Fig 1.
Time series plot of dengue case counts (left) and partial autocorrelation function plot of dengue case counts (right).
Fig 2.
Meteorological variables time series plots.
Time series plots of temperature, rainfall, solar radiation and relative humidity (top) and scatter plots of the average number of cases of dengue by intervals of the meteorological variables (bottom)
Fig 3.
Correlation matrix plot of weekly dengue case counts and lag-zero, lag-one and lag-two meteorological variables.
D: dengue disease cases. RF: rainfall. RH: relative humidity. SR: solar radiation. T: temperature.
Table 1.
DIC measures for models with constant coefficient (α), RW1 or RW2 TVCs (αt) for calendar trend with CC (βj) for the covariates.
Table 2.
Parameter estimates of models with CC (α) or RW1 or RW2 TVCs (αt) for calendar trend and CC (βj) for the covariates.
Table 3.
DIC measures for models with CC (α) or RW1 or RW2 TVCs (αt) for calendar trend with RW1 TVCs (bt,j) for the covariates.
Table 4.
DIC selection measures from models with RW1 TVCs (αt) for calendar trend and RW1 TVCs (bt,j) for the covariates.
bt,T: temperature. bt,RF: rainfall. bt,SR: solar radiation. bt,RH: relative humidity.
Fig 4.
Mean and 95% CI for the TVCs (bt,j) for temperature, rainfall, solar radiation and relative humidity from the saturated model.
Fig 5.
Mean and 95% CI for the predicted case counts of dengue disease (red lines) from the selected model, and observed counts (gray line).
Arrows representing the EW were short-term predictions of dengue case counts at one, two, three and four weeks.
Table 5.
Median of the MCMC simulations for the mean absolute percentage error (MAPE) to evaluate the short-term predictive performance of the final model in selected EWs after the first EW of January 2008.
Fig 6.
Median of the MCMC simulations for the mean absolute percentage error (MAPE) to evaluate the short-term predictive performance of the final model in selected EWs after the first EW of January 2008.