Agent-based modeling: methods and techniques for simulating human systems

Investigating resilience in the childcare context through the agent-based paradigm

Resilience in child protection professional teams (CPP) involves complex interactions at the individual, team and organizational levels. Understanding these multiscale, multimodal dynamics is critical to improving the resilience of CPP teams under enormous stress due to factors such as high work demands, pandemics, and geopolitical events, etc. While resilience has been considered a systemic phenomenon, the dynamic interactions between the individual, team, and organization; how they inform resilience; and how they adapt in the face of adversity remains to be adequately formalized. This paper explores the study of the complex dynamics of team resilience in the context of CPP and how agent-based modelling (ABM) can be used to systematically investigate resilience. Specifically, we explore the attributes of ABM that make it ideal to capturing resilience, outline the steps the uninitiated can take to adopt ABM, and showcase investigations that have used ABM in the childcare and resilience context. This review highlights the potential of ABM in modelling the multifaceted nature of team resilience, predicting outcomes and developing strategies that are critical to long-term success and well-being in dynamic and challenging environments.

A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change

Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.

Analysis of inoculation strategies during COVID-19 pandemic with an agent-based simulation approach

BACKGROUND

The severity of recent Coronavirus (COVID-19) pandemics has revealed the importance of development of inoculation strategies in case of limited vaccine availability. Authorities have implemented inoculation strategies based on perceived risk factors such as age and existence of other chronic health conditions for survivability from the disease. However, various other factors can be considered for identifying the preferred inoculation strategies depending on the vaccine availability and disease spread levels. This study explores the effectiveness of inoculating different groups of population in case of various vaccine availabilities and disease spread levels by means of some performance metrics namely: Attack Rate (AR), Death Rate (DR) and Hospitalization Rate (HR).

METHOD

In this study we have implemented a highly detailed Agent-Based Simulation (ABS) model that extends classical SEIR Model by including five more additional states: Asymptomatic (A), Quarantine (Q), Hospitalized (H), Dead (D) and Immune (M) which can be used as a decision support tool to prioritize the groups of the population inoculated. The approach employs the modelling of daily mobility of individuals, their interactions and transmission of virus among individuals. The population is heterogeneously clustered according to age, family size, work status, transportation and leisure preferences with 17 different groups in order to find the most appropriate one to inoculate. Three different Disease Spread Levels (DSL) (low, mid, high) are experimented with four different Vaccine Available Percentages (VAP) (25%, 50%, 75% and 85%) with a total of 84 scenarios.

RESULTS

As the benchmark, under the No Vaccine case Attack Rate, Hospitalization Rate, and Death Rate goes as high as 99.53%, 16.96%, and 1.38%, respectively. Corresponding highest performance metrics (rates) are 72.33%, 15.95%, and 1.35% for VAP = 25%; 50.25%, 9.55%, and 0.94% for VAP = 50%; 24.53%, 2.62%, and 0.25% for VAP = 75%; and 11.51%, 0.002%, and 0.08% for VAP = 85%. The results of our study shows that the common practice of inoculation based on the age of individual does not yield the best outcome in terms of performance metrics across all DSL and VAP values. The groups containing workers and students that represent highly interactive individuals, i.e. Group (9, 10), Group (9, 11, 10‾) and Group (9, 10, 11, 12‾) emerge as a commonly recommended choice for inoculation in the majority of cases. As expected, we observe that the higher is the VAP levels the more is the number of alternative inoculation groups.

CONCLUSIONS

Findings of this study present that: (i) inoculation considerably decreases the number of infected individuals, the number of deaths and the number of hospitalized individuals due to the disease, (ii) the best inoculation group/groups with respect to performance metrics varies depending on the vaccine availability percentages and disease spread levels, (iii) simultaneous implementation of both inoculation and precautions like lock-down, social distances and quarantines, yields a stronger impact on disease spread and its consequences.

An epidemiological extension of the El Farol Bar problem

This paper presents an epidemiological extension of the El Farol Bar problem, where both a social and an epidemiological dimension are present. In the model, individual agents making binary decisions-to visit a bar or stay home-amidst a non-fatal epidemic. The extension of the classic social dilemma is implemented as an agent-based model, and it is later explored by sampling the parameter space and observing the resulting behavior. The results of this analysis suggest that the infection could be contained by increasing the information available in the underlying social system and adjusting its structure.

Agent-based modeling of cellular dynamics in adoptive cell therapy

Adoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. While in vitro and in vivo experiments are required in this endeavor, they are typically expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), an in silico approach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous "virtual cells" created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic context indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT, in silico trials can be performed systematically to inform ACT product development and predict optimal treatment strategies.

Forecasting cardiovascular disease risk and burden in China from 2020 to 2030: a simulation study based on a nationwide cohort

BACKGROUND

Cardiovascular disease (CVD) remains a significant public health challenge in China. This study aimed to project the burden of CVD from 2020 to 2030 using a nationwide cohort and to simulate the potential impact of various control measures on morbidity and mortality.

METHODS

An agent-based model was employed to simulate annual CVD incidence and mortality from 2021 to 2030. The effects of different prevention and treatment interventions, modelled on international strategies, were also explored.

RESULTS

The study included 106 259 participants. The annual CVD incidence rate is projected to increase from 0.74% in 2021 to 0.97% by 2030, with age-standardised and sex-standardised rates rising from 0.71% to 0.96%. CVD mortality is expected to rise from 0.39% in 2021 to 0.46% in 2024, after which it will stabilise at 0.44% by 2030. Community-based interventions and improved access to inpatient care are predicted to reduce the projected burden of CVD significantly.

CONCLUSIONS

The incidence of CVD in China is projected to increase steadily over the next decade, while mortality will plateau after 2024. Comprehensive interventions, including community-based screenings and enhanced healthcare access, could significantly mitigate the CVD burden.

TRIAL REGISTRATION NUMBER

NCT02536456.

Developing and Comparing Four Families of Bayesian Network Autocorrelation Models for Binary Outcomes: Estimating Peer Effects Involving Adoption of Medical Technologies

Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocorrelation models for a binary random variable defined by whether the peer effect (also termed social influence or contagion) acts on latent continuous outcomes leading to an indirect effect under a normal or a logistic distribution or on the probability of the observed outcome itself under a probit or a logit link function defining a direct effect to account for interdependence between outcomes. For all models, we use a Bayesian approach for model estimation under a uniform prior on a transformed peer effect parameter ( ρ $\rho$ ) designed to enhance model computation and compare results to those under the uniform prior for ρ $\rho$ . We use simulation to assess the performance of Bayesian point and interval estimators for each of the four models when the model that generated the data is used for estimation (precision assessment) and when each of the other three models instead generated the data (robustness assessment). We construct a United States New England region patient-sharing hospital network and apply the four network autocorrelation models to study the adoption of robotic surgery, a new medical technology, among hospitals using a cohort of United States Medicare beneficiaries in 2016 and 2017. Finally, we develop a deviance information criterion for each of the four models to compare their fit to the observed data and use posterior predictive p-values to assess the models' ability to recover specified features of the data. The results find that although the indirect peer effect of the propensity of peer hospital adoption on that of the focal hospital is positive under both latent response autocorrelation models, the direct peer effect of the peer hospital's probability of adopting robotic surgery on the probability of the focal hospital adopting robotic surgery decreases under both mean autocorrelation data models. However, neither of these associations is statistically significant.

Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder

Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just the affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy muscles from abnormal ones. Existing analysis applications, designed for other purposes, often lack essential software engineering features such as a user-friendly interface, infrastructure independence, usability and learning ability, cloud computing capabilities, and AI-based assistance. This research proposes a computer-based methodology to analyze human motion and differentiate between healthy and unhealthy muscles. First, an IoT-based approach is proposed to digitize human motion using smartphones instead of hardly accessible wearable sensors and markers. The motion data is then simulated to analyze the neuromusculoskeletal system. An agent-driven modeling method ensures the naturalness, accuracy, and interpretability of the simulation, incorporating neuromuscular details such as Henneman's size principle, action potentials, motor units, and biomechanical principles. The results are then provided to medical and clinical experts to aid in differentiating between healthy and unhealthy muscles and for further investigation. Additionally, a deep learning-based ensemble framework is proposed to assist in the analysis of the simulation results, offering both accuracy and interpretability. A user-friendly graphical interface enhances the application's usability. Being fully cloud-based, the application is infrastructure-independent and can be accessed on smartphones, PCs, and other devices without installation. This strategy not only addresses the current challenges in treating motion disorders but also paves the way for other clinical simulations by considering both scientific and computational requirements.

Modelling the impact of environmental and social determinants on mental health using generative agents

Mental health is shaped by socio-environmental determinants, yet traditional research approaches struggle to capture their complex interactions. This review explores the potential of generative agents, powered by large language models, to simulate human-like behaviour in virtual environments for mental health research. We outline potential applications including the modelling of adverse life events, urbanicity, climate change, discuss potential challenges and describe how generative agents could transform mental health research.

Contagious acquisition of antimicrobial resistance is critical for explaining emergence in western Canadian feedlots-insights from an agent-based modelling tool

Introduction

Antimicrobial resistance (AMR) is a growing threat to the efficacy of antimicrobials in humans and animals, including those used to control bovine respiratory disease (BRD) in high-risk calves entering western Canadian feedlots. Successful mitigation strategies require an improved understanding of the epidemiology of AMR. Specifically, the relative contributions of antimicrobial use (AMU) and contagious transmission to AMR emergence in animal populations are unknown.

Materials and methods

A stochastic, continuous-time agent-based model (ABM) was developed to explore the dynamics of population-level AMR in Mannheimia haemolytica in pens of high-risk cattle on a typical western Canadian feedlot. The model was directly informed and parameterized with proprietary data from partner veterinary practices and AMU/AMR surveillance data where possible. Hypotheses about how AMR emerges in the feedlot environment were represented by model configurations in which detectable AMR was impacted by (1) only selection arising from AMU; (2) only transmission between animals in the same pen; and (3) both AMU-linked selection and transmission. Automated calibration experiments were used to estimate unknown parameters of interest for select antimicrobial classes. Calibrated parameter values were used in a series of Monte Carlo experiments to generate simulated outputs at both the pen and feedlot levels. Key model outputs included the prevalence of AMR by class at multiple time points across the feeding period. This study compared the relative performances of these model configurations with respect to reproducing empirical AMR data.

Results

Across all antimicrobial classes of interest, model configurations which included the potential for contagious acquisition of AMR offered stronger fits to the empirical data. Notably, sensitivity analyses demonstrated that model outputs were more robust to changes in the assumptions underscoring AMU than to those affecting the likelihood of transmission.

Discussion

This study establishes a feedlot simulation tool that can be used to explore questions related to antimicrobial stewardship in the context of BRD management. The ABM stands out for its unique hierarchical depiction of AMR in a commercial feedlot and its grounding in robust epidemiological data. Future experiments will allow for both AMU-linked selection and transmission of AMR and can accommodate parameter modifications as required.

Evaluating the ability of large language models to emulate personality

For social sciences, recent advancements in Large Language Models (LLMs) have the potential to revolutionize the study of human behaviors by facilitating the creation of realistic agents characterized by a diverse range of individual differences. This research presents novel simulation studies assessing GPT-4's ability to role-play real-world individuals with diverse big five personality profiles. In simulation 1, emulated personality responses exhibited superior internal consistency, but also a more distinct and structured factor organization compared to the human counterparts they were based on. Furthermore, these emulated scores exhibited remarkably high convergent validity with the human self-reported personality scale scores. Simulation 2 replicated these findings but demonstrated that the robustness of GPT-4's role-playing appears to wane as the complexity of the roles increases. Introducing supplementary demographic information in conjunction with personality affected convergent validities for certain emulated traits. However, including additional demographic characteristics enhanced the validity of emulated personality scores for predicting external criteria. Collectively, the findings underscore a promising future of using LLMs to emulate realistic and real person-based agents with varied personality traits. The broader applied implications and avenues for future research are elaborated upon.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

BharatSim: An agent-based modelling framework for India

BharatSim is an open-source agent-based modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. This synthetic population defines individual agents with multiple attributes, among them age, gender, home and work locations, pre-existing health conditions, and socio-economic and employment status. BharatSim's domain-specific language provides a framework for the simulation of diverse models. Its computational core, coded in Scala, supports simulations of a large number of individual agents, up to 50 million. Here, we describe the design and implementation of BharatSim, using it to address three questions motivated by the COVID-19 pandemic in India: (i) When can schools be safely reopened given specified levels of hybrid immunity?, (ii) How do new variants alter disease dynamics in the background of prior infections and vaccinations? and (iii) How can the effects of varied non-pharmaceutical interventions (NPIs) be quantified for a model Indian city? Through its India-specific synthetic population, BharatSim allows disease modellers to address questions unique to this country. It should also find use in the computational social sciences, potentially providing new insights into emergent patterns in social behaviour.

Modelling patient trajectories in emergency department simulations using retrospective patient cohorts

Computer simulations of emergency departments (EDs) are tools that can support managing and optimising ED operations. A core component of ED simulation models is patient trajectories, defined as the series of activities patients undergo in the ED from arrival to discharge. The combined duration of these activities, and waiting times between them, determines a patient's length of stay (LOS). Patient trajectories are often calibrated and validated solely based on the estimated acuity of patients assigned upon arrival. However, acuity is a prospective patient indicator that inconsistently reflects patients' actual urgency and resource usage as seen retrospectively upon discharge. Here, we propose a data-driven ED simulation model in which patient trajectories are modelled based on both acuity and retrospective patient indicators. We show that including retrospective patient indicators recovers the observed LOS distributions more accurately than when using acuity alone. We also demonstrate how the use of retrospective patient indicators leads to more plausible estimates of the impact of increased stress in the ED on patients' LOS. Our work exemplifies how we can better utilise ED data to make the development and evaluation of ED simulation models more accurate and robust, enabling them to provide more reliable and useful operational insights.

Investigating immersion and migration decisions for agent-based modelling: A cautionary tale

Background

Agent-based modelling provides an appealing methodological choice for simulating human behaviour and decisions. The currently dominant approaches based on static transition rates or unverified assumptions are restrictive, and could be enhanced with insights from cognitive experiments on actual decision making. Here, one common concern is that standard surveys or experiments may lack ecological validity, limiting the extent to which research findings can be generalised to real-life settings. For complex, highly emotive decision-making scenarios, such as those related to irregular migration, the typically used short, methodical survey questions may not appropriately map onto complex real-world decisions of interest. Immersive contexts may offer more accurate representations of reality, potentially enhancing the usefulness of experimental information in multi-disciplinary modelling endeavours.

Methods

This preregistered study, aimed directly at examining the effect of immersion on risk-taking in the context of migration decisions, and indirectly at informing a multi-disciplinary construction of an agent-based model of migration, presents a choice-based interactive fiction game in which players make migration decisions to advance through a story.

Participants

(N = 1000 Prolific users) took part in one of four experimental conditions, three involving different renditions of the game attempting to create immersion, with the last condition presenting the decisions in standard survey format.

Results

Although addressing the lack of ecological validity in survey data is important for improving agent-based modelling methodology, the experimental design used to tackle this issue, while responding directly to modelling needs, proved too complex. The created experimental conditions ended up too distinct from each other, involving stimuli that differed in quantity and content. This introduced several unintended and uncontrolled confounds, making it impossible to meaningfully interpret the results of this experiment on its own. Our results act as a cautionary tale for agent-based modellers, highlighting that the modelling needs should not override the principles of experimental design, and provide motivation for more rigorous research on this topic.

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies

OBJECTIVE

To systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.

DATA SOURCE AND STUDY SELECTION

Four databases were searched for modelling studies of e-cigarette use on population health published between 2010 and 2023. A total of 32 studies were included.

DATA EXTRACTION

Data on study characteristics, model attributes and estimates of population impacts including health outcomes and smoking prevalence were extracted from each article. The findings were synthesised narratively.

DATA SYNTHESIS

The introduction of e-cigarettes was predicted to lead to decreased smoking-related mortality, increased quality-adjusted life-years and reduced health system costs in 29 studies. Seventeen studies predicted a lower prevalence of cigarette smoking. Models that predicted negative population impacts assumed very high e-cigarette initiation rates among non-smokers and that e-cigarette use would discourage smoking cessation by a large margin. The majority of the studies were based on US population data and few studies included factors other than smoking status, such as jurisdictional tobacco control policies or social influence.

CONCLUSIONS

A population increase in e-cigarette use may result in lower smoking prevalence and reduced burden of disease in the long run, especially if their use can be restricted to assisting smoking cessation. Given the assumption-dependent nature of modelling outcomes, future modelling studies should consider incorporating different policy options in their projection exercises, using shorter time horizons and expanding their modelling to low-income and middle-income countries where smoking rates remain relatively high.

Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

Effect of rotavirus vaccination on the burden of rotavirus disease and associated antibiotic use in India: A dynamic agent-based simulation analysis

BACKGROUND

Rotavirus is a leading cause of diarrhea in infants and young children in many low- and middle-income countries. India launched a childhood immunization program for rotavirus in 2016, starting with four states and expanding it to cover all states by 2019. The objective of this study was to estimate the effects of the rotavirus vaccination program in India on disease burden and antibiotic misuse.

METHODS

We built a dynamic agent-based model of rotavirus progression in children under five within each district in India. Simulations were run for various scenarios of vaccination coverage in the context of India's Universal Immunization Programme. Population data were obtained from the National Family Household Surveys and used to calibrate the models. Disease parameters were obtained from published studies. We estimated past and projected future reduction of disease burden and antibiotic misuse due to full vaccination nationwide, by state, and by wealth quintile.

RESULTS

We estimate that rotavirus vaccination in India has reduced the prevalence of rotavirus cases by 33.7% (prediction interval: 30.7-36.0%), total antibiotic misuse due to rotavirus by 21.8% (18.6-25.1%), and total deaths due to rotavirus by 38.3% (31.3-44.4%) for children under five. We estimate total antibiotic misuse due to rotavirus infection to be 7.6% (7.5-7.9%) of total antibiotic consumption in this demographic versus 9.6% (9.4-9.9%) in the absence of vaccination. We project rotaviral prevalence to drop to below one case for every 100,000 individuals in those below five if vaccination coverage is increased by 50.3% (45.2-58.5%) to 68.1% (63.1-76.4) nationwide.

CONCLUSION

Universal coverage of childhood rotavirus vaccination can substantially reduce inappropriate antibiotic use in India.

Relational Persistent Homology for Multispecies Data with Application to the Tumor Microenvironment

Topological data analysis (TDA) is an active field of mathematics for quantifying shape in complex data. Standard methods in TDA such as persistent homology (PH) are typically focused on the analysis of data consisting of a single entity (e.g., cells or molecular species). However, state-of-the-art data collection techniques now generate exquisitely detailed multispecies data, prompting a need for methods that can examine and quantify the relations among them. Such heterogeneous data types arise in many contexts, ranging from biomedical imaging, geospatial analysis, to species ecology. Here, we propose two methods for encoding spatial relations among different data types that are based on Dowker complexes and Witness complexes. We apply the methods to synthetic multispecies data of a tumor microenvironment and analyze topological features that capture relations between different cell types, e.g., blood vessels, macrophages, tumor cells, and necrotic cells. We demonstrate that relational topological features can extract biological insight, including the dominant immune cell phenotype (an important predictor of patient prognosis) and the parameter regimes of a data-generating model. The methods provide a quantitative perspective on the relational analysis of multispecies spatial data, overcome the limits of traditional PH, and are readily computable.

The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations

Investigating the complex interactions between microbiota and immunity is crucial for a fruitful understanding progress of human health and disease. This review assesses animal models, next-generation in vitro models, and in silico approaches that are used to decipher the microbiome-immunity axis, evaluating their strengths and limitations. While animal models provide a comprehensive biological context, they also raise ethical and practical concerns. Conversely, modern in vitro models reduce animal involvement but require specific costs and materials. When considering the environmental impact of these models, in silico approaches emerge as promising for resource reduction, but they require robust experimental validation and ongoing refinement. Their potential is significant, paving the way for a more sustainable and ethical future in microbiome-immunity research.

How large language models can reshape collective intelligence

Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals-even experts-resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the 'wisdom of crowds', online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public. Large language models, however, are transforming how information is aggregated, accessed and transmitted online. Here we focus on the unique opportunities and challenges this transformation poses for collective intelligence. We bring together interdisciplinary perspectives from industry and academia to identify potential benefits, risks, policy-relevant considerations and open research questions, culminating in a call for a closer examination of how large language models affect humans' ability to collectively tackle complex problems.

Feasibility of hepatitis C elimination by screening and treatment alone in high-income countries

BACKGROUND AND AIMS

Despite the availability of highly effective direct-acting antiviral therapy, chronic hepatitis C (CHC) continues to cause a major public health burden. In many high-income countries, treatment rates have been declining, which was exacerbated by the impact of the COVID-19 pandemic, threatening the ability to meet the World Health Organization (WHO)'s targets for eliminating HCV as a public health threat by 2030. We sought to model the impact of CHC in Canada, a resource-rich country with ongoing immigration from HCV-endemic regions; which relies exclusively on risk-based screening for case identification.

APPROACH AND RESULTS

We developed an agent-based model to characterize the HCV epidemic in a high-income country with ongoing immigration. Combinations of prevention such as harm reduction, screening, and treatment strategies were considered. Model parameters were estimated from the literature and calibrated against historical HCV data. Sensitivity analyses were performed to assess uncertainty. Under the current status quo of risk-based screening, we predict the incidence of CHC-induced decompensated cirrhosis, HCC, and liver-related deaths would decrease by 79.4%, 76.1%, and 62.1%, respectively, between 2015 and 2030, but CHC incidence would only decrease by 11.1%. The results were sensitive to HCV transmission rate and an annual number of people initiating treatment.

CONCLUSIONS

Current risk-based screening, and subsequent treatment, will be inadequate to achieve WHO goals. With extensive scale-up in screening, and treatment, the mortality target may be achievable, but the target for preventing new CHC cases is unlikely reachable, highlighting the importance of developing enhanced harm-reduction strategies for HCV elimination.

Anonymization: The imperfect science of using data while preserving privacy

Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such data is key for scientific and societal progress. Anonymization is considered by scientists and policy-makers as one of the main ways to share data while minimizing privacy risks. In this review, we offer a pragmatic perspective on the modern literature on privacy attacks and anonymization techniques. We discuss traditional de-identification techniques and their strong limitations in the age of big data. We then turn our attention to modern approaches to share anonymous aggregate data, such as data query systems, synthetic data, and differential privacy. We find that, although no perfect solution exists, applying modern techniques while auditing their guarantees against attacks is the best approach to safely use and share data today.

A three-dimensional lattice-free agent-based model of intracellular ice formation and propagation and intercellular mechanics in liver tissues

A successful cryopreservation of tissues and organs is crucial for medical procedures and drug development acceleration. However, there are only a few instances of successful tissue cryopreservation. One of the main obstacles to successful cryopreservation is intracellular ice damage. Understanding how ice spreads can accelerate protocol development and enable model-based decision-making. Previous models of intracellular ice formation in individual cells have been extended to one-cell-wide arrays to establish the theory of intercellular ice propagation in tissues. The current lattice-based ice propagation models do not account for intercellular forces resulting from cell solidification, which could lead to mechanical disruption of tissue structures during freezing. Moreover, these models have not been expanded to include more realistic tissue architectures. In this article, we discuss the development and validation of a stochastic model for the formation and propagation of ice in small tissues using lattice-free agent-based model. We have improved the existing model by incorporating the mechanical effects of water crystallization within cells. Using information from previous research, we have also created a new model that accounts for ice growth in tissue slabs, spheroids and hepatocyte discs. Our model demonstrates that individual cell freezing can have mechanical consequences and is consistent with earlier findings.

Interdependent water and power infrastructure model (IWPIM): A modeling approach for water and energy resource management in rural communities

The importance of the dependencies between water and power systems is more acutely perceived when challenges emerge. As both energy and water supply are limited, efficient use is a must for any sustainable future, especially in rural areas. Although important, a modeling tool that can analyze water-energy systems interdependencies in rural systems, at the architectural level highlighting the physical interconnections and synergies of these systems, is still lacking. We present a multi-agent system model that captures the features of both systems, at the same levels of fidelity and resolution, with coordinated operations and contingency components represented. Unlike other models, ours captures architectural features of both systems and technical constraints of the systems' components, which is critical to capture physical intricacies of the interplay between systems components and shed light on the impacts of disruptions of either system on the other. This model, which includes multiple infrastructure components, shows the importance of a holistic understanding of the systems, for cooperation across systems physical boundaries and enhanced benefits at larger scales. This study looks to investigate water-power resource management in an irrigation system via the analysis of physical links and highlight strengths and vulnerabilities. The effects of water shortage, water re-allocation and load shedding are analyzed through scenarios designed to illustrate the utility of such a model. Results highlights the importance of inter-reservoir relationships for alleviating effects of disruption and unforeseen rise in energy demand. Water storage is also critical, helping to mitigate the impacts of water scarcity, and by extension, to keep the energy system unaffected. It can be a viable part of the solution to compensate for the negative impact of shortage for both resources.

Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses

The response of tumors to anti-cancer therapies is defined not only by cell-intrinsic therapy sensitivities but also by local interactions with the tumor microenvironment. Fibroblasts that make tumor stroma have been shown to produce paracrine factors that can strongly reduce the sensitivity of tumor cells to many types of targeted therapies. Moreover, a high stroma/tumor ratio is generally associated with poor survival and reduced therapy responses. However, in contrast to advanced knowledge of the molecular mechanisms responsible for stroma-mediated resistance, its effect on the ability of tumors to escape therapeutic eradication remains poorly understood. To a large extent, this gap of knowledge reflects the challenge of accounting for the spatial aspects of microenvironmental resistance, especially over longer time frames. To address this problem, we integrated spatial inferences of proliferation-death dynamics from an experimental animal model of targeted therapy responses with spatial mathematical modeling. With this approach, we dissected the impact of tumor/stroma distribution, magnitude and distance of stromal effects. While all of the tested parameters affected the ability of tumor cells to resist elimination, spatial patterns of stroma distribution within tumor tissue had a particularly strong impact.

Balancing long-term and short-term strategies in a sustainability game

Our society is marked by a tension between short-term objectives, such as economic growth, and long-term sustainability goals, including mitigating resource depletion. In such a competitive setting, it is crucial to ascertain whether a system can maintain long-term viability and, if so, how. This article aims to enhance the understanding of this issue by analyzing how sustainability concerns change over time by means of a game, and the effect of this variation on the final status of a system. Leveraging insights from the game, we implement an agent-based model to elicit the tension between short-term objectives and sustainability, emphasizing the influence of individual actions on the overall system. The simulation results suggest that the likelihood of a collapse is contingent upon the availability of resources and the manner in which information regarding these resources is gathered and utilized. Finally, the paper proposes practical suggestions for managing this kind of system.

Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2

In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events.

Analysis of rural broadband adoption dynamics: A theory-driven agent-based model

Demand for broadband internet has far outpaced its availability. In addition, the "new normal" imposed by the COVID-19 pandemic has further disadvantaged unserved and underserved areas. To address this challenge, federal and state agencies are funding internet service providers (ISPs) to deploy broadband infrastructure in these areas. To support goals to provide broadband service to as many people as possible as quickly as possible, policymakers and ISPs may benefit from better tools to predict take rates and formulate effective strategies to increase the adoption of high-speed internet. However, there is typically insufficient data available to understand consumer attitudes. We propose using an agent-based model grounded in the Theory of Planned Behavior, a behavioral theory that explains the consumer's decision-making process. The model simulates residential broadband adoption by capturing the effect of market competition, broadband service attributes, and consumer characteristics. We demonstrate the effectiveness of this type of tool via a use case in Missouri to show how simulation results can inform predictions of broadband adoption. In the model, broadband take rates increase as the presence of existing internet users in the area increases and price decreases. With further development, this type of simulation can guide decision-making for infrastructure and digital literacy investment based on demand as well as support the design of market subsidies that aim to reduce the digital divide.

An Agent-Based Model Shows How Mixed Infections Drive Multiyear Pathotype Dynamics in a Plant-Virus System

Mathematical models are widely used to understand the evolution and epidemiology of plant pathogens under a variety of scenarios. Here, we used this approach to analyze the effects of different traits intrinsic and extrinsic to plant-virus interactions on the dynamics of virus pathotypes in genetically heterogeneous plant-virus systems. For this, we propose an agent-based epidemiological model that includes epidemiologically significant pathogen life-history traits related to virulence, transmission, and survival in the environment and allows for integrating long- and short-distance transmission, primary and secondary infections, and within-host pathogen competition in mixed infections. The study focuses on the tobamovirus-pepper pathosystem. Model simulations allowed us to integrate pleiotropic effects of resistance-breaking mutations on different virus life-history traits into the net costs of resistance breaking, allowing for predictions on multiyear pathotype dynamics. We also explored the effects of two control measures, the use of host resistance and roguing of symptomatic plants, that modify epidemiological attributes of the pathogens to understand how their populations will respond to evolutionary pressures. One major conclusion points to the importance of pathogen competition within mixed-infected hosts as a component of the overall fitness of each pathogen that, thus, drives their multiyear dynamics.

Electrical signaling in three-dimensional bacterial biofilms using an agent-based fire-diffuse-fire model

Agent-based models were used to describe electrical signaling in bacterial biofilms in three dimensions. Specifically, wavefronts of potassium ions in Escherichia coli biofilms subjected to stress from blue light were modeled from experimental data. Electrical signaling occurs only when the biofilms grow beyond a threshold size, which we have shown to vary with the K^{+} ion diffusivity, and the K^{+} ion threshold concentration, which triggered firing in the fire-diffuse-fire model. The transport of the propagating wavefronts shows superdiffusive scaling on time. K^{+} ion diffusivity is the main factor that affects the wavefront velocity. The K^{+} ion diffusivity and the firing threshold also affect the anomalous exponent for the propagation of the wavefront determining whether the wavefront is subdiffusive or superdiffusive. The geometry of the biofilm and its relation to the mean-square displacement (MSD) of the wavefront as a function of time was investigated for spherical, cylindrical, cubical, and mushroom-like structures. The MSD varied significantly with geometry; an additional regime to the kinetics occurred when the potassium wavefront leaves the biofilm. Adding cylindrical defects to the biofilm, which are known to occur in E. coli biofilms, also gave an extra kinetic regime to the wavefront MSD for the propagation through the defect.

Multi-agent simulation of doughnut deep fat frying considering two domain heating media and sample flipping

Two domain heating media and sample flipping during processing were considered when developing an agent-based model to explain coupled heat and mass transfer phenomena during deep fat frying of doughnuts. The model was validated by comparing the moisture content, oil content and temperature profiles obtained from the experimental results with those obtained from the model. Results of this study showed that the water content of crumb raised to 60% (based on dry weight) whereas, it decreased to less than 10% in the case of doughnut crust during deep fat frying. Simulated profile of oil penetration illustrated that the oil content of different parts of crust were not equal and were affected by frying temperature and crust structure. In general, as the surface of doughnut (a porous material) was heated from the surface, evaporation zones were formed in the thinner parts of the crust and gradually formed oil penetrating areas. Moreover, experimental and simulated data indicated that flipping of samples in the middle of processing time had an important effect on heat and mass transfer during frying. Variation of thermophysical properties in each part of doughnut had a unique behavior. The changes in the density, specific heat capacity and thermal conductivity of crumb followed a sigmoid pattern; whereas, a dominant falling rate period with some variations was observed in crust. Moreover, any changes in moisture content and temperature of crust occurred faster than the crumb. The output of simulation was in a good agreement with the experimental data. With the power of simulation now available for design, the results of this study greatly improve the design of fried foods and frying processes.

Coupling an agent-based model and an ensemble Kalman filter for real-time crowd modelling

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the ensemble Kalman filter (EnKF) with an agent-based crowd model to enhance its accuracy in real time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under 'normality' to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications.

Transformation scenarios towards multifunctional landscapes: A multi-criteria land-use allocation model applied to Jambi Province, Indonesia

In tropical regions, shifting from forests and traditional agroforestry to intensive plantations generates conflicts between human welfare (farmers' demands and societal needs) and environmental protection. Achieving sustainability in this transformation will inevitably involve trade-offs between multiple ecological and socioeconomic functions. To address these trade-offs, our study used a new methodological approach allowing the identification of transformation scenarios, including theoretical landscape compositions that satisfy multiple ecological functions (i.e., structural complexity, microclimatic conditions, organic carbon in plant biomass, soil organic carbon and nutrient leaching losses), and farmers needs (i.e., labor and input requirements, total income to land, and return to land and labor) while accounting for the uncertain provision of these functions and having an actual potential for adoption by farmers. We combined a robust, multi-objective optimization approach with an iterative search algorithm allowing the identification of ecological and socioeconomic functions that best explain current land-use decisions. The model then optimized the theoretical land-use composition that satisfied multiple ecological and socioeconomic functions. Between these ends, we simulated transformation scenarios reflecting the transition from current land-use composition towards a normative multifunctional optimum. These transformation scenarios involve increasing the number of optimized socioeconomic or ecological functions, leading to higher functional richness (i.e., number of functions). We applied this method to smallholder farms in the Jambi Province, Indonesia, where traditional rubber agroforestry, rubber plantations, and oil palm plantations are the main land-use systems. Given the currently practiced land-use systems, our study revealed short-term returns to land as the principal factor in explaining current land-use decisions. Fostering an alternative composition that satisfies additional socioeconomic functions would require minor changes ("low-hanging fruits"). However, satisfying even a single ecological indicator (e.g., reduction of nutrient leaching losses) would demand substantial changes in the current land-use composition ("moonshot"). This would inevitably lead to a profit decline, underscoring the need for incentives if the societal goal is to establish multifunctional agricultural landscapes. With many oil palm plantations nearing the end of their production cycles in the Jambi province, there is a unique window of opportunity to transform agricultural landscapes.

Agent-based models under uncertainty

Background

Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children's battleship game, where each battleship is an agent.

Methods

The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of an imperfect radar. In the interval method, the approach occludes the status of the agents (ships) and precludes an analyst from making decisions about them in real-time.

Results

In a highly uncertain environment, after many time steps, there can be many ships remaining whose status is unknown. In contrast, any MC simulation invariably tends to conclude with a small number of the remaining ships after many time steps. Thus, the interval approach misses the quantitative conclusion. However, some quantitative results are generated by the interval implementation, e.g. the identities of the surviving ships, which are revealed to be nearly mutual with the MC implementation, though with fewer identities in total compared to MC.

Conclusions

We have demonstrated that it is possible to implement intervals in an ABM, but the results are broad, which may be useful for generating the overall bounds of the system but do not provide insight on the expected outcomes and trends.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

MOTIVATION

Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties.

RESULTS

Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced.

AVAILABILITY AND IMPLEMENTATION

All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

Mathematical models of Plasmodium vivax transmission: A scoping review

Plasmodium vivax is one of the most geographically widespread malaria parasites in the world, primarily found across South-East Asia, Latin America, and parts of Africa. One of the significant characteristics of the P. vivax parasite is its ability to remain dormant in the human liver as hypnozoites and subsequently reactivate after the initial infection (i.e. relapse infections). Mathematical modelling approaches have been widely applied to understand P. vivax dynamics and predict the impact of intervention outcomes. Models that capture P. vivax dynamics differ from those that capture P. falciparum dynamics, as they must account for relapses caused by the activation of hypnozoites. In this article, we provide a scoping review of mathematical models that capture P. vivax transmission dynamics published between January 1988 and May 2023. The primary objective of this work is to provide a comprehensive summary of the mathematical models and techniques used to model P. vivax dynamics. In doing so, we aim to assist researchers working on mathematical epidemiology, disease transmission, and other aspects of P. vivax malaria by highlighting best practices in currently published models and highlighting where further model development is required. We categorise P. vivax models according to whether a deterministic or agent-based approach was used. We provide an overview of the different strategies used to incorporate the parasite's biology, use of multiple scales (within-host and population-level), superinfection, immunity, and treatment interventions. In most of the published literature, the rationale for different modelling approaches was driven by the research question at hand. Some models focus on the parasites' complicated biology, while others incorporate simplified assumptions to avoid model complexity. Overall, the existing literature on mathematical models for P. vivax encompasses various aspects of the parasite's dynamics. We recommend that future research should focus on refining how key aspects of P. vivax dynamics are modelled, including spatial heterogeneity in exposure risk and heterogeneity in susceptibility to infection, the accumulation of hypnozoite variation, the interaction between P. falciparum and P. vivax, acquisition of immunity, and recovery under superinfection.

Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions

Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.

Optimising peak energy reduction in networks of buildings

Buildings are amongst the world's largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management-analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.

A Life Outside

How do the morphologies of organisms affect their physical interactions with the environment and other organisms? My research in marine systems couples field studies of the physical habitats, life history strategies, and ecological interactions of organisms with laboratory analyses of their biomechanics. Here, I review how we pursued answers to three questions about marine organisms: (a) how benthic organisms withstand and utilize the water moving around them, (b) how the interaction between swimming and turbulent ambient water flow affects where small organisms go, and (c) how hairy appendages catch food and odors. I also discuss the importance of different types of mentors, the roadblocks for women in science when I started my career, the challenges and delights of interdisciplinary research, and my quest to understand how I see the world as a dyslexic.

Exploring the Use of Artificial Intelligence in Agent-Based Modeling Applications: A Bibliometric Study

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field’s impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka’s law is reflected in the author’s contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford’s Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.

A Scoping Review and Taxonomy of Epidemiological-Macroeconomic Models of COVID-19

OBJECTIVES

The COVID-19 pandemic placed significant strain on many health systems and economies. Mitigation policies decreased health impacts but had major macroeconomic impact. This article reviews models combining epidemiological and macroeconomic projections to enable policy makers to consider both macroeconomic and health objectives.

METHODS

A scoping review of epidemiological-macroeconomic models of COVID-19 was conducted, covering preprints, working articles, and journal publications. We assessed model methodologies, scope, and application to empirical data.

RESULTS

We found 80 articles modeling both the epidemiological and macroeconomic outcomes of COVID-19. Model scope is often limited to the impact of lockdown on health and total gross domestic product or aggregate consumption and to high-income countries. Just 14% of models assess disparities or poverty. Most models fall under 4 categories: compartmental-utility-maximization models, epidemiological models with stylized macroeconomic projections, epidemiological models linked to computable general equilibrium or input-output models, and epidemiological-economic agent-based models. We propose a taxonomy comparing these approaches to guide future model development.

CONCLUSIONS

The epidemiological-macroeconomic models of COVID-19 identified have varying complexity and meet different modeling needs. Priorities for future modeling include increasing developing country applications, assessing disparities and poverty, and estimating of long-run impacts. This may require better integration between epidemiologists and economists.

Agent-based simulation and child protection systems: Rationale, implementation, and verification

BACKGROUND AND OBJECTIVE

Simulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence.

PARTICIPANTS AND SETTING

In this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE.

METHODS

The governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement.

RESULTS

As an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE.

CONCLUSIONS

Management of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.

Antibody-dependent enhancement and neutralization against CVB4 investigated in vitro and in silico through an agent-based model

The infection with coxsackievirus B4 (CVB4) can be enhanced in vitro by antibodies directed against the viral capsid protein VP4. In peripheral blood mononuclear cells, antibody-dependent enhancement (ADE) of CVB4 infection leads to the production of interferon alpha (IFN-α). To investigate ADE of CVB4-induced production of IFN-α, an agent-based model was constructed with enhancing and neutralizing antibodies. The model recapitulates viral neutralization and ADE in silico. The enhancing and neutralizing activities of serum samples were evaluated in vitro to confront the model predictions with experimental results. Increasing the incubation time of CVB4 with serum samples improves virus neutralization in silico as well as in vitro. It also results in ADE at lower antibody numbers in silico, which is confirmed in vitro with IFN-α production at lower serum concentrations. Furthermore, incubation of CVB4 with serum at a low temperature does not induce IFN-α production in vitro. Thus, taken together our results suggest that enhancing antibodies bind cryptic epitopes, more accessible with longer incubation time and at higher temperature due to changes in capsid conformation, consistent with previous results indicating that enhancing antibodies are anti-VP4 antibodies.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

Review: The prevailing mathematical modeling classifications and paradigms to support the advancement of sustainable animal production

Mathematical modeling is typically framed as the art of reductionism of scientific knowledge into an arithmetical layout. However, most untrained people get the art of modeling wrong and end up neglecting it because modeling is not simply about writing equations and generating numbers through simulations. Models tell not only about a story; they are spoken to by the circumstances under which they are envisioned. They guide apprentice and experienced modelers to build better models by preventing known pitfalls and invalid assumptions in the virtual world and, most importantly, learn from them through simulation and identify gaps in pushing scientific knowledge further. The power of the human mind is well-documented for idealizing concepts and creating virtual reality models, and as our hypotheses grow more complicated and more complex data become available, modeling earns more noticeable footing in biological sciences. The fundamental modeling paradigms include discrete-events, dynamic systems, agent-based (AB), and system dynamics (SD). The source of knowledge is the most critical step in the model-building process regardless of the paradigm, and the necessary expertise includes (a) clear and concise mental concepts acquired through different ways that provide the fundamental structure and expected behaviors of the model and (b) numerical data necessary for statistical analysis, not for building the model. The unreasonable effectiveness of models to grow scientific learning and knowledge in sciences arise because different researchers would model the same problem differently, given their knowledge and experiential background, leading to choosing different variables and model structures. Secondly, different researchers might use different paradigms and even unalike mathematics to resolve the same problem; thus, model needs are intrinsic to their perceived assumptions and structures. Thirdly, models evolve as the scientific community knowledge accumulates and matures over time, hopefully resulting in improved modeling efforts; thus, the perfect model is fictional. Some paradigms are most appropriate for macro, high abstraction with less detailed-oriented scenarios, while others are most suitable for micro, low abstraction with higher detailed-oriented strategies. Modern hybridization aggregating artificial intelligence (AI) to mathematical models can become the next technological wave in modeling. AI can be an integral part of the SD/AB models and, before long, write the model code by itself. Success and failures in model building are more related to the ability of the researcher to interpret the data and understand the underlying principles and mechanisms to formulate the correct relationship among variables rather than profound mathematical knowledge.

Agent-based approach for elucidating the release from collective arrest of cell motion in corneal epithelial cell sheet

Changes in cell fluidity have been observed in various cellular tissues and are strongly linked to biological phenomena such as self-organization. Recent studies suggested variety of mechanisms and factors, which are still being investigated. This study aimed to investigate changes in cell fluidity in multi-layered cell sheets, by exploring the collective arrest of cell motion and its release in cultures of corneal epithelial cells. We constructed mathematical models to simulate the behaviors of individual cells, including cell differentiation and time-dependent changes in cell-cell connections, which are defined by stochastic or kinetic rules. Changes in cell fluidity and cell sheet structures were expressed by simulating autonomous cell behaviors and interactions in tissues using an agent-based model. A single-cell level spatiotemporal analysis of cell state transition between migratable and non-migratable states revealed that the release from collective arrest of cell motion was initially triggered by a decreased ability to form cell-cell connections in the suprabasal layers, and was propagated by chain migration. Notably, the disruption of cell-cell connections and stratification occurred in the region of migratable state cells. Hence, a modeling approach that considers time-dependent changes in cell properties and behavior, and spatiotemporal analysis at the single-cell level can effectively delineate emergent phenomena arising from the complex interplay of cells.