Protecting infants against RSV disease: an impact and cost-effectiveness comparison of long-acting monoclonal antibodies and maternal vaccination.

Background: Two new products for preventing Respiratory Syncytial Virus (RSV) in young children have been licensed: a single-dose long-acting monoclonal antibody (la-mAB) and a maternal vaccine (MV). To facilitate the selection of new RSV intervention programmes for large-scale implementation, this study provides an assessment to compare the costs of potential programmes with the health benefits accrued. Methods: Using an existing dynamic transmission model, we compared maternal vaccination to la-mAB therapy against RSV in England and Wales by calculating the impact and cost-effectiveness. We calibrated a statistical model to the efficacy trial data to accurately capture their immune waning and estimated the impact of seasonal and year-round programmes for la-mAB and MV programmes. Using these impact estimates, we identified the most cost-effective programme across pricing and delivery cost assumptions. Findings: For infants under six months old in England and Wales, a year-round MV programme with 60% coverage would avert 32% (95% CrI 22-41%) of RSV hospital admissions and a year-round la-mAB programme with 90% coverage would avert 57% (95% CrI 41-69%). The MV programme has additional health benefits for pregnant women, which account for 20% of the population-level health burden averted. A seasonal la-mAB programme could be cost-effective for up to £84 for purchasing and administration (CCPA) and a seasonal MV could be cost-effective for up to £80 CCPA. Interpretation: This modelling and cost-effectiveness analysis has shown that both the long-acting monoclonal antibodies and the maternal vaccine could substantially reduce the burden of RSV disease in the infant population. Our analysis has informed JCVI's recommendations for an RSV immunisation programme to protect newborns and infants. Funding: National Institute for Health Research.

Genomic epidemiology of SARS-CoV-2 variants during the first two years of the pandemic in Colombia.

BACKGROUND: The emergence of highly transmissible SARS-CoV-2 variants has led to surges in cases and the need for global genomic surveillance. While some variants rapidly spread worldwide, other variants only persist nationally. There is a need for more fine-scale analysis to understand transmission dynamics at a country scale. For instance, the Mu variant of interest, also known as lineage B.1.621, was first detected in Colombia and was responsible for a large local wave but only a few sporadic cases elsewhere. METHODS: To better understand the epidemiology of SARS-Cov-2 variants in Colombia, we used 14,049 complete SARS-CoV-2 genomes from the 32 states of Colombia. We performed Bayesian phylodynamic analyses to estimate the time of variants' introduction, their respective effective reproductive number, and effective population size, and the impact of disease control measures. RESULTS: Here, we detect a total of 188 SARS-CoV-2 Pango lineages circulating in Colombia since the pandemic's start. We show that the effective reproduction number oscillated drastically throughout the first two years of the pandemic, with Mu showing the highest transmissibility (Re and growth rate estimation). CONCLUSIONS: Our results reinforce that genomic surveillance programs are essential for countries to make evidence-driven interventions toward the emergence and circulation of novel SARS-CoV-2 variants.

Colombia reported its first COVID-19 case on 6th March 2020. By April 2022, the country had reported over 6 million infections and over 135,000 deaths. Here, we aim to understand how SARS-CoV-2, the virus that causes COVID-19, spread through Colombia over this time and how the predominant version of the virus (variant) changed over time. We found that there were multiple introductions of different variants from other countries into Colombia during the first two years of the pandemic. The Gamma variant was dominant earlier in 2021 but was replaced by the Delta variant. The Mu variant had the highest potential to be transmitted. Our findings provide valuable insights into the pandemic in Colombia and highlight the importance of continued surveillance of the virus to guide the public health response.

Inferring the multiplicity of founder variants initiating HIV-1 infection: a systematic review and individual patient data meta-analysis.

BACKGROUND: HIV-1 infections initiated by multiple founder variants are characterised by a higher viral load and a worse clinical prognosis than those initiated with single founder variants, yet little is known about the routes of exposure through which transmission of multiple founder variants is most probable. Here we used individual patient data to calculate the probability of multiple founders stratified by route of HIV exposure and study methodology. METHODS: We conducted a systematic review and meta-analysis of studies that estimated founder variant multiplicity in HIV-1 infection, searching MEDLINE, Embase, and Global Health databases for papers published between Jan 1, 1990, and Sept 14, 2020. Eligible studies must have reported original estimates of founder variant multiplicity in people with acute or early HIV-1 infections, have clearly detailed the methods used, and reported the route of exposure. Studies were excluded if they reported data concerning people living with HIV-1 who had known or suspected superinfection, who were documented as having received pre-exposure prophylaxis, or if the transmitting partner was known to be receiving antiretroviral treatment. Individual patient data were collated from all studies, with authors contacted if these data were not publicly available. We applied logistic meta-regression to these data to estimate the probability that an HIV infection is initiated by multiple founder variants. We calculated a pooled estimate using a random effects model, subsequently stratifying this estimate across exposure routes in a univariable analysis. We then extended our model to adjust for different study methods in a multivariable analysis, recalculating estimates across the exposure routes. This study is registered with PROSPERO, CRD42020202672. FINDINGS: We included 70 publications in our analysis, comprising 1657 individual patients. Our pooled estimate of the probability that an infection is initiated by multiple founder variants was 0·25 (95% CI 0·21-0·29), with moderate heterogeneity (Q=132·3, p<0·0001, I2=64·2%). Our multivariable analysis uncovered differences in the probability of multiple variant infection by exposure route. Relative to a baseline of male-to-female transmission, the predicted probability for female-to-male multiple variant transmission was significantly lower at 0·13 (95% CI 0·08-0·20), and the probabilities were significantly higher for transmissions in people who inject drugs (0·37 [0·24-0·53]) and men who have sex with men (0·30 [0·33-0·40]). There was no significant difference in the probability of multiple variant transmission between male-to-female transmission (0·21 [0·14-0·31]), post-partum transmission (0·18 [0·03-0·57]), pre-partum transmission (0·17 [0·08-0·33]), and intra-partum transmission (0·27 [0·14-0·45]). INTERPRETATION: We identified that transmissions in people who inject drugs and men who have sex with men are significantly more likely to result in an infection initiated by multiple founder variants, and female-to-male infections are significantly less probable. Quantifying how the routes of HIV infection affect the transmission of multiple variants allows us to better understand how the evolution and epidemiology of HIV-1 determine clinical outcomes. FUNDING: Medical Research Council Precision Medicine Doctoral Training Programme and a European Research Council Starting Grant.

Optimal Respiratory Syncytial Virus intervention programmes using Nirsevimab in England and Wales.

INTRODUCTION: Respiratory Syncytial Virus (RSV) is a major cause of acute lower respiratory tract infections (ALRI) in infants. There are no licensed vaccines and only one monoclonal antibody available to protect infants from disease. A new and potentially longer-lasting monoclonal antibody, Nirsevimab, showed promising results in phase IIb/III trials. We evaluate the cost-effectiveness of Nirsevimab intervention programmes in England and Wales. METHODS: We used a dynamic model for RSV transmission, calibrated to data from England and Wales. We considered a suite of potential Nirsevimab programmes, including administration to all neonates (year-round); only neonates born during the RSV season (seasonal); or neonates born during the RSV season plus infants less than six months old before the start of the RSV season (seasonal + catch-up). RESULTS: If administered seasonally to all infants at birth, we found that Nirsevimab would have to be priced at £63 or less per dose for at least 50% certainty that it could cost-effectively replace the current Palivizumab programme, using an ICER threshold of £20,000/QALY. An extended seasonal programme which includes a pre-season catch-up becomes the optimal strategy at a purchasing price of £32/dose or less for at least 50% certainty. At a purchasing price per dose of £5-32, the annual implementation costs of a seasonal programme could be as high as £2 million before a switch to a year-round strategy would be optimal. DISCUSSION: Nirsevimab has the potential to be cost-effective in England and Wales not only for use in high-risk infants.

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs.

Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral-state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. While these methods vary in accuracy, it is unclear why. To evaluate the performance of phylogenetic ancestral-state reconstruction to determine the transmission direction of HIV-1 infection, we inferred the transmission direction for 112 transmission pairs where transmission direction and detailed additional information were available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic, and phylogenetic factors influenced the outcome of the inference. Finally, we repeated the analysis under real-life conditions with only routinely available data. We found that whether ancestral-state reconstruction correctly infers the transmission direction depends principally on the phylogeny's topology. For example, under real-life conditions, the probability of identifying the correct transmission direction increases from 32%-when a monophyletic-monophyletic or paraphyletic-polyphyletic tree topology is observed and when the tip closest to the root does not agree with the state at the root-to 93% when a paraphyletic-monophyletic topology is observed and when the tip closest to the root agrees with the root state. Our results suggest that documenting larger differences in relative intrahost diversity increases our confidence in the transmission direction inference of linked pairs for population-level studies of HIV. These findings provide a practical starting point to determine our confidence in transmission direction inference from ancestral-state reconstruction.

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools.

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society's responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak.

Detection of HIV-1 Transmission Clusters from Dried Blood Spots within a Universal Test-and-Treat Trial in East Africa.

The Sustainable East Africa Research in Community Health (SEARCH) trial was a universal test-and-treat (UTT) trial in rural Uganda and Kenya, aiming to lower regional HIV-1 incidence. Here, we quantify breakthrough HIV-1 transmissions occurring during the trial from population-based, dried blood spot samples. Between 2013 and 2017, we obtained 549 gag and 488 pol HIV-1 consensus sequences from 745 participants: 469 participants infected prior to trial commencement and 276 SEARCH-incident infections. Putative transmission clusters, with a 1.5% pairwise genetic distance threshold, were inferred from maximum likelihood phylogenies; clusters arising after the start of SEARCH were identified with Bayesian time-calibrated phylogenies. Our phylodynamic approach identified nine clusters arising after the SEARCH start date: eight pairs and one triplet, representing mostly opposite-gender linked (6/9), within-community transmissions (7/9). Two clusters contained individuals with non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance, both linked to intervention communities. The identification of SEARCH-incident, within-community transmissions reveals the role of unsuppressed individuals in sustaining the epidemic in both arms of a UTT trial setting. The presence of transmitted NNRTI resistance, implying treatment failure to the efavirenz-based antiretroviral therapy (ART) used during SEARCH, highlights the need to improve delivery and adherence to up-to-date ART recommendations, to halt HIV-1 transmission.

SCoVMod - a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics.

Background: Mobility restrictions prevent the spread of infections to disease-free areas, and early in the coronavirus disease 2019 (COVID-19) pandemic, most countries imposed severe restrictions on mobility as soon as it was clear that containment of local outbreaks was insufficient to control spread. These restrictions have adverse impacts on the economy and other aspects of human health, and it is important to quantify their impact for evaluating their future value. Methods: Here we develop Scotland Coronavirus transmission Model (SCoVMod), a model for COVID-19 in Scotland, which presents unusual challenges because of its diverse geography and population conditions. Our fitted model captures spatio-temporal patterns of mortality in the first phase of the epidemic to a fine geographical scale. Results: We find that lockdown restrictions reduced transmission rates down to an estimated 12\% of its pre-lockdown rate. We show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not. However, poor health associated with deprivation has a considerable association with mortality; the Council Area (CA) with the greatest health-related deprivation was found to have a mortality rate 2.45 times greater than the CA with the lowest health-related deprivation considering all deaths occurring outside of carehomes. Conclusions: We find that in even an early epidemic with poor case ascertainment, a useful spatially explicit model can be fit with meaningful parameters based on the spatio-temporal distribution of death counts. Our simple approach is useful to strategically examine trade-offs between travel related restrictions and physical distancing, and the effect of deprivation-related factors on outcomes.

Characterising within-hospitalSARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves.

Hospital outbreaks of COVID19 result in considerable mortality and disruption to healthcare services and yet little is known about transmission within this setting. We characterise within hospital transmission by combining viral genomic and epidemiological data using Bayesian modelling amongst 2181 patients and healthcare workers from a large UK NHS Trust. Transmission events were compared between Wave 1 (1st March to 25th J'uly 2020) and Wave 2 (30th November 2020 to 24th January 2021). We show that staff-to-staff transmissions reduced from 31.6% to 12.9% of all infections. Patient-to-patient transmissions increased from 27.1% to 52.1%. 40%-50% of hospital-onset patient cases resulted in onward transmission compared to 4% of community-acquired cases. Control measures introduced during the pandemic likely reduced transmissions between healthcare workers but were insufficient to prevent increasing numbers of patient-to-patient transmissions. As hospital-acquired cases drive most onward transmission, earlier identification of nosocomial cases will be required to break hospital transmission chains.

Changing socio-economic and ethnic disparities in influenza/A/H1N1 infection early in the 2009 UK epidemic: a descriptive analysis.

BACKGROUND: Higher incidence of and risk of hospitalisation and death from Influenza A(H1N1)pdm09 during the 2009 pandemic was reported in ethnic minority groups in many high-income settings including in the United Kingdom (UK). Many of these studies rely on geographical and temporal aggregation of cases and can be difficult to interpret due to the spatial and temporal factors in outbreak spread. Further, it can be challenging to distinguish between disparities in health outcomes caused by variation in transmission risk or disease severity. METHODS: We used anonymised laboratory confirmed and suspected case data, classified by ethnicity and deprivation status, to evaluate how disparities in risk between socio-economic and ethnic groups vary over the early stages of the 2009 Influenza A(H1N1)pdm09 epidemic in Birmingham and London, two key cities in the emergence of the UK epidemic. We evaluated the relative risk of infection in key ethnic minority groups and by national and city level deprivation rank. RESULTS: We calculated higher incidence in more deprived areas and in people of South Asian ethnicity in both Birmingham and London, although the magnitude of these disparities reduced with time. The clearest disparities existed in school-aged children in Birmingham, where the most deprived fifth of the population was 2.8 times more likely to be infected than the most affluent fifth of the population. CONCLUSIONS: Our analysis shows that although disparities in reported cases were present in the early phase of the Influenza A(H1N1)pdm09 outbreak in both Birmingham and London, they vary substantially depending on the period over which they are measured. Further, the development of disparities suggest that clustering of social groups play a key part as the outbreak appears to move from one ethnic and socio-demographic group to another. Finally, high incidence and large disparities between children indicate that they may hold an important role in driving inequalities.

Within and between classroom transmission patterns of seasonal influenza among primary school students in Matsumoto city, Japan.

Schools play a central role in the transmission of many respiratory infections. Heterogeneous social contact patterns associated with the social structures of schools (i.e., classes/grades) are likely to influence the within-school transmission dynamics, but data-driven evidence on fine-scale transmission patterns between students has been limited. Using a mathematical model, we analyzed a large-scale dataset of seasonal influenza outbreaks in Matsumoto city, Japan, to infer social interactions within and between classes/grades from observed transmission patterns. While the relative contribution of within-class and within-grade transmissions to the reproduction number varied with the number of classes per grade, the overall within-school reproduction number, which determines the initial growth of cases and the risk of sustained transmission, was only minimally associated with class sizes and the number of classes per grade. This finding suggests that interventions that change the size and number of classes, e.g., splitting classes and staggered attendance, may have a limited effect on the control of school outbreaks. We also found that vaccination and mask-wearing of students were associated with reduced susceptibility (vaccination and mask-wearing) and infectiousness (mask-wearing), and hand washing was associated with increased susceptibility. Our results show how analysis of fine-grained transmission patterns between students can improve understanding of within-school disease dynamics and provide insights into the relative impact of different approaches to outbreak control.

Drug resistance mutations in HIV: new bioinformatics approaches and challenges.

Drug resistance mutations appear in HIV under treatment pressure. Resistant variants can be transmitted to treatment-naive individuals, which can lead to rapid virological failure and can limit treatment options. Consequently, quantifying the prevalence, emergence and transmission of drug resistance is critical to effectively treating patients and to shape health policies. We review recent bioinformatics developments and in particular describe: (1) the machine learning approaches intended to predict and explain the level of resistance of HIV variants from their sequence data; (2) the phylogenetic methods used to survey the emergence and dynamics of resistant HIV transmission clusters; (3) the impact of deep sequencing in studying within-host and between-host genetic diversity of HIV variants, notably regarding minority resistant variants.

Modeling the effect of vaccination on selection for antibiotic resistance in Streptococcus pneumonia e.

Vaccines against bacterial pathogens can protect recipients from becoming infected with potentially antibiotic-resistant pathogens. However, by altering the selective balance between antibiotic-sensitive and antibiotic-resistant bacterial strains, vaccines may also suppress-or spread-antibiotic resistance among unvaccinated individuals. Predicting the outcome of vaccination requires knowing what drives selection for drug-resistant bacterial pathogens and what maintains the circulation of both antibiotic-sensitive and antibiotic-resistant strains of bacteria. To address this question, we used mathematical modeling and data from 2007 on penicillin consumption and penicillin nonsusceptibility in Streptococcus pneumoniae (pneumococcus) invasive isolates from 27 European countries. We show that the frequency of penicillin resistance in S. pneumoniae can be explained by between-host diversity in antibiotic use, heritable diversity in pneumococcal carriage duration, or frequency-dependent selection brought about by within-host competition between antibiotic-resistant and antibiotic-sensitive S. pneumoniae strains. We used our calibrated models to predict the impact of non-serotype-specific pneumococcal vaccination upon the prevalence of S. pneumoniae carriage, incidence of disease, and frequency of S. pneumoniae antibiotic resistance. We found that the relative strength and directionality of competition between drug-resistant and drug-sensitive pneumococcal strains was the most important determinant of whether vaccination would promote, inhibit, or have little effect upon the evolution of antibiotic resistance. Last, we show that country-specific differences in pathogen transmission substantially altered the predicted impact of vaccination, highlighting that policies for managing antibiotic resistance with vaccines must be tailored to a specific pathogen and setting.

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

BACKGROUND: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. METHODS: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. RESULTS: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. CONCLUSIONS: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. TRIAL REGISTRATION: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Respiratory syncytial virus seasonality and prevention strategy planning for passive immunisation of infants in low-income and middle-income countries: a modelling study.

BACKGROUND: Respiratory syncytial virus (RSV) represents a substantial burden of disease in young infants in low-income and middle-income countries (LMICs). Because RSV passive immunisations, including maternal vaccination and monoclonal antibodies, can only grant a temporary period of protection, their effectiveness and efficiency will be determined by the timing of the immunisation relative to the underlying RSV seasonality. We aimed to assess the potential effect of different approaches for passive RSV immunisation of infants in LMICs. METHODS: We included 52 LMICs in this study on the basis of the availability of RSV seasonality data and developed a mathematical model to compare the effect of different RSV passive immunisation approaches (seasonal approaches vs a year-round approach). For each candidate approach, we calculated the expected annual proportion of RSV incidence among infants younger than 6 months averted (effectiveness) and the ratio of per-dose cases averted between that approach and the year-round approach (relative efficiency). FINDINGS: 39 (75%) of 52 LMICs included in the study had clear RSV seasonality, defined as having more than 75% of annual RSV cases occurring in 5 or fewer months. In these countries with clear RSV seasonality, the seasonal approach in which monoclonal antibody administration began 3 months before RSV season onset was only a median of 16% (IQR 13-18) less effective in averting RSV-associated acute lower respiratory infection (ALRI) hospital admissions than a year-round approach, but was a median of 70% (50-97) more efficient in reducing RSV-associated hospital admissions per dose. The seasonal approach that delivered maternal vaccination 1 month before the season onset was a median of 27% (25-33) less effective in averting hospital admissions associated with RSV-ALRI than a year-round approach, but was a median of 126% (87-177) more efficient at averting these hospital admissions per dose. INTERPRETATION: In LMICs with clear RSV seasonality, seasonal approaches to monoclonal antibody and maternal vaccine administration might optimise disease prevention by dose given compared with year-round administration. More data are needed to clarify if seasonal administration of RSV monoclonal antibodies or maternal immunisation is programmatically suitable and cost effective in LMICs. FUNDING: The Bill & Melinda Gates Foundation, World Health Organization.

Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England.

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

Implications of the school-household network structure on SARS-CoV-2 transmission under school reopening strategies in England.

In early 2020 many countries closed schools to mitigate the spread of SARS-CoV-2. Since then, governments have sought to relax the closures, engendering a need to understand associated risks. Using address records, we construct a network of schools in England connected through pupils who share households. We evaluate the risk of transmission between schools under different reopening scenarios. We show that whilst reopening select year-groups causes low risk of large-scale transmission, reopening secondary schools could result in outbreaks affecting up to 2.5 million households if unmitigated, highlighting the importance of careful monitoring and within-school infection control to avoid further school closures or other restrictions.

Cost-effectiveness of live-attenuated influenza vaccination among school-age children.

The current pediatric vaccination program in England and Wales administers Live-Attenuated Influenza Vaccine (LAIV) to children ages 2-16 years old. Annual administration of LAIV to this age group is costly and poses substantial logistical issues. This study aims to evaluate the cost-effectiveness of prioritizing vaccination to age groups within the 2-16 year old age range to mitigate the operational and resource challenges of the current strategy. We performed economic evaluations comparing the influenza vaccination program from 1995-2013 to seven alternative strategies targeted at low risk individuals along the school age divisions Preschool (2-4 years old), Primary school (5-11 years old), and Secondary school (12-16 years old). These extensions are evaluated incrementally on the status quo scenario (vaccinating subgroups at high risk of influenza-related complications and individuals 65+ years old). Impact of vaccination was assessed using a transmission model from a previously published study and updated with new data. At all levels of coverage, all strategies had a 100% probability of being cost-effective at the current National Health Service threshold, £20,000/QALY gained. The incremental analysis demonstrated vaccinating Primary School children was the most cost-efficient strategy compared incrementally against others with an Incremental Cost-Effectiveness Ratio of £639 spent per QALY gained (Net Benefit: 404 M£ [155, 795]). When coverage was varied between 30%, 55%, and 70% strategies which included Primary school children had a higher probability of being cost-effective at lower willingness-to-pay levels. Although children were the vaccine target the majority of QALY gains occurred in the 25-44 years old and 65+ age groups. Influenza strain A/H3N2 incurred the greatest costs and QALYs lost regardless of which strategy was used. Improvement could be made to the current LAIV pediatric vaccination strategy by eliminating vaccination of 2-4 year olds and focusing on school-based delivery to Primary and Secondary school children in tandem.