The model was coded and run in R version 2.10.1, using the ''simecol'' package [27] with the Runge-Kutta 4 th order algorithm for numerical integration of the differential equations. ArcGIS version 10 was used to map the calculated resource gaps and avoidable mortalities at provincial level. Figure 1 presents the geographical distribution of estimated resource gaps across provinces (or districts in the case of Indonesia) in each study country for the modeled pandemic influenza scenario, under our baseline assumptions and point estimates for parameter values. The corresponding statistical distributions of resource capacities across areas within each country can be found in Figure S1 . A summary of overall resource gaps for each country is presented in Table 1 . There was substantial variation in resource gaps both between and within countries, and across resources types (Figures 1 and S1) . Overall, the biggest gaps were generally seen in Cambodia and Lao PDR, particularly when standardized by population size, with almost all provinces in these countries displaying gaps in all resources, with the exception of nurses which were estimated to be sufficient in approximately half of the provinces in these countries. In contrast, relatively few provinces in Taiwan were estimated to have gaps, at least in general health system resources (beds, ventilators, and human resources), with quantities of these resources often considerably above those predicted to be needed for this scenario. Nevertheless, almost half of provinces in Taiwan were predicted to have insufficient oseltamivir supplies to treat all severe cases (although it should be noted that the results in Figures 1 and S1 do not account for central stockpiles which might be mobilized in the event of a pandemic, as discussed later).