Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies

Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

The impact of individualism on the efficiency of epidemic control and the underlying computational and psychological mechanisms

Coronavirus disease 2019 (COVID-19) is a global health crisis, and some countries experience difficulties in controlling the infection and mortality of COVID-19. Based on previous findings, we argue that individualistic cultural values are not conducive to the control of the epidemic. The results of the cross-cultural analysis showed that the individualistic cultural values positively predicted the number of deaths, deaths per million, and mortality of COVID-19, and the independent self-construct negatively predicted the efficiency of epidemic control in the early phase. The evolutionary game model and cross-cultural experiment further suggested that individualistic culture reduced the efficiency of overall epidemic control by enhancing individuals' fear of death in the context of the epidemic and increased individuals' tendency to violate epidemic control. Our results support the natural-behavioral-cultural co-evolution model, suggesting the impact of culture on the control of virus transmission and deaths during COVID-19, and provide an important scientific reference for countries to respond to global public health crises. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A hybrid agent-based model integrated with a multi-stage learning-based fuzzy cognitive map

PurposeThis article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.Design/methodology/approachThe model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.FindingsThe findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.Originality/valueThe proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.

Bsds-balance sheet dynamics simulator (application abm)

Disrupting events like COVID-19, climate change or new competitors (e.g., GAFAM) can permanently change the structure of a bank's balance sheet and the bank's risk profile. Agent-based modeling (ABM) is a versatile, interdisciplinary bottom-up approach that can be used to consider such effects in dynamic simulations of the balance sheet development. The authors present a concept for an agent-based model that simulates the effects of macroeconomic scenarios and competitive boundaries on the balance sheet dynamics of banks. An implementation of such a model could be used to explore stylized balance sheet developments over time and thereby provide a valuable planning tool for qualitative and quantitative risk management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility.

The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c1 of the long-tail distribution of distance k moved in the same-level container, p(k)∼k-c1·level, increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s11071-023-08489-5.

Resilient Hospital Design: From Crimean War to COVID-19.

OBJECTIVES: Serious COVID-19 nosocomial infection has demonstrated a need to design our health services in a different manner. Triggered by the current crisis and the interest in rapid deployable hospital, this article discusses how hospital building layouts can be improved to streamline the patient pathways and thus to reduce the risk of hospital-related infections. Another objective of this work is to explore the possibility to develop flexible and scalable hospital building layouts through modular construction. This enables hospitals to better cope with different future demands and thereby enhance the resilience of the healthcare facilities. BACKGROUND: During the first wave of COVID-19, approximate one-seventh to one-fifth COVID-19 patients and majority of infected healthcare workers acquired the disease in NHS hospitals. Similar issues emerged during the Crimean War (1853-1856) when more soldiers died from infectious diseases rather than of battlefield casualties in Scutari Hospital. This led to an important collaborative work between Florence Nightingale, who looked into this problem statistically, and Isambard Kingdom Brunel, who designed the rapid deployment Renkioi Hospital which yielded a death rate 90% lower than that in Scutari Hospital. While contemporary medical research and practice have moved beyond Nightingale's concept of contagion, challenges of optimizing hospital building layouts to support healing and effectively combat nosocomial infections still pose elusive problems that require further investigation. METHODS: Through case study investigations, this article evaluates the risk of nosocomial infections of airborne transmissions under different building layouts, and this provides essential data for infection control in the new-build or refurbished healthcare projects. RESULTS: Improved hospital layout can be achieved through reconfiguration of rooms and concourse. Design interventions through evidence-based infection risk analysis can reduce congestion and provide extra separation and compartmentalization which will contribute the reduced nosocomial infection rate. CONCLUSIONS: A resilient hospital shall be able to cope with unexpected circumstances and be flexible to change when new challenges arise, without compromising the safety and well-being of frontline medical staff and other patients. Such an organizational resilience depends on not only flexible clinical protocols but also flexible hospital building layouts. The latter allows hospitals to get better prepared for rapidly changing patient expectations, medical advances, and extreme weather events. The reconfigurability of an existing healthcare facility can be further enhanced through modular construction, standardization of building components, and additional space considered.

Using linear regression metamodels for evaluating interventions in an individual-based influenza epidemic model

Agent-based simulation modeling is frequently used to model and simulate the spread of transmissible diseases such as influenza, COVID-19, and HIV/AIDS in communities. Besides incorporating disease-specific parameters, these models include a set of parameters to observe the effect of different intervention combinations on the course of an epidemic, bringing the opportunity to use these models as virtual laboratories for decision-making. However, these models are primarily large-scale and complex, increasing the runtime of experimentation. As a solution, metamodeling approaches are frequently employed to represent input–output relationships of simulation models. Instead of running the time-consuming agent-based model, policymakers use the metamodel to obtain predicted outcomes in a comparatively short time. In addition to time-saving advantages, metamodels can provide insights into how disease-specific and intervention parameters affect the outcome of interest. In this regard, this study uses an influenza epidemic model, FluTE, as the experimental platform. Instead of using the original agent-based model, we fit linear regression metamodels to quantify the effect of interventions, such as vaccination, quarantine, and school closure, on the influenza attack rate. After validating the metamodel, we observe that the day on which interventions start, ascertainment delay, the daily number of vaccinations administered, isolation and quarantine compliance probabilities, and the number of school closure days stand as the significant intervention policies.

EpiDesktop-A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios

Human mobility has been recognized as one of the critical factors determining the spread of contagious diseases, such as SARS-CoV-2, a highly contagious and elusive virus. This virus disrupts the normal lives of more than half of the global population in one way or another, claiming the lives of millions. In such cases, mobility should be managed via the imposition of certain policies. This proposed study presents a newly developed spatial platform aimed at simulating and mapping the spread of infectious diseases and mobility patterns under different scenarios based on different epidemiological models. In addition to the "business as usual" scenario, other response scenarios can be defined to reflect real-world situations, taking into consideration various parameters, including the daily rise in infections and deaths, among others. The developed system provides insights to decision-makers about strategies to be implemented and measures for controlling the spread of the virus.

An Agent-Based Model for Localized COVID-19 Transmission Dynamics and Intervention Impacts

Throughout the COVID-19 pandemic, disease-modeling has guided government health officials in choosing appropriate interventions. However, most current models simulate disease spread on a more generalized scale, lacking specificity for localities such as towns or counties, leading to one-size-fits-all policies being instituted on the country or state-wide-level. However, localities differ in many social determinants of health, which impact disease dynamics therefore necessitating models tailored to individual locations. This research aims to answer this question: What local factors affect COVID-19 outbreak severity and intervention effectiveness? To do this, a novel agent-based disease model was created using NetLogo to simulate contextualized COVID-19 disease dynamics at the local level. Model inputs include population demographic composition, area size, vaccination ratio, interventions (mask, test-and-isolate, or lockdown), and compliance rate. Agents representing the simulated local population are assigned specified traits, and become "susceptible”, "exposed”, "infected”, "recovered”, "quarantined”, or "dead” as they interact with other agents. The model was validated using data from state and local health agencies for Westchester County, NY (84.2% accuracy). A sensitivity analysis demonstrated that a higher elderly population, a lower young population, a lower vaccination rate, and weaker interventions were all factors that increased outbreak severity. A comparison of selective localities representing metric axes of high/low age and high/low vaccination was conducted for four different U.S. counties and showed that 1) any intervention would dramatically reduce locality variations and 2) interventions have higher impact in higher risk localities. This model enables local officials to better focus limited resources when making health related decisions, and a website (www.localcovidmodel.org) has been created for model access. © 2022, Avestia Publishing. All rights reserved.

Agent-Based Social Simulation for Policy Making

In agent-based social simulations (ABSS), an artificial population of intelligent agents that imitate human behavior is used to investigate complex phenomena within social systems. This is particularly useful for decision makers, where ABSS can provide a sandpit for investigating the effects of policies prior to their implementation. During the Covid-19 pandemic, for instance, sophisticated models of human behavior enable the investigation of the effects different interventions can have and even allow for analyzing why a certain situation occurred or why a specific behavior can be observed. In contrast to other applications of simulation, the use for policy making significantly alters the process of model building and assessment, and requires the modelers to follow different paradigms. In this chapter, we report on a tutorial that was organized as part of the ACAI 2021 summer school on AI in Berlin, with the goal of introducing agent-based social simulation as a method for facilitating policy making. The tutorial pursued six Intended Learning Outcomes (ILOs), which are accomplished by three sessions, each of which consists of both a conceptual and a practical part. We observed that the PhD students participating in this tutorial came from a variety of different disciplines, where ABSS is mostly applied as a research method. Thus, they do often not have the possibility to discuss their approaches with ABSS experts. Tutorials like this one provide them with a valuable platform to discuss their approaches, to get feedback on their models and architectures, and to get impulses for further research. © 2023, Springer Nature Switzerland AG.

Multicentral Agent-Based Model of Four Waves of COVID-19 Spreading in Nizhny Novgorod Region of Russian Federation

To study the characteristics of the spread of the COVID-19 pandemic and introduce timely and effective measures, there is a need for models that can predict the impact of various restrictive factors on COVID-19 disease dynamics. In this regard, it seems expedient to employ agent-based models that can take into account various characteristics of the population (for example, age distribution and social activity) and restrictive measures, testing, etc., as well as random factors that are usually omitted in traditionally used modifications of Susceptible-Infected-Recovered (SIR) type models. This paper presents the development of the previously proposed agent model for numerical simulation of the spread of COVID-19, namely, the transition from a single-center model, in which all agents interact within one common pool, to a multi-center model, in which the agents under consideration are distributed over several centers of interactions, and are also redistributed over time to other pools. This model allows us to more accurately simulate the epidemic dynamic within one region, when the patient zero usually arrives at the regional center, after which the distribution chains capture the periphery of the region due to pendulum migration. This paper demonstrates the application of the developed model to analyze the epidemic spread in the Nizhny Novgorod region of Russian Federation. Simulated dynamics of the daily number of newly detected cases and COVID-19-associated deaths is in good agreement with official statistics. Modeling results suggest that the actual number of COVID-19 cases is 1.5–3 times higher than the number of reported cases. The developed model also takes into account the process of vaccination. It is shown that with the same modeling parameters, but without vaccination, the third and fourth waves of the pandemic would be characterized by a significant increase in the incidence and the formation of natural immunity, but the number of deaths would exceed the real one by about 9 times. © 2023 Journal of Biomedical Photonics & Engineering.

Agent-based model for COVID-19: The impact of social distancing and vaccination strategies

In this work, we study the transmission of the new coronavirus, SARS-CoV-2, which causes COVID-19. Our main aim is to analyze the disease prevalence when vaccination and social distancing strategies are used. Simulations are implemented using an agent-based model (ABM) adapted from a Susceptible-Exposed-Infectious-Recovered (SEIR) type compartmental model. Several scenarios are simulated using the most common vaccines available in Brazil. On each scenario, different fractions of the population are affected by vaccination and social distancing measures. Results show the importance to start public health interventions to reduce the size of the epidemic. Besides, simulations show that vaccination only is not capable to control the disease spread. [ FROM AUTHOR] Copyright of International Journal of Modern Physics C: Computational Physics & Physical Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Should we make predictions based on social simulations?

The general feeling is that no predictions can be made based on agent-based social simulations. The outcomes of social simulations are based on the behaviors of individuals and their interactions. Behavioral models are always incomplete and often, also incorrect with respect to real behavior and thus the outcomes of agent-based social simulations cannot be trusted as predictions. In this article, we argue that behavioral models do not have to be valid in all respects, but only in the essential aspects in order to be able to make useful predictions. Based on some case studies on the effectiveness of restrictions during the COVID-19 crisis, we show that what are essential aspects of a behavioral model that need to be valid depends on the specific situation that is simulated. The predictions that were needed for the COVID-19 crisis were made with an agent-based social simulation framework using a behavioral model based on needs. The predictions could indicate the relative increase or decrease of COVID-19 infections due to the introduction of a new restriction. It shows that useful predictions can be made based on social simulations, but that we have to be careful on what type of predictions to make.

Should we make predictions based on social simulations?

The general feeling is that no predictions can be made based on agent-based social simulations. The outcomes of social simulations are based on the behaviors of individuals and their interactions. Behavioral models are always incomplete and often, also incorrect with respect to real behavior and thus the outcomes of agent-based social simulations cannot be trusted as predictions. In this article, we argue that behavioral models do not have to be valid in all respects, but only in the essential aspects in order to be able to make useful predictions. Based on some case studies on the effectiveness of restrictions during the COVID-19 crisis, we show that what are essential aspects of a behavioral model that need to be valid depends on the specific situation that is simulated. The predictions that were needed for the COVID-19 crisis were made with an agent-based social simulation framework using a behavioral model based on needs. The predictions could indicate the relative increase or decrease of COVID-19 infections due to the introduction of a new restriction. It shows that useful predictions can be made based on social simulations, but that we have to be careful on what type of predictions to make. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Airplane Boarding Methods that Reduce Risk from COVID-19.

Airlines have recently instituted practices to reduce the risk of their passengers becoming infected with the novel coronavirus (SARS-CoV-2). Some airlines block their airplanes' middle seats to preserve social distancing among seated passengers. In this context, we present six new boarding methods and compare their performance with that of the two best boarding methods used to date with social distancing. We evaluate the eight boarding methods using three performance metrics related to passenger health and one operational metric (airplane boarding time) for a one-door airplane. The three health metrics reflect the risks of virus spread by passengers through the air and surfaces (e.g. headrests and seat arms) and consider the amount of aisle social distancing between adjacent boarding passengers walking towards their seats. For an airline that highly values the avoidance of window seat risk, the best method to use is one of the new methods: back-to-front by row - WilMA, though it will result in a longer time to complete boarding of the airplane. Airlines placing greater emphasis on fast boarding times- while still providing favorable values for the health metrics-will be best served by using new methods back-to-front by row - WilMA - offset 2 and - offset 3 when aisle social distancing is 1 m and 2 m respectively.

Agent-based simulation of COVID-19 containment measures: the case of lockdowns in cities.

The effectiveness and political feasibility of COVID-19 containment measures such as lockdowns, are contentious. This stems in part from an absence of tools for their rigorous evaluation. Common epidemiological models such as the SEIR model generally lack the spatial resolution required for micro-level containment actions, the visualization capabilities for communicating measures such as localized lockdowns and the scenario-testing capabilities for assessing different alternatives. We present an individual-level ABM that generates geo-social networks animated by agent-agent and agent-building interactions. The model simulates real-world contexts and is demonstrated for the city of Jerusalem. Simulation outputs yield much useful information for evaluating the effectiveness of lockdowns. These include network-generated socio-spatial contagion chains for individual agents, dynamic building level contagion processes and neighborhood-level patterns of COVID-19 imports and exports useful in identifying super-spreader neighborhoods. The policy implications afforded by these various outputs are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s12076-023-00336-w.

How to design virus containment policies? A joint analysis of economic and epidemic dynamics under the COVID-19 pandemic.

We analyze the impact of different designs of COVID-19-related lockdown policies on economic loss and mortality using a micro-level simulation model, which combines a multi-sectoral closed economy with an epidemic transmission model. In particular, the model captures explicitly the (stochastic) effect of interactions between heterogeneous agents during different economic activities on virus transmissions. The empirical validity of the model is established using data on economic and pandemic dynamics in Germany in the first 6 months after the COVID-19 outbreak. We show that a policy-inducing switch between a strict lockdown and a full opening-up of economic activity based on a high incidence threshold is strictly dominated by alternative policies, which are based on a low incidence threshold combined with a light lockdown with weak restrictions of economic activity or even a continuous weak lockdown. Furthermore, also the ex ante variance of the economic loss suffered during the pandemic is substantially lower under these policies. Keeping the other policy parameters fixed, a variation of the consumption restrictions during the lockdown induces a trade-off between GDP loss and mortality. Furthermore, we study the robustness of these findings with respect to alternative pandemic scenarios and examine the optimal timing of lifting containment measures in light of a vaccination rollout in the population.

Agent-Based Simulations for Aircraft Boarding: A Critical Review

Aircraft boarding has a direct influence on the operational cost of an airline. Therefore it has become imperative for the airline industry to find better boarding methods that minimize the boarding time thereby reducing the turn-around time of flights. Agent-Based Simulations (ABS) offer a way to investigate optimal boarding strategies. Complex interactions between multiple passengers during the process of boarding can be modelled using ABS to discover the factors causing most delays and to compare the performance of suggested new models. This study performs a critical review of 12 studies that investigate the aircraft boarding problem using ABS to compare their results. The study classifies the reviewed papers into three groups based on the objective of the investigation: (1) studies that evaluate the efficiency of various boarding strategies, (2) studies that evaluate new methods for aircraft boarding, and lastly, (3) studies that evaluate the impact of COVID-19 restrictions on aircraft boarding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Grey Clustering of the Variations in Reverse Pyramid Boarding Method Considering Pandemic Restrictions

The COVID-19 pandemic has significantly hit the airline industry mostly due to the reduced number of flights between regions, the implementation of different protocols, restrictions, and the reluctance of the passengers to travel by airplane. In this context, the airlines have tried to offer an appropriate environment for their customers by ensuring a safe boarding process while considering the imposed restrictions related to social distancing. According to the literature, the Reverse Pyramid boarding method offers superior results in terms of boarding time and health risks in times of pandemics when compared to other classical airplane boarding methods. As the variations in Reverse Pyramid implementation are numerous, the present paper aims to determine which of these variations can be used when the airplane boarding process is made through the front door of the airplane. For this purpose, an agent-based model is created and used for simulating the variations in the Reverse Pyramid boarding method, while grey clustering is applied for dividing the variations into categories based on their performance. Three performance indicators, as reported in the scientific literature related to airplane boarding in times of COVID-19, are used, namely the boarding time, aisle seat risk, and window seat risk. Different scenarios are presented and analyzed in depth.