Table 3 Accuracy of some published studies on dengue prediction using different models. Note: In this table, the \(\hbox {R}^2\) value is the maximum value reported in each study.

From: Innovative application of a traffic-prediction spatio-temporal graph convolutional network for dengue disease forecasting

Authors

Algorithms

Region

Accuracy

Conde-Gutiérrez et al. 202438

ANN

Mexico

\(\hbox {R}^2\): 0.968

da Silva et al. 202439

RF

Brazil

\(\hbox {R}^2\): 0.949

Saleh et al. 202140

LSTM

Malaysia

\(\hbox {R}^2\): 0.750

da Silva et al, 202439

RF

Colombia

\(\hbox {R}^2\): 0.953

Francisco et al. 202441

6 ML methods: RF, SVM, ANN

Philippines

\(\hbox {R}^2\): 0.920

da Silva et al. 202439

RF

Peru

\(\hbox {R}^2\): 0.887

Silitonga et al. 202142

ANN

Indonesia

\(\hbox {R}^2\): 0.910