Forecasting Contagion Processes on Heterogeneous Complex Networks
Dissertation proposal
Kunpeng Mu
Physics PhD Candidate
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Thursday
Apr 28, 2022
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Faced with the ongoing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), epidemic modeling played a critical role in the public health planning and response. Especially during the early stage of the pandemic when surveillance data was limited and highly unreliable, model projections can provide valuable information for understanding the magnitude and potential risks of the outbreak. Many countries created task forces to help with policy makers on integrating predictive modeling into decision-making processes. In response to the COVID-19 crisis and because of the lack of effective vaccines and drugs in 2020, governments across the world have implemented many non-pharmaceutical interventions (NPIs) in the early stages of the pandemic (such as: school and restaurant closure, social distancing, travel restriction, mask wearing, etc.) in order to slow down the spreading of SARS-CoV-2. However, these unprecedented interventions have caused a significant negative impact on economy and a widespread disruption to our normal social life, so the immunization of the population through vaccination is considered as the public health priority.

During the past a couple of years, I focused my research on infectious disease modeling and forecasting in heterogeneous complex networks. In my first project, I applied machine learning techniques on predicting air passenger volumes by using socio-economic, demographic and meteorological features to reproduce an analogous origin-destination network to the one obtained from the OAG, which will be integrated into the Global Epidemic and Mobility (GLEAM) model developed in our lab. In my second project, I applied the age-stratified contact networks in a multi-model project to evaluate the reopening strategies and study the trade-offs between public health and economic outcomes of a generic mid-sized US county in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. In the third project, I investigated the COVID-19 epidemics in the United States under several scenarios with different vaccination coverage rates and effectiveness and strength and implementation of NPIs by using a multi-scale modeling approach that combines two distinct epidemic models that work at different geographical resolutions: the Global Epidemic and Mobility model and the Local Epidemic and Mobility model in the US.


Dissertation Committee:

  • Alessandro Vespignani (chair), Network Science Institute, Northeastern University
  • Albert-László Barabási, Network Science Institute, Northeastern University
  • Dmitri Krioukov, Network Science Institute, Northeastern University
  • Guido Caldarelli, Research Institute for Complexity, Ca' Foscari University of Venice

About the speaker
About the speaker

Kunpeng is a fifth year PhD student working in MOBS Lab. He received his bachelor’s degree in physics from Shandong University in China. Currently, he is working on applying the gravity model to international collaboration network to uncover the role of geography in science.