Malaria Incidence in the Philippines: Prediction using the Autoregressive Moving Average Models

TL;DR

This paper develops an ARIMA model to accurately forecast weekly malaria cases in the Philippines, aiding public health planning and response efforts.

Contribution

It applies the Box-Jenkins ARIMA methodology to malaria incidence data, identifying an optimal model for prediction in the Philippine context.

Findings

ARIMA (2, 1, 0) best fits the data

Model effectively predicts future malaria incidence

Forecasts can inform health policy decisions

Abstract

The study was conducted to develop an appropriate model that could predict the weekly reported Malaria incidence in the Philippines using the Box-Jenkins method.The data were retrieved from the Department of Health(DOH) website in the Philippines. It contains 70 data points of which 60 data points were used in model building and the remaining 10 data points were used for forecast evaluation. The R Statistical Software was used to do all the necessary computations in the study. Box-Cox Transformation and Differencing was done to make the series stationary. Based on the results of the analysis, ARIMA (2, 1, 0) is the appropriate model for the weekly Malaria incidence in the Philippines.