# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## 2 Assessment of Hidden Ecological Factors
### Presentation of Results
The plot shown in Figure 5, clearly indicates that the data points for malaria incidence are deviating from the straight line. Hence, the malaria incidence dataset is therefore not normally distributed using the Q-Q plot. In Table 3, we show the results of the factor score estimates for path coefficients of SEM estimated using PLS path modelling, and three different structural model weighting schemes were analysed. We observed that Centroid (A) converges faster after 12 iterations, while factorial (B) and path weighting (C) converge after 15 iterations. The procedure of selecting the best weighting scheme is determined by the maximum number of iterations that will be used for calculating the PLS results and this algorithm did not stop until the maximum number of iterations is reached due to the stop criterion. From Table 3, we can observe that the B and C weighting schemes converge at the same maximum number of iterations in estimating the parameters of SEM. The weighting scheme provides the highest R\({}^{2}\) value for endogenous latent variables in the PLS path model specifications and estimations. This result shows that the C weighting scheme is better than A and B, as suggested by [42] in terms of robustness and also when the path model includes higher-order constructs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Lag 0** & **Lag 1** & **Lag 2** & **VIF** & **Kurtosis** & **Standard Error** \\ \hline Maximum temperature & 0.284 & 0.321 \({}^{b}\) & 0.092 & 2.4096 & 5.48 & 0.38 \\ Minimum temperature & \(-\)0.122 & 0.215 \({}^{b}\) & \(-\)0.237 & 8.7919 & 2.07 & 0.33 \\ Precipitation & \(-\)0.214 & \(-\)0.292 \({}^{a}\) & \(-\)0.155 & 1.4194 & 20.73 & 0.27 \\ Relative humidity & \(-\)0.134 & 0.254 \({}^{b}\) & \(-\)0.198 & 9.0065 & 1.42 & 0.02 \\ Solar radiation & - & - & - & 1.9000 & 6.73 & 0.50 \\ Wind speed & - & - & - & 1.3452 & \(-\)0.58 & 0.04 \\ \hline \hline \end{tabular} \({}^{a}\) negative association at lag 1. \({}^{b}\) positive association at lag 1.  
\end{table} 
Table 2: Cross-correlation between meteorological variables and malaria incidence; VIF:variance inflated factor. Table 4 presents the results of bootstrapping sampling for outer loadings of the observed variables and path coefficient of the latent variables estimated using PLS-PM. The results also show that all outer loadings and path coefficients are significant at \(\alpha=5\%\), except for the solar radiation with Factor II and wind speed with Factor III that contains zero-point in the bootstrap confidence interval. Furthermore, the interaction effects of the Factors between I and II, II and III were also investigated and the results revealed that none of the Factor combinations is significant in the incidence of malaria in the study area. This result provides sufficient evidence that high malaria incidence in the study area was attributed to the occurrence of minimum temperature and relative humidity which are identified as Factor I. The decision to select the most influential hidden ecological factor to the incidence of malaria is based on the communality and Dillon-Goldstein's indices. Furthermore, Table 5 summarizes the results, indicating some indices for selecting the hidden ecological factors to the high incidence of malaria in the study area. Among the three factors identified by EFA, we find that Factor I, indicated by minimum temperature and relative humidity, influences malaria transmission with communality index (0.94) and Dillon-Goldstein's \(\rho\) (0.97). This result is also consistent with the finding in [37], where a positive association exists between temperature and occurrence of dengue. Factor II and Factor III appear to have less influence on the malaria incidence. Figure 5: Graphical representation of quantile–quantile (Q–Q) plot normality tests.