Towards the development of societal twins

A digital twin is a virtual data-driven replica of a real-world system. Recently, digital twins have become popular in engineering and infrastructure planning where they provide insights into complex physical systems or processes. Yet, to date, considerably less research has explored how digital replicas of social systems - representing the decisions, behaviors and interactions of individual people, and, in turn, their emergent outcomes - might be developed and integrated with those of physical systems. In this position paper we discuss the need for such societal twins, what they might look like, and set out key challenges that will need to be overcome if these benefits are to be realised

Inferring epidemic dynamics using Gaussian process emulation of agent-based simulations

Computational models help decision makers understand epidemic dynamics to optimize public health interventions. Agent-based simulation of disease spread in synthetic populations allows us to compare and contrast different effects across identical populations or to investigate the effect of interventions keeping every other factor constant between ``digital twins''. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geo-spatial perspective using a synthetic population that is constructed based on the U.S. census data. In this paper, we show how Gaussian process regression can be used on FRED-synthesized data to infer the differing spatial dispersion of the epidemic dynamics for two disease conditions that start from the same initial conditions and spread among identical populations. Our results showcase the utility of agent-based simulation frameworks such as FRED for inferring differences between conditions where controlling for all confounding factors for such comparisons is next to impossible without synthetic data.Comment: To be presented in Winter Simulation Conference 2023, repository link: https://github.com/abdulrahmanfci/gpr-ab

Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model.

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agent-based model can approximately reproduce the patterns of migration involving overnight stays

Scale matters: Variations in spatial and temporal patterns of epidemic outbreaks in agent-based models

Agent-based modellers frequently make use of techniques to render simulated populations more computationally tractable on actionable timescales. Many generate a relatively small number of “representative” agents, each of which is “scaled up” to represent some larger number of individuals involved in the system being studied. The degree to which this “scaling” has implications for model forecasts is an underdeveloped field of study; in particular, there has been little known research on the spatial implications of such techniques. This work presents a case study of the impact of the simulated population size, using a model of the spread of COVID-19 among districts in Zimbabwe for the underlying system being studied. The impact of the relative scale of the population is explored in conjunction with the spatial setup, and crucial model parameters are varied to highlight where scaled down populations can be safely used and where modellers should be cautious. The results imply that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for researchers seeking to use scaled populations in their research. This article is an extension on work previously presented as part of the International Conference on Computational Science 2022 (Wise et al., 2022)[1]

INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

Modelling without queues: adapting discrete-event simulation for service operations

Discrete-event simulation (DES), which has largely grown out of modelling manufacturing systems, has increasingly been applied in the service sector. The approach, however, is not always appropriate for modelling service operations. In particular, it cannot help with detailed decisions about the layout of service operations in which the customers are present such as retail outlets and airports. An adapted DES approach is proposed for modelling such systems and the approach is demonstrated through a model of a coffee shop. A key innovation is that queues are not explicitly modelled. The benefit of the approach is that it simplifies the modelling of service systems in which the customers are present by reducing the number of components that need to be modelled. It can also aid decisions about the layout of a system. We ask whether the approach is in fact an agent-based simulation and identify ways in which the approach could be extended

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487

Computational Decision Support for Socio-Technical Awareness of Land-Use Planning under Complexity—A Dam Resilience Planning Case Study

Land-use planning for modern societies requires technical competence as well as social competence. We therefore propose an integrative solution enabling better land-use planning and management through better-informed decision-making. We adapt a method developed for cross-disciplinary team building to identify the stakeholders and their various objectives and value systems. We use these results to populate artificial societies embedded into a dynamic data analytics framework as a tool to identify, explore, and visualize the challenges resulting from the different objectives and value systems in land-use planning and management. To prove the feasibility of the proposed solution, we present two use cases from the dam resilience planning domain, show how to apply the process and tools, and present the results. The solution is not limited to such use cases but can be generalized to address challenges in socio-technical systems, such as water resource evaluations or climate change effects

J Infect Dis

Background:Pockets of undervaccinated individuals continue to raise concerns about their potential to sustain epidemic transmission of vaccine preventable diseases. Prior importations of live polioviruses (LPVs) into Amish communities in North America led to their recognition as a potential and identifiable linked network of undervaccinated individuals.Methods:We developed an individual-based model to explore the potential transmission of a LPV throughout the North American Amish population.Results:Our model demonstrates the expected limited impact associated with the historical importations, which occurred in the context of a high level of population immunity attributable to historical exposure to LPVs (wild and vaccine). We show that some conditions could potentially lead to wider circulation and paralytic cases in Amish communities if an importation occurred in or after 2013. The impact will depend on the uncertain historical immunity of members of the community to polioviruses.Conclusions:Heterogeneity in immunization coverage represents a risk factor for potential outbreaks of polio if a live virus introduction occurs, although overall high population immunity suggests that transmission would remain relatively limited. Efforts to prevent spread between Amish church districts with any feasible measures may offer the best opportunity to contain an outbreak and limit its size.20142021-03-10T00:00:00ZU2R GH001913/GH/CGH CDC HHS/United StatesU66 IP000519/IP/NCIRD CDC HHS/United StatesU66IP000519-01/IP/NCIRD CDC HHS/United States25316864PMC79444871040