Regional forecasting of COVID-19 caseload by non-parametric regression: a VAR epidemiological model.

ABSTRACT:

Objectives

The COVID-19 pandemic (caused by SARS-CoV-2) has introduced significant challenges for accurate prediction of population morbidity and mortality by traditional variable-based methods of estimation. Challenges to modelling include inadequate viral physiology comprehension and fluctuating definitions of positivity between national-to-international data. This paper proposes that accurate forecasting of COVID-19 caseload may be best preformed non-parametrically, by vector autoregression (VAR) of verifiable data regionally.

Methods

A non-linear VAR model across 7 major demographically representative New York City (NYC) metropolitan region counties was constructed using verifiable daily COVID-19 caseload data March 12-July 23, 2020. Through association of observed case

SUBMITTER: Shang AC 

PROVIDER: S-EPMC7870378 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature