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Dynamic downscaling and daily nowcasting from influenza surveillance data
Author(s) -
Paul Rajib,
Han Dan,
DeDoncker Elise,
Prieto Diana
Publication year - 2022
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9502
Subject(s) - nowcasting , preparedness , downscaling , outbreak , computer science , markov chain monte carlo , bayesian probability , medicine , pandemic , kalman filter , emergency medicine , statistics , medical emergency , covid-19 , disease , meteorology , virology , artificial intelligence , geography , mathematics , infectious disease (medical specialty) , precipitation , political science , law
Real‐time trends from surveillance data are important to assess and develop preparedness for influenza outbreaks. The overwhelming testing demand and limited capacity of testing laboratories for viral positivity render daily confirmed case data inaccurate and delay its availability in preparedness. Using Bayesian dynamic downscaling models, we obtained posterior estimates for daily influenza incidences from weekly estimates of the Centers for Disease Control and Prevention and daily reported constitutional and respiratory complaints during emergency department (ED) visits obtained from the state health departments. Our model provides one‐day and seven‐day lead forecasts along with 95 % $$ \% $$ prediction intervals. Our hybrid Markov Chain Monte Carlo and Kalman filter algorithms facilitate faster computation and enable us to update our estimates as new data become available. Our method is tested and validated using the State of Michigan data over the years 2009‐2013. Reported constitutional and respiratory complaints at the EDs showed strong correlations of 0.81 and 0.68 respectively, with influenza rates. In general, our forecast model can be adapted to track an outbreak with only one respiratory virus as a causative agent.