Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity.

ABSTRACT: We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model's requirement that variance should be larger than the expected value. Our version of an IB model is more appropriate, as it can accommodate all potential data scenarios in which the variance is smaller, equal, or larger than the mean. This is unlike the usual IB, which accommodates only the scenario in which the variance is more than the mean. Therefore, we propose a refined version of an IB model to be able to accommodate all potential data scenarios. The application of the approach is based on a restricted infectivity

SUBMITTER: Shanmugam R 

PROVIDER: S-EPMC8282037 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature