<p>Dynamic (blue) and static (red) forecasts were considered here assuming the specific infectiousness profile function and scalar of 0.50 for pre-existing immunity. See legend of Fig. 4 for further details about x-axis. Deviation in epidemic duration was calculated as difference in peak week of simulated and observed epidemic. Positive values should be interpreted as overestimation, by the forecast model, of the observed epidemic duration. Negative values, similarly, indicated underestimation of the observed epidemic duration. Metric values closer to zero indicated better predictive ability of the forecasting methodology.</p
Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many w...
<p>Accuracy was calculated over all forecasts (332,400 for each setting of the forecast system). Thi...
Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked wi...
<p>Dynamic (blue) and static (red) forecasts were considered here assuming the general infectiousnes...
<p>Dynamic (blue) and static (red) forecasts were considered here assuming the general infectiousnes...
<p>Dynamic (blue) and static (red) forecasts were considered here assuming the specific infectiousne...
<p>Overall fit was calculated under dynamic (blue) and static (red) forecasts assuming a specific in...
<p>Forecasts were based on best-fit baseline model in which only the level of vaccination coverage w...
<p>Forecasts were based on best-fit baseline model in which only the level of vaccination coverage w...
<p>The dashed lines represent the values observed after the end of the epidemic. Colours correspond ...
<p>Analysis was limited to countries providing ≥30 viruses for ≥3 yrs. Different colors represent di...
A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed a...
OBJECTIVE: Recent studies have used Bayesian methods to predict timing of influenza epidemics many w...
Accurate prediction of flu activity enables health officials to plan disease prevention and allocate...
Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many w...
Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many w...
<p>Accuracy was calculated over all forecasts (332,400 for each setting of the forecast system). Thi...
Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked wi...
<p>Dynamic (blue) and static (red) forecasts were considered here assuming the general infectiousnes...
<p>Dynamic (blue) and static (red) forecasts were considered here assuming the general infectiousnes...
<p>Dynamic (blue) and static (red) forecasts were considered here assuming the specific infectiousne...
<p>Overall fit was calculated under dynamic (blue) and static (red) forecasts assuming a specific in...
<p>Forecasts were based on best-fit baseline model in which only the level of vaccination coverage w...
<p>Forecasts were based on best-fit baseline model in which only the level of vaccination coverage w...
<p>The dashed lines represent the values observed after the end of the epidemic. Colours correspond ...
<p>Analysis was limited to countries providing ≥30 viruses for ≥3 yrs. Different colors represent di...
A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed a...
OBJECTIVE: Recent studies have used Bayesian methods to predict timing of influenza epidemics many w...
Accurate prediction of flu activity enables health officials to plan disease prevention and allocate...
Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many w...
Objective: Recent studies have used Bayesian methods to predict timing of influenza epidemics many w...
<p>Accuracy was calculated over all forecasts (332,400 for each setting of the forecast system). Thi...
Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked wi...