The COVID-19 epidemic created, at the time of writing the paper, highly unusual and uncertain socio-economic conditions. The world economy was severely impacted and business-as-usual activities severely disrupted. The situation presented the necessity to make a trade-off between individual health and safety on one hand and socio-economic progress on the other. Based on the current understanding of the epidemiological characteristics of COVID-19, a broad set of control measures has emerged along dimensions such as restricting people's movements, high-volume testing, contract tracing, use of face masks, and enforcement of social-distancing. However, these interventions have their own limitations and varying level of efficacy depending on factors such as the population density and the socio-economic characteristics of the area. To help tailor the intervention, we develop a configurable, fine-grained agent-based simulation model that serves as a virtual representation, i.e., a digital twin of a diverse and heterogeneous area such as a city. In this paper, to illustrate our techniques, we focus our attention on the Indian city of Pune in the western state of Maharashtra. We use the digital twin to simulate various what-if scenarios of interest to (1) predict the spread of the virus; (2) understand the effectiveness of candidate interventions; and(3) predict the consequences of introduction of interventions possibly leading to trade-offs between public health, citizen comfort, and economy. Our model is configured for the specific city of interest and used as an in-silico experimentation aid to predict the trajectory of active infections, mortality rate, load on hospital, and quarantine facility centers for the candidate interventions. The key contributions of this paper are: (1) a novel agent-based model that seamlessly captures people, place, and movement characteristics of the city, COVID-19 virus characteristics, and primitive set of candidate interventions, and(2) a simulation-driven approach to determine the exact intervention that needs to be applied under a given set of circumstances. Although the analysis presented in the paper is highly specific to COVID-19, our tools are generic enough to serve as a template for modeling the impact of future pandemics and formulating bespoke intervention strategies.

Abstract. We are in the business of delivering software intensive business systems using model-driven techniques. Developing suitable code generators is an important step in model-based development of purpose-specific business applications. Hence, it becomes critical to ensure that code generator development doesn't become a bottleneck for the project delivery. After establishing a sophisticated technology infrastructure to facilitate quick and easy adaptation of model-based code generators, we experimented with agile methodology. In this paper, we discuss why pure agile methodology does not work for model-driven software development. We propose a modification to the agile methodology in the form of meta-sprints as a golden mean between agile method and traditional plan-driven method. Early experience with the proposed development method is shared along with the lessons learnt.

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Part 3: Short PapersInternational audienceThis paper reviews the state-of-the-art enterprise modelling (EM) techniques with an objective to support decision-making. It describes a Systematic Mapping Study based on 173 publications chosen from 7622 EM related publications collated using five digital libraries: Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect and Web of Science. The study identifies 29 EM techniques and critically analyses them for suitability as an effective aid to complex dynamic decision-making vis-a-vis a set of characteristics. The paper also reports EM publications trends and the challenges aimed at providing effective aids to complex dynamic decision making

Abstract-This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decisionmaking. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined.

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