Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases
Published: 11 February 2025
Abstract Views: 1293
PDF: 324
Supplementary Materials: 218
HTML: 131
Supplementary Materials: 218
HTML: 131
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Wastewater-based epidemiology was utilized during the COVID-19 outbreak to monitor the circulation of SARS-CoV-2, the virus causing this disease. However, this approach is limited by the need for additional methods to accurately translate virus concentrations in wastewater to disease-positive human counts. Combined modelling of COVID-19 disease cases and the concentration of its causative virus, SARS-CoV-2, in wastewater will necessarily deepen our understanding. However, this requires addressing the technical differences between disease, population mobility and wastewater models. To that end, we developed an integrated Agent-Based Model (ABM) that facilitates analysis in space and time at various temporal resolutions, including disease spread, population mobility and wastewater production, while also being sufficiently generic for different types of infectious diseases or pathogens. The integrated model replicates the epidemic curve for COVID-19 and can estimate the daily infections at the household level, enabling the monitoring of the spatial patterns of infection intensity. Additionally, the model allows monitoring the estimated production of infected wastewater over time and spatially across the sewage and treatment plant. The model addresses differences between resolutions and can potentially support Early Warning Systems (EWS) for future pandemics.
Altmetrics
Downloads
Download data is not yet available.
Citations
An L, Grimm V, Sullivan A, Turner II BL, 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, Ecol Modell 457:109685. DOI: https://doi.org/10.1016/j.ecolmodel.2021.109685
Assaad RH, Assaf G, Boufadel M, 2023. Optimizing the maintenance strategies for a network of green infrastructure: An agent-based model for stormwater detention basins. J Environ Manag 330:117179. DOI: https://doi.org/10.1016/j.jenvman.2022.117179
Atinkpahoun CNH, Le ND, Steve Pontvianne S, Poirot H, Leclerc J-P, Pons M-N, Soclo HH, 2018. Population mobility and urban wastewater dynamics. Sci Total Environ 1:622–623: 1431–7. DOI: https://doi.org/10.1016/j.scitotenv.2017.12.087
Augustijn E-W. Doldersum T, Useya J, Augusttijin D, 2016. Agent-based modelling of cholera diffusion, Stoch Environ Res Risk Assess 30:2079–95. DOI: https://doi.org/10.1007/s00477-015-1199-x
Augustijn PWM. Herwig A, Chen Y, Abdulkareem S, 2022. Integration of governmental risk perception into a Covid-19 model for the Netherlands. Geogr Gesundheit 6:39–55.
Badr HS, Du H, Marshall M, Dong E, Squire MM, Gardner LM, 2020. Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study, Lancet Infect Dis 20:1247–54. DOI: https://doi.org/10.1016/S1473-3099(20)30553-3
Balushi AAL, Momin A, van Doninck J, 2024. CODECHECK of integrating agent-based disease, mobility, and wastewater models. Workshop at ITC faculty, University of Twente, Netherland on 26 Sept. 2024 doi.org/10.17605/OSF.IO/6NGYC.
Barat S, Parchure R, Darak S, Kulkarni V, Paranjape A, Gajrani M, Yadav A, Kulkarni V, 2021. An agent-based digital twin for exploring localized non-pharmaceutical interventions to control COVID-19 pandemic. Trans Indian Natl Acad Eng 6:323–53. DOI: https://doi.org/10.1007/s41403-020-00197-5
Cartenì A, Di Francesco L, Martino M, 2020. How mobility habits influenced the spread of the COVID-19 pandemic: results from the Italian case study. Sci Total Environ 741:140489. DOI: https://doi.org/10.1016/j.scitotenv.2020.140489
Cook LM, Samaras C, Van Briesen JM, 2018. A mathematical model to plan for long-term effects of water conservation choices on dry weather wastewater flows and concentrations’. J Environ Manag 206:684–97. DOI: https://doi.org/10.1016/j.jenvman.2017.10.013
Darbandsari P, Kerachian R, Malakpour-Estalaki S, Khorasani H, 2020. An agent-based conflict resolution model for urban water resources management, Sustain Cities Soc 57;102112. DOI: https://doi.org/10.1016/j.scs.2020.102112
DelaPaz Ruiz N., Augustijn EW, Farnaghi M, Zurita-Milla R,2021. Population census estimation for Spatial Microsimulation. Poster at Int Conf Geospat Inf Sci held at Mérida, Mexico 3-5 November 2021. doi.org/10.5281/zenodo.5543112.
DelaPaz-Ruíz N, Augustijn E-W, Farnaghi M, Zurita-Milla R, 2023a. Modeling spatiotemporal domestic wastewater variability: Implications for measuring treatment efficiency. J Environ Manag 351:119680. DOI: https://doi.org/10.1016/j.jenvman.2023.119680
DelaPaz-Ruíz N, Augustijn E-W, Farnaghi M, Zurita-Milla R, 2023b. Spatiotemporal domestic wastewater variability : Assessing implications of population mobility in pollutants dynamics. Association of Geographic Information Laboratories in Europe (AGILE), 4. doi.org/10.5194/agile-giss-4-23-2023. DOI: https://doi.org/10.5194/agile-giss-4-23-2023
DelaPaz-Ruíz, N. Augustijn E-W, Farnaghi M, Zurita-Milla R, 2024. Software. Reproducible results. Integrating agent-based disease, mobility, and wastewater models: Dealing with differences in spatiotemporal resolutions. (CODECHECK NL). University of Twente Research Information. Zenodo. doi.org/10.5281/zenodo.13734543.
Evans TP, Kelley H, 2004. Multi-scale analysis of a household level agent-based model of landcover change. J Enviro Manag 72:57–72. DOI: https://doi.org/10.1016/j.jenvman.2004.02.008
Faraway J. Boxall-Clasby J, Feil EJ, Gibbon MJ, Hatfield O, Kasprzyk-Hordern B, Smith T, 2022. Challenges in realising the potential of wastewater-based epidemiology to quantitatively monitor and predict the spread of disease. J Waterd Health 20:1038–50. DOI: https://doi.org/10.2166/wh.2022.020
Freni G, Mannina G, Viviani G, 2010. Urban storm-water quality management: centralized versus source control. J Water Resour Plan Manag 136:268–78. DOI: https://doi.org/10.1061/(ASCE)0733-9496(2010)136:2(268)
Heppenstall A, Crooks A, Malleson N, Manley E, Ge J, Batty M, 2021. Future developments in geographical agent-based models: challenges and opportunities, Geogr Anal 53:76–91. DOI: https://doi.org/10.1111/gean.12267
INEGI, 2020. Censo de Población y Vivienda 2020, Censos y conteos. Accessed: 7 October 2022. Available from: https://www.inegi.org.mx/programas/ccpv/2020/
Lovelace R, Dumont M, 2018. Spatial Microsimulation with R. Website ed. CRC Press. DOI: https://doi.org/10.1201/9781315381640
Mahdizadeh Gharakhanlou N, Hooshangi N, 2020. Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran), Inform Med Unlocked 20:100403. DOI: https://doi.org/10.1016/j.imu.2020.100403
Martin M, Goethals P, Newhart K, Rhodes E, Vogel J, Stevenson B, 2023. Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling. J Eng Appl Sci 70;11 DOI: https://doi.org/10.1186/s44147-023-00180-1
McLane AJ, Semeniuk Ch, McDermid GJ, Danielle J. Marceau DJ, 2011. The role of agent-based models in wildlife ecology and management. Ecol Modell 222(8):1544–56. DOI: https://doi.org/10.1016/j.ecolmodel.2011.01.020
McManus O, Christiansen LE, Nauta M, Wulff Krogsgaard L, Stolberg Bahrenscheer N, 2023. Predicting COVID-19 incidence using wastewater surveillance data, Denmark, October 2021–June 2022. Emerg Infect Dis 29:1589–97. DOI: https://doi.org/10.3201/eid2908.221634
NASEM, 2023. Wastewater-based Disease Surveillance for Public Health Action. National Academies of Sciences Engineering and Medicine (NASEM). pp. 1–152.
NASEM, 2024. Increasing the utility of wastewater-based disease surveillance for public health action: a phase 2 report.
Ngo AT, Nguyen GTH, Nong DH, See L, 2022. Simulating the spatial distribution of pollutant loads from pig farming using an agent-based modeling approach. Environ Sci Pollut Res 29:42037-54. DOI: https://doi.org/10.1007/s11356-021-17112-2
Oliva-Felipe L, Verdaguer M, Poch M, Vázquez-Salceda J, Cortés U, 2021. The organisational structure of an agent-based model for the management of wastewater systems, Water 13:1–24. DOI: https://doi.org/10.3390/w13091258
Petrucci G, De Bondt K, Claeys P, 2019. Experimental design to support water quality modelling of sewer systems. New Trend Urb Drain Modell 2:96–101.
Randazzo W, Cuevas-Ferrando E, Sanjuán R, Domingo-Calap P, Sánchez G, 2020. Metropolitan wastewater analysis for COVID-19 epidemiological surveillance. Int J Hyg Environ Health 230;113621. DOI: https://doi.org/10.1016/j.ijheh.2020.113621
Sheppard C, Railsback S, Kelter J, 2020. NetLogo/Time-Extension. Accessed: 30 January 2023. Available at: https://github.com/NetLogo/Time-Extension#behavior
Sheppard C, Railsback S, Kelter J, 2022. NetLogo 6.3.0 User Manual: Time Extension. Accessed: 30 January 2023. Available from: https://ccl.northwestern.edu/netlogo/docs/time.html
Tiwari A, Tiwari A, Radu E, Kreuzinger N, Ahmed W, Pitkänen T, 2024. Key considerations for pathogen surveillance in wastewater. Sci Total Environ 945:173862. DOI: https://doi.org/10.1016/j.scitotenv.2024.173862
Torabi F, Li G, Mole C, Nicholson G, Rowlingson B, Smith CR, Jersakova R, Diggle PJ, Blangiardo M, 2023. Wastewater-based surveillance models for COVID-19 : A focused review on spatio-tem poral models. Heliyon 9:e21734. DOI: https://doi.org/10.1016/j.heliyon.2023.e21734
Truszkowska, A, 2021. High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town. Adv Theory Simulat 4:2000277. DOI: https://doi.org/10.1002/adts.202000277
Verdaguer M, Clara N, Gutiérrez O, Poch M, 2014. Application of Ant-Colony-Optimization algorithm for improved management of first flush effects in urban wastewater systems. Sci Total Environ 485–486:143–52. DOI: https://doi.org/10.1016/j.scitotenv.2014.02.140
Wilensky U, 1999. NetLogo 6.3.0 User Manual: Programming Guide, Programming Guide. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Accessed: 30 May 2023. Available at: https://ccl.northwestern.edu/netlogo/docs/programming.html#tick-counter
Zhu Y, Oishi W, Maruo C, Saito M, Chen R, Kitajima M, Sano D, 2021. Early warning of COVID-19 via wastewater-based epidemiology: potential and bottlenecks. Sci Total Environ 767;145124. DOI: https://doi.org/10.1016/j.scitotenv.2021.145124
Supporting Agencies
CONACYT-Alianza FiiDEM; Reference: 2019-000005-01EXTF-0027How to Cite
Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases. (2025). Geospatial Health, 20(1). https://doi.org/10.4081/gh.2025.1326
Copyright (c) 2025 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Wentao Yang, Fengjie Wang, Yihan You, Zhixiong Fang, Xing Wang, Xiaoming Mei, Optimizing vaccination sites for infectious diseases based on heterogeneous travel modes in multiple scenarios , Geospatial Health: Vol. 20 No. 1 (2025)
- Ana M. Vicedo-Cabrera, Annibale Biggeri, Laura Grisotto, Fabio Barbone, Dolores Catelan, A Bayesian kriging model for estimating residential exposure to air pollution of children living in a high-risk area in Italy , Geospatial Health: Vol. 8 No. 1 (2013)
- Rudi Cassini, Paolo Mulatti, Claudia Zanardello, Giulia Simonato, Manuela Signorini, Stefania Cazzin, Pier Giorgio Tambalo, Mario Cobianchi, Mario Pietrobelli, Gioia Capelli, Retrospective and spatial analysis tools for integrated surveillance of cystic echinococcosis and bovine cysticercosis in hypo-endemic areas , Geospatial Health: Vol. 8 No. 2 (2014)
- Beatriz Martínez-Lòpez, Tsviatko Alexandrov, Lina Mur, Fernando Sánchez-Vizcaíno, José M. Sánchez-Vizcaíno, Evaluation of the spatial patterns and risk factors, including backyard pigs, for classical swine fever occurrence in Bulgaria using a Bayesian model , Geospatial Health: Vol. 8 No. 2 (2014)
- Vitomir Djokić, Ivana Klun, Vincenzo Musella, Laura Rinaldi, Giuseppe Cringoli, Smaragda Sotiraki, Olgica Djurković-Djaković, Spatial epidemiology of Toxoplasma gondii infection in goats in Serbia , Geospatial Health: Vol. 8 No. 2 (2014)
- Jia-Cheng Zhang, Wen-Dong Liu, Qi Liang, Jian-Li Hu, Jessie Norris, Ying Wu, Chang-Jun Bao, Fen-Yang Tang, Peng Huang, Yang Zhao, Rong-Bin Yu, Ming-Hao Zhou, Hong-Bing Shen, Feng Chen, Zhi-Hang Peng, Spatial distribution and risk factors of influenza in Jiangsu province, China, based on geographical information system , Geospatial Health: Vol. 8 No. 2 (2014)
- Naoko Nihei, Osamu Komagata, Kan-ichiro Mochizuki, Mutsuo Kobayashi, Geospatial analysis of invasion of the Asian tiger mosquito Aedes albopictus: competition with Aedes japonicus japonicus in its northern limit area in Japan , Geospatial Health: Vol. 8 No. 2 (2014)
- Kuo-Hsin Tseng, Song Liang, Motomu Ibaraki, Hyongki Lee, C. K. Shum, Study of the variation of schistosomiasis risk in Lake Poyang in the People's Republic of China using multiple space-borne sensors for monitoring and modelling , Geospatial Health: Vol. 8 No. 2 (2014)
- Ulrik B. Pedersen, Nicholas Midzi, Takafira Mduluza, White Soko, Anna-Sofie Stensgaard, Birgitte J. Vennervald, Samson Mukaratirwa, Thomas K. Kristensen, Modelling spatial distribution of snails transmitting parasitic worms with importance to human and animal health and analysis of distributional changes in relation to climate , Geospatial Health: Vol. 8 No. 2 (2014)
- Mohammadreza Rajabi, Ali Mansourian, Petter Pilesjö, Ahad Bazmani, Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks: a case study in north-western Iran , Geospatial Health: Vol. 9 No. 1 (2014)
You may also start an advanced similarity search for this article.
