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About the Model

This model was developed in collaboration with Carl Bergstrom and Ryan McGee from the University of Washington. This model can be used to simulate infection dynamics of the Omicron variant of SARS-CoV-2, and evaluate the impact that different testing strategies and vaccination haves on outbreaks in populations. You can learn more about the model and proactive testing here:

SEIRS+ Model

SEIR models are epidemiological models that are used to model the spread of disease in a population. Standard SEIR models are compartmental models, meaning they track the proportion of the population in different disease states over time. SEIR models include compartments for susceptible (S), exposed (E), infectious (I), and recovered (R) disease states.

The SEIRS+ model is an extended SEIR model, which incorporates the effects of stochastic dynamics, network structure, SARS-CoV-2 testing, and additional interventions in a population.

Further information and code for the SEIRS+ model framework can be found at: https://github.com/ryansmcgee/seirsplus

Model parameters

The parameter values used in the models are listed in the table below. Some of the parameters are represented by distributions or time-varying functions. These include:

Table 1. Table of parameters included in model

Parameter

Mean Value

Description

R0

2.25 (default, can be changed)

The R0, or reproductive number, is the expected average number of secondary infectious cases produced by a single infectious case. This level of baseline transmissibility (R0=1.5) assumes that basic mitigation strategies, such as mask-wearing and social distancing, are in place.

Latent period

3.0 days

The time from exposure to when the individual becomes infectious to others.

Presymptomatic infectious period

6.2 days 2–5

The time period during which an infected individual is infectious to others. For symptomatic cases, this includes the presymptomatic period.

Infectious period

6.2 days 2–5

The time period during which an infected individual is infectious to others. For symptomatic cases, this includes the presymptomatic period.

Test sensitivity

75% while presymptomatic, 90% during first 3 days of infectious period, and decreasing thereafter.6,7

Probability that a single test will correctly identify an infectious individual as having SARS-CoV-2.

Testing Compliance

100%

Probability that an individual will comply with testing, if any.

Percent asymptomatic

30%

Percentage of individuals infected with SARS-CoV-2 who do not develop symptoms.

Percent symptomatic who self-quarantine

20%

Percentage of symptomatic individuals who develop sufficient symptoms (i.e., fever) that they call in sick and stay home from work.

Test turnaround time

1 day

Length of time between testing and isolation for individuals who receive positive results.

Isolation Time

10 days 8,9

Isolation time for individuals who receive a positive test result, self-isolate due to symptoms, or quarantine in response to a known positive contact.

Vaccine Effectiveness

90% (default, can be adjusted)

Percentage reduction in per-encounter infection probability for boosted individuals.13-15

Limitations

Modeling can be extremely important to help us understand epidemic progression, however, all models have assumptions, limitations, and biases that make them imperfect estimates. While we do our best to pick the most accurate and evidence-based parameters about SARS-CoV-2 disease spread, estimates for these parameters vary and may change as we learn more about the SARS-CoV-2 virus. Because these parameter choices can have significant impacts on model outcomes, we cannot guarantee our choices are always correct, and any results produced by this model should not be interpreted to predict exact numbers of cases or outcomes.

We describe the limitations of our model in further detail in our pre-print.

References

Tindale L, Coombe M, Stockdale JE, et al. Transmission interval estimates suggest pre-symptomatic spread of COVID-19. Epidemiology. Published online March 6, 2020. doi:10.1101/2020.03.03.20029983

He X, Lau EHY, Wu P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020;26(5):672-675.

Wölfel R, Corman VM, Guggemos W, et al. Virological assessment of hospitalized patients with COVID-2019. Nature. 2020;581(7809):465-469.

Ganyani T, Kremer C, Chen D, et al. Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Euro Surveill. 2020;25(17). doi:10.2807/1560-7917.ES.2020.25.17.2000257

Young BE, Ong SWX, Kalimuddin S, et al. Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. JAMA. Published online March 3, 2020. doi:10.1001/jama.2020.3204

Wikramaratna P, Paton RS, Ghafari M, Lourenco J. Estimating false-negative detection rate of SARS-CoV-2 by RT-PCR. Epidemiology. Published online April 7, 2020. doi:10.1101/2020.04.05.20053355

Kucirka LM, Lauer SA, Laeyendecker O, Boon D, Lessler J. Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure. Ann Intern Med. Published online May 13, 2020. doi:10.7326/M20-1495

CDC. Duration of isolation and precautions for adults with COVID-19. Published December 1, 2020. Accessed December 16, 2020. https://www.cdc.gov/coronavirus/2019-ncov/hcp/duration-isolation.html

CDC. Options to reduce quarantine for contacts of persons with SARS-CoV-2 infection using symptom monitoring and diagnostic testing. Published December 2, 2020. Accessed December 16, 2020. https://www.cdc.gov/coronavirus/2019-ncov/more/scientific-brief-options-to-reduce-quarantine.html