Evaluating the Accuracy of Mortality Forecasting Models in Tanzania
DOI:
https://doi.org/10.47672/ajsas.2346Keywords:
Accuracy, Mortality, Forecasting ModelsAbstract
Purpose: The aim of the study was to assess the evaluating the accuracy of mortality forecasting models in Tanzania.
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: Advanced statistical methods and machine learning algorithms have significantly improved the precision of mortality forecasts. Models that incorporate a wide range of variables, including age-specific mortality rates, socioeconomic factors, and health trends, tend to produce more accurate predictions. Additionally, the use of ensemble methods, which combine predictions from multiple models, has been shown to enhance forecast reliability. However, challenges remain, such as dealing with uncertainties related to future health crises, changes in public health policies, and demographic shifts. Overall, while modern models have made substantial progress, continuous refinement and validation are necessary to ensure their accuracy and relevance in diverse population contexts.
Implications to Theory, Practice and Policy: Theory of stochastic modeling, actuarial life table theory and demographic transition theory may be used to anchor future studies on assessing the evaluating the accuracy of mortality forecasting models in Tanzania. To facilitate the adoption of advanced forecasting models, there is a need to develop user-friendly software tools and platforms that allow practitioners to easily apply these models in their work. Policymakers should incorporate insights from advanced mortality forecasting models into public health and social policy planning.
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