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Logo of ijhealthgeoBioMed CentralBiomed Central Web Sitesearchsubmit a manuscriptregisterthis articleInternational Journal of Health GeographicsJournal Front Page
Int J Health Geogr. 2010; 9: 57.
Published online Nov 1, 2010. doi:  10.1186/1476-072X-9-57
PMCID: PMC2988709

The Atlas of human African trypanosomiasis: a contribution to global mapping of neglected tropical diseases

Abstract

Background

Following World Health Assembly resolutions 50.36 in 1997 and 56.7 in 2003, the World Health Organization (WHO) committed itself to supporting human African trypanosomiasis (HAT)-endemic countries in their efforts to remove the disease as a public health problem. Mapping the distribution of HAT in time and space has a pivotal role to play if this objective is to be met. For this reason WHO launched the HAT Atlas initiative, jointly implemented with the Food and Agriculture Organization of the United Nations, in the framework of the Programme Against African Trypanosomosis.

Results

The distribution of HAT is presented for 23 out of 25 sub-Saharan countries having reported on the status of sleeping sickness in the period 2000 - 2009. For the two remaining countries, i.e. Angola and the Democratic Republic of the Congo, data processing is ongoing. Reports by National Sleeping Sickness Control Programmes (NSSCPs), Non-Governmental Organizations (NGOs) and Research Institutes were collated and the relevant epidemiological data were entered in a database, thus incorporating (i) the results of active screening of over 2.2 million people, and (ii) cases detected in health care facilities engaged in passive surveillance. A total of over 42 000 cases of HAT and 6 000 different localities were included in the database. Various sources of geographic coordinates were used to locate the villages of epidemiological interest. The resulting average mapping accuracy is estimated at 900 m.

Conclusions

Full involvement of NSSCPs, NGOs and Research Institutes in building the Atlas of HAT contributes to the efficiency of the mapping process and it assures both the quality of the collated information and the accuracy of the outputs. Although efforts are still needed to reduce the number of undetected and unreported cases, the comprehensive, village-level mapping of HAT control activities over a ten-year period ensures a detailed and reliable representation of the known geographic distribution of the disease. Not only does the Atlas serve research and advocacy, but, more importantly, it provides crucial evidence and a valuable tool for making informed decisions to plan and monitor the control of sleeping sickness.

Background

Efficient collection, management and dissemination of information are essential to intervene effectively against neglected tropical diseases and to monitor and evaluate strategies and actions towards their control. However, the neglect suffered by many diseases that affect the world's poorest people manifests itself in many guises, not least in the paucity of reliable information on their incidence and geographic distribution [1,2]. Evidence on the number and location of infections is badly needed to bring these neglected diseases out of the shadow [3] and, most crucially, to tackle them efficiently [4,5].

Disease maps are required for planning, managing and monitoring interventions across the whole spectrum of neglected tropical diseases. For conditions such as helminthiases and trachoma, against which safe and affordable drugs are available, baseline maps of endemicity are used to plan mass drug administration (MDA) campaigns [6-8]. As campaigns progress, spatially-explicit data enable implementation to be monitored and ultimately contribute to assessing the degree of success of the initiatives [9]. Geo-spatial information also plays a role in the post-MDA phase when surveillance aims at providing early-warning of the possible disease resurgence or reintroduction in freed areas.

A different group of diseases such as human African trypanosomiasis (HAT), Chagas disease and leishmaniasis, are characterized by the limited availability of safe and cost-effective control tools. This group includes infections of complex epidemiology, which, if untreated, can result in very high death rates. Strategies to control them hinge on early diagnosis and treatment, as well as on vector control. Accurate, spatially-explicit information is essential to target, monitor and evaluate interventions against these challenging and deadly diseases [10-12]

Geo-referenced data are also crucial when control strategies are successful and elimination or eradication of a disease can be contemplated. This is exemplified by the "Dracunculiasis Eradication Program", wherein village-level information on disease occurrence and programme implementation is routinely collected to monitor progress towards the programme's ultimate goal [13].

Disease distribution maps are also required if accurate estimates of the population at risk and of the burden of diseases are to be made. The issue of the most appropriate metrics to estimate the burden is subject of debate [14,15], but there is increasing recognition that the burden of neglected tropical diseases still needs to be fully appreciated [16] and that more detailed and reliable data on their incidence and impacts need to be collected [17-19].

The present paper focuses on HAT, also known as sleeping sickness, whose etiological agents are tsetse-transmitted protozoa of the species Trypanosoma brucei. Two sub-species are responsible for syndromes of markedly different epidemiology and geographical range. T. b. gambiense causes the chronic, anthroponotic form found in Western and Central Africa. Infection with T. b. rhodesiense results in the acute, zoonotic form of Eastern and Southern Africa. Both forms are considered fatal if untreated. No vaccine against HAT is available, and the toxicity of existing drugs precludes the adoption of control strategies based on preventive chemotherapy. As a result, the keystones of interventions against sleeping sickness are active and passive case-finding followed by treatment, vector control and animal reservoir management.

Following World Health Assembly resolutions 50.36 in 1997 [20] and 56.7 in 2003 [21], the World Health Organization (WHO) committed itself to supporting HAT-endemic countries in their efforts to remove the disease as a public health problem. Mapping disease distribution in time and space has a pivotal role to play if this objective is to be met.

Partly because of the focal nature of sleeping sickness geography, HAT control has always been intimately entwined with disease mapping [22]. For example, as early as 1903, detailed maps of HAT distribution were published in Uganda [23] and foci were mapped in Western and Central Africa in the period 1908-1910 [24,25]. In the last decade, mapping and geo-positioning have become much more accessible with the widespread diffusion of Geographic Information Systems (GIS) and satellite-aided positioning systems (e.g. the Global Positioning System (GPS)). These technical tools are at the basis of the systematic approach to sleeping sickness mapping embodied in the Atlas of HAT, the first attempt ever to geo-reference at the village level all sleeping sickness cases reported from affected countries [26]. Underpinning this initiative are the commitment of WHO and the Food and Agriculture Organization of the United Nations (FAO), the institutional umbrella provided by the Programme against African Trypanosomosis (PAAT) and the crucial input of National Sleeping Sickness Control Programmes (NSSCPs), Non-Governmental Organizations (NGOs) and Research Institutes.

This paper presents the process of building the Atlas of HAT for the period 2000-2009. The distribution of HAT is presented for 23 out of 25 sub-Saharan countries having reported on the status of sleeping sickness during the last ten years: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Congo, Côte d'Ivoire, Equatorial Guinea, Gabon, Ghana, Guinea, Kenya, Malawi, Mali, Mozambique, Nigeria, Rwanda, Sudan, Togo, Uganda, United Republic of Tanzania, Zambia and Zimbabwe. Work is in progress to process data from the two remaining countries having reported on HAT in the study period: Angola and the Democratic Republic of the Congo (DRC).

The presented maps are a fundamental milestone in the process of building the continental Atlas of HAT. The unprecedented accuracy and comprehensiveness of the Atlas are to make it an essential tool for disease control, research and advocacy.

Methods

The central feature of the Atlas of HAT is the mapping at village level of all new cases of HAT reported during a ten-year period (2000-2009, see Table Table11 for T. b. gambiense and Table Table22 for T. b. rhodesiense).

Table 1
T. b. gambiense Sleeping Sickness: new cases reported between 2000 and 2009
Table 2
T. b. rhodesiense Sleeping Sickness: new cases reported between 2000 and 2009

In addition to the new HAT reported cases, results of all active case-finding surveys are also included in the Atlas, even if no case was detected, as this provides important information on the populations screened and the epidemiological status of historical transmission areas.

Data from both active and passive surveillance are analyzed and imported into a single database, where a selected set of ancillary data are also recorded if available (i.e. number of people screened, number of people living in screened localities, disease stage).

Since the methodology used to build the HAT database has been described in detail elsewhere [26], this section elaborates only on those key methodological aspects that have repercussions on mapping accuracy and on the epidemiological interpretation of the outputs.

Where to map HAT cases

Data on HAT cases detected by active case-finding surveys are collected by mobile teams visiting villages in transmission areas. As a rule, people are tested in their village of residence, which therefore is used as the reference for mapping. However, participants in a mass-screening event carried out in a given village may also comprise people coming from neighbouring settlements, normally located only a few kilometres away. If these settlements of origin are explicitly mentioned in the mission report, it is possible to refer the relevant individuals back to their exact place of residence, thus more accurately reflecting in the Atlas the epidemiological milieu in which they live. If information on the exact place of residence is not available, cases are mapped in the village where the screening took place.

For HAT cases detected by passive surveillance in referral healthcare facilities, the patient's village of residence is normally recorded in laboratory and ward register books and thus it can be used for mapping. Therefore, the patient's village of residence constitutes the basic mapping unit for both actively and passively reported cases.

Hospital records pointing to villages of residence located far from known endemic areas are investigated on a case-by-case basis, with a view towards ascertaining the accuracy of the recorded information and clarifying its epidemiological significance. Indeed, cultural factors and communication difficulties may lead the patient to declare, or the doctor or technician to record, the patient's place of birth or the place of origin of their family as their village of residence. The mapping process often allows these issues to be identified.

More generally, careful verification is carried out for all outliers (e.g. places of residence that are inconsistent with the catchment area of the reporting healthcare structure, cases falling outside tsetse-infested areas, etc). Cases originating from areas with no reported history of HAT undergo special scrutiny, as they could point to new transmission zones requiring in-depth field investigation.

Other challenges to HAT mapping are posed by refugees and internally displaced people who are mapped in their camps if their village of residence is unknown; soldiers are mapped in the localities where they are stationed. Mapping of other categories of migrating workers, for whom establishing the most meaningful place of residence is a demanding endeavour, is guided by knowledge of the local epidemiology of the disease and perspectives given by the long field-experience of NSSCPs staff.

Importantly, notes included in the HAT database provide details on all cases that required ad-hoc investigation, so as to make the interpretation process transparent to users.

HAT cases are also occasionally diagnosed in a country that is different from the country where the patient contracted the infection. This is relatively frequent between HAT-endemic countries as a result of transboundary foci. To capture these cases the Atlas presents the special category "transboundary case". The corresponding individuals are mapped in the village of residence in the country of infection and flagged accordingly as "transboundary cases". The place and country of diagnosis are also recorded in the notes of the database with a view to understanding population movements within transboundary foci.

HAT infections are also diagnosed in countries that are not endemic for HAT, including countries outside Africa, mostly in migrants and tourists who come from or have visited endemic areas. The category of "exported case" has been created in the HAT Atlas to reflect this phenomenon. In the maps presented in this paper, exported cases are associated to the probable place of infection; in the case of tourists, this is most often represented by the centroid of a national park or another wildlife management area. The importing countries, as well as the places of diagnoses, are also recorded in the notes of the database, which will allow a "World map" of HAT exported cases to be generated.

How to georeference HAT cases

Once the village to be mapped has been determined, its geographic coordinates must be measured or estimated. A procedure was devised whereby coordinates are first sought in the epidemiological reports [26]. When not available there, coordinates are searched for in gazetteers (i.e. databases of named locations). These two sources enable the majority of villages to be mapped. As a third option, other sources of geographic information can be used, including paper and digital maps, as well as hand-drawn, out-of-scale sketches of the endemic areas that may be available in the epidemiological reports.

When all the available data sources have been exhausted, a limited but non-negligible number of settlements, especially the smaller or temporary, often remain unmapped, thus calling for further investigation. Direct consultation with first-line field workers of the NSSCPs (either via face-to-face interviews, cell-phone or e-mail) has proved the most speedy and cost-effective manner to locate - most frequently in relation to other known locations - hundreds of these small and often remote settlements.

Mapping accuracy

The various sources of geographic information used to build the HAT Atlas are characterized by different levels of accuracy. To estimate the overall spatial accuracy of the resulting maps we classified mapped locations into four broad categories of accuracy: very high, high, moderate and low.

The first category, i.e. "very high accuracy", includes locations whose coordinates were derived from GPS measurements carried out by field teams in charge of HAT reporting. These measurements can be affected by an error as little as a few metres, but we conservatively estimated an average error of 50 m. Certain coordinates obtained from gazetteers are also included in the first category of accuracy, but only if no rounding of the coordinates was carried out (i.e. coordinates must be available from gazetteers in full, as degrees, minutes and seconds, or down to the 4th decimal place if decimal degrees notation was used).

The second category ("high accuracy") includes all those cases where coordinates obtained from gazetteers have, for some reason, been rounded (or truncated) at the source. For example, in the GEOnet Names Server database, which provides the baseline for many, if not all, of the available gazetteers [30], more than half of the coordinates for Africa are rounded to the nearest minute, which results in errors of up to 928 m. For this second category we therefore estimated an average error of 500 m.

"Medium" and "low" accuracy refer to coordinates that were estimated in an approximate manner, i.e. by using digital and paper maps, out-of-scale sketches of survey areas, as well as qualitative information received from field-workers. The difference between the two categories lies in the detail of the qualitative information they are based upon. For example, "medium" accuracy may derive from indications such as "the village to be mapped is located at 1 kilometre from village ×, along the road to village y", with the obvious assumption that the position of villages × and y and the road connecting them be known. By contrast, "low" accuracy may result from less precise indications, such as "village × is located between village y and village z, along the river w". We estimate an average accuracy of 2,500 m and 5,000 m for the categories "medium" and "low" respectively.

Definition of HAT case

The Atlas aims to map all new HAT cases reported by affected countries. As a rule, NSSCPs consider parasitological confirmation (i.e. direct observation of trypanosomes in bodily fluids) as mandatory for HAT diagnosis. However, under certain circumstances, NSSCPs consider a seropositive individual as affected, even in the absence of parasitological confirmation. This occurs more frequently in T. b. gambiense highly endemic or epidemic areas, where the likelihood of false positives in serological tests is deemed lower. For the Atlas of HAT we simply follow national rules for the definition of HAT cases. However, in the database note is taken of all the instances when NSSCPs have included seropositives without parasitological confirmation in the totality of HAT cases.

Results

Mapping of HAT

As we write, mapping in 23 countries out of the 25 that reported on HAT occurrence in the period 2000-2009 has been completed (Figure (Figure1).1). Data processing is considered complete when all available data sources for the study period have been analysed and entered in the database. Approximately 1 500 unpublished files, including epidemiological reports, maps, databases and spreadsheets, have been analyzed for the 23 study countries, thus incorporating (i) the results of active screening of over 2.2 million people, and (ii) cases detected in health care facilities engaged in passive surveillance. A total of over 42 000 cases of HAT and 6 000 different localities have been included in the database. Work is in progress to process data from the 2 remaining countries having reported the largest burden of HAT cases in the study period: Angola and DRC (represented with a striped pattern in Figure Figure1).1). Eight countries listed as endemic have provided no report on the HAT situation in 2000-2009: Burundi, Ethiopia, Gambia, Guinea-Bissau, Liberia, Niger, Senegal and Sierra Leone (represented with a yellow chequerboard pattern in Figure Figure1).1). Efforts should be made to clarify the epidemiological status of HAT in these eight countries. In Botswana, Namibia and Swaziland (represented with a green chequerboard pattern in Figure Figure1),1), it is suggested that transmission of sleeping sickness may have been interrupted because of successful tsetse elimination campaigns recently implemented in Botswana and Namibia [31-33], as well as evidence indicating that Glossina austeni - a species not involved in HAT transmission [34,35] - is currently the only species of tsetse fly present in Swaziland [36].

Figure 1
The Atlas of human African trypanosomiasis: progress status. For each country, data processing is considered complete when all available data sources for the study period (2000-2009) have been analysed and included in the HAT database.

To date, not all cases reported from completed countries could be mapped. Table Table33 shows that approximately 10 percent of cases could not be geo-referenced at the village level yet, although the focus of origin is known. The majority of gaps in mapped cases originate from passive surveillance, as the number of sleeping sickness cases may be reported as focus-level aggregates, without specific reference to the patients' village of residence. Despite actions to visit the source for more detailed data, it occurs that the necessary village-level information may have been permanently lost, especially because of the violent conflicts that ravaged numerous HAT endemic areas in recent years.

Table 3
Progress status of mapping for HAT cases and geographic locations (period 2000 - 2009)

HAT distribution

Present results of the HAT Atlas initiative are shown in Figure Figure2,2, Figure Figure33 and Figure Figure4,4, which represent the distribution of both forms of HAT in 23 countries.

Figure 2
Cases of human African trypanosomiasis reported from Western Africa (period 2000-2009). Countries masked in white are (i) non-endemic for HAT, or (ii) those that did not report on the HAT epidemiological situation in the period 2000-2009. Areas masked ...
Figure 3
Cases of human African trypanosomiasis reported from Central Africa (period 2000-2009). Countries masked in white are (i) those where mapping is in progress, (ii) those that did not report on the HAT epidemiological situation in the period 2000-2009.
Figure 4
Cases of human African trypanosomiasis reported from Eastern and South-eastern Africa (period 2000-2009). Countries masked in white are (i) those were mapping is in progress, (ii) non-endemic for HAT, and (iii) those that did not report on the HAT epidemiological ...

Western Africa

In Western Africa (Figure (Figure2),2), three countries are still reporting significant levels of disease incidence: Guinea, Côte d'Ivoire and Nigeria. In Guinea, HAT cases originate from the mangrove biotope (Guinée Maritime) where people expose themselves to the risk of infection when fishing, growing rice and collecting firewood and salt. This region includes the foci of Boffa, Dubréka and Forécariah, the latter close to the border with Sierra Leone. The presence of the same mangrove biotope across this porous border suggests that the disease might also be present in the territory of Sierra Leone, even though no case is reported from this country. However, an exploratory mission carried out in early 2010 in 24 villages of Sierra Leone close to the Guinean border (results included in Figure Figure2)2) failed to find evidence of HAT [37]. In Guinea, HAT cases are also reported from the forest biotope (Guinée Forestière), namely from N'zérékoré and Guéckédou foci.

In Côte d'Ivoire, transmission is reported among workers of cocoa and coffee plantations in the Centre-west of the country that comprises the foci of Daloa (Haut-Sassandra Region), Sinfra and Bonon (Marahoué Region) and Oumé (Fromager Region). Sporadic cases are also reported from the South-eastern (Aboisso, Sud Comoe Region) and Western (Moyen Cavally Region) parts of the country.

Nigeria reports cases from Edo and Delta States, where security constraints prevent more accurate surveys that could delineate the exact extent of the focus. By contrast, extensive active screening surveys in Benin, Burkina Faso, Ghana, Mali and Togo have not revealed any evidence of transmission in vast areas of the Sudanian savannah ecoregion, which includes a number of sleeping sickness foci that had been active in the first part of the 20th century [29]. This evolution appears to have been driven by demographic, climatic and landscape dynamics that would have shrunk the distribution of the tsetse fly in the region and reduced the possibility of contact between people and the vector of HAT [38]. The few cases detected in Burkina Faso in the last decade are due to returnees who had been working in HAT-endemic areas in Côte d'Ivoire [29].

Central Africa

The most active HAT foci of the Central African countries presented in this paper - which does not include Angola and DRC - are found in Sudan, Central African Republic, Congo and Uganda (Figure (Figure3),3), most notably in riverine, forested biotopes where at-risk activities include attending to domestic chores, farming, fishing and hunting. A notable feature of HAT distribution in this region is the relevance of trans-boundary foci, associated to important population movements. These movements are often caused by insecurity, but also by the fact that borders frequently cut across areas inhabited by the same ethnic group.

In North-western Uganda most cases of Gambiense sleeping sickness are reported, listing from East to West, from the districts of Amuru, Adjumani, Moyo, Yumbe, Arua, Maracha-Terego and Koboko, the latter providing most of the cases reported in recent years. In Southern Sudan, the main foci are, from East to West: Nimule and Kajo-Keji (linked to the Ugandan foci of Adjumani and Moyo), Yei, Mundri, Maridi, Yambio-Ezo (linked to the foci of the Oriental Province in DRC) and Tambura-Source Yubu, related to the foci of Obo, Mboki and Zemio in the South East of Central African Republic (Haut-Mbomou Prefecture). The latter cluster of foci also extends southwards merging with the foci of Haut Uele in the Oriental Province of DRC. Central African Republic also includes the foci of Batangafo and Maitikoulou in the North West (Ouham Prefecture). Lower incidence is reported from the foci of Nola-Bilolo and Lobaye in the South, which is linked with the foci in Équateur Province of DRC. In Chad, HAT cases are reported only from the southern foci of Bodo and Moissala, associated to Mandoul and Nana Barya rivers and linked with the Central African foci of Batangafo and Maitikoulou. In Congo, HAT cases are concentrated in the Bouenza Region (foci of Loudima, Boko-Songho, Nkayi, Nkayes, Madingou, Mfouati and Yamba) and along the Congo River and its main tributaries (e.g. the Likouala and Sangha rivers), where the main foci are Loukolela and Mossaka (Cuvette Region), Gamboma, Mpouya and Makotimpoko (Plateaux Region) and Mindouli, Ignié and Ngabé (Pool Region). The transmission areas along the Congo River are also linked to the foci of Équateur, Bandundu, Kinshasa and Bas Congo Provinces in the DRC. Other less active foci in the forest biotope can be found in Cameroon (i.e. Mamfé and Fontem in the Sud-Ouest Province, Bipindi in the Littoral Province and Doumé in the Est Province).

The second important biotope of HAT transmission in Central Africa is the mangrove swamp along the Atlantic coast, where people are at risk of infection when engaged in fishing or hunting activities. In Cameroon we find the focus of Campo, also shared with Equatorial Guinea, which is followed southwards by the foci of Mbini and Kogo. The latter is linked to the focus of Noya in Gabon (Estuaire Province), and lastly the focus of Bendje (Ogooué-Maritime Province).

Eastern and South-eastern Africa

In Eastern and South-eastern Africa, affected by Rhodesiense HAT, at-risk activities are those that bring people into areas where livestock or wildlife interact with the tsetse vector. Among these activities are grazing livestock, collection of firewood and honey, hunting, fishing and poaching. Evidently, in addition to park rangers, tourists are among the categories at-risk, and a number of infections are diagnosed outside transmission areas in tourists that had visited protected areas. Infected tourists are considered as "exported cases" and mapped with purple triangles in points that are representative of the areas where they contracted the infection (most often the centroids of the visited parks).

Most reported cases of Rhodesiense sleeping sickness are linked to the livestock reservoir. Domestic cattle can be highly mobile, and their movements along trade routes and through livestock markets pose a serious challenge to Rhodesiense HAT control. In some countries these movements have been directly linked to the emergence of new foci of HAT transmission [40]. In particular, it has been shown that animals for sale at markets tend to have a higher prevalence of infection than animals in the general population; control of this spread therefore depends on close collaboration with veterinary services [41].

The livestock reservoir is especially important for South-eastern Uganda, which accounts for over 50 percent of T. b. rhodesiense cases reported from Africa in the period 2000-2009. Cases are mainly reported from the districts of Iganga, Soroti and Kaberamaido. The latter, together with the Dokolo and Lira Districts, only started to report a sizable number of cases in 2004 after the northwards movements of cattle and the movements of populations due to civil unrest. This northwards shift represents a real risk of merger with the Gambiense form that affects the North West of the country, which would pose significant problems for disease control [42]. Sporadic cases of T. b. rhodesiense have also been reported from the South-western part of Uganda in travellers visiting the Queen Elizabeth Park.

Domestic animals also play an important epidemiological role in other countries, including Tanzania, which reports over 30 percent of the total number of T. b. rhodesiense cases, and Kenya. In the latter country, HAT cases are reported from the Western Province (districts of Bungoma, Teso and Busia), with a few Kenyan cases being detected by health facilities across the Ugandan border. Sporadic cases are also reported from the Nyanza Province (Migori district).

Wildlife, especially game animals, is the other central actor in the epidemiology of Rhodesiense sleeping sickness.

Figure Figure44 shows the close spatial relationship between HAT infections and protected areas in those countries where the livestock (cattle) reservoir does not dominate. In a larger-scale example (Figure (Figure5)5) the distribution of sleeping sickness cases around the Nkhotakota Wildlife Reserve in Malawi is shown. We note that a few circles identifying HAT cases are also found within the park's boundary, as they refer to park's workers (e.g. rangers), normally residing inside the park.

Figure 5
Cases of human African trypanosomiasis (T. b. rhodesiense) reported from the Nkhotakota focus in Malawi (period 2000-2009). Autochthonous cases are mapped in their places of residence. One exported case is mapped in the centroid of the polygon representing ...

Zambia has been reporting cases linked to National Parks in the eastern and northern part of the country (i.e. National Parks of North and South Luangwa, Isangano, Kasanka and Lavushi Manda). HAT cases linked to Kafue National Park in the South-west of the country have also been reported. Cases detected in northern Zimbabwe are related to Mana Pools National Park and Kariba Lake. In Malawi, cases are clustered around Vwaza Marsh Wildlife Reserve (Northern Region), Nkhotakota Wildlife Reserve and Kasungu National Park (Central Region). Mozambique is reporting sporadic cases in Tete and Niassa Provinces. Tanzania reports cases of infection from the Serengeti and Tarangire National Parks and Ngorongoro Conservation Area in the northern part of the country. In the West, cases are reported from health facilities in the Kigoma Region related to the Moyowosi Game Reserve. Cases are also reported from Tabora Region linked to the Ugalla River Game Reserve and Rukwa Region linked to Luafi Game Reserve and Katavi National Park.

Interestingly, only the waters of Lake Tanganyika seem to separate the cases of T. b. gambiense in DRC from those of T. b. rhodesiense in Tanzania, thus posing a risk of merger between the two forms of the disease.

Mapping accuracy

The production of large scale, focus-level maps such as the one in Figure Figure55 is made possible by the accuracy of the geo-referencing procedures adopted for the Atlas. Results of the spatial accuracy assessment are summarized in Table Table4,4, which provides the number of villages by country and by category of accuracy.

Table 4
Geographical accuracy of mapped villages.

If the 23 study countries are considered as a whole, the proportion of villages belonging to the 4 categories of accuracy is 59.2 percent for "very high", 16.5 percent for "high", 17.1 percent for "medium" and 7.1 percent for "low". The resulting average accuracy, based on the estimated average error of the 4 categories, is approximately 900 m.

It must be noted that the global accuracy of the Atlas is also potentially affected by homonymies, which can cause a case of HAT to be associated to the wrong village for the only reason of this having the same name as the correct one. It is impossible to estimate how often this may have occurred; however, thorough verifications are carried out to limit this kind of error to a minimum.

The Atlas of HAT as an epidemiological tool for planning, monitoring and evaluation of disease control

Maps of disease distribution are the first, more visible output of the HAT Atlas initiative. However, when the HAT geo-database is coupled with GIS software, it enables a range of epidemiological analyses to be performed, which are of great value for planning, monitoring and evaluating HAT control programmes. A few examples of the possible outputs of the Atlas for epidemiological analysis are given below.

Figure Figure6A6A allows to monitor whether mobile teams correctly plan active case finding surveys.

Figure 6
HAT focus of Bodo in Chad (period 2005-2009). A: Villages where HAT cases were detected through passive surveillance (lilac circles) and villages where active case-finding activities were carried out (black dots). B: Disease stage ratio (S1: first stage, ...

The map shows that a sizable number of cases are reported by passive surveillance from an area north of Bodo (Chad) where no active screening was undertaken in the last five years. This indicates that, when planning for the next surveys, mobile teams should broaden the geographical scope of their activities to include this area of transmission.

Figure Figure6B6B allows to evaluate the NSSCP capacity for early case detection, which has vast implications for treatment outcome, people's welfare and long-term disease control. The ratio (S2 - S1)/(S1 + S2) is used, where S1 and S2 are the numbers of HAT cases in first and second stage respectively. We note that, predictably, cases reported from non-surveyed villages tend to display high rates of second stage infections ((S2 - S1)/(S1 + S2) > 0.50). It must be noted that there are gaps in the field 'disease stage' of the HAT DB, as stage is not always recorded and included in the reports. This explains why for a few of the lilac circles in (A) there are no corresponding circles in (B). In this respect, more recent data (i.e. 2005-2009) are comparatively more complete than older data (i.e. 2000-2004), due to improved recording and reporting.

Another example of the Atlas as a tool for monitoring and evaluation of control activities is in Figure Figure7,7, which shows a time series of maps for the focus of Mbini (Equatorial Guinea).

Figure 7
HAT focus of Mbini in Equatorial Guinea (period 2000-2009). Villages reporting HAT cases (red circles) and villages where active case-finding surveys were carried out (black dots).

Despite active case-finding surveys and subsequent treatment of detected cases, the focus of Mbini presents a fairly stable epidemiological pattern and the number of new infections does not show signs of abatement. The strategy for HAT control in the area should therefore be reinvigorated. Thanks to the HAT DB, another factor could be added to this analysis by estimating the ratio of 'number of people screened' to 'census', which would clarify whether poor performance in reducing the number of new HAT cases can be ascribed to insufficient coverage of active screenings, and therefore if sensitization activities should be contemplated.

Discussion

The dramatic development of geospatial technologies has revolutionized disease mapping by providing tools of unprecedented accuracy, coverage and affordability [43-45]. However, several other factors, many of which are disease-specific, impinge on the mapping of neglected tropical diseases. Epidemiology, ecology, diagnosis, treatment, as well as political and financial commitment, all contribute to shaping how global disease maps are or can be generated. If treatment is looked at, we notice that the existence of safe and affordable drugs against schistosomiasis and soil-transmitted helminthiases enables mass drug administration schemes to be implemented, thus making detection and mapping of individual cases unnecessary in many instances [46]; this fact, combined with the high prevalence of these infections, has led to mapping approaches based on reviews of community-based surveys, whose results can be extrapolated or interpolated to provide regional or global coverage [47-49]. Disease ecology is also central to mapping, in that it can help define bioclimatic and biogeographic envelopes within which a pathogen or its vector can occur. This is aptly demonstrated by rapid epidemiological mapping methods, first developed for targeting interventions against onchocerciasis [50,51], and now also proposed for soil-transmitted helminthiases [46], schistosomiasis [52], and loiasis [53]. Diagnostic methods, and their sensitivity, specificity and cost, can also have a major impact on disease mapping, as proved by the repercussions that a new diagnostic tool [54] had on global mapping of lymphatic filariasis [55,56] From an institutional standpoint, coordination at the international level is fundamental to develop protocols for systematic and comparable mapping, as demonstrated by such initiatives as the global Dracunculiasis Eradication Program [57], the Global Programme to eliminate lymphatic filariasis [58], the Onchocerciasis Control Programme in West Africa and the African Programme for Onchocerciasis Control [51].

All these factors also shape sleeping sickness mapping. The severity of HAT outcomes and the toxicity of available drugs require case-by-case diagnosis [59]. A complex interplay of ecological factors, not yet fully understood, determines the pronounced focal nature of HAT [22]. Notwithstanding variations in transmission intensity, the geographic distribution of sleeping sickness foci in Africa has proved fairly stable over time, especially for the Gambiense form, thus helping to target costly mass-screening activities to the known areas of endemicity. On the other hand, at a more local level, HAT also exhibits complex spatial and temporal dynamics, with abatements and resurgences [60,61], contractions, local eliminations [62], shifts and spreads [63], which call for a detailed representation in space and time. Another feature of sleeping sickness epidemiology that has relevance for mapping is the occurrence of disease-free villages lying alongside endemic ones. Heterogeneities at this fine scale make village-level mapping a necessity, both for disease control and management [10] and also in those contexts where the goal is local disease elimination [62]. The level of detail of this Atlas exceeds that of MDA programmes and is comparable with the level reached by the eradication programmes.

The importance of an adequate framework for coordination and collaboration among stakeholders can not be overstated. Several resolutions of the World Health Assembly calling to reinforce links with FAO and increase awareness in the international community [20,21] spurred WHO to bring together national health authorities in the affected countries and international partners (FAO/PAAT, NGOs, Research Institutes), thus making HAT global mapping possible. One of the many fields in which coordination contributes to global mapping is the WHO-promoted harmonization of survey and annual reports, which streamlined the flow of information and simplified data analysis.

When utilizing the outputs of the HAT Atlas initiative it is essential to consider that sleeping sickness is a disease of complex diagnosis and management, which occurs in remote rural areas where health facilities are weak or even absent. The risk of under-detection and underreporting is ever-present, due to possible lapses of the health information systems and difficulties in accessing some transmission areas because of remoteness or insecurity [64]. HAT can also be misdiagnosed with more common diseases presenting similar clinical signs and symptoms. The combined effect of these factors can confound the real status of the disease, and the risk of this happening was extremely high during the 1990s. However, this risk has since been reduced as a result of two major developments: (i) discontinuation of social upheavals and civil strife in most affected countries, which facilitated access to endemic areas; and (ii) intensified efforts by NGOs, bilateral cooperation and in particular by WHO that, thanks to a successful Public-Private Partnership, increased its support to affected countries, thus leading to strengthened HAT control and reporting. Keeping these efforts up will be crucial to sustain and scale up the achieved results.

Despite substantial progress in the accuracy and completeness of HAT reporting and mapping, a relatively small proportion of HAT reported cases (fewer that 10%) still can not be located at the village-level. Unravelling of these situations relies on progressive acquisition of more detailed information. The burdensome process of progressively filling the various gaps will continue, ultimately aiming at mapping all reported cases at the village level. Searches for data will only be discontinued if and where insurmountable hurdles to further improvement should arise. Importantly, all of the unmapped cases originate from known foci, so that their absence from the maps has a limited impact on the delineation of HAT transmission areas.

From the methodological standpoint, it must be stressed that no specific structure or large-scale data collection activity specifically aimed at global mapping was put in place for the Atlas of HAT. The Atlas ultimately relies on the reinforced network of NSSCPs, NGOs, Research Institutions and on the optimal use of HAT data available at WHO, coupled with the appropriate use of public-domain geographical databases.

Conclusions

The maps presented in this paper represent the state of knowledge of HAT geographic distribution in 23 out of 25 HAT-endemic countries that reported on sleeping sickness occurrence during the past ten years (period 2000-2009). These maps are the result of the coordinating and leadership role of WHO, the partnership with FAO/PAAT, and of the thorough analysis of unpublished epidemiological reports provided by all stakeholders involved in HAT control. These maps constitute a remarkable improvement on previous information available on this topic (e.g. [22]). The average mapping error (approximately 900 m), and the overall completeness (i.e. 90.3 percent of all reported cases were mapped) make the outputs suitable for a range of technical and scientific applications.

The Atlas of HAT provides more than maps. Ministries of Health, NGOs and Research Institutions can use the Atlas, and the HAT data repository that underpins it, to monitor the impact of control activities, assess epidemiological trends and plan control or research activities. Also, it will be possible to combine HAT maps with the increasing amount of geospatial datasets, including human and livestock population layers, tsetse distribution map, land cover datasets, etc. [65], with a view to improving risk assessment and generating evidence-based estimates of population at risk and disease burden. The HAT data repository will also help NSSCPs prevent data losses, which have been a frequent occurrence in the history of sleeping sickness, mainly due to the challenging circumstances in which HAT control is carried out.

Although initially designated to cover the period 2000 - 2009, the Atlas is a dynamic tool that will be regularly updated as new information becomes available. The present focus is on the completion of the continental map, by completing mapping in Angola and DRC. At the same time, the whole database will be refined by progressively including more precise geographic coordinates and attempting to map the remaining missing villages. Finally, HAT-silent countries (i.e. Burundi, Ethiopia, Gambia, Guinea-Bissau, Liberia, Niger, Senegal and Sierra Leone) will undertake HAT assessments to clarify their epidemiological status.

In order to maximize the impact and ensure sustainability of the HAT Atlas initiative, capacity at the country level will be strengthened through training and provision of equipment, so that optimal usage of the Atlas can be made by NSSCPs, and future yearly updating can be envisaged at the country-level. Capacity building will also aim at reinforcing the sense of ownership of this tool in HAT-affected countries. A range of dissemination media and outreach activities are anticipated, including an Atlas proper that will provide disease maps at different scales, as well as indexed and georeferenced endemic locations. At the end of the process, the Atlas will be made available in the public domain free-of-charge through WHO and FAO/PAAT websites.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

PPS coordinated the HAT Atlas initiative. GC supervised the technical aspects related to data management and GIS. PPS and GC jointly drafted the manuscript. PPS, JRF, AD, JAR and JGJ collated and screened epidemiological data used as input. GC, MP, EMF and PPS designed and developed the logical and physical structure of the database of HAT. MP, GC and FC implemented geo-positioning procedures and entered data. EMF and FC provided perspectives on HAT epidemiology. PPS, RCM and JGJ coordinated and supervised the collaboration between WHO and FAO in the framework of PAAT. All authors have contributed to conceptualizing the manuscript, and commented on and approved the final draft.

Disclaimers

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. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement.

The views expressed in this paper are those of the authors and do not necessarily reflect the views of WHO and FAO.

Acknowledgements

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 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 Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Congo, Côte d'Ivoire, Equatorial Guinea, Gabon, Ghana, Guinea, Kenya, Malawi, Mali, Mozambique, Nigeria, Rwanda, Sierra Leone, Sudan, Togo, Uganda, United Republic of Tanzania, Zambia and Zimbabwe; the NGOs "Médecins sans frontières", Epicentre, Malteser and Merlin; and the Research Institutions "Institut de Recherche pour le Développement", "Institut Pierre Richet" (Côte d'Ivoire), "Projets de Recherches Cliniques contre la Trypanosomiase" (Côte d'Ivoire), "Centre International de Recherche-Développement sur l'Élevage en zone Sub-humide" (Burkina Faso), The Centre for Infectious Disease, College of Medicine and Veterinary Medicine, The University of Edinburgh. This work also benefited from the HAT mapping activities carried out by Dr. P. Bureau and Mr. P. Lucas in Central Africa in the 1990s.

The work of GC was supported by two FAO projects: "Strengthening the Information System of PAAT" (GCP/RAF/403/IFA), and "Pro-poor Integrated Packages to Enhance Policy and Decision Making against the African Animal Disease Burden in sub-Saharan Africa" (GCP/RAF/442/IFA), both funded by the International Fund for Agricultural Development (IFAD). GC was also supported by WHO. Funds for MP's activities were provided by WHO and FAO. EMF was supported by the Wellcome Trust (085308) and WHO. FC was supported by the French "Ministère des Affaires Etrangères et Européennes" (project "Fonds de Santé Prioritaire - Recherche en Entomologie Formation et Stratégie").

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