Modelling Trends of Climatic Variability and Malaria in Ghana Using Vector Autoregression

Abstract

Malaria is considered endemic in over hundred countries across the globe. Many cases of malaria and deaths due to malaria occur in Sub-Saharan Africa. The disease is of great public health concern since it affects people of all age groups more especially pregnant women and children because of their vulnerability. This study sought to use vector autoregression (VAR) models to model the impact of climatic variability on malaria. Monthly climatic data (rainfall, maximum temperature, and relative humidity) from 2010 to 2015 were obtained from the Ghana Meteorological Agency while data on malaria for the same period were obtained from the Ghana Health Service. Results of the Granger and instantaneous causality tests led to a conclusion that malaria is influenced by all three climatic variables. The impulse response analyses indicated that the highest positive effect of maximum temperature, relative humidity, and rainfall on malaria is observed in the months of September, March, and October, respectively. The decomposition of forecast variance indicates varying degree of malaria dependence on the climatic variables, with as high as 12.65% of the variability in the trend of malaria which has been explained by past innovations in maximum temperature alone. This is quite significant and therefore, policy-makers should not ignore temperature when formulating policies to address malaria.

Volcanic activity controls cholera outbreaks in the East African Rift

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Modelling the Impact and Effects of Climatic Variability on Electricity Energy Consumption in the Yendi Municipality of Ghana

“…The general model of ( ) VAR p has many parameters, and due to complex connections and reaction between the variables in the model, they may be difficult to interpret [8]. As a consequence, numerous types of structural study are often used to summarize the complex properties of a VAR.…”

Vector Autoregressive (VAR) Model for Rain-Fall Forecasting in West Java Indonesia at the Peak of the Rainy Season

“…Then to test the level of parameter estimate using the t-test partially and the F-test simultaneously. Calculation of parameter estimates with the OLS method refers to equation (5). By using the R 3.5.1 software, the results of calculation of parameter estimation of the VAR model (3) in Table 5 show that by using the 10% significance level, the three equations are significant simultaneously.…”

“…One of the multivariate time series forecasting methods is the Vector Autoregressive (VAR) model. VAR is a system of dynamic equations, with the estimation of a variable in a given period depending on the movement of these variables and other variables involved in the system in previous periods [3].There are many studies that use the VAR model for forecasting, they are [6] using the VAR model to forecast rainfall and isohyet mapping in Semarang, [12] using the VAR model for forecasting rainfall in Indramayu, [7] constructing the VAR model for predicting ENSO, [9] applies the VAR model for rainfall and groundwater level analysis, and [5] uses the VAR mod-el to analyze trends in climate and malaria variability in Ghana. Based on the description above, this study will predict rainfall at the peak of the rainy season (December, January and February) in a multivariate manner.…”