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Purpose: To investigate the evolution and transmission patterns of HRSVA in Kenya Methods & Materials: This was a retrospective study using specimens collected for influenza surveillance from 8 government hospitals across Kenya between March 2007 and February 2010. Subjects sampled were aged ≥2 months presenting to outpatient clinics with fever (≥38°C) and either cough or sore throat. Multiplex real-time RT-PCR was used to screen 2300 nasopharyngeal specimens for RSV. A 650 bp attachment (G) gene fragment was amplified using a one-step RT-PCR and then sequenced using the Sanger method. The evolutionary rate was calculated using Bayesian analysis, and the geographical transmission patterns investigated using SPREAD3. Results: Of the 2300 specimens, 159 (6.9%) tested positive for HRSVA and 134 (5.8%) for HRSVB. In this dataset, three RSVA genotypes were identified: GA2 (33.1%), GA5 (14%), and NA1 (52.9%). The mean evolutionary rate for HRSVA was estimated at 2.367 × 10−3 substitutions per site per year (95% HPD; 1.647 × 10−3 to 3.103 × 10−3) with the TMRCA dated to 1956 (95% HPD = 1962 to 1945). The genotypes had the following mean evolutionary rates: GA2: 1.901 × 10−3 (95% HPD; 2.698 to 1.102 × 10−3), NA1: 2.286 × 10−3 (95% HPD; 3.309 to 1.315 × 10−3), and GA5: 3.067 × 10−3 (95% HPD 4.250 to 2.076 × 10−3). HRSVA first appeared in Kisii (West) before spreading to Kericho (West) and Nairobi (Central) and thereafter to the remaining locations. Phylogeographic analysis suggested the virus in Kericho spread to the sites in the West before dispersing to the East, whereas the virus in Nairobi dispersed to Isiolo (central-northern) and New Nyanza (West). Furthermore, six sites experienced multiple introductions of the virus during the study period, but data suggest the virus in Alupe and Malindi sites were from single source of New Nyanza and Port Reitz respectively. Conclusion: This study elucidates the evolution rates of the local HRSVA genotypes with the GA5 genotype evolving most rapidly. It further provides some insight into the patterns of spread around Kenya, which adds knowledge on seasonal patterns. This is of potential use in intervention timing, though the interpretation is limited by sample site and size.