Spatial analysis and time-series modelling of Lassa fever cases in Nigeria: Insights from 2018-2023 Lassa fever national surveillance data
Bibliographic record
Abstract
Introduction Lassa fever (LF) is a viral hemorrhagic fever endemic in Nigeria characterized by high morbidity and mortality rates. Despite significant efforts at reducing the burden of LF in Nigeria, it remains a public health concern with negative socio-economic and health impacts. Modeling and predicting LF outbreaks are crucial to ensure timely-targeted interventions. We therefore analyzed data to describe the recent five-year trend of LF, identify patterns, and made predictions. Methods We reviewed the LF historical surveillance data from the National Surveillance Database (Surveillance Outbreak Response Management and Analysis System – SORMAS) from January 2018 to December 2023. We summarized data using frequencies and percentages. We used a Multiplicative Time-series model to determine the trend and pattern of the Lassa fever cases. We then predicted cases for 2024 and 2025 by de-seasonalizing the observed cases using a seasonal variation index (SVI) adjustment mechanism. We employed R and QGIS (v.3.32.2) for spatial analysis. Results Two of the 36 states in Nigeria―Ondo and Edo―accounted for 65% of the total confirmed cases and 53% of total mortality in the country. The Lassa fever cases followed a downward trend (β=0.1777, R2=0.0018) from 2018 to 2023. Time-series analysis shows peak periods in the first quarter (January–March) accounting for 65% of the total cases and deaths yearly. The SVI was highest in quarter 1 (2.1637), and least in quarter 4 (0.3249). The projected confirmed cases for 2024 and 2025 were 453 and 392, respectively, with peaks in the first quarter. Conclusion A downward trajectory in confirmed cases of Lassa fever was observed in Nigeria. However, peak periods are expected in the first quarter of the year. Lassa fever burden was predominant in two states. Focusing on community-driven preventive interventions before the peak periods and in the hot spot areas will facilitate Lassa fever control in Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".