Volume 10, Number 7—July 2004
Research
Model Parameters and Outbreak Control for SARS
Gerardo Chowell*†
, Carlos Castillo-Chavez‡1, Paul W. Fenimore*, Christopher M. Kribs-Zaleta§, Leon Arriola*, and James M. Hyman*
, Carlos Castillo-Chavez‡1, Paul W. Fenimore*, Christopher M. Kribs-Zaleta§, Leon Arriola*, and James M. Hyman*
Table 3
Partial rank correlation coefficients (PRCCs) between each of the input parameters and R0 from Monte Carlo sampling of size 105 for different distributions of the relative infectiousness after isolation (l) as described in the text
| Probability distribution | Input parameters in order of decreasing PRCC (shown in parenthesis) |
|---|---|
| β (a = 2, b = 2) | β (0.92), l (0.57), δ (0.53), γ2 (0.35), α (0.32), k (0.13) |
| β (a = 1, b = 2) | β (0.90), l (0.60), δ (0.44), α (0.39), γ2 (0.26), k (0.12) |
| β (a = 2, b = 1) | β (0.92), δ (0.60), l (0.51), γ2 (0.40), α (0.22), k (0.11) |
1At the time this work was carried out, Dr. Castillo-Chavez was on sabbatical at Los Alamos National Laboratory and faculty of Cornell University.
2Recall that l = 0 corresponds to complete isolation, whereas l = 1 means no effective isolation occurs. Hence, a decrease in l means an increase in the effective isolation of the infected persons.
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