Abstract
After more than a year of non-pharmaceutical interventions, such as, lock-downs and masks, questions remain on how effective these interventions were and could have been. The vast differences in the enforcement of and adherence to policies adds complexity to a problem already surrounded with significant uncertainty. This necessitates a model of disease transmission that can account for these spatial differences in interventions and compliance. In order to measure and predict the spread of disease under various intervention scenarios, we propose a Microscopic Markov Chain Approach (MMCA) in which spatial units each follow their own Markov process for the state of disease but are also connected through an underlying mobility matrix. Cuebiq, an offline intelligence and measurement company, provides aggregated, anonymized cell-phone mobility data which reveal how population behaviors have evolved over the course of the pandemic. These data are leveraged to infer mobility patterns across regions and contact patterns within those regions. The data enables the estimation of a baseline for how the pandemic spread under the true ground conditions, so that we can analyze how different shifts in mobility affect the spread of the disease. We demonstrate the efficacy of the model through a case study of spring break and it’s impact on how the infection spread in Florida during the spring of 2020, at the onset of the pandemic.
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Acknowledgements
This model was built in collaboration with Applied Intelligence Accenture Federal Services. The past year we served as members of their COVID-19 response team where we built and deployed models for decision support of various federal clients. We also appreciate Cuebiq’s Data for Good program for providing the mobility data needed for this analysis.
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Kent, T.G., Phillips, N.E., McCulloh, I., Pavon-Harr, V., Patsolic, H.G. (2022). Microscopic Markov Chain Approach for Measuring Mobility Driven SARS-CoV-2 Transmission. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_26
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DOI: https://doi.org/10.1007/978-3-030-93413-2_26
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