →

Organizers