Epidemiological review of confirmed Lassa fever cases during 2016–2018, in Plateau State, North Central Nigeria
Bibliographic record
Abstract
Lassa fever (LF) is endemic in West Africa and constitutes a significant public health concern due to its potential for epidemics and associated high mortality. The first reported case and management of Lassa fever in Plateau State occurred more than 50 years ago. We set out to undertake a three-year epidemiological review of LF cases in Plateau State, North Central Nigeria. This is a retrospective study of all confirmed LF cases in Plateau State between 2016 and 2018. Plateau state Lassa fever- Line list and patient case records were used to extract relevant data. Lassa PCR was carried out at the NCDC accredited Laboratory network. Data analysis was done using STATA version SE14.1. Forty-four persons (44) had confirmed LF over the examined period, 18 (41%) in 2016, 15 (34%) in 2017 and 11 (25%) in 2018. The mean age was 29.7±14.6 years and 53% were males. Sixty-six percent (66%) of the patients resided in rural areas. It affected all local government areas (LGA) in the state except Pankshin, Jos East and Kanke LGAs. Twenty-five percent (25%) of the cases occurred among underprivileged communities of Jos North and another 25% in rural dwellers of Langtang North. Fifty-nine percent (59%) of cases occurred during the 1st quarter, 27% the 2nd quarter and 18% the 3rd quarter of the year. The case fatality rate was 57%. LF is endemic in Plateau State. Prevention strategies must be sustained year round and target the youth, urban and rural underprivileged communities. There is also need for case management improvement to reduce mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".