BackgroundA recent longitudinal study in the Dadaab refugee camp near the Kenya-Somalia border identified unusual biannual respiratory syncytial virus (RSV) epidemics. We characterized the genetic variability of the associated RSV strains to determine if viral diversity contributed to this unusual epidemic pattern.MethodsFor 336 RSV positive specimens identified from 2007 through 2011 through facility-based surveillance of respiratory illnesses in the camp, 324 (96.4%) were sub-typed by PCR methods, into 201 (62.0%) group A, 118 (36.4%) group B and 5 (1.5%) group A-B co-infections. Partial sequencing of the G gene (coding for the attachment protein) was completed for 290 (89.5%) specimens. These specimens were phylogenetically analyzed together with 1154 contemporaneous strains from 22 countries.ResultsOf the 6 epidemic peaks recorded in the camp over the period, the first and last were predominantly made up of group B strains, while the 4 in between were largely composed of group A strains in a consecutive series of minor followed by major epidemics. The Dadaab group A strains belonged to either genotype GA2 (180, 98.9%) or GA5 (2, < 1%) while all group B strains (108, 100%) belonged to BA genotype. In sequential epidemics, strains within these genotypes appeared to be of two types: those continuing from the preceding epidemics and those newly introduced. Genotype diversity was similar in minor and major epidemics.ConclusionRSV strain diversity in Dadaab was similar to contemporaneous diversity worldwide, suggested both between-epidemic persistence and new introductions, and was unrelated to the unusual epidemic pattern.

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The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data are lacking. We isolated, sequenced, and analyzed 383 A(H1N1)pdm09 viral genomes from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. The transmission dynamics of A(H1N1)pdm09 virus in Kenya were characterized by (i) multiple virus introductions into Kenya over the study period, although only a few of those introductions instigated local seasonal epidemics that then established local transmission clusters, (ii) persistence of transmission clusters over several epidemic seasons across the country, (iii) seasonal fluctuations in effective reproduction number (Re) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres, (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009–2011 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012–2017, and (v) virus spread across Kenya. Considerable influenza virus diversity circulated within Kenya, including persistent viral lineages that were unique to the country, which may have been capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle-income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.

Background: The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data is lacking. Methods: We isolated, sequenced, and analyzed 383 influenza A(H1N1)pdm09 viral genomes isolated from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. Results: The transmission dynamics of influenza A(H1N1)pdm09 virus in Kenya was characterized by: (i) multiple virus introductions into Kenya over the study period, although these were remarkably few, with only a few of those introductions instigating seasonal epidemics that then established local transmission clusters; (ii) persistence of transmission clusters over several epidemic seasons across the country; (iii) seasonal fluctuations in effective reproduction number (Re) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres; (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009-11 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012-17; and (v) virus migration from multiple geographical regions to multiple geographical destinations in Kenya. Conclusion: Considerable influenza virus diversity circulates within Africa, as demonstrated in this report, including virus lineages that are unique to the region, which may be capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.

Background We used postmortem minimally invasive tissue sampling (MITS) to assess the effect of time since death on molecular detection of pathogens among respiratory illness–associated deaths. Methods Samples were collected from 20 deceased children (aged 1–59 months) hospitalized with respiratory illness from May 2018 through February 2019. Serial lung and/or liver and blood samples were collected using MITS starting soon after death and every 6 hours thereafter for up to 72 hours. Bodies were stored in the mortuary refrigerator for the duration of the study. All specimens were analyzed using customized multipathogen TaqMan® array cards (TACs). Results We identified a median of 3 pathogens in each child’s lung tissue (range, 1–8; n = 20), 3 pathogens in each child’s liver tissue (range, 1–4; n = 5), and 2 pathogens in each child’s blood specimen (range, 0–4; n = 5). Pathogens were not consistently detected across all collection time points; there was no association between postmortem interval and the number of pathogens detected (P = .43) and no change in TAC cycle threshold value over time for pathogens detected in lung tissue. Human ribonucleoprotein values indicated that specimens collected were suitable for testing throughout the study period. Conclusions Results suggest that lung, liver, and blood specimens can be collected using MITS procedures up to 4 days after death in adequately preserved bodies. However, inconsistent pathogen detection in samples needs careful consideration before drawing definitive conclusions on the etiologic causes of death.