A statistical model of Rift Valley fever activity in Egypt

Abstract

Rift Valley fever (RVF) is a viral disease of animals and humans and a global public health concern due to its ecological plasticity, adaptivity, and potential for spread to countries with a temperate climate. In many places, outbreaks are episodic and linked to climatic, hydrologic, and socioeconomic factors. Although outbreaks of RVF have occurred in Egypt since 1977, attempts to identify risk factors have been limited. Using a statistical learning approach (lasso-regularized generalized linear model), we tested the hypotheses that outbreaks in Egypt are linked to (1) River Nile conditions that create a mosquito vector habitat, (2) entomologic conditions favorable to transmission, (3) socio-economic factors (Islamic festival of Greater Bairam), and (4) recent history of transmission activity. Evidence was found for effects of rainfall and river discharge and recent history of transmission activity. There was no evidence for an effect of Greater Bairam. The model predicted RVF activity correctly in 351 of 358 months (98.0%). This is the first study to statistically identify risk factors for RVF outbreaks in a region of unstable transmission.

Keywords: Egypt; Rift valley fever; forecast; regularized regression.

Figure 1

Location of sampling stations upstream and downstream of Aswan High Dam. Average annual precipitation is shown in mm/y.

Figure 2

Model selection by lasso. The penalization parameter (λ=0.00729) was chosen to minimize the average deviance of the withheld observation in leave-one-out cross-validation. Red points show the average error. Error bars are standard deviations across cross-validation folds. Dashed vertical lines show (A) the value of λ at which average cross-validation deviance is minimized and (B) the largest value of λ at which the average cross-validation deviance is within one standard error of the minimum.

Figure 3

Rift Valley fever epidemics, lagged transmission activity and mosquito abundance, and and rainfall at Gambeila. To illustrate the data used in this study, monthly rainfall at Gambeila is plotted over the study period. To show how this relates to RVF transmission activity, epidemic periods are colored red and non-epidemic periods are colored grey. Points misclassified by the fit model are circled, showing poor ability to anticipate the origination of a new outbreak, but satisfactory performance predicting duration (three out of five epidemics terminated at the predicted time). The most important predictor identified by this model was the joint realization of RVF transmission activity at lag 1 and mosquito presence at the current time (RVF-Lag1 × Mosquito-Lag0). Intervals during which this categorical variable is true are signified by dark bars across the top of the plot.