Utilizing the Box-Jenkins Time Series Model for Predicting Diarrheal Mortality in Kenya
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Abstract
In Kenya, diarrheal illnesses remain a major public health concern since they account for a disproportionate share of the country's overall death toll. Using the Autoregressive Integrated Moving Average (ARIMA) model, the authors of this study project how the number of fatalities in Kenya attributable to diarrhoeal causes would change in the future. To assure the dependability and accuracy of the forecasting model, a thorough study was undertaken, incorporating several diagnostic tests such as the Augmented Dickey-Fuller (ADF) test, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and the Box-Jenkins approach. This study's findings will help policymakers and healthcare authorities in Kenya establish evidence-based solutions to address this critical public health challenge by shedding light on the underlying patterns and dynamics of diarrheal illnesses in the country.
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