Modelling the transmission dynamics of RSV and the impact of routine vaccination

Introduction: Respiratory Syncytial Virus is the major viral cause of lower respiratory tract disease in young children worldwide, with the greatest burden of disease in infants aged 1-3 months. Consequently, vaccine development has centered on a vaccine to directly protect the infants in this age group. The fundamental problem is that these young infants are poor responders to candidate RSV vaccines. This thesis focuses on the use of mathematical models to explore the merits of vaccination. Methods: Following development and analysis of a simple non-age-structured ODE model, we elaborate this to a Realistic Age Structured model (RAS) capturing the key epidemiological characteristics of RSV and incorporating age-specific vaccination options. The compartmental ODE model was calibrated using agespecific and time series hospitalization data from a rural coastal Kenyan population. The determination of Who Acquires Infection From Whom (WAIFW) matrix was done using social contact data from 1) a synthetic mixing matrix generated from primarily household occupancy data and 2) a diary study that we conducted in the Kilifi Health and Demographic Surveillance System (KHDSS). The vaccine was assumed to elicit partial immunity equivalent to wild type infection and its impact was measured by the ratio of hospitalized RSV cases after to before introduction. of vaccination. Uncertainty and sensitivity analysis were undertaken using Latin Hypercube Sampling (LHS) and partial rank correlation respectively. Given the importance of households in the transmission of respiratory infections, an exploratory household model was developed to capture the transmission dynamics of RSV A and B in a population of households. Results: From the analytical work of the simple ODE model, we have demonstrated that the model has the potential to exhibit a backward bifurcation curve within realistic parameter ranges. Both the diary and the synthetic mixing matrices had similar characteristics i.e. strong assortative mixing in individuals less than 30 years old and strong mixing between children less than 5 years and adults between 20 and 50 years old. When the two matrices were jointly linearly regressed, their elements were well correlated with an R2 ~ 0.6. The RAS model was capable of capturing the age-specific disease and the temporal epidemic nature of RSV in the specified location. Introduction of routine universal vaccination at ages varying from the first month of life to the 10th year of life resulted in optimal long-term benefit at 7 months (for the diary contact model) and 5 months (for the synthetic contact model). The greatest benefit arose under the assumption of age-related mixing with the contact diary data with no great deal of effectiveness lost when the vaccine is delayed between 5 and 12 months of age from birth. Vaccination was also shown to change the temporal dynamics of RSV hospitalizations and also to increase the average age at primary infection. From the sensitivity analysis, we identified the duration of RSV specific maternal antibodies, duration of primary and tertiary infections as the most important parameters in explaining the imprecision observed in predicting both the age specific hospitalizations and the optimal month at vaccination. Results from the household model have demonstrated that the household epidemic profile may be different from the general population with strong interaction of the viruses in the household that do not necessarily reflect at the population level. Conclusion: The synthetic matrix method would be a preferable alternative route in estimating mixing patterns in populations with the required socio-demographic data. Retrospectively, the synthetic mixing data can be used to reconstruct contact patterns in the past and therefore beneficial in assessing the effect of demographic transition in disease transmission. Universal infant vaccination has the potential to significantly reduce the burden of RSV associated disease, even with delayed vaccination between 5 and 12 months. This age class represents the group that is being targeted by vaccines that are currently under development. More accurate data measuring the duration of RSV specific maternal antibodies and the duration of infections are required to reduce the uncertainty in the model predictions

Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

International audienc

Contact mixing patterns.

<p>Part A: Distribution of overall number of contacts (with mean shown as a dashed line). Part B: Mean (dashed line) contact rate per person per day, with boxplots showing median (centre line) and interquartile range (IQR) of contact rates per age group per day. Part C: Contact rate surface (heat map) expressing the mean number of contacts between an individual participant in each age group with individuals in each age group. Part D: Population level numbers of contacts per day within and between age groups (estimated from the matrix defined in (C) scaled by the age-specific resident population size).</p

Age group specific contact rates with 95% CI^‡.

‡<p>Confidence intervals based on 2,000 bootstraps.</p><p>^*Age group in years.</p

Age specific contact matrices.

<p>Mixing patterns for 371 participants in rural areas (Part A) and 197 participants in semiurban areas (Part B). The description of the images, from left to right, follows that in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104786#pone-0104786-g002" target="_blank">Figure 2</a> Parts A, B and C, respectively.</p

Mean number of contacts per day stratified by gender, age group (years), presence of shadow, season, residence, days of week of 568 diary participants from the Kilifi Health and Demographic Surveillance System, Kenya.

‡<p>95% CI: 95% confidence intervals derived from 2,000 bootstraps.</p>$<p>Season: Dry  =  January, August, December; Wet  =  September – November</p>&<p>Location. Rural: Ngerenya, Roka, Matsangoni; Semiurban: Kilifi Township, Tezo.</p

Baseline characteristics of 10,042 contacts by participants in a diary study in the Kilifi Health and Demographic Surveillance System, Kenya.

╞<p>Missing records as a proportion of the total contacts 10,042): Relationship to participant (198, 2.0%); Sleep in same room (22, 0.8%); Ever met the contact before (298, 3.0%); Frequency of meeting (38, 0.4%).</p>$<p>While 63% of contacts with family members (parents, spouses, children and siblings),only 28% live in the same household. Members of the same family could be living in different households and share a common compound (homestead).</p>□<p>Frequency of meeting: daily (on a day-to-day basis); regularly (more than four times a week); often (once or twice a week); rarely (once or twice a month).</p

Map of the study area.

<p>The inset shows the location of the KHDSS in relation to the former Kilifi District (part of Kilifi County). The study area locations are conventionally categorised as semiurban (Kilifi Township [denoted A] and Tezo [B]), and rural (Ngerenya [C], Roka [D] and Matsangoni [E]).</p