A CyberGIS Approach to Spatiotemporally Explicit Uncertainty and Global Sensitivity Analysis for Agent-Based Modeling of Vector-Borne Disease Transmission

References

• Achee, N. L., F. Gould, T. A. Perkins, R. C. Reiner, A. C. Morrison, S. A. Ritchie, D. J. Gubler, R. Teyssou, and T. W. Scott. 2015. A critical assessment of vector control for dengue prevention. PLoS Neglected Tropical Diseases 9 (5):e0003655. doi: 10.1371/journal.pntd.0003655.
   PubMed Web of Science ®Google Scholar
• Aldstadt, J. 2007. An incremental Knox test for the determination of the serial interval between successive cases of an infectious disease. Stochastic Environmental Research and Risk Assessment 21 (5):487–500. doi: 10.1007/s00477-007-0132-3.
   Web of Science ®Google Scholar
• An, L., M. Linderman, J. Qi, A. Shortridge, and J. Liu. 2005. Exploring complexity in a human–environment system: An agent‐based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers 95 (1):54–79. doi: 10.1111/j.1467-8306.2005.00450.x.
   Web of Science ®Google Scholar
• Bitterman, P., and D. A. Bennett. 2016. Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models. Ecology and Society 21 (3):21. doi: 10.5751/ES-08677-210321.
   Web of Science ®Google Scholar
• Boyd, R., S. Roy, R. Sibly, R. Thorpe, and K. Hyder. 2018. A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to Atlantic mackerel. Ecological Modelling 382:9–17. doi: 10.1016/j.ecolmodel.2018.04.015.
   Web of Science ®Google Scholar
• Chao, D. L., S. B. Halstead, M. E. Halloran, and I. M. Longini. 2012. Controlling dengue with vaccines in Thailand. PLoS Neglected Tropical Diseases 6 (10):e1876. doi: 10.1371/journal.pntd.0001876.
   PubMed Web of Science ®Google Scholar
• Crooks, A., C. Castle, and M. Batty. 2008. Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems 32 (6):417–30. doi: 10.1016/j.compenvurbsys.2008.09.004.
   Web of Science ®Google Scholar
• Crooks, A., and A. Hailegiorgis. 2014. An agent-based modeling approach applied to the spread of cholera. Environmental Modelling & Software 62:164–77. doi: 10.1016/j.envsoft.2014.08.027.
   Web of Science ®Google Scholar
• Crosetto, M., and S. Tarantola. 2001. Uncertainty and sensitivity analysis: Tools for GIS-based model implementation. International Journal of Geographical Information Science 15 (5):415–37. doi: 10.1080/13658810110053125.
   Web of Science ®Google Scholar
• Crosetto, M., S. Tarantola, and A. Saltelli. 2000. Sensitivity and uncertainty analysis in spatial modelling based on GIS. Agriculture, Ecosystems & Environment 81 (1):71–79. doi: 10.1016/S0167-8809(00)00169-9.
   Web of Science ®Google Scholar
• Endy, T. P., S. Chunsuttiwat, A. Nisalak, D. H. Libraty, S. Green, A. L. Rothman, D. W. Vaughn, and F. A. Ennis. 2002. Epidemiology of inapparent and symptomatic acute dengue virus infection: A prospective study of primary school children in Kamphaeng Phet, Thailand. American Journal of Epidemiology 156 (1):40–51. doi: 10.1093/aje/kwf005.
   PubMed Web of Science ®Google Scholar
• Fedriani, J. M., T. Wiegand, D. Ayllón, F. Palomares, A. Suárez‐Esteban, and V. Grimm. 2018. Assisting seed dispersers to restore oldfields: An individual‐based model of the interactions among badgers, foxes and Iberian pear trees. Journal of Applied Ecology 55 (2):600–611. doi: 10.1111/1365-2664.13000.
   Web of Science ®Google Scholar
• Getis, A., and J. K. Ord. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis 24 (3):189–206. doi: 10.1111/j.1538-4632.1992.tb00261.x.
   Web of Science ®Google Scholar
• Grant, T. J., H. R. Parry, M. P. Zalucki, and S. P. Bradbury. 2018. Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory. Ecological Modelling 374:37–50. doi: 10.1016/j.ecolmodel.2018.02.011.
   Web of Science ®Google Scholar
• Grimm, V., U. Berger, D. L. DeAngelis, J. G. Polhill, J. Giske, and S. F. Railsback. 2010. The ODD protocol: A review and first update. Ecological Modelling 221 (23):2760–68. doi: 10.1016/j.ecolmodel.2010.08.019.
   Web of Science ®Google Scholar
• Grimm, V., and S. F. Railsback. 2012. Pattern-oriented modelling: A “multi-scope” for predictive systems ecology. Philosophical Transactions of the Royal Society B: Biological Sciences 367 (1586):298–310. doi: 10.1098/rstb.2011.0180.
   PubMed Web of Science ®Google Scholar
• Grimm, V., E. Revilla, U. Berger, F. Jeltsch, W. M. Mooij, S. F. Railsback, H. H. Thulke, J. Weiner, T. Wiegand, and D. L. DeAngelis. 2005. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science 310 (5750):987–91. doi: 10.1126/science.1116681.
   PubMed Web of Science ®Google Scholar
• Harrington, L. C., J. P. Buonaccorsi, J. D. Edman, A. Costero, P. Kittayapong, G. G. Clark, and T. W. Scott. 2001. Analysis of survival of young and old Aedes aegypti (Diptera: Culicidae) from Puerto Rico and Thailand. Journal of Medical Entomology 38 (4):537–47. doi: 10.1603/0022-2585-38.4.537.
   PubMed Web of Science ®Google Scholar
• Harrington, L. C., J. J. Jones, S. Kitthawee, R. Sithiprasasna, J. D. Edman, and T. W. Scott. 2008. Age-dependent survival of the dengue vector Aedes aegypti (Diptera: Culicidae) demonstrated by simultaneous release–recapture of different age cohorts. Journal of Medical Entomology 45 (2):307–13. doi: 10.1093/jmedent/45.2.307.
   PubMed Web of Science ®Google Scholar
• Harrington, L. C., T. W. Scott, K. Lerdthusnee, R. C. Coleman, A. Costero, G. G. Clark, … R. Sithiprasasna. 2005. Dispersal of the dengue vector Aedes aegypti within and between rural communities. The American Journal of Tropical Medicine and Hygiene 72 (2):209–20.
   PubMed Web of Science ®Google Scholar
• Hu, H., T. Lin, S. Wang, and L. F. Rodriguez. 2017. A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization. Applied Energy 203:26–40. doi: 10.1016/j.apenergy.2017.03.107.
   Web of Science ®Google Scholar
• Kang, J.-Y., and J. Aldstadt. 2017. The influence of spatial configuration of residential area and vector populations on dengue incidence patterns in an individual-level transmission model. International Journal of Environmental Research and Public Health 14 (7):E792.
   Web of Science ®Google Scholar
• Kang, J.-Y., and J. Aldstadt. 2019a. Examining time-dependent effects of water, sanitation, and hygiene (WASH) interventions using an agent-based model. Tropical Medicine & International Health 24 (8): 962–71. doi: 10.1111/tmi.13280.
   Web of Science ®Google Scholar
• Kang, J.-Y., and J. Aldstadt. 2019b. Using multiple scale space–time patterns in variance-based global sensitivity analysis for spatially explicit agent-based models. Computers, Environment, and Urban Systems 75:170–83. doi: 10.1016/j.compenvurbsys.2019.02.006.
   Web of Science ®Google Scholar
• Kang, J.-Y., and J. Aldstadt. 2019c. Using multiple scale spatio-temporal patterns for validating spatially explicit agent-based models. International Journal of Geographical Information Science 33 (1):193–213. doi: 10.1080/13658816.2018.1535121.
   PubMed Web of Science ®Google Scholar
• Kim, I.-H., M.-H. Tsou, and C.-C. Feng. 2015. Design and implementation strategy of a parallel agent-based Schelling model. Computers, Environment and Urban Systems 49:30–41. doi: 10.1016/j.compenvurbsys.2014.09.004.
   Web of Science ®Google Scholar
• Knox, E. G. 1964. The detection of space–time interactions. Applied Statistics 13 (1):25–29. doi: 10.2307/2985220.
   Web of Science ®Google Scholar
• Ligmann-Zielinska, A. 2013. Spatially-explicit sensitivity analysis of an agent-based model of land use change. International Journal of Geographical Information Science 27 (9):1764–81. doi: 10.1080/13658816.2013.782613.
   Web of Science ®Google Scholar
• Ligmann-Zielinska, A. 2018. “Can you fix it?” Using variance-based sensitivity analysis to reduce the input space of an agent-based model of land use change. In GeoComputational analysis and modeling of regional systems, 77–99. Cham: Springer.
   Google Scholar
• Ligmann-Zielinska, A., D. B. Kramer, K. S. Cheruvelil, and P. A. Soranno. 2014. Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance. PLoS ONE 9 (10):e109779. doi: 10.1371/journal.pone.0109779.
   PubMed Web of Science ®Google Scholar
• Ligmann-Zielinska, A., and L. Sun. 2010. Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change. International Journal of Geographical Information Science 24 (12):1829–50. doi: 10.1080/13658816.2010.490533.
   Web of Science ®Google Scholar
• Liukkonen, L., D. Ayllón, M. Kunnasranta, M. Niemi, J. Nabe-Nielsen, V. Grimm, and A.-M. Nyman. 2018. Modelling movements of Saimaa ringed seals using an individual-based approach. Ecological Modelling 368:321–35. doi: 10.1016/j.ecolmodel.2017.12.002.
   Web of Science ®Google Scholar
• Mammen, M. P., C. Pimgate, C. J. M. Koenraadt, A. L. Rothman, J. Aldstadt, A. Nisalak, R. G. Jarman, et al. 2008. Spatial and temporal clustering of dengue virus transmission in Thai villages. PLoS Medicine 5 (11):e205. doi: 10.1371/journal.pmed.0050205.
   PubMed Web of Science ®Google Scholar
• Manson, S. M. 2005. Agent-based modeling and genetic programming for modeling land change in the Southern Yucatan peninsular region of Mexico. Agriculture, Ecosystems & Environment 111 (1):47–62. doi: 10.1016/j.agee.2005.04.024.
   Web of Science ®Google Scholar
• Mao, L. 2011. Agent-based simulation for weekend-extension strategies to mitigate influenza outbreaks. BMC Public Health 11 (1):522. doi: 10.1186/1471-2458-11-522.
   Google Scholar
• Mao, L., and L. Bian. 2010. Spatial–temporal transmission of influenza and its health risks in an urbanized area. Computers, Environment and Urban Systems 34 (3):204–15. doi: 10.1016/j.compenvurbsys.2010.03.004.
   PubMed Web of Science ®Google Scholar
• Nisalak, A., T. P. Endy, S. Nimmannitya, S. Kalayanarooj, R. M. Scott, D. S. Burke, … D. W. Vaughn. 2003. Serotype-specific dengue virus circulation and dengue disease in Bangkok, Thailand from 1973 to 1999. The American Journal of Tropical Medicine and Hygiene 68 (2):191–202.
   PubMed Web of Science ®Google Scholar
• Openshaw, S. 1984. The modifiable areal unit problem. Norwich, UK: Geo Books.
   Google Scholar
• Rahmandad, H., and J. Sterman. 2008. Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science 54 (5):998–1014. doi: 10.1287/mnsc.1070.0787.
   Web of Science ®Google Scholar
• Saltelli, A., S. Tarantola, and K.-S. Chan. 1999. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41 (1):39–56. doi: 10.1080/00401706.1999.10485594.
   Web of Science ®Google Scholar
• Scott, T. W., and A. C. Morrison. 2010. Vector dynamics and transmission of dengue virus: Implications for dengue surveillance and prevention strategies. In Dengue virus, ed. A. Rothman, 115–28. Berlin: Springer.
   Google Scholar
• Shook, E., and S. Wang. 2015. Investigating the influence of spatial and temporal granularities on agent‐based modeling. Geographical Analysis 47 (4):321–48. doi: 10.1111/gean.12080.
   Web of Science ®Google Scholar
• Tang, W., and M. Jia. 2014. Global sensitivity analysis of a large agent-based model of spatial opinion exchange: A heterogeneous multi-GPU acceleration approach. Annals of the Association of American Geographers 104 (3):485–509. doi: 10.1080/00045608.2014.892342.
   Web of Science ®Google Scholar
• Tang, W., S. Wang, D. A. Bennett, and Y. Liu. 2011. Agent-based modeling within a cyberinfrastructure environment: A service-oriented computing approach. International Journal of Geographical Information Science 25 (9):1323–46. doi: 10.1080/13658816.2011.585342.
   Web of Science ®Google Scholar
• Thomas, S. J., J. Aldstadt,, R. G. Jarman, D. Buddhari, S. Yoon, I.-K. Suntayakorn, J. H. Richardson, A. L. Rothman, et al. 2015. Improving dengue virus capture rates in humans and vectors in Kamphaeng Phet Province, Thailand, using an enhanced spatiotemporal surveillance strategy. The American Journal of Tropical Medicine and Hygiene 93 (1):24–32.
   Web of Science ®Google Scholar
• Vaughn, D. W., S. Green, S. Kalayanarooj, B. L. Innis, S. Nimmannitya, S. Suntayakorn, T. P. Endy, B. Raengsakulrach, A. L. Rothman, F. A. Ennis, et al. 2000. Dengue viremia titer, antibody response pattern, and virus serotype correlate with disease severity. The Journal of Infectious Diseases 181 (1):2–9. doi: 10.1086/315215.
   PubMed Web of Science ®Google Scholar
• Wang, S. 2010. A cyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis. Annals of the Association of American Geographers 100 (3):535–57. doi: 10.1080/00045601003791243.
   Web of Science ®Google Scholar
• World Health Organization. 2012. Global strategy for dengue prevention and control 2012–2020. Accessed February 16, 2020. http://apps.who.int/iris/bitstream/10665/75303/1/9789241504034_eng.pdf
   Google Scholar
• Yoon, I.-K., A. Getis, J. Aldstadt, A. L. Rothman, D. Tannitisupawong, C. J. M. Koenraadt, T. Fansiri, J. W. Jones, A. C. Morrison, R. G. Jarman, et al. 2012. Fine scale spatiotemporal clustering of dengue virus transmission in children and Aedes aegypti in rural Thai villages. PLoS Neglected Tropical Diseases 6 (7):e1730. doi: 10.1371/journal.pntd.0001730.
   PubMed Web of Science ®Google Scholar