Towards malaria elimination: analysis of travel history and case forecasting using the SARIMA model in Limpopo Province

Abstract

Despite various efforts and policy implementation aimed at controlling and eliminating malaria, imported malaria remains a major factor posing challenges in places that have made progress in malaria elimination. The persistence of malaria in Limpopo Province has largely been attributed to imported cases, thus reducing the pace of achieving the malaria-free target by 2025. Data from the Limpopo Malaria Surveillance Database System (2010-2020) was analyzed, and a seasonal auto-regressive integrated moving average (SARIMA) model was developed to forecast malaria incidence based on the incidence data's temporal autocorrelation. The study found that out of 57,288 people that were tested, 51,819 (90.5%) cases were local while 5469 (9.5%) cases were imported. Mozambique (44.9%), Zimbabwe (35.7%), and Ethiopia (8.5%) were the highest contributors of imported cases. The month of January recorded the highest incidence of cases while the least was in August. Analysis of the yearly figures showed an increasing trend and seasonal variation of recorded malaria cases. The SARIMA (3,1,1) X (3,1,0) [12] model used in predicting expected malaria case incidences for three consecutive years showed a decline in malaria incidences. The study demonstrated that imported malaria accounted for 9.5% of all cases. There is a need to re-focus on health education campaigns on malaria prevention methods and strengthening of indoor residual spray programs. Bodies collaborating toward malaria elimination in the Southern Africa region need to ensure a practical delivery of the objectives.

Keywords: Imported malaria; South Africa; Surveillance; Travel history.

Fig. 1

Map of South Africa with the districts in Limpopo Province showing degrees of malaria-endemic regions

Fig. 2

a Graph showing malaria incidences in locations with a high number of cases. b Graph showing malaria incidences in locations with a low number of cases

Fig. 3

Time Plot of the cases of Malaria from January 2010 to December 2020

Fig. 4

a The generated autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs of the observed data. b The generated autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs of the differenced data

Fig. 5

Graph depicting the observed and forecasted values for years 2021 to 2023

Fig. 6

Graph showing the residuals analysis from SARIMA (3,1,1) x (3,1,0) [12]

Fig. 7

Seasonal indices graph (January 2010–December 2020)