Publications by authors named "M Grave"
Background: Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes.
View Article and Find Full Text PDFThe outbreak of COVID-19, beginning in 2019 and continuing through the time of writing, has led to renewed interest in the mathematical modeling of infectious disease. Recent works have focused on partial differential equation (PDE) models, particularly reaction-diffusion models, able to describe the progression of an epidemic in both space and time. These studies have shown generally promising results in describing and predicting COVID-19 progression.
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- The computer simulation of personalized cancer models using clinical and imaging data can improve predictions of tumor growth and treatment responses, which is crucial for tailoring cancer management.
- However, running these complex simulations can be very resource-intensive, limiting their real-world application in clinical settings.
- To overcome this, the authors propose using dynamic-mode decomposition (DMD), an efficient machine learning method, to simplify cancer model simulations while still achieving accurate predictions, demonstrating promising results with minimal computational costs.
Dynamic mode decomposition (DMD) is a powerful data-driven method used to extract spatio-temporal coherent structures that dictate a given dynamical system. The method consists of stacking collected temporal snapshots into a matrix and mapping the nonlinear dynamics using a linear operator. The classical procedure considers that snapshots possess the same dimensionality for all the observable data.
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- The COVID-19 pandemic sparked a heightened interest in mathematical models for infectious diseases, particularly compartmental models that categorize the population based on characteristics and track disease progression.
- Recent studies have expanded this by using partial differential equations (PDEs) to account for spatial variations in epidemics, yielding effective results in understanding COVID-19 dynamics.
- This research validates the modeling framework by comparing outcomes from Lombardy, Italy, and then applying it to the U.S. state of Georgia and Brazil's Rio de Janeiro, demonstrating strong alignment with actual epidemiological data across multiple regions.