Descriptive epidemiology of Lassa fever, its trend, seasonality, and mortality predictors in Ebonyi State, South- East, Nigeria, 2018—2022
Bibliographic record
Abstract
Nigeria is an epicenter for Lassa fever. Ebonyi state is located in the South-Eastern region of Nigeria where a high burden of Lassa fever has been reported. Therefore, this study was designed to assess the epidemiology of Lassa fever, its seasonality, trend, and mortality predictors in Ebonyi state, South-East, Nigeria. We analyzed data extracted from Ebonyi State Integrated Disease Surveillance and Response (IDSR) system over five years (2018–2022). A total of 1578 reported Lassa fever cases were captured in the IDSR out of which 300 were laboratory-confirmed. Data were analyzed using descriptive statistics, additive time series model, quadratic equation, and logistic regression model (α 0.05 ). Spatial distribution of reported Lassa fever cases was conducted using Arc G.I.S. The mean age of the individuals with the reported cases of Lassa fever was 29.4 ± 17.8 years. Lassa fever showed a seasonal trend across the years. The quadratic model provided the best fit for predicting Lassa fever cumulative cases (R 2 = 98.4%, P-value < 0.05). Projected cases of Lassa fever for the year 2023 were 123 in the 1st quarter, 23 in the 2nd quarter, 42 in the 3rd quarter, and 17 in the 4th quarter. The seasonality index was + 70.76, -28.42, -9.09, and -33.2 in the 1st, 2nd, 3rd, and 4th quarters respectively. The reported cases of Lassa fever followed a declining trend (slope = -0.1363). Farmers were 70% less likely to die from Lassa fever compared to those not working (aOR:0.3, CI: 0.17–0.83). The hot spots for Lassa fever were Abakaliki and Ezza Local Government Areas. Although the reported Lassa fever cases followed a declining trend in Ebonyi state, there was a seasonality in the disease pattern. Being a farmer was protective against the risk of dying from Lassa fever. While efforts to eliminate and mitigate the spread of the disease in Ebonyi state should be strengthened, more attention should target the peak period of the disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".