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Emulation of epidemics via Bluetooth-based virtual safe virus spread: Experimental setup, software, and data [1]

['Azam Asanjarani', 'Department Of Statistics', 'The University Of Auckland', 'Auckland', 'New Zealand', 'Aminath Shausan', 'School Of Mathematics', 'Physics', 'The University Of Queensland', 'Brisbane']

Date: 2023-01

We describe an experimental setup and a currently running experiment for evaluating how physical interactions over time and between individuals affect the spread of epidemics. Our experiment involves the voluntary use of the Safe Blues Android app by participants at The University of Auckland (UoA) City Campus in New Zealand. The app spreads multiple virtual safe virus strands via Bluetooth depending on the physical proximity of the subjects. The evolution of the virtual epidemics is recorded as they spread through the population. The data is presented as a real-time (and historical) dashboard. A simulation model is applied to calibrate strand parameters. Participants’ locations are not recorded, but participants are rewarded based on the duration of participation within a geofenced area, and aggregate participation numbers serve as part of the data. The 2021 experimental data is available as an open-source anonymized dataset, and once the experiment is complete, the remaining data will be made available. This paper outlines the experimental setup, software, subject-recruitment practices, ethical considerations, and dataset description. The paper also highlights current experimental results in view of the lockdown that started in New Zealand at 23:59 on August 17, 2021. The experiment was initially planned in the New Zealand environment, expected to be free of COVID and lockdowns after 2020. However, a COVID Delta strain lockdown shuffled the cards and the experiment is currently extended into 2022.

In this paper, we describe the Safe Blues Android app experimental setup and a currently running experiment at the University of Auckland City Campus. This experiment is designed to evaluate how physical interactions over time and between individuals affect the spread of epidemics. The Safe Blues app spreads multiple virtual safe virus strands via Bluetooth based on the subjects’ unobserved social and physical proximity. The app does not record the participants’ locations, but participants are rewarded based on the duration of participation within a geofenced area, and aggregate participation numbers serve as part of the data. The 2021 experimental data is available, and once the experiment is complete, the remaining data will be made available. The experimental setup, software, subject recruitment practices, ethical considerations, and dataset description are all described in this paper. In addition, we present our current experimental results in view of the lockdown that started in New Zealand at 23:59 on August 17, 2021. The information we provide here may be useful to other teams planning similar experiments in the future.

Funding: Funds for incidentals and research assistance were supplied by the University of Queensland’s AI for Pandemics initiative as well as consultancy funds at The University of Melbourne. A.A. is supported by the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and UoA Faculty of Science Research & Development Fund no. 3721832. H.M.J. is supported by the Australian Research Council (ARC) under grant no. DP180101602. S.G.H. is supported by the National Science Foundation under grants CMMI 2035086 and DMS 2230023. Y.N. is supported by the Australian Research Council (ARC) under grant no. DP180101602 and UQ Research Support Package: Strategic Research Investment. A.S. is supported by the UQ Research Support Package: Strategic Research Investment. K.R.S is supported by NHMRC investigator grant 2007919. P.G.T. is supported by the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) under grant no. CE140100049. I.Z. is supported by Te Pūnaha Matatini. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

The COVID-19 pandemic is the most significant global event of the 21st century to date. In response to the pandemic, multiple solutions have been and are still being developed and deployed, including vaccines and contact tracing technologies. As part of this effort, various initiatives that integrate digital health and “AI systems” (artificial intelligence for pandemics) are being thought out. A key initiative includes measuring the spread of pathogens as well as the level of physical human contact. The Safe Blues project is one such idea, where virtual safe virus-like tokens are spread between cellular phones in an attempt to mimic biological virus spread for purposes of measurement and analysis, while respecting the privacy and safety of the population.

Much COVID-19 data is being gathered by contact-tracing apps to aid in identifying infected people or their contacts. However, there can be a time lag of 1 to 2 weeks between being infected and being diagnosed as positive with the result that data obtained in this way is always lagging and biased. Asymptomatic cases who may have already spread the virus to others are frequently missed by such methods. Data delays and bias make it difficult for public health officials and others who want to use the data to implement timely mitigation measures. Also, many contact tracing apps do not save information about the number, distance, and duration of contacts on a centralised server for scientific research. Our approach, on the other hand, is specifically designed to make inferences about characteristics of an epidemic in real-time, allowing governments to implement relevant mitigation measures in a timely fashion.

Safe Blues, introduced in [1, 2], works by spreading virtual ‘virus-like’ tokens, which we call strands. The strands can be of Susceptible-Exposed-Infectious-Removed (SEIR), Susceptible-Infectious-Removed (SIR), Susceptible-Exposed-Infectious (SEI), or Susceptible-Infectious (SI) type. Each strand is artificially seeded into the system at chosen times and can then spread between phones of users. At any given time, a phone can be infected with many strands, and the phone reports its strand infections to the server periodically. Individuals’ identities and social contacts are not recorded in this reporting, ensuring anonymity. A key aim of the Safe Blues idea is to give policymakers another tool that they can use in their effort to track the real-time spread of an epidemic. In contrast to those systems that model population contact and implement agent-based simulations, Safe Blues is an emulation of a group of epidemics based upon a contact process that takes place in the population itself.

We devised a campus-wide experiment at The University of Auckland City Campus. This is the first attempt to implement such a system. An outcome of this experiment is an open-source (virtual) epidemic spread dataset which can be used for further modeling, training, and analysis. Our initial plan was to conclude the experiment during November 2021, with the release of data afterwards. However, due to an extensive lockdown in Auckland, the experiment will now run through the second half of 2022, After requesting an ethics amendment, we have now released the data from 2021. In this paper, our primary focus is on the experiment’s methods and the experience gained. Also, we illustrate general outcomes and results to date. The details we present may be valuable to other teams planning similar experiments in the future. Table 1 describes the phases of the experiment, their timelines, and the period at the University of Auckland during which these phases run.

As an illustration of the experiment and some of the collected results, consider Fig 1 where we depict the timeline July 28—September 9, 2021. Phones of participants were “infected” with strands on July 29 and the figure presents the trajectories of the ensuing epidemics along with the number of participants who attended the campus during that period. There are multiple Safe Blues strand trajectories, the (artificial) infection on July 29 included multiple repeats of the same type of strand and multiple types of strands. In fact, not displayed in this figure, about 600 strands were seeded into the participating population. The black trajectory depicts the daily count of campus participants. The weekly attendance pattern, with lower attendance at weekends, can be seen clearly. The green and red trajectories represent the hourly counts of participants whose phones were in the states of exposed (infected but not infectious) and infectious, respectively. As is apparent from the plot, Safe Blues infections continued until the week of August 17 at which point the campus was closed due to a (real) government lockdown. At that point, the number of participants who attended the campus immediately dropped to fewer than 5 per day. As a result, the number of new infections (exposed participants) immediately decreased and within several weeks the number of infectious participants also decreased to zero.

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TIFF original image Download: Fig 1. The effect of campus closure, due to actual lockdown in New Zealand on August 17, on the virtual Safe Blues epidemic. Red and green trajectories show the daily counts of participants whose phones were infected (red) and exposed (green) respectively. These strands depict an SEIR type epidemic. For all strands the initial probability of infection and maximal infection radius were 0.1 and 50, respectively. Their infection strength was set as 0.08, 0.16 and 0.48. The black trajectory shows the daily count of participants who attended the campus. https://doi.org/10.1371/journal.pdig.0000142.g001

The Safe Blues experiment was not intended to interact with actual COVID numbers or lockdowns. In fact, we chose New Zealand as a destination because it was essentially COVID free for the second half of 2020 and the first half of 2021 and we believed that a university campus could serve as a good first testbed for Safe Blues. In making this decision, we were aware that the university campus did not directly mimic the population dynamics in all of New Zealand. For instance, during the Auckland lockdown in Phase 2, the campus was completely shut down, while in contrast, people in greater New Zealand still interacted, for example, to go shopping. We did not foresee this lockdown in planning the experiment. Nevertheless, the closure of the campus due to the actual physical lockdown served to illustrate the key point of Safe Blues: safe virtual virus strands that are measured in real-time can give an indication of how actual viruses are spreading, and with enough data, the application of machine learning techniques allows us to carry out prediction and state estimation. The Safe Blues system could thus be applied to predict the spread of viral diseases within a subgroup of the population.

Machine learning based prediction using Safe Blues data was initially developed in [1, 2] where both standard neural networks and scientific machine learning based techniques were employed. The measurements of Safe Blues data together with viral data were artificially simulated using several alternative models, and this synthetic data was used to calibrate and test the machine learning techniques. Specifically, scientific machine learning methods which include universal ODE (ordinary differential equations) estimation using techniques as described in [3] were used. Future research using data collected from the current Safe Blues experiment will be used to further fine-tune and develop machine learning techniques.

The focus of this current paper is not on the machine learning, estimation, and prediction per-se but is rather on the experimental setup, software, subject recruitment practices, ethical considerations, and dataset description of the experiment. We also presents initial experimental results. Our goal in doing so is to showcase the methodologies and experience gained from the experiment. The source code for the project is freely and openly available at [4]. Further, data collected in the experiment during 2021 (and used for the dispalys in this paper) is available via [5].

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[1] Url: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000142

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