Skip to main content

Table 1 Candidate models used to choose the best model

From: Assessment of the fatality rate and transmissibility taking account of undetected cases during an unprecedented COVID-19 surge in Taiwan

Models

Description

AIC

1

\(\alpha + \beta_{1} R_{tc}\)

95.9

2

\(\alpha + \beta_{1} R_{tc} + \beta_{2} P_{ntd}\)

91.0

3

\(\alpha + \beta_{1} R_{tc} + \beta_{2} P_{ntd} + \beta_{3} C_{d}\)

93.0

4

\(\alpha + \beta_{1} R_{tc} + \beta_{2} P_{ntd} + \beta_{3} C_{d} + \beta_{4} T_{d}\)

95.0

5

\(\alpha + \beta_{1} R_{tc} + \beta_{2} P_{ntd} + \beta_{3} C_{d} + \beta_{4} T_{d} + \beta_{5} D_{d}\)

96.9

  1. \(\alpha { }\) and \(\beta\) s are model coefficients, whereas the proportion of contact tracing delay (\(P_{ntd}\)), the ratio of the number of tests conducted to reported cases (\(R_{tc}\)), the delay in testing (\(T_{d}\)), the delay in reporting (\(C_{d}\)), and the delay in deaths (\(D_{d}\)) are predictors. AIC represents the Akaike information criterion