Model: NotreDame-FRED

Model: NotreDame-FRED - NotreDame-FRED

Name:	NotreDame-FRED
Abbreviation:	NotreDame-FRED
Owner:	covid19hub
Team name:	NotreDame-FRED
Description:	FRED is an agent-based model developed for influenza with parameters modified to represent the natural history of COVID-19
Contributors:	guido espana <guido.espana@nd.edu>, rachel oidtman, sean cavany, alan costello, anneliese wieler, anita lerch, carly barbera, marya poterek, quan tran, sean moore, alex perkins <taperkins@nd.edu>
License:	Creative Commons Attribution 4.0
Notes:	secondary
Citation:	Missing
Methods:	We used an existing agent-based model, FRED (Framework for Reconstructing Epidemic Dynamics) which was developed by the university of Pittsburgh in response to the 2009 influenza pandemic (Grefenstette et al. 2013). We modified the model parameters to represent the natural history of COVID-19 and calibrated a set of parameters to reproduce current trends of deaths due to COVID-19 in the US. FRED simulates the spread of the virus in a population by recreating interactions among people on a daily basis. To accurately represent the population of each of the states simulated, we used a synthetic population of the US that represents demographic and geographic characteristics of the population in 2010 (Wheaton 2012). Each human is modeled as an agent that visits a set of places defined by their activity space. This activity space contains places such as households, schools, workplaces, or neighborhood locations. Transmission of SARS-CoV-2 can occur when an infected person visits the same location as a susceptible person on the same day. Numbers of contacts per person are specific to each place type. For instance, school contacts do not depend on the size of the school but the age of the agent. Infected agents have a probability to stay at home if they develop symptoms. Those who do not stay at home continue their daily activities. Public health interventions are included in the model to represent the changes in agents’ behavior in response to an epidemic. In this study, we limited the interventions to school closure and shelter in place. Schools are closed on a specific date to represent state-level guidelines (IHME COVID-19 health service utilization forecasting Team and Murray 2020). In the case that schools are closed, students limit their activity space to household or neighborhood locations. Shelter-in-place interventions reduce each agent’s activity space to their household at a compliance level from 0-100%, which was estimated as part of the model calibration. Agents who do not comply with the shelter-in-place orders continue with their daily routines. We used state-level orders to determine the date at which people are advised to shelter in place (IHME COVID-19 health service utilization forecasting Team and Murray 2020). See the methods file in our repo for more detailed methods and model limitations. Updates 2020-05-11 - We updated our likelihood used in our calibration, switching from a Poisson to a negative binomial distribution in order to allow for greater over-dispersion in the distribution of deaths. - We changed the way that we calculate the proportion of the population that is sheltering in place. We no longer use the dates at which government advisories were issued. Instead, we now exclusively use the google mobility data (https://www.google.com/covid19/mobility/) on journeys made to residential locations. We calibrate the maximum compliance to shelter in place, and use a generalized additive model fitted to the google data to forecast movement in the coming 5 weeks. For more details on this approach, see the methods file in our repo.
Home:	https://github.com/confunguido/covid19_ND_forecasting
Auxiliary data:	Missing

Forecasts (36)

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2020-04-27	2020-04-27-NotreDame-FRED.csv	2020-05-19 03:00:01 UTC	2020-05-19 12:00:00 UTC	1 of 2
2020-04-27	2020-04-27-NotreDame-FRED.csv	2021-05-04 20:52:14 UTC	2020-07-06 12:00:00 UTC	2 of 2
2020-05-04	2020-05-04-NotreDame-FRED.csv	2020-05-19 03:00:08 UTC	2020-05-19 12:00:00 UTC
2020-05-11	2020-05-11-NotreDame-FRED.csv	2020-05-19 03:00:20 UTC	2020-05-19 12:00:00 UTC
2020-05-18	2020-05-18-NotreDame-FRED.csv	2020-05-19 03:00:36 UTC	2020-05-19 12:00:00 UTC
2020-05-25	2020-05-25-NotreDame-FRED.csv	2020-05-26 00:14:11 UTC	2020-05-26 12:00:00 UTC
2020-06-01	2020-06-01-NotreDame-FRED.csv	2020-06-01 20:40:43 UTC	2020-06-01 12:00:00 UTC
2020-06-08	2020-06-08-NotreDame-FRED.csv	2020-06-08 23:24:42 UTC	2020-06-08 12:00:00 UTC
2020-06-15	2020-06-15-NotreDame-FRED.csv	2020-06-15 21:59:35 UTC	2020-06-15 12:00:00 UTC
2020-06-22	2020-06-22-NotreDame-FRED.csv	2020-06-22 22:12:17 UTC	2020-06-22 12:00:00 UTC
2020-06-29	2020-06-29-NotreDame-FRED.csv	2020-06-29 20:14:31 UTC	2020-06-29 12:00:00 UTC
2020-07-06	2020-07-06-NotreDame-FRED.csv	2020-07-07 13:44:37 UTC	2020-07-07 12:00:00 UTC
2020-07-13	2020-07-13-NotreDame-FRED.csv	2020-07-14 04:09:01 UTC	2020-07-14 12:00:00 UTC
2020-07-20	2020-07-20-NotreDame-FRED.csv	2020-07-21 03:27:23 UTC	2020-07-21 12:00:00 UTC
2020-07-27	2020-07-27-NotreDame-FRED.csv	2020-07-28 00:59:21 UTC	2020-07-28 12:00:00 UTC
2020-08-03	2020-08-03-NotreDame-FRED.csv	2020-08-04 01:18:20 UTC	2020-08-04 12:00:00 UTC
2020-08-10	2020-08-10-NotreDame-FRED.csv	2020-08-11 00:53:08 UTC	2020-08-11 12:00:00 UTC
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2020-08-24	2020-08-24-NotreDame-FRED.csv	2020-08-25 00:53:56 UTC	2020-08-25 12:00:00 UTC
2020-08-31	2020-08-31-NotreDame-FRED.csv	2020-09-01 06:28:24 UTC	2020-09-01 12:00:00 UTC
2020-09-07	2020-09-07-NotreDame-FRED.csv	2020-09-08 06:28:41 UTC	2020-09-08 12:00:00 UTC
2020-09-14	2020-09-14-NotreDame-FRED.csv	2020-09-15 18:25:08 UTC	2020-09-15 12:00:00 UTC
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2021-02-22	2021-02-22-NotreDame-FRED.csv	2021-02-23 01:21:40 UTC	2021-02-22 12:00:00 UTC
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2021-03-08	2021-03-08-NotreDame-FRED.csv	2021-03-09 01:08:43 UTC	2021-03-08 12:00:00 UTC
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