A multinational Delphi consensus to end the COVID-19 public health threat.

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.

Geographical fragmentation of the global network of Twitter communications.

Understanding the geography of society represents a challenge for social and economic sciences. The recent availability of data from social media enables the observation of societies at a global scale. In this paper, we study the geographical structure of the Twitter communication network at the global scale. We find a complex structure where self-organized patches with clear cultural, historical, and administrative boundaries are manifested and first-world economies centralize information flows. These patches unveil world regions that are socially closer to each other with direct implications for processes of collective learning and identity creation.

On single point forecasts for fat-tailed variables.

We discuss common errors and fallacies when using naive "evidence based" empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management. We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al. (2020).

Accurate market price formation model with both supply-demand and trend-following for global food prices providing policy recommendations.

Recent increases in basic food prices are severely affecting vulnerable populations worldwide. Proposed causes such as shortages of grain due to adverse weather, increasing meat consumption in China and India, conversion of corn to ethanol in the United States, and investor speculation on commodity markets lead to widely differing implications for policy. A lack of clarity about which factors are responsible reinforces policy inaction. Here, for the first time to our knowledge, we construct a dynamic model that quantitatively agrees with food prices. The results show that the dominant causes of price increases are investor speculation and ethanol conversion. Models that just treat supply and demand are not consistent with the actual price dynamics. The two sharp peaks in 2007/2008 and 2010/2011 are specifically due to investor speculation, whereas an underlying upward trend is due to increasing demand from ethanol conversion. The model includes investor trend following as well as shifting between commodities, equities, and bonds to take advantage of increased expected returns. Claims that speculators cannot influence grain prices are shown to be invalid by direct analysis of price-setting practices of granaries. Both causes of price increase, speculative investment and ethanol conversion, are promoted by recent regulatory changes-deregulation of the commodity markets, and policies promoting the conversion of corn to ethanol. Rapid action is needed to reduce the impacts of the price increases on global hunger.

Multilevel and kin selection in a connected world.

Wild et al. argue that the evolution of reduced virulence can be understood from the perspective of inclusive fitness, obviating the need to evoke group selection as a contributing causal factor. Although they acknowledge the mathematical equivalence of the inclusive fitness and multilevel selection approaches, they conclude that reduced virulence can be viewed entirely as an individual-level adaptation by the parasite. Here we show that their model is a well-known special case of the more general theory of multilevel selection, and that the cause of reduced virulence resides in the opposition of two processes: within-group and among-group selection. This distinction is important in light of the current controversy among evolutionary biologists in which some continue to affirm that natural selection centres only and always at the level of the individual organism or gene, despite mathematical demonstrations that evolutionary dynamics must be described by selection at various levels in the hierarchy of biological organization.

Evolution and stability of ring species.

Neutral models, in which genetic change arises through random variation without fitness differences, have proven remarkably successful in describing observed patterns of biodiversity, despite the manifest role of selection in evolution. Here we investigate the effect of barriers on biodiversity by simulating the expansion of a population around a barrier to form a ring species, in which the two ends of the population are reproductively isolated despite ongoing gene flow around the ring. We compare the spatial and genetic properties of a neutral agent-based population model to the greenish warblers' complex, a well-documented example of an actual ring species in nature. Our results match the distribution of subspecies, the principal components of genetic diversity, and the linear spatial-genetic correlation of the observed data, even though selection is expected to be important for traits of this species. We find that ring species are often unstable to speciation or mixing but can persist for extended times depending on species and landscape features. For the greenish warblers, our analysis implies that the expanded area near the point of secondary contact is important for extending the duration of the ring, and thus, for the opportunity to observe this ring species. Nevertheless it also suggests the ring will break up into multiple species in 10,000 to 50,000 y. These results imply that simulations can be used to accurately describe empirical data for complex spatial-genetic traits of an individual species.

Programed Death is Favored by Natural Selection in Spatial Systems.

Standard evolutionary theories of aging and mortality, implicitly based on mean-field assumptions, hold that programed mortality is untenable, as it opposes direct individual benefit. We show that in spatial models with local reproduction, programed deaths instead robustly result in long-term benefit to a lineage, by reducing local environmental resource depletion via spatiotemporal patterns causing feedback over many generations. Results are robust to model variations, implying that direct selection for shorter life span may be quite widespread in nature.

Conditions for neutral speciation via isolation by distance.

The branching of new species from an ancestral population requires the evolution of reproductive isolation between groups of individuals. Geographic separation of sub-populations by natural barriers, if sustained for sufficiently long times, may lead to the accumulation of independent genetic changes in each group and to mating incompatibilities (Mayr, 2001; Fitzpatrick et al., 2009). A similar phenomenon may occur in the absence of barriers via isolation by distance if the population is distributed over large areas (de Aguiar et al., 2009; Etienne and Haegeman, 2011; Gavrilets et al., 2000). The first demonstration of this process was based on computer simulations employing agent-based models. Recently, analytical results were derived combining network theory, to model the spatial structure of the population, and an ansatz that accounts for the effect of forbidding mating between individuals that are too different genetically (de Aguiar and Bar-Yam, 2011). The main result obtained with this approach is an expression that indicates when speciation is possible as a function of the parameters describing the population. The aim of this work is to test this analytical result by comparing it with numerical simulations for a hermaphroditic population (de Aguiar et al., 2009) and for a population whose individuals are explicitly separated into males and females (Baptestini et al., 2013). We show that the analytical formula is indeed a very good overall description of the simulations and that the exponents describing dependence of the critical threshold of speciation with the parameters are in good agreement with the simulations.

Recovering population parameters from a single gene genealogy: an unbiased estimator of the growth rate.

We show that the number of lineages ancestral to a sample, as a function of time back into the past, which we call the number of lineages as a function of time (NLFT), is a nearly deterministic property of large-sample gene genealogies. We obtain analytic expressions for the NLFT for both constant-sized and exponentially growing populations. The low level of stochastic variation associated with the NLFT of a large sample suggests using the NLFT to make estimates of population parameters. Based on this, we develop a new computational method of inferring the size and growth rate of a population from a large sample of DNA sequences at a single locus. We apply our method first to a sample of 1,212 mitochondrial DNA (mtDNA) sequences from China, confirming a pattern of recent population growth previously identified using other techniques, but with much smaller confidence intervals for past population sizes due to the low variation of the NLFT. We further analyze a set of 63 mtDNA sequences from blue whales (BWs), concluding that the population grew in the past. This calls for reevaluation of previous studies that were based on the assumption that the BW population was fixed.

Developing a mobile produce distribution system for low-income urban residents in food deserts.

Low-income households in the contemporary city often lack adequate access to healthy foods, like fresh produce, due to a variety of social and spatial barriers that result in neighborhoods being underserved by full-service supermarkets. Because of this, residents commonly resort to purchasing food at fast food restaurants or convenience stores with poor selections of produce. Research has shown that maintaining a healthy diet contributes to disease prevention and overall quality of life. This research seeks to increase low-income residents' access to healthy foods by addressing spatial constraints through the characterization of a mobile market distribution system model that serves in-need neighborhoods. The model optimally locates mobile markets based on the geographic distribution of these residents. Using data from the medium-sized city of Buffalo, New York, results show that, with relatively few resources, the model increases these residents' access to healthy foods, helping to create a healthier city.

Strategizing COVID-19 lockdowns using mobility patterns.

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.

Unmasking the mask studies: why the effectiveness of surgical masks in preventing respiratory infections has been underestimated.

BACKGROUND: Pre-pandemic empirical studies have produced mixed statistical results on the effectiveness of masks against respiratory viruses, leading to confusion that may have contributed to organizations such as the WHO and CDC initially not recommending that the general public wear masks during the coronavirus disease 2019 pandemic. METHODS: A threshold-based dose-response curve framework is used to analyse the effects of interventions on infection probabilities for both single and repeated exposure events. Empirical studies on mask effectiveness are evaluated with a statistical power analysis that includes the effect of adherence to mask usage protocols. RESULTS: When the adherence to mask usage guidelines is taken into account, the empirical evidence indicates that masks prevent disease transmission: all studies we analysed that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100% effective, the studies in question would still have been unlikely to find a statistically significant effect. We also provide a framework for understanding the effect of masks on the probability of infection for single and repeated exposures. The framework demonstrates that masks can have a disproportionately large protective effect and that more frequently wearing a mask provides super-linearly compounding protection. CONCLUSIONS: This work shows (1) that both theoretical and empirical evidence is consistent with masks protecting against respiratory infections and (2) that non-linear effects and statistical considerations regarding the percentage of exposures for which masks are worn must be taken into account when designing empirical studies and interpreting their results.

Theory predicts the uneven distribution of genetic diversity within species.

Global efforts to conserve species have been strongly influenced by the heterogeneous distribution of species diversity across the Earth. This is manifest in conservation efforts focused on diversity hotspots. The conservation of genetic diversity within an individual species is an important factor in its survival in the face of environmental changes and disease. Here we show that diversity within species is also distributed unevenly. Using simple genealogical models, we show that genetic distinctiveness has a scale-free power law distribution. This property implies that a disproportionate fraction of the diversity is concentrated in small sub-populations, even when the population is well-mixed. Small groups are of such importance to overall population diversity that even without extrinsic perturbations, there are large fluctuations in diversity owing to extinctions of these small groups. We also show that diversity can be geographically non-uniform--potentially including sharp boundaries between distantly related organisms--without extrinsic causes such as barriers to gene flow or past migration events. We obtained these results by studying the fundamental scaling properties of genealogical trees. Our theoretical results agree with field data from global samples of Pseudomonas bacteria. Contrary to previous studies, our results imply that diversity loss owing to severe extinction events is high, and focusing conservation efforts on highly distinctive groups can save much of the diversity.

Segregation dynamics with reinforcement learning and agent based modeling.

Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Rewards are key to understand people's choices and decisions. For instance, individual preferences of where to live may lead to the emergence of social segregation. In this paper, we combine Reinforcement Learning (RL) with Agent Based Modeling (ABM) in order to address the self-organizing dynamics of social segregation and explore the space of possibilities that emerge from considering different types of rewards. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions inspired on the rules of the Schelling Segregation model and rewards for interactions. Despite the segregation reward, our experiments show that spatial integration can be achieved by establishing interdependencies among agents of different kinds. They also reveal that segregated areas are more probable to host older people than diverse areas, which attract younger ones. Through this work, we show that the combination of RL and ABM can create an artificial environment for policy makers to observe potential and existing behaviors associated to rules of interactions and rewards.

A new phylogenetic diversity measure generalizing the shannon index and its application to phyllostomid bats.

Protecting biodiversity involves preserving the maximum number and abundance of species while giving special attention to species with unique genetic or morphological characteristics. In balancing different priorities, conservation policymakers may consider quantitative measures that compare diversity across ecological communities. To serve this purpose, a measure should increase or decrease with changes in community composition in a way that reflects what is valued, including species richness, evenness, and distinctness. However, counterintuitively, studies have shown that established indices, including those that emphasize average interspecies phylogenetic distance, may increase with the elimination of species. We introduce a new diversity index, the phylogenetic entropy, which generalizes in a natural way the Shannon index to incorporate species relatedness. Phylogenetic entropy favors communities in which highly distinct species are more abundant, but it does not advocate decreasing any species proportion below a community structure-dependent threshold. We contrast the behavior of multiple indices on a community of phyllostomid bats in the Selva Lacandona. The optimal genus distribution for phylogenetic entropy populates all genera in a linear relationship to their total phylogenetic distance to other genera. Two other indices favor eliminating 12 out of the 23 genera.

Unusual percolation in simple small-world networks.

We present an exact solution of percolation in a generalized class of Watts-Strogatz graphs defined on a one-dimensional underlying lattice. We find a nonclassical critical point in the limit of the number of long-range bonds in the system going to zero, with a discontinuity in the percolation probability and a divergence in the mean finite-cluster size. We show that the critical behavior falls into one of three regimes depending on the proportion of occupied long-range to unoccupied nearest-neighbor bonds, with each regime being characterized by different critical exponents. The three regimes can be united by a single scaling function around the critical point. These results can be used to identify the number of long-range links necessary to secure connectivity in a communication or transportation chain. As an example, we can resolve the communication problem in a game of "telephone."

US social fragmentation at multiple scales.

Despite global connectivity, societies seem to be increasingly polarized and fragmented. This phenomenon is rooted in the underlying complex structure and dynamics of social systems. Far from homogeneously mixing or adopting conforming views, individuals self-organize into groups at multiple scales, ranging from families up to cities and cultures. In this paper, we study the fragmented structure of American society using mobility and communication networks obtained from geo-located social media data. We find self-organized patches with clear geographical borders that are consistent between physical and virtual spaces. The patches have multi-scale structure ranging from parts of a city up to the entire nation. Their significance is reflected in distinct patterns of collective interests and conversations. Finally, we explain the patch emergence by a model of network growth that combines mechanisms of geographical distance gravity, preferential attachment and spatial growth. Our observations are consistent with the emergence of social groups whose separated association and communication reinforce distinct identities. Rather than eliminating borders, the virtual space reproduces them as people mirror their offline lives online. Understanding the mechanisms driving the emergence of fragmentation in hyper-connected social systems is imperative in the age of the Internet and globalization.

Segregation and polarization in urban areas.

Social behaviours emerge from the exchange of information among individuals-constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces.