_ Biogeographical patterns of African trypanosomoses for improved planning and implementation of field interventions _

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Biogeographical patterns of African trypanosomoses for improved planning and implementation of field

interventions

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Giuliano Cecchi

Université libre de Bruxelles

École Interfacultaire de Bioingénieurs Laboratoire de lutte biologique et écologie spatiale

Thèse présentée en vue de l’obtention du grade de Docteur en sciences agronomiques et ingénierie biologique

Supervisor: Marius Gilbert

2011

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Acronyms

AAT African Animal Trypanosomosis

AU-IBAR African Union Interafrican Bureau for Animal Resources AW-IPM Area-Wide Integrated Pest Management

CAR Central African Republic

CIAT International Center for Tropical Agriculture

CIESIN Center for International Earth Science Information Network DALY Disability-Adjusted Life Year

DDT Dichloro-Diphenyl-Trichloroethane

FAO Food and Agriculture Organization of the United Nations GIS Geographic Information System

GLC2000 Global Land Cover for the year 2000 GRUMP Global Rural–Urban Mapping Project HAT Human African Trypanosomosis IAEA International Atomic Energy Agency

IFAD International Fund for Agricultural Development IFPRI International Food Policy Research Institute LCCS Land Cover Classification System

NGO Non-Governmental Organization

NSSCP National Sleeping Sickness Control Programme PAAT Programme Against African Trypanosomosis

PAAT-IS Programme Against African Trypanosomosis-Information System PATTEC Pan African Tsetse and Trypanosomosis Eradication Campaign REMO Rapid Epidemiological Mapping Methods

SAT Sequential Aerosol Technique

SPOT Satellite Pour l’Observation de la Terre

WB World Bank

WHO World Health Organization

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Abstract

Spatially-explicit information is essential for planning and implementing interventions against vector-borne diseases. This is also true for African trypanosomoses, a group of diseases of both humans and animals caused by protozoa of the Genus Trypanosoma, and transmitted by tsetse flies (Genus Glossina).

In this thesis the knowledge gaps and the requirements for an evidence-based decision making in the field of tsetse and trypanosomoses are identified, with a focus on georeferenced data and Geographic Information Systems (GIS). Datasets, tools and analyses are presented that aim to fill some of the identified knowledge gaps.

For the human form of the disease, also known as sleeping sickness, case detection and treatment are the mainstay of control, so that accurate knowledge of the geographic distribution of infections is paramount. In this study, an Atlas was developed that provides village-level information on the reported occurrence of sleeping sickness. The geodatabase underpinning the Atlas also includes the results of active screening activities, even when no cases were detected.

The Atlas enables epidemiological maps to be generated at a range of scales, from local to global, thus providing evidence for strategic and technical decision making.

In the field of animal trypanosomosis control, also known as nagana, much emphasis has recently been placed on the vector. Accurate delineation of tsetse habitat appears as an essential component of ongoing and upcoming interventions against tsetse. The present study focused on land cover datasets and tsetse habitat. The suitability for tsetse of standardized land cover classes was explored at continental, regional and national level, using a combination of inductive and deductive approaches. The land cover classes most suitable for tsetse were identified and described, and tailored datasets were derived.

The suite of datasets, methodologies and tools presented in this thesis provides evidence for informed planning and implementation of interventions against African trypanosomoses at a range of spatial scales.

This thesis draws on 5 core papers, as well as on a few ancillary technical and scientific publications. Paper I describes the methodology to develop the continental Atlas of sleeping sickness (Cecchi et al. 2009b). In Paper II the

methodology presented in Paper I is applied at a regional level in Western Africa (Cecchi et al. 2009a). In Paper III results at the continental level for the methodology illustrated in Paper I are presented (Simarro et al. 2010). Paper IV presents a methodology to map the risk of T. b. gambiense infection and the results of its application in 6 countries of Central Africa (Simarro et al. 2011a). In Paper V a combination of inductive and deductive approaches are used to explore the patterns of association between land-cover and the habitat of the tsetse fly (Cecchi et al. 2008a). Full details on the core and ancillary publications are in the Annex at Page 32.

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Resumé

L’information spatialisée est essentielle pour planifier et mettre en œuvre les interventions de lutte contre les maladies transmises par vecteur. Cela s'applique en particulier pour les trypanosomoses africaines, un groupe de maladies qui affecte à la fois les humains et les animaux. Ces maladies sont causées par des protozoaires du genre Trypanosoma, et sont transmises par les mouches tsé-tsé (Genus Glossina).

Cette thèse a pour objectif d'identifier les lacunes dans l'état des connaissances sur la biogéographie des trypanosomes et d'identifier les besoins en matière de données spatialisées, en terme de bases de données géo-référencées et de Systèmes d’Information Géographique (SIG). La thèse présente les données, les outils et des analyses qui visent à combler certaines des lacunes identifiées.

Pour la forme humaine de la maladie, connue sous le nom de "maladie du sommeil", le dépistage et le traitement des cas forment la base des stratégies de contrôle. Par conséquent, il est fondamental de connaître avec précision la répartition géographique des infections. Dans cette étude, nous avons développé un Atlas qui fournit des cartes de distribution des cas rapportés de trypanosomose humaine africain à l’échelle du village. La base de donnée géographique développée pour constituer cet Atlas inclut également les résultats des activités de dépistage active, même lorsque aucun cas n’a pas été identifié. L’Atlas permet de produire des cartes épidémiologiques à différentes échelles, du local au continental et permet ainsi d'améliorer la prise de décisions techniques et stratégiques de contrôle.

Dans le domaine du contrôle des trypanosomoses animales, l’accent est généralement posé sur la lutte contre les vecteurs. Par conséquent, les interventions actuelles et futures contre la trypanosomose animale nécessitent une cartographie précise de l’habitat des mouches tsé-tsé. Notre étude se concentre sur la relation entre les données de couverture du sol ("landcover") et l’habitat des glossines. Nous avons étudié les habitats favorables aux vecteurs en utilisant des classes de couverture du sol standardisées à l’échelle continentale, régionale et nationale. La relation entre ces classes et l'habitat des mouches tsé-tsé est établie à l'aide d'une combinaison d’approches inductives et déductives. Les classes de couverture du sol les plus favorable aux tsé-tsé sont identifiées et décrites, ce qui permet de produire de nouvelles couches d'information dérivées concernant l'habitat de ces vecteurs.

L'ensemble des données, méthodes et outils présentés dans cette thèse offre un support à une meilleure planification et mise en œuvre des interventions contre les trypanosomoses africaines à différentes échelles spatiales.

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1 Introduction

Traditionally, biogeography has been defined as the study of the past and present distributions of organisms, and it now encompasses the broader goal of documenting and understanding the spatial patterns of biological diversity (Lomolino et al. 2006).

The geographic and ecological distribution of species, including pathogens, can be seen in terms of interactions among abiotic requirements, biotic constraints, and dispersal abilities of species (Peterson 2008). However, frameworks for thinking about species distributional patterns (Soberon and Peterson 2005) encounter particular challenges when applied to disease biogeography (Peterson 2008). These include the particular importance of interaction among species, stability or instability of interactions and variability of dispersal abilities. As a result, compared to macro-organisms, relatively little is known about whether human pathogens conform to more common biogeographic patterns (Smith 2009). This is, in part, also due to a lack of synthesized historical, epidemiological, and geographical data on the pathogens that infect humans (Woolhouse and Gowtage-Sequeria 2005).

The studies summarized in this thesis focus on African trypanosomoses and on geospatial datasets and tools, which have revolutionized disease mapping by providing tools of unprecedented accuracy, coverage and affordability (Kitron 1998; Bergquist 2001; Rogers and Randolph 2003). As a neglected disease affecting the poor, trypanosomosis is typical in the challenges it poses to data collection, reporting and harmonization. Also, as a group of vector-borne, zoonotic diseases, African trypanosomoses illustrate well the complex interplay between pathogens, hosts (both human and animal) and environment. This interplay shapes disease epidemiology and influences options for disease control. Depending on the specific circumstances, interventions against trypanosomosis will focus either on the parasite, the vector, the host or the environment. Consequently, priorities in terms of information requirements for decision making may vary, as illustrated by the case studies included in the present thesis.

1.1 African trypanosomoses

The problem of tsetse and trypanosomoses provides a classic example of a parasitic disease whose biogeography is shaped by a complex interplay of epidemiological features and environmental patterns. Tsetse-transmitted trypanosomosis is a major constraint to socio-economic development in sub-Saharan Africa, and it also represents a public health issue that requires accurate information in space and time if sound decisions on its control are to be made.

African trypanosomoses are caused by protozoa of the Genus Trypanosoma Gruby, 1843 (Stevens and Brisse 2004). As established over a century ago (Bruce 1895), trypanosomes are transmitted by the tsetse fly (Genus Glossina Wiedemann, 1830). Although other species of haematophagous biting flies can be involved in mechanical transmission, the tsetse fly is the only cyclical

vector, and it sustains transmission in endemic areas of Africa (Luckins and Dwinger 2004).

Trypanosomosis in humans, also known as sleeping sickness, is caused by two sub-species of Trypanosoma brucei: T. b. gambiense (Dutton 1902), which is the agent of a late-onset, chronic form that is endemic in Western and Central Africa, and T. b. rhodesiense (Stephens and Fantham 1910), which is responsible for an early-onset, acute disease found in Eastern and Southern Africa. Both forms are known to be lethal in the absence of treatment (Brun et al. 2010). The disease is unique to Africa, although cases are sporadically reported from other continents, especially among tourists and migrants (Simarro et al. In press).

Rhodesiense sleeping sickness has clear zoonotic connotations, having been transmitted experimentally to humans from both wild (Heisch et al. 1958) and domestic animals (Onyango et al. 1966). Moreover, livestock and wildlife are known to be able to play important roles both during epidemics and as reservoirs (Hide 1999; Fèvre et al.

2001; Kaare et al. 2007). Gambiense sleeping sickness is primarily anthroponotic, even though the role played by hosts other than humans still awaits clarification (Njiokou et al. 2006; Simo et al. 2006).

Human African trypanosomosis (HAT) is to a large extent a disease of rural areas, where human and the tsetse flies carrying the parasite come into contact. However, it is not infrequent that cases be reported from urban and peri- urban areas, such as Kinshasa and Luanda (Cattand et al.

2001; Ebeja et al. 2003). Although most of these infections may originate in neighbouring rural areas (Robays et al.

2004), local transmission in urban and peri-urban areas can not be ruled out (Grébaut et al. 2009), especially in the light of the high adaptability of some species of tsetse to man-made environments (De Deken et al. 2005).

African animal trypanosomosis (AAT or nagana) is a group of diseases of ruminants, camels, equines, swine and carnivores caused by T. congolense, T. vivax, T. simiae, T.

b. brucei and T. suis. The major pathogenic species in African cattle are T. congolense, T. vivax, and, to a lesser extent, T. b. brucei (Taylor and Authié 2004).

Both human and animal trypanosomosis pose major constraints on African development. The burden of HAT across Africa was first estimated for the year 1990 at 1.78 million disability-adjusted life years (DALYs) (World Bank 1993). More recent estimates for the years 2001 and 2002 range from 1.33 to 1.60 million DALYs (World Health Organization 2002; World Health Organization 2003b; Lopez 2006). The latest global estimates indicate that 60 million people are at risk of contracting the disease (World Health Organization 1998).

AAT constrains agriculture in the areas of Africa that hold the continent’s greatest potential for expanded production (Swallow 2000). The disease directly impacts on livestock productivity by increasing mortality and reducing fertility, milk yield and ability to work as traction animals. Indirect effects are related to the limits AAT poses on production opportunities (e.g. choice of livestock breeds, use of animal traction, grazing patterns and migration) (Shaw 2004).

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1.2 Tsetse flies

Tsetse flies are classified into one genus, Glossina, family Glossinidae, Order Diptera. 31 species and subspecies are identified (Rogers and Robinson 2004), which are grouped into three subgenera: Austenina (fusca group), Nemorhina (palpalis group) and Glossina s.s. (morsitans group). Classification of tsetse is largely based on morphological differences in the structure of genitalia (Newstead 1911a; Newstead 1911b), but subgeneric classification is also related to habitat (Leak 1998).

Tsetse flies, both males and females, are obligate blood feeders, and their hosts include mammals and reptiles.

Early in evolutionary time, the latter may have been their original hosts. The fly’s ability to feed on wildlife, livestock and humans alike is responsible for the zoonotic nature of African trypanosomosis.

Tsetse adult females produce one single egg at a time, which hatches and develops to a larva in the uterus.

Thereafter, the larva is deposited and it pupates in the soil. After a temperature-dependent puparial period, the adult fly will emerge. Females only produce one full- grown larva every 9-10 days, so that only a few offspring are produced by each female. In this respect, tsetse flies differ from most other insects, which are normally characterized by higher rates of reproduction.

As for may other insects, temperature and humidity regulate may aspects of tsetse biology, to the extent that climate is arguably the most important factor controlling the basic pattern of distribution at the macro-scale (Leak 1998). At a local scale, hosts availability and the presence of suitable vegetation determine the overall habitat suitability. In particular, the association between tsetse and vegetation types has been the subject of much research, because of its relevance in identifying areas at risk for trypanosomoses and in determining priorities for control.

Tsetse vegetation requirements can be defined with a fair level of accuracy at the level of subgenus (group).

The fusca group is predominantly constituted by forest flies, and in evolutionary terms it is generally considered as the most primitive. The palpalis group is referred to as riverine group, and it may have arisen because of climatic changes consisting of alternating wet and dry periods, which determined expansions and contractions of forested areas (Machado 1954) that would have caused separation and speciation of tsetse populations (Challier 1973). The morsitans group, which is normally referred to as the savannah group, might have evolved from the fusca group to adapt to drier environments and the appearance of more open vegetation (Bursell 1958).

Tsetse flies are the only known cyclical vector of trypanosomosis. Other biting flies (e.g. Stomoxys, tabanids), are know to be able to transmit trypanosomes mechanically, although it is unclear whether they can

sustain transmission in the absence of the cyclical vector.

As a rule, tsetse rates of infection with trypanosomes are low. Rates are particularly low with trypanosomes infective to humans, which rarely exceed 0.1 percent.

Infection rates for livestock-infective trypanosome species can be higher by one or two orders of magnitude. The differential capacity to carry human- or animal-infective parasites may contribute to explaining the more widespread distribution of the animal form of the disease as compared to the human form. Differences also exist in the trypanosome infection rates exhibited by different tsetse groups and species, with flies of the morsitans group typically showing rates of 10-15 percent, as compared to the 5 percent normally displayed by flies of the palpalis group (Jordan 1986).

The fusca group has received the attention of much fewer studies, as its distribution and habitat requirements make it a marginal player in the epidemiology of human and livestock trypanosomoses.

1.3 Control of African trypanosomoses

Different tools, and combinations thereof, can be used to control African trypanosomoses. Interventions can target the parasite, through chemotherapy and/or chemoprophylaxis, the vector, through tsetse control and/or elimination, or the host, through host management and/or the use of trypanotolerant breeds.

1.3.1 Parasite control

Surveillance, screening, diagnosis and treatment of infected individuals with curative drugs are at the core of sleeping sickness control (Simarro et al. 2008). Most of the available drugs to treat HAT were developed many decades ago, and all of the current therapies are unsatisfactory for various reasons, including unacceptable toxicity, poor efficacy, undesirable route of administration, and drug resistance (Fairlamb 2003). Also, it is unlikely that new medicines against HAT will become available soon, and recent efforts have focused on finding optimum therapeutic regimens and on development of combination therapy with drugs already registered or those used to treat related diseases (Brun et al. 2010).

For AAT, conventional parasite control measures are based on the use of curative (chemotherapy) and preventive (chemoprophylaxis) drugs. In areas where large-scale, structured control operations are not undertaken, drugs are often the sole tool farmers can use to limit the impact of nagana on their animals.

Isometamidium chloride, diminazene aceturate and homidium (bromide and chloride) are the only three drugs available, and these compounds have been on the market for over four decades. Although the demand for trypanocides by African farmers is high, the cost of developing new compounds stops pharmaceutical companies from pursuing the quest for new drugs (Holmes et al. 2004).

The development and spread of drug resistance in parasite populations is perhaps the greatest risk to the sustained use

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of existing trypanocides (Holmes et al. 2004). Until 1998, resistance to one or more of the trypanocidal drugs used in cattle had been reported in 13 countries of sub-Saharan Africa, including Burkina Faso, Central African Republic, Chad, Côte d'Ivoire, Ethiopia, Kenya, Nigeria, Somalia, Sudan, United Republic of Tanzania, Uganda, Zambia and Zimbabwe (Geerts and Holmes 1998). Subsequently, resistance has also been reported from Mali (Diall et al.

2003), Cameroon (Mamoudou et al. 2008), Ghana (Allegye - Cudjoe et al. 2009), Guinea (Barry 2007) and Nigeria (Mamman and et al. 2011).

1.3.2 Vector control

Tsetse populations can be targeted either directly, by affecting their mortality and/or fertility, and indirectly, by acting on their hosts and/or on their habitat.

Insecticides, by increasing natural tsetse mortality rates, are at the basis of most tsetse suppression measures.

Organochlorines, notably dichloro-diphenyl- trichloroethane (DDT) and dieldrin, and more recently the synthetic pyrethroids, are the two chemical groups that have enjoyed the wider success in the control of tsetse flies (Allsopp et al. 2004). Delivery methods include (i) depositing residual insecticide, either discriminatively (on to the resting and breeding sites of tsetse by ground spraying) (Davies 1967) or less selectively (treating the entire habitat from the air); (ii) application of insecticides on animals (epicutaneous use, the so-called pour-on or live-bait technique) (Hargrove et al. 2000); (iii) repeated spraying of non-residual insecticide, either over large areas using aircraft (sequential aerosol technique - SAT) (Allsopp and Phillemon-Motsu 2002) or in more localized areas using hand-held or vehicle mounted fogging machines; (iv) attracting tsetse to devices such as screens or traps treated with insecticide (Vale 1993; Van den Bossche et al. 2004).

As opposed to insecticides, which affect mortality, the Sterile Insect Technique affects birth rate (Klassen and Curtis 2005). Reproductively sterile male insects are released among the indigenous target population so that, upon insemination, female insects become infertile for the remainder of their lifespans (Feldmann 2004).

In the past, some tsetse control methods targeted the tsetse habitat and hosts, and included ruthless (Hocking et al.

1963) or partial clearing of vegetation (Ford et al. 1970), as well as elimination of wild animals (Wooff 1968).

These methods are no longer deliberately used to control tsetse, although anthropogenic land cover change and reduction of wild animal populations may have similar effects (Bourn et al. 2001).

Tsetse control has long been an important option for reducing the impact of African trypanosomosis and many methods have been used successfully. Sustainability of the results following interventions has often proved much more challenging (Jordan 1986; Allsopp 2001).

1.3.3 Host management

Host management was the mainstay of trypanosomosis control in pre-colonial times, and it continues to play an

important role to date. Host management may imply avoidance of tsetse infested areas (Kjekshus 1977), or limited but continued contact to favour the emergence of a degree of immunity (Ford 1971).

In addition to host management, trypanotolerance is another host-related feature that plays an important role in trypanosomosis control. Certain breeds of African cattle posses the ability to survive and to be productive in tsetse- infested areas where other breeds rapidly succumb to the disease. This trait is termed trypanotolerance (Murray et al. 2004), and it is usually attributed to the Bos taurus.

Trypanotolerant livestock play a significant role in the control of trypanosomosis in that their use in tsetse- affected areas allows livestock production and related development to occur that would otherwise not be possible with other breeds (Agyemang 2005).

1.3.4 The present situation of African trypanosomoses control

Despite having ranked high in the list of priorities of colonial powers (Lyons 1992), African trypanosomoses have been seriously neglected in recent decades (Smith et al. 1998; Veeken and Pécoul 2000; Barrett et al. 2007), to a large extent because they affect the health and livelihoods of rural poor with little voice in the public arena (Mattioli et al. 2004). However, important initiatives at the international level are presently trying to redress this, by setting ambitious targets for tsetse and trypanosomoses control at the continental level.

In 1997, the 29^th Conference of the Food and agriculture organization of the United Nations (FAO) established the Programme against African Trypanosomosis (PAAT) (Resolution 5/1997), bringing together the efforts of FAO, the World Health Organization (WHO), the International Atomic Energy Agency (IAEA) and the African Union Interafrican Bureau for Animal Resources (AU-IBAR).

PAAT promotes concerted international action to assist African affected countries in their efforts to control and eventually eliminate this devastating disease (Hursey 2001).

At the same time, successive resolutions of the World Health Assembly prompted WHO to strengthen its support to countries affected by sleeping sickness (World Health Organization 1997; World Health Organization 2003a), with the objective to eliminate HAT as a public health problem and to establish sustained surveillance systems in all disease-endemic countries.

In parallel, decisions of the Heads of State and Government of the African Union in 2000 (AHG/156 – XXXVI) and 2001 (AHG/169 – XXXVII) endorsed and committed to the plan of action for the Pan-African Tsetse and Trypanosomosis Eradication Campaign (PATTEC) (Kabayo 2002). PATTEC objective is to render Africa tsetse and trypanosomosis-free.

1.4 Making evidence-based decisions:

information requirements and knowledge gaps

The ongoing important initiatives against tsetse and trypanosomosis need reliable epidemiological information

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at a range of spatial scales, from continental to local.

However, knowledge gaps in the biogeographical patterns of African trypanosomoses pose serious constraints to an evidence-based decision making. This section summarizes the typology of decisions that should be based on geospatial evidence, the information requirements for decision making and the knowledge gaps.

1.4.1 What kind of decisions

The range of decisions to be made when planning, implementing and monitoring interventions against African trypanosomoses is broad. It includes both strategic decisions, such as the choice of priority areas for intervention (Mattioli et al. 2004), and technical decisions such as the identification of the control tools that are most appropriate in each epidemiological setting. Many of these decisions should be based on, or at least be informed by, geo-referenced evidence, and they would benefit from a deeper knowledge of the biogeography of African trypanosomoses.

In essence, decisions on interventions against tsetse and trypanosomosis requiring geospatial datasets can be grouped into two broad categories: where to intervene, and how to intervene.

1.4.1.1 Where to carry out interventions against African trypanosomoses

The question of “where to intervene against African trypanosomoses” is a complex, multi-faceted one and it can be asked at different geographical scales. For a continental-level initiative such as PATTEC, priority countries for intervention need to be selected in a coherent manner in order to address the transboundary nature of the disease. However, the question “in which countries?” can not be disconnected from the selection of specific intervention areas. This is because the stated goal of PATTEC, i.e. eradication, would call for interventions based on the concept of area-wide integrated pest management (AW-IPM), whereby isolated, or isolable, pest populations are targeted to create, and progressively expand, pest-free zones (Vreysen et al. 2007).

At the same time, at a national level each country needs to prioritize actions on the basis of a broad spectrum of public health and socio-economic considerations, which can also point to intervention zones where AW-IPM may not be readily applicable.

In the specific case of HAT, there is a clear need to rationally allocate limited resources by identifying priority areas for intervention. On the other hand, ethical principles impose to control the disease wherever it occurs, and to limit the risk of it spreading into free areas.

1.4.1.2 How to intervene against African trypanosomoses

Several technical decisions on how to intervene against African trypanosomoses should be grounded on geospatial datasets.

For HAT, knowledge of the local levels of disease incidence and prevalence, as well as an understanding of the role of livestock and wildlife reservoirs may help define the optimal frequency of active screening activities,

the strategy for passive surveillance, and the emphasis to be placed on vector control and/or animal reservoir management.

For AAT, geo-referenced information on epidemiological patterns (e.g. structure of tsetse populations, levels of disease endemicity, etc.) and socio-economic and environmental datasets should contribute to identifying which control technique (or combination thereof) is the most appropriate for selected intervention areas.

1.4.2 Information requirements

Schematically, data requirements for an evidence-based decision making in trypanosomoses control can be grouped into two categories: (i) data on trypanosomosis (both human and animal) and its biological vector (tsetse fly), and (ii) data on hosts (both human and animal) and other socio-economic and environmental factors. While for the latter, data providers are diverse and ever growing (Cecchi and Mattioli 2009b), for the former, the responsibility for data collection and management is vested in a relatively restricted circle of stakeholders, including national health and veterinary authorities, international organizations and research institutions dealing with the tsetse and trypanosomosis problem. This work focuses on the first category, but a review of geospatial datasets for African trypanosomosis management is provided in (Cecchi and Mattioli 2009b).

In a nutshell, decisions on where and how to intervene against trypanosomoses should be grounded on recent and accurate data on the occurrence of the diseases and its vector. More detailed information requirements are included in the section “Materials and Methods” (§ 3).

1.4.3 Knowledge gaps

The latest continental maps of sleeping sickness foci and estimates of people at risk were provided in 1995 by a WHO Expert Committee (World Health Organization 1998). This information was based on the opinion of experts, and the need for a systematic, spatially-explicit approach to sleeping sickness data management is therefore well recognized (Cattand et al.

2001); I.

As concerns AAT, there is currently no detailed and consistent spatial dataset on the presence and occurrence of the disease in sub-Saharan Africa (Cecchi and Mattioli 2009b; Cecchi et al. 2011a), to the extent that global estimates of domestic animals at risk (Kristjanson et al. 1999; Gilbert et al. 2001; Cecchi and Mattioli 2009b) can only be based on the predicted presence of tsetse fly rather than on the intensity of disease transmission. Also the maps of environmental suitability for tsetse at the continental level (Wint and Rogers 2000b; Rogers and Robinson 2004) are, to a large extent, based on data collated decades ago (Ford and Katondo 1975; Ford and Katondo 1977a; Ford and Katondo 1977b) and the need to assemble more recent and accurate data is urgent (Cecchi et al. 2011b).

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1.4.4 Data users

The knowledge gaps identified above can not be adequately addressed unless the perspective of the end- users is not given the highest prominence. Who does utilize geospatial data for decision making in the field of tsetse and trypanosomosis interventions? What are their priorities and needs? What skills are available with stakeholders in affected countries to make good use of the developed GIS datasets and techniques? How can dissemination and uptake of these tools be promoted?

For HAT, the main stakeholders involved in disease control are the National Sleeping Sickness Control Programmes (NSSCPs), Non-Governmental Organizations (NGOs) and Research Institutes, with WHO providing coordination at the international level.

Because of the complexities inherent in HAT control (from case detection to treatment), the structure of NSSCPs is very often strongly centralized at the national level, and NSSCPs are often responsible for planning and conducting active screening activities, promoting passive surveillance and ensuring treatment of affected individuals. To effectively discharge these functions, NSSCPs need accurate information on the distribution of HAT cases in space and time, and on the level of endemicity thereof. The common utilization of hand-drawn, sketchy maps of HAT foci demonstrates that the need for spatially-explicit epidemiological data is strongly felt at the field level. However, GIS skills within NSSCPs are very limited and capacity building in this technical area is strongly needed. In this context, tools developed to support NSSCPs’ activities must be user-friendly and clearly documented. In addition, uptake must be promoted through training courses and workshops and the utilization of customized applications, preferably based on Freeware Open Source Software (e.g. Quantum GIS (Quantum GIS Development Team 2011)). Preferential use of Open Access publishing can also enhance dissemination and maximize accessibility.

For AAT, National Animal Health Authorities (e.g.

Ministries of livestock, veterinary extension services, etc.) have the responsibility of planning and implementing large-scale operations against animal trypanosomosis and tsetse flies. Their information needs for decision making include guidelines for baseline data collection (Leak et al. 2008), as well as GIS datasets and techniques for analysis and decision making (Cecchi and Mattioli 2009a). As a rule, GIS skills within National Animal Health Authorities are higher than within NSSCPs. Many national projects for tsetse and trypanosomosis control or elimination employ or recruit GIS experts (Koudougou et al. 2009) or have access to GIS expertise (Bouyer et al. 2010;

Adam et al. In press), although this is not always the case. The main challenge in the use of GIS for AAT control is to build the capacity where it is not available.

Where adequate skills are available, efforts towards harmonization of methodologies and information sharing are necessary. Regardless of the initial GIS expertise, optimal use of available public-domain

datasets must be promoted, as a cost-effective, convenient route to promote uptake of GIS tools.

2 Objectives

2.1 General objective

The general objective of the present study was to develop methodologies, datasets and tools based on Geographic Information Systems (GIS) to improve our knowledge of the biogeographical patterns of African trypanosomoses.

The methodologies, datasets and tools must help to improve planning, implementation and monitoring of interventions against African trypanosomoses at a range of geographical scales, from continental to local.

2.2 Specific objectives

For HAT, the specific objective was to develop methodologies to map the geographic distribution of the disease and the patterns of disease risk.

For AAT, the specific objective was to explore how standardized, public-domain land cover datasets can be used to map tsetse habitat and assist planning and implementation of interventions against AAT.

3 Materials and methods

The objectives of the present study were identified considering the information requirements and knowledge gaps in the field of tsetse and trypanosomosis decision making, as summarized in § 1.4. In view of the presence of trypanosomoses in 37 African countries, priority was given to methodologies, datasets and tools with a potential for application at the continental and regional levels. In this regard, the growing range of public domain, global geospatial datasets commanded attention. Furthermore, efforts were made to ensure that methodologies be scalable so that they could also be applied at a local level, and therefore have an impact on interventions in the field.

The important differences between HAT and AAT in relation to epidemiology, data availability and prevailing control strategies are at the basis of the different methodologies developed in this study.

From the epidemiological standpoint, HAT exhibits a markedly focal geographical pattern, while AAT is much more widespread across the entire tsetse belt (Jordan 1986). As concerns availability of epidemiological datasets, WHO has ensured collation of HAT data over many years, thereby laying the foundations for a systematic mapping endeavour (I).

For AAT and tsetse, longer term efforts will be needed to achieve comparable results in the fields of data collection, collation and harmonization (Cecchi et al.

2011a; Cecchi et al. 2011b). Finally, case-detection and treatment are the cornerstones of HAT control. By contrast, one of the prevailing strategies for sustainable AAT control hinges on the creation and progressive expansion of tsetse-free zones (e.g. PATTEC).

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3.1 Human African trypanosomosis

Available global maps of HAT distribution provide brush- brush representations of the limits of HAT foci (World Health Organization 1998). This type of qualitative information layer proved of great value in the past, but it is not adequate to underpin present control efforts aiming at the elimination of HAT as a public health problem. The success of these efforts will depend, among many other factors, on the availability of up-to-date and accurate information on the geographic distribution of infections.

Despite the neglect suffered by HAT, a substantial amount of data on its occurrence has been collected over many years by NSSCPs, NGOs and Research Institutes. These data were progressively collated by WHO, as part of its coordinating actions of HAT control activities at the continental level. Data available at WHO span over many years, and provided a solid starting point for a global mapping initiative. However, heterogeneities in data formats and data gaps, which affected in particular geographic coordinates of endemic locations, called for the development of a more systematic approach to HAT data management and mapping.

In the next section the requirements of an optimal information system for HAT global mapping are outlined.

A proposed methodology follows, which provides pragmatic solutions to the problem (I). Subsequently, the requirements for novel HAT risk estimations are listed, and a GIS-based methodology for risk mapping is proposed (IV).

3.1.1 Requirements for mapping HAT distribution

Ideally, a methodology to map the distribution of sleeping sickness should meet a series of requirements:

• To include all infections

Because of high mortality rates and the toxicity of existing drugs that precludes preventive chemotherapy, HAT control hinges on the detection and treatment of individual infections. Therefore, the prevailing strategies for HAT control call for mapping approaches based on geo-location of each individual diagnosed with the disease.

• To include several locations of epidemiological relevance for each infected individual.

Several locations are important, and should ideally be recorded, in the history of a sleeping sickness case.

Among these, the place of infection, the place of residence, the place of diagnosis and the place of treatment.

• To have the highest possible level of geographical accuracy

Geo-positioning of the locations of epidemiological relevance should be as accurate as possible. If the georeferencing of the places of residence is taken as an example, geographic coordinates of the exact address of residence should ideally be sought. In the absence of these, mapping should be at least at the village-level (i.e.

coordinates of the village, neighbourhood, town or city of residence should be used). The need to have at least

village-level mapping stems from the frequent occurrence of disease-free villages lying alongside disease-endemic ones. Also, planning and implementation of HAT control activities crucially depend on village-level information (e.g. planning active screening missions).

• To include the geographic accuracy of recorded information

Recording the estimated accuracy of the geographic coordinates is useful when deciding at which scale data can be utilized. It also enables to account for this source of uncertainty when developing geospatial prediction models.

• To include results of active screening activities Regardless of whether the active screening mission detected HAT cases or not, results of all active screenings should be included in the maps, as this will provide guidance on where and how to conduct further active screenings and to carry out passive surveillance.

• To include as much epidemiological information as possible

Epidemiological information on each sleeping sickness case should be recorded, including disease stage, sex and age of the patient, drug used, treatment outcome, etc...

• To be applicable at different geographical scales In the control of a transboundary disease such as HAT, decisions are made at different geographical scales ranging from local to continental. Therefore, data harmonization should ensure that consistent maps can be generated at all scales of interest.

3.1.2 A methodology to map HAT distribution

In the light of the above requirements, and considering the amount, accuracy and format of the input data available, a methodology to map the distribution of HAT at the continental level was developed under the umbrella of the

“Atlas of HAT” initiative (I). The key points of the methodology are summarized in this section.

The vast amount of unpublished epidemiological reports and datasets collected by NSSCPs, NGOs and Research Institutes and collated by WHO provided the epidemiological information used as input. Additional sources (e.g. gazetteers (Dooley 2005), etc.) and methodologies (direct communications with NSSCPs staff, workshops, etc...) were used to complement the geographic information (i.e. the geographic coordinates) available in the epidemiological data sources, as well as to validate the output maps.

Selected input data were imported into a single geodatabase, thereby taking a crucial step towards harmonization. All HAT cases reported over a 10-year period (2000-2009) were imported, as well as the results of all active screening activities. A substantial amount of data was also collated by WHO for the years prior to 2000, but a 10-year time frame was considered sufficient to inform a broad range of strategic and technical decisions.

Information on the village of residence of patients was available for the vast majority of HAT reported cases,

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regardless of whether the infection was detected through active screening or by referral health facilities in charge of passive surveillance. Therefore, the village of residence was chosen as the basic geographic unit for mapping.

As demonstrated by similar regional and global mapping efforts (Michael et al. 1996; Brooker et al. 2000; Guerra et al. 2007), the choice of a unifying variable is a fundamental step to map the limits and risk of infection.

For the geo-database underpinning the Atlas of HAT “the number of new HAT cases reported from a given village in a given year” was chosen as the unifying variable. A set of selected ancillary data was also recorded, including the infective agent (i.e. T. b. gambiense or T. b. rhodesiense), disease stage (i.e. first – haemolymphatic - or second – meningoencephalitic - stage), surveillance type (either active or passive) and, in case of active screenings, census and number of people screened.

Semi-quantitative estimates of mapping accuracy enabled locations to be ranked in four broad categories of accuracy, ranging from “high” (i.e. mapping error of approximately 50 m) to low (mapping error of approximately 5 km). These categories were used to estimate the average accuracy of output maps.

3.1.3 Requirements for HAT risk estimation and mapping

In order to improve on previous global estimates of disease risk (World Health Organization 1998) and to assist planning, monitoring and evaluation of HAT control activities, a novel methodology to map the risk of HAT should be:

• Based on evidence

Rather than expert opinion, epidemiological evidence and an objective and transparent methodology should underpin risk estimates.

• Spatially-explicit

The latest available estimates of disease risk provided country-level figures, but had otherwise no explicit geographical dimension. The methodology to estimate risk should be able to describe how risk varies in space, and therefore be based on GIS tools.

• Applicable at a range of scales

A major impetus to developing a novel methodology for HAT risk estimation came for the need to update the estimates at the national and continental levels provided by a WHO expert committee (World Health Organization 1998) and extensively cited in the literature. However, risk estimates (and risk maps) are needed at a range of scales, down to the local, focus-level, in order to inform planning and implementation of field activities (e.g. to determine the optimal frequency of active screening activities, to define surveillance strategies that be appropriate to the different levels of endemicity, etc.).

• Applicable to both forms of sleeping sickness Although characterized by considerable epidemiological differences, gambiense and rhodesiense sleeping sickness have nonetheless important commonalities, and it is

therefore desirable to be able to generate risk estimates that be comparable between the two forms.

• Updatable

Risk estimates should be updated regularly (e.g. yearly) so that improvement or worsening of the epidemiological situation can be reflected in the risk estimates.

3.1.4 A methodology to map HAT risk In an attempt to address the above requirements, a methodology was developed to estimate and map the risk of HAT infection. The methodology was tested in 6 countries of central Africa (Cameroon, Chad, Central African Republic, Equatorial Guinea and Gabon) where the gambiense form is endemic (IV).

In the proposed methodology, risk was looked at as the likelihood of exposure to transmission, where the likelihood was estimated as a function of geographic proximity to HAT cases reported over a 10-year period.

Estimates of HAT risk were based on epidemiological data assembled by the Atlas of HAT for the period 2000-2009 (III). In the study region, the Atlas provided village-level mapping for 15,083 HAT cases, corresponding to 94.2 percent of all cases reported from the six countries. The average spatial accuracy for these cases was estimated at 800 m. For the remaining 5.8 percent of cases, village- level information was unavailable but the focus of origin was known. Therefore, these cases were distributed among the endemic villages of their focus by means of proportional allocation, whereby a proportionally higher number of unmapped cases is attributed to mapped villages that reported more cases during the study period.

In addition to village-level data on HAT occurrence, geospatial datasets of human population were needed. We explored two sources of human population datasets available at the global level: Landscan (Dobson et al.

2000; Bhaduri et al. 2002) and the Global Rural–Urban Mapping Project (GRUMP) database - alpha version (CIESIN et al. 2004).

Landscan provides global grids where census counts are allocated to grid nodes by means of probability coefficients. Probability coefficients are based on land cover, elevation, slope, roads and populated areas/points.

Landscan spatial resolution is less than 1 km at the equator and the population layers are updated yearly.

GRUMP consists of estimates of human population in five-year steps (from 1990 to 2000). GRUMP has the same resolution as Landscan, but it further distinguished between urban and rural population. “Proportional allocation” or “areal weighting”, combined with a urban- rural mask, are used by GRUMP distribute the population to a grid based on administrative polygons.

A comparative case-study on Kenya suggested that GRUMP database may provide a more accurate baseline than Landscan for a certain category of disease risk studies (Hay et al. 2005). However, a more recent study indicated that in different areas the opposite is true. (Tatem et al.

2011)

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For the proposed HAT risk methodology, Landscan datasets represented a more suitable input, as exemplified in Figure 1. In this figure, Landscan 2000 and GRUMP 2000 are compared for an HAT-endemic area (the area of Libreville in Gabon). GRUMP would indicate that the

population density is zero in vast areas whence a sizable number of HAT cases are reported. This type of feature makes GRUMP unsuitable for epidemiological analysis conducted at a high spatial resolution, such as that proposed in this study.

Figure 1 (a) Landscan 2000, and (b) GRUMP 2000 - alpha version. HAT cases reported in 2000 are overlaid on population surfaces in both (a) and (b).

The average number of HAT cases in the period 2000- 2009 and the average human population (as per Landscan datasets from 2000 to 2009) were regarded as spatial point processes and subjected to spatial smoothing.

Spatial smoothing techniques enable to calculate the intensity of a point process (i.e. the number of events per unit area) (Diggle 1983) and they rely on a moving window whose size and shape determine how far the effect of each event will reach (Pfeiffer et al. 2008).

For this study, a kernel function k(·) was used, whereby the intensity estimate

λ ˆ

τ

( s )

can be expressed as:

⎟ ⎠

⎜ ⎞

⎝

= ∑ ⎛ −

=

τ τ

λ

τ ⁱ

n i

s

s ) k s

ˆ (

1 2

1

Here, s is a location anywhere in the study region R, s1,.., sn are the locations of the n observed events, k(·) represents the kernel weighting function, τ > 0 is referred to as the bandwidth or search radius, and si are the events that lie within the area of influence as controlled by τ.

In particular, a quadratic kernel function (Silverman 1986), with a 30 km bandwidth was used. Figure 2 illustrates graphically the result of spatial smoothing on one single event. In this instance, the event represents one case of HAT localized at the centre of the 5-km resolution grid, which therefore corresponds to the apex of the disease intensity surface.

Figure 2 Graphical rendering of the disease intensity surface for one case of HAT, as derived from spatial smoothing (Kernel function k(·):

quadratic; bandwidth τ: 30 km; output resolution: 1 km)

Whilst the choice on the shape of the kernel (in this case quadratic) has relatively little effect on the resulting intensity estimate (Silverman 1986; Gatrell et al. 1996) a more important choice is the selection of the bandwidth, the rule being that the higher the bandwidth, the smoother the intensity surface. Different techniques are proposed for selecting the bandwidth (Berman and Diggle 1989; Scott 1992; Wand and Jones 1995), but no optimal value exists

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and characteristics of the biological process under study are often better suited to guide the choice (Pfeiffer et al.

2008). We explored different bandwidths, and the distance of 30 km was chosen by taking into account the epidemiological features of HAT, the behaviour of the vector and the mobility of people in the average rural African milieu where HAT occurs. In particular, a few studies investigated the daily distance covered by people living in HAT foci (Solano et al. 2003; Courtin et al. 2005;

Courtin et al. 2010) and found that this tends not to exceed 15 km. We conservatively used a distance of 30 km to take into account, at least in part, also people’s movements that do not occur on a daily basis.

Importantly, both intensity surfaces (for HAT cases and exposed population) were generated using the same

bandwidth (Kelsall and Diggle 1995). No attempt was made to account for ‘edge effects’ of smoothing (Pfeiffer et al. 2008); however, this is not expected to matter unduly because our final object was to estimate the ratio of two intensities (Bithell 1990).

Spatial smoothing resulted in two surfaces representing the average annual estimates of disease intensity and population intensity. The ratio of these two surfaces can be defined as the disease risk (R) (Pfeiffer et al. 2008).

Thresholds were subsequently applied to the risk surface in order to distinguish different categories of risk, ranging from ‘very high’ to ‘very low’ (Table 1). Where the risk function R was less then 1 HAT case per 10⁶ people per annum, risk was considered ‘marginal’.

Table 1 Thresholds for the definition of sleeping sickness risk categories. Thresholds are applied to the ratio between the average annual intensity of HAT cases (period 2000 – 2009) and the intensity of exposed population (based on the average population in the period 2000 - 2009)

Category of risk Risk function (R) HAT cases per annum Very high R ≥ 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

Having thus mapped the risk categories, Landscan 2009 was used to calculate the number of people that at the end of the study period lived in areas at different levels of risk.

In view of the somewhat arbitrary choice of the search radius, sensitivity analysis was conducted to investigate the effect that different search radii would have on the demarcation of risk areas and on the subsequent estimation of people at risk. Procedures for mapping risk areas and calculating the population at risk were implemented for all radii ranging from 5 to 100 km (at 5 km intervals) and results were compared with the 30 km benchmark.

3.2 The habitat of the tsetse fly and standardized land cover

datasets

The high cost of controlling trypanosomoses indefinitely poses serious concerns of sustainability and calls for permanent solutions to the problem. As a result, large scale interventions against African trypanosomoses (and especially against the animal form of the disease) are increasingly focusing on the sustainable creation of disease-free zones (Vreysen et al. 2000; Allsopp and Phillemon-Motsu 2002). To achieve this goal, the elimination of the tsetse vector is often seen as the most viable option, which would also break transmission of the human forms of the disease.

Projects carried out in the framework of PATTEC follow this approach, i.e. vector eradication to achieve disease eradication (Kabayo 2002). Recent and accurate information on the biogeography of tsetse appears therefore as an essential prerequisite for the success of a broad range of interventions.

Unfortunately, tsetse distribution maps presently available at the global (Wint and Rogers 2000b) and regional levels (Wint 2001) are affected by

shortcomings which limit their potential application in operational scenarios (Cecchi and Mattioli 2009b).

The need to collect and collate recent and accurate data on the geographic distribution of tsetse is recognized, but sustained, long-term efforts will be necessary to address this issue adequately (Cecchi et al. 2011b).

Consequently, developing tools to assist mapping of potential tsetse habitat is often seen as a research priority, both to fill the gaps in the known geographic distribution of tsetse and to target field surveys (Rogers and Randolph 1993; Kitron et al. 1996;

Rogers et al. 1996; Robinson et al. 1997; Wint and Rogers 2000a; Rogers and Robinson 2004).

Land cover maps have long been considered as a useful tool to map the habitat of arthropod vectors of diseases (Pope et al. 1992; Daniel et al. 2004;

Wimberly et al. 2008), including tsetse (de La Rocque et al. 2001; De Deken et al. 2005; Mahama et al. 2005;

Bouyer et al. 2006). Furthermore, several ecological variables known to affect environmental suitability for tsetse flies (e.g. host availability, climatic conditions, vicinity of water bodies, etc.) are correlated in different ways with land cover. However, most studies dealing with tsetse rely on land cover maps developed ad hoc, which often hinders uptake of research results in operational contexts. In this regard, the increasing amount of multi-purpose land cover maps developed using a standardized classification systems (i.e. the Land Cover Classification System - LCCS (Di Gregorio and Jansen 2000; Di Gregorio 2005)) has a potential as yet largely untapped. LCCS was developed by FAO and the United Nations Environment Programme to improve access to reliable and standardized information on land cover. It enables comparison of land cover classes regardless of

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mapping scale, land cover type, data collection method and geographic location.

In this section we explore how state-of-the-art, LCCS- compliant land cover maps available in the public domain at a range of scales (from global to national level) can be used to define areas where vegetation cover is suitable for tsetse. In the methodologies summarized below, a combination of inductive and deductive approaches were adopted to explore the patterns of association between tsetse habitat and classes of vegetation cover as provided by standardized land cover datasets.

An inductive approach was used at the continental level, and a deductive approach at the national and regional level. In inductive approaches the relationships between the ecological variables (e.g.

land cover) and the habitat requirements of a taxon (e.g. sub-genera of tsetse fly, Genus Glossina) are not assumed a priori but rather established through quantitative analysis of observed datasets. By contrast, in deductive approaches the species’ known ecological requirements as derived from the literature are used to define habitat suitability (Corsi et al. 2000).

The methodology and results of this study were published in Paper V, and they are also more extensively described in a technical publication (Cecchi et al. 2008b).

3.2.1 Land cover and the habitat of tsetse fly: an inductive approach at the continental level

The broad patterns of association between the three groups of tsetse flies (i.e. subgenus Austenina – fusca group, subgenus Nemorhina – palpalis group and subgenus Glossina s.s. – morsitans group) and land cover were explored using datasets at similar spatial resolution: the predicted areas of suitability for tsetse flies of the PAAT-IS (Wint and Rogers 2000b) and the Global Land Cover 2000 (GLC2000) (Bartholomé and Belward 2005).

The predicted areas of suitability for tsetse flies were produced by modelling the known presence of the tsetse flies (mostly based on work by Ford and Katondo (Ford and Katondo 1975; Ford and Katondo 1977b; Ford and Katondo 1977a), with a wide range of remotely sensed predictor variables (Hay et al. 1996). Ideally, recent, accurate and consistent field datasets on the presence and abundance of tsetse flies would have represented a more appropriate input, but this type of information is presently lacking (Cecchi et al.

2011b), and the PAAT-IS predicted areas of suitability were considered the most reliable substitute.

GLC2000 is based on medium resolution satellite images acquired for the most part by the fourth Satellite Pour l’Observation de la Terre (SPOT) satellite, SPOT 4, between November 1999 and December 2000. It provides land cover datasets

at a resolution of approximately 1 km over the whole globe. We used the regional product for Africa (Mayaux et al. 2004), which includes 28 land cover classes. Importantly, GLC2000 is based on LCCS.

The land cover preferences for each tsetse group were derived by overlaying the predicted areas of suitability for tsetse flies and GLC2000, which enabled to estimate the mean predicted area of presence within each class. Land cover classes were subsequently ranked with an index of suitability ranging from 3 to 0 (i.e. from ‘high’ to

‘nil’), based on the thresholds in Table 2.

Table 2 Categories of suitability of land cover classes for tsetse fly.

Predicted area of presence within the class (%)

Category of suitability for tsetse

Index of suitability

> 50 High 3

> 25 and ≤ 50 Moderate 2

> 5 and ≤ 25 Low 1

≤ 5 Nil 0

Having thus ranked GLC2000 land cover classes for tsetse environmental suitability, the chi- square (χ²) test was used to estimate the strength of the relationship. Chi-square is a simple non- parametric test of statistical significance for bivariate tabular analysis. In this context, the test was used to check the hypothesis that the different land cover classes help us to predict the presence or absence of tsetse flies. Also, symmetric measures based on the chi-square statistic are capable of measuring the strength of the relationship between the dependent and independent variable. In particular, the measure called shared variance¹ is the portion of the total distribution of the variables measured in the sample data that is accounted for by the relationship detected with the chi-square test.

3.2.2 Land cover and the habitat of tsetse fly: a deductive approach at the national and regional levels

At the national and regional levels, land cover datasets at a spatial resolution higher than GLC2000 were used (Africover). Africover maps (www.africover.org) are based on digital images acquired by Landsat satellites, which have a resolution of 15-30 metres. Africover datasets are available for 8 Eastern African countries infested by tsetse flies (Burundi, Democratic Republic of the Congo, Kenya, Rwanda, Somalia, Sudan, Uganda and United Republic of Tanzania). Their scale ranges from 1 : 250 000 to 1 : 100 000 (the latter being used for small countries or specific

1r² = χ² / N(k - 1), where χ² is chi-square, N is the total number of observations and k is the smaller of the number of rows or columns in the cross tabulation. In this exercise the tables contain 26 rows (land cover classes), and 2 columns (tsetse absence/presence).

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areas) and, as it is the case for GLC2000, they are based on LCCS.

A legend of 26 classes was derived for the 8 countries by thematic aggregation of the original Africover classes (over 500) by using the aggregation rules embedded in LCCS. The 26 classes were customized for tsetse habitat mapping because, for vegetation, they preserved a fairly high level of detail in the description of

‘physiognomy’ (i.e. ‘trees’, ‘shrubs’ or

‘herbaceous vegetation’) and ‘density’ (from

‘closed vegetation’ to ‘sparse vegetation’).

The 26 land cover classes were subsequently ranked to determine their suitability for each of the three tsetse fly groups. The scores were derived from a review of the most recent and comprehensive literature on the subject (Challier 1982; Pollock 1982; Jordan 1986; Leak 1998) and opinions of experts (deductive approach).

The lack of standardization in the description of vegetation types in the available literature warranted the use of qualitative suitability scores, which were based on four broad categories only (from ‘high’ – 3, to ‘nil’ - 0).

The coherence between the results of the continental, inductive approach and the regional, deductive approach was estimated by also calculating the suitability for tsetse of the Africover classes as an area-weighted average of the suitability for the corresponding GLC2000 classes.

The goal here was to try and estimate the suitability for tsetse of the Africover classes not through the literature (deductive approach), but rather using the results of the continental-level inductive approach (§ 3.2.1). In order to do so, the correspondence between the GLC2000 legend and the customized Africover legend needed to be established. Despite the fact that both legends are based on LCCS, it is impossible to establish a one-to-one thematic correspondence between the different land cover classes. Therefore, correspondence between the two legends was established spatially (i.e. with GIS overlay tools).

For each customized Africover class, the proportions of the corresponding GLC2000 classes were determined. These proportions enabled to calculate the land cover suitability of Africover classes as an area-weighted average of the suitability of the corresponding GLC2000 classes (more precisely, an area-weighted average of the “proportion of surface affected by tsetse inside the GLC2000 classes”, see Table 4 in § 4.2.1). The suitability thresholds in Table 2 were subsequently applied to the “calculated”

suitability, thus enabling comparison with the scores based on expert opinion.

4 Results

4.1 Human African trypanosomosis

4.1.1 HAT distribution

The methodology for mapping HAT was first developed and tested at the regional level in Central Africa (I), subsequently applied to Western Africa (II) and then expanded aiming to embrace all endemic countries in Africa (III).

The preliminary study in Central Africa (I) showed that datasets used as input and the methodology developed for the Atlas of HAT are adequate to generate national and regional disease maps that are substantially more accurate than previously available. Approximately 98 percent of the locations analysed in Central Africa could be mapped, thus demonstrating the efficacy of the georeferencing protocol.

Comparison between the preliminary outputs of the Atlas of HAT and previous cartographic products of similar geographic scope indicated that less than a third of the endemic locations mapped are situated within the boundaries of previously described transmission areas (I).

The study in Western Africa (II) confirmed that updated and comprehensive maps could be generated also in this region. From the epidemiological standpoint, results confirmed that in Western Africa significant levels of HAT occurrence continue to be reported only from costal Guinea and West Central Côte d’Ivoire (Courtin et al. 2008). South Eastern Guinea, coastal areas in Côte d’Ivoire and Ghana account for a few sporadic cases.

Following the two regional studies, the methodology for mapping HAT was applied to all affected countries. Results for 23 out of the 25 countries having reported on the status of HAT in the period 2000-2009 are summarized in Figure 3 and presented in detail in Paper III.

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Figure 3 Cases of human African trypanosomosis reported from Africa (period 2000-2009). Countries masked in semi-transparent white are (i) those where mapping is in progress (Angola and the Democratic Republic of the Congo), (ii) non-endemic for HAT (not labelled), (iii) those that did not report on the HAT epidemiological situation in the period 2000-2009 (Burundi, Ethiopia, Gambia, Guinea-Bissau, Liberia, Niger, Senegal and Sierra Leone), and Botswana, Namibia and Swaziland, for reasons explained in the text. Areas masked in dark grey correspond to disputed territories and non-self-governing territories (Food and Agriculture Organization of the United Nations 2008a).

_____________________________________________ Biogeographical patterns of African trypanosomoses for improved planning and implementation of field interventions _____________________________________________