Regional forecasting of COVID-19 caseload by non-parametric regression: a VAR epidemiological model
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...
Saved in:
Main Authors: | Aaron C Shang (Author), Kristen E Galow (Author), Gary G Galow (Author) |
---|---|
Format: | Book |
Published: |
AIMS Press,
2021-02-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach
by: Jonathan Roux, et al.
Published: (2024) -
Asymptomatic Cases, the Hidden Challenge in Predicting COVID-19 Caseload Increases
by: Brett Snider, et al.
Published: (2021) -
Application of non-parametric models for analyzing survival data of COVID-19 patients
by: Sarada Ghosh, et al.
Published: (2021) -
Clinicians' caseload management behaviours as explanatory factors in patients' length of time on caseloads: a predictive multilevel study in paediatric community occupational therapy
by: Kolehmainen Niina, et al.
Published: (2010) -
Breast cancer and occupation: Non-parametric and parametric net survival analyses among Swiss women (1990-2014)
by: Irina Guseva Canu, et al.
Published: (2023)