The authors have declared that no competing interests exist.
Conceived and designed the experiments: PPS GC. Performed the experiments: PPS GC JRF MP. Analyzed the data: PPS GC JRF MP AD JARP. Contributed reagents/materials/analysis tools: PPS GC JRF MP EMF. Wrote the paper: PPS GC JRF MP AD JARP EMF RCM JGJ.
Human African trypanosomiasis (HAT), also known as sleeping sickness, persists as a public health problem in several sub-Saharan countries. Evidence-based, spatially explicit estimates of population at risk are needed to inform planning and implementation of field interventions, monitor disease trends, raise awareness and support advocacy. Comprehensive, geo-referenced epidemiological records from HAT-affected countries were combined with human population layers to map five categories of risk, ranging from “very high” to “very low,” and to estimate the corresponding at-risk population.
Approximately 70 million people distributed over a surface of 1.55 million km2 are estimated to be at different levels of risk of contracting HAT.
Updated estimates of the population at risk of sleeping sickness were made, based on quantitative information on the reported cases and the geographic distribution of human population. Due to substantial methodological differences, it is not possible to make direct comparisons with previous figures for at-risk population. By contrast, it will be possible to explore trends in the future. The presented maps of different HAT risk levels will help to develop site-specific strategies for control and surveillance, and to monitor progress achieved by ongoing efforts aimed at the elimination of sleeping sickness.
The present thrust towards the elimination of human African trypanosomiasis (HAT, or sleeping sickness) requires accurate information on how many people are at risk of contracting the disease, and where they live. This information is crucial to target field interventions effectively and efficiently, as well as to monitor progress towards the elimination goal. In this paper, a Geographic Information System was used to delineate areas at different levels of risk. To this end, accurate data on the spatial distribution of HAT cases (period 2000–2009) were collated and combined with maps of human population. A total of 70 million people are estimated to be at risk of contracting sleeping sickness in Africa. This population is distributed over a surface of one and a half million square kilometres, an area six times that of the United Kingdom. Half of the people and of the areas at risk are found in the Democratic Republic of the Congo.
Human African trypanosomiasis (HAT), or sleeping sickness, is a vector-borne disease caused by two sub-species of the parasitic protozoa
In the early 1960s, the reported incidence of the disease was at a trough, with only a few thousand cases being reported annually. However, a decline in surveillance in the post-independence period allowed sleeping sickness to regain ground. By the end of the 20th century, the World Health Organization (WHO) estimated that 300,000 people contracted the infection every year
The magnitude of the recent advances in HAT control and surveillance is such that up-to-date estimates of the number and geographic distribution of people at risk are urgently needed.
In the past, estimates of sleeping sickness risk at the continental, regional and national levels could only be based on educated guess and rough estimations of experts, rather than on a clearly laid out, objective analysis of the epidemiological evidence. In 1985, a WHO Expert Committee indicated that a population of 78.5 million was at risk of HAT in sub-Saharan Africa
Since the latest estimations were made, HAT control and surveillance were scaled up
Till recently, geospatial analysis had never been used to estimate HAT risk at the regional or African scale. In 2008, the Atlas of HAT was launched, aiming at assembling, harmonizing and mapping datasets on the geographic distribution of sleeping sickness in sub-Saharan Africa
In the present study, the methodology tested in the six Central African countries was applied at the continental level in order to map the risk of sleeping sickness in sub-Saharan Africa and to estimate at-risk population. In an effort to generate comparable estimates for both
Georeferenced layers of sleeping sickness occurrence and human population for the period 2000–2009 constituted the input for the present HAT risk mapping exercise.
The number and the geographic distribution of HAT cases were provided by the latest update of the Atlas of HAT (reference date: 31 May 2012), thus including 170,492 cases of
The Atlas provided village-level mapping for 81.0% of the cases, corresponding to 19,828 different locations mapped. The average spatial accuracy for reported cases mapped was estimated at ≈1,000 m using methods already described
For the remaining 19.0% of the cases, village-level information was unavailable but the area of occurrence was known (e.g. focus, parish, health zone, etc.). For the purpose of risk estimation, these cases were apportioned among the endemic villages of their area of occurrence by means of proportional allocation
Reported cases also included those diagnosed in non-endemic countries – most notably in travellers and migrants – which in the Atlas of HAT are mapped in the probable place of infection and flagged as ‘exported’
The geographic distribution of human population was derived from Landscan ™ databases
To delineate risk areas, an average of the ten Landscan population datasets from 2000 to 2009 was used. Subsequently, Landscan 2009 was combined with the risk map to provide estimates of people at risk at the end of the study period
Both input layers (i.e. sleeping sickness cases and human population) can be regarded as spatial point processes, and thus amenable to spatial smoothing.
Spatial smoothing methods are used in epidemiology to facilitate data analysis, and they allow to transform point layers into continuous surfaces of intensity. In this context, the intensity
There are various shapes of kernel to choose from, all usually represented by symmetric bivariate functions decreasing radially. The choice of shape has relatively little effect on the resulting intensity estimate
By taking into account the epidemiological features of HAT, the behaviour of the tsetse vector and the mobility of people in the average rural African milieu where HAT occurs, a search radius of 30 km was chosen
Prior to spatial smoothing, the number of HAT cases reported in 2000–2009 was divided by ten, thus providing the average number of cases per annum (p.a.). Similarly, Landscan human population layers from 2000 to 2009 were averaged
Spatial smoothing resulted in the two surfaces
(a) Distribution of HAT cases; (b) Average population distribution (Landscan); (c) Annual intensity of HAT cases as derived from (a) through spatial smoothing; (d) Population intensity as derived from (b) through spatial smoothing.
The ratio between the intensity of HAT cases and the population intensity can be defined as the disease risk
Category of risk |
|
HAT cases per annum |
Very high | ≥10−2 | ≥1 per 102 people |
High | 10−3≤R<10−2 | ≥1 per 103 people AND<1 per 102 people |
Moderate | 10−4≤R<10−3 | ≥1 per 104 people AND<1 per 103 people |
Low | 10−5≤R<10−4 | ≥1 per 105 people AND<1 per 104 people |
Very low | 10−6≤R<10−5 | ≥1 per 106 people AND<1 per 105 people |
The map depicting the different categories of HAT risk was combined with Landscan 2009 dataset to estimate the number of people at risk at the end of the study period
An area of 1.55 million km2 in Africa is estimated to be at various levels of HAT risk, ranging from ‘very high’ to ‘very low’ (
Country | Total country area |
Area at risk (km2 ×102) | ||||||
Very High | High | Moderate | Low | Very Low | Total at risk | % of total country area | ||
Angola | 12,538 | - | 568 | 597 | 480 | 158 | 1,803 | 14.4 |
Cameroon | 4,664 | - | - | 22 | 79 | 71 | 173 | 3.7 |
Central African Republic | 6,244 | 55 | 141 | 204 | 161 | 97 | 659 | 10.6 |
Chad | 12,725 | - | 33 | 34 | 36 | 39 | 142 | 1.1 |
Congo | 3,385 | 21 | 199 | 388 | 372 | 182 | 1,162 | 34.3 |
Côte d'Ivoire | 3,214 | - | - | 23 | 82 | 182 | 286 | 8.9 |
Democratic Republic of the Congo | 23,041 | 27 | 996 | 2,717 | 2,599 | 1,563 | 7,902 | 34.3 |
Equatorial Guinea | 270 | - | 4 | 37 | 16 | 8 | 65 | 24.1 |
Gabon | 2,660 | - | 6 | 57 | 69 | 35 | 167 | 6.3 |
Guinea | 2,461 | - | 1 | 42 | 53 | 88 | 184 | 7.5 |
Nigeria | 9,089 | - | - | - | 16 | 55 | 70 | 0.8 |
Sierra Leone | 728 | - | - | - | 7 | 11 | 18 | 2.5 |
South Sudan | 6,334 | 21 | 260 | 379 | 265 | 76 | 1,001 | 15.8 |
Uganda | 2,055 | - | 13 | 91 | 42 | 28 | 175 | 8.5 |
Other Endemic Countries |
60,316 | - | - | - | - | - | - | - |
Total | 149,722 | 124 | 2,222 | 4,591 | 4,277 | 2,594 | 13,808 | 9.2 |
Land area. The area of surface water bodies as depicted in the Shuttle Radar Topography Mission – River-Surface Water Bodies dataset
Countries at marginal risk: Benin, Burkina Faso, Gambia, Ghana, Guinea-Bissau, Liberia, Mali, Niger, Senegal and Togo.
Country | Total country area |
Area at risk (km2 ×102) | ||||||
Very High | High | Moderate | Low | Very Low | Total at risk | % of total country area | ||
Burundi | 251 | - | - | - | - | 2 | 2 | 0.8 |
Kenya | 5,749 | - | - | - | 5 | 26 | 31 | 0.5 |
Malawi | 948 | - | - | 33 | 53 | 52 | 138 | 14.6 |
Mozambique | 7,791 | - | - | - | 5 | 34 | 39 | 0.5 |
United Republic of Tanzania | 8,863 | - | 16 | 125 | 229 | 286 | 657 | 7.4 |
Uganda | 2,055 | - | - | 45 | 146 | 97 | 288 | 14.0 |
Zambia | 7,425 | - | - | 33 | 221 | 224 | 478 | 6.4 |
Zimbabwe | 3,884 | - | - | - | 9 | 69 | 78 | 2.0 |
Other Endemic Countries |
25,685 | - | - | - | - | - | - | - |
Total | 62,650 | - | 16 | 236 | 667 | 792 | 1,711 | 2.7 |
Land area. The area of surface water bodies as depicted in the Shuttle Radar Topography Mission – River-Surface Water Bodies dataset
Countries at marginal risk: Botswana, Ethiopia, Namibia, Rwanda and Swaziland.
The total population at risk of sleeping sickness is estimated at 69.3 million (
Country | Total country population |
Population at risk (no. persons ×103) | ||||||
Very High | High | Moderate | Low | Very Low | Total at risk | % of total country population | ||
Angola | 12,799 | - | 740 | 749 | 3,049 | 229 | 4,767 | 37.2 |
Cameroon | 18,879 | - | - | 28 | 238 | 365 | 631 | 3.3 |
Central African Republic | 4,511 | 28 | 41 | 130 | 138 | 99 | 435 | 9.6 |
Chad | 10,329 | - | 109 | 114 | 120 | 123 | 465 | 4.5 |
Congo | 4,013 | 4 | 109 | 451 | 1,825 | 177 | 2,566 | 63.9 |
Côte d'Ivoire | 20,617 | - | - | 230 | 722 | 1,720 | 2,672 | 13.0 |
Democratic Republic of the Congo | 68,693 | 23 | 3,546 | 10,767 | 15,674 | 6,237 | 36,247 | 52.8 |
Equatorial Guinea | 633 | - | 2 | 27 | 8 | 6 | 43 | 6.8 |
Gabon | 1,515 | - | 2 | 21 | 19 | 761 | 803 | 53.0 |
Guinea | 10,058 | - | - | 187 | 488 | 1,932 | 2,606 | 25.9 |
Nigeria | 149,229 | - | - | - | 368 | 1,814 | 2,183 | 1.5 |
Sierra Leone | 5,132 | - | - | 1 | 83 | 87 | 170 | 3.3 |
South Sudan | 6,996 | 15 | 401 | 453 | 334 | 67 | 1,270 | 18.2 |
Uganda | 32,370 | - | 142 | 1,275 | 456 | 251 | 2,124 | 6.6 |
Other Endemic Countries |
103,673 | - | - | - | - | - | - | - |
Total | 449,447 | 70 | 5,092 | 14,431 | 23,521 | 13,869 | 56,983 | 12.7 |
As per Landscan 2009.
Countries at marginal risk: Benin, Burkina Faso, Gambia, Ghana, Guinea-Bissau, Liberia, Mali, Niger, Senegal and Togo.
Country | Total country population |
Risk (no. persons ×103) | ||||||
Very High | High | Moderate | Low | Very Low | Total at risk | % of total country population | ||
Burundi | 9,511 | - | - | - | 5 | 33 | 38 | 0.4 |
Kenya | 39,003 | - | - | - | 254 | 870 | 1,124 | 2.9 |
Malawi | 15,029 | - | - | 194 | 217 | 499 | 910 | 6.1 |
Mozambique | 21,669 | - | - | - | 5 | 53 | 58 | 0.3 |
United Republic of Tanzania | 41,049 | - | 22 | 373 | 621 | 808 | 1,824 | 4.4 |
Uganda | 32,370 | - | - | 847 | 4,734 | 2,295 | 7,877 | 24.3 |
Zambia | 11,863 | - | - | 14 | 122 | 279 | 416 | 3.5 |
Zimbabwe | 11,393 | - | - | - | 5 | 88 | 94 | 0.8 |
Other Endemic Countries |
101,420 | - | - | - | - | - | - | - |
Total | 283,306 | - | 22 | 1,429 | 5,964 | 4,927 | 12,341 | 4.4 |
As per Landscan 2009.
Countries at marginal risk: Botswana, Ethiopia, Namibia, Rwanda and Swaziland.
The geographic distribution of risk areas in central Africa, western Africa and eastern-southern Africa are presented in
A total of 57 million people are estimated to be at risk of contracting Gambian sleeping sickness (
The risk patterns in Cameroon, Central African Republic, Chad, Congo, Equatorial Guinea, and Gabon have already been described in some detail elsewhere
The Democratic Republic of the Congo is, by far, the country with the highest number of people at risk (≈36.2 million) and the largest at-risk area (≈790 thousand km2). Areas at risk can be found in the provinces of Bandundu, Bas Congo, Équateur, Kasai-Occidental, Kasai-Oriental, Katanga, Kinshasa, Maniema, Orientale, and South Kivu. More details on the risk and the geographic distribution of sleeping sickness in the Democratic Republic of the Congo will be provided in a separate paper.
In South Sudan, a sizable area (≈100 thousand km2) and over a million people are estimated to be at risk of sleeping sickness, including a number of high to very high risk areas in Central and Western Equatoria provinces. These findings highlight the need for continued surveillance in this country
In Angola, sleeping sickness is found in the northwestern part of the country (≈180 thousand km2 – 4.8 million people at risk), and most of the high-risk areas are located in the Provinces of Bengo, Kwanza Norte, Uige and Zaire.
In western Africa, the most affected endemic areas are categorized at moderate risk and they are localized in costal Guinea and central Côte d'Ivoire (
Rhodesian sleeping sickness is estimated to threaten a total of 12.3 million people in eastern and southern Africa (
In Uganda, Rhodesian HAT threatens a population of ≈7.9 million, and the risk area (29 thousand km2) stretches from the northern shores of Lake Victoria up to Lira District, north of Lake Kyoga. The areas in Uganda where risk is relatively higher (i.e. ‘moderate’) broadly correspond to the districts of Soroti, Kaberamaido and northwestern Iganga.
Because of a comparatively lower human population density, some areas in the United Republic of Tanzania are estimated to be characterized by higher levels of risk than Uganda, despite fewer reported cases of HAT. In particular, risk is estimated to be high in proximity to the Ugalla River Forest Reserve (Tabora Province). Also all of the other risk areas in the United Republic of Tanzania are associated in one way or another to protected areas, most notably the Moyowosi Game Reserve and the natural reservations in the northeast of the country (i.e. Serengeti, Ngorongoro and Tarangire). Overall, ≈1.8 million people (66 thousand km2) are estimated to be at risk in this country.
In Kenya, HAT risk ranging from low to very low is localized in the western part of the country, adjacent to risk areas in neighbouring Uganda. Also, although no cases were reported from the Masai Mara National Reserve during the study period, part of its area is estimated to be at risk, as influenced by the risk observed in the neighbouring Serengeti National Park (United Republic of Tanzania). Interestingly, two cases have been reported recently (2012) in travellers visiting the Masai Mara
Nature reserves also shape the patterns of HAT risk at the southernmost limit of
Approximately 70 million people (1.55 million km2) are estimated to be at various levels of HAT risk in Africa. This corresponds to 10% of the total population and 7.4% of the total area of the endemic countries. This figure is not far from estimates made by WHO over the last thirty years, (78.54 million in 1985
By contrast, the present methodology is quantitative, reproducible, based on evidence and provides a categorization of risk. The use of global human population layers
The presented maps of different HAT risk categories will help to plan the most appropriate site-specific strategies for control and surveillance, and they will contribute to ongoing efforts aimed at the sustainable elimination of the sleeping sickness.
However, the reported incidence levels underpinning the different risk categories differ by orders of magnitude, so that a more accurate representation of HAT risk can be given by focusing on the different risk categories. For example, 21 million of people (0.7 million km2) are estimated to live at ‘moderate’ to ‘very high’ risk of infection. These are the areas where the most intensive control measures need to be deployed. Low to very low risk categories account for ≈48 million people (0.8 million km2). In these areas, cost-effective and adapted measures must be applied for a sustainable control.
From the methodological standpoint, assumptions affect all estimates of disease risk, including those presented in this paper. One important assumption in the proposed methodology is that it is possible to use the same approach based on human cases of trypanosomiasis to estimate risk of both forms of sleeping sickness. This assumption met the primary goal of generating continental risk estimates in a consistent fashion. However, especially for
Another important choice in the proposed methodology is that of the 30 km bandwidth – the distance from affected locations beyond which disease intensity is considered zero. Sensitivity analysis conducted for six central African countries showed that there is a positive linear relationship between bandwidth on the one hand, and the extent of risk areas and the at-risk population on the other
The estimates presented here also rest on the assumption of isotropy for the risk function. In the future, anisotropy may be explored in an effort to account for the linear nature of some important landscape features such as rivers or roads.
When interpreting the presented risk estimates it is important to acknowledge the uncertainty inherent in the human population datasets used as denominator
For the chronic
The temporal dimension is also crucial when interpreting risk maps. The proposed estimates were based on an average of HAT reported cases for a ten-year period. No weighting for the different reporting years was applied, despite the fact that a reduction in reported cases was observed during the last years of the study period. As a result, all cases contributed equally regardless of when exactly they were reported.
Importantly, the estimates of people at risk presented in this paper, being based on reported cases, can not account for the possible future spread of HAT, and the risk thereof, into presently unaffected areas. Other approaches to risk modelling could be more interested in predicting the future risk of sleeping sickness, focusing on the environmental suitability for HAT rather than on its present occupancy. To this end, the relationships are to be explored between HAT occurrence and a range of factors, including human and livestock population movements
Where estimates of prevalence are available, most notably in
Maps of distribution of population at risk of human African trypanosomiasis in 21 disease endemic countries, where any level of risk has been identified during the period 2000–2009. Countries are organized on geographical order, west to east + north to south, and from T.b.gambiense to T.b.rhodesiense endemic countries: Guinea, Sierra Leone, Côte d'Ivoire, Nigeria, Cameroon, Chad, Central African Republic, South Sudan, Equatorial Guinea, Gabon, Congo, The Democratic Republic of the Congo, Angola, Uganda, Kenya, United Republic of Tanzania, Burundi, Zambia, Malawi, Mozambique and Zimbabwe.
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The activities described in this paper are an initiative of the Department of Control of Neglected Tropical Diseases - World Health Organization. They were implemented through a technical collaboration between WHO and FAO in the framework of the Programme against African Trypanosomosis (PAAT).
The authors would like to acknowledge all institutions that provided the epidemiological data used as input to this study: the National Sleeping Sickness control Programmes and national health authorities of Angola, Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Congo, Côte d'Ivoire, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Guinea, Kenya, Malawi, Mali, Mozambique, Nigeria, Rwanda, Sierra Leone, South Sudan, Togo, Uganda, United Republic of Tanzania, Zambia and Zimbabwe; the NGOs “
The boundaries and names shown and the designations used on the maps presented in this paper do not imply the expression of any opinion whatsoever on the part of WHO and FAO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
The views expressed in this paper are those of the authors and do not necessarily reflect the views of WHO and FAO.