Predicting malaria incidence in Kenya using the ARIMA and SARIMA models
Date
2021
Authors
Ali, Amira Abdulkadir
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Malaria is considered a public health challenge across the world. Approximately 40 percent
of the population of the world is at risk of malaria. In this study we will employ time series
analysis models to predict malatia incidence and it will also use climate variables such as
temperature and rainfall as exogenous inputs. Future malaria incidences will be projected
based on detennined trend patterns. The Auto-regressive integrated moving average
(ARIMA) and Seasonal Auto-regressive integrated (SARIMA) models were used in this
study to predict and forecast monthly malaria incidence (the spread of malaria in Kenya).
Considering the results, ARIMA (0, 1, 0) appeared fit for forecasting monthly malaria
incidence in Kenya further on the SARIMA model was used to compare which model had the
best results and to remove seasonality from the data the best fit for SARIMA model was
(0, 1 ,0) (0, 1 ,0)[ 12]. The models that usually give slightly better results are the ones that have
the lowest AICc values. In addition to that a regression analysis was carried out to detennine
the effects of rainfall and temperature on malaria incidence in Kenya. The variables have
different orders; we estimate a V AR regression because V AR regression enables us to
dynamically measure variables with combination I different order. The results obtained from
the regression analysis indicate that temperature has no significant impact on the number of
malaria cases however rainfall has a significant impact.
Description
Submitted in partial fulfillment of the requirements for the Degree of Bachelor of Business Science in Financial Economics at Strathmore University