Smart investment of virus RNA testing resources to enhance Covid-19 mitigation.

A variety of mitigation strategies have been employed against the Covid-19 pandemic. Social distancing is still one of the main methods to reduce spread, but it entails a high toll on personal freedom and economic life. Alternative mitigation strategies that do not come with the same problems but are effective at preventing disease spread are therefore needed. Repetitive mass-testing using PCR assays for viral RNA has been suggested, but as a stand-alone strategy this would be prohibitively resource intensive. Here, we suggest a strategy that aims at targeting the limited resources available for viral RNA testing to subgroups that are more likely than the average population to yield a positive test result. Importantly, these pre-selected subgroups include symptom-free people. By testing everyone in these subgroups, in addition to symptomatic cases, large fractions of pre- and asymptomatic people can be identified, which is only possible by testing-based mitigation. We call this strategy smart testing (ST). In principle, pre-selected subgroups can be found in different ways, but for the purpose of this study we analyze a pre-selection procedure based on cheap and fast virus antigen tests. We quantify the potential reduction of the epidemic reproduction number by such a two-stage ST strategy. In addition to a scenario where such a strategy is available to the whole population, we analyze local applications, e.g. in a country, company, or school, where the tested subgroups are also in exchange with the untested population. Our results suggest that a two-stage ST strategy can be effective to curb pandemic spread, at costs that are clearly outweighed by the economic benefit. It is technically and logistically feasible to employ such a strategy, and our model predicts that it is even effective when applied only within local groups. We therefore recommend adding two-stage ST to the portfolio of available mitigation strategies, which allow easing social distancing measures without compromising public health.

Influence of linear birefringence in the computation of scattering phase functions.

Birefringent media, like biological tissues, are usually assumed to be uniaxial. For biological tissues, the influence of linear birefringence on the scattering phase function is assumed to be neglectable. In order to examine this, a numerical study of the influence of linear birefringence on the scattering phase function and the resulting backscattering Mueller matrices was performed. It is assumed that the media consist of spherical scattering particles embedded in a nonabsorbing medium, which allows us to employ the Lorenz-Mie theory. In the Monte Carlo framework, the influence of linear birefringence on the components of the electric field vector is captured through the Jones N-matrix formalism. The Lorenz-Mie theory indicates that a given linear birefringence value Δn has a bigger impact on the scattering phase function for large particles. This conclusion is further supported by Monte Carlo simulations, where the phase function was calculated based on the refractive index once in the ordinary direction and once in the extraordinary one. For large particles, comparisons of the resulting backscattering Mueller matrices show significant differences even for small Δn values.

Stochastic modeling of polarized light scattering using a Monte Carlo based stencil method.

This paper deals with an efficient and accurate simulation algorithm to solve the vector Boltzmann equation for polarized light transport in scattering media. The approach is based on a stencil method, which was previously developed for unpolarized light scattering and proved to be much more efficient (speedup factors of up to 10 were reported) than the classical Monte Carlo while being equally accurate. To validate what we believe to be the new stencil method, a substrate composed of spherical non-absorbing particles embedded in a non-absorbing medium was considered. The corresponding single scattering Mueller matrix, which is required to model scattering of polarized light, was determined based on the Lorenz-Mie theory. From simulations of a reflected polarized laser beam, the Mueller matrix of the substrate was computed and compared with an established reference. The agreement is excellent, and it could be demonstrated that a significant speedup of the simulations is achieved due to the stencil approach compared with the classical Monte Carlo.

Stochastic modeling of light scattering with fluorescence using a Monte Carlo-based multiscale approach.

This work deals with the efficient and accurate modeling of fluorescence in the context of stochastic Monte Carlo methods for which we propose a novel multiscale method. As in other approaches of this category, the transport theory is employed to describe the physics. The new framework was successfully applied for a quantitative assessment of halftone reflectance measurements with three different devices. It could be demonstrated that the described method is faster than classical Monte Carlo by multiple orders of magnitude, and that it is capable of correctly handling the geometrical device differences. It is also shown that optical dot gain is accurately predicted for the whole ink coverage range.