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Abstract Human mobility is fundamental to a range of applications including epidemic control, urban planning, and traffic engineering. While laws governing individual movement trajectories and population flows across locations have been extensively studied, the predictability of population-level mobility during the COVID-19 pandemic driven by specific activities such as work, shopping, and recreation remains elusive. Here we analyze mobility data for six place categories at the US county level from 2020 February 15 to 2021 November 23 and measure how the predictability of these mobility metrics changed during the COVID-19 pandemic. We quantify the time-varying predictability in each place category using an information-theoretic metric, permutation entropy. We find disparate predictability patterns across place categories over the course of the pandemic, suggesting differential behavioral changes in human activities perturbed by disease outbreaks. Notably, predictability change in foot traffic to residential locations is mostly in the opposite direction to other mobility categories. Specifically, visits to residences had the highest predictability during stay-at-home orders in March 2020, while visits to other location types had low predictability during this period. This pattern flipped after the lifting of restrictions during summer 2020. We identify four key factors, including weather conditions, population size, COVID-19 case growth, and government policies, and estimate their nonlinear effects on mobility predictability. Our findings provide insights on how people change their behaviors during public health emergencies and may inform improved interventions in future epidemics.
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Estimating spread of contact-based contagions in a population through sub-sampling
Various phenomena such as viruses, gossips, and physical objects (e.g., packages and marketing pamphlets) can be spread through physical contacts. The spread depends on how people move, i.e., their mobility patterns. In practice, mobility patterns of an entire population is never available, and we usually have access to location data of a subset of individuals. In this paper, we formalize and study the problem of estimating the spread of a phenomena in a population, given that we only have access to sub-samples of location visits of some individuals in the population. We show that simple solutions that estimate the spread in the sub-sample and scale it to the population, or more sophisticated solutions that rely on modeling location visits of individuals do not perform well in practice. Instead, we directly model the co-locations between the individuals. We introduce PollSpreader and PollSusceptible, two novel approaches that model the co-locations between individuals using a contact network , and infer the properties of the contact network using the sub-sample to estimate the spread of the phenomena in the entire population. We analytically show that our estimates provide an upper bound and a lower bound on the spread of the disease in expectation. Finally, using a large high-resolution real-world mobility dataset, we experimentally show that our estimates are accurate in practice, while other methods that do not correctly account for co-locations between individuals result in entirely wrong observations (e.g, premature prediction of herd-immunity).
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- PAR ID:
- 10332821
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- Volume:
- 14
- Issue:
- 9
- ISSN:
- 2150-8097
- Page Range / eLocation ID:
- 1557 to 1569
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.14778/3461535.3461544
