Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity
Date
2021-07
Authors
Shanmugam, Ramalingam
Ledlow, Gerald
Singh, Karan P.
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
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 rate and methodology on COVID-19 data, which exhibit two clusters of infectivity. Cluster 1 has a smaller number of primary cases and exhibits larger variance than the expected cases with a negative correlation of 28%, implying that the number of secondary cases is lesser when the number of primary cases increases and vice versa. The traditional IB model is appropriate for Cluster 1. The probability of contracting COVID-19 is estimated to be 0.13 among the primary, but is 0.75 among the secondary in Cluster 1, with a wider gap. Cluster 2, with a larger number of primary cases, exhibits smaller variance than the expected cases with a correlation of 79%, implying that the number of primary and secondary cases do increase or decrease together. Cluster 2 disqualifies the traditional IB model and requires its refined version. The probability of contracting COVID-19 is estimated to be 0.74 among the primary, but is 0.72 among the secondary in Cluster 2, with a narrower gap. The advantages of the proposed approach include the model’s ability to estimate the community’s health system memory, as future policies might reduce COVID’s spread. In our approach, the current hazard level to be infected with COVID-19 and the odds of not contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable.
Description
Keywords
COVID-19, medical risk factors, binomials, pandemics, convolution, health care policy, immunity, virus testing, Health Administration
Citation
Shanmugam, R., Ledlow, G., & Singh, K. P. (2021). Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity. PLoS One, 16(7), e0254313.
Rights
Rights Holder
© 2021 Shanmugam et al.
Rights License
This work is licensed under a Creative Commons Attribution 4.0 International License.