Prognostics for respiratory epidemic dynamics by multivariate gaidai risk assessment methodology

Abstract

Introduction

current study introduces an accurate prediction spatiotemporal model for epidemic outbreaks risk assessment.

Methods

utilize state-of-the-art statistical methodology on raw/unfiltered clinical datasets. In order to provide trustworthy long-term forecasts of viral outbreak risks, this research suggests a novel biosystem bio-reliability approach that works particularly well for multi-regional biological, environmental, and public health systems that are monitored over a representative time-lapse.

Results

study made use of daily clinically reported patient counts from COVID-19 (SARS-CoV-2) throughout all impacted Dutch administrative areas. The objective of this research was to establish new benchmark for novel bio-reliability methodology that enables efficient risk analysis, based on recorded raw clinical patient numbers, with accounting for pertinent area mapping.

Remarks and significance

by effectively employing various clinical survey datasets that are now accessible, the proposed technique may be used for contemporary biomedical applications, as well as the general welfare.