Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic.

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

Analysis of the diverse antigenic landscape of the malaria protein RH5 identifies a potent vaccine-induced human public antibody clonotype.

The highly conserved and essential Plasmodium falciparum reticulocyte-binding protein homolog 5 (PfRH5) has emerged as the leading target for vaccines against the disease-causing blood stage of malaria. However, the features of the human vaccine-induced antibody response that confer highly potent inhibition of malaria parasite invasion into red blood cells are not well defined. Here, we characterize 236 human IgG monoclonal antibodies, derived from 15 donors, induced by the most advanced PfRH5 vaccine. We define the antigenic landscape of this molecule and establish that epitope specificity, antibody association rate, and intra-PfRH5 antibody interactions are key determinants of functional anti-parasitic potency. In addition, we identify a germline IgG gene combination that results in an exceptionally potent class of antibody and demonstrate its prophylactic potential to protect against P. falciparum parasite challenge in vivo. This comprehensive dataset provides a framework to guide rational design of next-generation vaccines and prophylactic antibodies to protect against blood-stage malaria.

A reply to "TCR+/BCR+ dual-expressing cells and their associated public BCR clonotype are not enriched in type 1 diabetes".

We have recently identified a novel lymphocyte that is a dual expresser (DE) of TCRαß and BCR. DEs in T1D patients are predominated by a public BCR clonotype (clone-x) that encodes a potent autoantigen that cross-activates insulin-reactive T cells. Betts and colleagues were able to detect DEs but alleged to not detect high DE frequency, clone-x, or similar clones in T1D patients. Unfortunately, the authors did not follow our methods and when they did, their flow cytometric data at two sites were conflicting. Moreover, contrary to their claim, we identified clones similar to clone-x in their data along with clones bearing the core motif (DTAMVYYFDYW). Additionally, their report of no increased usage of clone-x VH/DH genes by bulk B cells confirms rather than challenges our results. Finally, the authors failed to provide data verifying purity of their sorted DEs, making it difficult to draw reliable conclusion of their repertoire analysis. This Matters Arising Response paper addresses the Japp et al. (2021) Matters Arising paper, published concurrently in Cell.

TCR+/BCR+ dual-expressing cells and their associated public BCR clonotype are not enriched in type 1 diabetes.

Ahmed and colleagues recently described a novel hybrid lymphocyte expressing both a B and T cell receptor, termed double expresser (DE) cells. DE cells in blood of type 1 diabetes (T1D) subjects were present at increased numbers and enriched for a public B cell clonotype. Here, we attempted to reproduce these findings. While we could identify DE cells by flow cytometry, we found no association between DE cell frequency and T1D status. We were unable to identify the reported public B cell clone, or any similar clone, in bulk B cells or sorted DE cells from T1D subjects or controls. We also did not observe increased usage of the public clone VH or DH genes in B cells or in sorted DE cells. Taken together, our findings suggest that DE cells and their alleged public clonotype are not enriched in T1D. This Matters Arising paper is in response to Ahmed et al. (2019), published in Cell. See also the response by Ahmed et al. (2021), published in this issue.

Longitudinal Analysis of the Human B Cell Response to Ebola Virus Infection.

Ebola virus (EBOV) remains a public health threat. We performed a longitudinal study of B cell responses to EBOV in four survivors of the 2014 West African outbreak. Infection induced lasting EBOV-specific immunoglobulin G (IgG) antibodies, but their subclass composition changed over time, with IgG1 persisting, IgG3 rapidly declining, and IgG4 appearing late. Striking changes occurred in the immunoglobulin repertoire, with massive recruitment of naive B cells that subsequently underwent hypermutation. We characterized a large panel of EBOV glycoprotein-specific monoclonal antibodies (mAbs). Only a small subset of mAbs that bound glycoprotein by ELISA recognized cell-surface glycoprotein. However, this subset contained all neutralizing mAbs. Several mAbs protected against EBOV disease in animals, including one mAb that targeted an epitope under evolutionary selection during the 2014 outbreak. Convergent antibody evolution was seen across multiple donors, particularly among VH3-13 neutralizing antibodies specific for the GP1 core. Our study provides a benchmark for assessing EBOV vaccine-induced immunity.

A large-scale systematic survey reveals recurring molecular features of public antibody responses to SARS-CoV-2.

Global research to combat the COVID-19 pandemic has led to the isolation and characterization of thousands of human antibodies to the SARS-CoV-2 spike protein, providing an unprecedented opportunity to study the antibody response to a single antigen. Using the information derived from 88 research publications and 13 patents, we assembled a dataset of ∼8,000 human antibodies to the SARS-CoV-2 spike protein from >200 donors. By analyzing immunoglobulin V and D gene usages, complementarity-determining region H3 sequences, and somatic hypermutations, we demonstrated that the common (public) responses to different domains of the spike protein were quite different. We further used these sequences to train a deep-learning model to accurately distinguish between the human antibodies to SARS-CoV-2 spike protein and those to influenza hemagglutinin protein. Overall, this study provides an informative resource for antibody research and enhances our molecular understanding of public antibody responses.

A vaccine-induced public antibody protects against SARS-CoV-2 and emerging variants.

The emergence of SARS-CoV-2 antigenic variants with increased transmissibility is a public health threat. Some variants show substantial resistance to neutralization by SARS-CoV-2 infection- or vaccination-induced antibodies. Here, we analyzed receptor binding domain-binding monoclonal antibodies derived from SARS-CoV-2 mRNA vaccine-elicited germinal center B cells for neutralizing activity against the WA1/2020 D614G SARS-CoV-2 strain and variants of concern. Of five monoclonal antibodies that potently neutralized the WA1/2020 D614G strain, all retained neutralizing capacity against the B.1.617.2 variant, four also neutralized the B.1.1.7 variant, and only one, 2C08, also neutralized the B.1.351 and B.1.1.28 variants. 2C08 reduced lung viral load and morbidity in hamsters challenged with the WA1/2020 D614G, B.1.351, or B.1.617.2 strains. Clonal analysis identified 2C08-like public clonotypes among B cells responding to SARS-CoV-2 infection or vaccination in 41 out of 181 individuals. Thus, 2C08-like antibodies can be induced by SARS-CoV-2 vaccines and mitigate resistance by circulating variants of concern.

Vaccination with prefusion-stabilized respiratory syncytial virus fusion protein induces genetically and antigenically diverse antibody responses.

An effective vaccine for respiratory syncytial virus (RSV) is an unrealized public health goal. A single dose of the prefusion-stabilized fusion (F) glycoprotein subunit vaccine (DS-Cav1) substantially increases serum-neutralizing activity in healthy adults. We sought to determine whether DS-Cav1 vaccination induces a repertoire mirroring the pre-existing diversity from natural infection or whether antibody lineages targeting specific epitopes predominate. We evaluated RSV F-specific B cell responses before and after vaccination in six participants using complementary B cell sequencing methodologies and identified 555 clonal lineages. DS-Cav1-induced lineages recognized the prefusion conformation of F (pre-F) and were genetically diverse. Expressed antibodies recognized all six antigenic sites on the pre-F trimer. We identified 34 public clonotypes, and structural analysis of two antibodies from a predominant clonotype revealed a common mode of recognition. Thus, vaccination with DS-Cav1 generates a diverse polyclonal response targeting the antigenic sites on pre-F, supporting the development and advanced testing of pre-F-based vaccines against RSV.

Health Digital Twins in Life Science and Health Care Innovation.

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

The role of positive emotion in harmful health behavior: Implications for theory and public health campaigns.

Meta-analyses have concluded that positive emotions do not reduce appetitive risk behaviors (risky behaviors that fulfill appetitive or craving states, such as smoking and excessive alcohol use). We propose that this conclusion is premature. Drawing on the Appraisal Tendency Framework and related theories of emotion and decision-making, we hypothesized that gratitude (a positive emotion) can decrease cigarette smoking, a leading cause of premature death globally. A series of multimethod studies provided evidence supporting our hypothesis (collective N = 34,222). Using nationally representative US samples and an international sample drawn from 87 countries, Studies 1 and 2 revealed that gratitude was inversely associated with likelihood of smoking, even after accounting for numerous covariates. Other positive emotions (e.g., compassion) lacked such consistent associations, as expected. Study 3, and its replication, provided further support for emotion specificity: Experimental induction of gratitude, unlike compassion or sadness, reduced cigarette craving compared to a neutral state. Study 4, and its replication, showed that inducing gratitude causally increased smoking cessation behavior, as evidenced by enrollment in a web-based cessation intervention. Self-reported gratitude mediated the effects in both experimental studies. Finally, Study 5 found that current antismoking messaging campaigns by the US Centers for Disease Control and Prevention primarily evoked sadness and compassion, but seldom gratitude. Together, our studies advance understanding of positive emotion effects on appetitive risk behaviors; they also offer practical implications for the design of public health campaigns.

Correcting misperceptions of the material benefits associated with union membership increases Americans' interest in joining unions.

How accurate are Americans' perceptions of the material benefits associated with union membership, and do these perceptions influence their support for, and interest in joining, unions? We explore these questions in a preregistered, survey experiment conducted on a national sample, representative of the US population on a number of demographic benchmarks (n = 1,430). We find that Americans exhibit large and consistent underestimates of the benefits associated with unionization, as compared to evidence from the Bureau of Labor Statistics and peer-reviewed academic research. For example, 89% of Americans underestimated the life-time income premium associated with union membership, 72% underestimated the percentage of union members who receive health insurance from their employer, and 97% overestimated the average union dues rate. We next randomly assigned half of the participants to receive a brief, informational correction conveying results of academic and government research on the material benefits associated with union membership, or not. Those who received the correction reported 11.6% greater interest in joining a union, 7.8% greater support for unions, and 6.9% greater interest in helping to organize a union in their workplace, as compared to the control group. These results suggest that, overall, Americans tend to underestimate the material benefits associated with unionization, misperceptions of these benefits are causally linked to Americans' support for unionization, and correcting these misperceptions increases a range of pro-union sentiments in the American mass public.

The impact of marriage equality campaigns on stress: Did a Swiss public vote get under the skin?

Lesbian, gay, bisexual, trans, intersex, and queer (LGBTIQ+) individuals encounter persistent structural inequalities and discrimination that can lead to detrimental psychological and physiological health outcomes. Amid evolving legal landscapes, little attention has been directed toward understanding the physiological health effects of societal shifts on these communities. This study aims to explore the impact of a national marriage equality vote and associated debates on psychological and biological stress among LGBTIQ+ individuals and cisgender, heterosexual, endosex individuals (termed cis-heterosexual) in Switzerland. We gathered longitudinal survey and biological data collected in hair samples among LGBTIQ+ and cis-heterosexual individuals before, during, and after the 2021 national vote (survey data: NT1T2 = 954; NT2T3 = 880; biological data: NT1T2 = 393; NT2T3 = 354). Preregistered analyses reveal a notable increase in biological stress levels (i.e., cortisol and cortisone levels), but not perceived stress, among both LGBTIQ+ as well as cis-heterosexual individuals who were close to them during the campaign. Results further point out the negative impacts of the campaign against marriage equality (i.e., no-campaign) on LGBTIQ+ individuals' biological stress levels as well as on those of their allies. These effects were, however, moderated by exposure to the campaign for marriage equality (i.e., yes-campaign), indicating the powerful buffering effects of the yes-campaign on the impact of discrimination on individuals' health. However, these positive effects appear to come at a cost, potentially impacting the well-being of individuals engaged in advocating for the yes-campaign. This research underscores the lasting impact of political campaigns on individuals' health.

Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.

Schistosomiasis is a neglected tropical disease affecting over 150 million people. Hotspots of Schistosoma transmission-communities where infection prevalence does not decline adequately with mass drug administration-present a key challenge in eliminating schistosomiasis. Current approaches to identify hotspots require evaluation 2-5 y after a baseline survey and subsequent mass drug administration. Here, we develop statistical models to predict hotspots at baseline prior to treatment comparing three common hotspot definitions, using epidemiologic, survey-based, and remote sensing data. In a reanalysis of randomized trials in 589 communities in five endemic countries, a regression model predicts whether Schistosoma mansoni infection prevalence will exceed the WHO threshold of 10% in year 5 ("prevalence hotspot") with 86% sensitivity, 74% specificity, and 93% negative predictive value (NPV; assuming 30% hotspot prevalence), and a regression model for Schistosoma haematobium achieves 90% sensitivity, 90% specificity, and 96% NPV. A random forest model predicts whether S. mansoni moderate and heavy infection prevalence will exceed a public health goal of 1% in year 5 ("intensity hotspot") with 92% sensitivity, 79% specificity, and 96% NPV, and a boosted trees model for S. haematobium achieves 77% sensitivity, 95% specificity, and 91% NPV. Baseline prevalence is a top predictor in all models. Prediction is less accurate in countries not represented in training data and for a third hotspot definition based on relative prevalence reduction over time ("persistent hotspot"). These models may be a tool to prioritize high-risk communities for more frequent surveillance or intervention against schistosomiasis, but prediction of hotspots remains a challenge.

Reassessing the role of urban green space in air pollution control.

The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly variable and context specific. We revisited the link between vegetation and air quality using satellite-derived changes of urban green space and air pollutant concentrations from 2,615 established monitoring stations over Europe and the United States. Between 2010 and 2019, stations recorded declines in ambient NO2, (particulate matter) PM10, and PM2.5 (average of -3.14% y-1), but not O3 (+0.5% y-1), pointing to the general success of recent policy interventions to restrict anthropogenic emissions. The effect size of total green space on air pollution was weak and highly variable, particularly at the street scale (15 to 60 m radius) where vegetation can restrict ventilation. However, when isolating changes in tree cover, we found a negative association with air pollution at borough to city scales (120 to 16,000 m) particularly for O3 and PM. The effect of green space was smaller than the pollutant deposition and dispersion effects of meteorological drivers including precipitation, humidity, and wind speed. When averaged across spatial scales, a one SD increase in green space resulted in a 0.8% (95% CI: -3.5 to 2%) decline in air pollution. Our findings suggest that while urban greening may improve air quality at the borough-to-city scale, the impact is moderate and may have detrimental street-level effects depending on aerodynamic factors like vegetation type and urban form.

Confusion cannot explain cooperative behavior in public goods games.

Some scholars find that behavioral variation in the public goods game is explained by variations in participants' understanding of how to maximize payoff and that confusion leads to cooperation. Their findings lead them to question the common assumption in behavioral economics experiments that choices reflect motivations. We conduct two experiments, in which we minimize confusion by providing participants with increased training. We also introduce a question that specifically assesses participants' understanding of payoff maximization choices. Our experimental results show that the distribution of behavior types is significantly different when participants play with computers versus humans. A significant increase in contributions is also observed when participants play with humans compared to when they play with computers. Moreover, social norms may be the main motive for contributions when playing with computers. Our findings suggest that social preferences, rather than confusion, play a crucial role in determining contributions in public goods games when playing with humans. We therefore argue that the assumption in behavioral economics experiments that choices reveal motivations is indeed valid.

Climate change and public health in California: A structured review of exposures, vulnerable populations, and adaptation measures.

California faces several serious direct and indirect climate exposures that can adversely affect public health, some of which are already occurring. The public health burden now and in the future will depend on atmospheric greenhouse gas concentrations, underlying population vulnerabilities, and adaptation efforts. Here, we present a structured review of recent literature to examine the leading climate risks to public health in California, including extreme heat, extreme precipitation, wildfires, air pollution, and infectious diseases. Comparisons among different climate-health pathways are difficult due to inconsistencies in study design regarding spatial and temporal scales and health outcomes examined. We find, however, that the current public health burden likely affects thousands of Californians each year, depending on the exposure pathway and health outcome. Further, while more evidence exists for direct and indirect proximal health effects that are the focus of this review, distal pathways (e.g., impacts of drought on nutrition) are more uncertain but could add to this burden. We find that climate adaptation measures can provide significant health benefits, particularly in disadvantaged communities. We conclude with priority recommendations for future analyses and solution-driven policy actions.

Social competition drives collective action to reduce informal waste burning in Uganda.

Improving urban air quality is a pressing challenge in the Global South. A key source of air pollution is the informal burning of household waste. Reducing informal burning requires governments to develop formal systems for waste disposal and for residents to adopt new disposal behaviors. Using a randomized experiment, we show that social competitions between pairs of neighborhoods in Nansana municipality, Uganda, galvanized leadership and inspired collective action to reduce informal burning. All 44 neighborhoods in the study received a public health campaign, while 22 treated neighborhoods were paired and competed to reduce waste burning over an 8-mo period. Treated neighborhoods showed a 24 percent reduction (95% CI: 11 to 35 percent) in waste burning relative to control neighborhoods at the end of the competition period. There is no evidence that treated neighborhoods experienced a rebound in waste burning several months after the competitions. Community leaders reported greater effort in coordinating residents and more pride in their neighborhood when assigned to the competition treatment. These results suggest that creating focal points for leadership and collective action can be an effective and low-cost strategy to address policy problems that require broad participation and costly behavior change.

The European Union Emissions Trading System might yield large co-benefits from pollution reduction.

Mitigating greenhouse gas emissions and reducing air pollution represent two pressing and interwoven environmental challenges. While international carbon markets, such as the European Union emissions trading system (EU ETS), have demonstrated their effectiveness in curbing carbon emissions (CO[Formula: see text]), their indirect impact on hazardous co-pollutants remains understudied. This study investigates how key toxic air pollutants-sulfur dioxide (SO[Formula: see text]), fine particulate matter (PM[Formula: see text]), and nitrogen oxides (NO[Formula: see text])-evolved after the introduction of the EU ETS with a comparative analysis of regulated and unregulated sectors. Leveraging the generalized synthetic control method, we offer an ex post analysis of how the EU ETS and concurrent emission standards may have jointly generated sizable pollution reductions in regulated sectors between 2005 and 2021. We provide an aggregate assessment that these pollution reductions could translate into large health co-benefits, potentially in the hundreds of billions of Euros, even when bounding the effect of emission standards. These order-of-magnitude estimates underscore key implications for policy appraisal and motivate further microlevel research around the health co-benefits of carbon abatement.

Efficiency and resilience of cooperation in asymmetric social dilemmas.

Direct reciprocity is a powerful mechanism for cooperation in social dilemmas. The very logic of reciprocity, however, seems to require that individuals are symmetric, and that everyone has the same means to influence each others' payoffs. Yet in many applications, individuals are asymmetric. Herein, we study the effect of asymmetry in linear public good games. Individuals may differ in their endowments (their ability to contribute to a public good) and in their productivities (how effective their contributions are). Given the individuals' productivities, we ask which allocation of endowments is optimal for cooperation. To this end, we consider two notions of optimality. The first notion focuses on the resilience of cooperation. The respective endowment distribution ensures that full cooperation is feasible even under the most adverse conditions. The second notion focuses on efficiency. The corresponding endowment distribution maximizes group welfare. Using analytical methods, we fully characterize these two endowment distributions. This analysis reveals that both optimality notions favor some endowment inequality: More productive players ought to get higher endowments. Yet the two notions disagree on how unequal endowments are supposed to be. A focus on resilience results in less inequality. With additional simulations, we show that the optimal endowment allocation needs to account for both the resilience and the efficiency of cooperation.

Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty.

Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.