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One approach to risk-limiting audits (RLAs) compares ran- domly selected cast vote records (CVRs) to votes read by human auditors from the corresponding ballot cards. Historically, such methods reduce audit sample sizes by considering how each sampled CVR di!ers from the corresponding true vote, not merely whether they di!er. Here we investigate the latter approach, auditing by testing whether the total number of mismatches in the full set of CVRs exceeds the minimum number of CVR errors required for the reported outcome to be wrong (the “CVR margin”). This strategy makes it possible to audit more social choice functions and simplifies RLAs conceptually, which makes it easier to explain than some other RLA approaches. The cost is larger sample sizes. “Mismatch-based RLAs” only require a lower bound on the CVR margin, which for some social choice functions is easier to calculate than the e!ect of particular errors. When the population rate of mismatches is low and the lower bound on the CVR margin is close to the true CVR margin, the increase in sample size is small. However, the increase may be very large when errors include errors that, if corrected, would widen the CVR margin rather than narrow it; errors a!ect the margin between candidates other than the reported winner with the fewest votes and the reported loser with the most votes; or errors that a!ect di!erent margins.
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Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics
This work quanti es mobility changes observed during the di erent phases of the pandemic world-wide at multiple resolutions { county, state, country { using an anonymized aggregate mobility map that captures population ows between geographic cells of size 5 km2. As we overlay the global mobility map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell and has since then seen a slow but steady increase in ows. Further, in order to understand mixing within a region, we propose a new metric to quantify the e ect of social distancing on the basis of mobility.Taking two very di erent countries sampled from the global spectrum, We analyze in detail the mobility patterns of the United States (US) and India. We then carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. Finally, we quantify the e ect of college students returning back to school for the fall semester on COVID-19 dynamics in the surrounding community. We employ the data from a recent university outbreak (reported on August 16, 2020) to infer possible Re values and mobility ows combined with daily prevalence data and census data to obtain an estimate of new cases that might arrive on a college campus. We nd that maintaining social distancing at existing levels would be e ective in mitigating the extra seeding of cases. However, potential behavioral change and increased social interaction amongst students (30% increase in Re ) along with extra seeding can increase the number of cases by 20% over a period of one month in the encompassing county. To our knowledge, this work is the rst to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.
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- PAR ID:
- 10213762
- Date Published:
- Journal Name:
- medRxiv
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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