A new way to do epidemic modeling
Name
Abhijit Dandekar_rajd_PhD_CEE_2022.pdf
Description
Thesis PDF
Size
9.7 MB
Format
Adobe PDF
Checksum (MD5)
82e7b4146ab8c180491989ea82831544
Author(s)
Abhijit Dandekar, Raj
Advisor(s)
Edelman, Alan
Rackauckas, Chris
Date Issued
September 2022
Publisher
Massachusetts Institute of Technology
Abstract
The Coronavirus respiratory disease 2019 originating from the virus SARS-COV-2 led to a global pandemic, leading to more than 500 million confirmed global cases and approximately 6 million deaths in more than 50 countries. Since the outbreak of this pandemic, a number of modeling frameworks have been used to analyze various
aspects of the pandemic such as prediction of infected and recovered case counts, hospitalizations, travel restrictions, reopening and non-pharmaceutical interventions. These frameworks can be divided broadly into the following categories: (a) compartment models which are interpretable but cannot capture complex effects and (b) agent based models which can capture varying ranges of complexity; but are generally non interpretable.
In this thesis, we introduce another category for epidemic modeling, which is rooted in Scientific Machine Learning. Scientific Machine Learning (SciML) leverages the interpretability of ODEs with the expressivity of neural networks. We thus aim to retain the interpretability of compartment models along with the complexity of
agent based models using the SciML modeling paradigm. Using such a framework, we tackle a wide variety of application based problems including:
• How quarantine control policies shaped the outbreak evolution in different countries around the world.
• Effect of early reopening in the Southern and West Central US states; and how it led to an exponential explosion of infected cases in the USA during the period of June-Aug 2020.
• Virtual Virus spread through Bluetooth tokens; and how it can be used to obtain real time estimates of the pandemic.
Towards the end, we analyze the robustness of the proposed SciML methodology and provide a general set of guidelines for training such models in other domains.
MIT Department
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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