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The Malaria Atlas Project: Developing Global Maps of Malaria Risk

The Malaria Atlas Project: Developing Global Maps of Malaria Risk

  • Simon I Hay, 
  • Robert W Snow
PLOS
x

In allocating public health resources, the guiding principle should be an evidence-based quantification of need. A significant effort to categorize diseases by their global morbidity and mortality impact has developed during the last decade, epitomized by the Global Burden of Diseases [1] and the Disease Control Priorities projects [2]. But despite these efforts, the evidence base for allocating resources for malaria control on a global scale is poor.

National reporting on malaria continues to be fanciful; Kenya, for example, reported only 135 malaria deaths in 2002 to the World Health Organization [3]. In addition, less than half (22/49) of the malaria-endemic countries in Africa provided information for the most-recent reporting year, 2003; the rest were older [3]. Information on the global burden of malaria remains the subject of best guesses rooted in national reporting systems [3], informed estimation based on epidemiological data linked to historical malaria distributions [4], or unvalidated models of malaria distribution in Africa [5–7]. As a corollary, resource allocations for malaria interventions remain driven by perceptions and politics, rather than an objective assessment of need. This status quo is untenable when global and national financial resources must be defined to meet needs for new, expensive antimalarial drugs and commodities to prevent infection, and to ensure that these interventions are optimally targeted.

It has been almost 40 years since the last global map of malaria endemicity was constructed [8], and a decade since the need for maps of malaria transmission in Africa was first advocated [9]. Although substantial progress has been made [10–21], an evidence-based map of malaria transmission intensity for Africa remains illusive, and there have been no recent efforts to construct a credible evidence-based global malaria map.

A New Mapping Project

The primary goal of the recently launched Malaria Atlas Project (MAP) is to develop the science of malaria cartography. Our approach will be first to define the global limits of contemporary malaria transmission; we have initiated this process [12,13], but will substantially refine these layers with additional medical intelligence in future years.

Within these limits, we plan to then model endemicity using a global evidence base of malaria parasite prevalence. This Health in Action concentrates mostly on how we intend to achieve this important goal. Once we have created these global endemicity maps, these will then provide a baseline to facilitate estimation of populations at risk of malaria and more-credible predictions of disease burden. These maps will also provide a platform to help target intervention needs, and may provide a means to measure progress toward national and international malaria public health goals at a global scale.

Why Do We Need Maps of Malaria Transmission?

Malaria parasite transmission intensity is spatially heterogeneous [6,2224]. This heterogeneity has important implications for risks and age patterns of progression from malaria infection to disease, disability, and death [5,25].

Endemicity is a measure of the level of malaria challenge in a human population, and determines the average age of first exposure, the rate of development of immunity, and thus, the expected clinical spectrum of disease [25,26]. Therefore, suites of relevant interventions should be tailored to these basic epidemiological foundations [9,2729].

This is obvious for malaria early warning systems, for example, that have a rationale only in epidemic-prone areas [30,31]. In addition, intermittent presumptive treatment of infants is likely to have little impact on the incidence of clinical malaria and anaemia in areas of exceptionally low transmission [32]. Moreover, where one should withhold iron supplementation in young children demands an understanding of the balanced risks of iron deficiency, malaria disease incidence, and intensity of transmission [33,34]. Furthermore, optimizing the introduction of diagnostics to rationalize the use of new, expensive therapies will require better tools to target where this is cost efficient and where presumptive treatment remains appropriate. We anticipate that other interventions are likely to have health impacts and cost-effectiveness balances that may vary under different endemicity conditions, and we propose to conduct a detailed systematic review of the evidence.

It is often not immediately apparent when reading guidelines for malaria control that there are many intervention options available, that these may need to be appropriately combined, and that the optimal mix could depend on the intensity of malaria transmission in a given area. This would be as true for Africa as it is for other malarious territories of the Old and New Worlds. Global maps of malaria endemicity should therefore be essential in every step, from selecting appropriate intervention options and identifying requirements and budgeting, to planning, implementing, and monitoring at subnational, national, and regional scales.

Box 1. The MAP Web Site

The MAP Web site (http://www.map.ox.ac.uk) was launched on May 1, 2006, to further the aims and ambitions of MAP. The Web site allows users to visualize the current distribution of the assembled PR data through static maps in Web browsers, or more interactively through “.kmz” files that enable the data to be displayed in Google Earth (http://earth.google.com). We are currently interested in gathering additional PR data from the public health community, and to facilitate communication we have translated the entire Web site into Spanish and French.

MAP is different than previous attempts at mapping malaria, primarily because it is a global initiative, but also because it aims to share data from the outset. Those supplying useful PR data will be provided with the full database for their country of interest, provided full permission is granted from the data owners for distribution. In addition, the entire database will be released in the public domain after component outputs have been peer reviewed. We have set a June 1, 2009, deadline for this release.

A second unique feature of MAP is that it operates with strict inclusion criteria for PR data: only random or complete community-sample surveys conducted post-1985, where parasite species and age groups are defined and the survey involves more than 50 persons to minimize sampling error [68]. Extensive details of these and additional inclusion rules are provided online in English, Spanish, French, Chinese, and Swahili.

Thirdly, the MAP project will collect data on P. falciparum malaria, as well as the often neglected P. vivax parasite. The Web site also allows formal acknowledgment of those interested individuals and institutions who contribute data. We encourage you to have a look and send us feedback at map@zoo.ox.ac.uk.

Large Area Efforts to Map Malaria since the 1960s

The fuzzy climate-suitability map for stable Plasmodium falciparum malaria transmission was a milestone in the mapping of malaria in Africa [10]. It represented the first attempt for several decades to provide a map of P. falciparum transmission at a continental scale, and has been widely used and cited by scientists, international agencies, and national malaria control programmes [6,35,36].

However, it has also been widely misinterpreted, as it represents a measure of the likelihood that stable transmission can occur, rather than ranges of transmission intensity. Furthermore, it has never been formally evaluated against contemporary parasite rate (PR) data outside of Kenya [37]. What is required for defining both disease risks and intervention need is a spatial model that predicts levels of endemicity, defined and validated by empirical data and constructed at a global scale. This approach to assembling epidemiological “training” data, environmental “predictor” data, and a suite of statistical mapping techniques to relate the two is considered below.

An Archive of Parasite Prevalence

There are many ways to measure the abundance of malaria in a given location, and they all have their advantages and disadvantages that have been reviewed elsewhere [23,24,38]. Regardless of any epidemiological preferences, PR data indisputably constitute the bulk of the global information available on the distribution of malaria endemicity. The PR is the proportion of a sampled population that is confirmed positive for malaria parasites, canonically by identifying immature “ring stage” trophozoites in blood slides [39].

We have adopted a single and traditional classification of malaria endemicity based on the PR [40], to standardize our definition of risk globally. Endemicity is defined by the PR in the two- to ten-year age cohort (hypoendemic, less than 0.1; mesoendemic, 0.11–0.5; hyperendemic, 0.51–0.75), except for the holoendemic class (greater than 0.75) where the PR refers to the one-year age group [40]. This is important because “risk” is a geographically relative concept: nation states in Latin America identify areas of “high” risk that would be classified as low risk in sub-Saharan Africa.

To gather global data on PR surveys of a sufficient extent and density to generate endemicity surfaces at moderate spatial resolution requires combinations of traditional and nontraditional search strategies. This process has involved electronic searches of formal literature and grey literature databases, as well as using personal contacts with malaria research scientists and malaria control personnel. Most recently, we have developed a Web site to guide people in identifying additional data sources from areas where information is lacking (Box 1). As of September 10, 2006, our search has provided 3,036 spatially independent geopositioned PR surveys undertaken since January 1985 from an aggregate sample of 2,143,979 blood slides in 79 malaria-endemic countries. The data included 2,728 survey locations reporting P. falciparum prevalence (Figure 1) and 1,379 locations reporting P. vivax prevalence.

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Figure 1. Distribution of the n = 3,036 PR Data Points Collected and Geopositioned by September 10, 2006

There are n = 420 PR surveys conducted in the 1985–1989 period, n = 557 in the 1990–1994 period, n = 556 in the 1995–1999 period, and n = 1,503 in the 2000–2006 period.

https://doi.org/10.1371/journal.pmed.0030473.g001

In addition, the distribution of the main Anopheles malaria vectors and the frequency of the inherited haemoglobin disorders will constrain malaria infection risks and disease outcomes globally. Both will be subject to a similar intensity of data search and assembly, which will be described elsewhere.

An Archive of Global Environmental Data

Malaria is a vector-borne disease and the culpable anopheline mosquitoes are very sensitive to climate. This has been exploited in many ways to predict the distribution of malaria in time [14,30,31,41,42] and space [15,20,43]. Mapping the distribution of malaria requires spatially referenced data (e.g., altitude, temperature, rainfall, and vegetation extent) to be matched to the PR-survey positions, to establish uni and/or multivariate statistical relationships between malaria endemicity and the environment. These relationships can then be applied to the environmental data, in more or less sophisticated ways (see below), to generate continuous global maps [20].

Our proclivities are for such information derived from Earth-observing satellites [44–48] because they are globally consistent measurements, often more contemporary and often of higher spatial resolution than interpolated climatologies [47]. Examples of these environmental data [47] have been made available in the public domain, and will be hosted on a MAP Web site when all necessary distribution rights have been negotiated. There is significant potential for improvement in these environmental themes through data collected by new generations of satellite sensors [48]. Continual improvement of these data will be part of the ongoing commitment of MAP.

First Steps to Maps of Force of Infection

As a cross-sectional measure of prevalence, the PR is a less direct measure of malaria transmission than the entomological inoculation rate (the number of infective bites per capita, often expressed annually for P. falciparum, hence APfEIR) [22,23], the vectorial capacity (canonically, C) or the basic reproductive number (canonically, R0) [38]. These “force of infection” metrics, however, are much less frequently measured, [6,22] and while not recorded at sufficient frequency to enable mapping, will be archived by the MAP as companion data to inform modelling.

Moreover, in high-endemicity areas, PR samples are often restricted to children, but in areas of low endemicity, surveys are usually extended to include all age groups. PR is therefore confounded by the interacting factors of the age of the population sampled, its immune status, and the “detectability” of peripheral parasitaemia [23,24]. It is necessary to transform PR into a measure of the force of infection of malaria controlling for these factors. This is because these interrelated measures are more closely related to the life-history characteristics and dynamics of the Anopheles vector populations that we will attempt to model with environmental data. Our goal is to generate APfEIR, C, and ultimately R0 surfaces from our PR data for mapping, and the modelling framework within which to perform these conversions has already been developed [23,24].

Those techniques required to standardize PR for age represent an ongoing challenge, although methods by which to achieve this have been suggested [11]. The models written to perform these conversions will be made freely available in the public domain pending peer review. R is the chosen MAP platform as it is a programming environment that is free to all (http://www.r-project.org).

Measuring Risk and Managing Uncertainty

Quantifying the uncertainty in prediction has been a neglected area in the field of malaria mapping and in ecology, more generally [49]. Our aim is to present all risk maps generated through MAP with uncertainty guides, companion maps that show the spatial variation in predictive accuracy. We also intend to evaluate the most-accurate procedures for achieving the basic mapping with appropriate robustness measures [11,5054]. There are alternative methods available to achieve mapping with error estimations: Bayesian [11,21,55], discriminant analyses [52], and logistic-regression [50,52] techniques, among others [53,54], and these will all be systematically tested as part of the MAP project. The code written to implement these techniques will again be written in R and distributed freely upon acceptance of its products through peer review.

Where People Live

Accurate population data are critical for the assessment of the effects of human population density on malaria risk and the attribution of risk to populations [56]. These databases are also becoming increasingly accessible [47,56,57]. Areas of the world for which we have a particularly poor understanding of human population distribution will limit the accuracy of MAP and other databases to derive population at-risk estimates. Countries of specific concern are highlighted in the ancillary data section of the MAP Web site. More contemporary or higher spatial resolution census data supplied to MAP will be forwarded with permission to collaborators developing the Gridded Population of the World, version 3 (GPWv3) and the Global Rural–Urban Mapping Project (GRUMP) (http://sedac.ciesin.columbia.edu).

Future Applications

These planned malaria-endemicity maps will provide the basis for increasing the fidelity of morbidity [4], mortality [5,6] and co-infection burden estimates [58,59]. These studies lead logically to more-accurate commodity demand and budget estimation. As we have argued, these maps may also provide a means to help determine the distribution of intervention types and mixes within countries. Companion maps of the global distribution of the main anopheline vector species will also be particularly important in helping inform the appropriate modes of control. At the very least, these map suites should augment the objective monitoring and evaluation of our interventions in the coming years. There are strong arguments for these exercises being conducted independently of international agencies responsible for the implementation and evaluation of interventions [60–62].

While we strive to assemble data to define an endemicity baseline, the static maps we generate will represent a “snapshot” of a dynamic malaria epidemiology. It is important to establish this epidemiological baseline because history has shown that changes independent of planned interventions are inevitable [63]. These global environmental changes will affect the populations at risk of malaria. Land-use changes [64], such as deforestation [65], may modify vector population dynamics, for example. Population growth, urbanization [6], and climate change [66] will additionally affect human population dynamics. Other factors such as the progression of the HIV/AIDS pandemic and changes in undernutrition and socioeconomic status [66,67] will influence the ability of human population to cope with malaria infection. These changes will be significant over the time span of international development goals and targets. MAP aims to develop plausible scenarios for many of these influences and techniques to model their potential impact, as they will be confounders in our ability to evaluate critically interventions at scale.

Conclusions

The distribution of populations exposed to the risk of P. falciparum and P. vivax malaria is poorly understood at the global level. Considerable effort as part of MAP is required to improve our basic maps of malaria transmission intensity and help identify the global distribution of populations at risk of malaria. This will involve assembling the largest-ever collection of PR data and a significant parallel investment in establishing the required environmental, population, and malaria vector data.

A very considerable research effort is also required to evaluate those statistical techniques needed to relate the PR and environmental data for extensive map predictions with confidence intervals. This will take time and succeed only with the cooperation of the malaria control community. To encourage interaction, MAP will be connected with the philosophy of open access, so that all data collected and techniques developed can be made available in the public domain rapidly after peer review.

Acknowledgments

We thank Drs. Carlos Guerra and Andrew Tatem for commenting on the manuscript.

References

  1. 1. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ (2006) Global and regional burden of disease and risk factors, 2001: Systematic analysis of population health data. Lancet 367: 1747–1757.AD LopezCD MathersM. EzzatiDT JamisonCJ Murray2006Global and regional burden of disease and risk factors, 2001: Systematic analysis of population health data.Lancet36717471757
  2. 2. Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, et al. (2006) Disease control priorities in developing countries. Washington (D. C.): World Bank Publications. 1352 p.DT JamisonJG BremanAR MeashamG. AlleyneM. Claesoneditors. 2006Disease control priorities in developing countries.Washington (D. C.)World Bank Publications1352 editors.
  3. 3. The World Health Organization (2005) The world malaria report 2005. Geneva: World Health Organization. The World Health Organization2005The world malaria report 2005.GenevaWorld Health OrganizationAvailable: http://www.rbm.who.int/wmr2005. Accessed 10 September 2006. Available: http://www.rbm.who.int/wmr2005. Accessed 10 September 2006.
  4. 4. Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI (2005) The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature 434: 214–217.RW SnowCA GuerraAM NoorHY MyintSI Hay2005The global distribution of clinical episodes of Plasmodium falciparum malaria.Nature434214217
  5. 5. Snow RW, Craig M, Deichmann U, Marsh K (1999) Estimating mortality, morbidity and disability due to malaria among Africa's non-pregnant population. Bull World Health Organ 77: 624–640.RW SnowM. CraigU. DeichmannK. Marsh1999Estimating mortality, morbidity and disability due to malaria among Africa's non-pregnant population.Bull World Health Organ77624640
  6. 6. Hay SI, Guerra CA, Tatem AJ, Atkinson PM, Snow RW (2005) Urbanization, malaria transmission and disease burden in Africa. Nat Rev Microbiology 3: 81–90.SI HayCA GuerraAJ TatemPM AtkinsonRW Snow2005Urbanization, malaria transmission and disease burden in Africa.Nat Rev Microbiology38190
  7. 7. Rowe AK, Rowe SY, Snow RW, Korenromp EL, Armstrong Schellenberg JR, et al. (2006) The burden of malaria mortality among African children in the year 2000. Int J Epidemiol 35: 691–704.AK RoweSY RoweRW SnowEL KorenrompJR Armstrong Schellenberg2006The burden of malaria mortality among African children in the year 2000.Int J Epidemiol35691704
  8. 8. (1968) [Geography of malaria: A medico-geographic profile of an ancient disease]. In: Lebedew AW, editor. Medicinskaja Geografija. Moscow: Academy of Sciences. pp. 25–146.AY LysenkoIN Semashko1968[Geography of malaria: A medico-geographic profile of an ancient disease].In:. AW Lebedeweditor. Medicinskaja GeografijaMoscowAcademy of Sciences25146 editor.
  9. 9. Snow RW, Marsh K, le Sueur D (1996) The need for maps of transmission intensity to guide malaria control in Africa. Parasitol Today 12: 455–457.RW SnowK. MarshD. le Sueur1996The need for maps of transmission intensity to guide malaria control in Africa.Parasitol Today12455457
  10. 10. Craig MH, Snow RW, le Sueur D (1999) A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today 15: 105–111.MH CraigRW SnowD. le Sueur1999A climate-based distribution model of malaria transmission in sub-Saharan Africa.Parasitol Today15105111
  11. 11. Gemperli A, Vounatsou P, Sogoba N, Smith T (2006) Malaria mapping using transmission models: Application to survey data from Mali. Am J Trop Med Hyg 163: 289–297.A. GemperliP. VounatsouN. SogobaT. Smith2006Malaria mapping using transmission models: Application to survey data from Mali.Am J Trop Med Hyg163289297
  12. 12. Guerra CA, Snow RW, Hay SI (2006) Defining the global spatial limits of malaria transmission in 2005. Adv Parasitol 62: 157–179.CA GuerraRW SnowSI Hay2006Defining the global spatial limits of malaria transmission in 2005.Adv Parasitol62157179
  13. 13. Guerra CA, Snow RW, Hay SI (2006) Mapping the global extent of malaria in 2005. Trends Parasitol 22: 353–358.CA GuerraRW SnowSI Hay2006Mapping the global extent of malaria in 2005.Trends Parasitol22353358
  14. 14. Hay SI, Snow RW, Rogers DJ (1998) Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 92: 12–20.SI HayRW SnowDJ Rogers1998Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data.Trans R Soc Trop Med Hyg921220
  15. 15. Hay SI, Omumbo JA, Craig MH, Snow RW (2000) Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa. Adv Parasitol 47: 173–215.SI HayJA OmumboMH CraigRW Snow2000Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa.Adv Parasitol47173215
  16. 16. Kleinschmidt I, Bagayoko M, Clarke GPY, Craig M, Le Sueur D (2000) A spatial statistical approach to malaria mapping. Int J Epidemiol 29: 355–361.I. KleinschmidtM. BagayokoGPY ClarkeM. CraigD. Le Sueur2000A spatial statistical approach to malaria mapping.Int J Epidemiol29355361
  17. 17. Kleinschmidt I, Omumbo J, Briet O, van de Giesen N, Sogoba N, et al. (2001) An empirical malaria distribution map for West Africa. Trop Med Int Health 6: 779–786.I. KleinschmidtJ. OmumboO. BrietN. van de GiesenN. Sogoba2001An empirical malaria distribution map for West Africa.Trop Med Int Health6779786
  18. 18. Omumbo JA, Hay SI, Goetz SJ, Snow RW, Rogers DJ (2002) Updating historical maps of malaria transmission intensity in East Africa using remote sensing. Photogrammetric Eng Remote Sens 68: 161–166.JA OmumboSI HaySJ GoetzRW SnowDJ Rogers2002Updating historical maps of malaria transmission intensity in East Africa using remote sensing.Photogrammetric Eng Remote Sens68161166
  19. 19. Omumbo JA, Hay SI, Snow RW, Tatem AJ, Rogers DJ (2005) Modelling malaria risk in East Africa at high-spatial resolution. Trop Med Int Health 10: 557–566.JA OmumboSI HayRW SnowAJ TatemDJ Rogers2005Modelling malaria risk in East Africa at high-spatial resolution.Trop Med Int Health10557566
  20. 20. Rogers DJ, Randolph SE, Snow RW, Hay SI (2002) Satellite imagery in the study and forecast of malaria. Nature 415: 710–715.DJ RogersSE RandolphRW SnowSI Hay2002Satellite imagery in the study and forecast of malaria.Nature415710715
  21. 21. Gemperli A, Sogoba N, Fondjo E, Mabaso M, Bagayoko M, et al. (2006) Mapping malaria transmission in West and Central Africa. Trop Med Int Health 11: 1032–1046.A. GemperliN. SogobaE. FondjoM. MabasoM. Bagayoko2006Mapping malaria transmission in West and Central Africa.Trop Med Int Health1110321046
  22. 22. Hay SI, Rogers DJ, Toomer JF, Snow RW (2000) Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, Internet access and review. Trans R Soc Trop Med Hyg 94: 113–127.SI HayDJ RogersJF ToomerRW Snow2000Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, Internet access and review.Trans R Soc Trop Med Hyg94113127
  23. 23. Smith DL, Dushoff J, Snow RW, Hay SI (2005) The entomological inoculation rate and Plasmodium falciparum infection in African children. Nature 438: 492–495.DL SmithJ. DushoffRW SnowSI Hay2005The entomological inoculation rate and Plasmodium falciparum infection in African children.Nature438492495
  24. 24. Smith DL, McKenzie FE, Snow RW, Hay SI (2007) Revisiting the basic reproductive number for malaria and its implications for malaria control. PLoS Biol. DL SmithFE McKenzieRW SnowSI Hay2007Revisiting the basic reproductive number for malaria and its implications for malaria control.PLoS BiolIn press. In press.
  25. 25. Snow RW, Omumbo JA, Lowe B, Molyneux CS, Obiero JO, et al. (1997) Relation between severe malaria morbidity in children and level of Plasmodium falciparum transmission in Africa. Lancet 349: 1650–1654.RW SnowJA OmumboB. LoweCS MolyneuxJO Obiero1997Relation between severe malaria morbidity in children and level of Plasmodium falciparum transmission in Africa.Lancet34916501654
  26. 26. Snow RW, Marsh K (2002) The consequences of reducing transmission of Plasmodium falciparum in Africa. Adv Parasitol 52: 235–264.RW SnowK. Marsh2002The consequences of reducing transmission of Plasmodium falciparum in Africa.Adv Parasitol52235264
  27. 27. Greenwood BM, Bojang K, Whitty CJM, Targett GAT (2005) Malaria. Lancet 365: 1487–1498.BM GreenwoodK. BojangCJM WhittyGAT Targett2005Malaria.Lancet36514871498
  28. 28. Shiff C (2002) Integrated approach to malaria control. Clin Microbiology Rev 15: 278–293.C. Shiff2002Integrated approach to malaria control.Clin Microbiology Rev15278293
  29. 29. The World Health Organization (2006) Malaria vector control and personal protection: Report of a WHO study group. Geneva: World Health Organization. 62 p.The World Health Organization2006Malaria vector control and personal protection: Report of a WHO study group.WHO technical report series, No. 936. GenevaWorld Health Organization62 WHO technical report series, No. 936.
  30. 30. Hay SI, Rogers DJ, Shanks GD, Myers MF, Snow RW (2001) Malaria early warning in Kenya. Trends Parasitol 17: 95–99.SI HayDJ RogersGD ShanksMF MyersRW Snow2001Malaria early warning in Kenya.Trends Parasitol179599
  31. 31. Hay SI, Were EC, Renshaw M, Noor AM, Ochola SA, et al. (2003) Forecasting, warning, and detection of malaria epidemics: A case study. Lancet 361: 1705–1706.SI HayEC WereM. RenshawAM NoorSA Ochola2003Forecasting, warning, and detection of malaria epidemics: A case study.Lancet36117051706
  32. 32. Schellenberg D, Cisse B, Menedez C (2006) The IPTi Consortium: Research for policy and action. Trends Parasitol 22: 296–300.D. SchellenbergB. CisseC. Menedez2006The IPTi Consortium: Research for policy and action.Trends Parasitol22296300
  33. 33. English M, Snow RW (2006) Iron and folic acid supplementation and malaria risk. Lancet 367: 90–91.M. EnglishRW Snow2006Iron and folic acid supplementation and malaria risk.Lancet3679091
  34. 34. Sazawal S, Black RE, Ramsan M, Chwaya HM, Stoltzfus RJ, et al. (2006) Effects of routine prophylactic supplementation with iron and folic acid on admission to hospital and mortality in preschool children in a high malaria transmission setting: Community-based, randomised, placebo-controlled trial. Lancet 367: 133–143.S. SazawalRE BlackM. RamsanHM ChwayaRJ Stoltzfus2006Effects of routine prophylactic supplementation with iron and folic acid on admission to hospital and mortality in preschool children in a high malaria transmission setting: Community-based, randomised, placebo-controlled trial.Lancet367133143
  35. 35. Small J, Goetz SJ, Hay SI (2003) Climatic suitability for malaria transmission in Africa, 1911–1995. Proc Natl Acad Sci U S A 100: 15341–15345.J. SmallSJ GoetzSI Hay2003Climatic suitability for malaria transmission in Africa, 1911–1995.Proc Natl Acad Sci U S A1001534115345
  36. 36. Thomas CJ, Davies G, Dunn CE (2004) Mixed picture for changes in stable malaria distribution with future climate in Africa. Trends Parasitol 20: 216–220.CJ ThomasG. DaviesCE Dunn2004Mixed picture for changes in stable malaria distribution with future climate in Africa.Trends Parasitol20216220
  37. 37. Omumbo JA, Hay SI, Guerra CA, Snow RW (2004) The relationship between the Plasmodium falciparum parasite ratio in childhood and climate estimates of malaria transmission in Kenya. Malaria J 3: 17.JA OmumboSI HayCA GuerraRW Snow2004The relationship between the Plasmodium falciparum parasite ratio in childhood and climate estimates of malaria transmission in Kenya.Malaria J317
  38. 38. Smith DL, McKenzie FE (2004) Statics and dynamics of malaria infection in Anopheles mosquitoes. Malaria J 3: 13.DL SmithFE McKenzie2004Statics and dynamics of malaria infection in Anopheles mosquitoes.Malaria J313
  39. 39. (1993) The malaria parasites. In: Gilles HM, Warrel DA, editors. Bruce-Chwatt's essential malariology. 3rd ed. London: Arnold. pp. 12–34.HM Gilles1993The malaria parasites.In. HM GillesDA Warreleditors. Bruce-Chwatt's essential malariology3rd edLondonArnold1234 editors.
  40. 40. Metselaar D, Van Thiel PH (1959) Classification of malaria. Trop Geogr Med 11: 157–161.D. MetselaarPH Van Thiel1959Classification of malaria.Trop Geogr Med11157161
  41. 41. Hay SI, Renshaw M, Ochola SA, Noor AM, Snow RW (2003) Performance of forecasting, warning and detection of malaria epidemics in the highlands of western Kenya. Trends Parasitol 19: 394–399.SI HayM. RenshawSA OcholaAM NoorRW Snow2003Performance of forecasting, warning and detection of malaria epidemics in the highlands of western Kenya.Trends Parasitol19394399
  42. 42. Hay SI, Snow RW, Rogers DJ (1998) From predicting mosquito habitat to malaria seasons using remotely sensed data: Practice, problems and perspectives. Parasitol Today 14: 306–313.SI HayRW SnowDJ Rogers1998From predicting mosquito habitat to malaria seasons using remotely sensed data: Practice, problems and perspectives.Parasitol Today14306313
  43. 43. Hay SI, Packer MJ, Rogers DJ (1997) The impact of remote sensing on the study and control of invertebrate intermediate host and vectors for disease. Int J Remote Sens 18: 2899–2930.SI HayMJ PackerDJ Rogers1997The impact of remote sensing on the study and control of invertebrate intermediate host and vectors for disease.Int J Remote Sens1828992930
  44. 44. Hay SI (2000) An overview of remote sensing and geodesy for epidemiology and public health application. Adv Parasitol 47: 1–35.SI Hay2000An overview of remote sensing and geodesy for epidemiology and public health application.Adv Parasitol47135
  45. 45. Hay SI, Lennon JJ (1999) Deriving meteorological variables across Africa for the study and control of vector-borne disease: A comparison of remote sensing and spatial interpolation of climate. Trop Med Int Health 4: 58–71.SI HayJJ Lennon1999Deriving meteorological variables across Africa for the study and control of vector-borne disease: A comparison of remote sensing and spatial interpolation of climate.Trop Med Int Health45871
  46. 46. Hay SI, Tucker CJ, Rogers DJ, Packer MJ (1996) Remotely sensed surrogates of meteorological data for the study of the distribution and abundance of arthropod vectors of disease. Ann Trop Med Parasitol 90: 1–19.SI HayCJ TuckerDJ RogersMJ Packer1996Remotely sensed surrogates of meteorological data for the study of the distribution and abundance of arthropod vectors of disease.Ann Trop Med Parasitol90119
  47. 47. Hay SI, Tatem AJ, Graham AJ, Goetz SJ, Rogers DJ (2006) Global environmental data for mapping infectious disease distribution. Adv Parasitol 62: 37–77.SI HayAJ TatemAJ GrahamSJ GoetzDJ Rogers2006Global environmental data for mapping infectious disease distribution.Adv Parasitol623777
  48. 48. Tatem AJ, Goetz SJ, Hay SI (2004) Terra and Aqua: New data for epidemiology and public health. Int J Appl Earth Obs Geoinform 6: 33–46.AJ TatemSJ GoetzSI Hay2004Terra and Aqua: New data for epidemiology and public health.Int J Appl Earth Obs Geoinform63346
  49. 49. Clark JS (2003) Uncertainty in ecological inference and forecasting. Ecology 84: 1349–1350.JS Clark2003Uncertainty in ecological inference and forecasting.Ecology8413491350
  50. 50. Augustin NH, Mugglestone MA, Buckland ST (1996) An autologistic model for the spatial distribution of wildlife. J Appl Ecol 33: 339–347.NH AugustinMA MugglestoneST Buckland1996An autologistic model for the spatial distribution of wildlife.J Appl Ecol33339347
  51. 51. Brooker S, Hay SI, Bundy DAP (2002) Tools from ecology: Useful for evaluating infection risk models? Trends Parasitol 18: 70–74.S. BrookerSI HayDAP Bundy2002Tools from ecology: Useful for evaluating infection risk models?Trends Parasitol187074
  52. 52. Rogers DJ (2006) Models for vectors and vector-borne diseases. Adv Parasitol 62: 1–35.DJ Rogers2006Models for vectors and vector-borne diseases.Adv Parasitol62135
  53. 53. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, et al. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129–151.J. ElithCH GrahamRP AndersonM. DudikS. Ferrier2006Novel methods improve prediction of species' distributions from occurrence data.Ecography29129151
  54. 54. Segurado P, Araujo MB (2004) An evaluation of methods for modelling species distributions. J Biogeography 31: 1555–1568.P. SeguradoMB Araujo2004An evaluation of methods for modelling species distributions.J Biogeography3115551568
  55. 55. Gemperli A, Vounatsou P (2006) Strategies for fitting large, geostatistical data in MCMC simulation. Commun Stat Simul Comput 35: 331–345.A. GemperliP. Vounatsou2006Strategies for fitting large, geostatistical data in MCMC simulation.Commun Stat Simul Comput35331345
  56. 56. Balk DL, Deichmann U, Yetman G, Pozzi F, Hay SI, et al. (2006) Determining global population distribution: Methods, applications and data. Adv Parasitol 62: 119–156.DL BalkU. DeichmannG. YetmanF. PozziSI Hay2006Determining global population distribution: Methods, applications and data.Adv Parasitol62119156
  57. 57. Hay SI, Noor AM, Nelson A, Tatem AJ (2005) The accuracy of human population maps for public health application. Trop Med Int Health 10: 1073–1086.SI HayAM NoorA. NelsonAJ Tatem2005The accuracy of human population maps for public health application.Trop Med Int Health1010731086
  58. 58. Korenromp EL, Williams BG, de Vlas SJ, Gouws E, Gilks CF, et al. (2005) Malaria attributable to the HIV-1 epidemic, sub-Saharan Africa. Emerg Infect Dis 11: 1410–1419.EL KorenrompBG WilliamsSJ de VlasE. GouwsCF Gilks2005Malaria attributable to the HIV-1 epidemic, sub-Saharan Africa.Emerg Infect Dis1114101419
  59. 59. Mwangi TW, Bethony J, Brooker S (2006) Worms and malaria interactions: An epidemiological viewpoint. Ann Trop Med Parasitol 100: 551–570.TW MwangiJ. BethonyS. Brooker2006Worms and malaria interactions: An epidemiological viewpoint.Ann Trop Med Parasitol100551570
  60. 60. Murray CJL, Lopez AD, Wibulpolprasert S (2004) Monitoring global health: Time for new solutions. BMJ 329: 1096–1100.CJL MurrayAD LopezS. Wibulpolprasert2004Monitoring global health: Time for new solutions.BMJ32910961100
  61. 61. Attaran A (2005) An immeasurable crisis? A criticism of the millennium development goals and why they cannot be measured. PLoS Med 2: e318.A. Attaran2005An immeasurable crisis? A criticism of the millennium development goals and why they cannot be measured.PLoS Med2e318
  62. 62. Horton R (2005) The Ellison Institute: Monitoring health, challenging WHO. Lancet 366: 179–181.R. Horton2005The Ellison Institute: Monitoring health, challenging WHO.Lancet366179181
  63. 63. Hay SI, Guerra CA, Tatem AJ, Noor AM, Snow RW (2004) The global distribution and population at risk of malaria: Past, present, and future. Lancet Infect Dis 4: 327–336.SI HayCA GuerraAJ TatemAM NoorRW Snow2004The global distribution and population at risk of malaria: Past, present, and future.Lancet Infect Dis4327336
  64. 64. Feddema JJ, Oleson KW, Bonan GB, Mearns LO, Buja LE, et al. (2005) The importance of land-cover change in simulating future climates. Science 310: 1674–1678.JJ FeddemaKW OlesonGB BonanLO MearnsLE Buja2005The importance of land-cover change in simulating future climates.Science31016741678
  65. 65. Guerra CA, Snow RW, Hay SI (2006) A global assessment of closed forests, deforestation and malaria risk. Ann Trop Med Parasitol 100: 189–204.CA GuerraRW SnowSI Hay2006A global assessment of closed forests, deforestation and malaria risk.Ann Trop Med Parasitol100189204
  66. 66. Hay SI, Tatem AJ, Guerra CA, Snow RW (2006) Infectious diseases: Preparing for the future. London: Department of Trade and Industry. SI HayAJ TatemCA GuerraRW Snow2006Infectious diseases: Preparing for the future.Report T8.2: Population at malaria risk in Africa: 2005, 2015 and 2030. LondonDepartment of Trade and IndustryAvailable: http://www.foresight.gov.uk/Previous_Projects/Detection_and_Identification_of_Infectious_Diseases/Reports_and_Publications/Final_Reports/T/T8_2.pdf. Accessed 10 September 2006. Available: http://www.foresight.gov.uk/Previous_Projects/Detection_and_Identification_of_Infectious_Diseases/Reports_and_Publications/Final_Reports/T/T8_2.pdf. Accessed 10 September 2006.
  67. 67. Snow RW, Hay SI, Marsh K (2006) Infectious diseases: Preparing for the future. London: Department of Trade and Industry. RW SnowSI HayK. Marsh2006Infectious diseases: Preparing for the future.Report T5.8: Malaria in Africa: Sources, risks, drivers and disease burden: 2005–2030. LondonDepartment of Trade and Industryhttp://www.foresight.gov.uk/Previous_Projects/Detection_and_Identification_of_Infectious_Diseases/Reports_and_Publications/Final_Reports/T/t5_8.pdf. Accessed 10 September 2006. http://www.foresight.gov.uk/Previous_Projects/Detection_and_Identification_of_Infectious_Diseases/Reports_and_Publications/Final_Reports/T/t5_8.pdf. Accessed 10 September 2006.
  68. 68. Jovani R, Tella JL (2006) Parasite prevalence and sample size: Misconceptions and solutions. Trends Parasitol 22: 214–218.R. JovaniJL Tella2006Parasite prevalence and sample size: Misconceptions and solutions.Trends Parasitol22214218