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. Author manuscript; available in PMC: 2015 Sep 7.

Published in final edited form as: J Theor Biol. 2014 May 4;356:174–191. doi: 10.1016/j.jtbi.2014.04.033

These plots show how the for dengue (top 3 plots) and chikungunya (bottom 3 plots) change as the parameters vary (one at a time) within the ranges given in Table 2, while all other parameters are set at baseline values. Baseline values for the models (indicated by a *) correspond to Table 6. The local sensitivity indices in Table 6 are the scaled slope at the baseline values. For sensitivity curves close to a straight line (e.g. for ν_v), the local sensitivity index is relatively accurate across the parameter range. However, if the sensitivity curve is more nonlinear (e.g. for σ_v), the local sensitivity index is indeed only accurate locally. Effective early mitigation strategies target the control POI that reduce the most.

Inline graphic — These plots show how the for dengue (top 3 plots) and chikungunya (bottom 3 plots) change as the parameters vary (one at a time) within the ranges given in Table 2, while all other parameters are set at baseline values. Baseline values for the models (indicated by a *) correspond to Table 6. The local sensitivity indices in Table 6 are the scaled slope at the baseline values. For sensitivity curves close to a straight line (e.g. for ν_v), the local sensitivity index is relatively accurate across the parameter range. However, if the sensitivity curve is more nonlinear (e.g. for σ_v), the local sensitivity index is indeed only accurate locally. Effective early mitigation strategies target the control POI that reduce the most.