BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Mathematical Biology Seminar - Christina Wang and Maia Richard
 s-Dinger
X-WR-TIMEZONE:Mountain Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260609T205842Z
UID:tag:localist.com\,2008:EventInstance_50524766099977
DTSTART:20251002T170000Z
DTEND:20251002T180000Z
DESCRIPTION:Christina Wang and Maia Richards-Dinger\, Department of Applied
  Mathematics\, University of Colorado Boulder\n\nChristina: mRNA sequence 
 design via dynamic programming and tensor networks\n\nMaia: Climate inform
 ed Bayesian forecasting of seasonal influenza dynamics using a non-station
 ary ARIMA model\n\nChristina's Abstract: TBD\n\nMaia's Abstract: \n\nSeaso
 nal influenza recurs in temperate regions each winter\, causing a major he
 alth burden. The timing and intensity of influenza outbreaks vary substant
 ially across years\, raising the need for accurate forecasts to guide publ
 ic health messaging and inform resource allocation. Cold\, dry weather enh
 ances influenza transmission\; thus\, influenza forecasts may benefit from
  including temperature and humidity data. Some models have incorporated we
 ather into their near-term predictions. However\, this approach does not u
 se climate variables to predict important seasonal parameters\, such as pe
 ak week. We propose a novel forecasting approach that uses meteorological 
 data to predict both the overall shape of an influenza season and its shor
 t-term dynamics. \n\nWe developed a climate-driven non-stationary ARIMA mo
 del to predict influenza incidence at the US state level. We used ILI+\, a
  combination of outpatient visits due to ILI and positive influenza test p
 ercentages\, as a proxy for influenza incidence. For each year and state\,
  we fit a piecewise linear function to the log of ILI+ and recorded the re
 sulting values of our target parameters: up slope\, down slope\, peak week
 \, and peak value. We fit a Bayesian multivariate linear model to predict 
 these four parameters using climate and population variables. We compared 
 models using all possible variable combinations. The best model included p
 opulation density\, percent of population under age 18\, maximum temperatu
 re\, and latitude. Higher temperatures correlated with later and lower pea
 ks\, and slower growth. To forecast future influenza seasons\, we used pre
 dicted climate variables to create posterior distributions for our target 
 parameters for each state. We forecast deviations from the predicted gener
 al trajectory using an ARIMA model.
GEO:40.006791;-105.262818
LOCATION:Engineering Center\, ECCR 257
SUMMARY:Mathematical Biology Seminar - Christina Wang and Maia Richards-Din
 ger
URL;VALUE=URI:https://calendar.colorado.edu/event/mathematical-biology-semi
 nar-christina-wang-and-maia-richards-dinger
CATEGORIES:Colloquium/Seminar
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END:VCALENDAR