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1.
2022 Winter Simulation Conference, WSC 2022 ; 2022-December:593-604, 2022.
Article in English | Scopus | ID: covidwho-2275595

ABSTRACT

We present a case study on modeling and predicting the course of Covid-19 in the Indian city of Pune. The results presented in this paper are concerned primarily with the wave of infections triggered by the Delta variant during the period between February and June 2021. Our work demonstrates the necessity for bringing together compartmental stock-and-flow and agent-based models and the limitations of each approach when used individually. Some of the work presented here was carried out in the process of advising the local city administration and reflects the challenges associated with employing these models in a real-world environment with its uncertainties and time pressures. Our experience, described in the paper, also highlights the risks associated with forecasting the course of an epidemic with evolving variants. © 2022 IEEE.

2.
2022 Winter Simulation Conference, WSC 2022 ; 2022-December:557-568, 2022.
Article in English | Scopus | ID: covidwho-2251210

ABSTRACT

Predicting the evolution of Covid19 pandemic has been a challenge as it is significantly influenced by the characteristics of people, places and localities, dominant virus strains, extent of vaccination, and adherence to pandemic control interventions. Traditional SEIR based analyses help to arrive at a coarse-grained 'lumped up' understanding of pandemic evolution which is found wanting to determine locality-specific measures of controlling the pandemic. We comprehend the problem space from system theory perspective to develop a fine-grained simulatable city digital-twin for 'in-silico' experimentations to systematically explore - Which indicators influence infection spread to what extent? Which intervention to introduce, and when, to control the pandemic with some a-priori assurance? How best to return to a new normal without compromising individual health safety? This paper presents a digital twin centric simulation-based approach, illustrates it in a real-world context of an Indian City, and summarizes the learning and insights based on this experience. © 2022 IEEE.

3.
2022 Annual Modeling and Simulation Conference, ANNSIM 2022 ; 54:701-714, 2022.
Article in English | Scopus | ID: covidwho-2227924

ABSTRACT

Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of Covid19 pandemic related uncertainties. The uncertainty exists along multiple dimensions such as virus mutations, infectivity and severity of new mutants, efficacy of vaccines against new mutants, waning of vaccine induced immunity over time, and lockdown/opening-up policies effected by city authorities. Moreover, this uncertainty plays out in a non-uniform manner across nations, states, cities, and even within the cities thus leading to highly heterogeneous evolution of pandemic. While Work From Home (WFH) strategy has served well to meet ever-increasing business demands without compromising on individual health safety, there has been an undeniable reduction in social capital. With Covid19 pandemic showing definite waning trends, organizations are considering the possibility of safe transition from WFH to Work From Office (WFO) or a hybrid mode of operation. An effective strategy needs to score equally well on possibly interfering dimensions such as risk of infection, project delivery, and employee wellness. As large organizations will typically have a large number of offices spread across a geography, the problem of arriving at office-specific strategies becomes non-trivial. Moreover, the strategies need to adapt over time to changes that cannot be deduced upfront. This calls for an approach that is amenable to quick and easy adaptation. Our contribution in this regard is constructing a Digital Twin by leveraging various modelling techniques to realistically represent the above mentioned aspects of interest that can be subjected to what-if scenario analysis. We further demonstrate its efficacy using a case study from a large organization. © 2022 Society for Modeling & Simulation International (SCS)

4.
24th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020 ; 13753 LNAI:314-330, 2023.
Article in English | Scopus | ID: covidwho-2148644

ABSTRACT

Predicting the evolution of the Covid-19 pandemic during its early phases was relatively easy as its dynamics were governed by few influencing factors that included a single dominant virus variant and the demographic characteristics of a given area. Several models based on a wide variety of techniques were developed for this purpose. Their prediction accuracy started deteriorating as the number of influencing factors and their interrelationships grew over time. With the pandemic evolving in a highly heterogeneous way across individual countries, states, and even individual cities, there emerged a need for a contextual and fine-grained understanding of the pandemic to come up with effective means of pandemic control. This paper presents a fine-grained model for predicting and controlling Covid-19 in a large city. Our approach borrows ideas from complex adaptive system-of-systems paradigm and adopts a concept of agent as the core modeling ion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

5.
20th International Conference on Practical Applications of Agents and Multi-Agent Systems , PAAMS 2022 ; 13616 LNAI:24-35, 2022.
Article in English | Scopus | ID: covidwho-2128472

ABSTRACT

Open economy, globalization and effect of Covid19 pandemic are transforming the consumer behavior rapidly. The business is nudging consumers towards hyper consumption through online shopping, e-commerce and other conveniences with affordable cost. The companies from courier, express and parcel (CEP) industry are trying to capitalize on this opportunity by tying up with business to consumers (B2C) companies with a promise of delivering parcels to the doorstep in an ever-shrinking time window. In this endeavor, the conventional optimization-based planning approach to manage the fixed parcel payload is turning out to be inadequate. The CEP companies need to quickly adapt to the situation more frequently so as to be efficient and resilient in this growing demand situation. We propose an agent-based digital twin of the sorting terminal, a key processing element of parcel delivery operation, as an experimentation aid to: (i) explore and arrive at the right configuration of the existing sorting terminal infrastructure, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. This paper presents digital twin of the sorting terminal and demonstrates its use as “in silico” experimentation aid for domain experts to support evidence-backed decision-making. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

6.
2022 Annual Modeling and Simulation Conference, ANNSIM 2022 ; : 126-139, 2022.
Article in English | Scopus | ID: covidwho-2056827

ABSTRACT

Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of Covid19 pandemic related uncertainties. The uncertainty exists along multiple dimensions such as virus mutations, infectivity and severity of new mutants, efficacy of vaccines against new mutants, waning of vaccine induced immunity over time, and lockdown / opening-up policies effected by city authorities. Moreover, this uncertainty plays out in a non-uniform manner across nations, states, cities, and even within the cities thus leading to highly heterogeneous evolution of pandemic. While Work From Home (WFH) strategy has served well to meet ever-increasing business demands without compromising on individual health safety, there has been an undeniable reduction in social capital. With Covid19 pandemic showing definite waning trends, organizations are considering the possibility of safe transition from WFH to Work From Office (WFO) or a hybrid mode of operation. An effective strategy needs to score equally well on possibly interfering dimensions such as risk of infection, project delivery, and employee wellness. As large organizations will typically have a large number of offices spread across a geography, the problem of arriving at office-specific strategies becomes non-trivial. Moreover, the strategies need to adapt over time to changes that cannot be deduced upfront. This calls for an approach that is amenable to quick and easy adaptation. Our contribution in this regard is constructing a Digital Twin by leveraging various modelling techniques to realistically represent the above mentioned aspects of interest that can be subjected to what-if scenario analysis. We further demonstrate its efficacy using a case study from a large organization. © 2022 SCS.

7.
Cureus ; 14(8): e27993, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-2006493

ABSTRACT

The COVID-19 pandemic has proven to be a challenge for public health professionals, researchers, clinicians, and patients. One group that has experienced significant difficulties during this time is cancer patients. Data regarding this vulnerable population is scarce, despite novel information about vaccine efficacy, therapeutics, mutations, and comorbidities. In this article, we discuss the need for a greater study of social determinants of health (SDOH) for cancer patients in the context of the COVID-19 pandemic. The effects of SDOH on population health are generally well-understood, but their effects on cancer patients are poorly understood. We further pose questions that may be starting points for the investigation of SDOH in cancer patients during this time. Using SDOH as a tool for more effective clinical care will promote the development of targeted interventions to study and improve outcomes in this population.

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