Spatial phylodynamics of HIV-1 epidemic emergence in east Africa

Abstract

Design: We sought to investigate the evolutionary and historical reasons for the different epidemiological patterns of HIV-1 in the early epidemic. In order to characterize the demographic history of HIV-1 subtypes A and D in east Africa, we examined molecular epidemiology, geographical and historical data.

Methodology: We employed high-resolution phylodynamics to investigate the introduction of HIV-1A and D into east Africa, the geographic trends of viral spread, and the demographic growth of each subtype. We also used geographic information system data to investigate human migration trends, population growth, and human mobility.

Results: HIV-1A and D were introduced into east Africa after 1950 and spread exponentially during the 1970s, concurrent with eastward expansion. Spatiotemporal data failed to explain the establishment and spread of HIV based on urban population growth and migration. The low prevalence of the virus in the Democratic Republic of Congo before and after the emergence of the pandemic was, however, consistent with regional accessibility data, highlighting the difficulty in travel between major population centers in central Africa. In contrast, the strong interconnectivity between population centers across the east African region since colonial times has likely fostered the rapid growth of the epidemic in this locale.

Conclusion: This study illustrates how phylodynamic analysis of pathogens informed by geospatial data can provide a more holistic and evidence-based interpretation of past epidemics. We advocate that this 'landscape phylodynamics' approach has the potential to provide a framework both to understand epidemics' spread and to design optimal intervention strategies.

Fig. 1. Genealogy and phylogeography of HIV-1A in Africa

Bayesian maximum clade credibility trees assuming relaxed molecular clock and Bayesian skyline plot coalescent prior for p24 (a) and gp41 (b), generated from the posterior distribution of trees less a 50% burn-in. Branch lengths are shown in years according to the scale bar at the bottom of each panel. Posterior probabilities for major nodes are indicated. Tip branches are colored to represent the country of sampling: red = Uganda, green = Kenya, yellow = Tanzania or Rwanda, blue = west/central Africa, black = outgroups. Internal branches are colored according to the maximum parsimony reconstruction of the ancestral geographic origin. The complete phylogenetic trees are depicted in Fig. S1. (c and d) The bar charts represent the proportion of the total number of lineages (y-axis) present in the HIV-1 genealogies for each 5-year interval (x-axis) beginning with 1955–1960. Genetic lineages that originated in Uganda and Kenya (as inferred from the genealogies in panel a and b) are shown in red and green, respectively. (c) HIV-1A p24 gene. (d) HIV-1A gp41 gene.

Fig. 2. HIV-1 subtype A and D epidemic history and net migration trends for central and east Africa

Bayesian skyline plots of strains included in the A1 clade for HIV-1 A p24 (a) and gp41 (b), and in the east African clade for HIV-1D p24 (c) and gp41 (d). Nonparametric estimates of HIV-1 effective population size (effective number of infections, Ne) over time were inferred for the four datasets. The blue line represents the median estimate of Ne with the shaded area showing the 95% high posterior density (HPD) estimates. Broken vertical lines indicate the period of exponential growth for all four analyses.

Fig. 3. Net migration rates over 5-year intervals for east Africa

The symbols represent the net migration (number of people in or out-migrating per 1000 individuals on the y-axis) for 5-year intervals (x-axis) from 1955 to 2000. Countries are represented by different colors (DRC, yellow; Kenya, light blue; Sudan, purple; Tanzania, pink; Uganda, red; Rwanda, green). Data were obtained from the United Nations World Population Prospects database http://esa.un.org/unpp/.

Fig. 4. Population distribution changes in east Africa

Percentage change in population per grid square is mapped for (a) 1960–1970 and (b) 1970–1980.

Fig. 5. Accessibility and subtype distribution in central and east Africa

(a) The gradient of colors indicate the estimated travel time to the nearest city of population more than 500 000 people, with yellow at one extreme indicating low travel times (<30 min) and red at the other extreme indicating long travel times (>10 h). Major roadways are indicated in blue. The data are available for download from http://gem.jrc.ec.europa.eu/gam/download.htm [26]. (b) HIV-1 subtype distribution in major cities from sources as follows Addis Ababa [27], Dar es Salaam [18], Kampala [28], Kigali [29], Kinshasa [30], Kisangani [31], Luanda [32], Lubambashi [31], Lusaka [33], Moshi [18], Nairobi [34], and Rakai [35].