A Weather-Based Prediction Model of Malaria Prevalence in Amenfi West District, Ghana

Abstract

This study investigated the effects of climatic variables, particularly, rainfall and temperature, on malaria incidence using time series analysis. Our preliminary analysis revealed that malaria incidence in the study area decreased at about 0.35% annually. Also, the month of November recorded approximately 21% more malaria cases than the other months while September had a decreased effect of about 14%. The forecast model developed for this investigation indicated that mean minimum (P = 0.01928) and maximum (P = 0.00321) monthly temperatures lagged at three months were significant predictors of malaria incidence while rainfall was not. Diagnostic tests using Ljung-Box and ARCH-LM tests revealed that the model developed was adequate for forecasting. Forecast values for 2016 to 2020 generated by our model suggest a possible future decline in malaria incidence. This goes to suggest that intervention strategies put in place by some nongovernmental and governmental agencies to combat the disease are effective and thus should be encouraged and routinely monitored to yield more desirable outcomes.

Figure 1

Map of Ghana showing study area in Amenfi West District.

Figure 2

Time series plot—malaria cases showing the LLIN intervention in May 2010 (a) and rainfall data (b).

Figure 3

Time series plot of temperature ((a) min.; (b) max.; and (c) avg.).

Figure 4

Plot of preintervention forecast compared to observed malaria cases.

Figure 5

Plot of in-sample forecasts compared to training set data.

Figure 6

Plot of out-of-sample forecasts compared to the test set.

Figure 7

Forecast of malaria Incidence (i.e., based on back-transformed output) with 80% and 95% prediction intervals (PI).