Phylodynamics of HIV-1 Circulating Recombinant Forms 12_BF and 38_BF in Argentina and Uruguay

Retrovirology

Table 4 Bayesian estimates of population dynamic parameters of the HIV-1 CRF12_BF and CRF38_BF epidemics.

Subtype	Demographic model	Molecular Clock	Gene	r	λ
CRF12_BF	Logistic growth	Strict	pol (PR-RT)	1.08 (0.79-1.44)	0.64 (0.48-0.88)
		Relaxed		1.22 (0.85-1.64)	0.57 (0.42-0.81)
CRF38_BF	Logistic growth	Strict	pol (PR-RT)	0.83 (0.31-1.81)	0.83 (0.38-2.24)
		Relaxed		0.92 (0.41-1.75)	0.75 (0.40-1.69)
CRF12_BF^a	Logistic growth	Relaxed	vpu	2.24 (0.21-4.56)	0.31 (0.15-3.30)
B^b	Logistic growth		env (C2-V3)	0.46 (0.33-0.59)	1.51 (1.22-2.10)
		Strict
			pol (PR-RT)	0.56 (0.35-0.80)	1.24 (0.87-1.98)
F^b	Logistic growth		env (C2-V3)	0.61 (0.40-0.86)	1.14 (0.81-1.73)
		Strict
			pol (PR-RT)	0.59 (0.31-0.92)	1.17 (0.75-2.24)
C^c	Logistic growth	Strict	pol (RT)	0.70 (0.41-1.00)	0.99 (0.69-1.69)
		Relaxed		0.81 (0.40-1.26)	0.86 (0.55-1.73)
CRF31_BC^c	Logistic growth	Strict	pol (RT)	1.26 (0.61-2.10)	0.55 (0.33-1.14)
		Relaxed		1.27 (0.44-2.26)	0.55 (0.31-1.57)

Estimates of the median growth rate (r, yr^-1) and epidemic doubling time (λ, yr) for the HIV-1 CRF12_BF and CRF38_BF epidemics (95% HPD in parentheses). Growth rate estimates were used to calculate the time taken for the epidemic to double in size (λ) using the relation λ = ln(2)/r. ^a Data from Aulicino et al. [26]. ^bData from Bello et al. [23]. ^c Data from Bello et al. [47].

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