Per- and polyfluoroalkyl substances (PFAS), ubiquitous in a myriad of consumer and industrial products, and depending on the doses of exposure represent a hazard to both environmental and public health, owing to their persistent, mobile, and bio accumulative properties. These substances exhibit long half-lives in humans and can induce potential immunotoxic effects at low exposure levels, sparking growing concerns. While the European Food Safety Authority (EFSA) has assessed the risk to human health related to the presence of PFAS in food, in which a reduced antibody response to vaccination in infants was considered as the most critical human health effect, a comprehensive grasp of the molecular mechanisms spearheading PFAS-induced immunotoxicity is yet to be attained. Leveraging modern computational tools, including the Agent-Based Model (ABM) Universal Immune System Simulator (UISS) and Physiologically Based Kinetic (PBK) models, a deeper insight into the complex mechanisms of PFAS was sought. The adapted UISS serves as a vital tool in chemical risk assessments, simulating the host immune system\'s reactions to diverse stimuli and monitoring biological entities within specific adverse health contexts. In tandem, PBK models unravelling PFAS\' biokinetics within the body i.e. absorption, distribution, metabolism, and elimination, facilitating the development of time-concentration profiles from birth to 75 years at varied dosage levels, thereby enhancing UISS-TOX\'s predictive abilities. The integrated use of these computational frameworks shows promises in leveraging new scientific evidence to support risk assessments of PFAS. This innovative approach not only allowed to bridge existing data gaps but also unveiled complex mechanisms and the identification of unanticipated dynamics, potentially guiding more informed risk assessments, regulatory decisions, and associated risk mitigations measures for the future.

Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. They aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms - coupled with a graphical interface - is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to easily construct such models, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as a Supplementary Material and all models are provided at: https://physiboss.github.io/tutorial/.

To model complex systems, individual-based models (IBMs), sometimes called \"agent-based models\" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.

Purpose. To estimate the impact on mortality of nonpharmaceutical interventions (NPIs) implemented early in the COVID-19 pandemic. Methods. We implemented an agent-based modified SEIR model of COVID-19, calibrated to match death numbers reported in Pennsylvania from January 2020 to April 2021 and including representations of NPIs implemented in Pennsylvania. To investigate the impact of these strategies, we ran the calibrated model with no interventions and with varying combinations, timings, and levels of interventions. Results. The model closely replicated death outcomes data for Pennsylvania. Without NPIs, deaths in the early months of the pandemic were estimated to be much higher (67,718 deaths compared to actual 6,969). Voluntary interventions alone were relatively ineffective at decreasing mortality. Delaying implementation of interventions led to higher deaths (∼9,000 more deaths with just a 1-week delay). School closure was insufficient as a single intervention but was an important part of a combined intervention strategy. Conclusions. NPIs were effective at reducing deaths early in the COVID-19 pandemic. Agent-based models can incorporate substantial detail on infectious disease spread and the impact of mitigations. Policy Implications. The model supports the importance and effectiveness of NPIs to decrease morbidity from respiratory pathogens. This is particularly important for emerging pathogens for which no vaccines or treatments exist, but such strategies are applicable to a variety of respiratory pathogens.
UNASSIGNED: Nonpharmaceutical interventions were used extensively during the early period of the COVID-19 pandemic, but their use has remained controversial.Agent-based modeling of the impact of these mitigation strategies early in the COVID-19 pandemic supports the effectiveness of nonpharmaceutical interventions at decreasing mortality.Since such interventions are not specific to a particular pathogen, they can be used to protect against any respiratory pathogen, known or emerging. They can be applied rapidly when conditions warrant.

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector\'s evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity -as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being -reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors\' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.

While inhomogeneous diffusivity has been identified as a ubiquitous feature of the cellular interior, its implications for particle mobility and concentration at different length scales remain largely unexplored. In this work, we use agent-based simulations of diffusion to investigate how heterogeneous diffusivity affects the movement and concentration of diffusing particles. We propose that a nonequilibrium mode of membrane-less compartmentalization arising from the convergence of diffusive trajectories into low-diffusive sinks, which we call \'diffusive lensing,\' is relevant for living systems. Our work highlights the phenomenon of diffusive lensing as a potentially key driver of mesoscale dynamics in the cytoplasm, with possible far-reaching implications for biochemical processes.

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem\'s complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm\'s performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem\'s dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration.

Power outages can cause severe disruption to critical infrastructure. With the predicted increase in the electrification of the transport sector, society will become even more vulnerable to the effects of power outages. While increased electric vehicle (EV) adoption will contribute to the electrification process, EVs can also offer capabilities to provide services during an outage. This paper studied the use of a fleet of EVs during the aftermath of a disaster to provide disaster relief by donating power to a shelter, delivering critical supplies and people in need, and providing transport for personnel or performing inspections. While the bulk of the past work has focused on using EVs to increase the resilience of the distribution grid, or individual buildings, to a power outage, this paper was novel in its use of an agent-based model to study EVs that are performing functions to increase the resilience of a community to an outage. The fleet of EVs were provided access to a microgrid with a solar array, one or two EV fast chargers, and three possible sizes of a storage. Useful outputs were produced and studied for such features as daily energy donated to a shelter, daily energy used at the microgrid, and the length of outages that can be supported before the energy is depleted at the microgrid. Results showed that increasing storage size at the microgrid led to substantial increases in the outage length that could be support. Additionally, it was found that focusing a fleet on delivery and transport tasks, as opposed to energy donation, could also increase the length of outages that could be supported.

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.

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like \"Should we lockdown?\" During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.