Use of a 40-day rolling incidence to monitor pertussis in Nova Scotia, 2015
Bibliographic record
Abstract
BACKGROUND: Pertussis can cause a serious respiratory bacterial infection, especially in infants. Between January 1 and December 31, 2015, there was an increase in the number of reported pertussis cases in Nova Scotia (NS). Surveillance practices for pertussis in NS were challenging because immunization coverage data are not available and rate information was neither timely nor precise with respect to geography. Public health officials in NS decided to adopt a new surveillance technique to inform public health actions across the Province. OBJECTIVE: To assess the use of a 40-day rolling incidence rate to monitor pertussis activity in Nova Scotia. INTERVENTION: A 40-day rolling incidence rate was calculated for pertussis by age groups and various levels of geography. Public health authorities continued to anticipate new cases of pertussis if the contacts of known cases were still within the incubation period (range between six and 20 days). The 40-day incubation period was chosen to reflect twice the incubation period's upper range. Rates were calculated using Statistics Canada population projections for 2014 and then compared with traditional case counts and cumulative incidences. The usefulness of the statistics was assessed by public health decision makers. OUTCOMES: Increased pertussis activity was noted across NS, most notably in the South West region. The use of a 40-day rolling incidence rate as a surveillance tool provided more timely and geographically precise descriptions of ongoing trends in pertussis activity and helped to inform appropriate public health action. Health officials valued the information provided from the rolling incidence because it allowed them to manage activities based on weekly estimates at various levels of geography. CONCLUSION: Rolling incidence proved to be a useful tool to monitor a cyclical increase in pertussis cases in Nova Scotia and to inform related public health actions. The rolling incidence provided geographically precise and timely information that was useful to estimate new cases in the absence of reliable immunization coverage information. This method could supplement traditional epidemiological surveillance of future communicable disease events, especially those characterized by long incubation periods and low case counts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".