This file is automatically generated and should not be edited manually. Last updated on 2024-08-15.
The goal of this statistical analysis was to compare SARS-CoV-2 community incidence and mortality rates across schools that opened in in-person, remote, or hybrid mode. Measure(s) of Economic Impact: Infections and mortality
Contact: Richard Nelson (email: richard.nelson@utah.edu)
Links:
https://www.nature.com/articles/s41591-021-01563-8
The goal of this model was to estimate the net financial impact to the hospital of halting elective admissions due to a COVID-19 surge. Measure(s) of Economic Impact: Hospital profit
Contact: Richard Nelson (email: richard.nelson@utah.edu)
Links:
https://pubmed.ncbi.nlm.nih.gov/33427797/
Given the time lag between laboratory testing for disease and eventual reporting, we’ve developed a model which consumes data from both community and VA to predict “now” how many positive tests for a disease (COVID-19 in this context) will eventually be reported as positive. A VA HSR&D abstract is available.
Contact: Kelly Peterson (email: kelly.peterson2@va.gov ;kelly.peterson@hsc.utah.edu)
Links:
An abstract of this was presented at VS HSR&D meeting. The text of the abstract is here: https://www.hsrd.research.va.gov/meetings/2023/abstract-display.cfm?AbsNum= (There is also a poster)
We have supported an Operations system for processing time series for “aberrations” (aka, “alerts”, “potential outbreaks”) for 8+ years in VA BASIC. During this time, we’ve integrated several among the suite of algorithms available in the R package titled “surveillance”. This includes several classes of numerical methods, of which some are window-based, control chart-based or trained linear models. A brief presentation on this is attached here.
Contact: Kelly Peterson (email: kelly.peterson2@va.gov ;kelly.peterson@hsc.utah.edu)
Links:
The way that we implement this system has not been published, but there are links for “surveillance” (https://cran.r-project.org/web/packages/surveillance/index.html)and “epysurv” packages (https://epysurv.readthedocs.io/en/latest/).
We’ve used Mortality Mapping (mMAP), an existing epidemiologic and statistical analysis approach by Lu et al. [1,2] to estimate the cumulative incidence of symptomatic COVID-19 in Veterans in the United States. mMAP is a time series deconvolution method which infers the true COVID-19 case counts from reported COVID-19 deaths. It uses symptom onset to death distribution, smoothed time series of reported COVID-19 deaths, and symptomatic case fatality rate (sCFR) to estimate the distribution of symptomatic COVID-19 cases. We focus on the adjusted version of the mMAP approach presented by Lu et al., which tries to account for the unreported COVID-19 deaths. While Lu uses excess influenza and pneumonia deaths to account for unreported COVID-19 deaths, we have extended their approach by using excess all-cause deaths.
Contact: Vanessa Stevens or Wathsala Widanagamaachchi (email: vanessa.stevens@hsc.utah.edu; vanessa.stevens@va.gov or wathsala.widanagamaachchi@hsc.utah.edu)
Links:
Under the following existing papers from others: 1-https://www.medrxiv.org/content/10.1101/2020.04.18.20070821v3.full.pdf , 2-https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008994
Classic Serfling-type regression model for seasonal diseases. Can be used to estimate both excess risk (based on breaching a threshold), or estimate the peak week and other parameters of a yearly epidemic curve.
Contact: Eric Lofgren (email: Eric.Lofgren@wsu.edu)
Links:
https://link.springer.com/chapter/10.1007/978-3-540-72608-1_11
Parameter estimation using differential evolution.
Contact: Elissa Schwartz (email: ejs@wsu.edu)
Links:
https://www.dediscover.org/
Parameter estimation in non-linear mixed effect models. Parameter estimated: Any.
Contact: Elissa Schwartz (email: ejs@wsu.edu)
Links:
https://monolix.lixoft.com/
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.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
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.
Provides estimates for individual-level transmission distribution (mean and variance) based on data from prior isolated outbreak clusters. Useful for quantifying the risk of superspreading events and the risk that future outbreaks will exceed a given threshold.Parameter estimated: Basic reproduction number; dispersion parameter quantifying variance in transmission; extensions estimate changes in reproduction number by generation, e.g. post-control reproduction number. Estimated via maximum likelihood using final size distribution equations for branching process models.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
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. 2-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.
Simultaneously estimate household importation risk, household secondary attack rate, and associated variability / heterogeneities using point-prevalence data for antibody positivity among household members. Parameter estimated: Household importation probability, household secondary attack rate, sensitivity / specificity of data for indicating prior infection, variability (dispersion) in transmissibility, and heterogeneities in those quantities over age and/or other identifiable characteristics of individuals and households. Estimates derived via maximum likelihood for final household outbreak size equations.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
1-Toth D, Beams A, Keegan L, Zhang Y, Greene T, Orleans B, Seegert N, Looney A, Alder S, Samore M (2021). High variability in transmission of SARS-CoV-2 within households and implications for control. PLoS ONE 16(11): e0259097. https://doi.org/10.1371/journal.pone.0259097.
2-Manuscript with age-structured model in preparation
Estimating a competing risks mixture model, which provides both the odds ratio of one event occurring vs. another as well as a marginal estimate of the time to both respective events. Parameter estimated: Odds ratio of two different events occurring and the time to event estimate for both events.
Contact: Eric Lofgren (email: Eric.Lofgren@wsu.edu)
Links:
https://journals.lww.com/epidem/fulltext/2014/07000/hospital_acquired_clostridium_difficile.15.aspx
Estimate the extent of patient-to-patient transmission and other quantities using admission prevalence and cross-sectional point-prevalence data for carriage of organisms among healthcare facility in-patients. Parameter estimated: Patient-to-patient transmission rate, facility reproduction number, rate of progression to infection, intervention effects if applicable. Estimates derived via solving differential equations at equilibrium and facility reproduction number equations.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
o Toth D, Khader K, Beams A, Samore M (2019). Model-based Assessment of the Effect of Contact Precautions Applied to Surveillance-detected Carriers of Carbapenemase-producing Enterobacteriaceae in Long-term Acute Care Hospitals, Clinical Infectious Diseases 69(Supplement_3), S206–S213. https://doi.org/10.1093/cid/ciz557.
o Toth D, Samore M, Nelson R (2021), Economic Evaluations of New Antibiotics: The High Potential Value of Reducing Healthcare Transmission Through Decolonization, Clinical Infectious Diseases 72(Supplement_1), S34–S41. https://doi.org/10.1093/cid/ciaa1590.
Estimate infection probability from a given dose of exposure using data sets from experimental dose-quantified exposures of humans or animals; model-based results used to perform quantitative risk assessment for potential human exposure scenarios. Parameter estimated: Per-unit-dose infection probability and associated additional parameters for more complicated dose-response models, including those that quantify dose-dependent latent/incubation period; can convert estimates to common “infectious dose” quantification metrics such as “ID-50.” Estimates derived via maximum likelihood for multiple equation-based dose-response models fit to exposure-response data.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
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 (Chapter 8; Appendix J).
pomp R code for the estimation of unknown parameters in a two-population SEIR model from data collected in Whitman County, Washington
Build a two-population SEIR model using the pomp coding framework in R and estimate the unknown parameters in this model using the PMCMC algorithm. Parameter estimated: Transmission parameters (transmission within each population and transmission across the two-populations), testing probability, and overdispersion parameter.
Contact: Erin Clancey (email: erin.clancey@wsu.edu)
Links:
The manuscript is currently available as a preprint https://www.medrxiv.org/content/10.1101/2024.01.10.24301116v1.full.pdf and submitted to the journal Epidemics.
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).
Contact: Karim Khader (email: karim.khader@hsc.utah.edu)
Links:
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.
2.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.
3.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.
4.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.
5.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.
6.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.
7.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.
Describes an SIR model parameter estimation with an improved fit using least squares estimation over maximum likelihood estimation. Parameter estimated: Transmission rate, recovery rate, R0, number of initial infectives
Contact: Elissa Schwartz (email: ejs@wsu.edu)
Links:
https://doi.org/10.1016/j.mbs.2015.03.007
Estimate the mixing parameters for a two-population metapopulation model (i.e. Beta_Pop1, Beta_Pop2, Beta_Pop1*Pop2). Model currently fits timeseries data from pre-vaccination COVID in Fall 2020 in Whitman Co., WA (focusing on the town of Pullman).
Contact: Eric Lofgren (email: Eric.Lofgren@wsu.edu)
Links:
https://www.medrxiv.org/content/10.1101/2024.01.10.24301116v1
An ODE-based model to predict future disease prevalence given the impact of drug therapy levels and its mechanism of action (i.e., stopping progression to disease or reducing deaths). Parameter estimated: Growth rate, death rate, drug efficacy to block disease progression. Scenarios(s) Modeled: Rebound in prevalence due to insufficient therapy (e.g., like in Paxlovid rebound).
Contact: Elissa Schwartz (email: ejs@wsu.edu)
Links:
1-https://journals.lww.com/jasn/fulltext/2005/08000/highly_active_antiretroviral_therapy_and_the.23.aspx 2- https://doi.org/10.5206/mase/10852 3-https://ir.library.illinoisstate.edu/spora/vol6/iss1/6/.
An ODE-based 6-compartment SI model with asymptomatic carriers and vaccinated classes to forecast the number of infections prevented with imperfect vaccination and infections prevented per vaccine administered. Parameter estimated: Highly correlated parameters determined using LHS PRCC. Scenarios(s) Modeled: Prophylactic (pre-exposure) vaccines, Therapeutic (post-exposure) vaccines, Parameter(s) Estimated: Highly correlated parameters determined using LHS PRCC
Contact: Elissa Schwartz (email: ejs@wsu.edu)
Links:
1-https://doi.org/10.1086/429299 2-https://doi.org/10.4161/hv.4529
Analysis of time series data based on stochastic dynamical systems models
Contact: Elissa Schwartz (email: ejs@wsu.edu)
Links:
Unknown
A basic model to plan for the post-acute care needs following a hospital surge
Contact: Lindsay Keegan (email: Lindsay.Keegan@utah.edu)
Links:
https://www.medrxiv.org/content/10.1101/2020.06.12.20129551v1
Stochastic representation of an ICU in a tertiary care academic medical center. Scenarios(s) Modeled: Staffing changes, cohorting (with some effort), decolonization, fecal transplant, antibiotic stewardship (with some modifications). Others are possible.
Contact: Eric Lofgren (email: Eric.Lofgren@wsu.edu)
Links:
Published: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010352 for the canonical reference
A flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Scenarios(s) Modeled: Mask, social distance, lock down, comprehensive test/isolate, fatiguing interventions
Contact: Lindsay Keegan (email: Lindsay.Keegan@utah.edu)
Links:
https://www.nature.com/articles/s41598-021-86811-0
Stochastic representation a season of on-campus football during COVID. Scenarios(s) Modeled: Estimates a football season with high or low mixing between the visiting community and the hosting college student population, and with high or low prevalence in the visitors.
Contact: Eric Lofgren (email: Eric.Lofgren@wsu.edu)
Links:
https://www.mdpi.com/1660-4601/18/16/8260
Deterministic model of a stylized jail, with a number of potential interventions developed in collaboration with the ACLU. Scenarios(s) Modeled: Decarceration, social distancing, targeted or widespread arrest reduction, isolation.
Contact: Eric Lofgren (email: Eric.Lofgren@wsu.edu)
An ODE (ordinary differential equation)-based SVEIR model with 3 compartments for symptoms (mild, moderate, severe) that can quantify the tradeoffs between vaccine efficacy and vaccine hesitancy on reducing the disease burden. Scenarios(s) Modeled: R0, cumulative infections, and deaths due to varying values of transmission rate, vaccine coverage rate, vaccine efficacy, and vaccine hesitancy. The vaccine efficacy required to compensate for different levels of vaccine hesitancy.
Contact: Elissa Schwartz (email: ejs@wsu.edu)
Links:
https://doi.org/10.1080/17513758.2023.2298988
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.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
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.
2-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
Provides analytic framework for investigating patient-focused interventions to prevent transmission in high-risk healthcare facilities and their direct and population-level health impacts and threshold effects. Scenarios(s) Modeled: Intervention scenarios for surveillance and isolation (contact precautions), decolonization drugs, infection treatment, horizontal transmission reduction, and length of stay reduction.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
1-Toth D, Khader K, Beams A, Samore M (2019). Model-based Assessment of the Effect of Contact Precautions Applied to Surveillance-detected Carriers of Carbapenemase-producing Enterobacteriaceae in Long-term Acute Care Hospitals, Clinical Infectious Diseases 69(Supplement_3), S206–S213. https://doi.org/10.1093/cid/ciz557. 2-Toth D, Samore M, Nelson R (2021), Economic Evaluations of New Antibiotics: The High Potential Value of Reducing Healthcare Transmission Through Decolonization, Clinical Infectious Diseases 72(Supplement_1), S34–S41. https://doi.org/10.1093/cid/ciaa1590.
Simulate household importation and within-household transmission across a population of households, and intervention effects, e.g. vaccination. Scenarios(s) Modeled: Household importation and transmission with variability in transmissibility and heterogeneity across age groups and vaccination status.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
1-Toth D, Beams A, Keegan L, Zhang Y, Greene T, Orleans B, Seegert N, Looney A, Alder S, Samore M (2021). High variability in transmission of SARS-CoV-2 within households and implications for control. PLoS ONE 16(11): e0259097. https://doi.org/10.1371/journal.pone.0259097. 2-Manuscripts with age-structured model and vaccination model in preparation
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.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
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.
Agent-based simulation of person-to-person transmission at schools over data-based dynamic proximity contact networks. Scenarios(s) Modeled: Transmission modeled during school hours; can model interventions affecting the contact network and school attendance behavior.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
Toth D, Leecaster M, Pettey W, Gundlapalli A, Gao H, Rainey J, Uzicanin A, Samore M (2015). The role of heterogeneity in contact timing and duration in network models of influenza spread in schools. J R Soc Interface, 12(108), 20150279. https://doi.org/10.1098/rsif.2015.0279.
Provides flexible framework for agent-based simulation of outbreaks among in-patients in a generic-but-realistic network of short/long-stay hospitals and nursing homes, as well as interventions to mitigate transmission and inter-facility spread. Scenarios(s) Modeled: Initial invasion of new pathogen / strain and/or endemic pathogens. Intervention scenarios for patient-targeted efforts including surveillance and isolation (contact precautions), vaccination, and anti-microbial drug treatment.
Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
Links:
1-Slayton R, Toth D, Lee B, Tanner W, Bartsch S, Khader K, Wong K, Brown K, McKinnell J, Ray W, Miller L, Rubin M, Kim D, Adler F, Cao C, Avery L, Stone N, Kallen A, Samore M, Huang S, Fridkin S, Jernigan J (2015). Vital Signs: estimated effects of a coordinated approach for action to reduce antibiotic-resistant infections in health care facilities - United States. MMWR Morb Mortal Wkly Rep, 64(30), 826-831. https://doi.org/10.1111/ajt.13555. 2-Toth D, Khader K, Slayton R, Kallen A, Gundlapalli A, O'Hagan J, Fiore A, Rubin M, Jernigan J, Samore M (2017). The potential for interventions in a long-term acute care hospital to reduce transmission of carbapenem-resistant Enterobacteriaceae in affiliated healthcare facilities. Clin Infect Dis, 65(4), 581-587. https://doi.org/10.1093/cid/cix370., 3-Toth D, Keegan L, Samore M, Khader K, O'Hagan J, Yu H, Quintana A, Swerdlow D (2020). Modeling the potential impact of administering vaccines against Clostridioides difficile infection to individuals in healthcare facilities. Vaccine, 38(37), 5927-5932. https://doi.org/10.1016/j.vaccine.2020.06.081.
TDA tool that can possibly help us to view some trajectory data. Scenarios(s) Modeled: We demonstrate the utility of this new tool on real-world plant (e.g., maize) phenomics datasets.
Contact: Ananth Kalyanaraman (email: ananth@wsu.edu)
Links:
https://dl.acm.org/doi/abs/10.1145/3459930.3469511
Length of Stay prediction and unit transfer prediction tool. Scenarios(s) Modeled: In this multi-task learning model, we train the model to predict:Task-1: patient's unit label in next time step. Task-2: patient's remaining length of stay.The model inputs get updated on a daily basis and generates prediction accordingly.
Contact: Ananth Kalyanaraman (email: ananth@wsu.edu)
Links:
Preprint ready by June
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
Contact: Ananth Kalyanaraman (email: ananth@wsu.edu)
Links:
https://ojs.aaai.org/index.php/AAAI/article/view/30337
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.
Contact: George G. Vega Yon (email: george.vegayon@utah.edu)
Links:
https://github.com/UofUEpiBio/epiworld, https://github.com/UofUEpiBio/epiworldR/, https://github.com/UofUEpiBio/epiworldpy, https://github.com/UofUEpiBio/epiworldRShiny
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.
Contact: George G. Vega Yon, Milo Banks (email: george.vegayon@utah.edu)
Links:
https://github.com/UofUEpiBio/epiworldpy
An R package designed to create raster images from QuadKey-identified data (Microsoft Bing Maps Tile System), aiming to simplify the analysis of Facebook mobility data.
Contact: Florencia D'Andrea, Pilar Fernandez (email: NA)
Links:
https://github.com/Fernandez-Lab-WSU/quadkeyr, https://joss.theoj.org/papers/10.21105/joss.06500