Epidemiological insight into the possible drivers of Lassa fever in an endemic area of Southwestern Nigeria from 2017 and 2021
Bibliographic record
Abstract
ABSTRACT Background Lassa fever (LF) is a viral disease transmitted between animals and humans, commonly found in West Africa, including Nigeria. The region experiences an estimated annual total of about 2 million LF cases in humans, leading to 5,000 to 10,000 deaths. Strikingly, up to 80% of LF-infected individuals show no symptoms, making its true incidence hard to determine in endemic populations. We investigated LF distribution, mortality, survival patterns, and contributing factors during a local outbreak in Nigeria, from 2017 to 2021. Method Data from the Integrated Disease Surveillance and Response weekly line list for 2017 to 2021 were extracted. The survival pattern of LF patients was visualized with the Kaplan-Meier curve, binary logistic regression model was employed to explore LF-associated factors and level of statistical significance (α) was set at 5%. Result Overall, 4,554 participants were recruited between 2017 and 2021. Their average age varied from 31.82 ± 20.0 to 37.85 ± 17.89. LF-positive patients decreased from 26.9% in 2017 to 17.7% in 2021, paralleling the mortality trend. In 2021, patient survival ranged from 5 to 30 days. Male patients had lower survival odds in the initial 10 days of hospitalization, improved chances from days 10 to 20, and reduced probabilities beyond day 20. Residence location and age were significant factors (p<0.05) associated with LF in Ondo State. Conclusion The decline in LF cases in 2021 could be attributed to the ongoing intervention by Nigerian Centre for Disease Control or the disruption caused by the COVID-19 pandemic in 2020. To address LF challenges in hotspot areas, we propose Community Action Networks that would operate using the One Health approach involving local stakeholders sustainably to promote Early Warning/Early Response system in high-risk settings and mitigate LF-related issues. SUMMARY Lassa fever (LF) is an important disease of global public health concern that is endemic in West Africa. In Nigeria, the disease constitutes a major health challenge with outbreaks being recorded on an annual basis despite efforts channeled towards combating it by the government at various levels. This study analysed a five years data of LF in Ondo State southwestern Nigeria. The results identified age and location were identified as important factors associated with infection and mortality among LF patients as the incidence and case fatality rates were highest among adults (≥ 45 years), while the highest number of suspected, confirmed and dead cases was recorded in Owo Local Government Area. Furthermore, we identified drying of food items by the roadside where rodent vectors can access them, presence of a local market, poor and unsafe sewage disposal, and proximity of refuse dumps to residential areas as possible socio-ecological factors/practices fueling the endemicity and seasonal outbreak of LF. These findings emphasize the need for active involvement of community members in the already established national LF surveillance network to facilitate prompt case identification, and early reporting and response in the LF-endemic areas of the country.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".