Á. Briz Redón, A. Iftimi, J. Mateu, C. Romero García

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at the case level is highly useful in this regard. In particular, we propose a mechanistic spatio-temporal point process model to study a point pattern of COVID-19 cases detected in Valencia (Spain) during the first 11 months of the pandemic. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while previous works have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations.

Keywords: COVID-19, first-order intensity function, inhomogeneous point processes, mechanistic models, spatio-temporal models

Scheduled

Posters IV
June 10, 2022  10:10 AM
Faculty Hall


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Exploring Algorithmic Fairness on Diabetes Predicition

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Inferencia en encuestas no probabilísticas con Kernel Weighting Method.

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Diseños óptimos para funciones de crecimiento

M. J. Rivas Lopez, J. M. Rodriguez Diaz, I. Mariñas del Collado


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