metadata-UTA-ImmunoSEIRS.txt

team_name: UTA
model_name: ImmunoSEIRS
model_abbr: UTA-ImmunoSEIRS
model_version: "1.0"
model_contributors: Kaiming Bi (Lead modeler, University of Texas at Austin), <kaiming.bi@austin.utexas.edu>, Anass Bouchnita (University of Texas at El Paso), Shraddha R Bandekar (University of Texas at Austin), Spencer Fox (University of Georgia), Lauren Ancel Meyers (Senior author, University of Texas at Austin), and the UT COVID-19 Modeling Consortium
website_url: https://covid-19.tacc.utexas.edu/
license: cc-by-4.0
methods: (State) An age-stratified SEIRS model that explicitely caputres the changes in the population-immunity from heterogenous sources and its effect on susceptiblity and severity. 
modeling_NPI: NPIs levels are considered to remain the same as for the period of December 15, 2021 to January 08, 2022.
compliance_NPI: Compliance to NPIs is assumed to remain the same as for the period of December 15, 2021 to January 08, 2022.
contact_tracing: No changes in the contact tracing policy are assumed.
testing: No changes in the testing capacity are assumed.
vaccine_efficacy_transmission: "Not applicable"
vaccine_efficacy_delay: "Not applicable"
vaccine_hesitancy: Hesitancy levels are assumed to be similar to the US for all states.
vaccine_immunity_duration: The immunity of vaccines has a half-life time of 6 months, or tuned according to scenario.
natural_immunity_duration: The immunity of vaccines has a half-life time of 8 months, or tuned according to scenario.
case_fatality_rate: The infection fatality rate depends on the age group. Before Omicron emergence, we consider that around one out of four infections is reported at the beginning but can change depending on the immunity.
infection_fatality_rate: the age-specific infection fatality rate is [0.001608,  0.003823, 0.003823, 0.05943,  0.4420,  1.130] for the groups [0-4, 5-11, 12-17, 18-49, 50-64, 65+].
asymptomatics: 
age_groups: Before Omicron emergence, we consider that around one out of four infections is reported at the beginning but can change depending on the immunity.
importations: No importations are considered. The growth function for Omicron prevalence is taken similar to the US.
confidence_interval_method:  We generate 250 stochastic simulations where we consider microstochasticities and stochastic sampling for the transmission rate. Then, we compute the confidence interval.
calibration:  The model is fitted for the duration between August 14, 2021 to January 08, 2022. The immunity levels are initialized using vaccination coverages and seroprevalence data for each state.
spatial_structure: "Not applicable"
citation: Bouchnita, Anass, et al. "COVID-19 Scenario Projections: The Emergence of Omicron in the US-January 2022." (2022).
methods_long: "We use a novel stochastic age-structured SEIRS model that explicitly tracks the changes in immunity acquired from natural infection and vaccination. Population-level immunity changes depending on natural infection rates, vaccination rates, and the waning of immunity, and we modulate  transmission rates, symptomatic rates, hospitalization rates, and mortality rates, according to population immunity and the specific characteristics of the circulating variant. We initialize our model on August 14, 2021 using seroprevalence and vaccination data up to that date, and fit the transmission, hospitalization, and mortality rates to state data up to January 8, 2022."