Fig 1.
Plot of RSV incidence in Dadaab.
Fluctuations in the data are roughly constant over time, indicating that the RSV time series could likely be described using an additive model.
Fig 2.
Correlation-regression analysis.
A: Correlation between RSV incidence and wind speed; B: Correlation between RSV incidence and temperature; C: Correlation between RSV incidence and dew point; and D: Correlation between temperature and wind speed. In these plots, the regression lines of best fit are indicated by bold blue lines.
Fig 3.
Best model fit to the RSV incidence data (bold lines) with decomposed covariates.
A: Poisson, GLM. B: Poisson, GAM. The standard error bars to the model fit are indicated by the dotted lines (95% confidence bounds). The base year in all these plots was September 2009.
Fig 4.
Best model fit (Poisson, GAM) to the RSV incidence data with the signifficant decomposed covariates.
Seasonal, wind speed; Trend, wind speed; Seasonal, rainfall; Trend, rainfall; and Trend, visibility. The standard error bars to the model fit are indicated by the gray shade (95% confidence bounds). RSV incidence units as cases per 1,000 person months.