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Table 2 Detail of the "what-if" scenarios

From: Planning for resilience in screening operations using discrete event simulation modeling: example of HPV testing in Peru

Experiment

Description

Parameters varied

Values in "what-if" scenarios

(* indicates baseline value)

Screening system capacity with baseline resources (Figs. 2 and 3)

Assess capacity to meet the increasing demand for screening

Demand for screening

4000*, 4700, 5500, 6000 women per year

Resilience to changes in processing times (Fig. 4)

Assess resilience to longer or more variable processing times

Most likely central lab testing time (central parameter of triangular distribution)

Multiplied by a factor of 1*, 1.5, 2, and 3

Maximum central lab testing time (last parameter of triangular distribution)

Multiplied by a factor of 1*, 1.5, 2, and 3

Scaling up to meet a higher demand (Fig. 5)

Identify the resources required to meet a higher demand of 9500 women per year

Demand for screening

9500

Scheduled hours of operation for CxCa screening per day (assumes 5 days per week)

3*, 4, 5, and 6

Central lab testing capacity (number of GeneXpert testing wells)

8* and 12

Resilience to disruptions (Fig. 6)

Assess resilience to disruptions which result in cuts in laboratory operating hours or testing machine availability

Scheduled hours of operation for CxCa screening per day (assumes 5 days per week)

3*, 2, and 1

Central lab testing capacity (number of GeneXpert testing wells)

4, 8*, and 12