Telecommunications networks and public health responses during the COVID-19 pandemic: Evidence from a large national network operator in Canada
Bibliographic record
Abstract
The COVID-19 pandemic has demonstrated the fundamental importance of telecommunications networks. This paper utilizes data from a large North American network operator to examine the role of telecom networks in enabling public health and economic responses to the pandemic. During the pandemic, data usage grew significantly, with growth in wireline data use outstripping growth in wireless data use, 53% to 27%, between March and December 2020. Yet, even at pre-pandemic levels, data use is growing exponentially, doubling every 1.3 years between 2017 and 2020. This paper also considers three examples of public health and economic responses enabled by telecom networks: staying at home, adopting telemedicine, and teleworking. First, based on de-identified data on customer movement and location, this paper estimates that compliance with stay-at-home orders was about 20% lower in the 2nd wave than during the 1st wave, despite much cases counts, suggesting that Canadians suffered from isolation fatigue. Across Canada's six largest cities, controlling for population and GDP per capita, a 1% decrease in compliance is associated with an additional 700 COVID-19 infections. Second, based on data on two telemedicine apps, this paper highlights the potential for the rapid adoption of telemedicine. At the start of the pandemic, adoption of these apps doubled and then was sustained throughout 2020, with usage patterns reflecting digital divides in age and gender. Third, Canada's teleworking rate changed in lockstep with COVID-19 cases, even while the unemployment rate remained constant, suggesting that employers are adopting teleworking as a flexible way to adapt to evolving public health conditions and restrictions without resorting to layoffs. When viewed against this backdrop, telecommunication policy can support public health and social outcomes, in addition to economic outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".