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Table 1 Default point estimates of parameters for fitting and simulating a basic COVID-19 SCLAIV outbreak on \([0,t_\text{est}]\)). Also see Fig. 2

From: A versatile web app for identifying the drivers of COVID-19 epidemics

Parameter Symbol (\(\hbox {math}^{\dag }\)) Value Source/Comment
Transmission
 Contact \(\hbox {rate}^\mathsection\) \(\kappa\) Estimated Incidence \(\hbox {data}^{*}\)
 Nominal/effective pop size \(N_0=N_\text{eff}\) \(10^5-10^7\) \(\hbox {Varies}^\P\)
 Asymptomatic reduction \(\varepsilon\) 0.1 Unknown: see [12]
 Surveillance \(\delta _\text{sur}\) 0.5 \(\hbox {Unknown}^{**}\)
 Isolation/treatment \(\delta _\text{iso}\) 0.35 \(\hbox {Varies}^{\ddag }\)
 Contact rate reduction \(\delta _\text{con}\) 0.1 \(\hbox {Varies}^{*\ddag }\)
Progression
 Thwart period \(\pi _\text{thw}\) 1 Normalised
 Succumb \(\hbox {period}^\mathsection\) \(\pi _\text{suc}\) Estimated Incidence \(\hbox {data}^{*}\)
 Latent period \(\pi _\text{lat}\) 4\(^{\dag \dag }\) days [7, 12, 45]
 Asymptomatic period \(\pi _\text{asy}\) 5\(^{\dag \dag }\) days [7, 12, 45]
 Symptomatic/recovery period \(\pi _\text{rec}\) 7\(^{\ddag \ddag }\) days [12]
 Immune period \(\pi _\text{imm}\) 1000 days \(\hbox {Unknown}^\parallel\)
 Disease-induced mort./virulence \(\alpha ^{\dag \ddag }\) Estimated Mortality data
Initial values
 Initial susceptible \(S_0\) \(S_0=N_{\text{eff}}-C_0-L_0-A_0-I_0-V_0\)
 Initial contact \(C_0\) Estimated Incidence \(\hbox {data}^{*}\)
 Initial latent \(L_0\) \(\left( \frac{\pi _\text{asy} }{\pi _\text{rec}}\right) G I_0\) Requires \(G{^+}\)
 Initial asymptomatic \(A_0\) \(\left( \frac{\pi _\text{lat}\pi _\text{asy} }{\pi _\text{rec}^2}\right) G^2 I_0\) Requires \(G{^+}\)
 Initial symptomatic \(I_0\) Estimated Incidence \(\hbox {data}^{*}\)
 Initial immune \(V_0=0\) SARS-CoV-2 immunologically naïve pop
  1. \(^\dag\)Web app symbols are: \(\kappa =\mathtt{kappa}\), \(\pi _\text{thw}\)=P_thw, \(I_0\)=I_0, etc., as made clear in text
  2. \(^\mathsection\)See Eq. B.3 in Appendix B of Additional file 1 for relationship to SEIR model parameter \(\beta\)
  3. \(^*\)Best values estimated using MLE, ranges estimated using MCMC [17, 46, 47]
  4. \(^\P\)Depends on relative outbreak size (e.g., \(<10\)% of total pop.) for reliable forecasts
  5. \(^{**}\)Values fitted to the initial conditions will inversely scale with this value
  6. \(^{\ddag }\)Varies. This value implies individuals isolated within three days of entering class I or \(\hbox {I}_\text{r}\)
  7. \(^{\ddag \ddag }\)Varies. This rate assumes that sheltering-in-place reduces contact rates by 90%
  8. \(^{\dag \dag }\)Sources are highly variable, so we selected integer-valued ball park estimates
  9. \(^{*\ddag }\)Value early into epidemic since it depends on hospitalisation/isolation protocols
  10. \(^\parallel\)If sufficiently large, its influence on the initial stage of the outbreak is negligible
  11. \(^{\dag \ddag }\alpha\), being a driver, is replaced with \(\delta _\text{vir}\) in Appendix C, Additional file 1, and Vir_const in the Web App
  12. \(^+\)Requires G which, unlike \(R_0\), is a rate of increase over recovery rather than “serial” interval [1, 48]