Recent advances in random and deterministic modeling in biology/health sciences

Wednesday, June 16 at 02:15am (PDT)
Wednesday, June 16 at 10:15am (BST)
Wednesday, June 16 06:15pm (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS11" time block.
Note: this minisymposia has multiple sessions. The second session is MS17-CBBS (click here).

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Maria C.A. Leite, (University of South Florida St.Petersburg), Juan Carlos Cortés López (Instituto Universitario de Matemática Multidisciplinar. Universitat Politècnica de València, Spain), Rafael J. Villanueva Micó (Instituto Universitario de Matemática Multidisciplinar. Universitat Politècnica de València, Spain)


Mathematical modelling is well-known to be a vital tool for studying complex systems in biology and health sciences. They enable to study mathematically what is costly and/or impossible to do experimentally. There has been a large body of research on this area with amazing contributions in understanding complex biological/health sciences systems. In the spite of such, the innovative development of not only deterministic models but also models that include randomness is still critical for answering crucial questions in mathematical biology. Thus, the talks we envision for this mini-symposium will have a special focus on modeling approaches that have specific application to biology/health sciences, but will also be of interest to researchers in other areas. This mini-symposium will disseminate recent research and open questions in modeling biological/health sciences complex systems. It aims at inspiring mathematicians, modelers, and experimentalists to address several challenges in this field of research.

Óscar Angulo

(Universidad de Valladolid, Spain)
"Numerical integration of an age-structured model with unbounded age-domain"
The analysis of an unbounded life-span age-structured population model is motivated because, not only new models continue to appear in this framework, but also it is required by the study of the asymptotic behaviour of its dynamics. The numerical integration of the corresponding model is usually performed in bounded domains through the truncation of the age life-span. Here, we present a new numerical method that avoids the truncation of the unbounded age domain. We completely analyze it and we establish its second order of convergence. We finish with some experiments to exhibit numerically the theoretical results and the behaviour of the problem in the simulation of the evolution of the Nicholson's blowflies model.

Carla Pinto

(School of Engineering, Polytechnic of Porto, Portugal)
"Modified SIQR model for the COVID-19 outbreak"
In this talk we consider a generalization of the Susceptible-Infected-Quarantine-Recovered model, the mSIQR, for the COVID-19 pandemic. The main goal is to study the importance of the value of the contact rate, proportion of unkown infectious, and hospital care in the disease propagation. We test the model and fit the results for COVID-19 pandemic data from some countries, including France, US, and Portugal. We discuss the epidemiological relevance of the results and provide insights on future patterns, subjected to health policies.

Clara Burgos

(Instituto Universitario de Matemática Multidisciplinar. Universitat Politècnica de València, Spain)
"A computational procedure describe breast tumor growth capturing the uncertainty in the volume data"
The aim of this talk is to describe a theoretical-computational approach to model the breast tumor growth taking into account the uncertainty of the retrieved data. To do it, we will seek suitable random inputs of a discretized version of a logistic model. The random model parameters will be described via its probability density function. The theoretical-computational approach seems to be flexible enough to be adapted to describe different biological dynamics problems.

Gilberto Gonzalez-Parra

(Department of Mathematics, New Mexico Tech, USA)
"Mathematical modeling of COVID-19 pandemic under social behavior uncertainty (Pre-recorded)"
Mathematical modeling of COVID-19 pandemic has been challenging due to the complexity of the phenomena including the variability of the social behavior. The uncertainty in some of the mechanisms involved in the transmission of the SARS-CoV-2 and its fatality rate make forecasting an extremely difficult problem as the outcomes have shown. In this talk, we present a mathematical model approach to study the effect of uncertainty in social behavior on the COVID-19 pandemic. Specifically, we rely on stochastic differential equations to give some insights regarding this topic. We illustrate with some scenarios the consequences of social behavior uncertainty on the COVID-19 pandemic. Finally, we will show an application of computational tools such as bootstrapping and Markov chain Monte Carlo that allow us to investigate some uncertainties related to the mathematical modeling of COVID-19 pandemic.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.