Understanding the Impact of Geo-Social Human Interaction Patterns on Effective Vaccination Strategies


Thursday, October 17, 2019, 12:00pm to 1:30pm


CGIS South S050, 1730 Cambridge St., Cambridge, MA 02138

By: Dr. Wei Luo

Abstract: Human interaction and population mobility determine the spatio-temporal course of the spread of an airborne disease. This research views such spreads as geo-social interaction problems, because population mobility connects different groups of people over geographical locations via which the viruses transmit. Previous research argued that geo-social interaction patterns identified from population movement data can provide great potential in designing effective pandemic mitigation. However, little work has been done to examine the effectiveness of designing control strategies taking into account geo-social interaction patterns. To address this gap, this research proposes a new framework for effective disease control; specifically this framework proposes that disease control strategies should start from identifying geo-social interaction patterns, designing effective control measures accordingly, and evaluating the efficacy of different control measures. With the real world human interaction data in both school and urban environments, this research evaluates the efficacy of vaccination strategies considering the impacts of human spatial–social interaction patterns. The simulated results show that a proper spatial–social scale can help achieve the best control efficacy with a limited number of vaccines.

About the speaker: Dr. Wei Luo is a Research Associate at the Computational Health Informatics Program (CHIP) at Boston Children's Hospital and Harvard Medical School, and an Associate at the Center for Geographic Analysis at Harvard University. He has broad training backgrounds in Geography (GIScience), Public Health (Epidemiology), Computer Science (Visual Analytics), and Network Science. He proposed a new research area: geo-social visual analytics, which aims to integrate spatial analysis, social network analysis, and machine learning into visual analytics environments. He has designed and developed a series of geovisual analytics tools and high performance computational models for infectious disease transmission and control (i.e., influenza, HIV), international trade, social media, crime analysis, climate change impacts, and water crisis.