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. 2023 Sep 4:12:e85435.
doi: 10.7554/eLife.85435.

The role of migration networks in the development of Botswana's generalized HIV epidemic

Janet Song  1 , Justin T Okano  1 , Joan Ponce  1 , Lesego Busang  2 , Khumo Seipone  2 , Eugenio Valdano  3 , Sally Blower  1
Affiliations

The role of migration networks in the development of Botswana's generalized HIV epidemic

Janet Song et al. Elife. .

Abstract

The majority of people with HIV live in sub-Saharan Africa, where epidemics are generalized. For these epidemics to develop, populations need to be mobile. However, the role of population-level mobility in the development of generalized HIV epidemics has not been studied. Here we do so by studying historical migration data from Botswana, which has one of the most severe generalized HIV epidemics worldwide; HIV prevalence was 21% in 2021. The country reported its first AIDS case in 1985 when it began to rapidly urbanize. We hypothesize that, during the development of Botswana's epidemic, the population was extremely mobile and the country was highly connected by substantial migratory flows. We test this mobility hypothesis by conducting a network analysis using a historical time series (1981-2011) of micro-census data from Botswana. Our results support our hypothesis. We found complex migration networks with very high rates of rural-to-urban, and urban-to-rural, migration: 10% of the population moved annually. Mining towns (where AIDS cases were first reported, and risk behavior was high) were important in-flow and out-flow migration hubs, suggesting that they functioned as 'core groups' for HIV transmission and dissemination. Migration networks could have dispersed HIV throughout Botswana and generated the current hyperendemic epidemic.

Keywords: HIV/AIDS; epidemiology; global health; human; migration; mobility patterns; network science.

Plain language summary

Over 25 million people in sub-Saharan Africa live with HIV. After reporting its first AIDS case in 1985, Botswana is one of the most severely affected countries in the region, with one in five adults now living with HIV. Movement of the population is likely to have contributed to a geographically dispersed, and high-prevalence, HIV epidemic in Botswana. Since 1985, urbanization, rapid economic and population growth, and migration have transformed Botswana. Yet, few studies have analyzed the role of population-level movement patterns in the spread of HIV during this time. By studying micro-census data from Botswana between 1981 and 2011, Song et al. found that the country’s population was highly mobile during this period. Reconstructions of internal migration patterns show very high rates of rural-to-urban and urban-to-rural migration, with 10% of Botswana’s population moving each year. The first reported AIDS cases in Botswana occurred in mining towns and cities where high-risk behavior was prevalent. These areas were also migration hubs during this period and could have contributed to the rapid spread of HIV throughout the country as infected individuals moved back to rural districts. Understanding human migration patterns and how they affect the spread of infectious diseases using current data could help public health authorities in Botswana and additional sub-Saharan African countries design control strategies for HIV and other important infections that occur in the region.

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Conflict of interest statement

JS, JO, JP, LB, KS, EV, SB No competing interests declared

Figures

Figure 1.
Figure 1.. HIV prevalence at the district-level.
HIV prevalence estimated using BAIS III data (2008) for adults ages 15–69. Botswana’s two cities and five towns are denoted with circles. Districts for the Okavango Delta and Central Kgalagadi Game Reserve have been geographically incorporated into Ngamiland West and Ghanzi, respectively, due to their small population sizes.
Figure 2.
Figure 2.. Gender-stratified age profiles of internal migrants by census year.
(A) 1981, (B) 1991, (C) 2001, and (D) 2011.
Figure 3.
Figure 3.. Maps of net migratory flows by census year.
(A) 1981, (B) 1991, (C) 2001, and (D) 2011. Maps show the net migratory flows between any two districts (pink and green lines). Line thickness indicates the magnitude of the flow between districts, line color indicates the flow direction. Pink lines denote eastward flows, green lines denote westward flows. For example, in 1991, the thickest green line indicates a large (westward) migratory flow from Ngamiland East to Ngamiland West. Each district is shaded to indicate the total net flow of all migrations into and out of it. Cities and towns are represented with circles. Districts for the Okavango Delta and Central Kgalagadi Game Reserve have been geographically incorporated into Ngamiland West and Ghanzi, respectively, due to their small population sizes.
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Maps of turnover by census year.
(A) 1981, (B) 1991, (C) 2001, and (D) 2011. The turnover rate provides a measure of net flow for a district per hundred residents. Botswana’s two cities and five towns are denoted with circles. Districts for the Okavango Delta and Central Kgalagadi Game Reserve have been geographically incorporated into Ngamiland West and Ghanzi, respectively, due to their small population sizes.
Figure 3—figure supplement 2.
Figure 3—figure supplement 2.. Maps of within district migration intensity (WDMI) by census year.
(A) 1981, (B) 1991, (C) 2001, and (D) 2011. The WDMI for each district is the number of internal movements per hundred residents. Botswana’s two cities and five towns are denoted with circles. Districts for the Okavango Delta and Central Kgalagadi Game Reserve have been geographically incorporated into Ngamiland West and Ghanzi, respectively, due to their small population sizes.
Figure 4.
Figure 4.. Chord diagrams by census year.
(A) 1981, (B) 1991, (C) 2001, and (D) 2011. Each diagram shows the internal migration network of the general population in the 12 mo prior to the census. Each color represents a different district. The thickness of each line is proportional to the number of migrants that moved between the two connected districts. The angular width of each district is proportional to the total number of migrants who moved into, or out of, that district. For clarity, in (A–C) only connections with greater than 200 migrants are shown, and in (D) only connections with greater than 400 migrants are shown. Consequently, some districts are not shown in the chord diagram. The total number of migrants (in and out) of every district is listed in Supplementary file 1.
Figure 5.
Figure 5.. The top five in-flow and out-flow migration hubs by census year.
In-flow hubs are sorted by the rate per hundred residents of a district’s population that moves in from another district; out-flow hubs are sorted by the rate per hundred residents of a district’s population that moves out to another district. (A) Top in-flow hubs in 1981. (B) Top out-flow hubs in 1981. (C) Top in-flow hubs in 1991. (D) Top out-flow hubs in 1991. (E) Top in-flow hubs in 2001. (F) Top out-flow hubs in 2001. (G) Top in-flow hubs in 2011. (H) Top out-flow hubs in 2011.
Figure 6.
Figure 6.. Ego network of Jwaneng.
The chord diagrams show migrants flowing (A) out of, and (B) into, the diamond mining town in 1981.
Figure 7.
Figure 7.. Schemata of migration-urbanization framework by census year.
(A) 1981, (B) 1991, (C) 2001, and (D) 2011. Circles represent the five classes based on an urban/rural classification (see ‘Methods’). The radius of each circle is proportional to the number of residents living in the districts in that specific class. The color of each arrow indicates the size of the net migration between classes, as does the thickness of the line: the thicker the line, the greater the number of migrants.
Figure 8.
Figure 8.. Sankey diagrams showing the migration-urbanization framework by census year.
(A) 1981, (B) 1991, (C) 2001, and (D) 2011. The diagrams show the relative magnitude of migratory flows between the five classes over time. Cities are shown in purple, towns in blue, predominantly urban districts in gold, partially urban districts in magenta, and predominantly rural districts in green.
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