Adding YML processing

README.qmd

@@ -10,25 +10,29 @@ This repository is used for coordinating all the software development activities
 ```{r}
 #| label: load-data
 #| echo: false
-library(readxl)
+library(yaml)
 library(data.table)
 
-existing_software <- read_excel(
-    path = "data/box/ForeSITE_Tools surveyV2_ Tool D&I only.xlsx",
-    skip = 2
-    ) |> as.data.table()
+existing_software <- list.files(
+    "data", pattern = "ya?ml$", full.names = TRUE
+    ) |> 
+    lapply(FUN = yaml.load_file, as.named.list = TRUE) |>
+    lapply(as.data.table) |>
+    rbindlist()
+
+writexl::write_xlsx(x = existing_software, path="data/software.xlsx")
 
 # Renaming relevant columns
 cnames <- c(
-    Tool = "Tool Name",
-    Description = "Brief Description",
-    Languages = "Languages",
-    Repo = "GitHub Repo (new or old if existing one)",
-    Contact = "Name of developer, maintainer, or key contact",
-    Email = "Email of developer, maintainer or key contact",
-    Links = "Link to web page/documentation (optional)",
-    Type="Type of tool",
-    Diseases="Relevant disease(s)"
+    Tool = "tool_name",
+    Description = "brief_description",
+    Languages = "languages",
+    Repo = "github_repo_new_or_old_if_existing_one",
+    Contact = "name_of_developer_maintainer_or_key_contact",
+    Email = "email_of_developer_maintainer_or_key_contact",
+    Links = "link_to_web_page_documentation_optional",
+    Type="type_of_tool",
+    Diseases="relevant_disease_s"
 )
 
 setnames(existing_software, cnames, names(cnames))

data/README.md

@@ -0,0 +1,62 @@
+# List of existing software
+
+This folder holds information about existing software developed by the ForeSITE group. To add a new software, please create a new yaml file (template below) and add it to the list. Executing the [../README.qmd](../README.qmd) quarto file will automatically update the list of software included in the README.md at the root level of the repository and update the `software.xlsx` file included in this folder.
+
+## YAML Template
+Fill in the template fields with your project's information.
+```yaml
+tool_name: 
+brief_description:
+name_of_developer_maintainer_or_key_contact:
+email_of_developer_maintainer_or_key_contact:
+is_it_actively_maintained_yes_no:
+relevant_disease_s:
+maturity: 
+license: 
+languages:
+audience_type:
+required_expertise_to_use_tool:
+type_of_tool:
+type_of_data_input_needed:
+link_to_web_page_documentation_optional:
+link_to_source_code_optional:
+reviewer:
+github_repo_new_or_old_if_existing_one:
+complete_yes_no:
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes:
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project:
+```
+
+## Example Project YAML File 
+```yaml
+tool_name: 'epiworld: Fast Agent-Based Epi Models'
+brief_description: A flexible framework for Agent-Based Models (ABM), the 'epiworldR'
+  package provides methods for prototyping disease outbreaks and transmission models
+  using a 'C++' backend, making it very fast. It supports multiple epidemiological
+  models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed
+  (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary
+  mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility
+  rates as a function of agents' features, providing great complexity for the model
+  dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large
+  populations.
+name_of_developer_maintainer_or_key_contact: George G. Vega Yon
+email_of_developer_maintainer_or_key_contact: [email protected]
+is_it_actively_maintained_yes_no: 'Yes'
+relevant_disease_s: .na.character
+maturity: Published
+license: MIT
+languages: R, C++, Python, Webessembly
+audience_type: Modelers
+required_expertise_to_use_tool: TBD
+type_of_tool: Epidemic Model - Scenario Modeling
+type_of_data_input_needed: Parameter inputs for simulating the model
+link_to_web_page_documentation_optional: https://github.com/UofUEpiBio/epiworld, https://github.com/UofUEpiBio/epiworldR/,
+  https://github.com/UofUEpiBio/epiworldpy, https://github.com/UofUEpiBio/epiworldRShiny
+link_to_source_code_optional: .na.character
+reviewer: .na.character
+github_repo_new_or_old_if_existing_one: https://github.com/UofUEpiBio/epiworld
+complete_yes_no: 'yes'
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: .na.character
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: .na.character
+```
+

data/airborne_release_of_infectious_pathogens_simulator.yml

@@ -0,0 +1,31 @@
+tool_name: Airborne release of infectious pathogens simulator
+brief_description: 'Estimate airborne dispersal, human exposure, and infection probabilities
+  and timelines after a release of a quantity of infectious organisms. Scenarios(s)
+  Modeled: Airborne release and human inhalational exposure and infection, similar
+  the Sverdlovsk anthrax leak of 1979.'
+name_of_developer_maintainer_or_key_contact: Damon Toth
+email_of_developer_maintainer_or_key_contact: [email protected]
+is_it_actively_maintained_yes_no: 'No'
+relevant_disease_s: Any pathogen with supportable assumptions; we have applied our
+  tools to Anthrax
+maturity: R code is organized and documented but not publicly available. Could be
+  made publicly available or packaged for use with moderate effort.
+license: TBD
+languages: R
+audience_type: TBD
+required_expertise_to_use_tool: TBD
+type_of_tool: Epidemic Model - Scenario Modeling
+type_of_data_input_needed: Exposure localized to release area.
+link_to_web_page_documentation_optional: 1-Toth D, Gundlapalli A, Schell W, Bulmahn
+  K, Walton T, Woods C, Coghill C, Gallegos F, Samore M, Adler F (2013). Quantitative
+  models of the dose-response and time course of inhalational anthrax in humans. PLoS
+  Pathog, 9(8), e1003555. https://doi.org/10.1371/journal.ppat.1003555.                          2-Bulmahn
+  K, Canella M, Coghill C, Gallegos F, Gundlapalli A, Schell W, Toth D, Walton T,
+  Woods C (2012). Final Supplementary Risk Assessment for the Boston University National
+  Emerging Infectious Diseases Laboratories, National Institutes of Health. https://www.bu.edu/neidl/files/2013/01/SFEIR-Volume-III.pdf.
+link_to_source_code_optional: .na.character
+reviewer: George
+github_repo_new_or_old_if_existing_one: .na.character
+complete_yes_no: .na.character
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: Asked for the code
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: .na.character

data/arima_generalize_arima_vector_autoregression.yml

@@ -0,0 +1,23 @@
+tool_name: ARIMA; Generalize ARIMA; Vector Autoregression
+brief_description: To forecast the weekly positive test number
+name_of_developer_maintainer_or_key_contact: Yue Zhang
+email_of_developer_maintainer_or_key_contact: [email protected]
+is_it_actively_maintained_yes_no: N/A
+relevant_disease_s: RSV, influenza, COVID-19
+maturity: Research or Development phase
+license: TBD
+languages: Python
+audience_type: TBD
+required_expertise_to_use_tool: TBD
+type_of_tool: Epidemic Model - Forecasting
+type_of_data_input_needed: 'The objective of this study is to use multiple time series
+  data to predict weekly infection counts for mutiple virus. Timeframe: 12/2002-01/2024'
+link_to_web_page_documentation_optional: no pubication plan
+link_to_source_code_optional: .na.character
+reviewer: Andrew
+github_repo_new_or_old_if_existing_one: .na.character
+complete_yes_no: .na.character
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: Nothing to do but publish
+  analysis files.  Yue will publish those
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: Unsure
+  - Needs group thinking

data/attention_based_models_for_snow_water_equivalent_prediction.yml

@@ -0,0 +1,29 @@
+tool_name: Attention-Based Models for Snow-Water Equivalent Prediction
+brief_description: 'Transformer architectures for spatio-temporal prediction (or synthetic
+  data generation/imputation). Scenarios(s) Modeled: Predicting the SWE value for
+  multiple SNOTEL locations in the Western US using the Attention Models'
+name_of_developer_maintainer_or_key_contact: Ananth Kalyanaraman
+email_of_developer_maintainer_or_key_contact: [email protected]
+is_it_actively_maintained_yes_no: 'Yes'
+relevant_disease_s: Synthetic data generation/imputation
+maturity: Software page: https://github.com/Krishuthapa/SWE-Attention (currently tested
+  for SWE prediction application)
+license: TBD
+languages: Python
+audience_type: TBD
+required_expertise_to_use_tool: TBD
+type_of_tool: Epidemic Model - Scenario Modeling
+type_of_data_input_needed: SWE values vary spatiotemporally—affected by weather, topography,
+  and other environmental factors.
+link_to_web_page_documentation_optional: https://ojs.aaai.org/index.php/AAAI/article/view/30337
+link_to_source_code_optional: .na.character
+reviewer: George
+github_repo_new_or_old_if_existing_one: https://github.com/Krishuthapa/SWE-Attention
+complete_yes_no: 'yes'
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: "Potentially a python
+  module. There are functions and classes that are well-defined. Maybe exporting the
+  classes should be enough.\r\n\r\nBut it also has hardcoded stuff in the class def
+  files."
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: Needs
+  some work - Functions/classes are mixed with the code. Need to separate them and
+  add them to namespace/__init__.py file.

data/autoencoder_and_clustering_based_anomaly_detection.yml

@@ -0,0 +1,33 @@
+tool_name: Autoencoder and clustering based anomaly detection
+brief_description: Initially developed as a project with DHS Science and Technology,
+  this project took place from 2018 to 2019 (pre-COVID).  The approach here is to
+  find potentially anomalous cases which are also related.  To create anomaly “scores”,
+  a neural network autoencoder is used to process over patient data from the emergency
+  department visit and quantify how “rare” this kind of visit might be compared to
+  all other previous visits.  Then a density-based clustering is used to identify
+  any potential clusters of anomalous cases since one single case may not warrant
+  concern, but a group of patients with similar labs, signs/symptoms, etc might suggest
+  common exposures and conditions.
+name_of_developer_maintainer_or_key_contact: Kelly Peterson
+email_of_developer_maintainer_or_key_contact: [email protected] ;[email protected]
+is_it_actively_maintained_yes_no: N/A
+relevant_disease_s: NA
+maturity: Still early in its maturity.  Early investigations show that this currently
+  emits too many potential “anomaly clusters” to be useful, so sensitivity needs to
+  be reduced before being used in an operation capacity
+license: TBD
+languages: Python
+audience_type: TBD
+required_expertise_to_use_tool: TBD
+type_of_tool: Decision Support tool
+type_of_data_input_needed: Uses VA CDW data including Emergency Department visits,
+  associated ICD codes, health factors, labs, orders, medications, and procedures.
+  Python technology stacks.  Autoencoder models trained with PyTorch, and clustering
+  is performed with HDBScan.
+link_to_web_page_documentation_optional: Nothing published.
+link_to_source_code_optional: .na.character
+reviewer: Andrew
+github_repo_new_or_old_if_existing_one: .na.character
+complete_yes_no: .na.character
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: .na.character
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: .na.character

data/bayesian_transmission_model.yml

@@ -0,0 +1,52 @@
+tool_name: Bayesian Transmission Model
+brief_description: 'Provides estimates for critical epidemiological parameters that
+  characterize the spread of bacterial pathogens in healthcare settings. Parameter
+  estimated: Transmission rate (frequency-dependent or density-dependent mass action),
+  importation probability, clearance rate (loss of colonization per colonized person
+  per unit time), surveillance test sensitivity, surveillance test specificity, effect
+  of covariate on transmission (multiplier in relation to overall transmission rate).'
+name_of_developer_maintainer_or_key_contact: Karim Khader
+email_of_developer_maintainer_or_key_contact: [email protected]
+is_it_actively_maintained_yes_no: 'No'
+relevant_disease_s: Bacterial pathogens and other pathogens that result in both symptomatic
+  and asymptomatic disease states.
+maturity: C++ code can be compiled and run; customization may be required for specific
+  uses and to specify the underlying model/parameters of interest.
+license: TBD
+languages: C++
+audience_type: TBD
+required_expertise_to_use_tool: TBD
+type_of_tool: Parameter estimation 
+type_of_data_input_needed: Developed for use in a healthcare setting, accounts for
+  ‘flow’ of patients, can allow for multiple sub-units (multiple hospitals, multiple
+  wards within each hospital). Uses disease (random or uniform) surveillance data;
+  that is testing data that is not targeted to specific populations more/less likely
+  to be infected.
+link_to_web_page_documentation_optional: "1.Khader K, Thomas A, Stevens V, Visnovsky
+  L, Nevers M, Toth D, Keegan LT, Jones M, Rubin M, Samore MH (2021). Association
+  Between Contact Precautions And Transmission of Methicillin-Resistant Staphylococcus
+  Aureus in Veterans Affairs Hospitals. JAMA Netw Open.\r\n2.Khader K, Munoz-Price
+  LS, Hanson R, Stevens V, Keegan LT, Thomas A, Pezzin LE, Nattinger A, Singh S, Samore
+  MH (2021). Transmission Dynamics of Clostridioides difficile in 2 High-Acuity Hospital
+  Units. Clin Infect Dis.\r\n3.Khader K, Thomas A, Huskins WC, Stevens V, Keegan LT,
+  Visnovsky L, Samore MH (2021). Effectiveness of Contact Precautions to Prevent Transmission
+  of Methicillin-Resistant Staphylococcus aureus and Vancomycin-Resistant Enterococci
+  in Intensive Care Units. Clin Infect Dis.\r\n4.Khader K, Thomas A, Jones M, Toth
+  D, Stevens V, Samore MH (2019). Variation and trends in transmission dynamics of
+  Methicillin-resistant Staphylococcus aureus in veterans affairs hospitals and nursing
+  homes. Epidemics.\r\n5.Thomas A, Khader K, Redd A, Leecaster M, Zhang Y, Jones M,
+  Greene T, Samore M (2018). Extended models for nosocomial infection: parameter estimation
+  and model selection. Math Med Biol, 35(suppl_1), 29-49.\r\n6.Khader K, Thomas A,
+  Huskins WC, Leecaster M, Zhang Y, Greene T, Redd A, Samore MH (2017). A dynamic
+  transmission model to evaluate the effectiveness of infection control strategies.
+  Open Forum Infect Dis.\r\n7.Thomas A, Redd A, Khader K, Leecaster M, Greene T, Samore
+  M (2015). Efficient parameter estimation for models of healthcare-associated pathogen
+  transmission in discrete and continuous time. Math Med Biol, 32(1), 79-98."
+link_to_source_code_optional: .na.character
+reviewer: George
+github_repo_new_or_old_if_existing_one: https://github.com/EpiForeSITE/bayesian-transmission
+complete_yes_no: 'yes'
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: A large C++ program.
+  Probably easy to write a cmd line wrapper within R. We could also use Rcpp11.
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: Leave
+  for later - Too complex to address | No functional programming whatsoever

data/branching_process_outbreak_simulator.yml

@@ -0,0 +1,38 @@
+tool_name: Branching process outbreak simulator
+brief_description: 'Quantifies risk posed by individual importers of a novel transmissible
+  pathogen to a generic population, with intervention effects. Scenarios(s) Modeled:
+  Novel introduction of transmissible pathogen by infected traveler, by accidentally
+  infected laboratory worker, or similar scenario; intervention scenarios for improved
+  detection of initial case and for delayed mitigation after ongoing outbreak is detected.'
+name_of_developer_maintainer_or_key_contact: Damon Toth
+email_of_developer_maintainer_or_key_contact: [email protected]
+is_it_actively_maintained_yes_no: 'No'
+relevant_disease_s: Any emerging transmissible pathogen; we have applied the tool
+  to Ebola and Middle East respiratory syndrome (MERS).
+maturity: R code used for publication results is reasonably organized and documented
+  but not publicly available. Key functions have been shared and used by others; could
+  be made publicly available or packaged for use with reasonable effort.
+license: TBD
+languages: R
+audience_type: TBD
+required_expertise_to_use_tool: TBD
+type_of_tool: Epidemic Model - Scenario Modeling
+type_of_data_input_needed: Generic population model applicable to local community
+  experiencing the initial importation / infection.
+link_to_web_page_documentation_optional: "1-Toth D, Gundlapalli A, Khader K, Pettey
+  W, Rubin M, Adler F, Samore M (2015). Estimates of outbreak risk from new introductions
+  of Ebola with immediate and delayed transmission control. Emerg Infect Dis, 21(8),
+  1402-1408. https://doi.org/10.3201/eid2108.150170.\r\n2-Toth D, Tanner W, Khader
+  K, Gundlapalli A (2016). Estimates of the risk of large or long-lasting outbreaks
+  of Middle East respiratory syndrome after importations outside the Arabian Peninsula.
+  Epidemics, 16, 27-32. https://doi.org/10.1016/j.epidem.2016.04.002"
+link_to_source_code_optional: .na.character
+reviewer: George
+github_repo_new_or_old_if_existing_one: https://github.com/EpiForeSITE/branching_process/
+complete_yes_no: 'yes'
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: All code is functions
+  only. It could be bundled into an R pkg very easily. We only need to have an example
+  so it is more complete.
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: Easy
+  win - Functions are already in a separate file, just need to be added to a namespace/__init__.py
+  file.

data/carriage_duration_estimation_from_serial_testing_data.yml

@@ -0,0 +1,31 @@
+tool_name: Carriage duration estimation from serial testing data
+brief_description: 'Estimate the duration and heterogeneity of individuals’ colonization
+  episodes for organisms of interest. Parameter estimated: Average and distribution
+  of clearance rate(s) across multiple candidate model forms, average (re)acquisition
+  rate, sensitivity/specificity of testing. Estimates derived via maximum likelihood
+  techniques.'
+name_of_developer_maintainer_or_key_contact: Damon Toth
+email_of_developer_maintainer_or_key_contact: [email protected]
+is_it_actively_maintained_yes_no: TBD
+relevant_disease_s: Any pathogen with appropriate serialized test data; we have applied
+  our tools to S. aureus.
+maturity: R code used for publication results is publicly available on Github [https://github.com/alexbeams/StaphCarrierTypes]
+  with documentation. Could be packaged for wider use.
+license: TBD
+languages: R
+audience_type: TBD
+required_expertise_to_use_tool: TBD
+type_of_tool: Parameter estimation 
+type_of_data_input_needed: Appropriate for application to data sets from repeated
+  testing of the same individuals over long time periods relative to typical carriage
+  duration. Useful for understanding dynamics of background carriage in a wide population,
+  important to understand for evaluating intervention effectiveness.
+link_to_web_page_documentation_optional: Beams A, Keegan L, Adler F, Samore M, Khader
+  K, Toth D (2023), Are Staphylococcus aureus Carrier Types Evidence of Population
+  Heterogeneity? American Journal of Epidemiology 192(3), 455–466. https://doi.org/10.1093/aje/kwac201.
+link_to_source_code_optional: .na.character
+reviewer: Andrew
+github_repo_new_or_old_if_existing_one: .na.character
+complete_yes_no: .na.character
+pkg_dev_assessment_how_hard_is_to_make_into_a_package_notes: .na.character
+overall_assessment_easy_win_needs_some_work_needs_lots_of_work_long_term_project: .na.character