Surveillance study demonstrates early-warning forecasting models for weekly Lassa fever cases, indicating practical triggers for timely outbreak preparedness.
This study examines seasonal patterns, temporal changes, and one-week-ahead early-warning forecasting of weekly reported Lassa fever cases in Nigeria from 2020 to 2025. Using publicly available Nigeria Centre for Disease Control and Prevention surveillance data, it characterises recurring seasonal peaks, evaluates season-adjusted changes over time, and compares reproducible forecasting approaches. An interpretable forecast-exceedance warning rule is also demonstrated to support timely outbreak preparedness and public-health decision-making. The study additionally assesses data consistency and discusses discrepancies between compiled surveillance records and final NCDC situation reports. All findings should be interpreted at the national level and within the limitations of routinely reported surveillance data.
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Edward Okhumaile (2026) studied this question.
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