Shanmugam, R. | Search bioRxiv

bioRxiv2021

Heterogeneity versus the COVID-19 Pandemic

In this paper, heterogeneity is formally defined, and its properties are explored. We define and distinguish observable versus non-observable heterogeneity. It is proposed that heterogeneity among the vulnerable is a significant factor in the contagion impact of COVID-19, as demonstrated with incidence rates on a Diamond Princess Cruise ship in February 2020. Given the nature of the disease, its heterogeneity and human social norms, pre-voyage and post-voyage quick testing procedures may become the new standard for cruise ship passengers and crew. The technological advances in testing available today would facilitate more humanistic treatment as compared to more archaic quarantine and isolation practices for all onboard ship. With quick testing, identification of those infected and thus not allowed to embark on a cruise or quarantining those disembarking and other mitigation strategies, the popular cruise adventure could be available safely again. Whatever the procedures implemented, the methodological purpose of this study should add valuable insight in the modeling of disease and specifically, the COVID-19 virus.

scientific communication and education↗

bioRxiv2020

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

This article constructs a restricted infection rate inverse binomial-based approach to predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is unqualified to match the reality of COVID-19, because the data contradicts the models requirement that variance should be greater than expected value. A refined version of the IB model is a necessity to predict COVID-19 cases after family gatherings. Our refined version of an IB model is more appropriate and versatile, as it accommodates all potential data scenarios: equal, lesser, or greater variance than expected value. Application of the approach is based on a restricted infectivity rate and methodology on Fan et al.s COVID-19 data, which exhibits 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 inverse binomial (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. Conversely, Cluster 2, exhibits smaller variance than the expected cases with a correlation of 79%, implying the number of primary and secondary cases increase or decrease together. Cluster 2 disqualifies the traditional IB model and demands its refined version. 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 models ability to estimate the communitys health system memory for future policies to be developed is an asset of this approach. The current hazard level to be infected with COVID-19 among the primary and secondary groups are estimable and interpretable. Author SummaryCurrent statistical models are not able to accurately predict disease infection spread in the COVID-19 pandemic. We have applied a widely-used inverse binomial method to predict rates of infection after small gatherings, going from primary (original) cases to secondary (later) cases after family gatherings or social events, using the data from the Wuhan and Gansu provinces in China, where the virus first spread. The advantages of the proposed approach include that the models ability to estimate the communitys health system memory for future policies to be developed, as such policies might reduce COVIDs spread if not its control. In our approach, as demonstrated, the current hazard level of becoming infected with COVID-19 and the odds of contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable. We hope the proposed approach will be used in future epidemics.

scientific communication and education↗