Descriptive epidemiology of measles surveillance data, Osun state, Nigeria, 2016–2018
Bibliographic record
Abstract
BACKGROUND: Globally, an estimate of 254,928 measles cases in 2015 and 89,780 deaths in 2016 occur annually. In Nigeria, measles is the fifth leading causes of under-five child mortality with 342 confirmed cases found in the first 9 epidemic weeks in some states including Osun State. We described the distribution, trend and make projection of measles cases in Osun State. METHODS: The Osun State surveillance weekly reporting data on measles cases from all its 30 Local Government Area (LGA) were reviewed, from January 2016 to December 2018 (n = 1205). Data were analyzed using descriptive statistics and a multiplicative time series model (MTSM). The MTSM was used to determine the trend, seasonality in the data and make projections for 2019 and 2020. RESULTS: Cases of measles were reported across the 30 LGAs of the state between January 2016 and December 2018. The rate of reported cases of measles was 20.2, 34.4 and 28.8 per 100,000 populations in 2016, 2017 and 2018 respectively in Ede south LGA where the highest rates were reported in the 3-year period. Out of the three studied years, year 2017, recorded the highest number of reported cases of measles in Osun State. The trend line for the 3-year period showed a positive correlation (r = + 0.4979, p = 0.056). The computed quarterly variation for the studied years was 1.094 for the 1st quarter, 1.162 for the 2nd quarter, 0.861 for the 3rd quarter and 0.888 for the 4th quarter. A quarterly projection for 2019 and 2020 showed an increasing trend with the second quarter of each year likely to have the highest reported cases of measles. CONCLUSIONS: Ede south LGA has the highest proportion of reported measles cases in Osun State. Measles cases may increase in years ahead, but the second quarter of a year has the highest number. Government should strengthen the existing framework on measles reduction and more attention should be given to the second quarter of each year.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".