Predicting an epidemic based on syndromic surveillance

1. Predicting an epidemic based on syndromic surveillance

2. In This Paper... • A framework based on the stochastic SIR model of infection dynamics, with syndromic observations of number of infected people is proposed. • Unknown parameters of the SIR epidemic model are estimated via the sequential Monte Carlo method, with the prediction based on the dynamic model.

3. Introduction - Syndromic Surveillance • One of the promising methods for early detection of epidemics • Based on assumption that an increase in number of infected people in the community is usually associated with the changes of some other indicators (non-medical). • Examples - visits to pharmacies, sales of a particular product, number of hits of a particular web site, absenteeism from work/school etc.

4. Introduction • Syndromic Surveillance data must be assimilated into an epidemiological model. • Amplitude and peak time can be predicted from this model using estimation algos. • Inhomogenity parameter, mixing parameter are incorporated into SIR to account for non-homogenous population and mixing.

5. Epidemic Outbreak Model • q- non linear mixing term describing social contacts • v- Inhomogeneity parameter • ξ, ζ are two uncorrelated white Gaussian noise processes

6. Model of Syndromic Observations • Each syndrome is a linear function of the number of infected people. • bj, σj = const • ηj is zero-mean, unit variance white Gaussian noise • For Non linear data, it is assumed linearity holds for small values of i

7. Early Detection and prediction • State Vector • State evolution in discrete time tk • Transition function

8. Algorithm • Algo - Prediction and Update • Prediction in time steps k = l’, l’+ 1, · · · , l− 1 from a time l’ to a future time l • Initial PDF

9. Algorithm • Input :set of particles at the previous update time t and the received observation zj at the new (update) time t. • Prediction: Propagate particles as per • xk+1 ≈ fk(xk) + wk • Update : Based on new measurement after introduction of syndromic data • Predicting peak and amplitude using • ρ is the basic reproduction number

10. Results • Agent based model vs Modified SIR

11. Results • Prediction results from a random sample of 25 particles