Human rhinovirus spatial-temporal epidemiology in rural coastal Kenya, 2015-2016, observed through outpatient surveillance

Abstract

Background: Human rhinovirus (HRV) is the predominant cause of upper respiratory tract infections, resulting in a significant public health burden. The virus circulates as many different types (~160), each generating strong homologous, but weak heterotypic, immunity. The influence of these features on transmission patterns of HRV in the community is understudied. Methods: Nasopharyngeal swabs were collected from patients with symptoms of acute respiratory infection (ARI) at nine out-patient facilities across a Health and Demographic Surveillance System between December 2015 and November 2016. HRV was diagnosed by real-time RT-PCR, and the VP4/VP2 genomic region of the positive samples sequenced. Phylogenetic analysis was used to determine the HRV types. Classification models and G-test statistic were used to investigate HRV type spatial distribution. Demographic characteristics and clinical features of ARI were also compared. Results: Of 5,744 NPS samples collected, HRV was detected in 1057 (18.4%), of which 817 (77.3%) were successfully sequenced. HRV species A, B and C were identified in 360 (44.1%), 67 (8.2%) and 390 (47.7%) samples, respectively. In total, 87 types were determined: 39, 10 and 38 occurred within species A, B and C, respectively. HRV types presented heterogeneous temporal patterns of persistence. Spatially, identical types occurred over a wide distance at similar times, but there was statistically significant evidence for clustering of types between health facilities in close proximity or linked by major road networks. Conclusion: This study records a high prevalence of HRV in out-patient presentations exhibiting high type diversity. Patterns of occurrence suggest frequent and independent community invasion of different types. Temporal differences of persistence between types may reflect variation in type-specific population immunity. Spatial patterns suggest either rapid spread or multiple invasions of the same type, but evidence of similar types amongst close health facilities, or along road systems, indicate type partitioning structured by local spread.

Rhinovirus dynamics across different social structures

“…We analysed sequence data from five studies across Kenya, cumulatively undertaken across four social structures (Fig. 1 ): (i) intensive household surveillance in a rural location in coastal Kenya 19 , (ii) surveillance of respiratory viruses within a school setting 21 , (iii) outpatient surveillance of acute respiratory illness (ARI) within the Kilifi HDSS 22 , and (iv) countrywide surveillance of severe acute respiratory illness (SARI) among inpatients and influenza-like illness (ILI) among outpatients via sentinel hospital reporting 26 . For comparison with the households, school and Kilifi HDSS studies’ datasets, we used contemporaneous data from (v) long-term surveillance of severe pneumonia among pediatric inpatients at the Kilifi County Hospital (KCH) 15 , 27 – 29 .…”

“…The five studies above are distinct and independent of each other, and a summary of the individual studies and sampling for extensive RV analysis is described in Supplementary Table 1 . Detailed information on study designs is described in previous publications 11 , 14 , 15 , 29 – 31 and RV analyses have also been reported 13 , 19 , 21 , 22 , 26 . The geographic locations of the studies are depicted in Fig.…”

“…Current epidemiological understanding of RV is restricted to the genetic diversity and transmission within individual settings such as hospitals, schools, or households 7 , 15 , 18 – 21 . RV infections occur year-round and are characterized by localized type-specific ‘mini-epidemics’ 21 , 22 . Numerous types co-circulate within a setting, and their profiles change temporally 13 , 23 .…”

Spatio-temporal distribution of rhinovirus types in Kenya: A retrospective analysis, 2014

“…A sample was considered RV positive if the rt-RT-PCR cycle threshold (Ct) was ≤35.0 32 . The VP4/VP2 viral genomic region (~420 nucleotides long) of positive samples was amplified and sequenced as previously described 33 .…”

Identifying socio-ecological drivers of common cold in Bhutan: a national surveillance data analysis

“…However, a study in Vietnam found that rainfall and temperature lagged 1 week were positively associated with acute respiratory infection caused by respiratory syncytial virus (RSV) 26 . Evidence also suggest strong spatial clustering of respiratory pathogens in certain locations driven by their proximities and pattern of human activities 27 . Therefore, this study aimed to quantify the effect of climatic variables on the common cold and investigate the spatial and temporal patterns of common cold in Bhutan.…”