1.1 Introduction
Typhoid fever is a bacterial infection caused by Salmonella typhi, commonly transmitted through contaminated food or water. It leads to symptoms such as high fever, abdominal pain, and diarrhea (World Health Organization, 2021). Typhoid fever remains a significant public health concern globally, particularly in developing countries where sanitation and access to clean drinking water are inadequate. The World Health Organization (WHO) estimates that approximately 11–21 million cases of typhoid fever occur annually, leading to 128,000 to 161,000 deaths worldwide (World Health Organization, 2022). In Nigeria, the incidence of typhoid fever has been a persistent challenge, with varying rates reported across different regions and communities. The fluctuations in typhoid fever cases can be attributed to several factors, including environmental conditions, population density, and public health interventions. Time series analysis provides a robust framework for understanding the temporal patterns of typhoid fever cases, enabling researchers and policymakers to identify trends, seasonal variations, and potential predictors of outbreaks.
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Research hypothesis and questions, Limitation of the study and Definition of terms.
1.2 Background of Study
In the early 20th century, typhoid fever was a leading cause of morbidity and mortality, particularly in urban areas with inadequate sanitation. The introduction of public health measures, such as improved sanitation and vaccination programs, led to a notable decline in the incidence of the disease in many developed countries (Mason et al., 2018). However, despite these advancements, typhoid fever remains endemic in several developing countries, including Nigeria, where poor sanitation and limited access to clean water continue to contribute to its prevalence.
The advent of time series analysis in the late 20th century provided new opportunities to understand the dynamics of infectious diseases like typhoid fever. Researchers began to apply statistical methods to analyze historical data, identifying patterns and trends in disease incidence that could inform public health interventions (Hollingsworth et al., 2016). This approach has proven valuable in understanding the seasonal variations of typhoid fever, which are often influenced by environmental factors such as rainfall and temperature (Santos et al., 2019).
In Nigeria, the historical context of typhoid fever is characterized by periodic outbreaks and regional variations in incidence. The analysis of time series data has been utilized to assess the impact of various factors, including healthcare access and environmental conditions, on the rates of typhoid fever. A study by Akinyemi et al. (2020) highlights the persistent challenge of typhoid fever in Nigeria, emphasizing the need for continuous monitoring and evaluation of disease trends through time series analysis.
The prevalence of typhoid fever remains a critical public health issue, particularly in regions with inadequate sanitation and poor access to clean water. Typhoid fever, caused by the bacterium Salmonella enterica serotype Typhi, is transmitted through contaminated food and water, and it presents significant morbidity and mortality, particularly in low- and middle-income countries (Mason et al., 2018). In Nigeria, the burden of typhoid fever is exacerbated by factors such as rapid urbanization, increased population density, and challenges in public health infrastructure. According to a study by Akinyemi et al. (2020), the incidence of typhoid fever in Nigeria has remained consistently high, with reports indicating that thousands of new cases arise annually, particularly among vulnerable populations, including children and young adults.
The dynamics of typhoid fever cases can be influenced by a myriad of factors, including environmental conditions, healthcare access, and seasonal variations. Time series analysis allows for a systematic examination of these temporal patterns, facilitating an understanding of trends and fluctuations in the incidence of typhoid fever over time. Previous studies have employed this methodology to uncover the relationships between environmental factors, such as rainfall and temperature, and the incidence of typhoid fever (Bhandari et al., 2019). Therefore, in Nigeria where the research was carried out, the activities that was conducted is to analyze the temporal patterns of typhoid fever incidence using time series analysis.
1.3 Statement of Problems
Investigation revealed that the ongoing prevalence of typhoid fever presents significant public health challenges, particularly in developing countries where inadequate sanitation and limited access to clean water are prevalent. The incidence of typhoid fever in Nigeria remains alarmingly high, with reports indicating thousands of new cases annually (Akinyemi et al., 2020). A critical issue is the lack of comprehensive and systematic data on the temporal patterns of typhoid fever incidence, which hinders the ability to implement effective public health interventions.
Existing studies often rely on cross-sectional data, which fails to capture the dynamics of disease transmission over time (Mason et al., 2018). This gap in knowledge makes it difficult to identify trends, seasonal variations, and the impact of environmental factors on typhoid fever rates. As such, there is an urgent need for rigorous time series analysis to better understand the temporal patterns associated with typhoid fever, which is essential for informing policy and planning public health strategies.
Furthermore, the impact of external factors such as climate variability, urbanization, and healthcare access on the rate of typhoid fever is not adequately addressed in current literature (Santos et al., 2019). It is against the backdrop that this study seeks to address these problems by analyzing the temporal patterns of typhoid fever incidence using time series analysis.
1.4 Aim and Objectives of Study
The aim of the study is to analyze the temporal patterns of typhoid fever incidence using time series analysis to understand its trends, seasonality, and the impact of various external factors, with the ultimate goal of providing data-driven insights for improving public health interventions in Nigeria. In achieving this aim, the following specific objectives were laid out as follows:
- To evaluate the historical trends in the incidence of typhoid fever in Nigeria over a specific period.
- To identify seasonal variations and patterns in the occurrence of typhoid fever.
- To assess the relationship between environmental factors (such as rainfall and temperature) and the rate of typhoid fever using time series models.
- To predict future incidence rates of typhoid fever based on historical data trends and time series forecasting methods.
- To recommend appropriate strategies for reducing the incidence of typhoid fever based on the findings from the time series analysis.
1.5 Research Questions
The study came up with research questions so as to be able to ascertain the above stated objectives. The specific research questions for the study are stated below as follows:
- Are there any significant seasonal variations in the rate of typhoid fever in Nigeria?
- Can time series models predict future trends in the rate of typhoid fever?
- What are the historical trends in the incidence of typhoid fever in Nigeria over the past years?
- How do environmental factors such as rainfall and temperature influence the occurrence of typhoid fever?
- What strategies can be developed based on the time series analysis to mitigate the spread of typhoid fever?
1.6 Research Hypothesis
In order to pursue the objective of this study, the following generalized statements have been designed to guide and aids in obtaining the result for the experiment to be conducted. For this work, the null hypothesis will be represented with H0 while the alternative hypothesis will be represented with hypothesis H1.
- H01: There is no significant relationship between environmental factors (rainfall and temperature) and the incidence of typhoid fever in Nigeria.
- H02: There is a significant seasonal variation in the incidence of typhoid fever in Nigeria.
- H03: Environmental factors such as rainfall and temperature have a significant impact on the rate of typhoid fever in Nigeria.
- H04: Time series models can accurately predict future trends in the incidence of typhoid fever.
1.7 Significance of Study
The findings of this study will provide valuable insights into the temporal patterns and trends in the incidence of typhoid fever, which will aid in understanding how the disease spreads over time. Also, this study will contribute to the existing body of knowledge on the relationship between environmental factors, such as rainfall and temperature, and the incidence of typhoid fever.
Furthermore, the time series predictions from this study will serve as a basis for future public health forecasting, enabling governments and health agencies to prepare for potential outbreaks and implement timely interventions. This proactive approach will enhance the overall capacity to manage and control typhoid fever in affected areas.
Additionally, this research will guide future epidemiological studies on typhoid fever and other waterborne diseases, especially in regions with similar environmental and socio-economic conditions. It will also provide a methodological framework for using time series analysis in public health research.
Lastly, the recommendations from this study will be useful for improving public health strategies, such as enhancing water sanitation and vaccination programs, thereby contributing to the reduction of typhoid fever incidence and improving the health and well-being of the population.
1.8 Scope of the Study
The scope of the research is focused on time series analysis on the rate of typhoid fever. This study is designed to cover Aba, the commercial city of Abia state. Aba is a city and a big trading center in Abia state. It will also cover a period of eleven (11) years (2010-2022).
1.9 Limitations of the Study
The limitations of this study were influenced by several factors.
- First, the issue of insufficient data was a challenge, as some health records were either incomplete or unavailable, which affected the comprehensiveness of the time series analysis.
- Delays from respondents, especially in obtaining relevant health data from government and health institutions, were also encountered. This slowed down the data collection process, as some stakeholders were not readily available or willing to provide the necessary information.
- Financial constraints posed a limitation, as the cost of obtaining high-quality data, traveling to various locations, and accessing specialized software for time series analysis was higher than anticipated. This restricted the scope of the research to some extent.
- Time constraints were another challenge, as the research had to be completed within a limited period, restricting the ability to conduct more in-depth analysis or to explore additional variables that could have further enhanced the study.
1.10 Definition of Terms
Typhoid Fever: Typhoid fever is a bacterial infection caused by Salmonella typhi, commonly transmitted through contaminated food or water. It leads to symptoms such as high fever, abdominal pain, and diarrhea (World Health Organization, 2021).
Time Series Analysis: Time series analysis refers to a statistical technique used to analyze a sequence of data points collected at consistent time intervals to identify trends, patterns, or seasonal variations over time (Shumway & Stoffer, 2017).
Incidence Rate: Incidence rate refers to the frequency or rate at which new cases of a disease occur in a specific population during a defined period (Last, 2001). It is typically expressed as the number of new cases per population unit.
Seasonality: Seasonality is the recurring pattern in a time series where certain behaviors or trends are observed to repeat at regular intervals, often linked to calendar seasons or environmental factors (Chatfield, 2003).
Forecasting: Forecasting is the process of making predictions about future data points based on historical data. In time series analysis, forecasting is used to predict future values in a time-ordered dataset (Hyndman & Athanasopoulos, 2018).
Environmental Factors: Environmental factors refer to external elements such as climate conditions (rainfall, temperature) that influence the occurrence and spread of diseases like typhoid fever (Smith et al., 2019).
