Urban epidemiological death rate prognostics by the Gaidai multivariate approach

Abstract

Aim

COVID-19 (SARS-CoV-2), which was reported to be highly transmissible and to have a low case-fatality rate, has had a significant impact on the global public health system.

Subject and Methods

To provide accurate long-term spatiotemporal prognostics of future COVID-19 (SARS-CoV-2)-related infection and death rates, the authors benchmark state-of-the-art biosystem risk-evaluation methodologies, which are particularly suitable for multiregional environmental, biological, and public health systems. The main aim of this case study was to assess future coronavirus-related mortality rates for a specified return period within the relevant region. The authors applied a novel, population-based, multicentre, clinical-data-based statistical method directly to the raw clinical dataset. Extending Extreme Value Theory (EVT) and the Generalized Extreme Value (GEV) distribution from univariate (1D) to bivariate (2D) models presents specific challenges. 1D EVT/GEV cannot be readily extended to 2D, let alone to higher-dimensional biological systems. Epidemiological biosystem performance or limit state function depends on multiple random variables (e.g., covariates, resistance, bio-environmental factors). Engineering relevance: assessing the reliability of high-dimensional bio-systems where risks are low (e.g., less than 10⁻³).

Results

The proposed spatiotemporal method can be applied effectively across a wide range of national public health models, using raw data from national clinical surveys.

Novelty: This case study benchmarks a recently developed multivariate bio-reliability method, utilizing a raw (unfiltered) clinical data sample. Primary novelty lies in the ability to treat virtually infinite-dimensional biosystems without any prior knowledge of the underlying joint distribution, as required by classic methods like FORM and SORM (First and Second Order Reliability Methods, respectively).

Conclusion

The underlying issues in clinical dataset quality analysis are briefly discussed, along with significant methodological limitations.

Plane language summary

State-of-the-art public health system bio-reliability and risk evaluation method was benchmarked utilizing the Coronavirus Disease 2019 (COVID-19) raw clinical dataset.

Confidence ranges have been forecasted for predicted epidemiological risk levels.

Accurate multi-modal risk forecasts were provided.