Difference between revisions of "Epidemics:ferguson2008"

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=== Model ===
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=== Cauchemez et al Model<ref name="cauchemez2004">CAUCHEMEZ S, Carrat F, Viboud C, Valleron A J, Boelle P Y, ''A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data'', Stat. Med., 23, (2004), p3469</ref> ===
  
An example of so called SIR (Susceptible, Infectious, Recovered) model<ref name="cauchemez2004">CAUCHEMEZ S, Carrat F, Viboud C, Valleron A J, Boelle P Y, ''A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data'', Stat. Med., 23, (2004), p3469</ref>.
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An example of so called SIR (Susceptible, Infectious, Recovered) model.
  
 
Joint probability of observed (''Y''), unobserved variables (<math>\nu,\,\psi</math>), and parameters (<math>\theta</math>) is given by:
 
Joint probability of observed (''Y''), unobserved variables (<math>\nu,\,\psi</math>), and parameters (<math>\theta</math>) is given by:
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<math>\nu_{i}^{ih},\psi_{i}^{ih} (\nu_{i}^{ih}<\psi_{i}^{ih})</math> - unobserved variables corresponding to the start and the end of the infectious period for ''i''th individual of ''ih''th household
 
<math>\nu_{i}^{ih},\psi_{i}^{ih} (\nu_{i}^{ih}<\psi_{i}^{ih})</math> - unobserved variables corresponding to the start and the end of the infectious period for ''i''th individual of ''ih''th household
 
   
 
   
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=== Observation level ===
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<math>
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p(Y|\nu,\psi)=\left\{\begin{array}{ll}
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1, & \textrm{if\,for\,all\,infected\;}i\,(i\in I): \nu_i\in [Z_i-3,Z_i] \land \nu_i < \psi_i\\
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0, & \textrm{otherwise}
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\end{array} \right.
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</math>
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This level ensured that the unobserved data, <math>\nu,\,\psi</math>, agreed with observed data, ''Y''.
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=== Transmission level ===
 
=== Transmission level ===
  
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The duration of infectious period for ''i''th infective <math>d_i=\psi_i-\nu_i</math> is taken from gamma distribution with mean <math>\mu_i</math> and standard deviation <math>\sigma_i</math>
 
The duration of infectious period for ''i''th infective <math>d_i=\psi_i-\nu_i</math> is taken from gamma distribution with mean <math>\mu_i</math> and standard deviation <math>\sigma_i</math>
  
=== Observation level ===
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Revision as of 18:39, 20 November 2008

Virtual society Virus spread Literature Version Polish.png
Polish virtual society epidemics model
Guinea pigs epidemics model
Virus genetic evolution epidemics model





Cauchemez et al Model[1]

An example of so called SIR (Susceptible, Infectious, Recovered) model.

Joint probability of observed (Y), unobserved variables (\nu,\,\psi), and parameters (\theta) is given by:

p\big(Y,\nu,\psi,\theta) = p(Y|\nu,\psi)\,p(\nu,\psi|\theta)\,p(\theta),

where:

p\big(\theta\big), \leftarrow prior level (prior distribution of the parameters of the model).
p(\nu,\psi|\theta)=\prod_{ih}^{n_{ih}}p(\nu^{ih},\psi^{ih}|\theta), \leftarrow transmission level

p(Y|\nu,\psi)=\prod_{i}^{n_{ih}}p(Y^{ih}|\nu^{ih},\psi^{ih}), \leftarrow observation level

Y - indicator function: Y_{ij}^{ih}=1 (for ith, (i=1,2,..,n_{ih}) individual of ihth household (of size n_{ih}) on jth day (j=0,1,..,14)), if clinical influenza was observed, Y_{ij}^{ih}=0 otherwise. Y^{ih} - all observations from household ihth, Y - observations from all households.

I^{ih} - group of individuals at ihth household with at least 1 day of clinical influenza, S^{ih} - remaining members of the ihth household.

Z_{i}^{ih} - the 1st day of clinical influenza of ith individual in ihth household

\nu_{i}^{ih},\psi_{i}^{ih} (\nu_{i}^{ih}<\psi_{i}^{ih}) - unobserved variables corresponding to the start and the end of the infectious period for ith individual of ihth household

Observation level


p(Y|\nu,\psi)=\left\{\begin{array}{ll}
1, & \textrm{if\,for\,all\,infected\;}i\,(i\in I): \nu_i\in [Z_i-3,Z_i] \land \nu_i < \psi_i\\
0, & \textrm{otherwise}
\end{array} \right.

This level ensured that the unobserved data, \nu,\,\psi, agreed with observed data, Y.

Transmission level

The instantaneous risk of infection for an individual at time t in household of size n:

\lambda(t) = \alpha + \varepsilon \sum_{i\in I(t)} \beta_i / n,

where \alpha, \varepsilon - instantaneous risk of infection from the community and within household, respectively.

The duration of infectious period for ith infective d_i=\psi_i-\nu_i is taken from gamma distribution with mean \mu_i and standard deviation \sigma_i



Prior level

That is prior distribution of parameters, \theta=(\mu, \sigma, \alpha, \beta, \varepsilon, \eta)

References

  1. CAUCHEMEZ S, Carrat F, Viboud C, Valleron A J, Boelle P Y, A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data, Stat. Med., 23, (2004), p3469