Map of Sudan 2007, showing the states reporting RVF during the outbreak.

<p>Map of Sudan 2007, showing the states reporting RVF during the outbreak.</p

Sequence of events, actions, and response related to the RVF outbreak in Sudan 2007.

<p>Sequence of events, actions, and response related to the RVF outbreak in Sudan 2007.</p

Photograph of virus infected Vero cell monolayer showing plaque phenotypes based on plaque size.

<p>Vero cell monolayers were fixed in 0.5% crystal violet solution. Plaque size was measured using a Zeiss microscope.</p

Virus isolates obtained from diverse geographical regions and species in Kenya.

<p>Virus isolates obtained from diverse geographical regions and species in Kenya.</p

Survival curves of mice following infection with wild type parental and amplified plaque purified phenotypes of A)Bunyamwera (GSA/S4/11232) and B)Ngari (TND/S1/19801) virus isolates.

<p>Groups of mice (n = 12) were inoculated with 10⁹ PFU/ml of virus and observed for signs of clinical illness. The experiment was replicated three times. Survival functions were graphed for the two sets of viruses. Pairwise comparisons of survival curves were made using the Wilcoxon-Breslow test to test for equality of survivor functions.</p

Growth kinetics of wild type parental and amplified Plaque purified phenotypes of A–B) Bunyamwera and C–E) Ngari virus isolates.

<p>The viral isolates including the parental WT, i.e. mixture of SP and LP, were used to infect 90% confluent monolayers of Vero cells at a multiplicity of infection of 0.01. Aliquots of tissue culture fluid were collected at different timepoints and titers determined by plaque assay. The experiment was replicated thrice. The statistical package R was used for fitting exponential growth data using the Kruskal–Wallis test. The detection of correlated error structure in the growth curve data was carried out as follows; the log-transformed data was fit to linear mixed effects models using R, and an AR1 model was determined to fit the data better than a repeated measures model.</p

Potential risk^* of yellow fever virus transmission based on estimated <i>Aedes aegypti</i> indices in the long rains, short rains, and dry season in Kilifi, Kisumu, and Nairobi Counties, Kenya.

<p>Potential risk^{<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005858#t007fn001" target="_blank">*</a>} of yellow fever virus transmission based on estimated <i>Aedes aegypti</i> indices in the long rains, short rains, and dry season in Kilifi, Kisumu, and Nairobi Counties, Kenya.</p

Pearson correlations between the traditional <i>Stegomyia</i> indices in Kilifi, Kisumu, and Nairobi Counties, Kenya.

<p>Pearson correlations between the traditional <i>Stegomyia</i> indices in Kilifi, Kisumu, and Nairobi Counties, Kenya.</p

<i>Aedes aegypti</i> density, indoors and outdoors in Kilifi, Kisumu, and Nairobi Counties of Kenya.

<p>* Indicates significant differences between indoor and outdoor sampling, at P < 0.05 in each of the three peri-urban areas sampled.</p

Estimated dengue transmission risk levels in the long rains, short rains and dry season in Kilifi, Kisumu, and Nairobi Counties, Kenya.

<p>Estimated dengue transmission risk levels in the long rains, short rains and dry season in Kilifi, Kisumu, and Nairobi Counties, Kenya.</p