Incorporating and addressing testing bias within estimates of epidemic dynamics for SARS-CoV-2.

ABSTRACT:

Background

The disease burden of SARS-CoV-2 as measured by tests from various localities, and at different time points present varying estimates of infection and fatality rates. Models based on these acquired data may suffer from systematic errors and large estimation variances due to the biases associated with testing. An unbiased randomized testing to estimate the true fatality rate is still missing.

Methods

Here, we characterize the effect of incidental sampling bias in the estimation of epidemic dynamics. Towards this, we explicitly modeled for sampling bias in an augmented compartment model to predict epidemic dynamics. We further calculate the bias from differences in disease prediction from biased, and randomized sampling, proposing a strategy to obtain unbiased estim

SUBMITTER: Suhail Y 

PROVIDER: S-EPMC7789897 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature