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Publications in Applied Sciences by NOMIS researchers

New Frontiers: The Origins and Content of New Work, 1940–2018

NOMIS Researcher(s)

Published in

March 15, 2024

We answer three core questions about the hypothesized role of newly emerging job categories (“new work”) in counterbalancing the erosive effect of task-displacing automation on labor demand: what is the substantive content of new work, where does it come from, and what effect does it have on labor demand? We construct a novel database spanning eight decades of new job titles linked to U.S. Census microdata and to patent-based measures of occupations’ exposure to labor-augmenting and labor-automating innovations. The majority of current employment is in new job specialties introduced since 1940, but the locus of new-work creation has shifted from middle-paid production and clerical occupations over 1940–1980 to high-paid professional occupations and secondarily to low-paid services since 1980. New work emerges in response to technological innovations that complement the outputs of occupations and demand shocks that raise occupational demand. Innovations that automate tasks or reduce occupational demand slow new-work emergence. Although the flow of augmentation and automation innovations is positively correlated across occupations, the former boosts occupational labor demand while the latter depresses it. The demand-eroding effects of automation innovations have intensified in the past four decades while the demand-increasing effects of augmentation innovations have not.

Research field(s)
Information & Communication Technologies, Economics

DOI
10.1093/qje/qjae008

Publish or perish: Simulating the impact of publication policies on science

NOMIS Researcher(s)

Published in

February 26, 2024

Science can be viewed as a collective, epistemic endeavor. However, a variety of factors- such as the publish-or-perish culture, institutional incentives, and publishers who favor novel and positive findings- may challenge the ability of science to accurately aggregate information about the world. Evidence of the shortcomings in the current structure of science can be seen in the replication crisis that faces psychology and other disciplines. We analyze scientific publishing through the lens of cultural evolution, framing the scientific process as a multi-generational interplay between scientists and publishers in a multi-armed bandit setting. We examine the dynamics of this model through simulations, exploring the effect that different publication policies have on the accuracy of the published scientific record. Our findings highlight the need for replications and caution against behaviors that prioritize factors uncorrelated with result accuracy.

Research field(s)
Psychology & Cognitive Sciences, Psychology & Cognitive Sciences

Program-Based Strategy Induction for Reinforcement Learning

NOMIS Researcher(s)

Published in

February 26, 2024

Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idiosyncratic, discrete heuristics and strategies that people and animals appear to exhibit. Despite recent advances in strategy discovery using tools like recurrent networks that generalize the classic models, the resulting strategies are often onerous to interpret, making connections to cognition difficult to establish. We use Bayesian program induction to discover strategies implemented by programs, letting the simplicity of strategies trade off against their effectiveness. Focusing on bandit tasks, we find strategies that are difficult or unexpected with classical incremental learning, like asymmetric learning from rewarded and unrewarded trials, adaptive horizon-dependent random exploration, and discrete state switching.

Research field(s)
Artificial Intelligence & Image Processing, Psychology & Cognitive Sciences

DOI
10.48550/arXiv.2402.16668

Incoherent probability judgments in large language models

NOMIS Researcher(s)

Published in

January 30, 2024

Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments? We use probabilistic identities and repeated judgments to assess the coherence of probability judgments made by LLMs. Our results show that the judgments produced by these models are often incoherent, displaying human-like systematic deviations from the rules of probability theory. Moreover, when prompted to judge the same event, the mean-variance relationship of probability judgments produced by LLMs shows an inverted-U-shaped like that seen in humans. We propose that these deviations from rationality can be explained by linking autoregressive LLMs to implicit Bayesian inference and drawing parallels with the Bayesian Sampler model of human probability judgments.

Research field(s)
Artificial Intelligence & Image Processing, Psychology & Cognitive Sciences

DOI
10.48550/arXiv.2401.16646

Machine learning for modeling human decisions

NOMIS Researcher(s)

Published in

January 1, 2024

The rapid development of machine learning has led to new opportunities for applying these methods to the study of human decision making. We highlight some of these opportunities and discuss some of the issues that arise when using machine learning to model the decisions people make. We first elaborate on the relationship between predicting decisions and explaining them, leveraging findings from computational learning theory to argue that, in some cases, the conversion of predictive models to interpretable ones with comparable accuracy is an intractable problem. We then identify an important bottleneck in using machine learning to study human cognition—data scarcity—and highlight active learning and optimal experimental design as a way to move forward. Finally, we touch on additional topics such as machine learning methods for combining multiple predictors arising from known theories and specific machine learning architectures that could prove useful for the study of judgment and decision making. In doing so, we point out connections to behavioral economics, computer science, cognitive science, and psychology. (PsycInfo Database Record (c) 2024 APA, all rights reserved)

Research field(s)
Artificial Intelligence & Image Processing, Psychology & Cognitive Sciences

DOI
10.1037/dec0000242

Emergence and collapse of reciprocity in semiautomatic driving coordination experiments with humans

NOMIS Researcher(s)

Published in

December 11, 2023

Forms of both simple and complex machine intelligence are increasingly acting within human groups in order to affect collective outcomes. Considering the nature of collective action problems, however, such involvement could paradoxically and unintentionally suppress existing beneficial social norms in humans, such as those involving cooperation. Here, we test theoretical predictions about such an effect using a unique cyber-physical lab experiment where online participants (N = 300 in 150 dyads) drive robotic vehicles remotely in a coordination game. We show that autobraking assistance increases human altruism, such as giving way to others, and that communication helps people to make mutual concessions. On the other hand, autosteering assistance completely inhibits the emergence of reciprocity between people in favor of self-interest maximization. The negative social repercussions persist even after the assistance system is deactivated. Furthermore, adding communication capabilities does not relieve this inhibition of reciprocity because people rarely communicate in the presence of autosteering assistance. Our findings suggest that active safety assistance (a form of simple AI support) can alter the dynamics of social coordination between people, including by affecting the trade-off between individual safety and social reciprocity. The difference between autobraking and autosteering assistance appears to relate to whether the assistive technology supports or replaces human agency in social coordination dilemmas. Humans have developed norms of reciprocity to address collective challenges, but such tacit understandings could break down in situations where machine intelligence is involved in human decision-making without having any normative commitments.

Research field(s)
Experimental Psychology

DOI
10.1073/pnas.2307804120

Computation-Limited Bayesian Updating

NOMIS Researcher(s)

Published in

August 1, 2023

Effectively updating one’s beliefs requires sufficient empirical evidence (i.e., data) and the computational capacity to process it. Yet both data and computational resources are limited for human minds. Here, we study the problem of belief updating under limited data and limited computation. Using information theory to characterize constraints on computation, we find that the solution to the resulting optimization problem links the data and computational limitations together: when computational resources are tight, agents may not be able to integrate new empirical evidence. The resource-rational belief updating rule we identify offers a novel interpretation of conservative Bayesian updating.

Research field(s)
Psychology & Cognitive Sciences

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms

NOMIS Researcher(s)

Published in

August 1, 2023

On-line decision problems – in which a decision is made based on a sequence of past events without knowledge of the future – have been extensively studied in theoretical computer science. A famous example is the Prediction from Expert Advice problem, in which an agent has to make a decision informed by the predictions of a set of experts. An optimal solution to this problem is the Multiplicative Weights Update Method (MWUM). In this paper, we investigate how humans behave in a Prediction from Expert Advice task. We compare MWUM and several other algorithms proposed in the computer science literature against human behavior. We find that MWUM provides the best fit to people’s choices.

Research field(s)
Psychology & Cognitive Sciences

To each their own theory: Exploring the limits of individual differences in decisions under risk

NOMIS Researcher(s)

August 1, 2023

Theories in cognitive science are primarily aimed at explaining human behavior in general, appealing to universal constructs such as perception or attention. When it is considered, modeling of individual differences is typically performed by adapting model parameters. The implicit assumption of this standard approach is that people are relatively similar, employing the same basic cognitive processes in a given problem domain. In this work, we consider a broader evaluation of the way in which people may differ. We evaluate 23 models of risky choice on around 300 individuals, and find that most models—spanning various constructs from heuristic rules and attention to regret and subjective perception—explain the behavior of different subpopulations of individuals. These results may account for part of the difficulty in obtaining a single elegant explanation of behavior in some long-studied domains, and suggest a more serious consideration of individual variability in theory comparisons going forward.

Research field(s)
Psychology & Cognitive Sciences

Whole-body cellular mapping in mouse using standard IgG antibodies

NOMIS Researcher(s)

Published in

July 10, 2023

Whole-body imaging techniques play a vital role in exploring the interplay of physiological systems in maintaining health and driving disease. We introduce wildDISCO, a new approach for whole-body immunolabeling, optical clearing and imaging in mice, circumventing the need for transgenic reporter animals or nanobody labeling and so overcoming existing technical limitations. We identified heptakis(2,6-di-O-methyl)-β-cyclodextrin as a potent enhancer of cholesterol extraction and membrane permeabilization, enabling deep, homogeneous penetration of standard antibodies without aggregation. WildDISCO facilitates imaging of peripheral nervous systems, lymphatic vessels and immune cells in whole mice at cellular resolution by labeling diverse endogenous proteins. Additionally, we examined rare proliferating cells and the effects of biological perturbations, as demonstrated in germ-free mice. We applied wildDISCO to map tertiary lymphoid structures in the context of breast cancer, considering both primary tumor and metastases throughout the mouse body. An atlas of high-resolution images showcasing mouse nervous, lymphatic and vascular systems is accessible at http://discotechnologies.org/wildDISCO/atlas/index.php . © 2023, The Author(s).

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Biotechnology

DOI
10.1038/s41587-023-01846-0

Compromising improves forecasting

NOMIS Researcher(s)

Published in

May 17, 2023

Predicting the future can bring enormous advantages. Across the ages, reliance on supernatural foreseeing was substituted by the opinion of expert forecasters, and now by collective intelligence approaches which draw on many non-expert forecasters. Yet all of these approaches continue to see individual forecasts as the key unit on which accuracy is determined. Here, we hypothesize that compromise forecasts, defined as the average prediction in a group, represent a better way to harness collective predictive intelligence. We test this by analysing 5 years of data from the Good Judgement Project and comparing the accuracy of individual versus compromise forecasts. Furthermore, given that an accurate forecast is only useful if timely, we analyze how the accuracy changes through time as the events approach. We found that compromise forecasts are more accurate, and that this advantage persists through time, though accuracy varies. Contrary to what was expected (i.e. a monotonous increase in forecasting accuracy as time passes), forecasting error for individuals and for team compromise starts its decline around two months prior to the event. Overall, we offer a method of aggregating forecasts to improve accuracy, which can be straightforwardly applied in noisy real-world settings. © 2023 The Authors.

Research field(s)
Applied Sciences, Psychology & Cognitive Sciences, Experimental Psychology

DOI
10.1098/rsos.221216

Whole-mouse clearing and imaging at the cellular level with vDISCO

NOMIS Researcher(s)

Published in

January 25, 2023

Homeostatic and pathological phenomena often affect multiple organs across the whole organism. Tissue clearing methods, together with recent advances in microscopy, have made holistic examinations of biological samples feasible. Here, we report the detailed protocol for nanobody(VHH)-boosted 3D imaging of solvent-cleared organs (vDISCO), a pressure-driven, nanobody-based whole-body immunolabeling and clearing method that renders whole mice transparent in 3 weeks, consistently enhancing the signal of fluorescent proteins, stabilizing them for years. This allows the reliable detection and quantification of fluorescent signal in intact rodents enabling the analysis of an entire body at cellular resolution. Here, we show the high versatility of vDISCO applied to boost the fluorescence signal of genetically expressed reporters and clear multiple dissected organs and tissues, as well as how to image processed samples using multiple fluorescence microscopy systems. The entire protocol is accessible to laboratories with limited expertise in tissue clearing. In addition to its applications in obtaining a whole-mouse neuronal projection map, detecting single-cell metastases in whole mice and identifying previously undescribed anatomical structures, we further show the visualization of the entire mouse lymphatic system, the application for virus tracing and the visualization of all pericytes in the brain. Taken together, our vDISCO pipeline allows systematic and comprehensive studies of cellular phenomena and connectivity in whole bodies. © 2023, Springer Nature Limited.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1038/s41596-022-00788-2

Non-pyrogenic highly pure magnetosomes for efficient hyperthermia treatment of prostate cancer

NOMIS Researcher(s)

Published in

January 12, 2023

We report the fabrication of highly pure magnetosomes that are synthesized by magnetotactic bacteria (MTB) using pharmaceutically compatible growth media, i.e., without compounds of animal origin (yeast extracts), carcinogenic, mutagenic, or toxic for reproduction (CMR) products, and other heavy metals than iron. To enable magnetosome medical applications, these growth media are reduced and amended compared with media commonly used to grow these bacteria. Furthermore, magnetosomes are made non-pyrogenic by being extracted from these micro-organisms and heated above 400 °C to remove and denature bacterial organic material and produce inorganic magnetosome minerals. To be stabilized, these minerals are further coated with citric acid to yield M-CA, leading to fully reconstructed chains of magnetosomes. The heating properties and anti-tumor activity of highly pure M-CA are then studied by bringing M-CA into contact with PC3-Luc tumor cells and by exposing such assembly to an alternating magnetic field (AMF) of 42 mT and 195 kHz during 30 min. While in the absence of AMF, M-CA are observed to be non-cytotoxic, they result in a 35% decrease in cell viability following AMF application. The treatment efficacy can be associated with a specific absorption rate (SAR) value of M-CA, which is relatively high in cellular environment, i.e., SARcell = 253 ± 11 W/gFe, while being lower than the M-CA SAR value measured in water, i.e., SARwater = 1025 ± 194 W/gFe, highlighting that a reduction in the Brownian contribution to the SAR value in cellular environment does not prevent efficient tumor cell destruction with these nanoparticles. Key points: • Highly pure magnetosomes were produced in pharmaceutically compatible growth media • Non-pyrogenic and stable magnetosomes were prepared for human injection • Magnetosomes efficiently destroyed prostate tumor cells in magnetic hyperthermia © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Biotechnology

DOI
10.1007/s00253-022-12247-9

New approaches to epidemic modeling on networks

NOMIS Researcher(s)

Published in

January 10, 2023

In this article, we develop two independent and new approaches to model epidemic spread in a network. Contrary to the most studied models, those developed here allow for contacts with different probabilities of transmitting the disease (transmissibilities). We then examine each of these models using some mean field type approximations. The first model looks at the late-stage effects of an epidemic outbreak and allows for the computation of the probability that a given vertex was infected. This computation is based on a mean field approximation and only depends on the number of contacts and their transmissibilities. This approach shares many similarities with percolation models in networks. The second model we develop is a dynamic model which we analyze using a mean field approximation which highly reduces the dimensionality of the system. In particular, the original system which individually analyses each vertex of the network is reduced to one with as many equations as different transmissibilities. Perhaps the greatest contribution of this article is the observation that, in both these models, the existence and size of an epidemic outbreak are linked to the properties of a matrix which we call the R-matrix. This is a generalization of the basic reproduction number which more precisely characterizes the main routes of infection. © 2023, The Author(s).

Research field(s)
Applied Sciences, Information & Communication Technologies, Artificial Intelligence & Image Processing

DOI
10.1038/s41598-022-19827-9

Social Metacognition Drives Willingness to Commit

NOMIS Researcher(s)

Published in

January 1, 2023

Showing or telling others that we are committed to cooperate with them can boost social cooperation. But what makes us willing to signal our cooperativeness, when it is costly to do so? In two experiments,we tested the hypothesis that agents engage in social commitments if their subjective confidence in predicting the interaction partner’s behavior is low. In Experiment 1 (preregistered), 48 participants played a prisoner’s dilemma game where they could signal their intentions to their co-player by enduring a monetary cost. As hypothesized, low confidence in one’s prediction of the co-player’s intentions was associated with a higher willingness to engage in costly commitment. In Experiment 2 (31 participants), we replicate these findings and moreover provide causal evidence that experimentally lowering the predictability of others’ actions (and thereby confidence in these predictions) motivates commitment decisions. Finally, across both experiments, we show that participants possess and demonstrate metacognitive access to the accuracy of their mentalizing processes. Taken together, our findings shed light on the importance of confidence representations and metacognitive processes in social interactions © 2023 American Psychological Association

Research field(s)
Applied Sciences, Information & Communication Technologies, Artificial Intelligence & Image Processing

DOI
10.1037/xge0001419

Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences

NOMIS Researcher(s)

Published in

December 21, 2022

The dominant paradigm of experiments in the social and behavioral sciences views an experiment as a test of a theory, where the theory is assumed to generalize beyond the experiment’s specific conditions. According to this view, which Alan Newell once characterized as “playing twenty questions with nature,” theory is advanced one experiment at a time, and the integration of disparate findings is assumed to happen via the scientific publishing process. In this article, we argue that the process of integration is at best inefficient, and at worst it does not, in fact, occur. We further show that the challenge of integration cannot be adequately addressed by recently proposed reforms that focus on the reliability and replicability of individual findings, nor simply by conducting more or larger experiments. Rather, the problem arises from the imprecise nature of social and behavioral theories and, consequently, a lack of commensurability across experiments conducted under different conditions. Therefore, researchers must fundamentally rethink how they design experiments and how the experiments relate to theory. We specifically describe an alternative framework, integrative experiment design, which intrinsically promotes commensurability and continuous integration of knowledge. In this paradigm, researchers explicitly map the design space of possible experiments associated with a given research question, embracing many potentially relevant theories rather than focusing on just one. Researchers then iteratively generate theories and test them with experiments explicitly sampled from the design space, allowing results to be integrated across experiments. Given recent methodological and technological developments, we conclude that this approach is feasible and would generate more-reliable, more-cumulative empirical and theoretical knowledge than the current paradigm – and with far greater efficiency.

Research field(s)
Experimental Psychology, Social Sciences

DOI
10.1017/S0140525X22002874

Ultrasound and nanomaterial: an efficient pair to fight cancer

NOMIS Researcher(s)

Published in

December 1, 2022

Ultrasounds are often used in cancer treatment protocols, e.g. to collect tumor tissues in the right location using ultrasound-guided biopsy, to image the region of the tumor using more affordable and easier to use apparatus than MRI and CT, or to ablate tumor tissues using HIFU. The efficacy of these methods can be further improved by combining them with various nano-systems, thus enabling: (i) a better resolution of ultrasound imaging, allowing for example the visualization of angiogenic blood vessels, (ii) the specific tumor targeting of anti-tumor chemotherapeutic drugs or gases attached to or encapsulated in nano-systems and released in a controlled manner in the tumor under ultrasound application, (iii) tumor treatment at tumor site using more moderate heating temperatures than with HIFU. Furthermore, some nano-systems display adjustable sizes, i.e. nanobubbles can grow into micro-bubbles. Such dual size is advantageous since it enables gathering within the same unit the targeting properties of nano bubbles via EPR effect and the enhanced ultrasound contrasting properties of micro bubbles. Interestingly, the way in which nano-systems act against a tumor could in principle also be adjusted by accurately selecting the nano-system among a large choice and by tuning the values of the ultrasound parameters, which can lead, due to their mechanical nature, to specific effects such as cavitation that are usually not observed with purely electromagnetic waves and can potentially help destroying the tumor. This review highlights the clinical potential of these combined treatments that can improve the benefit/risk ratio of current cancer treatments. Graphical Abstract: [Figure not available: see fulltext.]

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Nanoscience & Nanotechnology

DOI
10.1186/s12951-022-01243-w

Multiscale optical and optoacoustic imaging of amyloid-β deposits in mice

NOMIS Researcher(s)

Published in

September 1, 2022

Deposits of amyloid-β (Aβ) in the brains of rodents can be analysed by invasive intravital microscopy on a submillimetre scale, or via whole-brain images from modalities lacking the resolution or molecular specificity to accurately characterize Aβ pathologies. Here we show that large-field multifocal illumination fluorescence microscopy and panoramic volumetric multispectral optoacoustic tomography can be combined to longitudinally assess Aβ deposits in transgenic mouse models of Alzheimer’s disease. We used fluorescent Aβ-targeted probes (the luminescent conjugated oligothiophene HS-169 and the oxazine-derivative AOI987) to transcranially detect Aβ deposits in the cortex of APP/PS1 and arcAβ mice with single-plaque resolution (8 μm) and across the whole brain (including the hippocampus and the thalamus, which are inaccessible by conventional intravital microscopy) at sub-150 μm resolutions. Two-photon microscopy, light-sheet microscopy and immunohistochemistry of brain-tissue sections confirmed the specificity and regional distributions of the deposits. High-resolution multiscale optical and optoacoustic imaging of Aβ deposits across the entire brain in rodents thus facilitates the in vivo study of Aβ accumulation by brain region and by animal age and strain.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Nanoscience & Nanotechnology

DOI
10.1038/s41551-022-00906-1

Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity

NOMIS Researcher(s)

Published in

June 15, 2022

Observing functional diversity continuously in time and space using satellite imagery forms the basis for studying impact, interactions, and feedback of environmental change mechanisms on ecosystems and biodiversity globally. Functional diversity of plant traits links ecosystem functioning and biodiversity. This work presents an approach to map and quantify functional diversity of physiological forest traits derived from 20 m Sentinel-2 data in a temperate forest ecosystem. We used two complementary data sources, namely high-resolution, as well as spatially resampled airborne imaging spectroscopy data and Sentinel-2 data, to ensure our methods support consistently mapping functional diversity from space. We retrieved three physiological traits related to forest health, stress, and potential productivity, namely chlorophyll, carotenoid, and water content, from airborne imaging spectroscopy and Sentinel-2 data using corresponding spectral indices as proxies. We analyzed changes in two functional diversity metrics, namely functional richness and divergence, at different spatial resolutions. Both functional diversity metrics depend on the size and number of pixels to derive functional diversity as a function of distance, leading to different interpretations. When mapping functional diversity using Sentinel-2 data, small-scale patterns <1.1 ha were no longer visible, implying a minimum calculation area with 60 m radius recommended for retrieval of functional diversity metrics. The spectrally convolved and spatially resampled airborne spectroscopy data and the native Sentinel-2 data were correlated with r = 0.747 for functional richness and r = 0.709 for divergence in a 3.1 ha neighborhood. Functional richness was more affected by the differences in trait maps between the acquisitions resulting from effects in illumination and topography compared with functional divergence. Further differences could be explained by varying illumination/observation effects and phenological status of the vegetation at acquisition. Our approach demonstrates the importance of spatial and spectral resolution when scaling diversity assessments from regional to continental scales.

Research field(s)
Applied Sciences, Engineering, Geological & Geomatics Engineering

DOI
10.1016/j.rse.2022.113024

Region Specific Brain Organoids to Study Neurodevelopmental Disorders

NOMIS Researcher(s)

Published in

January 1, 2022

Region specific brain organoids are brain organoids derived by patterning protocols using extrinsic signals as opposed to cerebral organoids obtained by self-patterning. The main focus of this review is to discuss various region-specific brain organoids developed so far and their application in modeling neurodevelopmental disease. We first discuss the principles of neural axis formation by series of growth factors, such as SHH, WNT, BMP signalings, that are critical to generate various region-specific brain organoids. Then we discuss various neurodevelopmental disorders modeled so far with these region-specific brain organoids, and findings made on mechanism and treatment options for neurodevelopmental disorders (NDD)

Research field(s)
Applied Sciences, Engineering, Biomedical Engineering

DOI
10.15283/IJSC22006

Characterizing super-spreaders using population-level weighted social networks in rural communities

NOMIS Researcher(s)

Published in

January 1, 2022

Sociocentric network maps of entire populations, when combined with data on the nature of constituent dyadic relationships, offer the dual promise of advancing understanding of the relevance of networks for disease transmission and of improving epidemic forecasts. Here, using detailed sociocentric data collected over 4 years in a population of 24 702 people in 176 villages in Honduras, along with diarrhoeal and respiratory disease prevalence, we create a social-network-powered transmission model and identify super-spreading nodes as well as the nodes most vulnerable to infection, using agent-based Monte Carlo network simulations. We predict the extent of outbreaks for communicable diseases based on detailed social interaction patterns. Evidence from three waves of population-level surveys of diarrhoeal and respiratory illness indicates a meaningful positive correlation with the computed super-spreading capability and relative vulnerability of individual nodes. Previous research has identified super-spreaders through retrospective contact tracing or simulated networks. By contrast, our simulations predict that a node’s super-spreading capability and its vulnerability in real communities are significantly affected by their connections, the nature of the interaction across these connections, individual characteristics (e.g. age and sex) that affect a person’s ability to disperse a pathogen, and also the intrinsic characteristics of the pathogen (e.g. infectious period and latency). This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.

Research field(s)
Applied Sciences, Enabling & Strategic Technologies, Bioinformatics

DOI
10.1098/rsta.2021.0123

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