BACKGROUND:Most longitudinal studies of COVID-19 incidence have used unlinked samples. The city of Manaus, Brazil, has a blood donation program which allows sample linkage, and was struck by two large COVID-19 epidemic waves between mid-2020 and early 2021. METHODS:We estimated the changing force of infection, i.e. incidence in susceptible individuals. Seroconversion was inferred by a mixture model for serial values from the Abbott Architect SARS-CoV-2 nucleocapsid (N) IgG assay. We estimated the number of suspected COVID-19 hospitalizations arising from each infection over calendar time. RESULTS:Whole blood donations between April 2020 and March 2021 were included from 6734 people, 2747 with two or more donations. The inferred criterion for seroconversion, and thus an incident infection, was a 6.07 fold increase in N IgG reactivity. The overall force of infection was 1.19 per person year (95% confidence interval 1.14-1.24) during the two main waves. The estimated number of suspected hospitalizations per infection, was approximately 4.1 times higher in the second wave than in the first. CONCLUSIONS:Serial values from this assay can be used to infer seroconversion over time, and in Manaus show a higher number of suspected COVID-19 hospitalizations per infection in the second wave relative to the first.

Abstract The proverbial benefits of prevention over cure are self-evident—and yet we are reluctant to invest in protecting or improving health. Resolution of this age-old dilemma begins with a timeless truth: the benefits of good health come at a cost; prevention is not better than cure at any price. Investment in health protection is more appealing when a high-risk, high-value hazard can be averted certainly, rapidly, and at a relatively low cost. Application of this idea helps to explain why prevention is neglected by health services, why the world was not ready for the COVID-19 pandemic, why the world’s most deadly infections are neglected, why cigarette smoking is still commonplace, why the idea of a ‘sin tax’ is misconceived, why billions still do not have access to safe sanitation, and why the response to climate change has been so slow. Although more money and effort are invested in health promotion and disease prevention today than is commonly thought, the enormous avoidable burden of ill-health is a reason to seek ways of investing still more. The search should begin, not with the usual expert prescriptions of how health choices ought to be made, but with investigations of how choices are made in practice. That depends on understanding, not only costs, hazards, and risks, but also the values, motives, and powers of all those who collectively make decisions about health.

Abstract Stable, familiar endemic infections are rarely seen as health emergencies, even though they kill millions of people each year. But there are ways to remedy the neglect, here illustrated by tuberculosis. Because curative treatment is currently the best form of prevention, the immediate priority for TB control is early diagnosis and chemotherapy, aided by developing novel diagnostics (easier) and therapeutics (harder), and carried out in the context of primary healthcare. This reinforces three major goals of global health at the same time—strengthen primary healthcare, reach Universal Health Coverage (UHC), and put TB on a path to elimination. Other motivating strategies have supporting roles: highlight new dangers from old hazards, such as the emergence of multidrug-resistant strains; neutralize major risks for TB, especially co-infection with human immunodeficiency virus (HIV); and mitigate the large number of weaker TB risks that have co-benefits for other health conditions (diabetes, undernutrition) and for society more widely (homelessness, crowding). Vaccination is the ultimate, transformative, preventive tool for TB but awaits a high-efficacy companion or successor to BCG. Whatever the virtues of new technologies for TB control, none will succeed without understanding the means and incentives for implementing them, as illustrated by the roll-out of antiretroviral therapy for HIV/AIDS and TB control in southern Africa.

Abstract Many consumers of tobacco, sugar and other causes of chronic diseases are trapped in a world of limited options (nothing but junk food), hard choices (tobacco addiction) and few resources (economic hardship), temporarily eased by postponing (discounting) the personal costs of future illness. Consumers are ultimately responsible for ‘behavioural’ risks to their health, but the behaviours in question are also those of manufacturers and governments. The burden of choice on consumers is lighter when shared. Governments, in particular, have the means to intervene between commercial supply and consumer demand. The most effective instruments of government are the most forceful—taxes and regulations—especially for the control of single, major causes of illness such as tobacco and sugar. In practice, governments under pressure from manufacturers tend to under-tax and under-regulate harmful commodities, so other enticements are needed too. Empirical studies show that campaigns to improve health (better food choices, greater physical activity) can complement those to prevent disease, especially when the joint benefits for health are large. The greater task, however, is to empower public demand for government action that favours health, and disempower commercial interests that drive the consumption of sugar, tobacco, and other causes of chronic disease.

Abstract If the threat of disaster—an earthquake, a pandemic, or a nuclear accident—is unlikely or uncertain in time, place, and scale then prevention and preparedness may not be better than treatment and cure. Tactics that favour the prevention of an unlikely disaster are routinely used by the insurance industry: spotlight preventable hazards, pool the risks, and share the costs. A hazard—such as COVID-19, Ebola, or Zika virus—is perceived to be more dangerous, and more likely to stimulate action, when classified as a public health emergency or a threat to national security, and when the severity of a hazard changes suddenly and unpredictably in space or time. The methods for pooling risks and sharing costs include: early detection and response systems for multiple pathogens, including an unknown, unpredictable Disease X; mechanisms for sharing genomic and other surveillance data; platform technologies for the development of new diagnostics and vaccines; and collaborations through the international conventions and regulations. Although primary prevention is preferable, secondary prevention is often a better interim investment: preventing the emergence of Disease X is not generally feasible; stopping its spread by surveillance, diagnosis, isolation, quarantine, and vaccination is practical and essential.

Abstract Prevention is an affordable part of primary healthcare in the sense that it is cheap compared with the mounting costs of medical treatment, and good value for money. The motives for investing in prevention differ at global (e.g. international vaccination initiatives), national (aspirations to Universal Health Coverage, UHC), and local levels (commitments to community health). But at all levels, there are ways to make prevention more attractive through efficiencies (low-cost preventive care targeted to people at high risk), technologies (novel vaccines and diagnostic devices), metrics (for health and wellbeing, not only death and disease), and partnerships (‘Prevention in All Policies’). There are, however, other obstacles to navigate. One is that funding is not transferable away from urgent medical care, so prevention needs additional, protected investment, which at first supplements but eventually supplants the treatment of illness.

This rapid systematic review of evidence asks whether (i) wearing a face mask, (ii) one type of mask over another and (iii) mandatory mask policies can reduce the transmission of SARS-CoV-2 infection, either in community-based or healthcare settings. A search of studies published 1 January 2020–27 January 2023 yielded 5185 unique records. Due to a paucity of randomized controlled trials (RCTs), observational studies were included in the analysis. We analysed 35 studies in community settings (three RCTs and 32 observational) and 40 in healthcare settings (one RCT and 39 observational). Ninety-five per cent of studies included were conducted before highly transmissible Omicron variants emerged. Ninety-one per cent of observational studies were at ‘critical’ risk of bias (ROB) in at least one domain, often failing to separate the effects of masks from concurrent interventions. More studies found that masks ( n = 39/47; 83%) and mask mandates ( n = 16/18; 89%) reduced infection than found no effect ( n = 8/65; 12%) or favoured controls ( n = 1/65; 2%). Seven observational studies found that respirators were more protective than surgical masks, while five found no statistically significant difference between the two mask types. Despite the ROB, and allowing for uncertain and variable efficacy, we conclude that wearing masks, wearing higher quality masks (respirators), and mask mandates generally reduced SARS-CoV-2 transmission in these study populations. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

A common basis to address the dynamics of directly transmitted infectious diseases, such as COVID-19, are compartmental (or SIR) models. SIR models typically assume homogenous population mixing, a simplification that is convenient but unrealistic. Here we validate an existing model of a scale-free fractal infection process using high-resolution data on COVID-19 spread in São Caetano, Brazil. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5. This model parameter correlated tightly with physical distancing measured by mobile phone data, such that in periods of greater distancing the model recovered a lower average number of contacts, and vice versa. We show that the SIR model is a special case of our scale-free fractal process model in which the parameter that reflects population structure is set at unity, indicating homogeneous mixing. Our more general framework better explained the dynamics of COVID-19 in São Caetano, used fewer parameters than a standard SIR model and accounted for geographically localized clusters of disease. Our model requires further validation in other locations and with other directly transmitted infectious agents.

This paper considers SEPIR, an extension of the well-known SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can be estimated from the data. SEPIR deploys an additional presymptomatic infectious compartment, not modelled in SEIR but known to exist in COVID-19. This stage can also be fitted to data. We focus on how to fit SEPIR to a first wave of COVID. Both SEIR and SEPIR and the existing SEIR models assume a homogeneous mixing population with parameters fixed. Moreover, neither includes dynamically varying control strategies deployed against the virus. If either model is to represent more than just a single wave of the epidemic, then the parameters of the model would have to be time dependent. In view of this, we also show how reproduction numbers can be calculated to investigate the long-term overall outcome of an epidemic.

BACKGROUND:The city of Manaus, north Brazil, was stricken by a second epidemic wave of SARS-CoV-2 despite high seroprevalence estimates, coinciding with the emergence of the Gamma (P.1) variant. Reinfections were postulated as a partial explanation for the second surge. However, accurate calculation of reinfection rates is difficult when stringent criteria as two time-separated RT-PCR tests and/or genome sequencing are required. To estimate the proportion of reinfections caused by Gamma during the second wave in Manaus and the protection conferred by previous infection, we identified anti-SARS-CoV-2 antibody boosting in repeat blood donors as a mean to infer reinfection. METHODS:We tested serial blood samples from unvaccinated repeat blood donors in Manaus for the presence of anti-SARS-CoV-2 IgG antibodies using two assays that display waning in early convalescence, enabling the detection of reinfection-induced boosting. Donors were required to have three or more donations, being at least one during each epidemic wave. We propose a strict serological definition of reinfection (reactivity boosting following waning like a V-shaped curve in both assays or three spaced boostings), probable (two separate boosting events) and possible (reinfection detected by only one assay) reinfections. The serial samples were used to divide donors into six groups defined based on the inferred sequence of infection and reinfection with non-Gamma and Gamma variants. RESULTS:From 3655 repeat blood donors, 238 met all inclusion criteria, and 223 had enough residual sample volume to perform both serological assays. We found 13.6% (95% CI 7.0-24.5%) of all presumed Gamma infections that were observed in 2021 were reinfections. If we also include cases of probable or possible reinfections, these percentages increase respectively to 22.7% (95% CI 14.3-34.2%) and 39.3% (95% CI 29.5-50.0%). Previous infection conferred a protection against reinfection of 85.3% (95% CI 71.3-92.7%), decreasing to respectively 72.5% (95% CI 54.7-83.6%) and 39.5% (95% CI 14.1-57.8%) if probable and possible reinfections are included. CONCLUSIONS:Reinfection by Gamma is common and may play a significant role in epidemics where Gamma is prevalent, highlighting the continued threat variants of concern pose even to settings previously hit by substantial epidemics.

There are large differences in the shape and size of regional SARS-CoV-2 epidemics in Brazil. Here we tested monthly blood donation samples for IgG antibodies from March 2020 to March 2021 in eight of Brazil’s most populous cities. There was large variation in the inferred attack rate adjusted for seroreversion across cities, and seroprevalence was consistently smaller in women and donors older than 55 years. The age-specific infection fatality rate differed between cities and consistently increased with age. The infection hospitalisation rate (IHR) increased significantly during the gamma-dominated second wave in Manaus, suggesting increased morbidity of the Gamma VOC compared to previous variants circulating in Manaus. The higher disease penetrance associated with the health system’s collapse increased the overall IFR by a minimum factor of 2.91 (95% CrI 2.43–3.53). These results demonstrate large heterogeneity in epidemic spread and highlight the utility of blood donor serosurveillance to monitor SARS-CoV-2 epidemics.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant of concern has spread rapidly across Brazil since late 2020, causing substantial infection and death waves. Here we used individual-level patient records after hospitalization with suspected or confirmed coronavirus disease 2019 (COVID-19) between 20 January 2020 and 26 July 2021 to document temporary, sweeping shocks in hospital fatality rates that followed the spread of Gamma across 14 state capitals, during which typically more than half of hospitalized patients aged 70 years and older died. We show that such extensive shocks in COVID-19 in-hospital fatality rates also existed before the detection of Gamma. Using a Bayesian fatality rate model, we found that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates were primarily associated with geographic inequities and shortages in healthcare capacity. We estimate that approximately half of the COVID-19 deaths in hospitals in the 14 cities could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization and pandemic preparedness are critical to minimize population-wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries.

SARS-CoV-2 serologic surveys estimate the proportion of the population with antibodies against historical variants, which nears 100% in many settings. New approaches are required to fully exploit serosurvey data. Using a SARS-CoV-2 anti-Spike (S) protein chemiluminescent microparticle assay, we attained a semi-quantitative measurement of population IgG titers in serial cross-sectional monthly samples of blood donations across seven Brazilian state capitals (March 2021–November 2021). Using an ecological analysis, we assessed the contributions of prior attack rate and vaccination to antibody titer. We compared anti-S titer across the seven cities during the growth phase of the Delta variant and used this to predict the resulting age-standardized incidence of severe COVID-19 cases. We tested ~780 samples per month, per location. Seroprevalence rose to >95% across all seven capitals by November 2021. Driven by vaccination, mean antibody titer increased 16-fold over the study, with the greatest increases occurring in cities with the highest prior attack rates. Mean anti-S IgG was strongly correlated (adjusted R2 = 0.89) with the number of severe cases caused by Delta. Semi-quantitative anti-S antibody titers are informative about prior exposure and vaccination coverage and may also indicate the potential impact of future SARS-CoV-2 variants.

New evidence confirms that fewer people die in better vaccinated communities

Background: Spatially-targeted approaches to screen for tuberculosis (TB) could accelerate TB control in high burden populations. We aimed to estimate gains in case-finding yield under an adaptive decision-making approach for spatially-targeted, mobile digital chest radiography (dCXR)-based screening in communities with varying levels of TB prevalence. Methods: We used a Monte-Carlo simulation model to simulate a spatially-targeted screening intervention in 24 communities with TB prevalence estimates derived from a large community-randomized trial. We implemented a Thompson sampling algorithm to allocate screening units based on Bayesian probabilities of local TB prevalence that are continuously updated during weekly screening rounds. Four mobile units for dCXR-based screening and subsequent Xpert Ultra-based testing were allocated among the communities during a 52-week period. We estimated the yield of bacteriologically-confirmed TB per 1000 screenings comparing scenarios of spatially targeted and untargeted resource allocation. Results: We estimated that under the untargeted scenario, an expected 666 (95% uncertainty interval 522-825) TB cases would be detected over one year, equivalent to 8.9 (7.5-10.3) per 1000 individuals screened. Allocating the screening units to the communities with the highest (prior-year) cases notification rates resulted in an expected 760 (617-926) TB cases detected, 10.1 (8.6-11.8) per 1000 screened. Adaptive, spatially-targeted screening resulted in an expected 1241 (995-1502) TB cases detected, 16.5 (14.5-18.7) per 1000 screened. Numbers of dCXR-based screenings needed to detect one additional TB case declined during the first 12-14 weeks as a result of Bayesian learning. Conclusion: We introduce a spatially-targeted screening strategy that could reduce the number of screenings necessary to detect additional TB in high-burden settings and thus improve the efficiency of screening interventions. trials are needed to determine whether this could be

Some asymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19. Here, we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate the relationship between asymptomatic proportion and age. Epidemiological data obtained before mass vaccination provide valuable insights into the nature of pathogenicity of SARS-CoV-2. The data were collected by multiple rounds of city-wide PCR testing with contact tracing, where each patient was monitored for symptoms through the whole course of infection. The clinical endpoint (asymptomatic or symptomatic) for each patient was recorded (the pre-symptomatic patients were classified as symptomatic). We find that the proportion of infections that are asymptomatic declines with age (coefficient = −0.006, 95% CI: −0.008 to −0.003, p < 0.01), falling from 42% (95% CI: 6–78%) in age group 0–9 years to 11% (95% CI: 0–25%) in age group greater than 60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern, together with the distribution of cases by age, can explain most of the reported variation in asymptomatic proportions among cities. Our analysis suggests that SARS-CoV-2 surveillance strategies should take account of the variation in asymptomatic proportion with age.