Data-Driven Modeling for Different Stages of Pandemic Response
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REVIEW ARTICLE
© Indian Institute of Science 2020.
Data‑Driven Modeling for Different Stages of Pandemic Response Aniruddha Adiga1, Jiangzhuo Chen1, Madhav Marathe1,3, Henning Mortveit1,2, Srinivasan Venkatramanan1* and Anil Vullikanti1,3
Abstract | Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who are at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision-making. As different countries and regions go through phases of the pandemic, the questions and data availability also change. Especially of interest is aligning model develop‑ ment and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse data‑ sets, ranging from disease outcomes, to mobility, behaviors, and socioeconomic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic. 1 Introduction As the SARS-CoV-2 pandemic has demonstrated, the spread of a highly infectious disease is a complex dynamical process. A large number of factors are at play as infectious diseases spread, including variable individual susceptibility to the pathogen (e.g., by age and health conditions), variable individual behaviors (e.g., compliance with social distancing and the use of masks), differing response strategies implemented by governments (e.g., school and workplace closure policies and criteria for testing), and potential availability of pharmaceutical interventions. Governments have been forced to respond to the rapidly changing dynamics of the pandemic, and are becoming increasingly reliant on different modeling and analytical techniques to understand, forecast, plan, and respond; this includes
statistical methods and decision support methods using multi-agent models, such as: (i) forecasting epidemic outcomes (e.g., case counts, mortality, and hospital demands), using a diverse set of data-driven methods, e.g., ARIMA type timeseries forecasting, Bayesian techniques and deep learning, e.g.,1–5, (ii) disease surveillance 6, 7, and (iii) counter-factual analysis of epidemics using multi-agent models, e.g.,8–13; indeed, the results of Refs.11, 14 were very influential in the early decisions for lockdowns in a number of countries. The specific questions of interest change with the stage of the pandemic. In the pre-pandemic stage, the focus was on understanding how the outbreak started,
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