Trace the History of HIV and Predict Its Future through Genetic Sequences

Abstract

Traditional methods of quantifying epidemic spread are based on surveillance data. The most widely used surveillance data are normally incidence data from case reports and hospital records, which are normally susceptible to human error, and sometimes, they even can be seriously error-prone and incomplete when collected during a destructive epidemic. In this manuscript, we introduce a new method to study the spread of infectious disease. We gave an example of how to use this method to predict the virus spreading using the HIV gene sequences data of China. First, we applied Bayesian inference to gene sequences of two main subtypes of the HIV virus to infer the effective reproduction number (GRe(t)) to trace the history of HIV transmission. Second, a dynamic model was established to forecast the spread of HIV medication resistance in the future and also obtain its effective reproduction number (MRe(t)). Through fitting the two effective reproduction numbers obtained from the two separate ways above, some crucial parameters for the dynamic model were obtained. Simply raising the treatment rate has no impact on lowering the infection rate, according to the dynamics model research, but would instead increase the rate of medication resistance. The negative relationship between the prevalence of HIV and the survivorship of infected individuals following treatment may be to blame for this. Reducing the MSM population's number of sexual partners is a more efficient strategy to reduce transmission per the sensitivity analysis.

Keywords: Bayesian phylogenetic method; HIV/AIDS; dynamic model; effective reproductive number; primary drug resistance; transmitted drug resistance.

Figure A1

The fitted curves of the two effective reproduction numbers ${}_G{\mathcal{R}}_e\left(t\right)$ and ${}_M{\mathcal{R}}_e\left(t\right)$ for four historical periods I (a), II (b), III (c) and IV (d), respectively.

Figure A2

Sensitivity analysis of treatment rate (a), infection rate after treatment (solid line in (b)) and course of the disease after treatment (dotted line in (b)) to $R_0^d\left(IV\right)$.

Figure A3

Sensitivity analysis of sexual partners to the reproduction number. (a) Only the number of annual sexual partners of various groups of people is changed and other parameters remain unchanged. (b) The impact of the number of sexual partners of bisexual men and the number of heterosexual men on the reproduction number.

Figure A4

The impact of changing the sexual preferences of bisexual men on the on the number of infected people (a) and the rate of drug resistance (b). The blue box plots are the situation of no change, the green ones are the situation that disconnect from male sex, and the red ones are the situation that disconnect from female sex. The continuous blue, green and red curves are their median lines respectively.

Figure 1

Transmission process diagram. The superscript g can be $w,mw,mm$ and b, respectively, which represents the four types of subgroup populations. Parameter a is an input of susceptible individuals; ${\theta }_i,i=1,2,3$ are the disease progression rates in infection stage 1, 2 and 3, respectively; ${\delta }_i,i=1,2,3$ are the treatment rates of patients in stage 1, 2 and 3, respectively; $\eta$ is the percentage of patients who give up HAART; $\gamma$ is rate of drug resistance after HAART; $\rho$ is the percentage of patients who are no longer resistant.

Figure 2

The weighted average of the effective reproduction number ${}_G{\mathcal{R}}_e\left(t\right)$ and its 95% HPD interval.

Figure 3

The number of HIV-positive people in the entire population (a) and drug resistance rate (b), where the continuous curve is the median line. Red dots in (a) are reported cases of 2017, 2018 and 2019 respectively.

Figure 4

Impact of target treatment rates on the HIV/AIDS epidemic: the infected rate (a) and the rate of drug resistance (b). The green box plots are based on the baseline parameters, while the red ones are based on target treatment rate. The continuous green and red curves are their median lines respectively.

Figure 5

Impacts of improving the treatment effect on the infected rate (a) and the rate of drug resistance (b). The blue box plots are based on the baseline parameters, the green ones are based on increase lifespan, and red ones are based on both increase lifespan and reduce infectivity. The continuous blue, green and red curves are their median lines respectively.