SAIFR:2133

Real-time Data Fusion to Guide Influenza Forecasting Models

(2020). Real-time Data Fusion to Guide Influenza Forecasting Models. ICTP South American Institute for Fundamental Research. https://scivideos.org/index.php/ictp-saifr/2133

Real-time Data Fusion to Guide Influenza Forecasting Models. ICTP South American Institute for Fundamental Research, Mar. 02, 2020, https://scivideos.org/index.php/ictp-saifr/2133 

          @misc{ scivideos_SAIFR:2133,
            doi = {},
            url = {https://scivideos.org/index.php/ictp-saifr/2133},
            author = {},
            keywords = {ICTP-SAIFR, IFT, UNESP},
            language = {en},
            title = {Real-time Data Fusion to Guide Influenza Forecasting Models},
            publisher = { ICTP South American Institute for Fundamental Research},
            year = {2020},
            month = {mar},
            note = {SAIFR:2133 see, \url{https://scivideos.org/index.php/ictp-saifr/2133}}
          }
Sara del Valle
Talk numberSAIFR:2133
Source RepositoryICTP – SAIFR
Collection
Talk Type Conference
Subject

Abstract

Globalization has created complex problems that can no longer be adequately understood and mitigated using traditional data analysis techniques and data sources. As such, there is a need for the integration of nontraditional data streams and approaches such as social media and machine learning to address these new challenges. In this talk, I will discuss how our team is applying approaches from the weather forecasting community including data collection, assimilating heterogeneous data streams into models, and quantifying uncertainty to forecast influenza and other infectious diseases. In addition, I will demonstrate that although epidemic forecasting is still in its infancy, it’s a growing field with great potential and mathematical modeling will play a key role in making this happen.