@article{TEXTUAL,
      recid = {5910},
      author = {Huang, Yi and Chattopadhyay, Ishanu},
      title = {Universal risk phenotype of US counties for flu-like  transmission to improve county-specific COVID-19 incidence  forecasts},
      journal = {PLOS Computational Biology},
      address = {2021-10-14},
      number = {TEXTUAL},
      abstract = {<p>The spread of a communicable disease is a complex  spatio-temporal process shaped by the specific transmission  mechanism, and diverse factors including the behavior,  socio-economic and demographic properties of the host  population. While the key factors shaping transmission of  influenza and COVID-19 are beginning to be broadly  understood, making precise forecasts on case count and  mortality is still difficult. In this study we introduce  the concept of a universal geospatial risk phenotype of  individual US counties facilitating flu-like transmission  mechanisms. We call this the Universal Influenza-like  Transmission (UnIT) score, which is computed as an  information-theoretic divergence of the local incidence  time series from an high-risk process of epidemic  initiation, inferred from almost a decade of flu season  incidence data gleaned from the diagnostic history of  nearly a third of the US population. Despite being computed  from the past seasonal flu incidence records, the UnIT  score emerges as the dominant factor explaining incidence  trends for the COVID-19 pandemic over putative demographic  and socio-economic factors. The predictive ability of the  UnIT score is further demonstrated via county-specific  weekly case count forecasts which consistently outperform  the state of the art models throughout the time-line of the  COVID-19 pandemic. This study demonstrates that knowledge  of past epidemics may be used to chart the course of future  ones, if transmission mechanisms are broadly similar,  despite distinct disease processes and causative  pathogens.</p>},
      url = {http://knowledge.uchicago.edu/record/5910},
}