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Enhancing COVID-19 Forecasting in Dagestan with Quasi-linear Recurrence Equations by using GLDM Algorithm

A journal article from the University of Alkafeel describes applying quasi-linear recurrence equations and a GLDM algorithm to enhance COVID-19 forecasting in Dagestan. The source is a published journal article dated 2024-10-01.

Ten-day ahead sub-epidemic model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods.
Illustrative image: Ten-day ahead sub-epidemic model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods. — Coauthors Kimberlyn Roosa, Yiseul Lee, Ruiyan Luo, Alexander Kirpich, Richard Rothenberg, James M. Hyman OrcID, Ping Yan, Gerardo Chowell/Wikimedia Commons, CC BY-SA 4.0

Categories: science-and-space, public-health

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence
5/10
Geographic reach
3/10
Global importance
3/10
Impact magnitude
3/10
Positivity
7/10
Urgency
2/10

Why it matters

The article reports a method intended to improve COVID-19 forecasting for Dagestan.

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