. 2022 Feb 18;540:111063. doi: 10.1016/j.jtbi.2022.111063

Table 1.

Model parameters estimated by Bayesian inference based on daily deaths until 1 February 2021 (with reduced models). Model selection based on maximum log-likelihood (LL) and Akaike information criterion (AIC). Best fitting models have lower AIC scores (bold). Infection fatality ratio, $ϕ = 0.9 %$ . Herd immunity threshold ( $H$ ) calculated from $R_{0}$ and CV using formulas (14) or (22), as appropriate. $T_{0}$ and $T_{2}$ parameterise linear reduction and increase in transmissibility, respectively, before and after first lockdown (larger T $\Leftrightarrow$ lower slope; Fig. 2).

	Heterogeneous susceptibility		Heterogeneous connectivity		Homogeneous
	Median	$95 %$ CI	Median	$95 %$ CI	Median	$95 %$ CI
Common parameters
$c_{1}$ ^a	$0.2968$	$(0.2958, 0.2988)$	$0.2993$	$(0.2976, 0.3015)$	$0.2751$	$(0.2740, 0.2772)$
$c_{2}$ ^b	$0.5761$	$(0.5731, 0.5843)$	$0.6601$	$(0.6535, 0.6661)$	$0.4803$	$(0.4776, 0.4869)$
$η$ ^c	12	${12}$	12	${12}$	9	${9, 10}$
$ν$ ^dCoefficient of variation (CV).	$1.475$	$(1.470, 1.487)$	$1.120$	$(1.111, 1.145)$	0	–

England
$T_{0}$	$3.616$	$(3.480, 3.759)$	$0.6549$	$(0.3925, 0.8821)$	$7.595$	$(6.955, 7.771)$
$T_{2}$	$283.5$	$(282.4, 286.0)$	$302.2$	$(295.8, 303.9)$	$416.1$	$(408.9, 418.3)$
$R_{0}$	$2.709$	$(2.701, 2.716)$	$2.807$	$(2.781, 2.827)$	$2.691$	$(2.683, 2.701)$

$H$	$26.94 %$	$(26.62 %, 27.10 %)$	$25.48 %$	$(24.61 %, 25.89 %)$	$62.84 %$	$(62.72 %, 62.98 %)$

Scotland
$T_{0}$	$10.10$	$(9.854, 10.72)$	$6.934$	$(6.460, 7.508)$	$11.78$	$(11.36, 12.58)$
$T_{2}$	$360.8$	$(359.0, 364.4)$	$399.9$	$(390.2, 406.3)$	$540.0$	$(524.9, 546.9)$
$R_{0}$	$2.874$	$(2.856, 2.912)$	$2.981$	$(2.948, 3.053)$	$2.978$	$(2.954, 2.995)$

$H$	$28.29 %$	$(27.88 %, 28.69 %)$	$26.75 %$	$(25.81 %, 27.52 %)$	$66.41 %$	$(66.14 %, 66.61 %)$

Model selection
LL	$- 3707$		$- 3684$		$- 5893$
AIC	7434		$7387$		11805

Transmissibility reduction due to lockdown 1.

Transmissibility reduction due to lockdowns 2 and 3.

Difference between mean-time-to-death and mean-time-to-recovery (sampled from a continuous interval and reduced to the nearest integer before entering the model).