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All Journal Mikailalsys Journal of Mathematics and Statistics
Chiwa Musa Dalah
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Engineering Mathematics Mechanical Engineering

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Statistical Time Series Analysis on Malaria Cases among Children (0-5 Years) in Damaturu Town (A Case Study of Primary Health Care Centers, Damaturu, Yobe State, Nigeria) Shuaibu Ibrahim Bulama; Chiwa Musa Dalah
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.10321

Abstract

This study applied statistical time series analysis to examine malaria cases among children aged 0–5 years in Primary Health Care (PHC) centers in Damaturu, Yobe State, using monthly data from 2017 to 2024. The study aimed to describe malaria patterns, examine long-term trends, identify seasonal components, fit an appropriate Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and forecast future malaria incidence. Descriptive analysis showed a sharp increase in cases from 3,503 in 2017 to 25,412 in 2024, with a total of 109,101 cases recorded during the study period. Seasonal decomposition revealed consistent peaks during the rainy months of August to October, with October recording the highest transmission levels. Stationarity was confirmed using the Augmented Dickey–Fuller test (p = 0.01). Model identification based on ACF, PACF, AIC, and BIC criteria selected SARIMA(2,0,0)(0,1,1)[12] with drift as the best-fitting model. Forecasts for 2025–2026 indicated continued increases in malaria incidence, with projected peaks exceeding 3,700 and 3,900 cases, respectively. The findings confirm a significant upward trend and strong seasonal variation in malaria incidence among children under five in Damaturu. This study concludes that malaria remains a persistent and increasing public health challenge in the study area. The findings contribute to public health surveillance and epidemiological forecasting by demonstrating the value of SARIMA-based modelling for anticipating seasonal malaria burden. Practical implications include the need to strengthen seasonal interventions, improve surveillance, enhance resource allocation, and adopt predictive modelling for timely malaria control. Future research should incorporate climatic and socio-behavioral variables to improve forecast accuracy.
Time Series Analysis on Infant Mortality Rates (A Case Study of Yobe State Specialist Hospital Geidam, 2014 - 2024) Mustapha Abdullahi; Chiwa Musa Dalah
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.10322

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.
Co-Authors Mustapha Abdullahi Shuaibu Ibrahim Bulama