# Mathematics

## Impacts on community transmission and disease burden of a clinical prediction tool to prioritize limited COVID testing

Community spread of coronavirus disease 2019 (COVID-19) continues to be high in many areas, likely due, in part, to insufficient testing and contact tracing. As regional test kit shortages are likely to continue with increased transmission, it is important that available testing capacity be used effectively. To date, testing for COVID-19 has largely been restricted to persons reporting symptoms, with no additional criteria being systematically employed to select who is tested. In situations when testing capacity is limited, we propose the use of a clinical prediction rule to allow for prioritized testing of people who are most likely to test positive for COVID-19. Using data from the University of Utah Health system, we developed a robust, deployable clinical prediction rule which incorporates data on demographics and clinical characteristics to predict which patients are most likely to test positive. We then incorporated prioritized testing into a stochastic SEIR model for COVID-19 to measure changes in disease burden compared to a model with indiscriminate testing. Our best performing clinical prediction rule achieved an AUC of 0.7. When incorporated into the SEIR model, prioritized testing resulted in a delay in the timing of the infection peak, a meaningful reduction in both the total number of infected individuals and the peak height of the infection curve, and thus a reduction in the excess demand on local hospital resources. These effects were strongest for lower values of Rt and higher proportions of infected individuals seeking testing.

## Spatiotemporal Transmission Dynamics of COVID-19 in Spain

Mathematical modelling of infectious diseases is an interdisciplinary area of increasing interest. Tracking and forecasting the full spatio-temporal evolution of an epidemic can help public health officials to plan their emergency response and health care. We present advanced methods of spatial data assimilation to epidemiology, in this case to the ebb and flow of COVID-19 across the landscape of Spain. Data assimilation is a general Bayesian technique for repeatedly and optimally updating an estimate of the current state of a dynamic model. We present a stochastic spatial Susceptible-Exposed-Infectious-Recovered-Dead (S-E-I-R-D) compartmental model to capture the transmission dynamics and the spatial spread of the ongoing COVID-19 outbreak in Spain. In this application the machinery of data assimilation acts to integrate incoming daily incidence data into a fully spatial population model, within a Bayesian framework for the tracking process. For the current outbreak in Spain we use registered data (CCAA-wide daily counts of total COVID-19 cases, recovered, hospitalized, and confirmed dead) from the Instituto de Salud Carlos III (ISCIII) situation reports. Our simulations show good correspondences between the stochastic model and the available sparse empirical data. A comparison between daily incidence data set and our SEIRD model coupled with Bayesian data assimilation highlights the role of a realization conditioned on all prior data and newly arrived data. In general, the SEIRD model with data assimilation gives a better fit than the model without data assimilation for the same time period. Our analyses may shed light more broadly on how the disease spreads in a large geographical area with places where no empirical data is recorded or observed. The analysis presented herein can be applied to a large class of compartmental epidemic models. It is important to remember that the model type is not particularly crucial for data assimilation, the Bayesian framework is the key. Data assimilation neither requires nor presupposes that the model of the infectious disease be in the family of S-I-R compartmental models. The projected number of newly infected and death cases up to August 1, 2020 are estimated and presented.

## Fast spread of SARS-CoV-2 in China, Europe and the US

SARS-CoV-2 is a novel pathogen causes the COVID-19 pandemic. Some of the basic epidemiological parameters, such as the exponential epidemic growth rate and R0 are debated. We collected and analyzed data from China, eight European countries and the US using a variety of inference approaches. In all countries, the early epidemic grew exponentially at rates between 0.19-0.29/day (epidemic doubling times between 2.4-3.7 days). I will discuss the appropriate serial intervals to estimate the basic reproductive number R0 and argue that existing evidence suggests a highly infectious virus with an R0 likely between 4.0 and 7.1. Further, we found that similar levels of intervention efforts are needed, no matter the goal is mitigation or containment. Early, strong and comprehensive intervention efforts to achieve greater than 74-86% reduction in transmission are necessary.

## Characterizing the spread of COVID-19 in Canada and the USA

Provincial and US state case, hospitalization, and death data can be characterized by relatively long periods of nearly constant growth/decline along with some large outbreaks. This talk will compare the spread in the different jurisdictions and how it has changed with relaxed social distancing measures.

## Flattening the curve and the effect of atypical events on mitigation measures

On March 23rd and March 30th, 2020, the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. In this work a mathematical model is used to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. Is shown that social distancing measures were successful in achieving a significant reduction of the epidemic curve growth rate in the early weeks of the intervention. However, “flattening the curve” had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (June 1st, 2020). If the peak indeed occurs in late May or early June, then the events of children's day and Mother’s Day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.

## Epidemiology CovidSimABM: An Agent-Based Model of Contagion

This is the prototype of an agent based model for a closed universe of a population experiencing a contagion-based epidemic, in which risk factors, movement, time of incubation and asymptomatic infection are all parameters. The model allows the operator to intervene at any step and change parameters, thus analytically visualizing the effect of policies like more testing, contract tracing, and shelter in place. Under current development, CovidSimMV is an ABM that supports a Multiverse of different environments, in which agents move from one to another according to ticket with stops. Each universe has its own characteristic mix of residents, transients and attached staff, and persons are able to adopt different roles and characteristics in different universes. The fundamental disease characteristics of incubation, asymptomatic infection, confirmed cases will be preserved. The Multiverse model will support a rich diversity of environments and interpersonal dynamics. These are JavaScript programs that can be run in a browser as HTML files. The code is open source, and available on github.com/ecsendmail.

## Counting social interactions for discrete subsets of the plane

Given a discrete subset V in the plane, how many points would you expect there to be in a ball of radius 100? What if the radius is 10,000? Due to the results of Fairchild and forthcoming work with Burrin, when V arises as orbits of non-uniform lattice subgroups of SL(2,R), we can understand asymptotic growth rate with error terms of the number of points in V for a broad family of sets. A crucial aspect of these arguments and similar arguments is understanding how to count pairs of saddle connections with certain properties determining the interactions between them, like having a fixed determinant or having another point in V nearby. We will spend the first 40 minutes discussing how these sets arise and counting results arise from the study of concrete translation surfaces. The following 40 minutes will be spent highlighting the proof strategy used to obtain these results, and advertising the generality and strength of this argument that arises from the computation of all higher moments of the Siegel--Veech transform over quotients of SL(2,R) by non-uniform lattices.

## Bohr and Measure Recurrent Sets

Given a probability preserving system (X, \mu, T) and a set U of positive measure contained in X we denote by N(U,U) the set of integers n such that the measure of U intersected with T^n(U) is positive. These sets are called return-time sets and are of very special nature. For instance, Poincaré recurrence theorem tells us that the set must have bounded gaps while Sarkozy-Furstenberg theorem tells us that it must have a square. The subject of this talk is a very old question (going back to Følner-1954 if not earlier) whether they give rise to the same family of the sets as when we restrict ourselves to compact group rotations. This was answered negatively by Kříž in 1987 and recently it was proved by Griesmer that a return-time set need not contain any translate of a return-time set arising from compact group rotations. In this talk, I will try to sketch some of these proofs and give a flavour of results and questions in this direction.

## An introduction to naive entropy

There are simple formulas defining "naive entropy" for continuous/measure preserving actions of a countable group G on a compact metric/probability space. It turns out that if G is amenable, then this naive entropy coincides with topological/Kolmogoro-Sinai entropy of the action, while for non-amenable groups both naive entropies take only two values: 0 or infinity. During my talk, I will try to sketch the proofs of these facts. I will follow: T. Downarowicz, B. Frej, P.-P. Romagnoli, Shearer's inequality and infimum rule for Shannon entropy and topological entropy. Dynamics and numbers, 63-75, Contemp. Math., 669, Amer. Math. Soc., Providence, RI, 2016. MR3546663 and P. Burton, Naive entropy of dynamical systems. Israel J. Math. 219 (2017), no. 2, 637-659. MR3649602.

## The Mathematics of Life: Making Diffusion Your Friend

Diffusion is the enemy of life. This is because diffusion is a ubiquitous feature of molecular motion that is constantly spreading things out, destroying molecular aggregates. However, all living organisms, whether single cell or multicellular have ways to use the reality of molecular diffusion to their advantage. That is, they expend energy to concentrate molecules and then use the fact that molecules move down their concentration gradient to do useful things. In this talk, I will show some of the ways that cells use diffusion to their advantage, to signal, to form structures and aggregates, and to make measurements of length and size of populations. Among the examples I will describe are signalling by nerves, cell polarization, bacterial quorum sensing, and regulation of flagellar molecular motors. In this way, I hope to convince you that living organisms have made diffusion their friend, not their enemy.