Search

Acknowledgements: We would like to thank the authors of the GBS burden paper for sharing data on the posterior estimates of parameters in the burden model. We thank the GBS Full Value of Vaccine Assessment project Scientific Advisory Group for helpful discussion. We also thank Clint Pecenka and Ranju Baral for sharing estimates of antenatal care coverage by gestational age. The views expressed in this article are those of the authors and do not necessarily represent the decisions, official policy, or opinions of WHO., BACKGROUND: Group B Streptococcus (GBS) can cause invasive disease (iGBS) in young infants, typically presenting as sepsis or meningitis, and is also associated with stillbirth and preterm birth. GBS vaccines are under development, but their potential health impact and cost-effectiveness have not been assessed globally. METHODS AND FINDINGS: We assessed the health impact and value (using net monetary benefit (NMB), which measures both health and economic effects of vaccination into monetary units) of GBS maternal vaccination in an annual cohort of 140 million pregnant women across 183 countries in 2020. Our analysis uses a decision tree model, incorporating risks of GBS-related health outcomes from an existing Bayesian disease burden model. We extrapolated country-specific GBS-related healthcare costs using data from a previous systematic review and calculated quality-adjusted life years (QALYs) lost due to infant mortality and long-term disability. We assumed 80% vaccine efficacy against iGBS and stillbirth, following the WHO Preferred Product Characteristics, and coverage based on the proportion of pregnant women receiving at least 4 antenatal visits. One dose was assumed to cost $50 in high-income countries, $15 in upper-middle income countries, and $3.50 in low-/lower-middle-income countries. We estimated NMB using alternative normative assumptions that may be adopted by policymakers. Vaccinating pregnant women could avert 127,000 (95% uncertainty range 63,300 to 248,000) early-onset and 87,300 (38,100 to 209,000) late-onset infant iGBS cases, 31,100 deaths (14,400 to 66,400), 17,900 (6,380 to 49,900) cases of moderate and severe neurodevelopmental impairment, and 23,000 (10,000 to 56,400) stillbirths. A vaccine effective against GBS-associated prematurity might also avert 185,000 (13,500 to 407,000) preterm births. Globally, a 1-dose vaccine programme could cost $1.7 billion but save $385 million in healthcare costs. Estimated global NMB ranged from $1.1 billion ($-0.2 to 3.8 billion) under the least favourable normative assumptions to $17 billion ($9.1 to 31 billion) under the most favourable normative assumptions. The main limitation of our analysis was the scarcity of data to inform some of the model parameters such as those governing health-related quality of life and long-term costs from disability, and how these parameters may vary across country contexts. CONCLUSIONS: In this study, we found that maternal GBS vaccination could have a large impact on infant morbidity and mortality. Globally, a GBS maternal vaccine at reasonable prices is likely to be a cost-effective intervention.

Additional file 1: Fig S1. Model Overview. Fig S2. Susceptibility and epidemic size comparison. Fig S3. Vaccine Immunity. Fig S4. Infections per 100’000 population. Fig S5. R0 values. Fig S6. Decision Tree. Fig S7. Cost-effectiveness acceptability curves. Fig S8. Immunity at 75% coverage. Fig S9. Cumulative infections at 75% coverage. Fig S10. Results at 75% coverage. Fig S11. Immunity at 25% coverage. Fig S12. Cumulative infections at 25% coverage. Fig S13. Results at 25% coverage. Fig S14. Infections with increased susceptibility after vaccination. Fig S15. Results with increased susceptibility after vaccination, Fig S16. Cumulative Infections with fixed reduction in susceptibility. S17. Results with fixed reduction in susceptibility. Fig S18. Immunity for exact efficacies sensitivity. Fig S19. Cumulative infections for exact efficacies sensitivity. Fig S20. Results for exact efficacies sensitivity. S21. Cost-effectiveness acceptability curves for different per-dose vaccine prices. Table S1. Parameters used in epidemic models. Table S2. Vaccine Efficacy. Table S3. Epidemic Timings. Table S4. Economic DALY parameters, Table S5. Economic cost parameters. Table S6. Willingness to pay thresholds. Table S7. Scenario output summaries. Table S8. Vaccine doses administered. Table S9. Incremental Net Monetary Benefits. Table S10. Threshold per-dose vaccine prices.

Additional file 1: Supplemental Figures: Figure S1. Vaccine roll-out trajectories by age group. Figure S2. Diagram of Transmission Model Structure. Figure S3. Countries included and excluded from this analysis. Figure S4-6. Performance of model fitting process, Part 1-3 (in alphabetical order). Figure S7. Fitted parameters. Figure S8-10. Health Outcomes Associated with Vaccine Roll-out Scenarios (by vaccine type and by health outcome). Figure S11. The association between the proportion of DALYs averted attributable to older adults and the performance of medium and fast scenarios. Figure S11. Effect sizes estimated in the multivariable regression model linking country characteristics to ICERs as proportions of GDP per capita. Figure S12. Target vaccine price under different perceived cost-effectiveness thresholds. Supplemental Tables: Table S1. Model Equations. Table S2. Epidemic and Healthcare Process Parameters. Table S3. Additional Data Sources. Table S4. Other vaccine and vaccination programme characteristics. Table S5. Variants of Concern Introduction. Table S6. List of countries with fitted models. Table S7. Itemised cost per dose per activity for base countries - viral vector / AstraZeneca-like vaccine. Table S8. Itemised Cost per dose per activity for base countries - mRNA / Pfizer-like vaccine. Table S9. Cost per dose for countries with fitted models. Table S10. CHEERS 2022 Checklist. Supplemental Methods: Methods S1. Further Model Descriptions. Methods S2. Fitting Process. Methods S3. Characterising behavioural change using data on non-pharmaceutical intervention and mobility. Methods S4. Calculating COVID-19 severe, critical, and death cases. Methods S5. Lengths of Stay (LoSs). Methods S6. Calculating Disability-adjusted Life Years (DALYs). Methods S7. Estimating Vaccine Delivery Costs. Methods S8. Extrapolating unit costs from base countries to other countries in Africa. Methods S9. Extrapolating vaccine unit costs for different roll-out scenarios. Methods S10. ICERs and Proportions of DALYs averted by those above 60 years.

Background High incidence of cases and deaths due to coronavirus disease 2019 (COVID-19) have been reported in prisons worldwide. This study aimed to evaluate the impact of different COVID-19 vaccination strategies in epidemiologically semi-enclosed settings such as prisons, where staff interact regularly with those incarcerated and the wider community. Methods We used a metapopulation transmission-dynamic model of a local prison in England and Wales. Two-dose vaccination strategies included no vaccination, vaccination of all individuals who are incarcerated and/or staff, and an age-based approach. Outcomes were quantified in terms of COVID-19-related symptomatic cases, losses in quality-adjusted life-years (QALYs), and deaths. Results Compared to no vaccination, vaccinating all people living and working in prison reduced cases, QALY loss and deaths over a one-year period by 41%, 32% and 36% respectively. However, if vaccine introduction was delayed until the start of an outbreak, the impact was negligible. Vaccinating individuals who are incarcerated and staff over 50 years old averted one death for every 104 vaccination courses administered. All-staff-only strategies reduced cases by up to 5%. Increasing coverage from 30 to 90% among those who are incarcerated reduced cases by around 30 percentage points. Conclusions The impact of vaccination in prison settings was highly dependent on early and rapid vaccine delivery. If administered to both those living and working in prison prior to an outbreak occurring, vaccines could substantially reduce COVID-19-related morbidity and mortality in prison settings.

Background: Data are limited regarding long-term consequences of invasive GBS (iGBS) disease in early infancy, especially from low- and middle-income countries (LMIC) where most cases occur. We aimed to estimate risk of neurodevelopmental impairment (NDI) in children with a history of iGBS disease. Methods: A multi-country matched cohort study was undertaken in South Africa, India, Mozambique, Kenya, and Argentina from October 2019 to April 2021. The exposure of interest was defined as a history of iGBS disease (sepsis or meningitis) before 90 days of age, amongst children now aged 1·5-18 years. Age and sex-matched, children without history of GBS were also recruited. Age-appropriate, culturally-adapted assessments were used to define NDI across multiple domains (cognitive, motor, hearing, vision, emotional-behaviour, growth). Pooled NDI risk was meta-analysed across sites. Association of iGBS exposure and NDI outcome was estimated using modified Poisson regression with robust variance estimator. Findings: Amongst 138 iGBS survivors and 390 non-iGBS children, 38·1% (95% confidence interval [CI]: 30·0% - 46·6%) of iGBS children had any NDI, compared to 21·7% (95% CI: 17·7% - 26·0%) of non- iGBS children, with notable between-site heterogeneity. Risk of moderate/severe NDI was 15·0% (95% CI: 3·4% - 30·8%) among GBS-meningitis, 5·6% (95% CI: 1·5% - 13·7%) for GBS-sepsis survivors. The adjusted risk ratio (aRR) for moderate/severe NDI among iGBS survivors was 1.27 (95% CI: 0.65, 2.45), when compared to non-GBS children. Mild impairment was more frequent in iGBS (27.6% (95% CI: 20.3 - 35.5%)) compared to non-GBS children (12.9% (95% CI: 9.7% - 16.4%)). The risk of emotional-behavioural problems was similar irrespective of iGBS exposure (aRR=0.98 (95% CI: 0.55, 1.77)). Interpretation: Our findings suggest that iGBS disease is on average associated with a higher risk of moderate/severe NDI, however substantial variation in risk was observed between sites and data are consistent with a wide range of values. Our study underlines the importance of long-term follow-up for at-risk neonates and more feasible, standardised assessments to facilitate diagnosis in research and clinical practice. Funding: This work was supported by a grant (INV-009018) from the Bill & Melinda Gates Foundation to the London School of Hygiene &Tropical Medicine.

BACKGROUND: Group B streptococcus (GBS) colonisation during pregnancy can lead to invasive GBS disease (iGBS) in infants, including meningitis or sepsis, with a high mortality risk. Other outcomes include stillbirths, maternal infections, and prematurity. There are data gaps, notably regarding neurodevelopmental impairment (NDI), especially after iGBS sepsis, which have limited previous global estimates. In this study, we aimed to address this gap using newly available multicountry datasets. METHODS: We collated and meta-analysed summary data, primarily identified in a series of systematic reviews published in 2017 but also from recent studies on NDI and stillbirths, using Bayesian hierarchical models, and estimated the burden for 183 countries in 2020 regarding: maternal GBS colonisation, iGBS cases and deaths in infants younger than 3 months, children surviving iGBS affected by NDI, and maternal iGBS cases. We analysed the proportion of stillbirths with GBS and applied this to the UN-estimated stillbirth risk per country. Excess preterm births associated with maternal GBS colonisation were calculated using meta-analysis and national preterm birth rates. FINDINGS: Data from the seven systematic reviews, published in 2017, that informed the previous burden estimation (a total of 515 data points) were combined with new data (17 data points) from large multicountry studies on neurodevelopmental impairment (two studies) and stillbirths (one study). A posterior median of 19·7 million (95% posterior interval 17·9-21·9) pregnant women were estimated to have rectovaginal colonisation with GBS in 2020. 231 800 (114 100-455 000) early-onset and 162 200 (70 200-394 400) late-onset infant iGBS cases were estimated to have occurred. In an analysis assuming a higher case fatality rate in the absence of a skilled birth attendant, 91 900 (44 800-187 800) iGBS infant deaths were estimated; in an analysis without this assumption, 58 300 (26 500-125 800) infant deaths from iGBS were estimated. 37 100 children who recovered from iGBS (14 600-96 200) were predicted to develop moderate or severe NDI. 40 500 (21 500-66 200) maternal iGBS cases and 46 200 (20 300-111 300) GBS stillbirths were predicted in 2020. GBS colonisation was also estimated to be potentially associated with considerable numbers of preterm births. INTERPRETATION: Our analysis provides a comprehensive assessment of the pregnancy-related GBS burden. The Bayesian approach enabled coherent propagation of uncertainty, which is considerable, notably regarding GBS-associated preterm births. Our findings on both the acute and long-term consequences of iGBS have public health implications for understanding the value of investment in maternal GBS immunisation and other preventive strategies. FUNDING: Bill & Melinda Gates Foundation.

dentifying the potential for SARS-CoV-2 reinfection is crucial for understanding possible long-term epidemic dynamics. We analysed longitudinal PCR and serological testing data from a prospective cohort of 4,411 United States employees in 4 states between April 2020 and February 2021. We conducted a multivariable logistic regression investigating the association between baseline serological status and subsequent PCR test result in order to calculate an odds ratio for reinfection. We estimated an odds ratio for reinfection ranging from 0.14 (95% CI: 0.019 to 0.63) to 0.28 (95% CI: 0.05 to 1.1), implying that the presence of SARS-CoV-2 antibodies at baseline is associated with around 72% to 86% reduced odds of a subsequent PCR positive test based on our point estimates. This suggests that primary infection with SARS-CoV-2 provides protection against reinfection in the majority of individuals, at least over a 6-month time period. We also highlight 2 major sources of bias and uncertainty to be considered when estimating the relative risk of reinfection, confounders and the choice of baseline time point, and show how to account for both in reinfection analysis. The authors received funding from the following sources: EF was funded by the Medical Research Council (MR/N013638/1); AJK was supported by Wellcome Trust (206250/Z/17/Z) and National Institute for Health Research (NIHR200908); RL was funded by a Royal Society Dorothy Hodgkin Fellowship (https://royalsociety.org). EN was supported by the US Centers for Disease Control and Prevention (U01 U01GH002238). AM was supported by the Translational Research Institute for Space Health through NASA Cooperative Agreement (https://www.nasa.gov/hrp/tri; NNX16AO69A). GA was supported by the Massachusetts Consortium on Pathogen Readiness (https://masscpr.hms.harvard.edu/; MassCPR), the National Institutes of Health (3R37AI080289-11S1, R01AI146785, U19AI42790-01, U19AI135995-02, 1U01CA260476-01) and the Musk Foundation (http://www.muskfoundation.org/). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. "Article signat per 18 autors/es: Emilie Finch ,Rachel Lowe,Stephanie Fischinger,Michael de St Aubin,Sameed M. Siddiqui,Diana Dayal,Michael A. Loesche,Justin Rhee,Samuel Beger,Yiyuan Hu,Matthew J. Gluck,Benjamin Mormann,Mohammad A. Hasdianda,Elon R. Musk,Galit Alter,Anil S. Menon ,Eric J. Nilles ,Adam J. Kucharski ,on behalf of the CMMID COVID-19 working group and the SpaceX COVID-19 Cohort Collaborative"

Background To contain the spread of COVID-19, a cordon sanitaire was put in place in Wuhan prior to the Lunar New Year, on 23 January 2020. We assess the efficacy of the cordon sanitaire to delay the introduction and onset of local transmission of COVID-19 in other major cities in mainland China. Methods We estimated the number of infected travellers from Wuhan to other major cities in mainland China from November 2019 to February 2020 using previously estimated COVID-19 prevalence in Wuhan and publicly available mobility data. We focused on Beijing, Chongqing, Hangzhou, and Shenzhen as four representative major cities to identify the potential independent contribution of the cordon sanitaire and holiday travel. To do this, we simulated outbreaks generated by infected arrivals in these destination cities using stochastic branching processes. We also modelled the effect of the cordon sanitaire in combination with reduced transmissibility scenarios to simulate the effect of local non-pharmaceutical interventions. Results We find that in the four cities, given the potentially high prevalence of COVID-19 in Wuhan between December 2019 and early January 2020, local transmission may have been seeded as early as 1–8 January 2020. By the time the cordon sanitaire was imposed, infections were likely in the thousands. The cordon sanitaire alone did not substantially affect the epidemic progression in these cities, although it may have had some effect in smaller cities. Reduced transmissibility resulted in a notable decrease in the incidence of infection in the four studied cities. Conclusions Our results indicate that sustained transmission was likely occurring several weeks prior to the implementation of the cordon sanitaire in four major cities of mainland China and that the observed decrease in incidence was likely attributable to other non-pharmaceutical, transmission-reducing interventions.

Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate that reporting and adherence are the most important predictors of programme impact but tracing coverage and speed plus diagnostic sensitivity also play an important role. We conclude that well-implemented contact tracing could bring small but potentially important benefits to controlling and preventing outbreaks, providing up to a 15% reduction in R. We reaffirm that contact tracing is not currently appropriate as the sole control measure.

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., Proceedings of the National Academy of Sciences, 119(37), art. no. e2203019119; 2022

Background:: Childhood immunisation services have been disrupted by the COVID-19 pandemic. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic. Methods:: We used two to three models per infection to estimate the health impact of 50% reduced routine vaccination coverage in 2020 and delay of campaign vaccination from 2020 to 2021 for measles vaccination in Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan, for meningococcal A vaccination in Burkina Faso, Chad, Niger, and Nigeria, and for yellow fever vaccination in the Democratic Republic of Congo, Ghana, and Nigeria. Our counterfactual comparative scenario was sustaining immunisation services at coverage projections made prior to COVID-19 (i.e. without any disruption). Results:: Reduced routine vaccination coverage in 2020 without catch-up vaccination may lead to an increase in measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns in Ethiopia and Nigeria by a year may significantly increase the risk of measles outbreaks (both countries did complete their supplementary immunisation activities (SIAs) planned for 2020). For yellow fever vaccination, delay in campaigns leads to a potential disease burden rise of >1 death per 100,000 people per year until the campaigns are implemented. For meningococcal A vaccination, short-term disruptions in 2020 are unlikely to have a significant impact due to the persistence of direct and indirect benefits from past introductory campaigns of the 1- to 29-year-old population, bolstered by inclusion of the vaccine into the routine immunisation schedule accompanied by further catch-up campaigns. Conclusions:: The impact of COVID-19-related disruption to vaccination programs varies between infections and countries. Planning and implementation of campaigns should consider country and infection-specific epidemiological factors and local immunity gaps worsened by the COVID-19 pandemic when prioritising vaccines and strategies for catch-up vaccination. Funding:: Bill and Melinda Gates Foundation and Gavi, the Vaccine Alliance.

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.

We assess the feasibility of reaching the herd immunity threshold against SARS-CoV-2 through vaccination, considering vaccine effectiveness (VE), transmissibility of the virus and the level of pre-existing immunity in populations, as well as their age structure. If highly transmissible variants of concern become dominant in areas with low levels of naturally-acquired immunity and/or in populations with large proportions of < 15 year-olds, control of infection without non-pharmaceutical interventions may only be possible with a VE ≥ 80%, and coverage extended to children. Initial reports of vaccine effectiveness against severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), the virus responsible for coronavirus disease (COVID-19), have suggested a substantial reduction of the risk of infection [1]. Nevertheless, with the emergence of more transmissible variants such as B.1.1.7 [2], how large-scale immunisation programmes against SARS-CoV-2 will perform is currently unclear. This study assesses the potential of COVID-19 vaccination to generate herd immunity and takes into account vaccine effectiveness, naturally-acquired immunity and achievable vaccination coverage (depending on the population age structure), as well as two transmissibility scenarios ((i) with pre-B.1.1.7, and (ii) with exclusively B.1.1.7 variants).