Adaptive pandemic management strategies for construction sites: An agent-based modeling approach

Chengqian LI, Qi FANG, Ke CHEN, Zhikang BAO, Zehao JIANG, Wenli LIU

PDF(14015 KB)
PDF(14015 KB)
Front. Eng ›› 2024, Vol. 11 ›› Issue (2) : 288-310. DOI: 10.1007/s42524-024-3061-7
Construction Engineering and Intelligent Construction
RESEARCH ARTICLE

Adaptive pandemic management strategies for construction sites: An agent-based modeling approach

Author information +
History +

Abstract

In the face of sudden pandemics, it becomes crucial for project managers to quickly adapt and make informed decisions that anticipate the consequences of their actions. This highlights the need for proactive management strategies to enhance epidemic response efforts. However, current research mainly emphasizes the negative impacts of pandemics, often neglecting the development of adaptable management approaches for construction sites. This study aims to fill this research void by developing strategies tailored to managing pandemics at construction sites. Using agent-based modeling, the study simulates the movement patterns of workers and the consequent spread of an epidemic under different risk scenarios and management tactics. The results indicate that measures such as wearing masks, managing group activities, and enforcing entry controls can significantly reduce epidemic spread on construction sites, with entry controls showing the greatest effectiveness.

Graphical abstract

Keywords

epidemic transmission / agent-based modeling / safety management / management strategy

Cite this article

Download citation ▾
Chengqian LI, Qi FANG, Ke CHEN, Zhikang BAO, Zehao JIANG, Wenli LIU. Adaptive pandemic management strategies for construction sites: An agent-based modeling approach. Front. Eng, 2024, 11(2): 288‒310 https://doi.org/10.1007/s42524-024-3061-7

References

[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[7]
AntunesMRibeiro JGomesDAguiarR 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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[11]
Aslan, S Türkakın, O H (2022). A construction project scheduling methodology considering COVID-19 pandemic measures. Journal of Safety Research, 80: 54–66
CrossRef Google scholar
[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
CrossRef Google scholar
[13]
Bohk-EwaldCDudel CMyrskyläM. 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
CrossRef Google scholar
[15]
CasiniLManzo G (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
CrossRef Google scholar
[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
CrossRef Google scholar
[19]
Cuevas, E (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine, 121: 103827
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[24]
Gerami Seresht, N (2022). Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach. Automation in Construction, 140: 104315
CrossRef Google scholar
[25]
GraduPZrnic TWangYJordanM 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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[44]
ReynoldsC JSwadling LGibbonsJ MPadeCJensenM P DinizM OSchmidt N MButlerD KAminO EBaileyS N L MurrayS MPieper F PTaylorSJonesJJonesM LeeW Y JRosenheim JChandranAJoyGDiGenova C TempertonNLambourne JCutino-MoguelTAndiapenMFontanaM SmitASemper AO’BrienBChainBBrooksT ManistyCTreibel TMoonJ CNoursadeghiMAltmannD M MainiM KMcKnight ÁBoytonR J (2020). Discordant neutralizing antibody and T cell responses in asymptomatic and mild SARS-CoV-2 infection. medRxiv 2020.10.13.20211763
[45]
RossAWillson V L (2017). One-way anova. Brill: Basic and advanced statistical tests. Brill: 21–24
[46]
SalimNChan W HMansorSNazira BazinN EAmaranS Mohd FaudziA AZainalAHuspiS H Jiun 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
CrossRef Google scholar
[48]
Sierra, F (2022). COVID-19: main challenges during construction stage. Engineering, Construction, and Architectural Management, 29( 4): 1817–1834
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[51]
StoddardMVan Egeren DJohnsonKRaoSFurgesonJ WhiteD ENolan R 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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar
[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
CrossRef Google scholar

Conflicts of Interest

The authors declare that they have no conflicts of interest.

RIGHTS & PERMISSIONS

2024 Higher Education Press
AI Summary AI Mindmap
PDF(14015 KB)

Accesses

Citations

Detail

Sections
Recommended

/