Integrating viral RNA sequence and epidemiological data to define transmission patterns for respiratory syncytial virus

The analyses contained herein focus on making comparisons between model inferences obtained using different scales of pathogen identification, with a particular focus on respiratory syncytial virus (RSV). A significant proportion of lower respiratory tract infections in children has been attributed to infection by RSV and as such, there has been global interest in understanding its transmission characteristics in order to plan for effective control. Mathematical models have often been used to explore potential mechanisms that drive the patterns observed in data collected at different scales. Several models have been used to explore how immunity to RSV is acquired and maintained, vaccination strategies and potential drivers of seasonality. However, most of these models do not make a distinction between the two antigenically and genetically distinct RSV groups (RSV A and RSV B), neither do they consider its ecological environment, in particular, potential interactions between RSV and other viral pathogens. This thesis therefore presents work done aimed at understanding the transmission characteristics of viral respiratory pathogens spreading in a group of households using a dynamic model of transmission The data analysed is cohort data collected between December 2009 and June 2010 from 493 individual distributed across 47 households from a rural coastal community in Kenya. Individuals in the study had nasopharyngeal swab samples collected twice weekly irrespective of symptom status. Infecting viral pathogens were identified using RT-PCR resulting in the identification of 4 main pathogens: RSV, human coronavirus, rhinovirus and adenovirus. RSV and coronavirus were further classified according to genetically distinct subgroups. Some of the RSV samples were sequenced to obtain whole genome sequences (WGS) and further classified into genetic clades/clusters. I first conducted a review of methods to identify the best way to integrate socialtemporal data and WGS genetic data into a single modelling framework for RSV. Given that the social-temporal data and genetic data were available at different sampling densities, I decided to use a model that focused on the data with the highest density. The results in this thesis are thus presented in three main chapters; the first focuses on analysing social-temporal shedding patterns of RSV identified at the group level (i.e. distinguish between RSV A and RSV B); the second incorporates the available genetic data into the model used to analyse the social-temporal data (i.e. separating RSV-A into 5 clusters, and RSV-B into 7 clusters); the third is an analysis of the interaction of two pathogens, RSV and coronavirus, identified at two different scales. One of the main findings in this thesis is that the household setting plays an important role in the spread of RSV, a finding that is made clearer with added detail on pathogen type. In the case of the data analysed here, and the social structuring from which it was collected, RSV clades appeared to mimic household structure as such identification at this level did not drastically change the transmission characteristic observed with identification at the group level. However, the combination of epidemiological and genetic data elucidated transmission chains within the household enabling the identification of the sources of infant RSV infections. For this particular study, it was inferred that the sources of infant RSV infections were both in the same household as the infant and from external sources. Where infant infections occurred in the household, the source of infection was often a child between the ages of 2-13 years. It was inferred that previous infection with one RSV group type reduced susceptibility to re-infection by heterologous group type within the same epidemic. Interactions were also observed between RSV and human coronavirus groups. In particular, previous infection with RSV B was estimated to increase susceptibility to corona OC43 by 81% (95% CrI: 40%, 134%). Detailed data of infection events in individual hosts can provide a wealth of knowledge. The inferences made from this study should be explored at larger spatial and temporal scales to determine the population level impact, and hence public-health significance, of pathogen interactions, whether these interactions are between strains of the same pathogen of between different pathogens. In planning for, and assessing the impact of, an intervention against a particular pathogen, investigators should not ignore the preexisting ecological balance and should make efforts to understand how this will be disrupted by an intervention against one or more pathogens

Defining the vaccination window for respiratory syncytial virus (RSV) using age-seroprevalence data for children in Kilifi, Kenya

Background Respiratory syncytial virus (RSV) is an important cause of lower respiratory tract disease in early life and a target for vaccine prevention. Data on the age-prevalence of RSV specific antibodies will inform on optimizing vaccine delivery. Methods Archived plasma samples were randomly selected within age strata from 960 children less than 145 months of age admitted to Kilifi County Hospital pediatric wards between 2007 and 2010. Samples were tested for antibodies to RSV using crude virus IgG ELISA. Seroprevalence (and 95% confidence intervals) was estimated as the proportion of children with specific antibodies above a defined cut-off level. Nested catalytic models were used to explore different assumptions on antibody dynamics and estimate the rates of decay of RSV specific maternal antibody and acquisition of infection with age, and the average age of infection. Results RSV specific antibody prevalence was 100% at age 0-<1month, declining rapidly over the first 6 months of life, followed by an increase in the second half of the first year of life and beyond. Seroprevalence was lowest throughout the age range 5–11 months; all children were seropositive beyond 3 years of age. The best fit model to the data yielded estimates for the rate of infection of 0.78/person/year (95% CI 0.65–0.97) and 1.69/person/year (95% CI 1.27–2.04) for ages 0-<1 year and 1-<12 years, respectively. The rate of loss of maternal antibodies was estimated as 2.54/year (95% CI 2.30–2.90), i.e. mean duration 4.7 months. The mean age at primary infection was estimated at 15 months (95% CI 13–18). Conclusions The rate of decay of maternal antibody prevalence and subsequent age-acquisition of infection are rapid, and the average age at primary infection early. The vaccination window is narrow, and suggests optimal targeting of vaccine to infants 5 months and above to achieve high seroconversion

COVID-19 transmission dynamics underlying epidemic waves in Kenya

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban–rural population structure are critical determinants of viral transmission in Kenya