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BackgroundTuberculosis is a leading infectious cause of death worldwide. Novel vaccines will be required to reach global targets and reverse setbacks from the COVID-19 pandemic. We estimated the impact of novel tuberculosis vaccines in low- and middle-income countries (LMICs) under alternative delivery scenarios.MethodsWe calibrated a tuberculosis model to 105 LMICs (93% of global tuberculosis incidence). Vaccine scenarios were implemented asBasecase: routine vaccination of 9-year-olds and a one-time vaccination campaign for ages ≥10 with country-specific introduction between 2028–2047 and 5-year scale-up to target coverage;Accelerated Scale-up: asBasecase, but all countries introducing in 2025 with instant scale-up; andRoutine Only: asBasecase, but routine vaccination only. Vaccines protected against disease for 10-years, with 50% efficacy.FindingsTheBasecasescenario prevented 44.0 (95% uncertainty range=37.2–51.6) million tuberculosis cases, and 5.0 (4.6–5.4) million tuberculosis deaths before 2050, including 2.2 million in the WHO South-East Asian region. TheAccelerated Scale-upscenario prevented 65.5 (55.6–76.0) million cases and 7.9 (7.3–8.5) million deaths before 2050. TheRoutine Onlyscenario prevented 8.8 (7.6–10.1) million cases and 1.1 (0.9–1.2) million deaths before 2050.InterpretationsNovel tuberculosis vaccines could have substantial impact, which will vary depending on delivery strategy. Including a campaign will be crucial for rapid impact. Accelerated introduction, more similar to the pace of COVID-19 vaccines, could increase lives saved by around 60%. Investment is required to support vaccine development, manufacturing, prompt introduction and scale-up.FundingWHO (2020/985800-0)

BackgroundHealthcare facilities are important sites for the transmission of pathogens spread via bioaerosols, such as Mycobacterium tuberculosis (Mtb). Natural ventilation can play an important role in reducing this transmission. In primary health care (PHC) clinics in low and middle-income settings, susceptible people, including healthcare workers, are exposed to individuals with infectious pulmonary tuberculosis. We measured rates of natural ventilation in PHC clinics in KwaZulu-Natal and Western Cape provinces, South Africa.Methods and FindingsWe measured ventilation in clinic spaces using a tracer-gas release method. In spaces where this was not possible, we estimated ventilation using data on indoor and outdoor carbon dioxide levels, under reasonable assumptions about occupants’ metabolic rates. Ventilation was measured i) under usual conditions and ii) with all windows and doors fully open. We used these ventilation rates to estimate the risk of Mtb transmission using the Wells-Riley Equation. We obtained ventilation measurements in 33 clinical spaces in 10 clinics: 13 consultation rooms, 16 waiting areas and 4 other clinical spaces. Under usual conditions, the absolute ventilation rate was much higher in waiting rooms (median 1769 m3/hr, range 338–4815 m3/hr) than in consultation rooms (median 197 m3/hr, range 0–1451 m3/hr). Ventilation was better in permanent than in temporary structures. When compared with usual conditions, fully opening existing doors and windows resulted in a median two-fold increase in ventilation. Our Wells-Riley estimates show that, following sustained exposure, or contact with highly infectious index cases, some risk of Mtb infection may persist in the best ventilated clinical spaces unless other components of transmission risk are also addressed.ConclusionsAmong the clinical spaces studied, we observed substantial variation in natural ventilation. Ventilation interventions may have considerable impact on Mtb transmission in this setting. We recommend these form part of a package of infection prevention and control interventions.

The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions.

Background England’s COVID-19 response transitioned from a national lockdown to localised interventions. In response to rising cases, these were supplemented by national restrictions on contacts (the Rule of Six), then 10 pm closing for bars and restaurants, and encouragement to work from home. These were quickly followed by a 3-tier system applying different restrictions in different localities. As cases continued to rise, a second national lockdown was declared. We used a national survey to quantify the impact of these restrictions on epidemiologically relevant contacts. Methods We compared paired measures on setting-specific contacts before and after each restriction started and tested for differences using paired permutation tests on the mean change in contacts and the proportion of individuals decreasing their contacts. Results Following the imposition of each measure, individuals tended to report fewer contacts than they had before. However, the magnitude of the changes was relatively small and variable. For instance, although early closure of bars and restaurants appeared to have no measurable effect on contacts, the work from home directive reduced mean daily work contacts by 0.99 (95% confidence interval CI] 0.03–1.94), and the Rule of Six reduced non-work and school contacts by a mean of 0.25 (0.01–0.5) per day. Whilst Tier 3 appeared to also reduce non-work and school contacts, the evidence for an effect of the lesser restrictions (Tiers 1 and 2) was much weaker. There may also have been some evidence of saturation of effects, with those who were in Tier 1 (least restrictive) reducing their contacts markedly when they entered lockdown, which was not reflected in similar changes in those who were already under tighter restrictions (Tiers 2 and 3). Conclusions The imposition of various local and national measures in England during the summer and autumn of 2020 has gradually reduced contacts. However, these changes are smaller than the initial lockdown in March. This may partly be because many individuals were already starting from a lower number of contacts.

Background Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories. Methods We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission using data from January to June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number (Rt) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in Rt, levels of NPI intensity, time-varying changes in NPI effect, and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. Results There was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced Rt. Another three NPIs (workplace closure, income support, and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective, restrictions on Conclusion Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects, and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many, although not all, actions policy-makers are taking to respond to the COVID-19 pandemic.

Background Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. Methods Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever ≥ 37.5 °C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. Results Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6 July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. As of 7 June, our seroprevalence estimates range from 0% (many countries) to 13% (95% CrI 5.6–24%) (Belgium). Conclusions We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country’s population infected with SARS-CoV-2 worldwide is generally low.

Markov model equations, model parameters and additional tables and figures. Text S1. Markov model formulae. Table S1. Definition of parameters for the Markov Model. Table S2. Uganda subset information for dataset 2 and matrix C. Figure S1. Results from applying transition probability (TP) matrices B and C on dataset 1 (full longitudinal Uganda data set). Figure S2. Results from applying TP matrices A–C on dataset 2 (selected Ugandan districts). Figure S3. Results from applying TP matrices B and C on dataset 3 (cross-sectional Ugandan dataset). Figure S4. Results from applying TP matrix D (baseline and year 1 Mali data for TPs) on dataset 4 (longitudinal Mali dataset). (DOC 6719 kb)

Model parameters for Schistosoma mansoni. Table S2. Model for the three age-intensity profiles. Table S3. Sensitivity of the relative increase in effectiveness when using annual community-wide versus school-based treatment to the treatment coverage in adults. Table S4. Sensitivity of the relative increase in effectiveness when using annual community-wide versus school-based treatment to the assumed mean life expectancy of the adult worms. Table S5. Sensitivity of the relative increase in effectiveness when using annual community-wide versus school-based treatment to the assumed level of systematic non-compliance. Table S6. Relative increase in effectiveness when using annual community-wide versus school-based treatment when assuming poor school enrolment. Table S7. The effect of using different fitting methods to account for the age-intensity profile on the projected incremental increase in effectiveness when using annual community-wide versus school-based treatment. Figure S1. The impact of annual school-based treatment on the mean worm burden in different age groups in three settings with a different relative pre-control worm burden in adults. Figure S2. Sensitivity of the number of years of annual treatment to achieve elimination of Schistosoma mansoni to the assumed level of systematic non-compliance. Figure S3. Sensitivity of the number of years of annual treatment to achieve elimination of Schistosoma mansoni to the assumed mean life expectancy of the worms. (PDF 1366 kb)

Model parameters for Schistosoma mansoni. Table S2. Model for the three age-intensity profiles. Table S3. Sensitivity of the relative increase in effectiveness when using annual community-wide versus school-based treatment to the treatment coverage in adults. Table S4. Sensitivity of the relative increase in effectiveness when using annual community-wide versus school-based treatment to the assumed mean life expectancy of the adult worms. Table S5. Sensitivity of the relative increase in effectiveness when using annual community-wide versus school-based treatment to the assumed level of systematic non-compliance. Table S6. Relative increase in effectiveness when using annual community-wide versus school-based treatment when assuming poor school enrolment. Table S7. The effect of using different fitting methods to account for the age-intensity profile on the projected incremental increase in effectiveness when using annual community-wide versus school-based treatment. Figure S1. The impact of annual school-based treatment on the mean worm burden in different age groups in three settings with a different relative pre-control worm burden in adults. Figure S2. Sensitivity of the number of years of annual treatment to achieve elimination of Schistosoma mansoni to the assumed level of systematic non-compliance. Figure S3. Sensitivity of the number of years of annual treatment to achieve elimination of Schistosoma mansoni to the assumed mean life expectancy of the worms. (PDF 1366 kb)