Qiao et al. Architectural design and building-level infections during the early stage of COVID-19: A study of 2597 public housing buildings in Hong Kong. Building and Environment,Volume 276,15 May 2025,112853 https://doi.org/10.1016/j.buildenv.2025.112853
C Ren,Z Shi,H Tian,R Zhao,C Huang,Q Qiao,J Yao. Estimating of the Causal Effect of Land Use Mixed on Adult Asthma Prevalence in New York State,Sustainable Cities and Society,106125,https://doi.org/10.1016/j.scs.2025.106125
Qiao et al. Associating COVID-19 Prevalence and Built Environment Design: An Explainable Machine Learning Approach. Journal of Urban Management,https://doi.org/10.1016/j.jum.2024.10.009
C Ren,X Huang,Q Qiao,M White. Street-Level Built Environment on SARS-CoV-2 Transmission: A Study of Hong Kong. Heliyon,Volume 10,Issue 19,e38405
Cheung,C. C.,Lai,K. Y.,Zhang,R.,Schuldenfrei,E.,Qiao,Q.,Webster,C.,& Sarkar,C. (2024). Associations of residential greenness with behavioural,physical,and mental health: a Hong Kong study during the fifth wave of COVID-19 pandemic. Cities & Health,1–14. https://doi-org.eproxy.lib.hku.hk/10.1080/23748834.2024.2381960
Qiao et al. “Architectural design and epidemic prevalence: Insights from Hong Kong's fifth wave,” Build. Environ,Volume 256,May 2024,doi.org/10.1016/j.buildenv.2024.111516
Qiao et al. “An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector,” Energy,Volume 286,1 January 2024,129499,doi.org/10.1016/j.energy.2023.129499
Q Qiao,C Cheung,A Yunusa-Kaltungo,P Manu,R Cao,Z Yuan. An interactive agent-based modelling framework for assessing COVID-19 transmission risk on construction site Safety Science 168,106312
Q. Qiao and A. Yunusa-Kaltungo,“A hybrid agent-based machine learning method for human-centred energy consumption prediction,” Energy Build,vol. 283,p. 112797,Mar. 2023,doi: 10.1016/j.enbuild.2023.112797.
Qiao et al. Developing a machine learning based building energy consumption prediction approach using limited data: Boruta feature selection and empirical mode decomposition. Energy Reports Volume 9 December 2023,Pages 3643-3660,doi.org/10.1016/j.egyr.2023.02.046
Qiao et al. Feature selection strategy for machine learning methods in building energy consumption prediction. Energy Reports Volume 8,November 2022,Pages 13621-13654
Qiao et al. Towards developing a systematic knowledge trend for building energy consumption prediction. Journal of Building Engineering 35(April):101967
