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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 SARS-CoV-2, and evaluate the impact different testing strategies and turnaround time has on outbreaks in populations. This model is used in Color’s Return to On-site SARS-CoV-2 Testing Protocol, 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.

You can learn more about the SEIRS+ model here:

Model parameters

The parameter values and descriptions used in the model are listed in the table below.

Table 1. Table of parameters included in model


Mean Value



1.0, 1.5, 2.0, 2.5 

The R0, or reproductive number, is the expected average number of secondary infectious cases produced by a single infectious case.

Latent period

3.0 days

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

Presymptomatic period

2.2 days1,2

The period when an individual infected with SARS-CoV-2 is contagious but has not yet developed symptoms.

Incubation period

5.2 days1,3–6

The total time from exposure to symptom onset — this is the sum of the latent period and presymptomatic period.

Infectious period

6.2 days2,7–9

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

Test sensitivity

76% while presymptomatic, 80% during first 5 days of infectious period, and decreasing thereafter10,11 

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

Percent asymptomatic


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

Percent  symptomatic who self-quarantine


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, 3 days, 5 days

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


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 SARS-CoV-2. 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.


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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.

Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199-1207.

Lauer SA, Grantz KH, Bi Q, et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann Intern Med. 2020;172(9):577-582.

Guan W-J, Ni Z-Y, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382(18):1708-1720.

Backer JA, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro Surveill. 2020;25(5). doi:10.2807/1560-7917.ES.2020.25.5.2000062

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. March 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. April 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. May 2020. doi:10.7326/M20-1495

Treibel TA, Manisty C, Burton M, et al. COVID-19: PCR screening of asymptomatic health-care workers at London hospital. Lancet. 2020;395(10237):1608-1610.

Nishiura H, Kobayashi T, Miyama T, et al. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). Int J Infect Dis. 2020;94:154-155.

Byambasuren O, Cardona M, Bell K, Clark J, McLaws M-L, Glasziou P. Estimating the extent of true asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. medRxiv. 2020.

Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill. 2020;25(10). doi:10.2807/1560-7917.ES.2020.25.10.2000180