Individual-level Interactions Research Articles

Projections of functional dependence among the late middle-aged and older population from 2018-2048 in China: a dynamic microsimulation

BackgroundThe population of China is aging rapidly. However, the long-term trajectories of functionally dependent late middle-aged and older Chinese people are currently absent. The present study aimed to estimate the population size and proportion of late middle-aged and older adults with difficulties and dependence on activities of daily living (ADL) and instrumental activities of daily living (IADL) in China from 2018 to 2048.MethodsWe constructed a dynamic microsimulation model to project the population size and proportions of late middle-aged and older Chinese people who have difficulty and dependence in ADL and IADL from 2018-2048. The model was populated with a representative sample of the target population and allowed individual-level interaction between risk factors, diseases, and health outcomes. Analyses by socioeconomic subgroups were also conducted.ResultsAlmost 25% and 38% of late middle-aged and older people in China will become ADL- and IADL-dependent by 2048, respectively. Also, 17% of the target population will be severely ADL-disabled by 2048. The inequity in functional status across subgroups by sex, educational level, and urban/rural residency will become substantial.ConclusionsThe numbers and percentages of China's functionally difficult and dependent late middle-aged and older population will increase by magnitudes as of the mid-21st century, the pressure of which is compounded by its disproportionate distribution across subgroups. To alleviate the overwhelming challenge, efforts to improve the functional status of the underserved subpopulation should also be iterated.

Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves

Collectively locomoting animals are often viewed as analogous to states of matter in that group-level phenomena emerge from individual-level interactions. Applying this framework to fish schools and bird flocks must account for visco-inertial flows as mediators of the physical interactions. Motivated by linear flight formations, here we show that pairwise flow interactions tend to promote crystalline or lattice-like arrangements, but such order is disrupted by unstably growing positional waves. Using robotic experiments on “mock flocks” of flapping wings in forward flight, we find that followers tend to lock into position behind a leader, but larger groups display flow-induced oscillatory modes – “flonons” – that grow in amplitude down the group and cause collisions. Force measurements and applied perturbations inform a wake interaction model that explains the self-ordering as mediated by spring-like forces and the self-amplification of disturbances as a resonance cascade. We further show that larger groups may be stabilized by introducing variability among individuals, which induces positional disorder while suppressing flonon amplification. These results derive from generic features including locomotor-flow phasing and nonreciprocal interactions with memory, and hence these phenomena may arise more generally in macroscale, flow-mediated collectives.

Generating synthetic population for simulating the spatiotemporal dynamics of epidemics.

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.

“They gotta beat us”: A qualitative investigation of multiple forms of violence and police interactions among urban youth

Interactions with police in neighborhoods shape how residents perceive justice and equity. A growing body of literature seeks to understand how young adults interact, experience, and perceive the criminal justice system with a focus on policing in their neighborhoods. However, to date much of the research in this area only focuses on singular forms of violence or vicarious police contact in relation to the views youth hold with regard to criminal justice. The purpose of this study is to identify the ways in which young adults living in Philadelphia conceptualize and experience multiple forms of violence embedded in policing practices. To capture young adult perceptions of policing at the intersection of multiple forms of violence we used multi-pronged qualitative methods, conducted 85 interviews and field observations, and collected geo-located heart rate data across 12 participants, aged 16 to 21. We find that exposure to multiple forms of violence (interpersonal, symbolic and structural) through policing practices lead to a mistrust of the criminal justice system. Key findings indicate that at times police escalate situations leading to increased violence and that youth may be negatively impacted not only through individual level interactions but also through the mere presence of law enforcement. These results highlight the importance of studying and intervening in violence in multiple forms, particularly within the context of policing.

Quantifying Rift Valley fever virus transmission efficiency in a lamb-mosquito-lamb model.

Rift Valley fever virus (RVFV) is a (re)emerging mosquito-borne pathogen impacting human and animal health. How RVFV spreads through a population depends on population-level and individual-level interactions between vector, host and pathogen. Here, we estimated the probability for RVFV to transmit to naive animals by experimentally exposing lambs to a bite of an infectious mosquito, and assessed if and how RVFV infection subsequently developed in the exposed animal. Aedes aegypti mosquitoes, previously infected via feeding on a viremic lamb, were used to expose naive lambs to the virus. Aedes aegypti colony mosquitoes were used as they are easy to maintain and readily feed in captivity. Other mosquito spp. could be examined with similar methodology. Lambs were exposed to either 1-3 (low exposure) or 7-9 (high exposure) infectious mosquitoes. All lambs in the high exposure group became viremic and showed characteristic signs of Rift Valley fever within 2-4 days post exposure. In contrast, 3 out of 12 lambs in the low exposure group developed viremia and disease, with similar peak-levels of viremia as the high exposure group but with some heterogeneity in the onset of viremia. These results suggest that the likelihood for successful infection of a ruminant host is affected by the number of infectious mosquitoes biting, but also highlights that a single bite of an infectious mosquito can result in disease. The per bite mosquito-to-host transmission efficiency was estimated at 28% (95% confidence interval: 15 - 47%). We subsequently combined this transmission efficiency with estimates for life traits of Aedes aegypti or related mosquitoes into a Ross-McDonald mathematical model to illustrate scenarios under which major RVFV outbreaks could occur in naïve populations (i.e., R0 >1). The model revealed that relatively high vector-to-host ratios as well as mosquitoes feeding preferably on competent hosts are required for R0 to exceed 1. Altogether, this study highlights the importance of experiments that mimic natural exposure to RVFV. The experiments facilitate a better understanding of the natural progression of disease and a direct way to obtain epidemiological parameters for mathematical models.

Trust, Interaction with Neighbors, and Vaccination during the COVID-19 Pandemic: A Cross-Sectional Analysis of Chinese Data.

The COVID-19 pandemic significantly impacted public health and quality of life, leading to government recommendations for vaccination. Using cross-sectional data from a nationwide population-based survey conducted in China (N = 6860), this study aimed to examine the associations between individual vaccine uptake and general trust in others, trust in government, and interaction with neighbors. We conducted a multilevel logistic regression analysis to examine the relevance of these factors at the individual and community levels. Among young adults, higher levels of general trust at both levels were positively associated with vaccination, with odds ratios (OR) of 1.35 (95% confidence interval [CI]: 1.07, 1.70) and 1.58 (95% CI: 1.14, 2.18), respectively. We also found a positive association between vaccination and community-level interaction with neighbors, with ORs of 1.55 (95% CI: 1.11, 2.17). In contrast, among older individuals, vaccination was positively associated only with individual-level interaction with neighbors, with an OR of 1.55 (95% CI: 1.15, 2.08). The results indicated that vaccine uptake was associated with an individual's views of society and the social environment of the community, with substantial variations between the young and the old. Our findings emphasize the significance of public health measures to strengthen neighborhood interactions among older adults.

Informal social control and the endorsement of police legitimacy: A confirmatory factor analysis

High-profile shootings by police officers and the mass protests that followed have highlighted the necessity of social work scholars to address new perspectives in community safety. Collective efficacy is a theoretical framework that explores how individual-level interactions impact community-level outcomes. Despite evidence that indicates collective efficacy is an effective mediator to community-level crime and violence, criticisms of the model question the influence of informal social control and whether or not it represents policing in another form. This paper seeks to understand the relationship between informal social control and police legitimacy to understand how residents’ inclination to intervene reflects a replication of the worst aspects of formal institutions of social control. I performed a confirmatory factor analysis (CFA) across 15 endogenous variables and two latent exogenous variables to create two pathways that assessed the relationship between informal social control and police legitimacy. Results from the CFA pathways indicate that increased levels of informal social control among neighborhood residents was related to decreased levels of police legitimacy. Implications of these findings for abolitionist social work scholars are discussed along with the study’s limitations and future directions for research.

Studying individual-level interactions can transform our understanding of avian mixed-species flocks

Abstract Avian mixed-species flocks are ubiquitous across habitats and a model for studying how heterospecific sociality influences the behavior and composition of animal communities. Here, we review the literature on mixed-species flocks and argue that a renewed focus on individual-level interactions among flock members can transform our understanding of this iconic, avian social system. Specifically, we suggest that an individual perspective will further our understanding of (1) how inter- and intraspecific variation in flock participation links to fitness costs and benefits, (2) the implications of familiarity between individuals in structuring mixed-species flock communities, and (3) how social roles within mixed-species flocks are related to social behavior within and across species. We summarize studies that use an individual perspective in each of these areas and discuss knowledge from conspecific social behavior to posit more broadly how individuals may shape mixed-species flocks. We encourage research approaches that incorporate individual variation in traits, relationships, and social roles in their assessment of mixed-species flocking dynamics. We propose that the analysis of individual variation in behavior will be particularly important for explicitly identifying fitness outcomes that led to the evolution of mixed-species flocks, which in turn affect community structure and resilience.

Self-organized multi-target trapping of swarm robots with density-based interaction

AbstractThe task of multi-target trapping in swarm robots can often be solved by global shape planning and target assignment, but it still remains a challenge to achieve fully self-organized multi-target trapping behavior based on local information. In this paper, inspired by the concept of spatial density in physics and biology, we proposed a novel density-based method to enable the swarm robots to entrap multiple targets with either single-ring, multi-ring or multi-subgroup formation in a distributed and self-organized way while neither communication among robots nor encirclement function is required. Each robot’s local spatial density is considered as the main clue for the individual’s motion decision-making and the enclosed configurations emerge from such individual-level interactions rather than being explicitly designed. Numerical simulations and real robotic experiments are conducted to validate the effectiveness of the proposed method. The results show that the proposed self-organized trapping method allows a swarm of robots to entrap multiple moving targets in a stable, flexible, noise-tolerate and size-scalable fashion.

Micro-Erasures: How an "Evidence-Based" Violence Intervention with Indigenous Youth Advances Cultural Genocide

Abstract: In this case study of an evidence-based violence intervention with Indigenous youth in the southwestern United States, Cruz examines four different types of what he calls "micro-erasures": individual-level interactions that correct, pathologize, punish, or otherwise supplant Indigenous ways of being with the taken-for-granted norms of settler society. Using critical realist grounded-theory methods, he demonstrates how these micro-level practices advance the colonial crimes of cultural imperialism and cultural genocide in often subtle and unconscious ways. To rectify the harms caused by these practices, recommendations for both healing and liberation (beyond traditional Marxism) are discussed.

Enhancing Clinical Data Analysis by Explaining Interaction Effects between Covariates in Deep Neural Network Models.

Deep neural network (DNN) is a powerful technology that is being utilized by a growing number and range of research projects, including disease risk prediction models. One of the key strengths of DNN is its ability to model non-linear relationships, which include covariate interactions. We developed a novel method called interaction scores for measuring the covariate interactions captured by DNN models. As the method is model-agnostic, it can also be applied to other types of machine learning models. It is designed to be a generalization of the coefficient of the interaction term in a logistic regression; hence, its values are easily interpretable. The interaction score can be calculated at both an individual level and population level. The individual-level score provides an individualized explanation for covariate interactions. We applied this method to two simulated datasets and a real-world clinical dataset on Alzheimer's disease and related dementia (ADRD). We also applied two existing interaction measurement methods to those datasets for comparison. The results on the simulated datasets showed that the interaction score method can explain the underlying interaction effects, there are strong correlations between the population-level interaction scores and the ground truth values, and the individual-level interaction scores vary when the interaction was designed to be non-uniform. Another validation of our new method is that the interactions discovered from the ADRD data included both known and novel relationships.

Effects of network topology and trait distribution on collective decision making

<abstract><p>Individual-level interactions shape societal or economic processes, such as infectious diseases spreading, stock prices fluctuating and public opinion shifting. Understanding how the interaction of different individuals affects collective outcomes is more important than ever, as the internet and social media develop. Social networks representing individuals' influence relations play a key role in understanding the connections between individual-level interactions and societal or economic outcomes. Recent research has revealed how the topology of a social network affects collective decision-making in a community. Furthermore, the traits of individuals that determine how they process received information for making decisions also change a community's collective decisions. In this work, we develop stochastic processes to generate networks of individuals with two simple traits: Being a conformist and being an anticonformist. We introduce a novel deterministic voter model for a trait-attributed network, where the individuals make binary choices following simple deterministic rules based on their traits. We show that the simple deterministic rules can drive unpredictable fluctuations of collective decisions which eventually become periodic. We study the effects of network topology and trait distribution on the first passage time for a sequence of collective decisions showing periodicity.</p></abstract>

A mechanistic understanding of microcolony morphogenesis: coexistence of mobile and sessile aggregates.

Most bacteria in the natural environment self-organize into collective phases such as cell clusters, swarms, patterned colonies, or biofilms. Several intrinsic and extrinsic factors, such as growth, motion, and physicochemical interactions, govern the occurrence of different phases and their coexistence. Hence, predicting the conditions under which a collective phase emerges due to individual-level interactions is crucial. Here we develop a particle-based biophysical model of bacterial cells and self-secreted extracellular polymeric substances (EPS) to decipher the interplay of growth, motility-mediated dispersal, and mechanical interactions during microcolony morphogenesis. We show that the microcolony dynamics and architecture significantly vary depending upon the heterogeneous EPS production. In particular, microcolony shows the coexistence of both motile and sessile aggregates rendering a transition towards biofilm formation. We identified that the interplay of differential dispersion and the mechanical interactions among the components of the colony determines the fate of the colony morphology. Our results provide a significant understanding of the mechano-self-regulation during biofilm morphogenesis and open up possibilities of designing experiments to test the predictions.

Contextual fallacy in MLMs with cross-level interaction: A critical review of neighborhood effects on psychiatric resilience

In the multilevel modeling literature, contextual effect is defined as or identified by the effect of the target group-level variable while controlling for the corresponding individual-level variable. This paper extends the notion of “contextual effects” (or “neighborhood” or “school” effects) to an interaction setting, such that the effect of one explanatory variable Xij on the outcome Yij is modeled as a function of a group-level ‘moderating’ or predisposing variable Zj* as well as its counterpart at the individual level Zij. Researchers frequently use regression models that only contain a cross-level interaction between Xij and Zj* to test contextual hypotheses in an interaction setting, but this modeling strategy is unable to discriminate the immediate rival hypothesis that attributes a causal role to the corresponding individual-level variable. This paper points out the prevalence of this type of fallacy through a review of past research on contextual determinants of psychiatric resilience. It is argued that the simple step of adding an appropriate individual-level interaction XijZij could help more robustly test substantive hypotheses about how neighborhood context alters the effect of proximal stressors on health outcomes.

Innovation ecosystems as structures: Actor roles, timing of their entrance, and interactions

Despite their importance little is known about how innovation ecosystems come into existence. We address this gap through an historical case study of Herceptin, a revolutionary drug developed for the treatment of ovarian and breast cancer, and the innovation ecosystem that emerged around this drug between 1978 and 1998. Through qualitative content analysis of a broad scope of archival documents (2474 in total), we define a cast of roles and determine their timing of entry onto the stage of ecosystem emergence, and in turn describe the interaction of these roles that govern emergence. We find that the locus of ecosystem emergence shifts gradually from discovery, resource provision and commitment, to the formation of connections and trust, and finally to complementarity and value creation. These activities are facilitated by specific roles that gain significance at various points in time. We additionally witness shifts in interaction dynamics, from individual level interactions early on, to interactions across levels, and finally to interactions at the organisational level. We synthesize these findings to propose a framework of a processual understanding of how innovation ecosystems come into existence.

Diversity strengthens competing teams.

How does the composition of a collection of individuals affect its outcome in competition with other collections of individuals? Assuming that individuals can be different, we develop a model to interpolate between individual-level interactions and collective-level consequences. Rooted in theoretical mathematics, the model is not constrained to any specific context. Potential applications include research, education, sports, politics, ecology, agriculture, algorithms and finance. Our first main contribution is a game theoretic model that interpolates between the internal composition of an ensemble of individuals and the repercussions for the ensemble as a whole in competition with others. The second main contribution is the rigorous identification of all equilibrium points and strategies. These equilibria suggest a mechanistic underpinning for biological and physical systems to tend towards increasing diversity due to the strength it imparts to the system in competition with others.

Grounded Model of Personal Rivalry and Covert Politics in Field-Identity Formation

Existing literatures by and large portray field-identity formation as an organic, distributed, and democratic meaning-making process—with members across the field negotiating to arrive at a consensus on shared meanings that constitute collective identity. Our inductive study of the formative years (1890-1920) of a century-old academic-professional field revealed however an unobserved dimension of field-identity formation—one marked by personal conflict and collusion to manipulate the field’s identity. Our emergent grounded model, pivoted around an episode of identity contestation in the emerging field, maps how a soured personal relationship and growing rivalry spurred the nascent field’s founder to employ covert politics to mount an overt contestation against his successor in defense of the central feature of the field’s founding identity—ultimately, forcing his successor out of the field and manipulating a consensus over the founding identity. Effectively, we theorize field identity construction as potentially Janus-faced in that it involves actions unfolding, in a complementary and recursive manner, across two levels: an overt level marked by open discourse and debates, to create an illusion of emergence of consensus over the field’s identity; and a covert level marked by personal rivalry and collusion to secure control over the field’s identity. Overall, beyond advancing theory on the hitherto underrecognized psychological and political dimensions of field-identity, our model uncovers the underexplored, dark side of institutional entrepreneurship, and reveals how micro, individual-level interactions inform macro-, field-level processes.

Delay-induced directional switches and mean switching time in swarming systems

Coordinated directional switches often emerge in moving biological groups replete with individual-level interactions. Recent self-propelled particles models can somewhat mimic the patterns of directional switches, but they usually do not include the effects of time delays in the interactions. Here, we focus on investigating the influence of time-delay interactions on the collective motion of swarming locusts, an experimentally well-studied system that exhibits ordered switches between clockwise and counterclockwise movement. We show, both analytically and numerically, that time delays of different types can affect the directional switches. Specifically, for the sufficiently small response delay, increasing the transmission delay can increase the mean switching time, while, for the large response delay, increasing the transmission delay may destroy the ordered directional switches. Our results decipher the role of time-delay interactions in the collective motion, which could be beneficial to the design of collective intelligent devices.

Crowd flow forecasting via agent-based simulations with sequential latent parameter estimation from aggregate observation

Unlike conventional crowd simulations for what-if analysis, agent-based crowd simulations for real-time applications are an emerging research topic and an important tool for better crowd managements in smart cities. Recent studies have attempted to incorporate the real-time crowd observations into crowd simulations for real-time crowd forecasting and management; however, crowd flow forecasting considering individual-level microscopic interactions, especially for large crowds, is still challenging. Here, we present a method that incorporates crowd observation data to forecast a large crowd flow, including thousands of individuals, using a microscopic agent-based model. By sequentially estimating both the crowd state and the latent parameter behind the crowd flows from the aggregate crowd density observation with the particle filter algorithm, the present method estimates and forecasts the large crowd flow using agent-based simulations that incorporate observation data. Numerical experiments, including a realistic evacuation scenario with 5000 individuals, demonstrated that the present method could successfully provide reasonable crowd flow forecasting for different crowd scenarios, even with limited information on crowd movements. These results support the feasibility of real-time crowd flow forecasting and subsequent crowd management, even for large but microscopic crowd problems.

Self-organization and information transfer in Antarctic krill swarms.

Antarctic krill swarms are one of the largest known animal aggregations, and yet, despite being the keystone species of the Southern Ocean, little is known about how swarms are formed and maintained. Understanding the local interactions between individuals that provide the basis for these swarms is fundamental to knowing how swarms arise in nature, and what potential factors might lead to their breakdown. Here, we analysed the trajectories of captive, wild-caught krill in 3D to determine individual-level interaction rules and quantify patterns of information flow. Our results demonstrate that krill align with near neighbours and that they regulate both their direction and speed relative to the positions of groupmates. These results suggest that social factors are vital to the formation and maintenance of swarms. Furthermore, krill operate a novel form of collective organization, with measures of information flow and individual movement adjustments expressed most strongly in the vertical dimension, a finding not seen in other swarming species. This research represents a vital step in understanding the fundamentally important swarming behaviour of krill.