New England Complex Systems Institute

In the version of this article originally published, the Global Dietary Database consortium was missing from the author list. The consortium is now listed as an author, with a list of members and their affiliations appearing online. The error has been corrected in the HTML and PDF versions of the article.

Despite notable scientific and medical advances, broader political, socioeconomic and behavioural factors continue to undercut the response to the COVID-19 pandemic1,2. Here we convened, as part of this Delphi study, a diverse, multidisciplinary panel of 386 academic, health, non-governmental organization, government and other experts in COVID-19 response from 112 countries and territories to recommend specific actions to end this persistent global threat to public health. The panel developed a set of 41 consensus statements and 57 recommendations to governments, health systems, industry and other key stakeholders across six domains: communication; health systems; vaccination; prevention; treatment and care; and inequities. In the wake of nearly three years of fragmented global and national responses, it is instructive to note that three of the highest-ranked recommendations call for the adoption of whole-of-society and whole-of-government approaches1, while maintaining proven prevention measures using a vaccines-plus approach2 that employs a range of public health and financial support measures to complement vaccination. Other recommendations with at least 99% combined agreement advise governments and other stakeholders to improve communication, rebuild public trust and engage communities3 in the management of pandemic responses. The findings of the study, which have been further endorsed by 184 organizations globally, include points of unanimous agreement, as well as six recommendations with >5% disagreement, that provide health and social policy actions to address inadequacies in the pandemic response and help to bring this public health threat to an end.

Evidence on what people eat globally is limited in scope and rigour, especially as it relates to children and adolescents. This impairs target setting and investment in evidence-based actions to support healthy sustainable diets. Here we quantified global, regional and national dietary patterns among children and adults, by age group, sex, education and urbanicity, across 185 countries between 1990 and 2018, on the basis of data from the Global Dietary Database project. Our primary measure was the Alternative Healthy Eating Index, a validated score of diet quality; Dietary Approaches to Stop Hypertension and Mediterranean Diet Score patterns were secondarily assessed. Dietary quality is generally modest worldwide. In 2018, the mean global Alternative Healthy Eating Index score was 40.3, ranging from 0 (least healthy) to 100 (most healthy), with regional means ranging from 30.3 in Latin America and the Caribbean to 45.7 in South Asia. Scores among children versus adults were generally similar across regions, except in Central/Eastern Europe and Central Asia, high-income countries, and the Middle East and Northern Africa, where children had lower diet quality. Globally, diet quality scores were higher among women versus men, and more versus less educated individuals. Diet quality increased modestly between 1990 and 2018 globally and in all world regions except in South Asia and Sub-Saharan Africa, where it did not improve.

Compartmental epidemic models have been widely used for predicting the course of epidemics, from estimating the basic reproduction number to guiding intervention policies. Studies commonly acknowledge these models’ assumptions but less often justify their validity in the specific context in which they are being used. Our purpose is not to argue for specific alternatives or modifications to compartmental models, but rather to show how assumptions can constrain model outcomes to a narrow portion of the wide landscape of potential epidemic behaviors. This concrete examination of well‐known models also serves to illustrate general principles of modeling that can be applied in other contexts.

During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.

Understanding the functions carried out by network subgraphs is important to revealing the organizing principles of diverse complex networks. Here, we study this question in the context of collaborative problem-solving, which is central to a variety of domains from engineering and medicine to economics and social planning. We analyze the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. The results reveal a strong association between a dynamic property of network subgraphs—synchronizability—and the frequency and significance of these subgraphs in problem-solving networks. In particular, we show that highly-synchronizable subgraphs are overrepresented in the networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that dynamical properties affect their prevalence, and thus the global structure of networks. We propose the possibility that selective pressures that favor more synchronizable subgraphs could account for their abundance in problem-solving networks. The empirical results also show that unrelated problem-solving networks display very similar local network structure, implying that network subgraphs could represent organizational routines that enable better coordination and control of problem-solving activities. The findings could also have potential implications in understanding the functionality of network subgraphs in other information-processing networks, including biological and social networks.

In contrast to the rapid integration of the world economy, many regional trade agreements (RTAs) have also emerged since the early 1990s. This contradiction has encouraged scholars and policymakers to explore the true effects of RTAs, including both regional and global trade relationships. This paper defines synthesized trade resistance and decomposes it into natural and artificial factors. Here, we separate the influence of geographical distance, economic volume, and overall increase in transportation and labor costs and use the expectation maximization algorithm to optimize the parameters and quantify the trade purity indicator, which describes the true global trade environment and relationships among countries. This indicates that although global and most regional trade relations gradually deteriorated during the period 2007–2017, RTAs generate trade relations among members, especially contributing to the relative prosperity of European Union (EU) and North American Free Trade Agreement (NAFTA) countries. In addition, we apply the network to reflect the purity of the trade relations among countries. The effects of RTAs can be analyzed by comparing typical trade unions and trade communities, which are presented using an empirical network structure. This analysis shows that the community structure is quite consistent with some trade unions, and the representative RTAs constitute the core structure of international trade network. However, the role of trade unions has weakened, and multilateral trade liberalization has accelerated in the past decade. This means that more countries have recently tended to expand their trading partners outside of these unions rather than limit their trading activities to RTAs.

In the age of hyperconnectivity, we are undergoing an explosive increase in the interdependence of the political, commercial, financial, and social spheres. The recent rise of deglobalization movements across the world highlights the local negative externalities of poorly designed networked structures at the global scale: high social complexity derived from immigration shocks, elevated risk of contagion in financial downturns, as well as increasing inequality and social polarization. While global interdependencies on networks enable opportunities for cultural and economic growth, they also establish channels for unresolved conflicts and design errors to propagate across social systems. We analyze failure propagation on networks as a function of density and centralization of inter-dependencies. We show that the risk of failure in both overly distributed and centralized systems behave similarly when the number of connections exceeds a system-dependent threshold number. The scale of interdependencies matters and must be considered for the design of policies targeted at increasing or decreasing the connectivity of social systems.

Introduction: Sodium, an important dietary requirement, is essential to many physiologic processes. High sodium intake affects serious health issues such as hypertension and cardiovascular disease, the largest cause of death globally. Consequently, many health organizations have recommended substantial reductions in sodium intake, to as little as 1,500 mg/d. Yet limited understanding exists for the entire range of the effect of sodium between high intake and the recommendations. Methods: We built a simulation using equations from the Uttamsingh model of the renal system to simulate the long-term mean arterial pressure (MAP) across sodium intake ranges. We used another existing physiology simulation platform, HumMod-3.0.4, for comparison. We compared the simulation results with empirical studies done on the global population. Results: We find a linear increase in MAP for consumption above 4,000 mg/d, but nearly constant MAP between 1,200 and 4,000 mg/d. Below 1,200 mg/d, the system cannot maintain homeostasis. Conclusion: Supporting the U-shape theory of sodium intake, which posits that too-high and too-low sodium intake rates increase cardiovascular disease risks, our results suggest that the homeostatic regulation by antidiuretic hormone and aldosterone transitions from sodium retention to sodium excretion at around 4,000 mg/d (a value that varies across individuals and conditions), indicating sodium saturation and evolutionary optimality. Our findings are consistent with recent empirical studies on large populations globally. We suggest that the current low-level recommendations are not supported by this physiologic model analysis and would require more compelling evidence.