JHU_UNC-flepiMoP round 18 results

data-processed/JHU_UNC-flepiMoP/LICENSE.txt

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+MIT License
+
+Copyright (c) 2020 Johns Hopkins IDDynamics Group
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

data-processed/JHU_UNC-flepiMoP/metadata-JHU_UNC-flepiMoP.txt

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+team_name: Johns Hopkins ID Dynamics and University of North Carolina Chapel Hill
+model_name: flepiMoP
+model_abbr: JHU_UNC-flepiMoP
+model_version: "1.0"
+model_contributors: Joseph C. Lemaitre (UNC), Sara Loo (Johns Hopkins Infectious Disease Dynamics), Joshua Kaminsky (Johns Hopkins Infectious Disease Dynamics), Claire P. Smith (Johns Hopkins Infectious Disease Dynamics), Clif McKee (Johns Hopkins Infectious Disease Dynamics), Alison Hill (Johns Hopkins Infectious Disease Dynamics), Sung-mok Jung (UNC), Erica Carcelen (Johns Hopkins Infectious Disease Dynamics), Koji Sato (Johns Hopkins Infectious Disease Dynamics), Elizabeth C. Lee (Johns Hopkins Infectious Disease Dynamics), Justin Lessler (UNC), Shaun Truelove (Johns Hopkins Infectious Disease Dynamics) <[email protected]>
+website_url: https://github.com/HopkinsIDD/flepiMoP
+license: mit
+methods: State-level metapopulation model with commuting and stochastic SEIR disease dynamics
+  with social-distancing indicators.
+modeling_NPI: Stay-at-home orders and 
+  \ subsequent social distancing interventions are updated at the state-level according \
+  \ to recent policy documents. Ongoing interventions continued through \
+  \ the remainder of the simulation period. Intervention effects are inferred \
+  \ where  possible.
+compliance_NPI: Not applicable
+contact_tracing: Not applicable
+testing: Not applicable
+vaccine_efficacy_transmission: "Vaccine efficacy is assumed to be against infection, not specifically \
+  \ transmission. Vaccine efficacy estimates are assumed based on the scenarios."
+vaccine_efficacy_delay: None.
+vaccine_hesitancy: Hesitancy is not assumed other than specifically indicated by the scenarios.
+vaccine_immunity_duration: Waning rate is fit.
+natural_immunity_duration: Waning rate is fit.
+case_fatality_rate: Age- and risk-adjusted based on population distribution. 
+infection_fatality_rate: Age- and risk-adjusted based on population distribution and age-distribution of those 
+  \ who have received at least one vaccine dose. US national CFR set at 5%.
+asymptomatics: Not applicable
+age_groups: 0-17, 18-64, 65+
+importations: Functionality in place, but not currently in use.
+confidence_interval_method: We compute confidence intervals as quantiles across 300 simulations.
+calibration: "We use an  MCMC-like inference procedure that calibrates model outputs to weekly \       
+  \ aggregations of incident hospitalizations and deaths. For the \
+  \ inference of the baseline reproductive number and the case confirmation to infection ratio."
+spatial_structure: state-level metapopulation accounting for spatial mobility using community data between states.
+citation: https://doi.org/10.1016/j.epidem.2024.100753
+methods_long: flepiMoP is a comprehensive open-source software pipeline designed for creating and \
+  \ simulating compartmental models of infectious disease transmission and inferring parameters through these models. \
+  \ A single YAML configuration file defines the inclusion and behavior of each submodule over the specified population \
+  \ structure. The core module, gempyor (“General Epidemics Modeling Pipeline with Ynterventions and Outcome Reporting”), \
+  \ is a fast and flexible disease transmission model that can simulate any compartmental structure and observation model. \
+  \ gempyor can be run standalone for projection scenarios (given a set of parameters and without inference) or within a \ 
+  \ tailored, distributed inference module that executes several parallel chains of repeated epidemic simulations in an \ 
+  \ MCMC-like fashion, allowing for calibration of the model to data and identification of model parameters.