diff --git a/evaluation/reporting.qmd b/evaluation/reporting.qmd
index 7600cce..d74525f 100644
--- a/evaluation/reporting.qmd
+++ b/evaluation/reporting.qmd
@@ -27,19 +27,19 @@ Of the **24** items in the checklist:
| 1.2 Model outputs | Define all quantitative performance measures that are reported, using equations where necessary. Specify how and when they are calculated during the model run along with how any measures of error such as confidence intervals are calculated. | ✅ Fully | `Materials and methods - Outcome measures`: "We estimated the the change in outpatient and inpatient workload during the epidemic in terms of COVID-positive negative and recovered, at each dialysis unit in the network. Estimates were produced over periods three to six months. We also estimated the number of patients who were required to travel to a different unit from normal and the change in travel time."
`Materials and methods - Dialysis model`: "The dialysis model is run 30 times to simulate 30 alternative years as, due to the randomness of infection, no two years will be exactly alike. Results show typical (median) and extreme years." |
| 1.3 Experimentation aims | If the model has been used for experimentation, state the objectives that it was used to investigate.
(A) Scenario based analysis – Provide a name and description for each scenario, providing a rationale for the choice of scenarios and ensure that item 2.3 (below) is completed.
(B) Design of experiments – Provide details of the overall design of the experiments with reference to performance measures and their parameters (provide further details in data below).
(C) Simulation Optimisation – (if appropriate) Provide full details of what is to be optimised, the parameters that were included and the algorithm(s) that was be used. Where possible provide a citation of the algorithm(s). | ✅ Fully | Model can technically explore multiple scenarios, but the paper presents a single scenario: worst-case three month spread. The parameters for this scenario are described and justified.
`Introduction`: "We aimed to provide reusable tools to provide rapid information under various scenarios including a worst case three month spread."
`Materials and methods - patient progression model`: "The baseline model takes a worst case progression of COVID, infecting 80% of the dialysis population over 3 months."
`Materials and methods - data sources`: "The worst case time of spread of COVID-positive was taken from Fergeson et al. Mortality rate, time a patient was COVID-positive before admission and inpatient length of stay were local parameters."
`Materials and methods - verification and validation`: "We instead worked closely with clinicians, managers and informatics specialists within the local health system to review iterative versions of the model. We also opted to model a range of likely scenarios including what is widely believed to be the worst case." |
| **Logic** |
-| 2.1 Base model overview diagram | Describe the base model using appropriate diagrams and description. This could include one or more process flow, activity cycle or equivalent diagrams sufficient to describe the model to readers. Avoid complicated diagrams in the main text. The goal is to describe the breadth and depth of the model with respect to the system being studied. | ✅ Fully | `Figure 1`: "Schematic representation of patient pathway"
{.lightbox} |
+| 2.1 Base model overview diagram | Describe the base model using appropriate diagrams and description. This could include one or more process flow, activity cycle or equivalent diagrams sufficient to describe the model to readers. Avoid complicated diagrams in the main text. The goal is to describe the breadth and depth of the model with respect to the system being studied. | ✅ Fully | `Figure 1`: "Schematic representation of patient pathway" (@allen_simulation_2020)
{.lightbox} |
| 2.2 Base model logic | Give details of the base model logic. Give additional model logic details sufficient to communicate to the reader how the model works. | ✅ Fully | `Materials and methods - Dialysis model` section provides a detailed description of how the model works, and how patients flow through it |
| 2.3 Scenario logic | Give details of the logical difference between the base case model and scenarios (if any). This could be incorporated as text or where differences are substantial could be incorporated in the same manner as 2.2. | N/A | Not applicable (single scenario). |
-| 2.4 Algorithms | Provide further detail on any algorithms in the model that (for example) mimic complex or manual processes in the real world (i.e. scheduling of arrivals/ appointments/ operations/ maintenance, operation of a conveyor system, machine breakdowns, etc.). Sufficient detail should be included (or referred to in other published work) for the algorithms to be reproducible. Pseudo-code may be used to describe an algorithm. | ✅ Fully | `Materials and methods - patient progression model`: "The proportions of patients and times in each phase is either fixed or sampled from stochastic distributions as given in Table 1"
`Table 1`: Baseline model parameters
{.lightbox}
`Materials and methods - unit search strategy`: "When allocating patients to units, the following search strategy is employed.
• COVID negative: First look for place in current unit attended. If no room there place in the closest unit (judged by estimated travel time) with available space.
• COVID-positive: Place all COVID-positive patients first in Queen Alexandra Hospital, Portsmouth, and if capacity there is fully utilised open up capacity in Basingstoke. If a new COVID session is required, the model will displace all COVID negative patients in that session, and seek to re-allocate them according to the rules for allocating COVID negative patients.
• COVID-positive inpatient: All inpatients are placed in Queen Alexandra Hospital, Portsmouth (though the model allows searching by travel time if another unit were to open to renal COVID-positive inpatients).
• COVID-recovered: Treat as COVID negative.
• Unallocated patients: If a patient cannot be allocated to any unit, the model attempts to allocate them each day." |
+| 2.4 Algorithms | Provide further detail on any algorithms in the model that (for example) mimic complex or manual processes in the real world (i.e. scheduling of arrivals/ appointments/ operations/ maintenance, operation of a conveyor system, machine breakdowns, etc.). Sufficient detail should be included (or referred to in other published work) for the algorithms to be reproducible. Pseudo-code may be used to describe an algorithm. | ✅ Fully | `Materials and methods - patient progression model`: "The proportions of patients and times in each phase is either fixed or sampled from stochastic distributions as given in Table 1"
`Table 1`: Baseline model parameters (@allen_simulation_2020)
{.lightbox}
`Materials and methods - unit search strategy`: "When allocating patients to units, the following search strategy is employed.
• COVID negative: First look for place in current unit attended. If no room there place in the closest unit (judged by estimated travel time) with available space.
• COVID-positive: Place all COVID-positive patients first in Queen Alexandra Hospital, Portsmouth, and if capacity there is fully utilised open up capacity in Basingstoke. If a new COVID session is required, the model will displace all COVID negative patients in that session, and seek to re-allocate them according to the rules for allocating COVID negative patients.
• COVID-positive inpatient: All inpatients are placed in Queen Alexandra Hospital, Portsmouth (though the model allows searching by travel time if another unit were to open to renal COVID-positive inpatients).
• COVID-recovered: Treat as COVID negative.
• Unallocated patients: If a patient cannot be allocated to any unit, the model attempts to allocate them each day." |
| 2.5.1 Components - entities | Give details of all entities within the simulation including a description of their role in the model and a description of all their attributes. | ✅ Fully | There is one entity - patients - and these can be in different COVID states, and inpatients or outpatients.
`Materials and methods - dialysis model`: "simulates the progression of patients through phases of COVID infection: negative, positive (with some requiring inpatient care) and recovered or died." |
| 2.5.2 Components - activities | Describe the activities that entities engage in within the model. Provide details of entity routing into and out of the activity. | ✅ Fully | Patients are allocated to units to receive dialysis three times a week. The unit search strategy for each patient type is provided.
`Materials and methods - patient progression model`: "all patients should receive dialysis three times weekly, with each patient allocated to a starting day for the week of either Monday or Tuesday"
`Materials and methods - unit search strategy`: "When allocating patients to units, the following search strategy is employed.
• COVID negative: First look for place in current unit attended. If no room there place in the closest unit (judged by estimated travel time) with available space.
• COVID-positive: Place all COVID-positive patients first in Queen Alexandra Hospital, Portsmouth, and if capacity there is fully utilised open up capacity in Basingstoke. If a new COVID session is required, the model will displace all COVID negative patients in that session, and seek to re-allocate them according to the rules for allocating COVID negative patients.
• COVID-positive inpatient: All inpatients are placed in Queen Alexandra Hospital, Portsmouth (though the model allows searching by travel time if another unit were to open to renal COVID-positive inpatients).
• COVID-recovered: Treat as COVID negative.
• Unallocated patients: If a patient cannot be allocated to any unit, the model attempts to allocate them each day." |
| 2.5.3 Components - resources | List all the resources included within the model and which activities make use of them. | ✅ Fully | There are nine units, and these are the only resource, used for dialysis appointments.
`Materials and methods - study setting`: "The service operates a network of nine centres... The Queen Alexandra will be used as the primary site for positive outpatients and inpatients with spillover to a second site (Basingstoke) when capacity is insufficient."
`Supplementary Materials 1` provides further details on the units (e.g. locations, inpatient facilities, whether accept COVID-19 positive). |
| 2.5.4 Components - queues | Give details of the assumed queuing discipline used in the model (e.g. First in First Out, Last in First Out, prioritisation, etc.). Where one or more queues have a different discipline from the rest, provide a list of queues, indicating the queuing discipline used for each. If reneging, balking or jockeying occur, etc., provide details of the rules. Detail any delays or capacity constraints on the queues. | N/A | Not applicable as there are no queues |
-| 2.5.5 Components - entry/exit points | Give details of the model boundaries i.e. all arrival and exit points of entities. Detail the arrival mechanism (e.g. ‘thinning’ to mimic a non-homogenous Poisson process or balking) | ❌ Not met | Not explicitly described in the paper. However, understand from Tom that it is a fixed population where patients exit the dialysis model at mortality. This is indicated by `Figure 1`, but as I did not feel it to be clearly detailed in the paper, this has been evaluated as not met.
{.lightbox} |
+| 2.5.5 Components - entry/exit points | Give details of the model boundaries i.e. all arrival and exit points of entities. Detail the arrival mechanism (e.g. ‘thinning’ to mimic a non-homogenous Poisson process or balking) | ❌ Not met | Not explicitly described in the paper. However, understand from Tom that it is a fixed population where patients exit the dialysis model at mortality. This is indicated by `Figure 1` (@allen_simulation_2020), but as I did not feel it to be clearly detailed in the paper, this has been evaluated as not met.
{.lightbox} |
| **Data** |
| 3.1 Data sources | List and detail all data sources. Sources may include:
• Interviews with stakeholders,
• Samples of routinely collected data,
• Prospectively collected samples for the purpose of the simulation study,
• Public domain data published in either academic or organisational literature. Provide, where possible, the link and DOI to the data or reference to published literature.
All data source descriptions should include details of the sample size, sample date ranges and use within the study. | ✅ Fully | `Materials and methods - study setting`: "We apply the service delivery modelling tools in the South of England in the region of Wessex: a mixed urban/rural setting where the renal dialysis service cares for 644 patients... In the analysis we excluded home patients (n = 80) and due to its separation from the mainland the Isle of Wight (n = 44)."
`Materials and methods - data sources`: "Researchers had no access to individual patient level data. To ensure confidentiality, patient geographic locations was provided at the UK postcode sector level (alternatives might be output areas or northings and eastings). Travel times between these sectors were estimated using Routino (routino.org) with data from OpenStreetMap (openstreetmap.org). The worst case time of spread of COVID-positive was taken from Fergeson et al. Mortality rate, time a patient was COVID-positive before admission and inpatient length of stay were local parameters." |
| 3.2 Pre-processing | Provide details of any data manipulation that has taken place before its use in the simulation, e.g. interpolation to account for missing data or the removal of outliers. | ✅ Fully | Generated travel time matrix - `Materials and methods - data sources`: "patient geographic locations was provided at the UK postcode sector level (alternatives might be output areas or northings and eastings). Travel times between these sectors were estimated using Routino (routino.org) with data from OpenStreetMap (openstreetmap.org)." |
-| 3.3 Input parameters | List all input variables in the model. Provide a description of their use and include parameter values. For stochastic inputs provide details of any continuous, discrete or empirical distributions used along with all associated parameters. Give details of all time dependent parameters and correlation.
Clearly state:
• Base case data
• Data use in experimentation, where different from the base case.
• Where optimisation or design of experiments has been used, state the range of values that parameters can take.
• Where theoretical distributions are used, state how these were selected and prioritised above other candidate distributions. | ✅ Fully | `Materials and methods - patient progression model`: "The proportions of patients and times in each phase is either fixed or sampled from stochastic distributions as given in Table 1... The baseline model takes a worst case progression of COVID, infecting 80% of the dialysis population over 3 months."
`Table 1`: Baseline model parameters
{.lightbox} |
+| 3.3 Input parameters | List all input variables in the model. Provide a description of their use and include parameter values. For stochastic inputs provide details of any continuous, discrete or empirical distributions used along with all associated parameters. Give details of all time dependent parameters and correlation.
Clearly state:
• Base case data
• Data use in experimentation, where different from the base case.
• Where optimisation or design of experiments has been used, state the range of values that parameters can take.
• Where theoretical distributions are used, state how these were selected and prioritised above other candidate distributions. | ✅ Fully | `Materials and methods - patient progression model`: "The proportions of patients and times in each phase is either fixed or sampled from stochastic distributions as given in Table 1... The baseline model takes a worst case progression of COVID, infecting 80% of the dialysis population over 3 months."
`Table 1`: Baseline model parameters (@allen_simulation_2020)
{.lightbox} |
| 3.4 Assumptions | Where data or knowledge of the real system is unavailable what assumptions are included in the model? This might include parameter values, distributions or routing logic within the model. | ✅ Fully | Assumes worst-case scenario (80% infected in 3 months with 15% mortality).
`Materials and methods - patient progression model`: "We assume that COVID patients must be separated from uninfected patients, and that patients who have recovered from a COVID episode do not mix with those currently testing COVID positive. We do not deal specifically with suspected COVID patients in the model, anticipating that rapid testing will soon be available to diagnose which group they belong to."
Assumes that patients travel alone - `Results - dialysis network`: "These patients typically require 20 minutes extra travel time to get to their temporary place of care (assuming they are travelling alone)"
`Discussion - limitations of the study`:
• "The model assumes that patients can be re-allocated to units/sessions immediately. In practice changes to session allocation (e.g. shifting from COVID-negative to COVID-positive are likely to be made a little in advance.
• The results reported here assume that current capacity is maintained throughout the COVID outbreak.
• We have not modelled the effect of reductions in capacity that may be caused by staff shortages. We have not modelled timing of sessions, but the model progressively allocates COVID-positive sessions as needed, and we would assume that these sessions would come later in the day, enabling cleaning at the end of the day, ready for any COVID-negative session the next morning.
• We have not included home dialysis patients, which may affect inpatient demand. A likely worst-case scenario (with home dialysis patients following the transmission spread, and need for inpatient care, of the dialysis units, is that inpatient demand may be increased 15%." |
| **Experimentation** |
| 4.1 Initialisation | Report if the system modelled is terminating or non-terminating. State if a warm-up period has been used, its length and the analysis method used to select it. For terminating systems state the stopping condition.
State what if any initial model conditions have been included, e.g., pre-loaded queues and activities. Report whether initialisation of these variables is deterministic or stochastic. | ❌ Not met | Not stated - but understand from Tom that the model is non-terminating. |
@@ -75,14 +75,14 @@ Of the **18** items in the checklist:
| **Model conceptualisation** |
| 1 Is the focused health-related decision problem clarified? | ...the decision problem under investigation was defined. DES studies included different types of decision problems, eg, those listed in previously developed taxonomies. | ✅ Fully | Planning service delivery when need to keep COVID-positive and COVID-negative patients seperate, testing the plan under worst-case scenario three-month spread.
`Introduction`: "Rapid guidelines for dialysis service delivery have been published. These include separation of COVID-positive and COVID-negative patients; dialysis units working with transport providers to minimise the risk of cross-infection; and continuing to treat patients as close to home as possible. Planning service delivery that separates COVID-positive patients is complicated, due to the uncertainty of the spread of SARS-CoV-2, the variability seen in symptom onset, length of infectivity, and regional delivery of dialysis. We therefore sought to support decision making in the period prior to peak infection by developing mathematical models of dialysis service delivery"
`Materials and methods - study setting`: "During the epidemic, COVID-positive patients will be treated separately from negative and recovered. The Queen Alexandra will be used as the primary site for positive outpatients and inpatients with spillover to a second site (Basingstoke) when capacity is insufficient. Patient transport services will provide COVID only ambulances with a policy of single patient transport" |
| 2 Is the modeled healthcare setting/health condition clarified? | ...the physical context/scope (eg, a certain healthcare unit or a broader system) or disease spectrum simulated was described. | ✅ Fully | Patients with Chronic Kidney Disease visiting dialysis units during COVID-19
`Introduction`: "Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) and the disease it causes COVID-19 (henceforth known as COVID) is causing widespread disruption to normal healthcare services, as the number COVID-positive cases increases... Although social distancing measures are in place both in the UK and internationally, patients with Chronic Kidney Disease who must visit dialysis units are limited in their ability to be fully isolated." |
-| 3 Is the model structure described? | ...the model’s conceptual structure was described in the form of either graphical or text presentation. | ✅ Fully | `Figure 1`: "Schematic representation of patient pathway"
{.lightbox}
`Materials and methods - Dialysis model` section provides a detailed description of how the model works, and how patients flow through it |
+| 3 Is the model structure described? | ...the model’s conceptual structure was described in the form of either graphical or text presentation. | ✅ Fully | `Figure 1`: "Schematic representation of patient pathway" (@allen_simulation_2020)
{.lightbox}
`Materials and methods - Dialysis model` section provides a detailed description of how the model works, and how patients flow through it |
| 4 Is the time horizon given? | ...the time period covered by the simulation was reported. | ✅ Fully | Three months - `Materials and methods - patient progression model`: "The baseline model takes a worst case progression of COVID, infecting 80% of the dialysis population over 3 months." |
| 5 Are all simulated strategies/scenarios specified? | ...the comparators under test were described in terms of their components, corresponding variations, etc | ✅ Fully | There is a single scenario: worst-case three month spread. The parameters for this scenario are described and justified.
`Materials and methods - patient progression model`: "The baseline model takes a worst case progression of COVID, infecting 80% of the dialysis population over 3 months." |
| 6 Is the target population described? | ...the entities simulated and their main attributes were characterized. | ✅ Fully | `Materials and methods - study setting:` - "We apply the service delivery modelling tools in the South of England in the region of Wessex: a mixed urban/rural setting where the renal dialysis service cares for 644 patients. The service operates a network of nine centres. The largest of which is located at the Queen Alexandra (QA) Hospital, Portsmouth. To access dialysis services 75% of patients make use of patient transport services."
`Results - dialysis network`: "Currently the median travel time from home to dialysis unit (one way, with a single passenger) is 14 minutes. The minimum, lower quartile, upper quartile, and maximum travel times are 1, 9, 22, and 76 minutes. Currently there is sufficient capacity for 668 dialysis patients in the outpatient sessions which are currently open, with 583 patients currently receiving dialysis (87% capacity utilisation)." |
| **Paramaterisation and uncertainty assessment** |
| 7 Are data sources informing parameter estimations provided? | ...the sources of all data used to inform model inputs were reported. | ✅ Fully | `Materials and methods - study setting`: "We apply the service delivery modelling tools in the South of England in the region of Wessex: a mixed urban/rural setting where the renal dialysis service cares for 644 patients... In the analysis we excluded home patients (n = 80) and due to its separation from the mainland the Isle of Wight (n = 44)."
`Materials and methods - data sources`: "Researchers had no access to individual patient level data. To ensure confidentiality, patient geographic locations was provided at the UK postcode sector level (alternatives might be output areas or northings and eastings). Travel times between these sectors were estimated using Routino (routino.org) with data from OpenStreetMap (openstreetmap.org). The worst case time of spread of COVID-positive was taken from Fergeson et al. Mortality rate, time a patient was COVID-positive before admission and inpatient length of stay were local parameters." |
-| 8 Are the parameters used to populate model frameworks specified? | ...all relevant parameters fed into model frameworks were disclosed. | ✅ Fully | `Materials and methods - patient progression model`: "The proportions of patients and times in each phase is either fixed or sampled from stochastic distributions as given in Table 1... The baseline model takes a worst case progression of COVID, infecting 80% of the dialysis population over 3 months."
`Table 1`: Baseline model parameters
{.lightbox} |
-| 9 Are model uncertainties discussed? | ...the uncertainty surrounding parameter estimations and adopted statistical methods (eg, 95% confidence intervals or possibility distributions) were reported. | ✅ Fully | All figures include the minimum and maximum from 30 trials (shown with fainter lines), alongside the median results (shown with bolder lines). Example from `Figure 2`:
{.lightbox} |
+| 8 Are the parameters used to populate model frameworks specified? | ...all relevant parameters fed into model frameworks were disclosed. | ✅ Fully | `Materials and methods - patient progression model`: "The proportions of patients and times in each phase is either fixed or sampled from stochastic distributions as given in Table 1... The baseline model takes a worst case progression of COVID, infecting 80% of the dialysis population over 3 months."
`Table 1`: Baseline model parameters (@allen_simulation_2020)
{.lightbox} |
+| 9 Are model uncertainties discussed? | ...the uncertainty surrounding parameter estimations and adopted statistical methods (eg, 95% confidence intervals or possibility distributions) were reported. | ✅ Fully | All figures include the minimum and maximum from 30 trials (shown with fainter lines), alongside the median results (shown with bolder lines). Example from `Figure 2` (@allen_simulation_2020):
{.lightbox} |
| 10 Are sensitivity analyses performed and reported? | ...the robustness of model outputs to input uncertainties was examined, for example via deterministic (based on parameters’ plausible ranges) or probabilistic (based on a priori-defined probability distributions) sensitivity analyses, or both. | ❌ Not met | Not reported. |
| **Validation** |
| 11 Is face validity evaluated and reported? | ...it was reported that the model was subjected to the examination on how well model designs correspond to the reality and intuitions. It was assumed that this type of validation should be conducted by external evaluators with no stake in the study. | ✅ Fully | `Materials and methods - verification and validation`: They "worked closely with clinicians, managers and informatics specialists within the local health system to review iterative versions of the model. We also opted to model a range of likely scenarios including what is widely believed to be the worst case." |
diff --git a/evaluation/reproduction_success.qmd b/evaluation/reproduction_success.qmd
index 80fd668..626d43f 100644
--- a/evaluation/reproduction_success.qmd
+++ b/evaluation/reproduction_success.qmd
@@ -1,5 +1,6 @@
---
title: "Reproduction success"
+bibliography: ../quarto_site/references.bib
---
Of the three items in the scope, 100% (3 out of 3) were considered to be **successfully reproduced**.
@@ -8,7 +9,7 @@ In each case, it was felt that there were **minimal variation** between the orig
## Figure 2
-Original figure:
+Original figure (@allen_simulation_2020):
{width=70% fig-align="center"}
@@ -18,7 +19,7 @@ Reproduction:
## Figure 3
-Original figure:
+Original figure (@allen_simulation_2020):
{width=70% fig-align="center"}
@@ -28,7 +29,7 @@ Reproduction:
## Figure 4
-Original figure:
+Original figure (@allen_simulation_2020):
{width=70% fig-align="center"}
diff --git a/evaluation/scope.qmd b/evaluation/scope.qmd
index 45b7f61..b6e7c3d 100644
--- a/evaluation/scope.qmd
+++ b/evaluation/scope.qmd
@@ -1,5 +1,6 @@
---
title: "Scope"
+bibliography: ../quarto_site/references.bib
---
This page outlines that parts of the journal article which we will attempt to reproduce.
@@ -10,7 +11,7 @@ This page outlines that parts of the journal article which we will attempt to re
## Figure 2
-{width=80%}
+{width=80%}
:::
@@ -18,7 +19,7 @@ This page outlines that parts of the journal article which we will attempt to re
## Figure 3
-
+
:::
@@ -26,7 +27,7 @@ This page outlines that parts of the journal article which we will attempt to re
## Figure 4
-
+
:::
@@ -38,7 +39,7 @@ This page outlines that parts of the journal article which we will attempt to re
Outside scope as it is a table of model parameters rather than outputs.
-
+
:::
@@ -48,7 +49,7 @@ Outside scope as it is a table of model parameters rather than outputs.
Outside scope as it is a flow chart representing the model pathways.
-
+
:::
@@ -58,7 +59,7 @@ Outside scope as it is a flow chart representing the model pathways.
Outside scope as it is a result of the Monte Carlo model.
-
+
:::
@@ -68,6 +69,6 @@ Outside scope as it is a result of the Monte Carlo model.
Outside scope as it is a result of the Monte Carlo model.
-
+
:::
\ No newline at end of file
diff --git a/logbook/posts/2024_05_22/index.qmd b/logbook/posts/2024_05_22/index.qmd
index b2fcf9a..d91274b 100644
--- a/logbook/posts/2024_05_22/index.qmd
+++ b/logbook/posts/2024_05_22/index.qmd
@@ -3,6 +3,7 @@ title: "Day 1"
author: "Amy Heather"
date: "2024-05-22"
categories: [setup, read, scope]
+bibliography: ../../../quarto_site/references.bib
---
::: {.callout-note}
@@ -79,13 +80,13 @@ Read article, making notes in `study_summary.qmd` (initially rough notes, then t
* Defined period (e.g. one year). Patients progress through phases of COVID (negative, positive, some with inpatient care, recovred, died). In each COVID state, model seeks to put them in appropriate unit and session, opening COVID-positive sessions in units that allow it. COVID-positive don't mis with others.
* Run 30 times, show median and extremes.
-{width=50%}
+{width=50%}
* All patients receive dialysis 3 times a week. Each patient starts on either Monday or Tuesday.
* Have proportion of patients either fixed or sampled from stochastic distribution for phases of COVID state and care.
* COVID seperate from uninfected and recovered.
-{width=50%}
+{width=50%}
For allocation to units, use search strategy:
@@ -109,11 +110,11 @@ COVID positive converted back to COVID negative when no longer needed.
* Reduces workflow in units not taking COVID positive patients.
* Displaced patients typically need 20 extra minutes to get to temporary care place (sometimes 50 minutes)
-{width=50%}
+{width=50%}
-{width=50%}
+{width=50%}
-{width=50%}
+{width=50%}
**Discusion:**
diff --git a/logbook/posts/2024_05_23/index.qmd b/logbook/posts/2024_05_23/index.qmd
index 83be8d6..1db313a 100644
--- a/logbook/posts/2024_05_23/index.qmd
+++ b/logbook/posts/2024_05_23/index.qmd
@@ -3,6 +3,7 @@ title: "Day 2"
author: "Amy Heather"
date: "2024-05-23"
categories: [read, reproduce]
+bibliography: ../../../quarto_site/references.bib
---
::: {.callout-note}
@@ -182,7 +183,7 @@ Updated the files accordingly.
### 14.19-15.12 Reproduction
-Original:
+Original (@allen_simulation_2020):
@@ -197,7 +198,7 @@ Run 2:
* Ran again and compared images to see if its varying between runs - it looked quite different! I saved each under new file names so not overwritten
* Model parameters input in the notebook look to match the paper (Table 1). Its 30 replications as in the paper too.
-
+
```
number_of_replications = 30
@@ -251,7 +252,7 @@ Change random_state in parameters.py from None to having a value for each in the
* NormalParams 1
* UniformParams 2
-Original:
+Original (@allen_simulation_2020):
diff --git a/logbook/posts/2024_05_24/index.qmd b/logbook/posts/2024_05_24/index.qmd
index eb63cd8..497d0b4 100644
--- a/logbook/posts/2024_05_24/index.qmd
+++ b/logbook/posts/2024_05_24/index.qmd
@@ -3,6 +3,7 @@ title: "Day 3"
author: "Amy Heather"
date: "2024-05-24"
categories: [reproduce]
+bibliography: ../../../quarto_site/references.bib
---
::: {.callout-note}
@@ -56,6 +57,8 @@ Exploring methods for overlaying figures. Not timed as not about reproduction of
Decided that it's not helpful to do this - spend more time fiddling around with getting them to resize and overlay correctly - and that the simplest option here would be to compare by eye.
+Base image sourced from @allen_simulation_2020
+
If timed, 13.33-13.57.
```{python}
diff --git a/logbook/posts/2024_06_03/index.qmd b/logbook/posts/2024_06_03/index.qmd
index b008dcf..bb34450 100644
--- a/logbook/posts/2024_06_03/index.qmd
+++ b/logbook/posts/2024_06_03/index.qmd
@@ -3,6 +3,7 @@ title: "Day 4"
author: "Amy Heather"
date: "2024-06-03"
categories: [reproduce]
+bibliography: ../../../quarto_site/references.bib
---
::: {.callout-note}
@@ -75,7 +76,7 @@ Examples of differences to spot between them:
* Interval of green line in Figure 2
::: {layout-ncol=3}
-Original Figure 2:
+Original Figure 2 (@allen_simulation_2020):
Base 2700:
@@ -86,7 +87,7 @@ Base 2100:
:::
::: {layout-ncol=3}
-Original Figure 3:
+Original Figure 3 (@allen_simulation_2020):
Base 2700:
@@ -97,7 +98,7 @@ Base 2100:
:::
::: {layout-ncol=3}
-Original Figure 4:
+Original Figure 4 (@allen_simulation_2020):
Base 2700:
diff --git a/quarto_site/license.qmd b/quarto_site/license.qmd
index ad6a3de..36612a1 100644
--- a/quarto_site/license.qmd
+++ b/quarto_site/license.qmd
@@ -1,5 +1,6 @@
---
-title: "Open Source License"
+title: "Code and article licenses"
+bibliography: ../quarto_site/references.bib
---
This repository is licensed under the MIT License.
@@ -12,7 +13,7 @@ This repository is licensed under the MIT License.
:::
-This is aligned with the original study, who also licensed their work under the MIT License.
+This is aligned with the original study, who also licensed their code under the MIT License.
::: {.callout-note appearance="minimal" collapse=true}
@@ -22,3 +23,13 @@ This is aligned with the original study, who also licensed their work under the
:::
+
+The original study was published in the journal "PLOS ONE". They distributed the article under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license. We have therefore been able to upload the article and images to this repository.
+
+::: {.callout-note appearance="minimal" collapse=true}
+
+## View copyright statement from journal
+
+"© 2020 Allen et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited." - @allen_simulation_2020
+
+:::
\ No newline at end of file
diff --git a/quarto_site/references.bib b/quarto_site/references.bib
index 0ba61ad..87a3ee8 100644
--- a/quarto_site/references.bib
+++ b/quarto_site/references.bib
@@ -549,7 +549,7 @@ @article{zhang_reporting_2020
year = {2020},
keywords = {discrete event simulation, healthcare decision modeling, reporting quality checklist},
pages = {506--514},
- file = {ScienceDirect Snapshot:/home/amy/Zotero/storage/YGMNWH9Z/S1098301520300401.html:text/html},
+ file = {Full Text:/home/amy/Zotero/storage/YW3KDRF5/Zhang et al. - 2020 - Reporting Quality of Discrete Event Simulations in.pdf:application/pdf;ScienceDirect Snapshot:/home/amy/Zotero/storage/YGMNWH9Z/S1098301520300401.html:text/html},
}
@misc{wickham_12_2023,
@@ -683,6 +683,56 @@ @article{monks_supplementary_2024
author = {Monks, Thomas and Harper, Alison},
month = jun,
year = {2024},
- keywords = {Discrete-Event Simulation, Healthcare, Open models, Open Science, Systematic review},
+ keywords = {Systematic review, Discrete-Event Simulation, Healthcare, Open models, Open Science},
file = {Snapshot:/home/amy/Zotero/storage/BIVEYHET/11490636.html:text/html},
}
+
+@misc{the_linux_foundation_docker_nodate,
+ title = {Docker containers: {What} are the open source licensing considerations?},
+ url = {https://www.linuxfoundation.org/resources/publications/docker-containers-what-are-the-open-source-licensing-considerations},
+ urldate = {2024-06-06},
+ journal = {The Linux Foundation},
+ author = {{The Linux Foundation}},
+ file = {Docker containers\: What are the open source licensing considerations?:/home/amy/Zotero/storage/X3LYAEL5/docker-containers-what-are-the-open-source-licensing-considerations.html:text/html},
+}
+
+@misc{noauthor_docker_nodate,
+ title = {Docker containers: {What} are the open source licensing considerations?},
+ shorttitle = {Docker containers},
+ url = {https://www.linuxfoundation.org/resources/publications/docker-containers-what-are-the-open-source-licensing-considerations},
+ abstract = {Tap into the latest open source publications. Discover insights from our projects and open technology thought leaders.},
+ language = {en},
+ urldate = {2024-06-06},
+ file = {Snapshot:/home/amy/Zotero/storage/XI6YY48D/docker-containers-what-are-the-open-source-licensing-considerations.html:text/html},
+}
+
+@misc{hoces_how_2020,
+ title = {How to {Teach} {Reproducibility} in {Classwork}},
+ url = {https://bitss.github.io/WEAI2020_slides},
+ abstract = {https://github.com/BITSS/WEAI2020\_slides},
+ urldate = {2024-06-12},
+ author = {Hoces, Fernando},
+ month = jun,
+ year = {2020},
+ file = {Hoces - 2020 - How to Teach Reproducibility in Classwork.pdf:/home/amy/Zotero/storage/P86MPQGM/Hoces - 2020 - How to Teach Reproducibility in Classwork.pdf:application/pdf},
+}
+
+@article{allen_simulation_2020,
+ title = {A simulation modelling toolkit for organising outpatient dialysis services during the {COVID}-19 pandemic},
+ volume = {15},
+ issn = {1932-6203},
+ url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237628},
+ doi = {10.1371/journal.pone.0237628},
+ abstract = {This study presents two simulation modelling tools to support the organisation of networks of dialysis services during the COVID-19 pandemic. These tools were developed to support renal services in the South of England (the Wessex region caring for 650 dialysis patients), but are applicable elsewhere. A discrete-event simulation was used to model a worst case spread of COVID-19, to stress-test plans for dialysis provision throughout the COVID-19 outbreak. We investigated the ability of the system to manage the mix of COVID-19 positive and negative patients, the likely effects on patients, outpatient workloads across all units, and inpatient workload at the centralised COVID-positive inpatient unit. A second Monte-Carlo vehicle routing model estimated the feasibility of patient transport plans. If current outpatient capacity is maintained there is sufficient capacity in the South of England to keep COVID-19 negative/recovered and positive patients in separate sessions, but rapid reallocation of patients may be needed. Outpatient COVID-19 cases will spillover to a secondary site while other sites will experience a reduction in workload. The primary site chosen to manage infected patients will experience a significant increase in outpatients and inpatients. At the peak of infection, it is predicted there will be up to 140 COVID-19 positive patients with 40 to 90 of these as inpatients, likely breaching current inpatient capacity. Patient transport services will also come under considerable pressure. If patient transport operates on a policy of one positive patient at a time, and two-way transport is needed, a likely scenario estimates 80 ambulance drive time hours per day (not including fixed drop-off and ambulance cleaning times). Relaxing policies on individual patient transport to 2-4 patients per trip can save 40-60\% of drive time. In mixed urban/rural geographies steps may need to be taken to temporarily accommodate renal COVID-19 positive patients closer to treatment facilities.},
+ language = {en},
+ number = {8},
+ urldate = {2024-06-17},
+ journal = {PLOS ONE},
+ author = {Allen, Michael and Bhanji, Amir and Willemsen, Jonas and Dudfield, Steven and Logan, Stuart and Monks, Thomas},
+ month = aug,
+ year = {2020},
+ note = {Publisher: Public Library of Science},
+ keywords = {Ambulances, COVID 19, Inpatients, Medical dialysis, Outpatients, Pandemics, Respiratory infections, Simulation and modeling},
+ pages = {e0237628},
+ file = {Full Text PDF:/home/amy/Zotero/storage/S5F2FSBS/Allen et al. - 2020 - A simulation modelling toolkit for organising outp.pdf:application/pdf},
+}
diff --git a/reproduction/README.md b/reproduction/README.md
index a39f323..ef4898f 100644
--- a/reproduction/README.md
+++ b/reproduction/README.md
@@ -8,17 +8,17 @@ This is a discrete-event simulation modeling patient allocation to dialysis unit
Model patient pathway figure from the original study:
-
+
## Scope of the reproduction
In this assessment, we attempted to reproduced three figures.
-{width=50%}
+{width=50%}
-{width=50%}
+{width=50%}
-{width=50%}
+{width=50%}
## Reproducing these results