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.

Population impact of SARS-CoV-2 variants with enhanced transmissibility and/or partial immune escape.

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from acquired immunity. Much effort has been devoted to measuring these phenotypes, but understanding their impact on the course of the pandemic-especially that of immune escape-has remained a challenge. Here, we use a mathematical model to simulate the dynamics of wild-type and variant strains of SARS-CoV-2 in the context of vaccine rollout and nonpharmaceutical interventions. We show that variants with enhanced transmissibility frequently increase epidemic severity, whereas those with partial immune escape either fail to spread widely or primarily cause reinfections and breakthrough infections. However, when these phenotypes are combined, a variant can continue spreading even as immunity builds up in the population, limiting the impact of vaccination and exacerbating the epidemic. These findings help explain the trajectories of past and present SARS-CoV-2 variants and may inform variant assessment and response in the future.

Academics are more specific, and practitioners more sensitive, in forecasting interventions to strengthen democratic attitudes.

Concern over democratic erosion has led to a proliferation of proposed interventions to strengthen democratic attitudes in the United States. Resource constraints, however, prevent implementing all proposed interventions. One approach to identify promising interventions entails leveraging domain experts, who have knowledge regarding a given field, to forecast the effectiveness of candidate interventions. We recruit experts who develop general knowledge about a social problem (academics), experts who directly intervene on the problem (practitioners), and nonexperts from the public to forecast the effectiveness of interventions to reduce partisan animosity, support for undemocratic practices, and support for partisan violence. Comparing 14,076 forecasts submitted by 1,181 forecasters against the results of a megaexperiment (n = 32,059) that tested 75 hypothesized effects of interventions, we find that both types of experts outperformed members of the public, though experts differed in how they were accurate. While academics' predictions were more specific (i.e., they identified a larger proportion of ineffective interventions and had fewer false-positive forecasts), practitioners' predictions were more sensitive (i.e., they identified a larger proportion of effective interventions and had fewer false-negative forecasts). Consistent with this, practitioners were better at predicting best-performing interventions, while academics were superior in predicting which interventions performed worst. Our paper highlights the importance of differentiating types of experts and types of accuracy. We conclude by discussing factors that affect whether sensitive or specific forecasters are preferable, such as the relative cost of false positives and negatives and the expected rate of intervention success.

Zoom out: An intervention on the virtual learning environment improves minority students' grades in two field experiments in Israel.

Closing the achievement gap for minority students in higher education requires addressing the lack of belonging these students experience. This paper introduces a psychological intervention that strategically targets key elements within the learning environment to foster the success of minority students. The intervention sought to enhance Palestinian minority student's sense of belonging by increasing the presence of their native language. We tested the effectiveness of the intervention in two field experiments in Israel (n > 20,000), at the height of the COVID-19 pandemic when all classes were held via Zoom. Lecturers in the experimental condition added a transcript of their names in Arabic to their default display (English/Hebrew only). Our findings revealed a substantial and positive impact on Palestinian student's sense of belonging, class participation, and overall grades. In experiment 1, Palestinian student's average grade increased by 10 points. In experiment 2, there was an average increase of 4 points among Palestinian students' semester grade. Our intervention demonstrates that small institutional changes when carefully crafted can have a significant impact on minority populations. These results have significant implications for addressing educational disparities and fostering inclusive learning environment.

Mortality risk information and health-seeking behavior during an epidemic.

In a context where pessimistic survival perceptions have been widespread as a result of the HIV/AIDS epidemic (Fig. 1 A), we study vaccine uptake and other health behaviors during the recent COVID-19 pandemic. Leveraging a longitudinal cohort study in rural Malawi that has been followed for up to 25 y, we document that a 2017 mortality risk information intervention designed to reduce pessimistic mortality perceptions (Fig. 1 B) resulted in improved health behavior, including COVID-19 vaccine uptake (Fig. 1 C). We also report indirect effects for siblings and household members. This was likely the result of a reinforcing process where the intervention triggered engagement with the healthcare system and stronger beliefs in the efficacy of modern biomedical treatments, which led to the adoption of health risk reduction behavior, including vaccine uptake. Our findings suggest that health information interventions focused on survival perceptions can be useful in promoting health behavior and participation in the formal healthcare system, even during health crises-such as the COVID-19 pandemic-that are unanticipated at the time of the intervention. We also note the importance of the intervention design, where establishing rapport, tailoring the content to the local context, and spending time with respondents to convey the information contributed to the salience of the message.

System-, teacher-, and student-level interventions for improving participation in online learning at scale in high schools.

Many school systems across the globe turned to online education during the COVID-19 pandemic. This context differs significantly from the prepandemic situation in which massive open online courses attracted large numbers of voluntary learners who struggled with completion. Students who are provided online courses by their high schools also have their behavior determined by actions of their teachers and school system. We conducted experiments to improve participation in online learning before, during, and right after the COVID-19 outbreak, with 1,151 schools covering more than 45,000 students in their final years of high school in Ecuador. These experiments tested light-touch interventions at scale, motivated by behavioral science, and were carried out at three levels: that of the system, teacher, and student. We find the largest impacts come from intervening at the system level. A cheap, online learning management system for centralized monitoring increased participation by 0.21 SD and subject knowledge by 0.13 SD relative to decentralized management. Centralized management is particularly effective for underperforming schools. Teacher-level nudges in the form of benchmarking emails, encouragement messages, and administrative reminders did not improve student participation. There was no significant impact of encouragement messages to students, or in having them plan and team-up with peers. Small financial incentives in the form of lottery prizes for finishing lessons did increase study time, but was less cost-effective, and had no significant impact on knowledge. The results show the difficulty in incentivizing online learning at scale, and a key role for central monitoring.

Correcting misperceptions of the other political party does not robustly reduce support for undemocratic practices or partisan violence.

A growing consensus suggests that a cause of support for undemocratic practices and partisan violence is that partisans misperceive the other side. That is, they vastly exaggerate the extent to which members of the other party support undemocratic practices and violence. When these misperceptions are corrected, citizens' own beliefs moderate. I present results from an experiment that show that misperception corrections do not have an effect in the presence of competing information (i.e., that challenges the validity of the correction or offers a conflicting narrative). Basic corrections do not constitute a robust way to counter democratic backsliding stemming from citizens' misperceptions. The results highlight the need to devise stronger misperception interventions and, more generally, to consider competing information environments when devising any scalable behavioral intervention.

Dynamics in a behavioral-epidemiological model for individual adherence to a nonpharmaceutical intervention.

The SARS-CoV-2 pandemic has highlighted the importance of behavioral drivers in epidemic dynamics. With the relaxation of mandated nonpharmaceutical interventions (NPIs) formerly in place to decrease transmission, such as mask-wearing or social distancing, adherence to an NPI is now the result of individual decision-making. To study these coupled dynamics, we embed a game-theoretic model for individual NPI adherence within an epidemiological model. When the disease is endemic, we find that our model has multiple (but none concurrently stable) equilibria: one each with zero, complete, or partial NPI adherence. Surprisingly, for the equilibrium with partial NPI adherence, the number of infections is independent of the transmission rate. Therefore, in that regime, a change in the rate of pathogen transmission, e.g., due to another (mandated) NPI or a new variant, has no effect on endemic infection levels. On the other hand, we show that vaccination successfully decreases endemic infection levels, and, unexpectedly, also reduces the number of susceptibles at equilibrium when there is partial adherence. From a game-theoretic perspective, we find that highly effective NPIs lead at most to partial adherence. As this effectiveness decreases, partially effective NPIs initially lead to increases in population-level adherence, especially if the risk is high enough. However, a completely ineffective NPI results in no adherence. Furthermore, we identify parameter regions where the individual incentives may not align with those of society as a whole. Overall, our findings illustrate complexities that can arise due to behavioral-epidemiological feedback and suggest appropriate measures to avoid more pessimistic population-level outcomes.

Utility-value intervention promotes persistence and diversity in STEM.

We tested the long-term effects of a utility-value intervention administered in a gateway chemistry course, with the goal of promoting persistence and diversity in STEM. In a randomized controlled trial (N = 2,505), students wrote three essays about course content and its personal relevance or three control essays. The intervention significantly improved STEM persistence overall (74% vs. 70% were STEM majors 2.5 y later). Effects were larger for students from marginalized and underrepresented racial/ethnic groups, who were 14 percentage points more likely to persist in STEM fields in the intervention condition (69% vs. 55%). Mediation analysis suggests that the intervention promoted persistence for these students by bolstering their motivation to attain a STEM degree and by promoting engagement with course assignments. This theory-informed curricular intervention is a promising tool for educators committed to retaining students in STEM.

The impact of threshold decision mechanisms of collective behavior on disease spread.

Humans are a hyper-social species, which greatly impacts the spread of infectious diseases. How do social dynamics impact epidemiology and what are the implications for public health policy? Here, we develop a model of disease transmission that incorporates social dynamics and a behavior that reduces the spread of disease, a voluntary nonpharmaceutical intervention (NPI). We use a "tipping-point" dynamic, previously used in the sociological literature, where individuals adopt a behavior given a sufficient prevalence of the behavior in the population. The thresholds at which individuals adopt the NPI behavior are modulated by the perceived risk of infection, i.e., the disease prevalence and transmission rate, costs to adopt the NPI behavior, and the behavior of others. Social conformity creates a type of "stickiness" whereby individuals are resistant to changing their behavior due to the population's inertia. In this model, we observe a nonmonotonicity in the attack rate as a function of various biological and social parameters such as the transmission rate, efficacy of the NPI, costs of the NPI, weight of social consequences of shirking the social norm, and the degree of heterogeneity in the population. We also observe that the attack rate can be highly sensitive to these parameters due to abrupt shifts in the collective behavior of the population. These results highlight the complex interplay between the dynamics of epidemics and norm-driven collective behaviors.

Efficiently exploring the causal role of contextual moderators in behavioral science.

Behavioral science interventions have the potential to address longstanding policy problems, but their effects are typically heterogeneous across contexts (e.g., teachers, schools, and geographic regions). This contextual heterogeneity is poorly understood, however, which reduces the field's impact and its understanding of mechanisms. Here, we present an efficient way to interrogate heterogeneity and address these gaps in knowledge. This method a) presents scenarios that vividly represent different moderating contexts, b) measures a short-term behavioral outcome (e.g., an academic choice) that is known to relate to typical intervention outcomes (e.g., academic achievement), and c) assesses the causal effect of the moderating context on the link between the psychological variable typically targeted by interventions and this short-term outcome. We illustrated the utility of this approach across four experiments (total n = 3,235) that directly tested contextual moderators of the links between growth mindset, which is the belief that ability can be developed, and students' academic choices. The present results showed that teachers' growth mindset-supportive messages and the structural opportunities they provide moderated the link between students' mindsets and their choices (studies 1 to 3). This pattern was replicated in a nationally representative sample of adolescents and did not vary across demographic subgroups (study 2), nor was this pattern the result of several possible confounds (studies 3 to 4). Discussion centers on how this method of interrogating contextual heterogeneity can be applied to other behavioral science interventions and broaden their impact in other policy domains.

Reimagining the machine learning life cycle to improve educational outcomes of students.

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout to assisting in university admissions and facilitating the rise of massive open online courses (MOOCs). Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand? We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

Masks and respirators for prevention of respiratory infections: a state of the science review.

SUMMARYThis narrative review and meta-analysis summarizes a broad evidence base on the benefits-and also the practicalities, disbenefits, harms and personal, sociocultural and environmental impacts-of masks and masking. Our synthesis of evidence from over 100 published reviews and selected primary studies, including re-analyzing contested meta-analyses of key clinical trials, produced seven key findings. First, there is strong and consistent evidence for airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory pathogens. Second, masks are, if correctly and consistently worn, effective in reducing transmission of respiratory diseases and show a dose-response effect. Third, respirators are significantly more effective than medical or cloth masks. Fourth, mask mandates are, overall, effective in reducing community transmission of respiratory pathogens. Fifth, masks are important sociocultural symbols; non-adherence to masking is sometimes linked to political and ideological beliefs and to widely circulated mis- or disinformation. Sixth, while there is much evidence that masks are not generally harmful to the general population, masking may be relatively contraindicated in individuals with certain medical conditions, who may require exemption. Furthermore, certain groups (notably D/deaf people) are disadvantaged when others are masked. Finally, there are risks to the environment from single-use masks and respirators. We propose an agenda for future research, including improved characterization of the situations in which masking should be recommended or mandated; attention to comfort and acceptability; generalized and disability-focused communication support in settings where masks are worn; and development and testing of novel materials and designs for improved filtration, breathability, and environmental impact.

A marginal structural model for normal tissue complication probability.

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

Correlation between the dynamic changes of γδT cells, Th17 cells, CD4+CD25+ regulatory T cells in peripheral blood and pharmacological interventions against bleomycin-induced pulmonary fibrosis progression in mice.

The involvement of γδT cells, Th17 cells, and CD4+CD25+ regulatory T cells (Tregs) is crucial in the progression of pulmonary fibrosis (PF), particularly in maintaining immune tolerance and homeostasis. However, the dynamics of these cells in relation to PF progression, especially under pharmacological interventions, remains poorly understood. This study aims to unravel the interplay between the dynamic changes of these cells and the effect of pharmacological agents in a mouse model of PF induced by intratracheal instillation of bleomycin. We analyzed changes in lung histology, lung index, hydroxyproline levels, and the proportions of γδT cells, Th17 cells, and Tregs on the 3rd, 14th, and 28th days following treatment with Neferine, Isoliensinine, Pirfenidone, and Prednisolone. Our results demonstrate that these drugs can partially or dynamically reverse weight loss, decrease lung index and hydroxyproline levels, and ameliorate lung histopathological damage. Additionally, they significantly modulated the abnormal changes in γδT, Th17, and Treg cell proportions. Notably, on day 3, the proportion of γδT cells increased in the Neferine and Prednisolone groups but decreased in the Isoliensinine and Pirfenidone groups, while the proportion of Th17 cells decreased across all treated groups. On day 14, the Neferine group showed an increase in all three cell types, whereas the Pirfenidone group exhibited a decrease. In the Isoliensinine group, γδT and Th17 cells increased, and in the Prednisolone group, only Tregs increased. By day 28, an increase in Th17 cell proportion was observed in all treatment groups, with a decrease in γδT cells noted in the Neferine group. These shifts in cell proportions are consistent with the pathogenesis changes induced by these anti-PF drugs, suggesting a correlation between cellular dynamics and pharmacological interventions in PF progression. Our findings imply potential strategies for assessing the efficacy and timing of anti-PF treatments based on these cellular changes.

Who Transmits Tuberculosis to Whom: A Cross-Sectional Analysis of a Cohort Study in Lima, Peru.

Rationale: The persistent burden of tuberculosis (TB) disease emphasizes the need to identify individuals with TB for treatment and those at a high risk of incident TB for prevention. Targeting interventions toward those at high risk of developing and transmitting TB is a public health priority. Objectives: We aimed to identify characteristics of individuals involved in TB transmission in a community setting, which may guide the prioritization of targeted interventions. Methods: We collected clinical and sociodemographic data from a cohort of patients with TB in Lima, Peru. We used whole-genome sequencing data to assess the genetic distance between all possible pairs of patients; we considered pairs to be the result of a direct transmission event if they differed by three or fewer SNPs, and we assumed that the first diagnosed patient in a pair was the transmitter and the second was the recipient. We used logistic regression to examine the association between host factors and the likelihood of direct TB transmission. Measurements and Main Results: Analyzing data from 2,518 index patients with TB, we identified 1,447 direct transmission pairs. Regardless of recipient attributes, individuals less than 34 years old, males, and those with a history of incarceration had a higher likelihood of being transmitters in direct transmission pairs. Direct transmission was more likely when both patients were drinkers or smokers. Conclusions: This study identifies men, young adults, former prisoners, alcohol consumers, and smokers as priority groups for targeted interventions. Innovative strategies are needed to extend TB screening to social groups such as young adults and prisoners with limited access to routine preventive care.

Computational mechanisms for context-based behavioral interventions: A large-scale analysis.

Choice context influences decision processes and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and to design behavioral interventions to influence and improve choices. We analyzed the effects of context-based behavioral interventions on the computational mechanisms underlying decision-making. We collected data from two large laboratory studies involving 19 prominent behavioral interventions, and we modeled the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allowed us to parametrize the biases induced by each intervention, to interpret these biases in terms of underlying decision mechanisms and their properties, to quantify similarities between interventions, and to predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision-making.

US Embassy air-quality tweets led to global health benefits.

The World Health Organization estimates that over 90% of the world's population is exposed to hazardous levels of local air pollution. Air pollution is markedly worse in low- and middle-income countries, yet air-quality monitoring is typically sparse. In 2008, the US Embassy in Beijing began tweeting hourly air-quality information from a newly installed pollution monitor, dramatically improving the information on air quality available to Beijing residents. Since then, the United States has installed over 50 monitors around the world, tweeting real-time reports on air quality in those locations. Using spatially granular measurements of local air pollution from satellite data that span the globe, we employ variation in whether and when US embassies installed monitors to evaluate the impact of air-quality information on pollution. We estimate that embassy monitors led to reductions in fine particulate concentration levels in host countries of 2 to 4 µg/m3. Our central estimate of the annual monetized benefit of the decrease in premature mortality due to this reduction in pollution is $127 million for the median city in 2019. Our findings point to the substantial benefits of improving the availability and salience of air-quality information in low- and middle-income countries.

Preventing soft skill decay among early-career women in STEM during COVID-19: Evidence from a longitudinal intervention.

As the workforce shifts to being predominantly hybrid and remote, how can companies help employees-particularly early-career women in science, technology, engineering, and mathematics (STEM) fields-develop greater confidence in their soft skills, shown to improve organizational retention? We evaluate the effects of an online longitudinal intervention to develop soft skills among early-career women employees at a North American biotechnology company during the height of the COVID-19 pandemic. Controlling for baseline levels collected immediately prior to nationwide lockdowns, we find that a 6-month online intervention increased early-career women's assessments of their soft skills at work by an average of 9% (P < 0.001), compared with a decrease of about 3.5% for a matched control group (P < 0.05), resulting in an average treatment effect of nearly 13% on the treated group. Furthermore, we find evidence that the intervention led to an increase in manager-assessed performance for early-career women relative to employees not in the intervention, and that overall, increased self-assessments of soft skill competencies were associated with greater odds of retention. Results show how employee soft skill development was affected by the pandemic and provide insights for a feasible and cost-effective method to train and engage a hybrid or fully remote workforce.

Transcatheter treatment of the tricuspid valve: current status and perspectives.

Transcatheter tricuspid valve interventions (TTVI) are emerging as alternatives to surgery in high-risk patients with isolated or concomitant tricuspid regurgitation. The development of new minimally invasive solutions potentially more adapted to this largely undertreated population of patients, has fuelled the interest for the tricuspid valve. Growing evidence and new concepts have contributed to revise obsolete and misleading perceptions around the right side of the heart. New definitions, classifications, and a better understanding of the disease pathophysiology and phenotypes, as well as their associated patient journeys have profoundly and durably changed the landscape of tricuspid disease. A number of registries and a recent randomized controlled pivotal trial provide preliminary guidance for decision-making. TTVI seem to be very safe and effective in selected patients, although clinical benefits beyond improved quality of life remain to be demonstrated. Even if more efforts are needed, increased disease awareness is gaining momentum in the community and supports the establishment of dedicated expert valve centres. This review is summarizing the achievements in the field and provides perspectives for a less invasive management of a no-more-forgotten disease.