Liang Mao
Associate Professor University of Florida
- Gainesville FL
Liang Mao's research aims to offer better understandings on the human-disease system with geospatial science approaches and technologies.
Biography
Areas of Expertise
Articles
Internet of people enabled framework for evaluating performance loss and resilience of urban critical infrastructures
Safety ScienceFaxi Yuan, et. al
2020-11-04
Critical infrastructures (CIs) such as road networks play a critical role in transporting affected people to hospitals and shelters during disasters. Timely evaluation of road networks’ performance loss and resilience enables emergency management agencies (EMAs) to make quick and optimized decisions on resource allocation to critical road segments and bridges.
Determinants of travel mode choice for seeking healthcare: A comparison between elderly and non-elderly patients
Journal of Transport GeographyFangye Du, et. al
2021-03-21
People's travel mode choices can vary significantly by age group due to different influencing factors, but relevant research on health-related travel is scarce. In this study, we explored and compared the determinants of travel mode choice for healthcare-seeking non-elderly and elderly patients in Beijing, China. A multinomial logit model was used to analyze data from a recent healthcare-seeking behavior survey.
A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities
Journal of The Royal Society InterfaceLing Yin, et. al
2021-08-25
Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China.
Agent-based Modeling to Evaluate Human–Environment Interactions in Community Flood Risk Mitigation
Risk AnalysisYu Han, et. al
2021-11-12
This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta–Bernoulli Bayesian learning model is first applied to measure changes of agents’ risk perceptions in response to stochastic storm surges.
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