“We didn't get much schooling because we were fishing all the time”: Potential impacts of irregular school attendance on the spread of epidemics
Bibliographic record
Abstract
OBJECTIVES: Especially in traditional, rural, and low-income areas, children attend school irregularly. School-based interventions are common mitigation strategies for infectious disease epidemics, but if daily attendance is not the norm, the impact of schools on disease spread might be overestimated. METHODS: We use an agent-based model of an early 20th century Newfoundland community to compare epidemic size and duration in three scenarios: (1) all school-aged children attend school each weekday, (2) students aged 10-15 have a chance of engaging in adult activities each day, and (3) students aged 10-15 have a chance of being reassigned to adult roles at the start of each simulation and thus never attend school. RESULTS: As the probability of not attending school increases, epidemics become smaller and peak earlier. The change in final size is larger with permanent reassignment (35% at baseline, 18% at maximum reassignment) than with daily nonattendance (35% vs. 22%). For both scenarios, the peak occurs 3 days earlier with maximum absence compared to the baseline. Benefits extend beyond the reassigned agents, as all school-aged agents are more likely to escape infection with increasing reassignment, and on average, 3-6 additional agents (2.6%-5.3%) escape infection compared to the baseline. CONCLUSIONS: This study demonstrates that absenteeism can have important impacts on epidemic outcomes. Thus, socioeconomic and other reasons for nonattendance of school, as well as how rates vary in different contexts, must be considered in models predicting epidemic outcomes or evaluating public health interventions in the face of major pandemics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".