Measuremenst of human interaction though proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this talk, I compare the power of Facebook’s social connectedness with cell phone-derived human mobility metrics for predicting county-level cases of COVID-19. Next, I explain our novel Spatio Temporal autoregressive eXtreme Gradient Boosting (STXGB) models for forecasting county-level new cases of COVID-19 in the coterminous US. We evaluate the model on ﬁve weekly forecast dates between October 24 and November 28, 2020 over one- to four-week prediction horizons. Comparing our predictions with a baseline Ensemble of 32-models currently used by the CDC indicates an average 58% improvement in prediction RMSEs over two- to four week prediction horizons, pointing to the strong predictive power of our model. I will conclude with a discussion on the practicality, requirements, advantages, and disadvantages of using machine learning for forecasting the spread of infectious diseases.
Ponente Dr. Morteza Karimzadeh
Dr Kanmzadeh is an assistant professor of Geography and affiliate assistant professor of Computer Science at the University of Colorado (CU) Boulder. Morteza is a geospatial data scientist, with research cutting across geographic information retrieval, machine learning, and visual analytics. His primary research focuses on method development, including social media analytics, environmental or public health data analysis, situational awareness, precision agriculture and disease forecasting. His approach to research and development is human-centered, from visual design to
ground truth creation, algorithm integration and evaluation, to domain deployment and field studies. His current research on sea ice classification an mapping is funded by a National Science Foundation EarthCube award.
03 DE MARZO DE 2021 - 12:00 HORAS
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