Test Ward Configuration

A hypothetical test ward, or a benchmark scenario, that represents the typical characteristics of all scenarios of the MRSA case study is created and its default input values are determined. As a principle, if an input parameter has the same value across all fourteen validation scenarios, then the same value will be used for the test ward as well. Otherwise, the input value will be re-estimated based on the pooled observed data from all scenarios.

Ward Layout

The test ward represents a typical study ward from the research project and should also be representative as a typical surgical ward in the UK hospital. It is assumed that, by default, the test ward consists of 34 beds, among which four are isolation beds. The remaining 30 beds are distributed evenly in five ward bays (e.g., each ward bay has six beds).

Model Experimentation and Analysis

Screening Test

The test ward carries out either admission screening or four day repeat screening. Regarding the turnaround time of the screening test, since no specific test method (e.g., conventional culture or rapid PCR test) is assumed to be applied to the test ward, an average value of two days is used.

Decolonisation Treatment

The duration and success rate of decolonisation treatment are set at five days and 74.6% respectively in the test ward as both values are shared by all validation scenarios. Like the validation models, the test ward also assumes that successful treatment needs to be confirmed by three successive weekly negative screening tests. As to the delay of decolonisation treatment after the detection of MRSA, an average delay of 0.77 day was estimated from the pooled observed data.

Vulnerability of Susceptible Patients

It is assumed that susceptible patients in the test ward have the same level of vulnerability which complies with the average vulnerability of patients in the research project.

Length of Stay

In the validation models, the patient’s length of stay is sampled from empirical distributions. For the test ward, the use of parametric distributions is more appropriate since it is easier to manipulate the mean and the shape of the length of stay by adjusting the parameter values of the chosen distribution. The use of parametric distributions can also help other researchers to replicate the model experiments.

Like the validation models, the length of stay in the test ward is classified into two categories: one for primary case patients and the other for non-primary case patients (see Section 5.2.1). The majority of patients are non-primary case patients, i.e., patients who are not colonised with MRSA on admission. The parametric distribution for the length of stay of non-primary case patients will be determined first. Due to the limited number of primary case patients, it is difficult to fit a separate parametric

Model Experimentation and Analysis

of the average length of stay of the primary case patients to the non-primary case patients.

Based on 13,417 observed lengths of stay, the parametric distribution for non-primary case patients selected by the distribution fitting package, Bestfit®, is a gamma distribution with the shape parameter, α, equal to 1.2, and the scale parameter, β, equal to 5.243. According to the property of the gamma distribution, the mean length of stay equals the product of α and β, which is 6.291 days. The Chi-square test is also satisfied (p<0.005). Figure 6.1 illustrates the comparison between the fitted gamma distribution and the histogram of the observed lengths of stay of non-primary case patients. Regarding the lengths of stay of primary case patients, a multiplying factor of 1.639, which is the ratio of average length of stay of primary case patients (i.e., 10.311 days) to the average length of stay of non-primary case patients (i.e., 6.291 days), is estimated from the observed data.

When the average length of stay needs to be changed during experimentation, the shape parameter, α, will keep the same value and the scale parameter, β, will be adjusted so that the product α and β equals the desired mean length of stay.

Figure 6.1 Comparison between the fitted gamma distribution and the observed lengths of stay of non-primary case patients

Model Experimentation and Analysis

Other Input Parameters

The default input parameter values of the arrival rate, the endemic setting and the availability of isolation beds are all estimated directly from the observed data. The average patient arrival rate during the study period is 4.7 patients per day. The endemic setting and the availability of isolation beds are 3.4% and 19.8% respectively. For the input parameters m (i.e., the proportion of transmission risk coming from within the same bay compared to the whole ward), k (i.e., the effectiveness of decolonisation treatment) and s (i.e., the inter-bay movement rate), the test ward adopts the same assumptions as the validation models which are 0.667, 0.4 and 0.1 respectively. The transmission coefficient of the test ward uses the weighted mean transmission coefficient estimated based on 13 scenarios which is 0.1414 (see Section 5.3.3). Table 6.3 summarises the default input parameter values of the test ward.

Model Experimentation and Analysis

Category Parameter Symbol Value

Total beds in the ward (beds) 34

Isolation beds in the ward (beds) 4

Beds in bay1 (beds) 6

Beds in bay2 (beds) 6

Beds in bay3 (beds) 6

Beds in bay4 (beds) 6

Ward layout

Beds in bay5 (beds) 6

Test turnaround time (days) 2

Screening

test Repeat screening interval (days) 4

Delay of decolonisation treatment (days) 0.77 Decolonisation treatment duration (days) 5 Decolonisat

ion

treatment Decolonisation treatment success rate 74.7% Length of stay distribution for non-primary

case patients (days)

Gamma(1.2

, 5.243) Non-primary case patients mean length of

stay (days) 6.291

Multiplying factor for primary case patients

length of stay 1.639

Primary case patients mean length of stay (days)

10.311

Length of stay

All patients mean length of stay (days) 6.404

Patient arrival rate 4.7

Endemic setting 3.4%

Other information

Availability of isolation beds 19.8%

Transmission coefficient C 0.1414

Proportion of risk within bay m 0.667

Effectiveness of decolonisation treatment k 0.4 Other

model assumptions

Inter-bay movement rate s 0.1