The effectiveness of backward contact tracing in networks

Discovering and isolating infected individuals is a cornerstone of epidemic control. Because many infectious diseases spread through close contacts, contact tracing is a key tool for case discovery and control. However, although contact tracing has been performed widely, the mathematical understanding of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing---tracing to whom disease spreads, "backward" tracing---tracing from whom disease spreads---is profoundly more effective. The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that even at a small probability of detecting infected individuals, strategically executed contact tracing can prevent a significant fraction of further transmissions. We also show that---in terms of the number of prevented transmissions per isolation---case isolation combined with a small amount of contact tracing is more efficient than case isolation alone. By demonstrating that backward contact tracing is highly effective at discovering super-spreading events, we argue that the potential effectiveness of contact tracing has been underestimated. Therefore, there is a critical need for revisiting current contact tracing strategies so that they leverage all forms of biases. Our results also have important consequences for digital contact tracing because it will be crucial to incorporate the capability for backward and deep tracing while adhering to the privacy-preserving requirements of these new platforms.Comment: 15 pages, 4 figure

Relatedness and synergies of kind and scale in the evolution of helping

Relatedness and synergy affect the selection pressure on cooperation and altruism. Although early work investigated the effect of these factors independently of each other, recent efforts have been aimed at exploring their interplay. Here, we contribute to this ongoing synthesis in two distinct but complementary ways. First, we integrate models of $n$-player matrix games into the direct fitness approach of inclusive fitness theory, hence providing a framework to consider synergistic social interactions between relatives in family and spatially structured populations. Second, we illustrate the usefulness of this framework by delineating three distinct types of helping traits ("whole-group", "nonexpresser-only" and "expresser-only"), which are characterized by different synergies of kind (arising from differential fitness effects on individuals expressing or not expressing helping) and can be subjected to different synergies of scale (arising from economies or diseconomies of scale). We find that relatedness and synergies of kind and scale can interact to generate nontrivial evolutionary dynamics, such as cases of bistable coexistence featuring both a stable equilibrium with a positive level of helping and an unstable helping threshold. This broadens the qualitative effects of relatedness (or spatial structure) on the evolution of helping.Comment: 38 pages, 2 figures, 4 table

A model for brain life history evolution

This work was funded by Swiss NSF grant PP00P3-146340 to LL http://www.snf.ch/en/Pages/default.aspx.Complex cognition and relatively large brains are distributed across various taxa, and many primarily verbal hypotheses exist to explain such diversity. Yet, mathematical approaches formalizing verbal hypotheses would help deepen the understanding of brain and cognition evolution. With this aim, we combine elements of life history and metabolic theories to formulate a metabolically explicit mathematical model for brain life history evolution. We assume that some of the brain’s energetic expense is due to production (learning) and maintenance (memory) of energy-extraction skills (or cognitive abilities, knowledge, information, etc.). We also assume that individuals use such skills to extract energy from the environment, and can allocate this energy to grow and maintain the body, including brain and reproductive tissues. The model can be used to ask what fraction of growth energy should be allocated at each age, given natural selection, to growing brain and other tissues under various biological settings. We apply the model to find uninvadable allocation strategies under a baseline setting (“me vs nature”), namely when energy-extraction challenges are environmentally determined and are overcome individually but possibly with maternal help, and use modern-human data to estimate model’s parameter values. The resulting uninvadable strategies yield predictions for brain and body mass throughout ontogeny and for the ages at maturity, adulthood, and brain growth arrest. We find that: (1) a me-vs-nature setting is enough to generate adult brain and body mass of ancient human scale and a sequence of childhood, adolescence, and adulthood stages; (2) large brains are favored by intermediately challenging environments, moderately effective skills, and metabolically expensive memory; and (3) adult skill is proportional to brain mass when metabolic costs of memory saturate the brain metabolic rate allocated to skills.Publisher PDFPeer reviewe

Evolution of semi-Kantian preferences in two-player assortative interactions with complete and incomplete information and plasticity

We develop a model for the evolution of preferences guiding behavior in pairwise interactions in groupstructured populations. The model uses the conceptual platform of long-term evolution theory and covers different interaction scenarios, including conditional preference expression upon recognition of interactant’s type. We apply the model to the evolution of semi-Kantian preferences at the fitness level, which combine self-interest and a Kantian interest evaluating own behavior in terms of consequences for own fitness if the interactant also adopted this behavior. We look for the convergence stable and uninvadable value of the Kantian coefficient, i.e., the weight attached to the Kantian interest, a quantitative trait varying between zero and one. We consider three scenarios: (a) incomplete information; (b) complete information and incomplete plasticity; and (c) complete information and complete plasticity, where individuals can, not only recognize the type of their interaction partner (complete information), but also conditionally express the Kantian coefficient upon it (complete plasticity). For (a), the Kantian coefficient tends to evolve to equal the coefficient of neutral relatedness between interacting individuals; for (b), it evolves to a value that depends on demographic and interaction assumptions, while for (c) individuals become pure Kantians when interacting with individuals of the same type, while they apply the Kantian coefficient that is uninvadable in a panmictic population under complete information when interacting with individuals with a different type. Overall, our model connects several concepts for analysing the evolution of behavior rules for strategic interactions that have been emphasized in different and sometimes isolated literatures

Life history and mutation rate joint evolution

The cost of germline maintenance gives rise to a trade-off between lowering the deleterious muta-tion rate and investing in life history functions. Therefore, life history and the mutation rate evolve jointly, but this coevolution is not well understood. We develop a mathematical model to analyse the evolution of resource allocation traits affecting simultaneously life history and the deleterious mutation rate. First, we show that the invasion fitness of such resource allocation traits can be approximated by the basic reproductive number of the least-loaded class; the expected lifetime pro-duction of offspring without deleterious mutations born to individuals without deleterious mutations. Second, we apply the model to investigate (i) the joint evolution of reproductive effort and germline maintenance and (ii) the joint evolution of age-at-maturity and germline maintenance. This analysis provides two biological predictions. First, under higher exposure to environmental mutagens (e.g. oxygen), selection favours higher allocation to germline maintenance at the expense of life history. Second, when exposure to environmental mutagens is higher, life histories tend to be faster with individuals having shorter life spans and smaller body sizes at maturity. Our results suggest that mutation accumulation via the cost of germline maintenance is a major force shaping life-history traits

Interest of major serum protein removal for Surface-Enhanced Laser Desorption/Ionization – Time Of Flight (SELDI-TOF) proteomic blood profiling

BACKGROUND: Surface-Enhanced Laser Desorption/Ionization – Time Of Flight (SELDI-TOF) has been proposed as new approach for blood biomarker discovery. However, results obtained so far have been often disappointing as this technique still has difficulties to detect low-abundant plasma and serum proteins. RESULTS: We used a serum depletion scheme using chicken antibodies against various abundant proteins to realized a pre-fractionation of serum prior to SELDI-TOF profiling. Depletion of major serum proteins by immunocapture was confirmed by 1D and 2D gel electrophoresis. SELDI-TOF analysis of bound and unbound (depleted) serum fractions revealed that this approach allows the detection of new low abundant protein peaks with satisfactory reproducibility. CONCLUSION: The combination of immunocapture and SELDI-TOF analysis opens new avenues into proteomic profiling for the discovery of blood biomarkers

Multichannel spectral mode of the ALOHA up-conversion interferometer

International audienceIn this paper, we propose a multichannel spectral configuration of the Astronomical Light Optical Hybrid Analysis (ALOHA) instrument dedicated to high resolution imaging. A frequency conversion process is implemented in each arm of an interferometer to transfer the astronomical light to a shorter wavelength domain. Exploiting the spectral selectivity of this non-linear optical process, we propose to use a set of independent pump lasers in order to simultaneously study multiple spectral channels. This principle is experimentally demonstrated with a dual-channel configuration as a proof-of-principle

The co-evolution of social institutions, demography, and large-scale human cooperation

Human cooperation is typically coordinated by institutions, which determine the outcome structure of the social interactions individuals engage in. Explaining the Neolithic transition from small‐ to large‐scale societies involves understanding how these institutions co‐evolve with demography. We study this using a demographically explicit model of institution formation in a patch‐structured population. Each patch supports both social and asocial niches. Social individuals create an institution, at a cost to themselves, by negotiating how much of the costly public good provided by cooperators is invested into sanctioning defectors. The remainder of their public good is invested in technology that increases carrying capacity, such as irrigation systems. We show that social individuals can invade a population of asocials, and form institutions that support high levels of cooperation. We then demonstrate conditions where the co‐evolution of cooperation, institutions, and demographic carrying capacity creates a transition from small‐ to large‐scale social groups

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them addresses specific needs, we re-designed and re-implemented PyKEEN, one of the first KGE libraries, in a community effort. PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs) based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. Besides, an automatic memory optimization has been realized in order to exploit the provided hardware optimally, and through the integration of Optuna extensive hyper-parameter optimization (HPO) functionalities are provided