Title: Multiscale models of SARS-CoV-2 infection
Abstract: Designing control strategies for the COVID-19 epidemic requires multi-scale understanding of individual infections, the probability of transmission through aerosol exposure, and the roles of vaccination and testing in limiting an outbreak at the population level. In the first part of this talk, I will present several studies investigating within-host and aeosol dynamics in animals and humans infected with SARS-CoV-2. They will focus on the tradeoff between viral infectiousness and viral positivity, as well as the biases induced by the scarcity of data early in the individual’s infection. In the second part, I will connect the virus profile of infected individuals with transmission, testing strategies, and vaccination at the population level through multi-scale immuno-epidemiological models. Using the multi-scale models, we will predict the best testing-vaccination combinations for limiting an outbreak with variants of increased transmissibility. Our findings can improve interventions.

Title: Optimal multiclass classification and prevalence estimation with applications to SARS-CoV-2 antibody assays
Abstract: Antibody tests are routinely used to identify past infection, with examples including Lyme disease and, of course, COVID-19. An accurate classification strategy is crucial to interpreting diagnostic test results and includes problems with more than two classes. Classification is further complicated when the relative fraction of the population in each class, or generalized prevalence, is unknown. We present a prevalence estimation method that is independent of classification and an associated classification scheme that minimizes false classifications. This work hinges on constructing probability models for data that are inputs to an optimal-decision theory framework. As an illustration, the method is applied to antibody data with SARS-CoV-2 naïve, previously infected, and vaccinated classes.

Title: Towards understanding the effect of fibrinogen interactions on fibrin gel structure
Abstract: Fibrin polymerization involves the conversion of fibrinogen molecules to fibrin monomers which polymerize to form a gel that is a major structural component of a blood clot. Fibrinogen plays a dual role in fibrin polymerization; it can occupy binding sites by binding to fibrin, inhibiting gelation, and fibrinogen can be converted to fibrin. A kinetic polymerization model is proposed, involving two types of monomers, each of which has two reaction sites that participate in different binding reactions. With a moment generating function approach, we track the temporal dynamics of a closed system of moment equations up until finite time blow-up. We examine the impact of fibrinogen-fibrin binding and fibrinogen conversion to fibrin on whether a gel forms and the resulting branch point density, if it does.

Title: On the impact of super spreaders on COVID-19 dynamics
Abstract: Superspreading phenomenon has been observed in many infectious diseases and contributes significantly to public health burden in many countries. Superspreading events have recently been reported in the transmission of the COVID-19 pandemic. The present study uses a set of nine ordinary differential equations to investigate the impact of superspreading on COVID-19 dynamics. The model developed in this study addresses the heterogeineity in infectiousness by taking into account two forms of transmission rate functions for superspreaders based on clinical (infectivity level) and social or environmental (contact level). The basic reproduction number has been derived and the contribution of each infectious compartment towards the generation of new COVID-19 cases is ascertained. Data fitting was performed and parameter values were estimated within plausible ranges. Numerical simulations performed suggest that control measures that decrease the effective contact radius and increase the transmission rate exponent will be greatly beneficial in the control of COVID-19 in the presence of superspreading phenomena.