Agent-based and phylogenetic analyses reveal how HIV-1 moves between risk groups: injecting drug users sustain the heterosexual epidemic in Latvia

Abstract

Injecting drug users (IDUs) are a driving force for the spread of HIV-1 in Latvia and other Baltic States, accounting for a majority of cases. However, in recent years, heterosexual cases have increased disproportionately. It is unclear how the changes in incidence patterns in Latvia can be explained, and how important IDUs are for the heterosexual sub-epidemic. We introduce a novel epidemic model and use phylogenetic analyses in parallel to examine the spread of HIV-1 in Latvia between 1987 and 2010. Using a hybrid framework with a mean-field description for the susceptible population and an agent-based model for the infecteds, we track infected individuals and follow transmission histories dynamically formed during the simulation. The agent-based simulations and the phylogenetic analysis show that more than half of the heterosexual transmissions in Latvia were caused by IDU, which sustain the heterosexual epidemic. Indeed, we find that heterosexual clusters are characterized by short transmission chains with up to 63% of the chains dying out after the first introduction. In the simulations, the distribution of transmission chain sizes follows a power law distribution, which is confirmed by the phylogenetic data. Our models indicate that frequent introductions reduced the extinction probability of an autonomously spreading heterosexual HIV-1 epidemic, which now has the potential to dominate the spread of the overall epidemic in the future. Furthermore, our model shows that social heterogeneity of the susceptible population can explain the shift in HIV-1 incidence in Latvia over the course of the epidemic. Thus, the decrease in IDU incidence may be due to local heterogeneities in transmission, rather than the implementation of control measures. Increases in susceptibles, through social or geographic movement of IDU, could lead to a boost in HIV-1 infections in this risk group. Targeting individuals that bridge social groups would help prevent further spread of the epidemic.

Figure 1

HIV-1 Epidemic in Latvia from 1987 to 2010. New HIV-1 diagnoses per year (A) and cumulative number of HIV-1 diagnoses (B) stratified by risk group. The lines in panel B show the best fit of Eq. (2) to the data.

Figure 2

Schematic of the modeling framework. Infected individuals are followed on an individual level characterized by different traits, e.g. the age of the individual or the viral load. The susceptible population is stratified into different risk groups according to the mode of HIV transmission (IDU, MSM, HET, SW), as well as into different social groups, indicating social and/or geographical distances. Furthermore, we distinguish between the sex of the individuals (black=male, grey=female). An infected individual can only infect other individuals which are in the same risk and current social groups as the infected individual.

Figure 3

Cumulative incidence curves for the sub-epidemics of MSM, IDU and HET transmission given the different social structures for the total susceptible population shown in Table 1. Panel A (upper row) shows the results for a scenario close to a well-mixed situation as defined in Table 2A. Panel B (lower row) shows the sub-epidemics for a more heterogeneous grouping scenario as described in Table 2B. For each of the different scenarios, 100 epidemics were simulated introducing one index case of each risk group into the susceptible population. Plotted values are scaled to the total population, as only 20% of the total susceptible population was simulated. The gray shaded area gives the 95%-quantiles and the dashed line the median over all successful epidemics (more than 10 infected cases in total). The solid line corresponds to the cumulative number of HIV-1 diagnoses in Latvia for the indicated sub-epidemic.

Figure 4

Fraction of IDU related heterosexual transmissions among all new heterosexual cases. The median of 100 epidemics for a homogenous (solid line) and heterogenous (dashed line) grouping scenario are shown referring to the heterosexual sub-epidemics depicted in Figure 3A and B, respectively.

Figure 5

Phylogenetic analysis of the mixing between IDU and HET. A Phylogenetic tree of the HIV-1 subtype A1 epidemic based on 232 samples in total (68 HET, 131 IDU, 4 MSM, 29 unknown) with black circles denoting the HET taxa. B Distribution of the number of introductions into the HET sub-epidemic based on 10,000 probable trees from a BMCMC phylogenetic reconstruction (mean=45, sd=2.8). C Distribution of the transmission chain sizes in the HET sub-epidemic based on the phylogenetic trees shown in B (HET-phylo, solid line), the total simulated HET-epidemics shown in Figure 3B (HET-total, dashed line), and a sample of 68 infected individuals from the total HET-epidemic, the size of the phylogenetic sample (HET-sample, dotted line). The average is based on 10,000 BMCMC-trees (HET-phylo), 100 simulations (HET-total), and 100 random samples of 68 individuals for each of the 100 simulations shown in Figure 3B (HET-sample). Estimated power-law-coefficients are given in the main text.

Figure 6

Fraction of HIV infected individuals in each social group and transmission links between the groups: The dynamics for one representative simulation of an epidemic in a susceptible population structured as in Table 2B is shown after 10 years (A) and after 22 years (B). Each circle represents the total number of susceptibles for a specific transmission risk in a certain social group, for simplicity one representative social group of each category of social groups is shown. Colored slices define the infections caused by mode of transmission. Different line widths between the circles quantify the amount of transmission between the different social groups. As only one representative group is chosen for each category of social groups, infections can also come from other sources than the groups indicated in the plot. The corresponding heat maps capture the average ratio of transmissions between the social groups indicating “spreader” (ratio> 0.5) or “recipient” groups. In detail, the value M(i, j), i ≠ j defines the ratio T_ij/(T_ij + T_ji), where T_ij denotes the number of transmissions from group i into group j. For i = j, M(i, i) = ∑_j T_ij/∑_j (T_ij + T_ji). White boxes denote no exchange between these groups. Values are averaged over 100 simulated epidemics and over all social groups belonging to a specific category (see Table 2B).