High GUD incidence in the early 20 century created a particularly permissive time window for the origin and initial spread of epidemic HIV strains

Abstract

The processes that permitted a few SIV strains to emerge epidemically as HIV groups remain elusive. Paradigmatic theories propose factors that may have facilitated adaptation to the human host (e.g., unsafe injections), none of which provide a coherent explanation for the timing, geographical origin, and scarcity of epidemic HIV strains. Our updated molecular clock analyses established relatively narrow time intervals (roughly 1880-1940) for major SIV transfers to humans. Factors that could favor HIV emergence in this time frame may have been genital ulcer disease (GUD), resulting in high HIV-1 transmissibility (4-43%), largely exceeding parenteral transmissibility; lack of male circumcision increasing male HIV infection risk; and gender-skewed city growth increasing sexual promiscuity. We surveyed colonial medical literature reporting incidences of GUD for the relevant regions, concentrating on cities, suffering less reporting biases than rural areas. Coinciding in time with the origin of the major HIV groups, colonial cities showed intense GUD outbreaks with incidences 1.5-2.5 orders of magnitude higher than in mid 20(th) century. We surveyed ethnographic literature, and concluded that male circumcision frequencies were lower in early 20(th) century than nowadays, with low rates correlating spatially with the emergence of HIV groups. We developed computer simulations to model the early spread of HIV-1 group M in Kinshasa before, during and after the estimated origin of the virus, using parameters derived from the colonial literature. These confirmed that the early 20(th) century was particularly permissive for the emergence of HIV by heterosexual transmission. The strongest potential facilitating factor was high GUD levels. Remarkably, the direct effects of city population size and circumcision frequency seemed relatively small. Our results suggest that intense GUD in promiscuous urban communities was the main factor driving HIV emergence. Low circumcision rates may have played a role, probably by their indirect effects on GUD.

Figure 1. Annual incidences of several GUDs (in %) in Kinshasa.

Declines of 1.5–2.5 orders of magnitude happened between the 1920s and the 1950s. (Data compilation in Text S1).

Figure 2. The growth of the most relevant Central African (A) and West African (B) cities.

Plotted the evolution of the population of the most relevant cities at or near: (A) chimpanzee and gorilla ranges in Central Africa; (B) sooty mangabey range in West Africa. The supporting references are listed in Text S2.

Figure 3. Male circumcision patterns in Central African (A) and West African (B) cities.

The charts show, for each city, and at the referred time, the proportional distribution of the male population by “circumcision classes” which are directly derived from the ethnographic literature and do not depend on additional assumptions. Each bar is based on either: i) a published census or survey partitioning by ethnicity; ii) assumption of the same ethnic distribution as in a neighboring time point for which there is a census or survey; iii) published numbers for some ethnic groups, and estimates for some relevant others. The proportions of red and orange in each bar indicate the proportions of the population belonging to groups which, respectively had not adopted circumcision by the time of the data point (red), or had adopted it, or started to generalize it from a situation in which it is described as far from general in the ethnographic literature, less than 15 years before the time of the data point (orange). So, higher proportions of red and orange (and, to a lesser extent, pink) mean lower circumcision frequencies. See supporting information in Text S2, and supporting calculations in Dataset S1.

Figure 4. Estimates of male circumcision frequencies in Central African (A) and West African (B) cities.

The charts show, for each city, and at the referred time, the upper and lower estimates of male circumcision frequency. The cities and times of estimates are the same that appear in the bars of Figure 3. Each estimate is based on either: i) a published census or survey partitioning by ethnicity (filled squares); ii) assumption of the same ethnic distribution as in a neighboring time point for which there is a census or survey (shallow squares); iii) published numbers for some ethnic groups, and estimates for some relevant others (lozenges); iv) present time estimates for each city are assumed to be similar to the national prevalences measured by the DHS, because the latter are above 95% for nearly all relevant countries, and this, considering the current high levels of ethnic mixing seen in African major cities, leaves little room for a major city to differ from the national average. Except for the situation iv) above, circumcision frequencies are estimated based on the ethnographic information about the circumcision practices of each group, according to an algorithm described in Text S2, and the supporting calculations are implemented in Dataset S1.

Figure 5. Comparison of early spread of HIV in simulated historical scenarios.

The graphs depict frequency distributions of the total number of infections per simulation (A), the duration of the epidemic (B) and the longest chain of transmission (C) from 1,000 simulations of Kinshasa in 1919 (red dots and bars), 1929 (blue dots and bars) and 1958 (green dots and bars), and a pre-colonial village (black dots and bars). The duration of an epidemic was defined as the time until the resolution of the last acute infection: its lower bound was defined by the length of acute infection in patient zero (12 weeks), its upper bound by the length of the simulations (52 weeks). The longest transmission chain was defined as the number of individuals in the longest chain of subsequent transmissions in each simulation. All frequencies (number of observations) are plotted on a log scale.

Figure 6. The effect of selected factors on the simulated early spread of HIV for Kinshasa 1919.

The graphs depict frequency distributions of the total number of infections (A), the duration of the epidemic (B) and the longest chain of transmission (C) from 1,000 simulations of Kinshasa with default parameters (black dots and bars), 10-fold reduced population size (red dots and bars), balanced sex ratio (blue dots and bars), no GUD (green dots and bars) and universal circumcision (gray dots and bars). The duration of an epidemic was defined as the time until the resolution of the last acute infection: its lower bound was defined by the length of acute infection in patient zero (12 weeks), its upper bound by the length of the simulations (52 weeks). The longest transmission chain was defined as the number of individuals in the longest chain of subsequent transmissions in each simulation. All frequencies (number of observations) are plotted on a log scale.