Operation Outbreak: An App-based Platform for Infectious Disease Education and Research

https://doi.org/10.56927/345434

Andres Colubri

ABSTRACT

The COVID-19 crisis brought to the forefront the importance of pandemic preparedness, predictive modeling for outbreak response, and literacy about infectious diseases, particularly in the midst of easy-to-spread misinformation and distrust about health interventions from the general public. Operation Outbreak (OO), a technology-based platform for infectious disease education and epidemiological modeling that my collaborators and I have been developing since 2016, provides an immersive learning experience that can be deployed in middle- and high-schools, colleges, conferences, and other settings. It is enabled by a smartphone app that spreads a virtual pathogen over Bluetooth, as well as accompanying tools for pre-simulation configuration and post-simulation sense-making. Furthermore, data from OO simulations could be used to develop and test predictive models to enable a better response to future outbreaks.

Article

Together with Harvard University Professor Pardis Sabeti and Dr. Todd Brown from The Inspire Project, we have been working since 2016 on the Operation Outbreak (OO) project. This project was initially motivated by the ever-present pandemic threat (at the time, made apparent by the West African Ebola outbreak) and the challenge of educating students about it in more engaging ways. Only five years later, COVID-19 reified epidemiologists’ predictions of a global pandemic caused by an emerging pathogen. ¹ As public health measures contributed to curbing the spread of COVID-19, innovative educational programs on infectious disease could also play a role in controlling this pandemic––and in preparing for or preempting the next one. OO started as a mock outbreak activity for middle schoolers using stickers to mimic pathogen transmission, and eventually evolved to comprise three interconnected components: (1) an academic curriculum and textbook on pathogen biology, epidemiology, public health, political decision-making, and science communication during health emergencies; (2) an outbreak simulation experiential learning activity that synthesizes curricular content with a Bluetooth-enabled smartphone app; and (3) an interactive dashboard that visualizes data generated during the outbreak simulation for informed reflection and skill development in epidemiology and quantitative data analysis. Facilitated by the smartphone app, the outbreak simulation spreads a virtual pathogen across participants’ phones via Bluetooth. Additionally, the simulation incorporates a series of role-playing activities for the students (e.g., governance, research, healthcare), taking place during the simulated outbreak and mirroring a real-world epidemic. The current OO app supports Bluetooth Low Energy beacons and QR codes that simulate infectious sources and protective items (e.g., face masks and hazmat suits), as well as interventions such as testing and vaccinations. ² During 2002 and 2021 we made substantial progress in the development of the app technology and conducting several large-scale pilots in middle and high schools, as well as colleges. Currently, my lab in the Program of Bioinformatics and Integrative Biology at the University of Massachusetts Chan Medical School is focused not only on developing the technology behind OO, but also using OO as a “real-life” simulator that could help create and validate epidemiological models to better respond to future outbreaks. ³ We created OO in collaboration with Sarasota Military Academy (SMA) Prep school in 2015 as a two-week curriculum in pandemic preparedness culminating in an experiential learning class-wide outbreak simulation. Stickers and other analog props initially mediated contagion. In 2018, we developed a simple app for iOS and Android to simulate person-to-person transmission using Bluetooth. The OO app-based simulations at SMA have involved more than 150 eighth-grade students who took on roles as general population, epidemiologists, health care workers, and government officials; they tried to prevent the virtual pathogen from infecting more than a predetermined fraction of the players in order to “win the game.” During these simulations, we sought to balance a realistic outbreak scenario with an engaging learning experience for middle schoolers. OO allows parameters to be adjusted in order to run different outbreak scenarios with existing pathogens, novel pathogens based on real microbes––or even fictional diseases. For the first simulation using the app in 2018, we chose Ebola virus as the pathogen, setting the app parameters––such as symptoms and case fatality rate––accordingly. In 2019 and early 2020, given reported risks of emerging respiratory viruses, ⁴ we simulated a coronavirus with the reproductive number of SARS, ⁵ the clinical symptoms of MERS, ⁶ and asymptomatic transmission, which turned out to be one of the defining characteristics of SARS-CoV-2. ⁷

Screen captures of the OO app, showing the welcome and main screen (left), the different “health states” (left) and the protective items that can be acquired by scanning QR codes (center bottom)

We have seen socio-behavioral parallels between our simulated outbreaks and real outbreaks that recapitulate observations from earlier research on students’ engagement and perceptions of authenticity. ⁸ The OO simulations have foreshadowed the political distrust and social protests that have occurred around the word during the COVID-19 pandemic. In one instance, a group of students tried to spread disinformation regarding “vaccine” availability to manipulate behaviors of the “general population.” In another instance, some students “falsified” health passes by taking screenshots of the app in the healthy state to avoid quarantine. Such realistic scenarios undeniably provide valuable insights into how citizenry behaves during outbreaks, leading to better learning outcomes. ⁹ Our preliminary observations suggest that the OO simulations’ realism and hands-on nature engages students of all backgrounds. Initial student interest in taking part in OO at SMA was nominal, but it increased dramatically with the introduction of the app. OO was the most anticipated lesson of the year by all classes in any subject in the school for the third straight year, based on survey results and parent reports. We found that students were eager to play the roles of epidemiologists and triage workers. In the last two years we run OO at SMA, 70 of 185 students signed up for this role: over 50% were female and 30% were underrepresented-in-STEM minorities (Hispanic or Black).

A large body of research indicates that simulation-based games and extended (virtual/augmented/mixed) reality environments can have a positive impact on learning outcomes. ^{10,11,12,13,14,15} Furthermore, a recent review study of NSF ITEST projects discovered that “game-based learning and game design experiences” can improve STEM learning outcomes, increase computational literacy, deepen engagement, and improve knowledge of industry opportunities. ¹⁶ Moreover, the study affirmed that such projects naturally broaden the participation of “underrepresented groups in computer science or STEM”. In the context of infectious disease education, there are many examples of outbreak simulation games, ranging from commercial tabletop (Pandemic, 2020) and computer games ^17,18 aimed at general audiences to more realistic exercises aimed to supplement classroom curricula on epidemiology from middle school to college levels. ^{19,20,21,22,23}

Realistic outbreak scenarios predict population behavior and increase engagement.

We have seen socio-behavioral parallels between our simulated outbreaks and real outbreaks that recapitulate observations from earlier research on students’ engagement and perceptions of authenticity.⁸ The OO simulations have foreshadowed the political distrust and social protests that have occurred around the word during the COVID-19 pandemic. In one instance, a group of students tried to spread disinformation regarding “vaccine” availability to manipulate behaviors of the “general population.” In another instance, some students “falsified” health passes by taking screenshots of the app in the healthy state to avoid quarantine. Such realistic scenarios undeniably provide valuable insights into how citizenry behaves during outbreaks, leading to better learning outcomes.⁹ Our preliminary observations suggest that the OO simulations’ realism and hands-on nature engages students of all backgrounds. Initial student interest in taking part in OO at SMA was nominal, but it increased dramatically with the introduction of the app. OO was the most anticipated lesson of the year by all classes in any subject in the school for the third straight year, based on survey results and parent reports. We found that students were eager to play the roles of epidemiologists and triage workers. In the last two years we run OO at SMA, 70 of 185 students signed up for this role: over 50% were female and 30% were underrepresented-in-STEM minorities (Hispanic or Black).

A large body of research indicates that simulation-based games and extended (virtual/augmented/mixed) reality environments can have a positive impact on learning outcomes.^{10,11,12,13,14,15} Furthermore, a recent review study of NSF ITEST projects discovered that “game-based learning and game design experiences” can improve STEM learning outcomes, increase computational literacy, deepen engagement, and improve knowledge of industry opportunities.¹⁶ Moreover, the study affirmed that such projects naturally broaden the participation of “underrepresented groups in computer science or STEM.” In the context of infectious disease education, there are many examples of outbreak simulation games, ranging from commercial tabletop (Pandemic, 2020) and computer games^17,18 aimed at general audiences to more realistic exercises aimed to supplement classroom curricula on epidemiology from middle school to college levels.^{19,20,21,22,23}

The idea of conducting real-life participatory simulations of infectious disease outbreaks mediated by portable computing devices has a rich history. Pioneering work at the MIT Media Lab around the “Thinking Tag” technology²⁴ enabled participants to become active “agents” in decentralized simulations of dynamic systems. Research on such simulations, originally with the aid of Thinking Tags,⁸ later through Palm computers,^25,26 and more recently with smartphone-enabled augmented reality support the conclusion that they can lead to more authentic experiences by bringing the simulation into the physical world of the learner.⁷ They can also engage students in topics “from biology (basics of epidemiology) to health (prevention of STDs) to history (transmission of the plague).”²⁶ Therefore, OO is part of a decades-long effort to incorporate mobile technologies in STEM education to make it more participatory and engaging. Perhaps the only difference with those earlier initiatives is that technologies have matured to the point that we can now realistically imagine country-level, or even international deployments of OO simulations.

Next steps: scaling up OO and using data for epidemiological research

Recent OO simulations: at the HERricane program, organized by New York City Emergency Management Department in April 2022 (left), and at Bronx Prep High School in March 2022 (right)

Despite the major challenges posed by the COVID-19 pandemic in the past two years, we were able to run several new deployments of OO across various educational settings: OO curriculum was incorporated into the Social Distancing Ambassador (SDA) program run by the City of Chicago as part of One Summer Chicago 2020 and over 250 SDA students (aged 16-24) participated in a 7-day OO simulation between July 27 and August 4. Another large-scale simulation with more than 350 college students occurred between October 29 and November 2 at Colorado Mesa University (CMU), which anticipated a spike of real COVID-19 infections as the data generated by the OO app suggested that some students flouted social distancing guidelines during Halloween celebrations. More recently, we ran further simulations at Brigham Young University, Rowan University, Galloway School, and Wisconsin Eagle School, among others. Between March and April of this year, we had a large-scale one-day simulation at Democracy Prep High School in Bronx, NY, involving over 300 students, and a multiple-day simulation with varying epidemiological parameters as part of the inaugural HERricane NYC program organized by New York City's Emergency Management Department to encourage young women in grades 9-12 to pursue careers and leadership roles in emergency management. After this stage of testing and piloting, our aim is two-pronged: on the educational front, we will be building teacher-centric tools to allow educators to customize the experiential learning simulation to their classroom and curriculum and developing a professional development program that will guide them in the process of using these tools, planning, and executing outbreak simulations, and integrating them into their teaching practices. On the research front, we are creating a genetic-epidemiology modeling framework that uses OO as a critical piece to generate high-quality outbreak data that contains “real-life” behavioral patterns that are hard to simulate otherwise,²⁷ and then would leverage OO ground-truth data to train and validate novel algorithms for outbreak reconstruction and risk prediction models.^28,29 We have recently shown how OO data can be used to inform pandemic mitigation by calculating potential transmission chains inferred from contacts during OO simulations,³⁰ contributing to a growing body of work on the use of proximity sensing in mobile phones to model disease spread.^31,32,33 These results support our belief that OO has the potential for beneficial impact on both STEM education and epidemiological research, and therefore we are fully committed to keep moving forward with the project.

ABOUT THE AUTHOR

Dr. Andres Colubri is a computational researcher at the Sabeti lab and a member of the Broad Institute and the Processing Project, who focuses on disease prediction models and works with several partners in the U.S. and West Africa that form the African Center of Excellence for Genomics of Infectious Diseases. He obtained a doctoral degree in mathematics at the Universidad Nacional del Sur, became a Burroughs Wellcome postdoctoral fellow at the Sosnick and Berry labs at the University of Chicago, earned an MFA degree from the Design Media Arts program at the University of California, Los Angeles, and was a professor of bioinformatics at Jeju National University, South Korea.

REFERENCES

1. Osterholm, M. (2007). Unprepared for a Pandemic. Foreign Affairs, 86(2), 47-57. Retrieved from from www.jstor.org/stable/20032283

2. Colubri A., Kemball M., Sani K., Boehm C., Mutch-Jones K., Fry B., Brown T., & Sabeti P.C.. (2020). Preventing Outbreaks through Interactive, Experiential Real-Life Simulations. Cell, 182(6), 1366-1371. DOI: 10.1016/j.cell.2020.08.042

3. The New York Times. (2021). The Future of Virus Tracking Can Be Found on This College Campus. The New York Times. https://www.nytimes.com/2021/05/17/health/coronavirus-broad-colorado-mesa-sabeti.html

4. New Take on Ubiquitous and Accessible Mobile Computing. (2005). Journal of Science Education and Technology, 14(3), 285-297. DOI: 10.1007/s10956-005-7194-0

5. Lipsitch, M. (2003). Transmission Dynamics and Control of Severe Acute Respiratory Syndrome. Science, 300(5627), 1966-1970.

6. Assiri, A., Al-Tawfiq, J. A., Al-Rabeeah, A. A, Al-Rabiah, F. A., Al-Hajjar, S., Al-Barrak, A., … Memish, Z. A. (2013). Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: A descriptive study. The Lancet Infectious Diseases, 13(9), 752-761.

7. Bai, Y., Yao, L., Wei, T., Tian, F., Jin, D. Y., Chen, L., & Wang, M. (2020). Presumed Asymptomatic Carrier Transmission of COVID-19. JAMA, 323(14), 1406–1407. https://doi.org/10.1001/jama.2020.2565

8. Colella, V. (2000) Participatory Simulations: Building Collaborative Understanding Through Immersive Dynamic Modeling. The Journal of the Learning Sciences, 9:4, 471-500, DOI: 10.1207/S15327809JLS0904_4

9. Freeman, S., Eddy, S.L., McDonough, M., Smith, M.K., Okoroafor, N., Jordt, H., Wenderoth, M.P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415.

10. Sitzman, T. (2011). A meta-analytic examination of the instructional effectiveness of computer- based simulation games. Personnel Psychology, 64(2), 489–528

11. Chen, J. A., Metcalf, S. J., & Tutwiler, M. S. (2014). Motivation and beliefs about the nature of scientific knowledge within an immersive virtual ecosystems environment. Journal of Contemporary Educational Psychology, 39(2), 112–123.

12. Grotzer, T. A., Powell, M., Kamarainen, A., Courter, C., Tutwiler, M. S., Metcalf, S., & Dede, C. (2015). Turning transfer inside out: The affordances of virtual worlds and mobile devices in real world contexts for teaching about causality across time and distance in ecosystems. Technology, Knowledge, and Learning, 20(1), 43–69.

13. Crompton, H., Burke, D., & Gregory, K. (2017). The use of mobile learning in PK-12 education: A systematic review. Computers & Education, 110, 51-63.

14. Thisgaard, M., & Makransky, G. (2017). Virtual Learning Simulations in High School: Effects on Cognitive and Non-cognitive Outcomes and Implications on the Development of STEM Academic and Career Choice. Frontiers in psychology, 8, 805. DOI: 10.3389/fpsyg.2017.00805

15. Falloon, G. (2019). Using simulations to teach young students science concepts: An Experiential Learning theoretical analysis. Computers & Education, 135, 138-159.

16. Vogt, K., Remold, J., & Parker, C. (2016). STEM Learning Games and Game Design in ITEST Projects. STEM Learning and Research Center, Education Development Center, Waltham, MA. Retrieved from https://go.edc.org/ITEST-Gaming.

17. Plague Inc. (2020). Retrieved from https://www.ndemiccreations.com/en/22-plague-inc

18. The New York Times. (2020). When a Gaming Fantasy Is Eerily Close to Reality. Retrieved from https://www.nytimes.com/2020/04/08/arts/plague-inc-video-game-gaming-coronavirus-covid-pandemic.html

19. Bellan, S. E., Pulliam, J. R., Scott, J. C., Dushoff, J., & MMED Organizing Committee (2012). How to make epidemiological training infectious. PLoS biology, 10(4), e1001295. https://doi.org/10.1371/journal.pbio.1001295

20. Barber, N. C, & Stark, L. A. (2015). Online resources for understanding outbreaks 
and infectious diseases. CBE Life Sciences Education, 14(1), Fe1.

21. Frimpong, J.A., Amo-Addae, M.P., Adewuyi, P.A., Park, M.M., Hall, C.D., & Nagbe, T.K. (2017). Investigating an outbreak of measles in Margibi County, Liberia, October 2015. The Pan African Medical Journal, 27(Suppl 1), 5.

22. Center for Disease Control and Prevention. (2018). Solve the Outbreak. Retrieved from https://www.cdc.gov/mobile/applications/sto/web-app.html

23. Marvasi, M., Sebastian, G., & Lorenzo, S. J. (2019). Fostering researcher identity in STEM distance education: Impact of a student-led on-line case study. FEMS Microbiology Letters, 366(6), FEMS microbiology letters, 2019-03-01, Vol.366 (6).

24. Borovoy, R., McDonald, M., Martin, F., & Resnick, M. (1996). Things that blink: Computationally augmented name tags. IBM Systems Journal, 35(3), 488-495

25. Soloway, E., Norris C., Blumenfeld, P., Fishman, B., Krajcik, J., & Marx, R. (2001). Handheld Devices are Ready-at-Hand. Communications of the Association for Computing Machinery 44, 15–20.

26. Klopfer, E., Yoon, S., & Perry, J. Using Palm Technology in Participatory Simulations of Complex Systems: A 

27. Lofgren ET, Fefferman NH. The untapped potential of virtual game worlds to shed light on real world epidemics. Lancet Infect Dis. 2007 Sep;7(9):625-9. doi: 10.1016/S1473-3099(07)70212-8. PMID: 17714675.

28. Lau, M. S., Marion, G., Streftaris, G., & Gibson, G. (2015). A Systematic Bayesian Integration of Epidemiological and Genetic Data. PLoS computational biology, 11(11), e1004633. https://doi.org/10.1371/journal.pcbi.1004633

29. Sahneh, F. D., Vajdi, A., Shakeri, H., Fan, F., & Scoglio, C. (2017). GEMFsim: A stochastic simulator for the generalized epidemic modeling framework. Journal of computational science, 22, 36-44.

30. Specht I, Sani K, Loftness BC, Hoffman C, Gionet G, Bronson A, Marshall J, Decker C, Bailey L, Siyanbade T, Kemball M, Pickett BE, Hanage WP, Brown T, Sabeti PC, Colubri A. Analyzing the Impact of a Real-life Outbreak Simulator on Pandemic Mitigation: an Epidemiological Modeling Study. medRxiv 2022.02.04.22270198; doi: https://doi.org/10.1101/2022.02.04.22270198

31. Yoneki, E. (2011, September). Fluphone study: Virtual disease spread using haggle. In Proceedings of the 6th ACM Workshop on Challenged Networks (pp. 65-66).

32. Klepac, P., Kissler, S., & Gog, J. (2018). Contagion! the bbc four pandemic–the model behind the documentary. Epidemics, 24, 49-59.

33. Dandekar, R., Henderson, S. G., Jansen, H. M., McDonald, J., Moka, S., Nazarathy, Y., ... & Vuorinen, A. (2021). Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics. Patterns, 2(3), 100220.