Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility Networks

61 Pages Posted: 29 Jun 2021 Last revised: 4 Nov 2021

See all articles by Shuai Hao

Shuai Hao

University of Illinois at Urbana-Champaign - College of Business

Yuqian Xu

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School

Ujjal Kumar Mukherjee

University of Illinois at Urbana-Champaign - College of Business

Sridhar Seshadri

University of Illinois at Urbana Champaign; Indian School of Business

Sebastian Souyris

Lally School of Management, Rensselaer Polytechnic Institute (RPI)

Anton Ivanov

University of Illinois at Urbana-Champaign

Mehmet Ahsen

University of Illinois at Urbana-Champaign - Department of Business Administration; Mount Sinai Health System - Icahn Institute for Genomics and Multiscale Biology

Padmavati Sridhar

University of California, Berkeley

Date Written: June 2, 2021

Abstract

The sudden emergence of epidemics, such as COVID-19, entails economic and social challenges requiring immediate attention from policy makers. An essential building block in implementing mitigation policies (e.g., lockdowns, testing, and vaccination) is the identification of potential hotspots, defined as locations that contribute significantly to the spatial diffusion of infections. During the initial stages of an epidemic, information related to the pathways of spatial diffusion of infection is not fully observable, making the detection of hotspots difficult. This work proposes a data-driven framework to identify hotspots using advanced analytical methodologies, specifically, a combination of interpretable long short-term memory (LSTM) model, multi-task learning, and transfer learning. Our methodology considers mobility within- and across-locations, which is the primary driving factor for the diffusion of infection over a network of connected locations. Additionally, to augment the signals of infection diffusion and the emergence of hotspots, we use transfer learning from past influenza transmission data, which follow a similar transmission mechanism as COVID-19. To illustrate the practical importance of our framework in deciding on lockdown policies, we compare the hotspots-based policy with a pure infection load-based policy and the state-wide lockdown policy used in practice. We show that the hotspots-based lockdown policy can achieve up to 21% improvement in reducing new infections as compared to an infection-based lockdown policy. In addition, we illustrate that locking down only top few hotspot counties can achieve almost similar performance as a state-wide lockdown policy used in practice. Finally, we demonstrate that the inclusion of transfer learning improves hotspot prediction accuracy by 53.4%. We also compare our model performance with the commonly used compartmental epidemiological model and demonstrate the superior prediction performance. Our paper addresses a practical problem with hotspot identification framework, which policy makers can use to improve mitigation decisions related to the control of epidemics.

Note: Funding Statement: The research work was partially supported by funds from the C3.ai Digital Transformation Institute.

Declaration of Interests: We have no competing interests to declare.

Ethics Approval Statement: COVID-19 related data was collected from a public source -Johns Hopkins Coronavirus Resource Center. Population mobility flows were collected from a proprietary source.

Keywords: COVID-19, Hotspots Identification, Data-Driven, Long Short-Term Memory, Multi-Task Learning, Transfer Learning, Deep Learning

Suggested Citation

Hao, Shuai and Xu, Yuqian and Mukherjee, Ujjal Kumar Mukherjee and Seshadri, Sridhar and Souyris, Sebastian and Ivanov, Anton and Ahsen, Mehmet Eren and Sridhar, Padmavati, Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility Networks (June 2, 2021). Available at SSRN: https://ssrn.com/abstract=3858274 or http://dx.doi.org/10.2139/ssrn.3858274

Shuai Hao (Contact Author)

University of Illinois at Urbana-Champaign - College of Business ( email )

Champaign, IL 61820
United States

Yuqian Xu

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School ( email )

McColl Building
Chapel Hill, NC 27599-3490
United States

Ujjal Kumar Mukherjee Mukherjee

University of Illinois at Urbana-Champaign - College of Business ( email )

Champaign, IL 61820
United States

Sridhar Seshadri

University of Illinois at Urbana Champaign ( email )

1206 South Sixth Street
Champaign, IL 61820
United States

Indian School of Business ( email )

Hyderabad, Gachibowli 500 019
India

Sebastian Souyris

Lally School of Management, Rensselaer Polytechnic Institute (RPI) ( email )

110 8th St
Troy, NY 12180
United States

Anton Ivanov

University of Illinois at Urbana-Champaign ( email )

4022 Business Instructional Facility
515 Gregory St
Champaign, IL 61820
United States

Mehmet Eren Ahsen

University of Illinois at Urbana-Champaign - Department of Business Administration ( email )

1206 South Sixth Street
Champaign, IL 61820
United States

Mount Sinai Health System - Icahn Institute for Genomics and Multiscale Biology ( email )

1425 Madison Ave
New York, NY 10029
United States

Padmavati Sridhar

University of California, Berkeley ( email )

310 Barrows Hall
Berkeley, CA 94720
United States

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