Covid-19 Endogenous Mutation Of Nb 1.8.1, Lf.7 And Kp.3: Applying A Multivariate Time-Series Model To Forecast Key Healthcare Indicators Through 2027

Abstract

Following the extensive devastation wrought by the global COVID-19 pandemic, the emergence of novel mutations persists on an international scale. The enduring impact of these mutations on public health infrastructure constitutes a matter of significant concern. Covid-19 variants NB.1.8.1, LF.7 and KP.3, both classified as Variants Under Monitoring (VUMs) by the World Health Organization, have been isolated in global wide. These variants highlight the ongoing evolution of SARS-CoV-2 and the need for continuous genomic surveillance to monitor their spread and impact on public health. It is difficult to determine the specific health complications they face, but observations from affected areas suggest certain patterns. This paper presents a long-term forecasting analysis of three crucial healthcare indicators—Daily New Deaths per Million, Hospital Patients per Million, and Test Positivity Rate—using time series models based on the Prophet algorithm. The analysis draws on data from multiple countries with varying healthcare dynamics and pandemic response strategies. Forecasts extend through 2027, offering insights into potential healthcare burdens and seasonal trends in an endemic COVID-19 landscape. The results demonstrate divergent trajectories among nations and highlight the value of data-driven health policy planning.