2026-09-18T21:12:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1473642023-01-20T03:27:13Zcom_1721.1_33226com_1721.1_7582com_1721.1_7581col_1721.1_145728col_1721.1_131022
Edelman, Alan
Rackauckas, Chris
Abhijit Dandekar, Raj
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
2023-01-19T18:48:22Z
2023-01-19T18:48:22Z
2022-09
2022-10-25T22:06:42.350Z
https://hdl.handle.net/1721.1/147364
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.
Ph.D.
Massachusetts Institute of Technology
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A new way to do epidemic modeling
Thesis
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Doctoral
Doctor of Philosophy
Publication
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<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.</Abstract>
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