Forecasting seasonal influenza with a state-space SIR model

Forecasting seasonal outbreaks of influenza.

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real...

The impact of school closures on pandemic influenza: Assessing potential repercussions using a seasonal SIR model.

When a new pandemic influenza strain has been identified, mass-production of vaccines can take several months, and antiviral drugs are expensive and usually in short supply. Social distancing measures, such as school closures, thus seem an attractive means to mitigate disease spread. However, the transmission of influenza is seasonal in nature, and as has been noted in previous studies, a decre...

A Space-Heterogeneous Stochastic SIR Model

Evolution-informed forecasting of seasonal influenza A (H3N2).

Interpandemic or seasonal influenza A, currently subtypes H3N2 and H1N1, exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus' antigenic evolution. We propose a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed model...

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model

Background Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. Methods We used the previously described "incidence decay with exponential adjustment" (IDEA) model, a 2...