Time Series Analysis on Infant Mortality Rates (A Case Study of Yobe State Specialist Hospital Geidam, 2014 - 2024)

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Abstract

This study examined the pattern and trend of infant mortality rates at Yobe State Specialist Hospital, Geidam, using retrospective secondary data from 2014 to 2024. The study aimed to analyze infant mortality patterns and forecast future trends using time series techniques. A quantitative retrospective design was adopted, and the data were analyzed using descriptive statistics and time series models, including moving averages and exponential smoothing, to identify trends, seasonal fluctuations, and forecast patterns within the study period. The findings revealed that infant mortality rates fluctuated across the years, showing both seasonal and irregular variations, with a slight downward trend toward the later years. The results suggest that improved maternal care, immunization programs, and increased public health awareness may have contributed to this decline. Forecast results indicate a gradual but continuous reduction in infant mortality if current health interventions are sustained and strengthened. The study concludes that time series analysis provides an effective framework for understanding the dynamics of infant mortality and supporting evidence-based policy decisions aimed at reducing infant deaths. The findings contribute to public health monitoring and forecasting by demonstrating the usefulness of time series techniques in assessing infant mortality trends. Practical implications include the need for state and local governments, through the Ministry of Health, to strengthen maternal and child health programs, with support from international organizations such as WHO and UNICEF.

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Article Details

How to Cite
Abdullahi, M., & Dalah, C. M. (2026). Time Series Analysis on Infant Mortality Rates (A Case Study of Yobe State Specialist Hospital Geidam, 2014 - 2024). Mikailalsys Journal of Mathematics and Statistics, 4(2), 454-473. https://doi.org/10.58578/mjms.v4i2.10322

References

Adama, Z. K., Mettle, F. O., Baiden, B. M., & Bii, N. K. (2025). Forecasting progress: Analyzing the trajectory of under-five child mortality for Ghana, Niger, Nigeria, and Sierra Leone towards SDG3 using ARIMA time series model. BMC Public Health, 25, Article 1607. https://doi.org/10.1186/s12889-025-22869-z

Adeyinka, D. A., & Muhajarine, N. (2020). Time series prediction of under-five mortality rates for Nigeria: Comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models. BMC Medical Research Methodology, 20, Article 292. https://doi.org/10.1186/s12874-020-01159-9

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley.

Chatfield, C. (2003). The analysis of time series: An introduction (6th ed.). Chapman & Hall/CRC. https://doi.org/10.4324/9780203491683

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts. https://otexts.com/fpp2/

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting (2nd ed.). Wiley.

Nwanze, L. D., Siuliman, A., & Ibrahim, N. (2023). Factors associated with infant mortality in Nigeria: A scoping review. PLOS ONE, 18(11), Article e0294434. https://doi.org/10.1371/journal.pone.0294434

Ogundunmade, T. P., Daniel, A. O., & Awwal, A. M. (2023). Modelling infant mortality rate using time series models. International Journal of Data Science, 4(2), 107–115. https://doi.org/10.18517/ijods.4.2.107-115.2023

R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: With R examples (4th ed.). Springer. https://doi.org/10.1007/978-3-319-52452-8

Ugoh, C. I., Echebiri, U. V., Abiodun, I. E., Besiru, M., & Guobadia, E. K. (2022). On forecasting infant mortality rate by sex using ARIMA model: A case of Nigeria. European Journal of Statistics and Probability, 10(2), 29–38. https://doi.org/10.37745/ejsp.2013/vol10n22938

United Nations Children’s Fund. (2024). Levels & trends in child mortality: Report 2023. UNICEF. https://data.unicef.org/resources/levels-trends-child-mortality-report-2023/

World Health Organization. (2024). World health statistics 2024: Monitoring health for the SDGs, Sustainable Development Goals. https://www.who.int/publications/i/item/9789240094703

Yobe State Specialist Hospital Geidam. (2014–2024). Infant mortality records [Unpublished internal hospital records].


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