Background: Acute respiratory illnesses (ARI) are a major cause of morbidity and mortality globally. With (re) emergence of novel viruses and increased access to childhood bacterial vaccines, viruses have assumed greater importance in the aetiology of ARI. There are now promising candidate vaccines against some of the most common endemic respiratory viruses. Optimal delivery strategies for these vaccines, and the need for interventions against other respiratory viruses, requires geographically diverse data capturing temporal variations in virus circulation.

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.Competing Interest StatementThe authors have declared no competing interest.Funding StatementFunding: The authors D.C.O. and C.N.A. were supported by the Initiative to Develop African Research Leaders (IDeAL) through the DELTAS Africa Initiative [DEL-15-003]. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)'s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa's Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust [107769/Z/10/Z] and the UK government. The study was also part funded by a Wellcome Trust grant [1029745] and the USA CDC grant [GH002133]. N.F.M. is supported by the Swiss National Science Foundation (PZEZP3_191891). This paper is published with the permission of the Director of KEMRI.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Kenya Medical Research Institute (KEMRI) and KEMRI-Wellcome Trust Research Programme Scientific and Ethics Review Unit (SERU), which is mandated to provide ethical approval for research work conducted in Kenya, provided ethical approval for the studies which collected and archived the samples used in these studies. These were approved under the following Scientific Steering Committee (SSC) approvals: 1. SSC No. 1899, SSC No. 2558 and SSC No. 2692; 2. KEMRI-Wellcome Trust Research Programme SSC No. 1055 and SSC No. 1433.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as Clini alTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll generated sequence data were deposited in the Global Initiative on Sharing All Influenza Data (GISAID). https://github.com/DCollinsOwuor/H1N1pdm09_Kenya_Phylodynamics/tree/main/Data/.

BACKGROUND: We describe the epidemiology and clinical features of Kenyan patients hospitalized with laboratory-confirmed influenza compared with those testing negative and discuss the potential contribution of severe acute respiratory illness (SARI) surveillance in monitoring a broader range of respiratory pathogens. METHODS: We described demographic and clinical characteristics of SARI cases among children (<18 years) and adults, separately. We compared disease severity (clinical features and treatment) of hospitalized influenza positive versus negative cases and explored independent predictors of death among SARI cases using a multivariable logistic regression model. RESULTS: From January 2014 to December 2018, 11,666 persons were hospitalized with SARI and overall positivity for influenza was ~10%. There were 10,742 (96%) children (<18 years)-median age of 1 year, interquartile range (IQR = 6 months, 2 years). Only 424 (4%) of the SARI cases were adults (≥18 years), with median age of 38 years (IQR 28 years, 52 years). There was no difference in disease severity comparing influenza positive and negative cases among children. Children hospitalized with SARI who had an underlying illness had greater odds of in-hospital death compared with those without (adjusted odds ratio 2.11 95% CI 1.09-4.07). No further analysis was done among adults due to the small sample size. CONCLUSION: Kenya's sentinel surveillance for SARI mainly captures data on younger children. Hospital-based platforms designed to monitor influenza viruses and associated disease burden may be adapted and expanded to other respiratory viruses to inform public health interventions. Efforts should be made to capture adults as part of routine respiratory surveillance.

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.

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 (R(e)) 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: In order to better understand respiratory syncytial virus (RSV) epidemiology and burden in tropical Africa, optimal case definitions for detection of RSV cases need to be identified. METHODS: We used data collected between September 2009 - August 2013 from children aged <5 years hospitalized with acute respiratory Illness at Siaya County Referral Hospital. We evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of individual signs, symptoms and standard respiratory disease case definitions (severe acute respiratory illness [SARI]; hospitalized influenza-like illness [hILI]; integrated management of childhood illness [IMCI] pneumonia) to detect laboratory-confirmed RSV infection. We also evaluated an alternative case definition of cough or difficulty breathing plus hypoxia, in-drawing, or wheeze. RESULTS: Among 4714 children hospitalized with ARI, 3810 (81 %) were tested for RSV; and 470 (12 %) were positive. Among individual signs and symptoms, cough alone had the highest sensitivity to detect laboratory-confirmed RSV [96 %, 95 % CI (95-98)]. Hypoxia, wheezing, stridor, nasal flaring and chest wall in-drawing had sensitivities ranging from 8 to 31 %, but had specificities >75 %. Of the standard respiratory case definitions, SARI had the highest sensitivity [83 %, 95 % CI (79-86)] whereas IMCI severe pneumonia had the highest specificity [91 %, 95 % CI (90-92)]. The alternative case definition (cough or difficulty breathing plus hypoxia, in-drawing, or wheeze) had a sensitivity of [55 %, 95 % CI (50-59)] and a specificity of [60 %, 95 % CI (59-62)]. The PPV for all case definitions and individual signs/symptoms ranged from 11 to 20 % while the negative predictive values were >87 %. When we stratified by age <1 year and 1- < 5 years, difficulty breathing, severe pneumonia and the alternative case definition were more sensitive in children aged <1 year [70 % vs. 54 %, p < 0.01], [19 % vs. 11 %, p = 0.01] and [66 % vs. 43 %, p < 0.01] respectively, while non-severe pneumonia was more sensitive [14 % vs. 26 %, p < 0.01] among children aged 1- < 5 years. CONCLUSION: The sensitivity and specificity of different commonly used case definitions for detecting laboratory-confirmed RSV cases varied widely, while the positive predictive value was consistently low. Optimal choice of case definition will depend upon study context and research objectives.

INTRODUCTION: Every year the World Health Organization (WHO) recommends which influenza virus strains should be included in a northern hemisphere (NH) and a southern hemisphere (SH) influenza vaccine. To determine the best vaccine formulation for Kenya, we compared influenza viruses collected in Kenya from April 2007 to May 2013 to WHO vaccine strains. METHODS: We collected nasopharyngeal and oropharyngeal (NP/OP) specimens from patients with respiratory illness, tested them for influenza, isolated influenza viruses from a proportion of positive specimens, tested the isolates for antigenic relatedness to vaccine strains, and determined the percentage match between circulating viruses and SH or NH influenza vaccine composition and schedule. RESULTS: During the six years, 7.336 of the 60,072 (12.2%) NP/OP specimens we collected were positive for influenza: 30,167 specimens were collected during the SH seasons and 3717 (12.3%) were positive for influenza; 2903 (78.1%) influenza A, 902 (24.2%) influenza B, and 88 (2.4%) influenza A and B positive specimens. We collected 30,131 specimens during the NH seasons and 3978 (13.2%) were positive for influenza; 3181 (80.0%) influenza A, 851 (21.4%) influenza B, and 54 (1.4%) influenza A and B positive specimens. Overall, 362/460 (78.7%) isolates from the SH seasons and 316/338 (93.5%) isolates from the NH seasons were matched to the SH and the NH vaccine strains, respectively (p<0.001). Overall, 53.6% and 46.4% SH and NH vaccines, respectively, matched circulating strains in terms of vaccine strains and timing. CONCLUSION: In six years of surveillance in Kenya, influenza circulated at nearly equal levels during the SH and the NH influenza seasons. Circulating viruses were matched to vaccine strains. The vaccine match decreased when both vaccine strains and timing were taken into consideration. Either vaccine formulation could be suitable for use in Kenya but the optimal timing for influenza vaccination needs to be determined.

In some influenza surveillance systems, timely transport to laboratories for reverse transcription-polymerase chain reaction (RT-PCR) testing is challenging. Guidelines suggest that samples can be stored at 4°C for up to 96 hours but the effect of longer storage times has not been systematically evaluated. We collected nasopharyngeal and oropharyngeal specimens from patients in Kenya and stored them in viral transport medium at 2 to 8°C before testing for influenza A and B using real-time RT-PCR. From April 2008 to November 2010, we collected 7,833 samples; 940 (12%) were positive for influenza. In multivariable analysis, specimens stored for six days were less likely to be influenza-positive compared to specimens stored between zero and one day (adjusted odds ratio (aOR): 0.49, 95% confidence interval (CI): 0.27–0.93). There was no statistically significant difference in influenza positivity of specimens stored for five days compared to zero to one day. There was no statistically significant relationship between days in refrigeration and cycle threshold (Ct) values for positive samples (p=0.31). We found that samples could remain in storage for at least five days without affecting the proportion-positive of samples, potentially increasing the feasibility of including influenza surveillance sites in remote areas.

BACKGROUND: Although health care-associated infections are an important cause of morbidity and mortality worldwide, the epidemiology and etiology of respiratory health care-associated infections (rHAIs) have not been documented in Kenya. In 2010, the Ministry of Health, Kenya Medical Research Institute, and Centers for Disease Control and Prevention initiated surveillance for rHAIs at 3 hospitals. METHODS: At each hospital, we surveyed intensive care units (ICUs), pediatric wards, and medical wards to identify patients with rHAIs, defined as any hospital-onset (≥3 days after admission) fever (≥38 degrees C) or hypothermia (<35 degrees C) with concurrent signs or symptoms of acute respiratory infection. Nasopharyngeal and oropharyngeal specimens were collected from these patients and tested by real-time reverse transcription polymerase chain reaction for influenza and 7 other viruses. RESULTS: From April 2010-September 2012, of the 379 rHAI cases, 60.7% were men and 57.3% were children <18 years old. The overall incidence of rHAIs was 9.2 per 10,000 patient days, with the highest incidence in the ICUs. Of all specimens analyzed, 45.7% had at least 1 respiratory virus detected; 92.2% of all positive viral specimens were identified in patients <18 years old. CONCLUSION: We identified rHAIs in all ward types under surveillance in Kenyan hospitals. Viruses may have a substantial role in these infections, particularly among pediatric populations. Further research is needed to refine case definitions and understand rHAIs in ICUs.

BACKGROUND: A 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. METHODS: For 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. RESULTS: Of 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. CONCLUSION: RSV 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.