The effects of spatial population dataset choice on estimates of population at risk of disease

Background

The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.

Methods

The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.

Results

The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.

Conclusions

Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

Testing spatiotemporal hypothesis of bacterial evolution using methicillin-resistant Staphylococcus aureus ST239 genome-wide data within a bayesian framework

Staphylococcus aureus is a common cause of infections that has undergone rapid global spread over recent decades. Formal phylogeographic methods have not yet been applied to the molecular epidemiology of bacterial pathogens because the limited genetic diversity of data sets based on individual genes usually results in poor phylogenetic resolution. Here, we investigated a whole-genome single nucleotide polymorphism (SNP) data set of health care-associated Methicillin-resistant S. aureus sequence type 239 (HA-MRSA ST239) strains, which we analyzed using Markov spatial models that incorporate geographical sampling distributions. The reconstructed timescale indicated a temporal origin of this strain shortly after the introduction of Methicillin, followed by global pandemic spread. The estimate of the temporal origin was robust to the molecular clock, coalescent prior, full/intergenic/synonymous SNP inclusion, and correction for excluded invariant site patterns. Finally, phylogeographic analyses statistically supported the role of human movement in the global dissemination of HA-MRSA ST239, although it was unable to conclusively resolve the location of the root. This study demonstrates that bacterial genomes can indeed contain sufficient evolutionary information to elucidate the temporal and spatial dynamics of transmission. Future applications of this approach to other bacterial strains may provide valuable epidemiological insights that may justify the cost of genome-wide typing.

Ranking of elimination feasibility between malaria-endemic countries

Experience gained from the Global Malaria Eradication Program (1955-72) identified a set of shared technical and operational factors that enabled some countries to successfully eliminate malaria. Spatial data for these factors were assembled for all malaria-endemic countries and combined to provide an objective, relative ranking of countries by technical, operational, and combined elimination feasibility. The analysis was done separately for Plasmodium falciparum and Plasmodium vivax, and the limitations of the approach were discussed. The relative rankings suggested that malaria elimination would be most feasible in countries in the Americas and Asia, and least feasible in countries in central and west Africa. The results differed when feasibility was measured by technical or operational factors, highlighting the different types of challenge faced by each country. The results are not intended to be prescriptive, predictive, or to provide absolute assessments of feasibility, but they do show that spatial information is available to facilitate evidence-based assessments of the relative feasibility of malaria elimination by country that can be rapidly updated.

Mapping malaria risk in Bangladesh using Bayesian geostatistical models

Background malaria-control programs are increasingly dependent on accurate risk maps to effectively guide the allocation of interventions and resources. Advances in model-based geostatistics and geographical information systems (GIS) have enabled researchers to better understand factors affecting malaria transmission and thus, more accurately determine the limits of malaria transmission globally and nationally. Here, we construct Plasmodium falciparum risk maps for Bangladesh for 2007 at a scale enabling the malaria-control bodies to more accurately define the needs of the program. A comprehensive malaria-prevalence survey (N = 9,750 individuals; N = 354 communities) was carried out in 2007 across the regions of Bangladesh known to be endemic for malaria. Data were corrected to a standard age range of 2 to less than 10 years. Bayesian geostatistical logistic regression models with environmental covariates were used to predict P. falciparum prevalence for 2- to 10-year-old children (PfPR(2-10)) across the endemic areas of Bangladesh. The predictions were combined with gridded population data to estimate the number of individuals living in different endemicity classes. Across the endemic areas, the average PfPR(2-10) was 3.8%. Environmental variables selected for prediction were vegetation cover, minimum temperature, and elevation. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. Risk maps generated from the model showed a heterogeneous distribution of PfPR(2-10) ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR(2-10) greater than 1%. Contemporary GIS and model-based geostatistics can be used to interpolate malaria risk in Bangladesh. Importantly, malaria risk was found to be highly varied across the endemic regions, necessitating the targeting of resources to reduce the burden in these areas.

Phylodynamics and human-mediated dispersal of a zoonotic virus

Understanding the role of humans in the dispersal of predominately animal pathogens is essential for their control. We used newly developed Bayesian phylogeographic methods to unravel the dynamics and determinants of the spread of dog rabies virus (RABV) in North Africa. Each of the countries studied exhibited largely disconnected spatial dynamics with major geo-political boundaries acting as barriers to gene flow. Road distances proved to be better predictors of the movement of dog RABV than accessibility or raw geographical distance, with occasional long distance and rapid spread within each of these countries. Using simulations that bridge phylodynamics and spatial epidemiology, we demonstrate that the contemporary viral distribution extends beyond that expected for RABV transmission in African dog populations. These results are strongly supportive of human-mediated dispersal, and demonstrate how an integrated phylogeographic approach will turn viral genetic data into a powerful asset for characterizing, predicting, and potentially controlling the spatial spread of pathogens.

At least 15 million doses of anti-rabies post-exposure prophylaxis are administered annually worldwide, and an estimated 55,000 people die of rabies every year. Over 99% of these deaths occur in developing countries, predominantly in Asia and in Africa where rabies is endemic in domestic dogs. Despite the global health burden due to rabies, little is known about the patterns of the spread of dog rabies in these endemic regions. We used recently developed Bayesian analytical methods to unravel the dynamics and determinants of the spatial diffusion of dog rabies viruses in North Africa based on viral genetic data. Our analysis reveals a combination of restricted spread across administrative borders, the occasional long-distance movement of rabies viruses, and a strong fit between spatial spread of the virus and road distances between localities. Together, these data indicate that by transporting dogs, humans have played a key role in the dispersal of a major animal pathogen. Our studies therefore provide essential new information on the transmission dynamics of rabies in Africa, and in doing so will greatly assist in future intervention strategies.

A high resolution spatial population database of Somalia for disease risk mapping

BACKGROUND

Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data.

RESULTS

Here land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 × 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach.

CONCLUSIONS

The results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.org.

Measles hotspots and epidemiological connectivity

Though largely controlled in developed countries, measles remains a major global public health issue. Regional and local transmission patterns are rooted in human mixing behaviour across spatial scales. Identifying spatial interactions that contribute to recurring epidemics helps define and predict outbreak patterns. Using spatially explicit reported cases from measles outbreaks in Niger, we explored how regional variations in movement and contact patterns relate to patterns of measles incidence. Because we expected to see lower rates of re-introductions in small, compared to large, populations, we measured the population-size corrected proportion of weeks with zero cases across districts to understand relative rates of measles re-introductions. We found that critical elements of spatial disease dynamics in Niger are agricultural seasonality, transnational contact clusters, and roads networks that facilitate host movement and connectivity. These results highlight the need to understand local patterns of seasonality, demographic characteristics, and spatial heterogeneities to inform vaccination policy.

The international limits and population at risk of Plasmodium vivax transmission in 2009

BACKGROUND

A research priority for Plasmodium vivax malaria is to improve our understanding of the spatial distribution of risk and its relationship with the burden of P. vivax disease in human populations. The aim of the research outlined in this article is to provide a contemporary evidence-based map of the global spatial extent of P. vivax malaria, together with estimates of the human population at risk (PAR) of any level of transmission in 2009.

METHODOLOGY

The most recent P. vivax case-reporting data that could be obtained for all malaria endemic countries were used to classify risk into three classes: malaria free, unstable (<0.1 case per 1,000 people per annum (p.a.)) and stable (> or =0.1 case per 1,000 p.a.) P. vivax malaria transmission. Risk areas were further constrained using temperature and aridity data based upon their relationship with parasite and vector bionomics. Medical intelligence was used to refine the spatial extent of risk in specific areas where transmission was reported to be absent (e.g., large urban areas and malaria-free islands). The PAR under each level of transmission was then derived by combining the categorical risk map with a high resolution population surface adjusted to 2009. The exclusion of large Duffy negative populations in Africa from the PAR totals was achieved using independent modelling of the gene frequency of this genetic trait. It was estimated that 2.85 billion people were exposed to some risk of P. vivax transmission in 2009, with 57.1% of them living in areas of unstable transmission. The vast majority (2.59 billion, 91.0%) were located in Central and South East (CSE) Asia, whilst the remainder were located in America (0.16 billion, 5.5%) and in the Africa+ region (0.10 billion, 3.5%). Despite evidence of ubiquitous risk of P. vivax infection in Africa, the very high prevalence of Duffy negativity throughout Central and West Africa reduced the PAR estimates substantially.

CONCLUSIONS

After more than a century of development and control, P. vivax remains more widely distributed than P. falciparum and is a potential cause of morbidity and mortality amongst the 2.85 billion people living at risk of infection, the majority of whom are in the tropical belt of CSE Asia. The probability of infection is reduced massively across Africa by the frequency of the Duffy negative trait, but transmission does occur on the continent and is a concern for Duffy positive locals and travellers. The final map provides the spatial limits on which the endemicity of P. vivax transmission can be mapped to support future cartographic-based burden estimations.

Estimating the global clinical burden of Plasmodium falciparum malaria in 2007

BACKGROUND

The epidemiology of malaria makes surveillance-based methods of estimating its disease burden problematic. Cartographic approaches have provided alternative malaria burden estimates, but there remains widespread misunderstanding about their derivation and fidelity. The aims of this study are to present a new cartographic technique and its application for deriving global clinical burden estimates of Plasmodium falciparum malaria for 2007, and to compare these estimates and their likely precision with those derived under existing surveillance-based approaches.

METHODS AND FINDINGS

In seven of the 87 countries endemic for P. falciparum malaria, the health reporting infrastructure was deemed sufficiently rigorous for case reports to be used verbatim. In the remaining countries, the mapped extent of unstable and stable P. falciparum malaria transmission was first determined. Estimates of the plausible incidence range of clinical cases were then calculated within the spatial limits of unstable transmission. A modelled relationship between clinical incidence and prevalence was used, together with new maps of P. falciparum malaria endemicity, to estimate incidence in areas of stable transmission, and geostatistical joint simulation was used to quantify uncertainty in these estimates at national, regional, and global scales. Combining these estimates for all areas of transmission risk resulted in 451 million (95% credible interval 349-552 million) clinical cases of P. falciparum malaria in 2007. Almost all of this burden of morbidity occurred in areas of stable transmission. More than half of all estimated P. falciparum clinical cases and associated uncertainty occurred in India, Nigeria, the Democratic Republic of the Congo (DRC), and Myanmar (Burma), where 1.405 billion people are at risk. Recent surveillance-based methods of burden estimation were then reviewed and discrepancies in national estimates explored. When these cartographically derived national estimates were ranked according to their relative uncertainty and replaced by surveillance-based estimates in the least certain half, 98% of the global clinical burden continued to be estimated by cartographic techniques.

CONCLUSIONS AND SIGNIFICANCE

Cartographic approaches to burden estimation provide a globally consistent measure of malaria morbidity of known fidelity, and they represent the only plausible method in those malaria-endemic countries with nonfunctional national surveillance. Unacceptable uncertainty in the clinical burden of malaria in only four countries confounds our ability to evaluate needs and monitor progress toward international targets for malaria control at the global scale. National prevalence surveys in each nation would reduce this uncertainty profoundly. Opportunities for further reducing uncertainty in clinical burden estimates by hybridizing alternative burden estimation procedures are also evaluated.

Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu

Background

The Ministry of Health in the Republic of Vanuatu has implemented a malaria elimination programme in Tafea Province, the most southern and eastern limit of malaria transmission in the South West Pacific. Tafea Province is comprised of five islands with malaria elimination achieved on one of these islands (Aneityum) in 1998. The current study aimed to establish the baseline distribution of malaria on the most malarious of the province's islands, Tanna Island, to guide the implementation of elimination activities.

Methods

A parasitological survey was conducted in Tafea Province in 2008. On Tanna Island there were 4,716 participants from 220 villages, geo-referenced using a global position system. Spatial autocorrelation in observed prevalence values was assessed using a semivariogram. Backwards step-wise regression analysis was conducted to determine the inclusion of environmental and climatic variables into a prediction model. The Bayesian geostatistical logistic regression model was used to predict malaria risk, and associated uncertainty across the island.

Results

Overall, prevalence on Tanna was 1.0% for Plasmodium falciparum (accounting for 32% of infections) and 2.2% for Plasmodium vivax (accounting for 68% of infections). Regression analysis showed significant association with elevation and distance to coastline for P. vivax and P. falciparum, but no significant association with NDVI or TIR. Colinearity was observed between elevation and distance to coastline with the later variable included in the final Bayesian geostatistical model for P. vivax and the former included in the final model for P. falciparum. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability.

Conclusion

Malaria in Tanna Island, Vanuatu, has a focal and predominantly coastal distribution. As Vanuatu refines its elimination strategy, malaria risk maps represent an invaluable resource in the strategic planning of all levels of malaria interventions for the island.

Climate change and the global malaria recession

The current and potential future impact of climate change on malaria is of major public health interest. The proposed effects of rising global temperatures on the future spread and intensification of the disease, and on existing malaria morbidity and mortality rates, substantively influence global health policy. The contemporary spatial limits of Plasmodium falciparum malaria and its endemicity within this range, when compared with comparable historical maps, offer unique insights into the changing global epidemiology of malaria over the last century. It has long been known that the range of malaria has contracted through a century of economic development and disease control. Here, for the first time, we quantify this contraction and the global decreases in malaria endemicity since approximately 1900. We compare the magnitude of these changes to the size of effects on malaria endemicity proposed under future climate scenarios and associated with widely used public health interventions. Our findings have two key and often ignored implications with respect to climate change and malaria. First, widespread claims that rising mean temperatures have already led to increases in worldwide malaria morbidity and mortality are largely at odds with observed decreasing global trends in both its endemicity and geographic extent. Second, the proposed future effects of rising temperatures on endemicity are at least one order of magnitude smaller than changes observed since about 1900 and up to two orders of magnitude smaller than those that can be achieved by the effective scale-up of key control measures. Predictions of an intensification of malaria in a warmer world, based on extrapolated empirical relationships or biological mechanisms, must be set against a context of a century of warming that has seen marked global declines in the disease and a substantial weakening of the global correlation between malaria endemicity and climate.

Quantifying the number of pregnancies at risk of malaria in 2007: a demographic study

BACKGROUND

Comprehensive and contemporary estimates of the number of pregnancies at risk of malaria are not currently available, particularly for endemic areas outside of Africa. We derived global estimates of the number of women who became pregnant in 2007 in areas with Plasmodium falciparum and P. vivax transmission.

METHODS AND FINDINGS

A recently published map of the global limits of P. falciparum transmission and an updated map of the limits of P. vivax transmission were combined with gridded population data and growth rates to estimate total populations at risk of malaria in 2007. Country-specific demographic data from the United Nations on age, sex, and total fertility rates were used to estimate the number of women of child-bearing age and the annual rate of live births. Subregional estimates of the number of induced abortions and country-specific stillbirths rates were obtained from recently published reviews. The number of miscarriages was estimated from the number of live births and corrected for induced abortion rates. The number of clinically recognised pregnancies at risk was then calculated as the sum of the number of live births, induced abortions, spontaneous miscarriages, and stillbirths among the population at risk in 2007. In 2007, 125.2 million pregnancies occurred in areas with P. falciparum and/or P. vivax transmission resulting in 82.6 million live births. This included 77.4, 30.3, 13.1, and 4.3 million pregnancies in the countries falling under the World Health Organization (WHO) regional offices for South-East-Asia (SEARO) and the Western-Pacific (WPRO) combined, Africa (AFRO), Europe and the Eastern Mediterranean (EURO/EMRO), and the Americas (AMRO), respectively. Of 85.3 million pregnancies in areas with P. falciparum transmission, 54.7 million occurred in areas with stable transmission and 30.6 million in areas with unstable transmission (clinical incidence <1 per 10,000 population/year); 92.9 million occurred in areas with P. vivax transmission, 53.0 million of which occurred in areas in which P. falciparum and P. vivax co-exist and 39.9 million in temperate regions with P. vivax transmission only.

CONCLUSIONS

In 2007, 54.7 million pregnancies occurred in areas with stable P. falciparum malaria and a further 70.5 million in areas with exceptionally low malaria transmission or with P. vivax only. These represent the first contemporary estimates of the global distribution of the number of pregnancies at risk of P. falciparum and P. vivax malaria and provide a first step towards a more informed estimate of the geographical distribution of infection rates and the corresponding disease burden of malaria in pregnancy.

The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents

BACKGROUND

Malaria endemicity in Zanzibar has reached historically low levels, and the epidemiology of malaria transmission is in transition. To capitalize on these gains, Zanzibar has commissioned a feasibility assessment to help inform on whether to move to an elimination campaign. Declining local transmission has refocused attention on imported malaria. Recent studies have shown that anonimized mobile phone records provide a valuable data source for characterizing human movements without compromising the privacy of phone users. Such movement data in combination with spatial data on P. falciparum endemicity provide a way of characterizing the patterns of parasite carrier movements and the rates of malaria importation, which have been used as part of the malaria elimination feasibility assessment for the islands of Zanzibar.

DATA AND METHODS

Records encompassing three months of complete mobile phone usage for the period October-December 2008 were obtained from the Zanzibar Telecom (Zantel) mobile phone network company, the principal provider on the islands of Zanzibar. The data included the dates of all phone usage by 770,369 individual anonymous users. Each individual call and message was spatially referenced to one of six areas: Zanzibar and five mainland Tanzania regions. Information on the numbers of Zanzibar residents travelling to the mainland, locations visited and lengths of stay were extracted. Spatial and temporal data on P. falciparum transmission intensity and seasonality enabled linkage of this information to endemicity exposure and, motivated by malaria transmission models, estimates of the expected patterns of parasite importation to be made.

RESULTS

Over the three month period studied, 88% of users made calls that were routed only through masts on Zanzibar, suggesting that no long distance travel was undertaken by this group. Of those who made calls routed through mainland masts the vast majority of trips were estimated to be of less than five days in length, and to the Dar Es Salaam Zantel-defined region. Though this region covered a wide range of transmission intensities, data on total infection numbers in Zanzibar combined with mathematical models enabled informed estimation of transmission exposure and imported infection numbers. These showed that the majority of trips made posed a relatively low risk for parasite importation, but risk groups visiting higher transmission regions for extended periods of time could be identified.

CONCLUSION

Anonymous mobile phone records provide valuable information on human movement patterns in areas that are typically data-sparse. Estimates of human movement patterns from Zanzibar to mainland Tanzania suggest that imported malaria risk from this group is heterogeneously distributed; a few people account for most of the risk for imported malaria. In combination with spatial data on malaria endemicity and transmission models, movement patterns derived from phone records can inform on the likely sources and rates of malaria importation. Such information is important for assessing the feasibility of malaria elimination and planning an elimination campaign.

A world malaria map: Plasmodium falciparum endemicity in 2007

BACKGROUND

Efficient allocation of resources to intervene against malaria requires a detailed understanding of the contemporary spatial distribution of malaria risk. It is exactly 40 y since the last global map of malaria endemicity was published. This paper describes the generation of a new world map of Plasmodium falciparum malaria endemicity for the year 2007.

METHODS AND FINDINGS

A total of 8,938 P. falciparum parasite rate (PfPR) surveys were identified using a variety of exhaustive search strategies. Of these, 7,953 passed strict data fidelity tests for inclusion into a global database of PfPR data, age-standardized to 2-10 y for endemicity mapping. A model-based geostatistical procedure was used to create a continuous surface of malaria endemicity within previously defined stable spatial limits of P. falciparum transmission. These procedures were implemented within a Bayesian statistical framework so that the uncertainty of these predictions could be evaluated robustly. The uncertainty was expressed as the probability of predicting correctly one of three endemicity classes; previously stratified to be an informative guide for malaria control. Population at risk estimates, adjusted for the transmission modifying effects of urbanization in Africa, were then derived with reference to human population surfaces in 2007. Of the 1.38 billion people at risk of stable P. falciparum malaria, 0.69 billion were found in Central and South East Asia (CSE Asia), 0.66 billion in Africa, Yemen, and Saudi Arabia (Africa+), and 0.04 billion in the Americas. All those exposed to stable risk in the Americas were in the lowest endemicity class (PfPR2-10 < or = 5%). The vast majority (88%) of those living under stable risk in CSE Asia were also in this low endemicity class; a small remainder (11%) were in the intermediate endemicity class (PfPR2-10 > 5 to < 40%); and the remaining fraction (1%) in high endemicity (PfPR2-10 > or = 40%) areas. High endemicity was widespread in the Africa+ region, where 0.35 billion people are at this level of risk. Most of the rest live at intermediate risk (0.20 billion), with a smaller number (0.11 billion) at low stable risk.

CONCLUSIONS

High levels of P. falciparum malaria endemicity are common in Africa. Uniformly low endemic levels are found in the Americas. Low endemicity is also widespread in CSE Asia, but pockets of intermediate and very rarely high transmission remain. There are therefore significant opportunities for malaria control in Africa and for malaria elimination elsewhere. This 2007 global P. falciparum malaria endemicity map is the first of a series with which it will be possible to monitor and evaluate the progress of this intervention process.

The worldwide airline network and the dispersal of exotic species: 2007-2010

International air travel has played a significant role in driving recent increases in the rates of biological invasion and spread of infectious diseases. By providing high speed, busy transport links between spatially distant, but climatically similar regions of the world, the worldwide airline network (WAN) increases the risks of deliberate or accidental movements and establishment of climatically sensitive exotic organisms. With traffic levels continuing to rise and climates changing regionally, these risks will vary, both seasonally and year-by-year. Here, detailed estimates of air traffic trends and climate changes for the period 2007-2010 are used to examine the likely directions and magnitudes of changes in climatically sensitive organism invasion risk across the WAN. Analysis of over 144 million flights from 2007-2010 shows that by 2010, the WAN is likely to change little overall in terms of connecting regions with similar climates, but anticipated increases in traffic and local variations in climatic changes should increase the risks of exotic species movement on the WAN and establishment in new areas. These overall shifts mask spatially and temporally heterogenous changes across the WAN, where, for example, traffic increases and climatic convergence by July 2010 between parts of China and northern Europe and North America raise the likelihood of exotic species invasions, whereas anticipated climatic shifts may actually reduce invasion risks into much of eastern Europe.

Insecticide-treated net coverage in Africa: mapping progress in 2000-07

BACKGROUND

Insecticide-treated bednets (ITNs) provide a means to improve child survival across Africa. Sales figures of these nets and survey coverage data presented nationally mask inequities in populations at biological and economic risk, and do not allow for precision in the estimation of unmet commodity needs. We gathered subnational ITN coverage sample survey data from 40 malaria-endemic countries in Africa between 2000 and 2007.

METHODS

We computed the projected ITN coverage among children aged less than 5 years for age-adjusted population data that were stratified according to malaria transmission risks, proximate determinants of poverty, and methods of ITN delivery.

FINDINGS

In 2000, only 1.7 million (1.8%) African children living in stable malaria-endemic conditions were protected by an ITN and the number increased to 20.3 million (18.5%) by 2007 leaving 89.6 million children unprotected. Of these, 30 million were living in some of the poorest areas of Africa: 54% were living in only seven countries and 25% in Nigeria alone. Overall, 33 (83%) countries were estimated to have ITN coverage of less than 40% in 2007. On average, we noted a greater increase in ITN coverage in areas where free distribution had operated between survey periods.

INTERPRETATION

By mapping the distribution of populations in relation to malaria risk and intervention coverage, we provide a means to track the future requirements for scaling up essential disease-prevention strategies. The present coverage of ITN in Africa remains inadequate and a focused effort to improve distribution in selected areas would have a substantial effect on the continent's malaria burden.

Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity

BACKGROUND

The efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (PfPR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.

METHODS

First, an existing population surface was examined to determine if it was sufficiently detailed to be used reliably as a mask to identify areas of very low and very high population density as malaria free regions. Second, the potential of international travel and health guidelines (ITHGs) for identifying malaria free cities was examined. Third, the differences in PfPR values between surveys conducted in author-defined rural and urban areas were examined. Fourth, the ability of various global urban extent maps to reliably discriminate these author-based classifications of urban and rural in the PfPR database was investigated. Finally, the urban map that most accurately replicated the author-based classifications was analysed to examine the effects of urban classifications on PfPR values across the entire MAP database.

RESULTS

Masks of zero population density excluded many non-zero PfPR surveys, indicating that the population surface was not detailed enough to define areas of zero transmission resulting from low population densities. In contrast, the ITHGs enabled the identification and mapping of 53 malaria free urban areas within endemic countries. Comparison of PfPR survey results showed significant differences between author-defined 'urban' and 'rural' designations in Africa, but not for the remainder of the malaria endemic world. The Global Rural Urban Mapping Project (GRUMP) urban extent mask proved most accurate for mapping these author-defined rural and urban locations, and further sub-divisions of urban extents into urban and peri-urban classes enabled the effects of high population densities on malaria transmission to be mapped and quantified.

CONCLUSION

The availability of detailed, contemporary census and urban extent data for the construction of coherent and accurate global spatial population databases is often poor. These known sources of uncertainty in population surfaces and urban maps have the potential to be incorporated into future malaria burden estimates. Currently, insufficient spatial information exists globally to identify areas accurately where population density is low enough to impact upon transmission. Medical intelligence does however exist to reliably identify malaria free cities. Moreover, in Africa, urban areas that have a significant effect on malaria transmission can be mapped.

Using remotely sensed night-time light as a proxy for poverty in Africa

BACKGROUND

Population health is linked closely to poverty. To assess the effectiveness of health interventions it is critical to monitor the spatial and temporal changes in the health indicators of populations and outcomes across varying levels of poverty. Existing measures of poverty based on income, consumption or assets are difficult to compare across geographic settings and are expensive to construct. Remotely sensed data on artificial night time lights (NTL) have been shown to correlate with gross domestic product in developed countries.

METHODS

Using national household survey data, principal component analysis was used to compute asset-based poverty indices from aggregated household asset variables at the Administrative 1 level (n = 338) in 37 countries in Africa. Using geographical information systems, mean brightness of and distance to NTL pixels and proportion of area covered by NTL were computed for each Administrative1 polygon. Correlations and agreement of asset-based indices and the three NTL metrics were then examined in both continuous and ordinal forms.

RESULTS

At the Administrative 1 level all the NTL metrics distinguished between the most poor and least poor quintiles with greater precision compared to intermediate quintiles. The mean brightness of NTL, however, had the highest correlation coefficient with the asset-based wealth index in continuous (Pearson correlation = 0.64, p < 0.01) and ordinal (Spearman correlation = 0.79, p < 0.01; Kappa = 0.64) forms.

CONCLUSION

Metrics of the brightness of NTL data offer a robust and inexpensive alternative to asset-based poverty indices derived from survey data at the Administrative 1 level in Africa. These could be used to explore economic inequity in health outcomes and access to health interventions at sub-national levels where household assets data are not available at the required resolution.

The limits and intensity of Plasmodium falciparum transmission: implications for malaria control and elimination worldwide

BACKGROUND

The efficient allocation of financial resources for malaria control using appropriate combinations of interventions requires accurate information on the geographic distribution of malaria risk. An evidence-based description of the global range of Plasmodium falciparum malaria and its endemicity has not been assembled in almost 40 y. This paper aims to define the global geographic distribution of P. falciparum malaria in 2007 and to provide a preliminary description of its transmission intensity within this range.

METHODS AND FINDINGS

The global spatial distribution of P. falciparum malaria was generated using nationally reported case-incidence data, medical intelligence, and biological rules of transmission exclusion, using temperature and aridity limits informed by the bionomics of dominant Anopheles vector species. A total of 4,278 spatially unique cross-sectional survey estimates of P. falciparum parasite rates were assembled. Extractions from a population surface showed that 2.37 billion people lived in areas at any risk of P. falciparum transmission in 2007. Globally, almost 1 billion people lived under unstable, or extremely low, malaria risk. Almost all P. falciparum parasite rates above 50% were reported in Africa in a latitude band consistent with the distribution of Anopheles gambiae s.s. Conditions of low parasite prevalence were also common in Africa, however. Outside of Africa, P. falciparum malaria prevalence is largely hypoendemic (less than 10%), with the median below 5% in the areas surveyed.

CONCLUSIONS

This new map is a plausible representation of the current extent of P. falciparum risk and the most contemporary summary of the population at risk of P. falciparum malaria within these limits. For 1 billion people at risk of unstable malaria transmission, elimination is epidemiologically feasible, and large areas of Africa are more amenable to control than appreciated previously. The release of this information in the public domain will help focus future resources for P. falciparum malaria control and elimination.

Climatic similarity and biological exchange in the worldwide airline transportation network

Recent increases in the rates of biological invasion and spread of infectious diseases have been linked to the continued expansion of the worldwide airline transportation network (WAN). Here, the global structure of the WAN is analysed in terms of climatic similarity to illuminate the risk of deliberate or accidental movements of climatically sensitive organisms around the world. From over 44 000 flight routes, we show, for each month of an average year, (i) those scheduled routes that link the most spatially distant but climatically similar airports, (ii) the climatically best-connected airports, and (iii) clusters of airports with similar climatic features. The way in which traffic volumes alter these findings is also examined. Climatic similarity across the WAN is skewed (most geographically close airports are climatically similar) but heavy-tailed (there are considerable numbers of geographically distant but climatically similar airports), with climate similarity highest in the June–August period, matching the annual peak in air traffic. Climatically matched, geographically distant airports form subnetworks within the WAN that change throughout the year. Further, the incorporation of passenger and freight traffic data highlight at greater risk of invasion those airports that are climatically well connected by numerous high capacity routes.

High resolution population maps for low income nations: combining land cover and census in East Africa

Background

Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.

Methodology/Principal Findings

We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.

Conclusions

We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km². The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.

Investigating the endemic transmission of the hepatitis C virus

The hepatitis C virus (HCV) infects at least 3% of people worldwide and is a leading global cause of liver disease. Although HCV spread epidemically during the 20th century, particularly by blood transfusion, it has clearly been present in human populations for several centuries. Here we attempt to redress the paucity of investigation into how long-term endemic transmission of HCV has been maintained. Such transmission not only represents the 'natural' route of infection but also contributes to new infections today. As a first step, we investigate the hypothesis that HCV can be mechanically transmitted by biting arthropods. Firstly, we use a combined bioinformatic and geographic approach to build a spatial database of endemic HCV infection and demonstrate that this can be used to geographically compare endemic HCV with the range distributions of potential vector species. Second, we use models from mathematical epidemiology to investigate if the parameters that describe the biting behaviour of vectors are consistent with a proposed basic reproduction number (R0) for HCV, and with the sustained transmission of the virus by mechanical transmission. Our analyses indicate that the mechanical transmission of HCV is plausible and that much further research into endemic HCV is needed.

Assembling a global database of malaria parasite prevalence for the Malaria Atlas Project

BACKGROUND

Open access to databases of information generated by the research community can synergize individual efforts and are epitomized by the genome mapping projects. Open source models for outputs of scientific research funded by tax-payers and charities are becoming the norm. This has yet to be extended to malaria epidemiology and control.

METHODS

The exhaustive searches and assembly process for a global database of malaria parasite prevalence as part of the Malaria Atlas Project (MAP) are described. The different data sources visited and how productive these were in terms of availability of parasite rate (PR) data are presented, followed by a description of the methods used to assemble a relational database and an associated geographic information system. The challenges facing spatial data assembly from varied sources are described in an effort to help inform similar future applications.

RESULTS

At the time of writing, the MAP database held 3,351 spatially independent PR estimates from community surveys conducted since 1985. These include 3,036 Plasmodium falciparum and 1,347 Plasmodium vivax estimates in 74 countries derived from 671 primary sources. More than half of these data represent malaria prevalence after the year 2000.

CONCLUSION

This database will help refine maps of the global spatial limits of malaria and be the foundation for the development of global malaria endemicity models as part of MAP. A widespread application of these maps is envisaged. The data compiled and the products generated by MAP are planned to be released in June 2009 to facilitate a more informed approach to global malaria control.