Predicting the neonatal, infant, and under-five mortality outcomes in the Philippines using Count TS GLM and GLARMA

Kyle Lawrence Verona; Zandro Castroñero; Xandro Alexi Nieto

doi:10.32890/jcia2026.5.2.2

Authors

Kyle Lawrence Verona University of Santo Tomas
Zandro Castroñero University of Santo Tomas
Xandro Alexi Nieto University of Santo Tomas

DOI:

https://doi.org/10.32890/jcia2026.5.2.2

Keywords:

Child mortality, forecasting, GLARMA, TS GLM

Abstract

Mortality statistics are often considered indicators of overall population health, highlighting the need for accurate forecasting to support public health planning and policy development. While various statistical models have been applied to mortality forecasting globally, research on the use of count time series modeling for mortality trends in the Philippines remains limited. Addressing this gap, this study provides the first systematic comparison of Count Time Series Generalized Linear Models (TS GLM) and Generalized Linear Autoregressive Moving Average (GLARMA) models for forecasting Philippine neonatal, infant, and under-five mortality. The predictive performance of these models was evaluated for the period 2023–2027. Prior to model implementation, stationarity was assessed using the KPSS test, after which predictive models were developed and compared, with the best-performing specifications selected based on the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE). Results show that TS GLM consistently outperforms GLARMA in predictive accuracy, with the negative binomial distribution yielding the best results. The optimal TS GLM models for neonatal (1,2), infant (1,2), and under-five mortality (1,5) surpass their GLARMA counterparts. Projected trends indicate rising absolute numbers of deaths across all subgroups. These findings provide valuable guidance for government agencies, health institutions, and insurance providers in monitoring child mortality, anticipating healthcare demand, improving actuarial projections, and informing targeted strategies, while also serving as a reference for future research on mortality forecasting using count time-series models.

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