ABSTRACT
Model-driven policymaking for epidemic control is a challenging collaborative process. It begins when a team of public-health officials, epidemiologists, and economists construct a reasonably predictive disease model representative of the team’s region of interest as a function of its unique socio-economic and demographic characteristics. As the team considers possible interventions such as school closures, social distancing, vaccination drives, etc., they need to simultaneously model each intervention’s effect on disease spread and economic cost. The team then engages in an extensive what-if analysis process to determine a cost-effective policy: a schedule of when, where and how extensively each intervention should be applied. This policymaking process is often an iterative and laborious programming-intensive effort where parameters are introduced and refined, model and intervention behaviors are modified, and schedules changed. We have designed and developed EpiPolicy to support this effort.
EpiPolicy is a policy aid and epidemic simulation tool that supports the mathematical specification and simulation of disease and population models, the programmatic specification of interventions and the declarative construction of schedules. EpiPolicy’s design supports a separation of concerns in the modeling process and enables capabilities such as the iterative and automatic exploration of intervention plans with Monte Carlo simulations to find a cost-effective one. We report expert feedback on EpiPolicy. In general, experts found EpiPolicy’s capabilities powerful and transformative, when compared with their current practice.
Supplemental Material
Available for Download
- 2016. Retraction [retraction of: Henson KE, Jagsi R, Cutter D, McGale P, Taylor C, Darby SC. J Clin Oncol. 2016 Mar 10;34(8):803-9]. Journal of Clinical Oncology 34, 27 (2016), 3358–3359. https://doi.org/10.1200/JCO.2016.69.0875 PMID: 27528722.Google Scholar
- David Adam. 2020. Special report: The simulations driving the world’s response to COVID-19.Nature 580, 7802 (2020), 316–319.Google Scholar
- Marco Ajelli, Bruno Gonçalves, Duygu Balcan, Vittoria Colizza, Hao Hu, José J Ramasco, Stefano Merler, and Alessandro Vespignani. 2010. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC infectious diseases 10, 1 (2010), 1–13.Google Scholar
- Anna Bershteyn, Jaline Gerardin, Daniel Bridenbecker, Christopher W Lorton, Jonathan Bloedow, Robert S Baker, Guillaume Chabot-Couture, Ye Chen, Thomas Fischle, Kurt Frey, 2018. Implementation and applications of EMOD, an individual-based multi-disease modeling platform. Pathogens and disease 76, 5 (2018), fty059.Google Scholar
- Jayanti Bhandari Neupane, Ram P Neupane, Yuheng Luo, Wesley Y Yoshida, Rui Sun, and Philip G Williams. 2019. Characterization of leptazolines A–D, polar oxazolines from the cyanobacterium Leptolyngbya sp., reveals a glitch with the “Willoughby–Hoye” scripts for calculating NMR chemical shifts. Organic letters 21, 20 (2019), 8449–8453.Google Scholar
- Derdei Bichara and Abderrahman Iggidr. 2018. Multi-patch and multi-group epidemic models: a new framework. Journal of mathematical biology 77, 1 (2018), 107–134.Google ScholarCross Ref
- George EP Box. 1979. Robustness in the strategy of scientific model building. In Robustness in statistics. Elsevier, 201–236.Google Scholar
- Tom Britton, Lisa Jeng, Graham Carver, Paul Cheak, and Tomer Katzenellenbogen. 2013. Reversible debugging software. Judge Bus. School, Univ. Cambridge, Cambridge, UK, Tech. Rep (2013).Google Scholar
- Shannon Brownlee and Jeanne Lenzer. 2020. The Ioannidis Affair: A Tale of Major Scientific Overreaction. https://www.scientificamerican.com/article/the-ioannidis-affair-a-tale-of-major-scientific-overreaction/ Accessed 05-04-2021.Google Scholar
- Caitlin Rivers. 2015. Epipy: Python tools for epidemiology. https://cmrivers.github.io/epipy/Google Scholar
- Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus A Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. Stan: a probabilistic programming language.Grantee Submission 76, 1 (2017), 1–32.Google Scholar
- Coronavirus Resource Center. 2021. MORTALITY ANALYSES. https://coronavirus.jhu.edu/data/mortality Accessed 05-04-2021.Google Scholar
- Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ Accessed 13-07-2020.Google Scholar
- GUILLAUME M. J-B. CHASLOT, MARK H. M. WINANDS, H. JAAP VAN DEN HERIK, JOS W. H. M. UITERWIJK, and BRUNO BOUZY. 2008. PROGRESSIVE STRATEGIES FOR MONTE-CARLO TREE SEARCH. New Mathematics and Natural Computation 04, 03 (2008), 343–357. https://doi.org/10.1142/S1793005708001094 arXiv:https://doi.org/10.1142/S1793005708001094Google ScholarCross Ref
- Rémi Coulom. 2006. Efficient selectivity and backup operators in Monte-Carlo tree search. In In: Proceedings Computers and Games 2006. Springer-Verlag.Google Scholar
- Janet Davis, Harry Hochheiser, Juan Pablo Hourcade, Jeff Johnson, Lisa Nathan, and Janice Tsai. 2012. Occupy CHI! engaging US policymakers. In CHI’12 Extended Abstracts on Human Factors in Computing Systems. 1139–1142.Google Scholar
- Judith V Douglas, Simone Bianco, Stefan Edlund, Tekla Engelhardt, Matthias Filter, Taras Günther, Kun Hu, Emily J Nixon, Nereyda L Sevilla, Ahmad Swaid, 2019. STEM: an open source tool for disease modeling. Health security 17, 4 (2019), 291–306.Google Scholar
- Stefan B Edlund, Matthew A Davis, and James H Kaufman. 2010. The spatiotemporal epidemiological modeler. In Proceedings of the 1st ACM International Health Informatics Symposium. 817–820.Google ScholarDigital Library
- Neil Ferguson, Daniel Laydon, Gemma Nedjati Gilani, Natsuko Imai, Kylie Ainslie, Marc Baguelin, Sangeeta Bhatia, Adhiratha Boonyasiri, ZULMA Cucunuba Perez, Gina Cuomo-Dannenburg, 2020. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. (2020).Google Scholar
- John J Grefenstette, Shawn T Brown, Roni Rosenfeld, Jay DePasse, Nathan TB Stone, Phillip C Cooley, William D Wheaton, Alona Fyshe, David D Galloway, Anuroop Sriram, 2013. FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC public health 13, 1 (2013), 1–14.Google Scholar
- Thomas Herndon, Michael Ash, and Robert Pollin. 2014. Does high public debt consistently stifle economic growth? A critique of Reinhart and Rogoff. Cambridge journal of economics 38, 2 (2014), 257–279.Google ScholarCross Ref
- David James Heslop, Abrar Ahmad Chughtai, Chau Minh Bui, and C Raina MacIntyre. 2017. Publicly available software tools for decision-makers during an emergent epidemic—Systematic evaluation of utility and usability. Epidemics 21(2017), 1–12.Google ScholarCross Ref
- John PA Ioannidis. 2020. A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data. Stat 17(2020).Google Scholar
- Samuel M Jenness, Steven M Goodreau, and Martina Morris. 2018. EpiModel: an R package for mathematical modeling of infectious disease over networks. Journal of statistical software 84 (2018).Google Scholar
- Michael A Johansson, Ann M Powers, Nicki Pesik, Nicole J Cohen, and J Erin Staples. 2014. Nowcasting the spread of chikungunya virus in the Americas. PloS one 9, 8 (2014), e104915.Google ScholarCross Ref
- Levente Kocsis and Csaba Szepesvári. 2006. Bandit Based Monte-Carlo Planning. In Machine Learning: ECML 2006, Johannes Fürnkranz, Tobias Scheffer, and Myra Spiliopoulou(Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 282–293.Google Scholar
- Jonathan Lazar. 2015. Public policy and HCI: making an impact in the future. Interactions 22, 5 (2015), 69–71.Google ScholarDigital Library
- Jonathan Lazar, Julio Abascal, Simone Barbosa, Jeremy Barksdale, Batya Friedman, Jens Grossklags, Jan Gulliksen, Jeff Johnson, Tom McEwan, Loïc Martínez-Normand, 2016. Human–computer interaction and international public policymaking: a framework for understanding and taking future actions. now publishers.Google Scholar
- Jonathan Lazar, Simone Barbosa, Jan Gulliksen, Tom McEwan, Loïc Martinez Normand, Philippe Palanque, Raquel Prates, Janice Tsai, Marco Winckler, and Volker Wulf. 2013. Workshop on engaging the human-computer interaction community with public policymaking internationally. In CHI’13 Extended Abstracts on Human Factors in Computing Systems. 3279–3282.Google Scholar
- BY Lee, LA Haidari, and MS Lee. 2013. Modelling during an emergency: the 2009 H1N1 influenza pandemic. Clinical Microbiology and Infection 19, 11 (2013), 1014–1022.Google ScholarCross Ref
- Zhicheng Liu and Jeffrey Heer. 2014. The effects of interactive latency on exploratory visual analysis. IEEE transactions on visualization and computer graphics 20, 12(2014), 2122–2131.Google ScholarCross Ref
- Christopher W Lorton, Joshua L Proctor, Min K Roh, and Philip A Welkhoff. 2019. Compartmental Modeling Software: a fast, discrete stochastic framework for biochemical and epidemiological simulation. In International Conference on Computational Methods in Systems Biology. Springer, 308–314.Google ScholarDigital Library
- Jennifer Manuel and Clara Crivellaro. 2020. Place-Based Policymaking and HCI: Opportunities and Challenges for Technology Design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–16.Google ScholarDigital Library
- Omid Mohaddesi, Yifan Sun, Rana Azghandi, Rozhin Doroudi, Sam Snodgrass, Ozlem Ergun, Jacqueline Griffin, David Kaeli, Stacy Marsella, and Casper Harteveld. 2020. Introducing Gamettes: A Playful Approach for Capturing Decision-Making for Informing Behavioral Models. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
- MRC Centre for Global Infectious Disease Analysis. 2020. COVID-19 CovidSim Model. https://github.com/mrc-ide/covid-sim Accessed 13-07-2021.Google Scholar
- Lisa A Rosenfeld, Claude Earl Fox, Debora Kerr, Erin Marziale, Amy Cullum, Kanchan Lota, Jonathan Stewart, and Mary Zack Thompson. 2009. Use of computer modeling for emergency preparedness functions by local and state health officials: a needs assessment. Journal of public health management and practice 15, 2 (2009), 96–104.Google ScholarCross Ref
- Kankoé Sallah, Roch Giorgi, Linus Bengtsson, Xin Lu, Erik Wetter, Paul Adrien, Stanislas Rebaudet, Renaud Piarroux, and Jean Gaudart. 2017. Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model. International journal of health geographics 16, 1 (2017), 1–11.Google ScholarCross Ref
- Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, and Stefano Tarantola. 2008. Global sensitivity analysis: the primer. John Wiley & Sons.Google Scholar
- Dalmeet Singh Chawla. 2020. Critiqued coronavirus simulation gets thumbs up from code-checking efforts.Nature (2020), 323–324.Google Scholar
- Alex Smajgl, Daniel G Brown, Diego Valbuena, and Marco GA Huigen. 2011. Empirical characterisation of agent behaviours in socio-ecological systems. Environmental Modelling & Software 26, 7 (2011), 837–844.Google ScholarDigital Library
- Anne Spaa, Abigail Durrant, Chris Elsden, and John Vines. 2019. Understanding the Boundaries between Policymaking and HCI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–15.Google ScholarDigital Library
- Jia Bin Tan, Matthew James Cook, Prishanee Logan, Liudmila Rozanova, and Annelies Wilder-Smith. 2021. Singapore’s Pandemic Preparedness: An Overview of the First Wave of COVID-19. International journal of environmental research and public health 18, 1(2021), 252.Google Scholar
- Charles A Taylor, Christopher Boulos, and Douglas Almond. 2020. Livestock plants and COVID-19 transmission. Proceedings of the National Academy of Sciences 117, 50(2020), 31706–31715.Google ScholarCross Ref
- WHO Ebola Response Team. 2014. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. New England Journal of Medicine 371, 16 (2014), 1481–1495.Google ScholarCross Ref
- Vanessa Thomas, Christian Remy, Mike Hazas, and Oliver Bates. 2017. HCI and environmental public policy: Opportunities for engagement. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 6986–6992.Google ScholarDigital Library
- Bill Tomlinson, M Six Silberman, Andrew W Torrance, Kurt Squire, Paramdeep S Atwal, Ameya N Mandalik, Sahil Railkar, and Rebecca W Black. 2020. A Participatory Simulation of the Accountable Capitalism Act. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
- Wouter Van den Broeck, Corrado Gioannini, Bruno Gonçalves, Marco Quaggiotto, Vittoria Colizza, and Alessandro Vespignani. 2011. The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC infectious diseases 11, 1 (2011), 1–14.Google Scholar
- Maria D Van Kerkhove and Neil M Ferguson. 2012. Epidemic and intervention modelling: a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic. Bulletin of the World Health Organization 90 (2012), 306–310.Google ScholarCross Ref
- Danielle Varda, Jo Ann Shoup, and Sara Miller. 2012. A systematic review of collaboration and network research in the public affairs literature: implications for public health practice and research. American journal of public health 102, 3 (2012), 564–571.Google Scholar
- Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods 17, 3 (2020), 261–272.Google Scholar
- Hadley Wickham. 2017. The tidyverse. R package ver 1, 1 (2017), 1.Google Scholar
- Jianyong Wu, Radhika Dhingra, Manoj Gambhir, and Justin V Remais. 2013. Sensitivity analysis of infectious disease models: methods, advances and their application. Journal of The Royal Society Interface 10, 86 (2013), 20121018.Google ScholarCross Ref
- Xin Ye, Karthik Konduri, Ram M Pendyala, Bhargava Sana, and Paul Waddell. 2009. A methodology to match distributions of both household and person attributes in the generation of synthetic populations. In 88th Annual Meeting of the Transportation Research Board, Washington, DC.Google Scholar
- Ye, Xin, Karthik Konduri, Ram Pendyala, Bhargava Sana and Paul Waddell. 2009. SynthPop. https://github.com/UDST/synthpop Accessed 13-07-2020.Google Scholar
Recommendations
A Patch Model for Pandemic Influenza Simulation in Korea
E-SCIENCEW '10: Proceedings of the 2010 Sixth IEEE International Conference on e-Science WorkshopsFor years, we have researched epidemiology model suitable for Korea based on InfluSim model, to analyze the spread of pandemic influenza. However, there is a problem with InfluSim, because the demographic characteristics and the movement of population ...
A contact-network-based simulation model for evaluating interventions under "what-if" scenarios in epidemic
WSC '12: Proceedings of the Winter Simulation ConferenceInfectious disease pandemics/epidemics have been serious concerns worldwide. Simulations for public health interventions are practically helpful in assisting policy makers to make wise decisions to control and mitigate the spread of infectious diseases. ...
Smart medication management system and multiple interventions for medication adherence
To keep healthcare costs under control, a high-level of medication adherence, or compliance with medication regimen, must be achieved. The multifaceted nature of medication adherence, due to a large number of underlying factors, presents several ...
Comments