[1]
Alfadil, M O Kassem, M A Ali, K N Alaghbari, W (2022). Construction industry from perspective of force majeure and environmental risk compared to the COVID-19 outbreak: A systematic literature review. Sustainability, 14( 3): 1135, 1–22
https://doi.org/10.3390/su14031135
[2]
Allan-Blitz, L T Turner, I Hertlein, F Klausner, J D (2020). High frequency and prevalence of community-based asymptomatic SARS-CoV-2 infection. MedRxiv, 20246249
https://doi.org/10.1101/2020.12.09.20246249
[3]
Allen, A J Boudreau, M C Roberts, N J Allard, A Hébert-Dufresne, L (2022). Predicting the diversity of early epidemic spread on networks. Physical Review Research, 4( 1): 013123
https://doi.org/10.1103/PhysRevResearch.4.013123
[4]
Alsharef, A Banerjee, S Uddin, S M J Albert, A Jaselskis, E (2021). Early impacts of the COVID-19 pandemic on the United States construction industry. International Journal of Environmental Research and Public Health, 18( 4): 1559
https://doi.org/10.3390/ijerph18041559
[5]
Althouse, B M Wenger, E A Miller, J C Scarpino, S V Allard, A Hébert-Dufresne, L Hu, H (2020). Superspreading events in the transmission dynamics of SARS-CoV-2: Opportunities for interventions and control. PLoS Biology, 18( 11): e3000897
https://doi.org/10.1371/journal.pbio.3000897
[6]
An, L Grimm, V Sullivan, A Turner , II B L Malleson, N Heppenstall, A Vincenot, C Robinson, D Ye, X Liu, J Lindkvist, E Tang, W (2021). Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457: 109685
https://doi.org/10.1016/j.ecolmodel.2021.109685
[7]
AntunesMRibeiroJGomesDAguiarR L (2018). Knee/Elbow Point Estimation through Thresholding. IEEE, 413–419
[8]
Araya, F (2021a). Modeling the spread of COVID-19 on construction workers: An agent-based approach. Safety Science, 133: 105022
https://doi.org/10.1016/j.ssci.2020.105022
[9]
Araya, F (2021b). Modeling working shifts in construction projects using an agent-based approach to minimize the spread of COVID-19. Journal of Building Engineering, 41: 102413
https://doi.org/10.1016/j.jobe.2021.102413
[10]
Araya, F (2022). Modeling the influence of multiskilled construction workers in the context of the COVID-19 pandemic using an agent-based ap-proach. Revista de la construcción, 21( 1): 105–117
https://doi.org/10.7764/RDLC.21.1.105
[11]
Aslan, S Türkakın, O H (2022). A construction project scheduling methodology considering COVID-19 pandemic measures. Journal of Safety Research, 80: 54–66
https://doi.org/10.1016/j.jsr.2021.11.007
[12]
Atkeson, A (2020). On using SIR models to model disease scenarios for COVID-19. Federal Reserve Bank of Minneapolis Quarterly Review, 41( 01): 1–35
https://doi.org/10.21034/qr.4111
[13]
Bohk-EwaldCDudelCMyrskyläM. A demographic scaling model for estimating the total number of COVID-19 infections. medRxiv, p. 2020.04.23.20077719, 2020, doi: 10.1101/2020.04.23.20077719
[14]
Briggs, B Friedland, C J Nahmens, I Berryman, C Zhu, Y (2022). Industrial construction safety policies and practices with cost impacts in a COVID-19 pandemic environment: A Louisiana DOW case study. Journal of Loss Prevention in the Process Industries, 76: 104723
https://doi.org/10.1016/j.jlp.2021.104723
[15]
CasiniLManzoG (2016). Agent-based models and causality: A methodological appraisal. Linköping University Electronic Press.
[16]
Centersfor Disease ControlPrevention (2022). How to determine a close contact for COVID-19.
[17]
Centola, D (2020). Considering network interventions. Proceedings of the National Academy of Sciences of the United States of America, 117( 52): 32833–32835
https://doi.org/10.1073/pnas.2022584118
[18]
Cooper, I Mondal, A Antonopoulos, C G (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons, and Fractals, 139: 110057
https://doi.org/10.1016/j.chaos.2020.110057
[19]
Cuevas, E (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine, 121: 103827
https://doi.org/10.1016/j.compbiomed.2020.103827
[20]
Devarajan, J P Manimuthu, A Sreedharan, V R (2023). Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics. IEEE Transactions on Engineering Management, 70( 9): 3229–3243
https://doi.org/10.1109/TEM.2021.3076603
[21]
Dobrucali, E Sadikoglu, E Demirkesen, S Zhang, C Tezel, A (2024). Exploring the impact of COVID-19 on the United States construction industry: Challenges and opportunities. IEEE Transactions on Engineering Management, 71: 1245–1257
https://doi.org/10.1109/TEM.2022.3155055
[22]
Ebekozien, A Aigbavboa, C (2021). COVID-19 recovery for the Nigerian construction sites: The role of the fourth industrial revolution technologies. Sustainable Cities and Society, 69: 102803
https://doi.org/10.1016/j.scs.2021.102803
[23]
Gan, W H Koh, D (2021). COVID-19 and return-to-work for the construction sector: Lessons from Singapore. Safety and Health at Work, 12( 2): 277–281
https://doi.org/10.1016/j.shaw.2021.04.001
[24]
Gerami Seresht, N (2022). Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach. Automation in Construction, 140: 104315
https://doi.org/10.1016/j.autcon.2022.104315
[25]
GraduPZrnicTWangYJordanM I (2022). Valid inference after causal discovery. arXiv preprint arXiv: 2208.05949
[26]
HinzeJ (2004). Construction Planning and Scheduling. NJ: Pearson/Prentice Hall Upper Saddle River
[27]
Karamoozian, A Wu, D (2024). A hybrid approach for the supply chain risk assessment of the construction industry during the COVID-19 pandemic. IEEE Transactions on Engineering Management, 71: 4035–4050
https://doi.org/10.1109/TEM.2022.3210083
[28]
Kermack, W O McKendrick, A G Walker, G T (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Containing papers of a mathematical and physical character, 115( 772): 700–721
https://doi.org/10.1098/rspa.1927.0118
[29]
KöhnH FHubertL J (2014). Hierarchical cluster analysis, Wiley StatsRef: statistics reference online, 1–13
[30]
Lakoba, T I Kaup, D J Finkelstein, N M (2005). Modifications of the Helbing-Molnár-Farkas-Vicsek social force model for pedestrian evolution. Simulation, 81( 5): 339–352
https://doi.org/10.1177/0037549705052772
[31]
Li, J Zhong, J Ji, Y M Yang, F (2021). A new SEIAR model on small-world networks to assess the intervention measures in the COVID-19 pandemics. Results in Physics, 25: 104283
https://doi.org/10.1016/j.rinp.2021.104283
[32]
Li, M Zhao, Y He, L Chen, W Xu, X (2015). The parameter calibration and optimization of social force model for the real-life 2013 Ya’an earthquake evacuation in China. Safety Science, 79: 243–253
https://doi.org/10.1016/j.ssci.2015.06.018
[33]
Liu, X (2021). A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan. Results in Physics, 20: 103712
https://doi.org/10.1016/j.rinp.2020.103712
[34]
Luo, H Liu, J Li, C Chen, K Zhang, M (2020). Ultra-rapid delivery of specialty field hospitals to combat COVID-19: Lessons learned from the Leishenshan Hospital project in Wuhan. Automation in Construction, 119: 103345
https://doi.org/10.1016/j.autcon.2020.103345
[35]
Mahmood, I Arabnejad, H Suleimenova, D Sassoon, I Marshan, A Serrano-Rico, A Louvieris, P Anagnostou, A J E Taylor, S Bell, D Groen, D (2022). FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions. Journal of Simulation, 16( 4): 355–373
https://doi.org/10.1080/17477778.2020.1800422
[36]
Michigangovernment (2022). Outbreak reporting.
[37]
Milne, G Hames, T Scotton, C Gent, N Johnsen, A Anderson, R M Ward, T (2021). Does infection with or vaccination against SARS-CoV-2 lead to lasting immunity. Lancet. Respiratory Medicine, 9( 12): 1450–1466
https://doi.org/10.1016/S2213-2600(21)00407-0
[38]
Mukherjee, U K Bose, S Ivanov, A Souyris, S Seshadri, S Sridhar, P Watkins, R Xu, Y (2021). Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation. Scientific Reports, 11( 1): 6264
https://doi.org/10.1038/s41598-021-84192-y
[39]
MüllnerD (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.237
[40]
Naili, M Bourahla, M Naili, M (2019). Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method. International Journal of Simulation and Process Modelling, 14( 1): 97702–97718
https://doi.org/10.1504/IJSPM.2019.097702
[41]
Nnaji, C Jin, Z Karakhan, A (2022). Safety and health management response to COVID-19 in the construction industry: A perspective of fieldworkers. Process Safety and Environmental Protection, 159: 477–488
https://doi.org/10.1016/j.psep.2022.01.002
[42]
Onishi, K Iida, A Yamakawa, M Tsubokura, M (2022). Numerical analysis of the efficiency of face masks for preventing droplet airborne infections. Physics of Fluids, 34( 3): 033309
https://doi.org/10.1063/5.0083250
[43]
Onumanyi, A J Molokomme, D N Isaac, S J Abu-Mahfouz, A M (2022). AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset. Applied Sciences, 12( 15): 7515
https://doi.org/10.3390/app12157515
[44]
ReynoldsC JSwadlingLGibbonsJ MPadeCJensenM PDinizM OSchmidtN MButlerD KAminO EBaileyS N LMurrayS MPieperF PTaylorSJonesJJonesMLeeW Y JRosenheimJChandranAJoyGDiGenova CTempertonNLambourneJCutino-MoguelTAndiapenMFontanaMSmitASemperAO’BrienBChainBBrooksTManistyCTreibelTMoonJ CNoursadeghiMAltmannD MMainiM KMcKnightÁBoytonR J (2020). Discordant neutralizing antibody and T cell responses in asymptomatic and mild SARS-CoV-2 infection. medRxiv 2020.10.13.20211763
[45]
RossAWillsonV L (2017). One-way anova. Brill: Basic and advanced statistical tests. Brill: 21–24
[46]
SalimNChanW HMansorSNazira BazinN EAmaranSMohd FaudziA AZainalAHuspiS HJiun HooiE KShithilS M (2020). COVID-19 epidemic in Malaysia: Impact of lockdown on infection dynamics. medRxiv, 20057463
[47]
Shamil, M S Farheen, F Ibtehaz, N Khan, I M Rahman, M S (2021). An agent-based modeling of COVID-19: Validation, analysis, and recommendations. Cognitive Computation, 14( 1): 1–12
https://doi.org/10.1007/s12559-020-09801-w
[48]
Sierra, F (2022). COVID-19: main challenges during construction stage. Engineering, Construction, and Architectural Management, 29( 4): 1817–1834
https://doi.org/10.1108/ECAM-09-2020-0719
[49]
Sticco, I M Frank, G A Dorso, C O (2021). Social Force Model parameter testing and optimization using a high stress real-life situation. Physica A, 561: 125299
https://doi.org/10.1016/j.physa.2020.125299
[50]
Stieler, D Schwinn, T Leder, S Maierhofer, M Kannenberg, F Menges, A (2022). Agent-based modeling and simulation in architecture. Automation in Construction, 141: 104426
https://doi.org/10.1016/j.autcon.2022.104426
[51]
StoddardMVan EgerenDJohnsonKRaoSFurgesonJWhiteD ENolanR PHochbergNChakravartyA (2020). Model-based evaluation of the impact of noncompliance with public health measures on COVID-19 disease control. medRxiv, 20240440
[52]
SunSZhengY (2021). The research of SEIJR model with time-delay based on 2019-nCov. IEEE Access: Practical Innovations, Open Solutions, 9: 117949–117956
[53]
Szabo, C Teo, Y M Chengleput, G K (2014). Understanding complex systems: Using interaction as a measure of emergence. Proceedings of the Winter Simulation Conference, 207–218
https://doi.org/10.1109/WSC.2014.7019889
[54]
TaojiangCounty People’s Government (2021). Wear masks, travel less, muster less, isolate rigorously and vaccinate quickly.
[55]
TennesseeTribune (2020). Metro public health department releases list of area COVID-19 clusters.
[56]
Wang, M Flessa, S (2020). Modelling COVID-19 under uncertainty: What can we expect?. European Journal of Health Economics, 21( 5): 665–668
https://doi.org/10.1007/s10198-020-01202-y
[57]
Wang, Y Lv, Z Sheng, Z Sun, H Zhao, A (2022). A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic. Advanced Engineering Informatics, 53: 101678
https://doi.org/10.1016/j.aei.2022.101678
[58]
WashingtonState Department of Health (2022). Statewide COVID-19 Outbreak Report.
[59]
Wu, J T Leung, K Bushman, M Kishore, N Niehus, R de Salazar, P M Cowling, B J Lipsitch, M Leung, G M (2020). Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan. Nature Medicine, 26( 4): 506–510
https://doi.org/10.1038/s41591-020-0822-7
[60]
Xu, Z Zhang, H Huang, Z (2022). A continuous Markov-Chain model for the simulation of COVID-19 epidemic dynamics. Biology, 11( 2): 190
https://doi.org/10.3390/biology11020190