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In this paper, we present a unifying framework for analyzing equilibria and designing interventions for large network games sampled from a stochastic network formation process represented by a graphon. To this end, we introduce a new class of infinite population games, termed graphon games, in which a continuum of heterogeneous agents interact according to a graphon, and we show that equilibria of graphon games can be used to approximate equilibria of large network games sampled from the graphon. This suggests a new approach for design of interventions and parameter inference based on the limiting infinite population graphon game. We show that, under some regularity assumptions, such approach enables the design of asymptotically optimal interventions via the solution of an optimization problem with much lower dimension than the one based on the entire network structure. We illustrate our framework on a synthetic data set and show that the graphon intervention can be computed efficiently and based solely on aggregated relational data.

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Targeted interventions have been delivered to neighbors of cholera cases in major epidemic responses globally despite limited evidence for the impact of such targeting. Using data from urban epidemics in Chad and Democratic Republic of the Congo, we estimate the extent of spatiotemporal zones of increased cholera risk around cases. In both cities, we found zones of increased risk of at least 200 meters during the 5 days immediately after case presentation to a clinic. Risk was highest for those living closest to cases and diminished in time and space similarly across settings. These results provide a rational basis for rapidly delivering targeting interventions.

Background School environments are thought to play an important role in the community spread of infectious diseases such as influenza because of the high mixing rates of school children. The closure of schools has therefore been proposed as an efficient mitigation strategy. Such measures come however with high associated social and economic costs, making alternative, less disruptive interventions highly desirable. The recent availability of high-resolution contact network data from school environments provides an opportunity to design models of micro-interventions and compare the outcomes of alternative mitigation measures. Methods and results We model mitigation measures that involve the targeted closure of school classes or grades based on readily available information such as the number of symptomatic infectious children in a class. We focus on the specific case of a primary school for which we have high-resolution data on the close-range interactions of children and teachers. We simulate the spread of an influenza-like illness in this population by using an SEIR model with asymptomatics, and compare the outcomes of different mitigation strategies. We find that targeted class closure affords strong mitigation effects: closing a class for a fixed period of time – equal to the sum of the average infectious and latent durations – whenever two infectious individuals are detected in that class decreases the attack rate by almost 70% and significantly decreases the probability of a severe outbreak. The closure of all classes of the same grade mitigates the spread almost as much as closing the whole school. Conclusions Our model of targeted class closure strategies based on readily available information on symptomatic subjects and on limited information on mixing patterns, such as the grade structure of the school, shows that these strategies might be almost as effective as whole-school closure, at a much lower cost. This may inform public health policies for the management and mitigation of influenza-like outbreaks in the community.

Due to the unique characteristics of breast cancer initiation sites and significant alterations in tumor metabolism, breast cancer cells rely on lipid metabolic reprogramming to effectively regulate metabolic programs during the disease progression cascade. This adaptation enables them to meet the energy demands required for proliferation, invasion, metastasis, and responses to signaling molecules in the breast cancer microenvironment. In this review, we comprehensively examined the distinctive features of lipid metabolic reprogramming in breast cancer and elucidated the underlying mechanisms driving aberrant behavior of tumor cells. Additionally, we emphasize the potential role and adaptive changes in lipid metabolism within the breast cancer microenvironment, while summarizing recent preclinical studies. Overall, precise control over lipid metabolism rewiring and understanding of plasticity within the breast cancer microenvironment hold promising implications for developing targeted treatment strategies against this disease. Therefore, interventions targeting the lipid metabolism in breast cancer may facilitate innovative advancements in clinical applications.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a global health crisis. Increasing evidence underlines the key role of competent immune responses in resisting SARS-CoV-2 infection and manifests the disastrous consequence of host immune dysregulation. Elucidating the mechanisms responsible for deregulated host immunity in COVID-19 may provide a theoretical basis for further research on new treatment modalities. Gut microbiota comprises trillions of microorganisms colonizing the human gastrointestinal tract and has a vital role in immune homeostasis and the gut-lung crosstalk. Particularly, SARS-CoV-2 infection can lead to the disruption of gut microbiota equilibrium, a condition called gut dysbiosis. Due to its regulatory effect on host immunity, gut microbiota has recently received considerable attention in the field of SARS-CoV-2 immunopathology. Imbalanced gut microbiota can fuel COVID-19 progression through production of bioactive metabolites, intestinal metabolism, enhancement of the cytokine storm, exaggeration of inflammation, regulation of adaptive immunity and other aspects. In this review, we provide an overview of the alterations in gut microbiota in COVID-19 patients, and their effects on individuals’ susceptibility to viral infection and COVID-19 progression. Moreover, we summarize currently available data on the critical role of the bidirectional regulation between intestinal microbes and host immunity in SARS-CoV-2-induced pathology, and highlight the immunomodulatory mechanisms of gut microbiota contributing to COVID-19 pathogenesis. In addition, we discuss the therapeutic benefits and future perspectives of microbiota-targeted interventions including faecal microbiota transplantation (FMT), bacteriotherapy and traditional Chinese medicine (TCM) in COVID-19 treatment.

People who experience mental ill-health are typically more disadvantaged across a range of social and economic domains compared with the general population. This disadvantage is further heightened for people from marginalised communities. Social and economic adversities can limit both the access to, and effectiveness of, interventions for mental ill-health; however, these challenges are often overlooked by mental health services. Therefore, adequate support for social needs is urgently required, particularly for those from marginalised and vulnerable groups. We conducted a PRISMA-compliant systematic review of three academic databases to identify social and/or economic interventions which were adapted or developed bespoke for people from marginalised or minoritised communities living with mental ill-health. All records were screened blind by two reviewers; quality appraisal was conducted with the Kmet tool. Seventy-eight papers were included, deriving mostly from high-income countries. The identified interventions targeted nine sociodemographic or socioeconomic groups including: people experiencing homelessness or unstable housing (n = 50), people with an offending history (n = 9), mothers (n = 6), people experiencing economic disadvantage (n = 3), older adults (n = 3), caregivers (n = 2), minority ethnic groups (n = 2), women with experience of intimate partner violence (n = 1), and people with comorbid intellectual disabilities (n = 1). All identified interventions demonstrated feasibility, acceptability, or effectiveness on at least one social and/or economic outcome measure, suggesting that targeted intervention can help to address social and economic needs and reduce systemic inequalities in mental health care. However, the evidence base is still sparse, and further replication is warranted to inform commissioners and policy makers.

Background Alcohol-related health inequalities remain a major public health challenge in England, with those from more disadvantaged socioeconomic backgrounds experiencing the greatest burden of harm despite consuming similar or lower levels of alcohol compared to those from more advantaged backgrounds. Yet, most research in this area has relied on single or composite measures of socioeconomic status (SES) that do not capture the overlapping dimensions of advantage and disadvantage that shape people’s lives and can be difficult for policymakers to interpret. We used a person-centred approach to examine how differing latent SES profiles relate to alcohol consumption before and during the COVID-19 pandemic. Methods We analysed data from 8,204 adults in 2019 and 5,880 adults in 2021 from the Health Survey for England. A latent class analysis was conducted on seven indicators of SES in 2019 (income, education, occupational grade, housing tenure, benefit receipt, car ownership and employment status), and six in 2021 as occupational grade was not collected that year. Multinomial logistic regression, adjusting for age, sex, ethnicity and marital status, examined associations between latent SES classes and four alcohol consumption risk categories (non-drinker, low-risk, increasing-risk and high-risk). Results Analysis revealed five latent classes in 2019 and four in 2021, each representing different constellations of social and economic conditions. Across both years, similar latent classes were identified and drinking patterns across classes were consistent despite the disrupting effects of the COVID-19 pandemic. Compared to non-drinkers in the Professional/Employed Homeowners class, (Skilled) Low-Income Renters and Retired Homeowners had far lower odds of drinking at all risk levels, while Professional/Employed Private Renters had odds of increasing and high-risk drinking similar to the reference group. Conclusions The identification of similar latent SES classes in 2019 and 2021 supports the utility of latent class analysis for capturing the multidimensional nature of SES over time and strengthens the case for future research to employ person-centred approaches when examining the relationship between alcohol consumption and SES. Capturing these constellations of SES indicators through latent class analysis may provide a stronger evidence base for designing targeted interventions and assessing the equity impacts of population-level alcohol control policies.

Background Since 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach. Methods This study used individual and household-level data from the 2015 and 2018 annual malaria indicator surveys on Bioko Island, as well as remotely-sensed environmental data in multilevel logistic regression models to quantify the odds of malaria infection. The analyses were stratified by urban and rural settings and by survey year. Results Malaria prevalence was higher in 10–14-year-old children and similar between female and male individuals. After adjusting for demographic factors and other covariates, many of the variables investigated showed no significant association with malaria infection. The factor most strongly associated was history of travel to mainland Equatorial Guinea (mEG), which increased the odds significantly both in urban and rural settings (people who travelled had 4 times the odds of infection). Sleeping under a long-lasting insecticidal net decreased significantly the odds of malaria across urban and rural settings and survey years (net users had around 30% less odds of infection), highlighting their contribution to malaria control on the Island. Improved housing conditions indicated some protection, though this was not consistent across settings and survey year. Conclusions Malaria risk on Bioko Island is heterogeneous and determined by a combination of factors interacting with local mosquito ecology. These interactions grant further investigation in order to better adapt control according to need. The single most important risk factor identified was travel to mEG, in line with previous investigations, and represents a great challenge for the success of malaria control on the Island.

Optimal control strategies for Taenia solium taeniasis and cysticercosis have not been determined. We conducted a 2-year cluster randomized trial in Peru by assigning 23 villages to 1 of 3 geographically targeted intervention approaches. For ring screening (RS), participants living near pigs with cysticercosis were screened for taeniasis; identified cases were treated with niclosamide. In ring treatment (RT), participants living near pigs with cysticercosis received presumptive treatment with niclosamide. In mass treatment (MT), participants received niclosamide treatment every 6 months regardless of location. In each approach, half the villages received targeted or mass oxfendazole for pigs (6 total study arms). We noted significant reductions in seroincidence among pigs in all approaches (67.1% decrease in RS, 69.3% in RT, 64.7% in MT; p<0.001), despite a smaller proportion of population treated by targeted approaches (RS 1.4%, RT 19.3%, MT 88.5%). Our findings suggest multiple approaches can achieve rapid control of T. solium transmission.