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Minimal and adaptive numerical strategy for critical resource planning in a pandemic

Prakash, MK and Kaushal, S and Bhattacharya, S and Chandran, A and Kumar, A and Ansumali, S (2020) Minimal and adaptive numerical strategy for critical resource planning in a pandemic. In: Physical Review E, 102 (2).

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Official URL: https://dx.doi.org/10.1103/PhysRevE.102.021301


Current epidemiological models can in principle model the temporal evolution of a pandemic. However, any such model will rely on parameters that are unknown, which in practice are estimated using stochastic and poorly measured quantities. As a result, an early prediction of the long-term evolution of a pandemic will quickly lose relevance, while a late model will be too late to be useful for disaster management. Unless a model is designed to be adaptive, it is bound either to lose relevance over time, or lose trust and thus not have a second chance for retraining. We propose a strategy for estimating the number of infections and the number of deaths, that does away with time-series modeling, and instead makes use of a "phase portrait approach."We demonstrate that, with this approach, there is a universality to the evolution of the disease across countries, that can then be used to make reliable predictions. These same models can also be used to plan the requirements for critical resources during the pandemic. The approach is designed for simplicity of interpretation, and adaptivity over time. Using our model, we predict the number of infections and deaths in Italy and New York State, based on an adaptive algorithm which uses early available data, and show that our predictions closely match the actual outcomes. We also carry out a similar exercise for India, where in addition to projecting the number of infections and deaths, we also project the expected range of critical resource requirements for hospitalizations in a location. © 2020 American Physical Society.

Item Type: Journal Article
Publication: Physical Review E
Publisher: American Physical Society
Additional Information: The copyright of this article belongs to American Physical Society
Keywords: Adaptive algorithms; Disaster prevention; Disasters; Forecasting; Long Term Evolution (LTE); Stochastic systems, Adaptive numerical strategies; Critical resources; Disaster management; Early prediction; Epidemiological models; Principle modeling; Temporal evolution; Time series modeling, Stochastic models
Department/Centre: Others
Date Deposited: 07 Sep 2020 11:43
Last Modified: 07 Sep 2020 11:43
URI: http://eprints.iisc.ac.in/id/eprint/66482

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