TIME SERIES MODELING ON TYPHOID FEVER INCIDENT AMONG UNDER FIVE YEARS CHILD (A CASE STUDY OF RASHEED SHEKONI TEACHING HOSPITAL DUTSE, FROM 2016 TO 2024)

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Typhoid fever remains a major public health concern, particularly in developing countries where sanitation and access to clean water are inadequate (World Health Organization [WHO], 2021). It is an infectious disease caused by Salmonella enterica serovar Typhi, transmitted mainly through the ingestion of contaminated food or water (Crump & Mintz, 2019). The disease continues to be a major contributor to morbidity and mortality among children under five years, especially in sub-Saharan Africa and South Asia (Goswami et al., 2020).

In Nigeria, typhoid fever constitutes a significant burden on the health system. Studies have shown that children are particularly vulnerable due to their weak immune systems, poor hygiene practices, and exposure to unsafe water and food sources (Ezeigbo et al., 2018). According to the Nigeria Centre for Disease Control (NCDC, 2023), recurrent outbreaks of typhoid fever are observed in both rural and urban areas, often with seasonal patterns influenced by rainfall, sanitation, and water supply conditions.

Time series modelling provides a quantitative approach to analyzing disease trends over time, helping health policymakers identify patterns, make forecasts, and design preventive strategies (Chatfield, 2019). Applying time series models such as the Autoregressive Integrated Moving Average (ARIMA) model to disease incidence data can reveal temporal patterns and predict future occurrences (Box et al., 2016).

In this study, time series analysis will be applied to typhoid fever incidence data among under-five children at Rasheed Shekoni Teaching Hospital, Dutse, from 2016 to 2024. The results will help understand the temporal behaviour of the disease and provide a predictive tool for improved disease control and prevention strategies in Jigawa State.

1.2 Statement of the Problem

Despite global advances in disease surveillance and treatment, typhoid fever continues to pose significant challenges in Nigeria, particularly among children under five. The persistence of the disease indicates that current preventive and control measures remain inadequate. Hospitals and health institutions, including Rasheed Shekoni Teaching Hospital, frequently report periodic spikes in typhoid cases, yet little empirical work has been done to model or predict these trends statistically.

The absence of a systematic time-series analysis has limited the ability of public health planners to anticipate outbreaks, allocate resources effectively, and implement timely interventions. Furthermore, there is insufficient data interpretation on how seasonality, environmental changes, and human behavior contribute to the periodicity of typhoid fever among children. Therefore, this study seeks to apply time series modelling to examine the pattern and trend of typhoid fever among under-five children from 2016 to 2024 in order to forecast future incidence and inform public health policy.

1.3 Aim and Objectives of the Study

To model and forecast the incidence of typhoid fever among under-five children at Rasheed Shekoni Teaching Hospital, Dutse, using time series analysis from 2016 to 2024.

The aim of this research can be achieved by the following objectives:-

1.      To examine the trend of typhoid fever cases among under-five children from 2016 to 2024.

2.      To develop an appropriate time series model (e.g., ARIMA) for predicting future cases.

3.      To forecast for 2025 and 2026.

1.4 Research Questions

1.      What is the trend of typhoid fever incidence among under-five children from 2016 to 2024?

2.      Are there seasonal or periodic variations in the pattern of typhoid fever occurrences?

3.      Which time series model best fits the typhoid fever incidence data?

4.      How can the model’s forecast be used to enhance health planning and disease prevention?

1.5 Research Hypotheses

·         H₀: There is no significant trend in the incidence of typhoid fever among under-five children between 2016 and 2024.

·         H₁: There is significant trend in the incidence of typhoid fever among under-five children between 2016 and 2024.

·         H₀: There is no significant seasonal variation in typhoid fever incidence among under-five children.

·         H₁: There is significant seasonal variation in typhoid fever incidence among under-five children.

·         H₀: The selected time series model does not significantly predict future incidence of typhoid fever

·         H₁: The selected time series model that significantly predict future incidence of typhoid fever.

1.6 Significance of the Study

This study is significant because it applies statistical modelling to public health surveillance data, providing evidence-based insights into disease patterns. It will benefit hospital administrators and public health agencies by improving understanding of typhoid fever dynamics and supporting proactive interventions (Adebayo & Aluko, 2020). Moreover, the findings will contribute to academic literature on time series applications in epidemiology, particularly in resource-limited settings. Policymakers and healthcare providers can use the forecast results to allocate resources, plan awareness campaigns, and implement sanitation programs targeting under-five children the most vulnerable age group (WHO, 2021).

1.7 Scope and Limitations of the Study

This research focuses on typhoid fever incidence among under-five children at Rasheed Shekoni Teaching Hospital, Dutse, Jigawa State, between 2016 and 2024. It involves the use of hospital records on confirmed cases within this period. The study is restricted to time series modelling using statistical techniques such as ARIMA, moving averages, and trend analysis. Other factors such as socioeconomic and environmental variables are not modeled explicitly but may be discussed qualitatively.

The study is limited by the availability and accuracy of hospital records, which may contain incomplete or missing data for certain periods. Other limitations include the potential under-reporting of cases due to self-medication and limited diagnostic facilities in the region. Additionally, the study is confined to a single institution, which may restrict the generalizability of findings to the wider population of Jigawa State.

1.9 Operational Definition of Terms

Typhoid Fever: A bacterial infection caused by Salmonella typhi transmitted through contaminated food or water.

Incidence: The number of new cases of a disease occurring within a specified time period.

Under-Five Children: Children below the age of five years.

Time Series Modelling: A statistical method used to analyze data points collected or recorded at specific time intervals to identify patterns and forecast future values.

ARIMA Model: Autoregressive Integrated Moving Average model, a forecasting technique used in time series analysis.