This function returns a keyed list of priors related to progression to various health states. Called with no arguments, the default values are returned. Custom hyperpriors can be specified by passing values for the parameters specified below. All parameters must be non-negative real numbers. The return value of this function must be added to a covidestim object using the addition operator (see examples).

priors_transitions(
  p_sym_if_inf = c(5.143, 3.536),
  p_sev_if_sym = c(1.8854, 20.002),
  p_die_if_sev = c(28.239, 162.3),
  p_die_if_inf = c(15.915, 3167.1),
  ifr_decl_OR = c(12.031, 8.999)
)

Arguments

p_sym_if_inf

A two-element numeric vector containing c(alpha, beta) parameters/hyperpriors of a Beta distribution modeling the probability of becoming symptomatic if infectious.

Sources for default value:

p_sev_if_sym

A two-element numeric vector containing c(alpha, beta) parameters/hyperpriors of a Beta distribution modeling the probability of transitioning into the "severe" health state if symptomatic. "Severe" disease is defined as disease that would likely require hospitalization.

Sources for default value:

  • CDC COVID-19 Response Team, CDC COVID-19 Response Team, Bialek S, Boundy E, Bowen V, Chow N, Cohn A, Dowling N, Ellington S, Gierke R, Hall A, MacNeil J, Patel P, Peacock G, Pilishvili T, Razzaghi H, Reed N, Ritchey M, Sauber-Schatz E (2020). “Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020.” MMWR. Morbidity and Mortality Weekly Report, 69(12), 343--346. ISSN 0149-2195, 1545-861X, doi: 10.15585/mmwr.mm6912e2 , http://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm?s_cid=mm6912e2_w.

  • Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NM (2020). “Estimates of the severity of coronavirus disease 2019: a model-based analysis.” The Lancet Infectious Diseases, 20(6), 669--677. ISSN 14733099, doi: 10.1016/S1473-3099(20)30243-7 , https://linkinghub.elsevier.com/retrieve/pii/S1473309920302437.

p_die_if_sev

A two-element numeric vector containing c(alpha, beta) parameters/hyperpriors of a Beta distribution modeling the probability of dying if in the "severe" health state.

Source for default value: CDC COVID-19 Response Team, CDC COVID-19 Response Team, Bialek S, Boundy E, Bowen V, Chow N, Cohn A, Dowling N, Ellington S, Gierke R, Hall A, MacNeil J, Patel P, Peacock G, Pilishvili T, Razzaghi H, Reed N, Ritchey M, Sauber-Schatz E (2020). “Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020.” MMWR. Morbidity and Mortality Weekly Report, 69(12), 343--346. ISSN 0149-2195, 1545-861X, doi: 10.15585/mmwr.mm6912e2 , http://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm?s_cid=mm6912e2_w.

p_die_if_inf

A two-element numeric vector containing c(alpha, beta) parameters/hyperpriors of a Beta distribution modeling the probability of dying if infected (e.g. the infection fatality rate). This prior represents a national average value, which is later adjusted for state and county-level factors, and to reflect higher fatality rates early in the epidemic.

Source for default value: Adummy A (2021). “Not avalable.” Failed to insert reference with key = ODriscoll_Nature_2020 from package = 'covidestim'. Possible cause --- missing or misspelled key.

ifr_decl_OR

A two-element numeric vector containing c(shape, rate) parameters/hyperpriors of a Gamma distribution modeling the elevated IFR in early 2020, relative to the present. Default value represents an IFR 30% higher in March of 2020, with 95% interval 10-50% higher.

Source for default value: Expert opinion

Value

An S3 object of class priors

Examples

cfg <- covidestim(ndays = 50, region = 'New York') + priors_transitions(p_sym_if_inf = c(0.5, 0.2))