 Research Article
 Open Access
 Open Peer Review
This article has Open Peer Review reports available.
Evaluating vaccination strategies for reducing infant respiratory syncytial virus infection in lowincome settings
 Piero Poletti^{1, 2}Email author,
 Stefano Merler^{2},
 Marco Ajelli^{2},
 Piero Manfredi^{3},
 Patrick K Munywoki^{4},
 D James Nokes^{4, 5} and
 Alessia Melegaro^{1}
https://doi.org/10.1186/s129160150283x
© Poletti et al.; licensee BioMed Central. 2015
Received: 14 October 2014
Accepted: 22 January 2015
Published: 10 March 2015
Abstract
Background
Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract disease and related hospitalization of young children in least developed countries. Individuals are repeatedly infected, but it is the first exposure, often in early infancy, that results in the vast majority of severe RSV disease. Unfortunately, due to immunological immaturity, infants are a problematic RSV vaccine target. Several trials are ongoing to identify a suitable candidate vaccine and target group, but no immunization program is yet in place.
Methods
In this work, an individualbased model that explicitly accounts for the sociodemographic population structure is developed to investigate RSV transmission patterns in a rural setting of Kenya and to evaluate the potential effectiveness of alternative population targets in reducing RSV infant infection.
Results
We find that household transmission is responsible for 39% of infant infections and that schoolage children are the main source of infection within the household, causing around 55% of cases. Moreover, assuming a vaccineinduced protection equivalent to that of natural infection, our results show that annual vaccination of students is the only alternative strategy to routine immunization of infants able to trigger a relevant and persistent reduction of infant infection (on average, of 35.6% versus 41.5% in 10 years of vaccination). Interestingly, if vaccination of pregnant women boosts maternal antibody protection in infants by an additional 4 months, RSV infant infection will be reduced by 31.5%.
Conclusions
These preliminary evaluations support the efforts to develop vaccines and related strategies that go beyond targeting vaccines to those at highest risk of severe disease.
Keywords
 Computational models
 Infectious diseases
 RSV
 Vaccination
 Household transmission
Background
Respiratory syncytial virus (RSV) is characterized worldwide by recurrent epidemics [14] and represents a leading cause of hospitalization of young children in least developed countries (LDCs) [57], where the vast majority of severe disease (and deaths) caused by RSV occurs [6]. Indeed, RSV can be considered one of the predominant viral pathogens among hospitalized infants [5,7].
Primary RSV infection usually arises in the first two years of life, and repeated reinfections occur throughout life [3,4,8,9]. However, most severe disease occurs among individuals infected during their first year of life, usually as a consequence of primary RSV infection [36,8,1012].
During the 1960s, trials of a formalininactivated RSV vaccine administered to RSVnaive US children led to enhanced lower respiratory tract disease (LRTD) following natural RSV challenge in the immunized group [13]. After the failure of this immunization experience, different live attenuated RSV vaccines were evaluated in clinical trials in adults, young children, and infants, but none has gone beyond earlystage clinical trials or been licensed yet [14]. Indeed, although the highest priority target population for vaccine development is the RSVnaive child [3,14], clinical evaluation of various vaccine candidates has met a range of problems related to the lack of immunological maturity and vaccine tolerance in this vulnerable age group that has thwarted progress [13,14]. It is still unclear which vaccine would result in an effective protection against RSV infection and RSVassociated disease, and which, if any, would be suitable for being administered safely to infants.
The first objective of this work is to investigate, through mathematical modeling techniques, RSV transmission patterns in a lowincome setting in order to realistically describe and understand the transmission chain leading to RSV infant infection, which causes most of RSVassociated serious disease. An empirical basis for this work is household studies [9,15] that have suggested a significant role of schoolage children in introducing and spreading RSV infection in the household. The second objective of this work is to assess which immunization strategies can be effective in preventing RSV at a population level. More specifically, we aim to identify which subgroup of the population should be targeted, and with which schedule, in order to achieve an effective reduction of RSV infection rates in those age groups at highest risk of severe disease. The exploration of the various alternatives might provide useful indications for current and future vaccine development efforts by assessing which age targets can be considered as valuable for interrupting the chain of transmission and reducing infection rates in the first year of life.
Methods
Individualbased transmission models of infectious diseases represent a powerful tool to evaluate and optimize interventions aimed at controlling human infectious diseases [1619]. The need for detailed epidemiological and sociodemographic data to calibrate microsimulation models makes the implementation of similar tools for LDCs a modeling challenge. In this work, a highly detailed sociodemographic and disease transmission model is developed and used to investigate the RSV transmission dynamics in a predominantly rural location of a lowincome setting of coastal Kenya, representing an illustrative case of a highburden region with limited financial resources. Specifically, the RSV infection process is simulated over a synthetic population of individuals structured in households and schools that is consistent with the main descriptive statistics of the Kenyan population as reported in the Demographic and Health Surveys (DHS, [20], accessed Jul 2014).
Modeling sociodemographic characteristics of a subSaharan population
The simulated synthetic population consists of about 200,000 individuals, all explicitly represented in the model and grouped into households and primary schools. In particular, the simulated households structure mirrors existing generational age gaps between parents and their children and the observed complex heterogeneity in the Kenyan population, such as the colocation of several generations (up to four) within a single household. This was achieved through intensive use of available sociodemographic data for Kenya from the Demographic and Health Surveys (DHS) and other available data. DHS data were used to randomly assign age and colocate individuals in households. Individuals were assigned to schools according to the agespecific school attendance rates computed by using the DHS dataset and school size distribution as provided by the Open Kenya dataset ([21], accessed Jun 2014). Annual fertility rates by age were computed according to the DHS statistics for the years 20022008. Annual mortality rates by age were obtained from the World Population Prospects of the United Nations ([22], accessed Jun 2014). The model was capable of reproducing the observed distributions of both household and school size, the age structure of the Kenyan population and the agespecific school attendance rates. Further details on the procedure used for generating the synthetic population and the validation process against the available sociodemographic data are provided in Additional file 1.
RSV transmission model
The epidemiological model distinguishes between primary RSV infection, related to the first exposure to RSV in life, and subsequent infections. In agreement with epidemiological evidence [4] and with past modeling works [2325], temporary (waning) immunity is combined in our model with a lifelong reduced susceptibility of individuals who have already experienced a first RSV infection.

i,h_{ i },s_{ i } identify, respectively, an individual, his/her household, and (if any) his/her school;

\(\phantom {\dot {i}\!}I,I^{h_{i}},I^{s_{i}}\) represent the number of infectious individuals in the population, in household h_{ i }, and in school s_{ i };

\(\phantom {\dot {i}\!}N, N^{h_{i}},N^{s_{i}}\) represent the number of individuals in the population, in household h_{ i } and in school s_{ i };

ρ_{ i } is the susceptibility to infection of individual i, which depends on the infection history of i; ρ=1 if i has never been infected and ρ=x<1 if i has already experienced an RSV infection;

β is the settingindependent RSV transmission rate.
After each infection episode, infectious individuals recover with probability γΔt, where 1/γ is the average length of the infectious period, which is assumed to be 11 days, in agreement with the estimated average duration of shedding given in the literature [27]. Recovered individuals are initially fully protected against reinfection, but become susceptible (at least partially) again with probability δΔt, where 1/δ is the average duration of the temporary complete immunity against reinfection. At the beginning of the simulated epidemic, the fraction of individuals by age who had already experienced an RSV infection in the past was approximated by the agespecific fraction of seropositive individuals as observed in the cohorts followed between 2002 and 2005 [4]. Incidence rates of primary and repeated RSV infections by age were also derived from the same individuals and used to calibrate our model. Such approximations can be justified by the evidence that the RSV serological profile does not significantly change over a short period of time (from 2002 to 2005). Monthly importation of new RSV cases is considered in model simulations in order to account for a population that is not fully closed to RSV infections generated outside of the study area (see Additional file 1). Imported cases are randomly chosen in the population of susceptible resident individuals. The Kenyan school calendar year is considered in model simulations; that is, school enrollment is implemented at the beginning of January and school closures are implemented in April, August, and December (World Data on Education [28], accessed Jun 2014); RSV transmission in school is interrupted during these periods (that is, we assume \(I_{s_{i}}(t)/N_{s_{i}}(t) = 0\)).
We assume homogeneous mixing among individuals who share the same setting, and a settingindependent transmission rate. This means that: 1) each individual has contacts at random with all the individuals who share with her/him the same setting (for example, the same school or the same household); 2) the probability that a specific individual i has a contact with a specific individual j is inversely proportional to the setting size; 3) each contact between a susceptible and an infected individual generates a new infection with a probability that does not depend on the setting where the contact occurs.
A sensitivity analysis is reported in Additional file 1, where we assume three distinct transmission rates, one for each of the three distinct settings considered in the model: household, school, and general community.
It is shown that the set of strategies resulting in effective prevention of RSV infant infection does not change when the assumption of a settingindependent transmission rate is relaxed.
Model calibration
The transmission model is calibrated by performing a Bayesian statistical analysis [29,30] of primary and repeated infection incidence data collected from a birth cohort of 635 children in Kilifi, Kenya, each of whom was followed over three consecutive RSV seasons (from 2002 to 2005) [3,4,31]. The model has the following free parameters: 1) the RSV transmission rate β; 2) the average duration of complete immunity generated from each infection event 1/δ; 3) the relative susceptibility to RSV reinfection once temporary immunity has waned x.
Vaccination
Vaccinated individuals (including those who have already experienced RSV) are assumed to gain a temporary full protection against infection. Similarly to natural infection, once temporary vaccine protection has waned, immunized individuals are assumed to become susceptible to RSV infection.
Given the unknown efficacy of potential RSV vaccines and the uncertainties surrounding future coverage, we consider three scenarios on the percentage of successfully immunized individuals in the target population, namely, 100%, 80%, and 60%. Also, three different scenarios on mean duration of vaccine protection against RSV infection are evaluated: 4, 6, and 12 months.
We first considered a routine universal vaccination strategy at 3 months of age, with and without a catchup campaign targeting individuals between 3 months and 15 years of age for the first year of the program. Immunization at 3 months of age is simulated in order to evaluate potential benefits resulting from the availability of a vaccine suitable for being administered to infants. Routine immunization of infants is assumed to occur within the third month of age of each child.
The second targeted age group was schoolage children. Specifically, a oneoff vaccination at first school enrollment is evaluated, with and without a catchup campaign in the first year of the program targeting only students of primary schools. Potential effects of repeating the vaccination campaign annually, as recently implemented in the UK [32] for controlling seasonal influenza, are also explored by assuming all primary students as our annual target population. The effects of targeting only students cohabiting with infants are also explored. Vaccination at school enrollment, vaccination of all students, and all catchup strategies are implemented on the first day of January.
Thirdly, in line with renewed interest in targeting pregnant women for RSV vaccination [14] (see also [33], accessed Jun 2014), we explored this strategy under two different assumptions: a) the vaccine has no direct protective effect on the offspring of immunized women who will benefit only indirectly from herd immunity effects; b) the vaccine administered to pregnant women provides a longer duration of maternal antibody protection against RSV infection to their newborns. In the latter case we assume that vaccination of pregnant women extends the duration of natural maternal protection provided by unvaccinated mothers (which is assumed to be 4 months) to 5, 6, and 8 months, representing, respectively, an additional 1, 2, and 4 months protection due to vaccine boosting. Vaccination of pregnant women is performed at each birth.
Results and discussion
RSV transmission in the Kilifi cohort: data and simulation
RSV transmissibility potential
In order to provide useful insight on the transmissibility potential of RSV in the considered population a range of possible reproductive numbers based on the proposed transmission model and on the parameter estimates obtained by model calibration were computed.
In particular, we first compute the basic reproduction number R_{0} and the effective reproductive number R_{ e }, which are respectively defined as the average number of individuals infected by a typical infectious individual in a fully susceptible population and when a fraction of the population is  at least partially  protected against the infection. Second, the transmission potential of RSV infection has also been investigated by computing the R^{ i n d e x } and the \(R_{e}^{index}\) which are respectively defined as the average number of individuals infected by the first infectious individual (the index case) in a fully susceptible population and in a partially immune population. The procedures adopted for computing R_{0},R_{ e },R^{ i n d e x }, and \(R_{e}^{index}\) are detailed in Additional file 1.
The obtained estimates of the RSV transmission potential are in satisfactory agreement with independent estimates obtained elsewhere by using an SIRS model (that is, with no partial lifelong immunity) [24]. In addition, by computing the average number of individuals infected by the first infectious individual in a partially immune population within her/his household we inferred the chance of observing a household outbreak after the introduction of RSV in a household. Our results suggest that a randomly sampled index case produces on average 0.50 case (95% CI 0.41,0.55) within the household, therefore resulting in a household outbreak in 50% of cases. This estimate is in line with results obtained in [Munywoki et al. 2014] where out of 73 separate RSV introductions into households, only 32 (43%) generated a household outbreak.
Finally, estimates of R_{0}, R_{ e }, R^{ i n d e x }, and \(R_{e}^{index}\) are lower when no transmission in schools is assumed (for example, when schools are closed). This suggests that schools and students play a relevant role in the transmissibility of RSV in the population. This is supported by the investigation of \(R_{e}^{index}\) stratified by age classes which highlight that the \(R_{e}^{index}\) of students is 67% higher than the average \(R_{e}^{index}\), but that it is reduced by 40% when schools are closed.
The RSV transmission chain: the role played by households and schoolage children
In agreement with a recent epidemiological study which investigated the role of household transmission in a rural community in the Kilifi district [15], we found that the percentage of infant primary infections generated by household transmission in a specific subgroup of households (see [15] for details) is on average 49.5% (CI 95% 41.0,58.5) (versus 53% in [15]). Such results refer to a set of households chosen among those with at least one infant and one or more older siblings under 13 years of age [15].
By extending our analysis to all types of households and by accounting for both primary and repeated infections, we found that, on average, 38.3% (CI 95% 35.4,40.9) of infants and 38.6% (95% CI 37.4,40.0) of children under 5 years of age contract RSV as a consequence of contacts occurring within households. On the other hand, among older children (510 years), where school attendance rates are high (see Additional file 1), school transmission is predicted to be responsible for about 30% of RSV infections (see Figure 2a).
The contribution of household contacts to overall RSV transmission (that is, irrespective of age) increases with household size. For instance, while 33.0% (95% CI 32.4,33.4) of transmission in the overall population occurs in the household, if we restrict our analysis to households of two individuals, the resulting average proportion of cases generated within the household is less than 12%, while it is about 35% when considering households with ten individuals or more (details are reported in Additional file 1).
Our analysis allows the identification of individuals who introduce the infection into the households (index cases) as well as the first cases generated by household contacts (secondary cases). We found that, on average, 54.6% (CI 95% 41.6,65.8) of index cases responsible for a household outbreak were schoolage children. Moreover, the role played by different age categories in introducing RSV into households was highlighted by computing for each age the ratio between the probability of being an index case and the probability of being the first secondary case generated by household transmission. The resultant ratio is presented in Figure 2b and shows that schoolage children are, on average, up to 50% more likely to be index cases than first secondary cases.
We further investigated household transmission by recording, for each case generated within households, both the age of the infected individual and the age of its infector. Figure 2d aims to highlight where (in terms of ages) most of the infections occur by showing the proportion of incidence of within household transmission due to contacts occurring between individuals of different ages. Specifically, in Figure 2d matrix M_{i,j} is reported, where \(M_{i,j} = 100 \cdot C_{i,j} /\sum _{i}\sum _{j} C_{i,j}\) and C_{i,j} is the recorded number of household infection episodes where an individual of age i was infected by an individual of age j.
Our results show that most of the transmission occurs between siblings of similar age and between parents and their children. However, schoolage children are predicted to be the main source of household transmission, causing approximately 55% of all RSV cases generated within household and 40% of infant cases. This indicates that vaccination of schoolage children might represent a valuable strategy for decreasing the occurrence of infection within households and, in turn, among infants.
Preventing RSV in infants with shortlived vaccine immunity
Our results show that routine immunization at 3 months of age and annual repeated vaccination of all students are the only two strategies able to trigger a relevant and persistent reduction of RSV infection occurrence in infants. Specifically, in 10 years of vaccination, the former strategy is predicted to reduce RSV infant infection incidence by 41.5% (95% CI 40.2,63.8) with little impact on the other age groups, while the latter strategy is predicted to generate a reduction of 35.6% (95% CI 13.7,89.4) in infants and 48.0% (95% CI 23.1,85.4) in the general population. The benefits resulting from catchup campaigns performed in the first year of vaccination completely wane after a few years (less than five), and require a significant increase in the number of administered vaccines (see Figure 3 and Additional file 1).
Although repeated vaccination of students entails a relatively high number of annually administered vaccines, namely 0.19 dose per person per year, this strategy is expected to be the most valuable alternative to infant immunization if RSV vaccines would not be suitable to be administered safely to infants. In addition, by employing this immunization strategy, the median age at first infection is expected to increase significantly, from 16.0 months to 21.0 months on average (see Additional file 1), possibly leading to a reduction in the incidence of severe disease.
It is worth noting that vaccination targeting only students (or new students) cohabiting with infants is not enough to break the RSV transmission chain leading to infant infection, entailing only negligible delays in the median age at first infection (see Additional file 1).
The potential benefits of longer vaccine immunity
Unfortunately, for a vaccineinduced immunity shorter than the protection provided by natural infection (for example, 4 months), the ability of the latter strategies to prevent RSV infection in infancy is markedly reduced (see Figure 4). Indeed, in this case, the expected average reduction of RSV infant incidence after 10 years of vaccination with routine immunization and annual vaccination of students ranges between 23.4% and 24.2%, on average.
The scenario of suboptimal coverage
If the fraction of successfully immunized individuals in the target population is reduced  as a consequence of either a lower vaccine coverage or a limited vaccine efficacy  the expected reduction in RSV incidence among infants and in the general population achieved after 10 years of vaccination decreases for all the considered strategies (see Figure 4). However, if the vaccineinduced immunity lasts 1 year, even when only 60% of the targeted population is protected, routine immunization of infants and repeated vaccination of all students are predicted to reduce infant RSV incidence by on average 39.6% and 45.6%, respectively, after 10 years of vaccination.
In contrast, the effectiveness of newborn immunization through vaccination of pregnant women seems to be only slightly affected by suboptimal coverage. Indeed, under all assumptions on the duration of vaccineinduced immunity, when the fraction of protected infants through vaccination of pregnant women is 60%, the reduction of RSV infections in infants is predicted to be at most 4% lower than what could be achieved with 100% coverage and a fully effective vaccine. The robustness of this strategy is especially relevant considering that the delivery of a vaccine through antenatal care may be suboptimal because of possibly low women attendance rates to antenatal clinics, and also in terms of protection because of premature births. This means that, if a vaccine able to significantly boost maternal protection of newborns through vaccination of pregnant women would be available, this strategy might possibly represent the best tradeoff between the ability of preventing RSV in infants and a limited number of doses required.
Conclusions
Although vaccine development aimed at prevention of RSV disease in young children dates back to the 1960s, and several trials are ongoing to identify a suitable candidate product, no vaccine has been licensed yet [14]. A better understanding of the RSV transmission chain leading to infant infection can be critical to the selection of suitable candidate vaccines and population targets [14,37].
With this objective in mind, we have developed an individualbased model for investigating RSV transmission patterns in a lowincome setting of rural coastal Kenya. The chosen modeling framework has allowed us to disentangle the contribution of different social settings in the spread of the infection in the population, to characterize the transmission chain leading to RSV infant infections, and to investigate the impact of a range of immunization schedules that are plausible alternatives to routine early infant vaccination. Specifically, these options include vaccination of school children, siblings of susceptible naive infants, and pregnant women.
Our analysis determined household transmission to be responsible, on average, for about 39% of infant infections and found that schoolage children play a key role in introducing RSV infection into the household, causing about 55% of household outbreaks. Moreover, our results showed that RSV infection provides complete natural immunity against reinfection for a period of around 6 months, after which resistance to reinfection appears very limited.
The vaccination strategies considered are shown to reduce RSV infection incidence both in infants and in the general population in different ways, and their impact critically depends on the duration of vaccineinduced protection. However, model predictions robustly suggest that as an alternative to direct vaccination of very young children, prevention of RSV in infancy can be achieved by reducing the transmission in the general population through the vaccination of students. In particular, in the case of a shortlived vaccineinduced immunity, for example, 6 months, the annual vaccination of all students is predicted to be the most valuable alternative to the direct immunization of young infants, reducing RSV infection occurrence in those less than 1 year of age by more than 35% after 10 years of vaccination, as opposed to a 41% reduction of infant infections when directly targeting 3monthold babies. Additionally, a maternal vaccine able to increase the average duration of passive RSV protection by only 4 months represents an effective strategy for preventing RSV occurrence in infancy, even in the case of suboptimal coverage. In particular, in this case, our results show that RSV infant infection incidence could be reduced through vaccination of pregnant women by at least 30%.
In conclusion, our results robustly show that schoolage children should be considered for alternative effective vaccination programs in case direct immunization of highrisk infants is not achievable. In addition, vaccination of pregnant women also has the potential of being an effective strategy. Clearly, for strategies targeting the transmitters of infection rather than those most vulnerable to disease following infection, education of the public will be a priority to ensure the acceptability and sustainability of such immunization programs [32,38].
Finally, note that in this work there is no attempt to quantify the impact of vaccination on the risk of disease following infection. Since the changes in incidence predicted are accompanied by changes in the age distribution of infection (to a degree varying by strategy), and because the risk of disease is highly dependent on age at infection, then the outcomes of vaccine impact on infection and on disease are not directly proportional. Thus, for example, the effect of repeated vaccination to school age children with large effect on age at infection would result in a proportionally higher effect on RSV disease. Also, the effect of maternal vaccination and increased duration of passive immunity in infants would directly protect those most at risk of severe disease, and so the impact on disease would be more significant than the reported impact on infections.
Declarations
Acknowledgements
The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/20072013)/ERC Grant agreement 283955 (DECIDE) to PP and AM. The work was funded in part by the Wellcome Trust, UK (077092), (084633), (102975).
Authors’ Affiliations
References
 Waris M. Pattern of respiratory syncytial virus epidemics in Finland: twoyear cycles with alternating prevalence of groups A and B. J Infect Dis. 1991; 163:464–9.View ArticlePubMedGoogle Scholar
 MlinaricGalinovic G, Welliver RC, VilibicCavlek T, LjubinSternak S, Drazenovic V, Galinovic I, et al. The biennial cycle of respiratory syncytial virus outbreaks in Croatia. Virol J. 2008; 5:18.View ArticlePubMedPubMed CentralGoogle Scholar
 Nokes DJ, Okiro EA, Ngama M, Ochola R, White LJ, Scott PD, et al. Respiratory syncytial virus infection and disease in infants and young children observed from birth in Kilifi District, Kenya. Clin Infect Dis. 2008; 46:50–7.View ArticlePubMedPubMed CentralGoogle Scholar
 Ohuma EO, Okiro EA, Ochola R, Sande CJ, Cane PA, Medley GF, et al. The natural history of respiratory syncytial virus in a birth cohort: the influence of age and previous infection on reinfection and disease. Am J Epidemiol. 2012; 176:794–802.View ArticlePubMedPubMed CentralGoogle Scholar
 Nokes DJ, Ngama M, Bett A, Abwao J, Munywoki PK, English M, et al. Incidence and severity of respiratory syncytial virus pneumonia in rural Kenyan children identified through hospital surveillance. Clin Infect Dis. 2009; 49:1341–9.View ArticlePubMedPubMed CentralGoogle Scholar
 Nair H, Nokes DJ, Gessner BD, Dherani M, Madhi SA, Singleton RJ, et al. Global burden of acute lower respiratory infections due to respiratory syncytial virus in young children: a systematic review and metaanalysis. Lancet. 2010; 375:1545–55.View ArticlePubMedPubMed CentralGoogle Scholar
 Berkley JA, Munywoki P, Ngama M, Kazungu S, Abwao J, Bett A, et al. Viral etiology of severe pneumonia among Kenyan infants and children. J Am Med Assoc. 2010; 303:2051–7.View ArticleGoogle Scholar
 Glezen WP, Taber LH, Frank AL, Kasel JA. Risk of primary infection and reinfection with respiratory syncytial virus. Am J Dis Child. 1986; 140:543–6.PubMedGoogle Scholar
 Hall CB, Geiman JM, Biggar R, Kotok DI, Hogan PM, Douglas RG. Respiratory syncytial virus infections within families. N Engl J Med. 1976; 294:414–41.View ArticlePubMedGoogle Scholar
 Hall CB, Weinberg GA, Iwane MK, Blumkin AK, Edwards KM, Staat MA, et al. The burden of respiratory syncytial virus infection in young children. N Engl J Med. 2009; 360:588–98.View ArticlePubMedPubMed CentralGoogle Scholar
 Okiro EA, White LJ, Ngama M, Cane PA, Medley GF, Nokes DJ. Duration of shedding of respiratory syncytial virus in a community study of Kenyan children. BMC Infect Dis. 2010; 10:15.View ArticlePubMedPubMed CentralGoogle Scholar
 Fletcher JN, Smyth RL, Thomas HM, Ashby D, Hart CA. Respiratory syncytial virus genotypes and disease severity among children in hospital. Arch Dis Child. 1997; 77:508–11.View ArticlePubMedPubMed CentralGoogle Scholar
 Polack FP, Karron RA. The future of respiratory syncytial virus vaccine development. Pediatr Infect Dis J. 2004; 23:65–73.View ArticleGoogle Scholar
 Anderson LJ, Dormitzer PR, Nokes DJ, Rappuoli R, Roca A, Graham BS. Strategic priorities for respiratory syncytial virus (RSV) vaccine development. Vaccine. 2013; 31:209–15.View ArticleGoogle Scholar
 Munywoki PK, Koech DC, Agoti CN, Lewa C, Cane PA, Medley GF, et al.The source of respiratory syncytial virus infection in infants: a household cohort study in rural Kenya. J Infect Dis. 2013; 209(10):1685–1692.PubMedPubMed CentralGoogle Scholar
 Ferguson NM, Cummings DAT, Cauchemez S, Fraser C, Riley S, Meeyai A, et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature. 2005; 437:209–14.View ArticlePubMedGoogle Scholar
 Merler S, Ajelli M. The role of population heterogeneity and human mobility in the spread of pandemic influenza. Proc R Soc B. 2010; 277(1681):557–65.View ArticlePubMedGoogle Scholar
 Merler S, Ajelli M, Pugliese A, Ferguson NM. Determinants of the spatiotemporal dynamics of the 2009 H1N1 pandemic in Europe: implications for realtime modelling. PLOS Comput Biol. 2011; 7:1002205.View ArticleGoogle Scholar
 Merler S, Ajelli M, Fumanelli L, Vespignani A. Containing the accidental laboratory escape of potential pandemic influenza viruses. BMC Med. 2013; 11(3):252.View ArticlePubMedPubMed CentralGoogle Scholar
 The DHS Program, Demographic and Health Surveys. http://dhsprogram.com/data/.
 Kenya Open Data. https://opendata.go.ke/.
 United Nations, Department of Economic and Social Affairs. World Population Prospects. http://www.un.org/en/development/desa/population/.
 Gomes MGM, White LJ, Medley GF. Infection, reinfection, and vaccination under suboptimal immune protection: epidemiological perspectives. J Theor Biol. 2004; 228:539–49.View ArticlePubMedGoogle Scholar
 Weber A, Weber M, Milligan P. Modeling epidemics caused by respiratory syncytial virus (RSV). Math Biosci. 2001; 172:95–113.View ArticlePubMedGoogle Scholar
 White LJ, Mandl NJ, Gomes MGM, Bodely–Tickell AT, Cane PA, Perez–Brena P, et al. Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models. Math Biosci. 2007; 209:222–39.View ArticlePubMedPubMed CentralGoogle Scholar
 Ochola R, Sande C, Fegan G, Scott PD, Medley GF, Cane PA, et al. The level and duration of RSVspecific maternal IgG in infants in Kilifi Kenya. PLOS ONE. 2009; 4:8088.View ArticleGoogle Scholar
 Munywoki PK, Koech DC, Agoti CN, Kibirige N, Kipoech J, Cane PA, et al. Influence of age, severity of infection, and coinfection on the duration of respiratory syncytial virus (RSV) shedding. Epidemiol Infect. 2014. http://0dx.doi.org.brum.beds.ac.uk/10.1017/S0950268814001393.
 UNESCO, International Bureau of Education. http://www.ibe.unesco.org/.
 Cauchemez S, Carrat F, Viboud C, Valleron AJ, Boëlle PY. A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data. Stat Med. 2004; 23:3469–87.View ArticlePubMedGoogle Scholar
 Merler S, Ajelli M. Deciphering the relative weights of demographic transition and vaccination in the descrease of measles incidence in Italy. Proc R Soc B. 2014; 281:20132676.View ArticlePubMedPubMed CentralGoogle Scholar
 Nokes DJ, Okiro EA, Ngama M, White LJ, Ochola R, Scott PD, et al. Respiratory syncytial virus epidemiology in a birth cohort from Kilifi District, Kenya: infection during the first year of life. J Infect Dis. 2004; 190:1828–32.View ArticlePubMedGoogle Scholar
 Baguelin M, Flasche S, Camacho A, Demiris N, Miller E, Edmunds WJ. Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study. PLOS Med. 2013; 10:1001527.View ArticleGoogle Scholar
 PATH’s Work in Vaccine Development. http://sites.path.org/vaccinedevelopment/.
 Glezen WP, Paredes A, Allison JE, Taber LH, Frank AL. Risk of respiratory syncytial virus infection for infants from lowincome families in relationship to age, sex, ethnic group, and maternal antibody level. J Pediatr. 1981; 98:708–15.View ArticlePubMedGoogle Scholar
 Ferguson NM, Cummings DAT, Fraser C, Cajka JC, Cooley PC, Burke DS. Strategies for mitigating an influenza pandemic. Nature. 2006; 442:448–52.View ArticlePubMedGoogle Scholar
 Ajelli M, Merler S. The impact of the unstructured contacts component in influenza pandemic modeling. PLOS ONE. 2008; 3:1519.View ArticleGoogle Scholar
 Graham BS. Protecting the family to protect the child: vaccination strategy guided by RSV transmission dynamics. J Infect Dis. 2014; 209:1679–81.View ArticlePubMedPubMed CentralGoogle Scholar
 d’Onofrio A, Manfredi P, Poletti P. The interplay of public intervention and private choices in determining the outcome of vaccination programmes. PLOS ONE. 2012; 7:45653.View ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.