Cell-phone traces reveal infection-associated behavioral change.

Epidemic preparedness depends on our ability to predict the trajectory of an epidemic and the human behavior that drives spread in the event of an outbreak. Changes to behavior during an outbreak limit the reliability of syndromic surveillance using large-scale data sources, such as online social media or search behavior, which could otherwise supplement healthcare-based outbreak-prediction methods. Here, we measure behavior change reflected in mobile-phone call-detail records (CDRs), a source of passively collected real-time behavioral information, using an anonymously linked dataset of cell-phone users and their date of influenza-like illness diagnosis during the 2009 H1N1v pandemic. We demonstrate that mobile-phone use during illness differs measurably from routine behavior: Diagnosed individuals exhibit less movement than normal (1.1 to 1.4 fewer unique tower locations; [Formula: see text]), on average, in the 2 to 4 d around diagnosis and place fewer calls (2.3 to 3.3 fewer calls; [Formula: see text]) while spending longer on the phone (41- to 66-s average increase; [Formula: see text]) than usual on the day following diagnosis. The results suggest that anonymously linked CDRs and health data may be sufficiently granular to augment epidemic surveillance efforts and that infectious disease-modeling efforts lacking explicit behavior-change mechanisms need to be revisited.

Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England.

BACKGROUND: Predicting the likely size of future SARS-CoV-2 waves is necessary for public health planning. In England, voluntary "plan B" mitigation measures were introduced in December 2021 including increased home working and face coverings in shops but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. METHODS: We developed a rapid online survey of risk mitigation behaviours ahead of the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/COVID Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we predicted the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. RESULTS: Over 95% of survey respondents (NALSPAC = 2686 and NTwins = 6155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to "plan B". We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 12,000 and 46,000 cumulative deaths, depending on assumptions about severity and vaccine effectiveness. The actual number of deaths was 15,208 (26 November 2021-1 March 2022). We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. CONCLUSIONS: Predicting future infection burden is affected by uncertainty in disease severity and vaccine effectiveness estimates. In addition to biological uncertainty, we show that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant but that voluntary measures alone would be unlikely to completely control transmission.

Estimating hepatitis B virus prevalence among key population groups for European Union and European Economic Area countries and the United Kingdom: a modelling study.

BACKGROUND: Hepatitis B virus (HBV) epidemiology in Europe differs by region and population risk group, and data are often incomplete. We estimated chronic HBV prevalence as measured by surface antigen (HBsAg) among general and key population groups for each country in the European Union, European Economic Area and the United Kingdom (EU/EEA/UK), including where data are currently unavailable. METHODS: We combined data from a 2018 systematic review (updated in 2021), data gathered directly by the European Centre for Disease Control (ECDC) from EU/EEA countries and the UK and further country-level data. We included data on adults from the general population, pregnant women, first time blood donors (FTBD), men who have sex with men (MSM), prisoners, people who inject drugs (PWID), and migrants from 2001 to 2021, with three exceptions made for pre-2001 estimates. Finite Mixture Models (FMM) and Beta regression were used to predict country and population group HBsAg prevalence. A separate multiplier method was used to estimate HBsAg prevalence among the migrant populations within each country, due to biases in the data available. RESULTS: There were 595 included studies from 31 countries (N = 41,955,969 people): 66 were among the general population (mean prevalence ([Formula: see text]) 1.3% [range: 0.0-7.6%]), 52 among pregnant women ([Formula: see text]1.1% [0.1-5.3%]), 315 among FTBD ([Formula: see text]0.3% [0.0-6.2%]), 20 among MSM ([Formula: see text]1.7% [0.0-11.2%]), 34 among PWID ([Formula: see text]3.9% [0.0-16.9%]), 24 among prisoners ([Formula: see text]2.9% [0.0-10.7%]), and 84 among migrants ([Formula: see text]7.0% [0.2-37.3%]). The FMM grouped countries into 3 classes. We estimated HBsAg prevalence among the general population to be < 1% in 24/31 countries, although it was higher in 7 Eastern/Southern European countries. HBsAg prevalence among each population group was higher in most Eastern/Southern European than Western/Northern European countries, whilst prevalence among PWID and prisoners was estimated at > 1% for most countries. Portugal had the highest estimated prevalence of HBsAg among migrants (5.0%), with the other highest prevalences mostly seen in Southern Europe. CONCLUSIONS: We estimated HBV prevalence for each population group within each EU/EAA country and the UK, with general population HBV prevalence to be < 1% in most countries. Further evidence is required on the HBsAg prevalence of high-risk populations for future evidence synthesis.

Estimates of hepatitis B virus prevalence among general population and key risk groups in EU/EEA/UK countries: a systematic review.

BackgroundThe burden of chronic hepatitis B virus (HBV) varies across the European Union (EU) and European Economic Area (EEA).AimWe aimed to update the 2017 HBV prevalence estimates in EU/EEA countries and the United Kingdom for 2018 to 2021.MethodsWe undertook a systematic review, adding to HBV prevalence estimates from an existing (2005-2017) database. Databases were searched for original English-language research articles including HBV surface antigen prevalence estimates among the general population, pregnant women, first-time blood donors (FTB), men who have sex with men (MSM), migrants and people in prison. Country experts contributed grey literature data. Risk of bias was assessed using a quality assessment framework.FindingsThe update provided 147 new prevalence estimates across the region (updated total n = 579). Median HBV prevalence in the general population was 0.5% and the highest was 3.8% (Greece). Among FTB, the highest prevalence was 0.8% (Lithuania). Estimates among pregnant women were highest in Romania and Italy (5.1%). Among migrants, the highest estimate was 31.7% (Spain). Relative to 2017 estimates, median prevalence among pregnant women decreased by 0.5% (to 0.3%) and increased by 0.9% (to 5.8%) among migrants. Among MSM, the highest estimate was 3.4% (Croatia). Prevalence among people in prison was highest in Greece (8.3%) and the median prevalence increased by 0.6% (to 2.1%).ConclusionsThe HBV prevalence is low in the general population and confined to risk populations in most European countries with some exceptions. Screening and treatment should be targeted to people in prison and migrants.

A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015.

Tuberculosis (TB) remains a public health threat in low TB incidence countries, through a combination of reactivated disease and onward transmission. Using surveillance data from the United Kingdom (UK) and the Netherlands (NL), we demonstrate a simple and predictable relationship between the probability of observing a cluster and its size (the number of cases with a single genotype). We demonstrate that the full range of observed cluster sizes can be described using a modified branching process model with the individual reproduction number following a Poisson lognormal distribution. We estimate that, on average, between 2010 and 2015, a TB case generated 0.41 (95% CrI 0.30,0.60) secondary cases in the UK, and 0.24 (0.14,0.48) secondary cases in the NL. A majority of cases did not generate any secondary cases. Recent transmission accounted for 39% (26%,60%) of UK cases and 23%(13%,37%) of NL cases. We predict that reducing UK transmission rates to those observed in the NL would result in 538(266,818) fewer cases annually in the UK. In conclusion, while TB in low incidence countries is strongly associated with reactivated infections, we demonstrate that recent transmission remains sufficient to warrant policies aimed at limiting local TB spread.

Sheep scab transmission: a spatially explicit dynamic metapopulation model.

Psoroptic mange (sheep scab), caused by the parasitic mite, Psoroptes ovis, is an important disease of sheep worldwide. It causes chronic animal welfare issues and economic losses. Eradication of scab has proved impossible in many sheep-rearing areas and recent reports of resistance to macrocyclic lactones, a key class of parasiticide, highlight the importance of improving approaches to scab management. To allow this, the current study aimed to develop a stochastic spatial metapopulation model for sheep scab transmission which can be adapted for use in any geographical region, exhibited here using data for Great Britain. The model uses agricultural survey and sheep movement data to geo-reference farms and capture realistic movement patterns. Reported data on sheep scab outbreaks from 1973 to 1991 were used for model fitting with Sequential Monte Carlo Approximate Bayesian Computation methods. The outbreak incidence predicted by the model was from the same statistical distribution as the reported outbreak data ([Formula: see text] = 115.3, p = 1) and the spatial location of sheep scab outbreaks predicted was positively correlated with the observed outbreak data by county ([Formula: see text] = 0.55, p < 0.001), confirming that the model developed is able to accurately capture the number of farms infected in a year, the seasonality of scab incidence and the spatial patterns seen in the data. This model gives insight into the transmission dynamics of sheep scab and will allow the exploration of more effective control strategies.

A dynamic model of bovine tuberculosis spread and control in Great Britain.

Bovine tuberculosis (TB) is one of the most complex, persistent and controversial problems facing the British cattle industry, costing the country an estimated £100 million per year. The low sensitivity of the standard diagnostic test leads to considerable ambiguity in determining the main transmission routes of infection, which exacerbates the continuing scientific debate. In turn this uncertainty fuels the fierce public and political disputes on the necessity of controlling badgers to limit the spread of infection. Here we present a dynamic stochastic spatial model for bovine TB in Great Britain that combines within-farm and between-farm transmission. At the farm scale the model incorporates stochastic transmission of infection, maintenance of infection in the environment and a testing protocol that mimics historical government policy. Between-farm transmission has a short-range environmental component and is explicitly driven by movements of individual cattle between farms, as recorded in the Cattle Tracing System. The resultant model replicates the observed annual increase of infection over time as well as the spread of infection into new areas. Given that our model is mechanistic, it can ascribe transmission pathways to each new case; the majority of newly detected cases involve several transmission routes with moving infected cattle, reinfection from an environmental reservoir and poor sensitivity of the diagnostic test all having substantive roles. This underpins our findings on the implications of control measures. Very few of the control options tested have the potential to reverse the observed annual increase, with only intensive strategies such as whole-herd culling or additional national testing proving highly effective, whereas controls focused on a single transmission route are unlikely to be highly effective.

Influence of commissioned provider type and deprivation score on uptake of the childhood flu immunization.

BACKGROUND: Since 2015/16 the UK seasonal influenza immunization programme has included children aged 5 and 6 years. In the South West of England school-based providers, GPs or community pharmacies were commissioned to deliver the vaccine depending on the locality. We aimed to assess variation in vaccine uptake in relation to the type of commissioned provider, and levels of socioeconomic deprivation. METHODS: Data from the South West of England (2015-16 season) were analysed using multilevel logistic regression to assess variation in vaccine uptake by type of commissioned provider, allowing for clustering of children within delivery sites. RESULTS: Overall uptake in 5 and 6 year olds was 34.3% (37 555/109 404). Vaccine uptake was highest when commissioned through school-based programmes 50.2% (9983/19 867) and lowest when commissioned through pharmacies, 23.1% (4269/18 479). Delivery through schools resulted in less variation by site and equal uptake across age groups, in contrast to GP and pharmacy delivery for which uptake was lower among 6 year olds. Vaccine uptake decreased with increasing levels of deprivation across all types of commissioned provider. CONCLUSION: School-based programmes achieve the highest and most consistent rates of childhood influenza vaccination. Interventions are still needed to promote more equitable uptake of the childhood influenza vaccine.

Social network analysis and whole genome sequencing in a cohort study to investigate TB transmission in an educational setting.

BACKGROUND: TB outbreaks in educational institutions can result in significant transmission and pose a considerable threat to TB control. Investigation using traditional microbiological and epidemiological tools can lead to imprecise screening strategies due to difficulties characterising complex transmission networks. Application of whole genome sequencing (WGS) and social network analysis can provide additional information that may facilitate rapid directed public health action. We report the utility of these methods in combination with traditional approaches for the first time to investigate a TB outbreak in an educational setting. METHODS: Latent tuberculosis infection (LTBI) cases were screenees with a positive T-SPOT®.TB test. Active TB cases were defined through laboratory confirmation of M. tuberculosis on culture or through clinical or radiological findings consistent with infection. Epidemiological data were collected from institutional records and screenees. Samples were cultured and analysed using traditional M. tuberculosis typing and WGS. We undertook multivariable multinomial regression and social network analysis to identify exposures associated with case status and risk communities. RESULTS: We identified 189 LTBI cases (13.7% positivity rate) and nine active TB cases from 1377 persons screened. The LTBI positivity rate was 39.1% (99/253) among persons who shared a course with an infectious case (odds ratio 7.3, 95% confidence interval [CI] 5.2 to 10.3). The community structure analysis divided the students into five communities based on connectivity, as opposed to the 11 shared courses. Social network analysis identified that the community including the suspected index case was at significantly elevated risk of active disease (odds ratio 7.5, 95% CI 1.3 to 44.0) and contained eight persons who were lost to follow-up. Five sputum samples underwent WGS, four had zero single nucleotide polymorphism (SNP) differences and one had a single SNP difference. CONCLUSION: This study demonstrates the public health impact an undiagnosed case of active TB disease can have in an educational setting within a low incidence area. Social network analysis and whole genome sequencing provided greater insight to evolution of the transmission network and identification of communities of risk. These tools provide further information over traditional epidemiological and microbiological approaches to direct public health action in this setting.

Estimating the effect of the 2005 change in BCG policy in England: a retrospective cohort study, 2000 to 2015.

BackgroundIn 2005 in England, universal Bacillus Calmette-Guérin (BCG) vaccination of school-age children was replaced by targeted BCG vaccination of high-risk neonates.AimEstimate the impact of the 2005 change in BCG policy on tuberculosis (TB) incidence rates in England.MethodsWe conducted an observational study by combining notifications from the Enhanced Tuberculosis Surveillance system, with demographic data from the Labour Force Survey to construct retrospective cohorts relevant to both the universal and targeted vaccination between 1 January 2000 and 31 December 2010. We then estimated incidence rates over a 5-year follow-up period and used regression modelling to estimate the impact of the change in policy on TB.ResultsIn the non-United Kingdom (UK) born, we found evidence for an association between a reduction in incidence rates and the change in BCG policy (school-age incidence rate ratio (IRR): 0.74; 95% credible interval (CrI): 0.61 to 0.88 and neonatal IRR: 0.62; 95%CrI: 0.44 to 0.88). We found some evidence that the change in policy was associated with an increase in incidence rates in the UK born school-age population (IRR: 1.08; 95%CrI: 0.97 to 1.19) and weaker evidence of an association with a reduction in incidence rates in UK born neonates (IRR: 0.96; 95%CrI: 0.82 to 1.14). Overall, we found that the change in policy was associated with directly preventing 385 (95%CrI: -105 to 881) cases.ConclusionsWithdrawing universal vaccination at school age and targeting vaccination towards high-risk neonates was associated with reduced incidence of TB. This was largely driven by reductions in the non-UK born with cases increasing in the UK born.

The perfect storm: incarceration and the high-risk environment perpetuating transmission of HIV, hepatitis C virus, and tuberculosis in Eastern Europe and Central Asia.

Despite global reductions in HIV incidence and mortality, the 15 UNAIDS-designated countries of Eastern Europe and Central Asia (EECA) that gained independence from the Soviet Union in 1991 constitute the only region where both continue to rise. HIV transmission in EECA is fuelled primarily by injection of opioids, with harsh criminalisation of drug use that has resulted in extraordinarily high levels of incarceration. Consequently, people who inject drugs, including those with HIV, hepatitis C virus, and tuberculosis, are concentrated within prisons. Evidence-based primary and secondary prevention of HIV using opioid agonist therapies such as methadone and buprenorphine is available in prisons in only a handful of EECA countries (methadone or buprenorphine in five countries and needle and syringe programmes in three countries), with none of them meeting recommended coverage levels. Similarly, antiretroviral therapy coverage, especially among people who inject drugs, is markedly under-scaled. Russia completely bans opioid agonist therapies and does not support needle and syringe programmes-with neither available in prisons-despite the country's high incarceration rate and having the largest burden of people with HIV who inject drugs in the region. Mathematical modelling for Ukraine suggests that high levels of incarceration in EECA countries facilitate HIV transmission among people who inject drugs, with 28-55% of all new HIV infections over the next 15 years predicted to be attributable to heightened HIV transmission risk among currently or previously incarcerated people who inject drugs. Scaling up of opioid agonist therapies within prisons and maintaining treatment after release would yield the greatest HIV transmission reduction in people who inject drugs. Additional analyses also suggest that at least 6% of all incident tuberculosis cases, and 75% of incident tuberculosis cases in people who inject drugs are due to incarceration. Interventions that reduce incarceration itself and effectively intervene with prisoners to screen, diagnose, and treat addiction and HIV, hepatitis C virus, and tuberculosis are urgently needed to stem the multiple overlapping epidemics concentrated in prisons.

Potential benefits of cattle vaccination as a supplementary control for bovine tuberculosis.

Vaccination for the control of bovine tuberculosis (bTB) in cattle is not currently used within any international control program, and is illegal within the EU. Candidate vaccines, based upon Mycobacterium bovis bacillus Calmette-Guérin (BCG) all interfere with the action of the tuberculin skin test, which is used to determine if animals, herds and countries are officially bTB-free. New diagnostic tests that Differentiate Infected from Vaccinated Animals (DIVA) offer the potential to introduce vaccination within existing eradication programs. We use within-herd transmission models estimated from historical data from Great Britain (GB) to explore the feasibility of such supplemental use of vaccination. The economic impact of bovine Tuberculosis for farmers is dominated by the costs associated with testing, and associated restrictions on animal movements. Farmers' willingness to adopt vaccination will require vaccination to not only reduce the burden of infection, but also the risk of restrictions being imposed. We find that, under the intensive sequence of testing in GB, it is the specificity of the DIVA test, rather than the sensitivity, that is the greatest barrier to see a herd level benefit of vaccination. The potential negative effects of vaccination could be mitigated through relaxation of testing. However, this could potentially increase the hidden burden of infection within Officially TB Free herds. Using our models, we explore the range of the DIVA test characteristics necessary to see a protective herd level benefit of vaccination. We estimate that a DIVA specificity of at least 99.85% and sensitivity of >40% is required to see a protective benefit of vaccination with no increase in the risk of missed infection. Data from experimentally infected animals suggest that this target specificity could be achieved in vaccinates using a cocktail of three DIVA antigens while maintaining a sensitivity of 73.3% (95%CI: 61.9, 82.9%) relative to post-mortem detection.

Eliminating bovine tuberculosis in cattle and badgers: insight from a dynamic model.

Bovine tuberculosis (BTB) is a multi-species infection that commonly affects cattle and badgers in Great Britain. Despite years of study, the impact of badgers on BTB incidence in cattle is poorly understood. Using a two-host transmission model of BTB in cattle and badgers, we find that published data and parameter estimates are most consistent with a system at the threshold of control. The most consistent explanation for data obtained from cattle and badger populations includes within-host reproduction numbers close to 1 and between-host reproduction numbers of approximately 0.05. In terms of controlling infection in cattle, reducing cattle-to-cattle transmission is essential. In some regions, even large reductions in badger prevalence can have a modest impact on cattle infection and a multi-stranded approach is necessary that also targets badger-to-cattle transmission directly. The new perspective highlighted by this two-host approach provides insight into the control of BTB in Great Britain.

Epidemic predictions in an imperfect world: modelling disease spread with partial data.

'Big-data' epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.

Measured dynamic social contact patterns explain the spread of H1N1v influenza.

Patterns of social mixing are key determinants of epidemic spread. Here we present the results of an internet-based social contact survey completed by a cohort of participants over 9,000 times between July 2009 and March 2010, during the 2009 H1N1v influenza epidemic. We quantify the changes in social contact patterns over time, finding that school children make 40% fewer contacts during holiday periods than during term time. We use these dynamically varying contact patterns to parameterise an age-structured model of influenza spread, capturing well the observed patterns of incidence; the changing contact patterns resulted in a fall of approximately 35% in the reproduction number of influenza during the holidays. This work illustrates the importance of including changing mixing patterns in epidemic models. We conclude that changes in contact patterns explain changes in disease incidence, and that the timing of school terms drove the 2009 H1N1v epidemic in the UK. Changes in social mixing patterns can be usefully measured through simple internet-based surveys.

Age-dependent patterns of bovine tuberculosis in cattle.

Bovine tuberculosis (BTB) is an important livestock disease, seriously impacting cattle industries in both industrialised and pre-industrialised countries. Like TB in other mammals, infection is life long and, if undiagnosed, may progress to disease years after exposure. The risk of disease in humans is highly age-dependent, however in cattle, age-dependent risks have yet to be quantified, largely due to insufficient data and limited diagnostics. Here, we estimate age-specific reactor rates in Great Britain by combining herd-level testing data with spatial movement data from the Cattle Tracing System (CTS). Using a catalytic model, we find strong age dependencies in infection risk and that the probability of detecting infection increases with age. Between 2004 and 2009, infection incidence in cattle fluctuated around 1%. Age-specific incidence increased monotonically until 24-36 months, with cattle aged between 12 and 36 months experiencing the highest rates of infection. Beef and dairy cattle under 24 months experienced similar infection risks, however major differences occurred in older ages. The average reproductive number in cattle was greater than 1 for the years 2004-2009. These methods reveal a consistent pattern of BTB rates with age, across different population structures and testing patterns. The results provide practical insights into BTB epidemiology and control, suggesting that targeting a mass control programme at cattle between 12 and 36 months could be beneficial.

Limited impact of contact tracing in a University setting for COVID-19 due to asymptomatic transmission and social distancing.

Contact tracing is an important tool for controlling the spread of infectious diseases, including COVID-19. Here, we investigate the spread of COVID-19 and the effectiveness of contact tracing in a university population, using a data-driven ego-centric network model constructed with social contact data collected during 2020 and similar data collected in 2010. We find that during 2020, university staff and students consistently reported fewer social contacts than in 2010, however those contacts occurred more frequently and were of longer duration. We find that contact tracing in the presence of social distancing is less impactful than without social distancing. By combining multiple data sources, we show that University-aged populations are likely to develop asymptomatic COVID-19 infections. We find that asymptomatic index cases cannot be reliably discovered through contact tracing and consequently transmission in their social network is not significantly reduced through contact tracing. In summary, social distancing restrictions had a large impact on limiting COVID-19 outbreaks in universities; to reduce transmission further contact tracing should be used in conjunction with alternative interventions.

Evaluation and deployment of isotype-specific salivary antibody assays for detecting previous SARS-CoV-2 infection in children and adults.

BACKGROUND: Saliva is easily obtainable non-invasively and potentially suitable for detecting both current and previous SARS-CoV-2 infection, but there is limited evidence on the utility of salivary antibody testing for community surveillance. METHODS: We established 6 ELISAs detecting IgA and IgG antibodies to whole SARS-CoV-2 spike protein, to its receptor binding domain region and to nucleocapsid protein in saliva. We evaluated diagnostic performance, and using paired saliva and serum samples, correlated mucosal and systemic antibody responses. The best-performing assays were field-tested in 20 household outbreaks. RESULTS: We demonstrate in test accuracy (N = 320), spike IgG (ROC AUC: 95.0%, 92.8-97.3%) and spike IgA (ROC AUC: 89.9%, 86.5-93.2%) assays to discriminate best between pre-pandemic and post COVID-19 saliva samples. Specificity was 100% in younger age groups (0-19 years) for spike IgA and IgG. However, sensitivity was low for the best-performing assay (spike IgG: 50.6%, 39.8-61.4%). Using machine learning, diagnostic performance was improved when a combination of tests was used. As expected, salivary IgA was poorly correlated with serum, indicating an oral mucosal response whereas salivary IgG responses were predictive of those in serum. When deployed to household outbreaks, antibody responses were heterogeneous but remained a reliable indicator of recent infection. Intriguingly, unvaccinated children without confirmed infection showed evidence of exposure almost exclusively through specific IgA responses. CONCLUSIONS: Through robust standardisation, evaluation and field-testing, this work provides a platform for further studies investigating SARS-CoV-2 transmission and mucosal immunity with the potential for expanding salivo-surveillance to other respiratory infections in hard-to-reach settings.

If a person has been previously infected with SARS-CoV-2 they will produce specific proteins, called antibodies. These are present in the saliva and blood. Saliva is easier to obtain than blood, so we developed and evaluated six tests that detect SARS-CoV-2 antibodies in saliva in children and adults. Some tests detected antibodies to a particular protein made by SARS-CoV-2 called the spike protein, and these tests worked best. The most accurate results were obtained by using a combination of tests. Similar tests could also be developed to detect other respiratory infections which will enable easier identification of infected individuals.

Epidemiologic inference from the distribution of tuberculosis cases in households in Lima, Peru.

BACKGROUND: Tuberculosis (TB) often occurs among household contacts of people with active TB. It is unclear whether clustering of cases represents household transmission or shared household risk factors for TB. METHODS: We used cross-sectional data from 764 households in Lima, Peru, to estimate the relative contributions of household and community transmission, the average time between cases, and the immunity afforded by a previous TB infection. RESULTS: The distribution of cases per household suggests that almost 7 of 10 nonindex household cases were infected in the community rather than in the household. The average interval between household cases was 3.5 years. We observed a saturation effect in the number of cases per household and estimated that protective immunity conferred up to 35% reduction in the risk of disease. CONCLUSIONS: Cross-sectional household data can elucidate the natural history and transmission dynamics of TB. In this high-incidence setting, we found that the majority of cases were attributable to community transmission and that household contacts of case patients derive some immunity from household exposures. Screening of household contacts may be an effective method of detecting new TB cases if carried out over several years.

Reassessing the evidence for universal school-age BCG vaccination in England and Wales: re-evaluating and updating a modelling study.

OBJECTIVES: In 2005, England and Wales switched from universal BCG vaccination against tuberculosis (TB) disease for school-age children to targeted vaccination of neonates. We aimed to recreate and re-evaluate a previously published model, the results of which informed this policy change. DESIGN: We recreated an approach for estimating the impact of ending the BCG schools scheme, correcting a methodological flaw in the model, updating the model with parameter uncertainty and improving parameter estimates where possible. We investigated scenarios for the assumed annual decrease in TB incidence rates considered by the UK's Joint Committee on Vaccination and Immunisation and explored alternative scenarios using notification data. SETTING: England and Wales. OUTCOME MEASURES: The number of vaccines needed to prevent a single notification and the average annual additional notifications caused by ending the policy change. RESULTS: The previously published model was found to contain a methodological flaw and to be spuriously precise. It greatly underestimated the impact of ending school-age vaccination compared with our updated, corrected model. The updated model produced predictions with wide CIs when parameter uncertainty was included. Model estimates based on an assumption of an annual decrease in TB incidence rates of 1.9% were closest to those estimated using notification data. Using this assumption, we estimate that 1600 (2.5; 97.5% quantiles: 1300, 2000) vaccines would have been required to prevent a single notification in 2004. CONCLUSIONS: The impact of ending the BCG schools scheme was found to be greater than previously thought when notification data were used. Our results highlight the importance of independent evaluations of modelling evidence, including uncertainty, and evaluating multiple scenarios when forecasting the impact of changes in vaccination policy.