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State of the art <p>Presented case study advocates state-of-the-art spatiotemporal multivariate prognostics approach for epidemic/pandemic outbreak risk/hazard assessment.</p> Methods <p>Novel statistical methodology has been applied to clinical unfiltered datasets. To provide robust and reliable long-term prognostics of flu-type outbreak future risks, current research advocates bio-reliability prognostic approach, suitable for multi-regional bio, environmental, public (national) health systems, clinically monitored across representative periods. Novel non-parametric deconvolution extrapolation scheme was employed.</p> Results <p>The current study utilized clinically daily reported patient COVID-19 or SARS-COV-2 related counts, throughout major administrative locations in the Netherlands. It is seen from Fig.&#xa0;7 that if epidemic would last 20 years instead of 1 year, then daily case number global maximum would increase less than twice.</p> Conclusions <p>A novel non-parametric deconvolution scheme was employed for extrapolation towards design return periods. The primary advantage of a non-parametric extrapolation scheme over existing parametric schemes lies within its numerical stability and accuracy.</p> Practical significance <p>Key objective was to benchmark proposed multimodal bio-reliability and risk assessment method, based on underlying recorded raw (clinical) patient data, accounting for territorial mapping. The latter has critical significance for early epidemiological prognostics.</p>

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Epidemiological forecast by state-of-the-art Gaidai multimodal bio-reliability scheme, combined with extrapolation by self-deconvolution

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Oleg Gaidai,
  • Shicheng He,
  • Yan Zhu

摘要

State of the art

Presented case study advocates state-of-the-art spatiotemporal multivariate prognostics approach for epidemic/pandemic outbreak risk/hazard assessment.

Methods

Novel statistical methodology has been applied to clinical unfiltered datasets. To provide robust and reliable long-term prognostics of flu-type outbreak future risks, current research advocates bio-reliability prognostic approach, suitable for multi-regional bio, environmental, public (national) health systems, clinically monitored across representative periods. Novel non-parametric deconvolution extrapolation scheme was employed.

Results

The current study utilized clinically daily reported patient COVID-19 or SARS-COV-2 related counts, throughout major administrative locations in the Netherlands. It is seen from Fig. 7 that if epidemic would last 20 years instead of 1 year, then daily case number global maximum would increase less than twice.

Conclusions

A novel non-parametric deconvolution scheme was employed for extrapolation towards design return periods. The primary advantage of a non-parametric extrapolation scheme over existing parametric schemes lies within its numerical stability and accuracy.

Practical significance

Key objective was to benchmark proposed multimodal bio-reliability and risk assessment method, based on underlying recorded raw (clinical) patient data, accounting for territorial mapping. The latter has critical significance for early epidemiological prognostics.