Spatial prediction of Plasmodium falciparum prevalence in Somalia

Predicting Continental Scale Malaria With Land Surface Water Predictors Based on Malaria Dispersal Mechanisms and High-Resolution Earth Observation Data

Despite malaria prevalence being linked to surface water through vector breeding, spatial malaria predictors representing surface water often predict malaria poorly. Furthermore, precipitation, which precursors surface water, often performs better. Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high-resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high-accuracy and resolution (5 m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% R 2, +17% Relative Contribution) and over the set of simpler predictors (+18% R 2, +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated.

Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventions

BACKGROUND

Since 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach.

METHODS

This study used individual and household-level data from the 2015 and 2018 annual malaria indicator surveys on Bioko Island, as well as remotely-sensed environmental data in multilevel logistic regression models to quantify the odds of malaria infection. The analyses were stratified by urban and rural settings and by survey year.

RESULTS

Malaria prevalence was higher in 10-14-year-old children and similar between female and male individuals. After adjusting for demographic factors and other covariates, many of the variables investigated showed no significant association with malaria infection. The factor most strongly associated was history of travel to mainland Equatorial Guinea (mEG), which increased the odds significantly both in urban and rural settings (people who travelled had 4 times the odds of infection). Sleeping under a long-lasting insecticidal net decreased significantly the odds of malaria across urban and rural settings and survey years (net users had around 30% less odds of infection), highlighting their contribution to malaria control on the Island. Improved housing conditions indicated some protection, though this was not consistent across settings and survey year.

CONCLUSIONS

Malaria risk on Bioko Island is heterogeneous and determined by a combination of factors interacting with local mosquito ecology. These interactions grant further investigation in order to better adapt control according to need. The single most important risk factor identified was travel to mEG, in line with previous investigations, and represents a great challenge for the success of malaria control on the Island.

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Background

Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).

Methods

A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.

Results

One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.

Conclusions

Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys

BACKGROUND

Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies.

METHODS

Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded.

RESULTS

We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure.

CONCLUSIONS

The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa

Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.

Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions?

As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.

Co-morbidity of malnutrition with falciparum malaria parasitaemia among children under the aged 6-59 months in Somalia: a geostatistical analysis

Background

Malnutrition and malaria are both significant causes of morbidity and mortality in African children. However, the extent of their spatial comorbidity remains unexplored and an understanding of their spatial correlation structure would inform improvement of integrated interventions. We aimed to determine the spatial correlation between both wasting and low mid upper arm circumference (MUAC) and falciparum malaria among Somalian children aged 6–59 months.

Methods

Data were from 49 227 children living in 888 villages between 2007 to 2010. We developed a Bayesian geostatistical shared component model in order to determine the common spatial distributions of wasting and falciparum malaria; and low-MUAC and falciparum malaria at 1 × 1 km spatial resolution.

Results

The empirical correlations with malaria were 0.16 and 0.23 for wasting and low-MUAC respectively. Shared spatial residual effects were statistically significant for both wasting and low-MUAC. The posterior spatial relative risk was highest for low-MUAC and malaria (range: 0.19 to 5.40) and relatively lower between wasting and malaria (range: 0.11 to 3.55). Hotspots for both wasting and low-MUAC with malaria occurred in the South Central region in Somalia.

Conclusions

The findings demonstrate a relationship between nutritional status and falciparum malaria parasitaemia, and support the use of the relatively simpler MUAC measurement in surveys. Shared spatial distribution and distinct hotspots present opportunities for targeted seasonal chemoprophylaxis and other forms of malaria prevention integrated within nutrition programmes.

Electronic supplementary material

The online version of this article (10.1186/s40249-018-0449-9) contains supplementary material, which is available to authorized users.

Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia

Background

Countries planning malaria elimination must adapt from sustaining universal control to targeted intervention and surveillance. Decisions to make this transition require interpretable information, including malaria parasite survey data. As transmission declines, observed parasite prevalence becomes highly heterogeneous with most communities reporting estimates close to zero. Absolute estimates of prevalence become hard to interpret as a measure of transmission intensity and suitable statistical methods are required to handle uncertainty of area-wide predictions that are programmatically relevant.

Methods

A spatio-temporal geostatistical binomial model for Plasmodium falciparum prevalence (PfPR) was developed using data from cross-sectional surveys conducted in Somalia in 2005, 2007–2011 and 2014. The fitted model was then used to generate maps of non-exceedance probabilities, i.e. the predictive probability that the region-wide population-weighted average PfPR for children between 2 and 10 years (PfPR_2–10) lies below 1 and 5%. A comparison was carried out with the decision-making outcomes from those of standard approaches that ignore uncertainty in prevalence estimates.

Results

By 2010, most regions in Somalia were at least 70% likely to be below 5% PfPR_2–10 and, by 2014, 17 regions were below 5% PfPR_2–10 with a probability greater than 90%. Larger uncertainty is observed using a threshold of 1%. By 2011, only two regions were more than 90% likely of being < 1% PfPR_2–10 and, by 2014, only three regions showed such low level of uncertainty. The use of non-exceedance probabilities indicated that there was weak evidence to classify 10 out of the 18 regions as < 1% in 2014, when a greater than 90% non-exceedance probability was required.

Conclusion

Unlike standard approaches, non-exceedance probabilities of spatially modelled PfPR_2–10 allow to quantify uncertainty of prevalence estimates in relation to policy relevant intervention thresholds, providing programmatically relevant metrics to make decisions on transitioning from sustained malaria control to strategies that encompass methods of malaria elimination.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2238-0) contains supplementary material, which is available to authorized users.

Characterizing environmental suitability of Aedes albopictus (Diptera: Culicidae) in Mexico based on regional and global niche models

The Asian tiger mosquito, Aedes albopictus (Skuse) (Diptera: Culicidae), is an invasive species and a vector of numerous human pathogens, including chikungunya, dengue, yellow fever, and Zika viruses. This mosquito had been reported from 36 geographic locations in Mexico by 2005, increasing to 101 locations by 2010 and 501 locations (spanning 16 states) by 2016. Here we modeled the occupied niche for Ae. albopictus in Mexico to characterize the environmental conditions related to its presence, and to generate updated environmental suitability maps. The predictors with the greatest contribution to characterizing the occupied niche for Ae. albopictus were NDVI and annual mean temperature. We also estimated the environmental suitability for Ae. albopictus in regions of the country where it has not been documented yet, by means of: 1) transferring its occupied niche model to these regions and 2) modeling its fundamental niche using global data. Our models will help vector control and public health institutions to identify areas where Ae. albopictus has not yet been recorded but where it may be present. We emphasize that most of Mexico has environmental conditions that potentially allow the survival of Ae. albopictus, which underscores the need for systematic mosquito monitoring in all states of the country.

Mapping multiple components of malaria risk for improved targeting of elimination interventions

There is a long history of considering the constituent components of malaria risk and the malaria transmission cycle via the use of mathematical models, yet strategic planning in endemic countries tends not to take full advantage of available disease intelligence to tailor interventions. National malaria programmes typically make operational decisions about where to implement vector control and surveillance activities based upon simple categorizations of annual parasite incidence. With technological advances, an enormous opportunity exists to better target specific malaria interventions to the places where they will have greatest impact by mapping and evaluating metrics related to a variety of risk components, each of which describes a different facet of the transmission cycle. Here, these components and their implications for operational decision-making are reviewed. For each component, related mappable malaria metrics are also described which may be measured and evaluated by malaria programmes seeking to better understand the determinants of malaria risk. Implementing tailored programmes based on knowledge of the heterogeneous distribution of the drivers of malaria transmission rather than only consideration of traditional metrics such as case incidence has the potential to result in substantial improvements in decision-making. As programmes improve their ability to prioritize their available tools to the places where evidence suggests they will be most effective, elimination aspirations may become increasingly feasible.

Modelling the Ecological Comorbidity of Acute Respiratory Infection, Diarrhoea and Stunting among Children Under the Age of 5 Years in Somalia

The aim of this study was to assess spatial co-occurrence of acute respiratory infections (ARI), diarrhoea and stunting among children of the age between 6 and 59 months in Somalia. Data were obtained from routine biannual nutrition surveys conducted by the Food and Agriculture Organization 2007-2010. A Bayesian hierarchical geostatistical shared component model was fitted to the residual spatial components of the three health conditions. Risk maps of the common spatial effects at 1×1 km resolution were derived. The empirical correlations of the enumeration area proportion were 0.37, 0.63 and 0.66 for ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. Spatially, the posterior residual effects ranged 0.03-20.98, 0.16-6.37 and 0.08-9.66 for shared component between ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. The analysis showed clearly that the spatial shared component between ARI, diarrhoea and stunting was higher in the southern part of the country. Interventions aimed at controlling and mitigating the adverse effects of these three childhood health conditions should focus on their common putative risk factors, particularly in the South in Somalia.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Assessing comorbidity and correlates of wasting and stunting among children in Somalia using cross-sectional household surveys: 2007 to 2010

OBJECTIVE

Wasting and stunting may occur together at the individual child level; however, their shared geographic distribution and correlates remain unexplored. Understanding shared and separate correlates may inform interventions. We aimed to assess the spatial codistribution of wasting, stunting and underweight and investigate their shared correlates among children aged 6-59 months in Somalia.

SETTING

Cross-sectional nutritional assessments surveys were conducted using structured interviews among communities in Somalia biannually from 2007 to 2010. A two-stage cluster sampling methodology was used to select children aged 6-59 months from households across three livelihood zones (pastoral, agropastoral and riverine). Using these data and environmental covariates, we implemented a multivariate spatial technique to estimate the codistribution and divergence of the risks and correlates of wasting and stunting at the 1 × 1 km spatial resolution.

PARTICIPANTS

73,778 children aged 6-59 months from 1066 survey clusters in Somalia.

RESULTS

Observed pairwise child level empirical correlations were 0.30, 0.70 and 0.73 between weight-for-height and height-for-age; height-for-age and weight-for-age, and weight-for-height and weight-for-age, respectively. Access to foods with high protein content and vegetation cover, a proxy of rainfall or drought, were associated with lower risk of wasting and stunting. Age, gender, illness, access to carbohydrates and temperature were correlates of all three indicators. The spatial codistribution was highest between stunting and underweight with relative risk values ranging between 0.15 and 6.20, followed by wasting and underweight (range: 0.18-5.18) and lowest between wasting and stunting (range: 0.26-4.32).

CONCLUSIONS

The determinants of wasting and stunting are largely shared, but their correlation is relatively variable in space. Significant hotspots of different forms of malnutrition occurred in the South Central regions of the country. Although nutrition response in Somalia has traditionally focused on wasting rather than stunting, integrated programming and interventions can effectively target both conditions to alleviate common risk factors.

Estimating malaria transmission intensity from Plasmodium falciparum serological data using antibody density models

Background

Serological data are increasingly being used to monitor malaria transmission intensity and have been demonstrated to be particularly useful in areas of low transmission where traditional measures such as EIR and parasite prevalence are limited. The seroconversion rate (SCR) is usually estimated using catalytic models in which the measured antibody levels are used to categorize individuals as seropositive or seronegative. One limitation of this approach is the requirement to impose a fixed cut-off to distinguish seropositive and negative individuals. Furthermore, the continuous variation in antibody levels is ignored thereby potentially reducing the precision of the estimate.

Methods

An age-specific density model which mimics antibody acquisition and loss was developed to make full use of the information provided by serological measures of antibody levels. This was fitted to blood-stage antibody density data from 12 villages at varying transmission intensity in Northern Tanzania to estimate the exposure rate as an alternative measure of transmission intensity.

Results

The results show a high correlation between the exposure rate estimates obtained and the estimated SCR obtained from a catalytic model (r = 0.95) and with two derived measures of EIR (r = 0.74 and r = 0.81). Estimates of exposure rate obtained with the density model were also more precise than those derived from catalytic models.

Conclusion

This approach, if validated across different epidemiological settings, could be a useful alternative framework for quantifying transmission intensity, which makes more complete use of serological data.

Predictors of the risk of malnutrition among children under the age of 5 years in Somalia

OBJECTIVE

To investigate the predictors of wasting, stunting and low mid-upper arm circumference among children aged 6-59 months in Somalia using data from household cross-sectional surveys from 2007 to 2010 in order to help inform better targeting of nutritional interventions.

DESIGN

Cross-sectional nutritional assessment surveys using structured interviews were conducted among communities in Somalia each year from 2007 to 2010. A two-stage cluster sampling methodology was used to select children aged 6-59 months from households across three livelihood zones (pastoral, agro-pastoral and riverine). Predictors of three anthropometric measures, weight-for-height (wasting), height-for-age (stunting) and mid-upper arm circumference, were analysed using Bayesian binomial regression, controlling for both spatial and temporal dependence in the data.

SETTING

The study was conducted in randomly sampled villages, representative of three livelihood zones in Somalia.

SUBJECTS

Children between the ages of 6 and 59 months in Somalia.

RESULTS

The estimated national prevalence of wasting, stunting and low mid-upper arm circumference in children aged 6-59 months was 21 %, 31 % and 36 %, respectively. Although fever, diarrhoea, sex and age of the child, household size and access to foods were significant predictors of malnutrition, the strongest association was observed between all three indicators of malnutrition and the enhanced vegetation index. A 1-unit increase in enhanced vegetation index was associated with a 38 %, 49 % and 59 % reduction in wasting, stunting and low mid-upper arm circumference, respectively.

CONCLUSIONS

Infection and climatic variations are likely to be key drivers of malnutrition in Somalia. Better health data and close monitoring and forecasting of droughts may provide valuable information for nutritional intervention planning in Somalia.

Use of a bibliometric literature review to assess medical research capacity in post-conflict and developing countries: Somaliland 1991-2013

OBJECTIVES

Effective healthcare systems require high-quality research to guide evidence-based interventions and strategic planning. In low- and middle-income countries, especially those emerging from violent conflict, research capacity often lags behind other aspects of health system development. Here, we sought to bibliometrically review health-related research output in Somaliland, a post-conflict self-declared, autonomous nation on the Horn of Africa, as a means of assessing research capacity.

METHODS

We reviewed articles on health-related research conducted in Somaliland between 1991 and 2013 that included a description of the experimental design, and articles were published in either a peer-reviewed journal or as part of a scholarly programme receiving formal review. We did not include policy or social science research that did not enrol or interact with subjects from Somaliland. Using online databases, all studies meeting minimum eligibility criteria were reviewed in regard to Somaliland-based co-authorship, topic of research and specific measures of quality.

RESULTS

A total of 37 studies were included in this review. Of these, only 19 (51%) included co-authorship by Somaliland-based researchers. Of the 21 studies reporting ethical approval, 16 (64%) received approval from the Somalia or Somaliland Ministry of Health, while five received approval from a university or national commission. More than two-thirds of published research was limited to a few areas of investigation with most (19, 51%) following basic cross-sectional study designs. The number of articles published per year increased from 0 to 1 in the years 1991-2007 to a maximum of 8 in 2013.

CONCLUSIONS

Research activity in Somaliland is extremely limited. Investigators from high-income countries have largely directed the research agenda in Somaliland; only half of the included studies list co-authors from institutions in Somaliland. Leadership and governance of health research in Somaliland is required to define national priorities, promote scholarly activity and guide the responsible conduct of research. The methods used here to assess research capacity may be generalisable to other low- and middle-income countries and post-conflict settings to measure the impact of research capacity-building efforts.

Spatial analysis of targeted surveillance for screw-worm fly (Chrysomya bezziana or Cochliomyia hominivorax) in Australia

OBJECTIVE

To assess Australia's targeted surveillance to detect an incursion of screw-worm fly (Chrysomya bezziana).

METHODS

A multi-criteria analysis shell was used to combine data on potential pathways of entry, availability of host species and environmental factors affecting survival of screw-worm fly in order to map spatial variation in the relative likelihood of a screw-worm fly incursion into Australia. Australia's current screw-worm fly surveillance activities were reviewed to determine whether they are located in the areas of highest likelihood of an incursion.

RESULTS

Under average environmental conditions, an incursion of screw-worm fly in Australia is relatively more likely to occur along the north coast, down the eastern seaboard or in the south-east. Cold winter temperatures would limit the environmental suitability for screw-worm fly survival to the north and north-east coast and adjacent inland areas. Australia's current targeted surveillance conducted by the Northern Australia Quarantine Strategy program of the Australian Department of Agriculture (adult screw-worm fly trapping and myiasis sampling) correlated well with areas considered to have a high relative likelihood of an incursion of screw-worm fly. Adult fly trapping conducted at sea ports was less well correlated.

DISCUSSION

Changes to surveillance at sea ports are proposed to better target areas considered to have a higher relative likelihood of screw-worm fly incursion. These include increasing the trapping intensity along the north and north-east coasts and shifting surveillance activity from the west coast to the south-east.

Malaria mapping: understanding the global endemicity of falciparum and vivax malaria

The mapping of malaria risk has a history stretching back over 100 years. The last decade, however, has seen dramatic progress in the scope, rigour and sophistication of malaria mapping such that its global distribution is now probably better understood than any other infectious disease. In this minireview we consider the main factors that have facilitated the recent proliferation of malaria risk mapping efforts and describe the most prominent global-scale endemicity mapping endeavours of recent years. We describe the diversification of malaria mapping to span a wide range of related metrics of biological and public health importance and consider prospects for the future of the science including its key role in supporting elimination efforts.

Distance to Anopheles sundaicus larval habitats dominant among risk factors for parasitemia in meso-endemic Southwest Sumba, Indonesia

BACKGROUND

The decline in intensity of malaria transmission in many areas now emphasizes greater importance of understanding the epidemiology of low to moderate transmission settings. Marked heterogeneity in infection risk within these populations creates opportunities to understand transmission and guide resource allocation to greater impact.

METHODS

In this study, we examined spatial patterns of malaria transmission in a hypo- to meso-endemic area of eastern Indonesia using malaria prevalence data collected from a cross-sectional socio-demographic and parasitological survey conducted from August to November 2010. An entomological survey performed in parallel, identified, mapped, and monitored local anopheline larval habitats.

RESULTS

A single spatial cluster of higher malaria prevalence was detected during the study period (relative risk=2.13; log likelihood ratio=20.7; P<0.001). In hierarchical multivariate regression models, risk of parasitemia was inversely correlated with distance to five Anopheles sundaicus known larval habitats [odds ratio (OR)=0.21; 95% confidence interval (CI)=0.14-0.32; P<0.001], which were located in a geographically restricted band adjacent to the coastline. Increasing distance from these sites predicted increased hemoglobin level across age strata after adjusting for confounders (OR=1.6; 95% CI=1.30-1.98; P<0.001).

CONCLUSION

Significant clustering of malaria parasitemia in close proximity to very specific and relatively few An. sundaicus larval habitats has direct implications for local control strategy, policy, and practice. These findings suggest that larval source management could achieve profound if not complete impact in this region.

Dynamics of Plasmodium falciparium and Plasmodium vivax in a micro-ecological setting, Southwest Ethiopia: effects of altitude and proximity to a dam

Background

Refining the spatial and temporal data on malaria transmissions at a defined ecological setting has practical implications for targeted malaria control and enhancing efficient allocation of resources. Spatial and temporal distribution of P. falciparium and P. vivax were explored around the Gilgel Gibe Hydroelectric Dam (GGHD) in southwest Ethiopia.

Methods

A review of confirmed malaria episodes recorded over eight years at primary health services was conducted. Using individual identifiers and village names malaria records were cross-linked to location and individual records of Gilgel Gibe Health and Demographic Surveillance System (HDSS) data, which had already been geo-referenced. The study setting was categorized in to buffer zones with distance interval of one kilometer. Similarly, altitude of the area was categorized considering 100 meters height intervals. Incidence rate ratios were estimated using Poisson model for the buffer zones and for the altitudinal levels by adjusting for the underlying population density as an offset variable. Yearly temporal variations of all confirmed malaria cases were also evaluated based on the Poisson model using STATA statistical software version 12.

Results

A considerable proportion (45.0%) of the P. falciparium episodes were registered within one kilometer radius of the GGHD. P. falciparium showed increment with distance from the GGHD up to five kilometers and with altitude above 1900 meters while P. vivax exhibited the increase with distance but, decrease with the altitude. Both species showed significantly higher infection among males than females (P <0.01). Temporally, malaria episodes manifested significant increments in the years between 2006/7 to 2009/10 while reduction of the malaria episodes was indicated during 2004/5, 2005/6 and 2010/11 compared to 2003/4 (P <0.01). On average, P. vivax was 52% less than P. falciparium over the time period considered. P. vivax was significantly higher in the years 2004/5 to 2007/8 and 2010/11 (P <0.001).

Conclusions

Spatial and temporal variations of malaria were observed. The spatial and temporal variations of malaria episodes were also different for the two main malaria species in the area.

A micro-epidemiological analysis of febrile malaria in Coastal Kenya showing hotspots within hotspots

Malaria transmission is spatially heterogeneous. This reduces the efficacy of control strategies, but focusing control strategies on clusters or 'hotspots' of transmission may be highly effective. Among 1500 homesteads in coastal Kenya we calculated (a) the fraction of febrile children with positive malaria smears per homestead, and (b) the mean age of children with malaria per homestead. These two measures were inversely correlated, indicating that children in homesteads at higher transmission acquire immunity more rapidly. This inverse correlation increased gradually with increasing spatial scale of analysis, and hotspots of febrile malaria were identified at every scale. We found hotspots within hotspots, down to the level of an individual homestead. Febrile malaria hotspots were temporally unstable, but 4 km radius hotspots could be targeted for 1 month following 1 month periods of surveillance.DOI: http://dx.doi.org/10.7554/eLife.02130.001.

Elevated dry-season malaria prevalence associated with fine-scale spatial patterns of environmental risk: a case-control study of children in rural Malawi

Background

Understanding the role of local environmental risk factors for malaria in holo-endemic, poverty-stricken settings will be critical to more effectively implement- interventions aimed at eventual elimination. Household-level environmental drivers of malaria risk during the dry season were investigated in rural southern Malawi among children < five years old in two neighbouring rural Traditional Authority (TA) regions dominated by small-scale agriculture.

Methods

Ten villages were randomly selected from TA Sitola (n = 6) and Nsamala (n = 4). Within each village, during June to August 2011, a census was conducted of all households with children under-five and recorded their locations with a geographic position system (GPS) device. At each participating house, a nurse administered a malaria rapid diagnostic test (RDT) to children under five years of age, and a questionnaire to parents. Environmental data were collected for each house, including land cover within 50-m radius. Variables found to be significantly associated with P. falciparum infection status in bivariate analysis were included in generalized linear models, including multivariate logistic regression (MLR) and multi-level multivariate logistic regression (MLLR). Spatial clustering of RDT status, environmental factors, and Pearson residuals from MLR and MLLR were analysed using the Getis-Ord Gi* statistic.

Results

Of 390 children enrolled from six villages in Sitola (n = 162) and four villages in Nsamala (n = 228), 45.6% tested positive (n = 178) for Plasmodium infection by RDT. The MLLR modelled the statistical relationship of Plasmodium positives and household proximity to agriculture (<25-m radius), controlling for the child sex and age (in months), bed net ownership, elevation, and random effects intercepts for village and TA-level unmeasured factors. After controlling for area affects in MLLR, proximity to active agriculture remained a significant predictor of positive RDT result (OR 2.80, 95% CI 1.41-5.55). Mapping of Pearson residuals from MLR showed significant clustering (Gi* z > 2.58, p < 0.01) predominantly within TA Sitola, while residuals from MLLR showed no such clustering.

Conclusion

This study provides evidence for significant, dry-season heterogeneity of malaria prevalence strongly linked to peridomestic land use, and particularly of elevated risk associated with nearby crop production.

Providing open access data online to advance malaria research and control

Background

To advance research on malaria, the outputs from existing studies and the data that fed into them need to be made freely available. This will ensure new studies can build on the work that has gone before. These data and results also need to be made available to groups who are developing public health policies based on up-to-date evidence. The Malaria Atlas Project (MAP) has collated and geopositioned over 50,000 parasite prevalence and vector occurrence survey records contributed by over 3,000 sources including research groups, government agencies and non-governmental organizations worldwide. This paper describes the results of a project set up to release data gathered, used and generated by MAP.

Methods

Requests for permission to release data online were sent to 236 groups who had contributed unpublished prevalence (parasite rate) surveys. An online explorer tool was developed so that users can visualize the spatial distribution of the vector and parasite survey data before downloading it. In addition, a consultation group was convened to provide advice on the mode and format of release for data generated by MAP’s modelling work. New software was developed to produce a suite of publication-quality map images for download from the internet for use in external publications.

Conclusion

More than 40,000 survey records can now be visualized on a set of dynamic maps and downloaded from the MAP website on a free and unrestricted basis. As new data are added and new permissions to release existing data come in, the volume of data available for download will increase. The modelled data output from MAP’s own analyses are also available online in a range of formats, including image files and GIS surface data, for use in advocacy, education, further research and to help parameterize or validate other mathematical models.

Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland

Background

As successful malaria control programmes move towards elimination, they must identify residual transmission foci, target vector control to high-risk areas, focus on both asymptomatic and symptomatic infections, and manage importation risk. High spatial and temporal resolution maps of malaria risk can support all of these activities, but commonly available malaria maps are based on parasite rate, a poor metric for measuring malaria at extremely low prevalence. New approaches are required to provide case-based risk maps to countries seeking to identify remaining hotspots of transmission while managing the risk of transmission from imported cases.

Methods

Household locations and travel histories of confirmed malaria patients during 2011 were recorded through routine surveillance by the Swaziland National Malaria Control Programme for the higher transmission months of January to April and the lower transmission months of May to December. Household locations for patients with no travel history to endemic areas were compared against a random set of background points sampled proportionate to population density with respect to a set of variables related to environment, population density, vector control, and distance to the locations of identified imported cases. Comparisons were made separately for the high and low transmission seasons. The Random Forests regression tree classification approach was used to generate maps predicting the probability of a locally acquired case at 100 m resolution across Swaziland for each season.

Results

Results indicated that case households during the high transmission season tended to be located in areas of lower elevation, closer to bodies of water, in more sparsely populated areas, with lower rainfall and warmer temperatures, and closer to imported cases than random background points (all p < 0.001). Similar differences were evident during the low transmission season. Maps from the fit models suggested better predictive ability during the high season. Both models proved useful at predicting the locations of local cases identified in 2012.

Conclusions

The high-resolution mapping approaches described here can help elimination programmes understand the epidemiology of a disappearing disease. Generating case-based risk maps at high spatial and temporal resolution will allow control programmes to direct interventions proactively according to evidence-based measures of risk and ensure that the impact of limited resources is maximized to achieve and maintain malaria elimination.

How well are malaria maps used to design and finance malaria control in Africa?

Introduction

Rational decision making on malaria control depends on an understanding of the epidemiological risks and control measures. National Malaria Control Programmes across Africa have access to a range of state-of-the-art malaria risk mapping products that might serve their decision-making needs. The use of cartography in planning malaria control has never been methodically reviewed.

Materials and Methods

An audit of the risk maps used by NMCPs in 47 malaria endemic countries in Africa was undertaken by examining the most recent national malaria strategies, monitoring and evaluation plans, malaria programme reviews and applications submitted to the Global Fund. The types of maps presented and how they have been used to define priorities for investment and control was investigated.

Results

91% of endemic countries in Africa have defined malaria risk at sub-national levels using at least one risk map. The range of risk maps varies from maps based on suitability of climate for transmission; predicted malaria seasons and temperature/altitude limitations, to representations of clinical data and modelled parasite prevalence. The choice of maps is influenced by the source of the information. Maps developed using national data through in-country research partnerships have greater utility than more readily accessible web-based options developed without inputs from national control programmes. Although almost all countries have stratification maps, only a few use them to guide decisions on the selection of interventions allocation of resources for malaria control.

Conclusion

The way information on the epidemiology of malaria is presented and used needs to be addressed to ensure evidence-based added value in planning control. The science on modelled impact of interventions must be integrated into new mapping products to allow a translation of risk into rational decision making for malaria control. As overseas and domestic funding diminishes, strategic planning will be necessary to guide appropriate financing for malaria control.

Malaria risk mapping for control in the republic of Sudan

Evidence shows that malaria risk maps are rarely tailored to address national control program ambitions. Here, we generate a malaria risk map adapted for malaria control in Sudan. Community Plasmodium falciparum parasite rate (PfPR) data from 2000 to 2010 were assembled and were standardized to 2-10 years of age (PfPR(2-10)). Space-time Bayesian geostatistical methods were used to generate a map of malaria risk for 2010. Surfaces of aridity, urbanization, irrigation schemes, and refugee camps were combined with the PfPR(2-10) map to tailor the epidemiological stratification for appropriate intervention design. In 2010, a majority of the geographical area of the Sudan had risk of < 1% PfPR(2-10). Areas of meso- and hyperendemic risk were located in the south. About 80% of Sudan's population in 2011 was in the areas in the desert, urban centers, or where risk was < 1% PfPR(2-10). Aggregated data suggest reducing risks in some high transmission areas since the 1960s.

Finding malaria hot-spots in northern Angola: the role of individual, household and environmental factors within a meso-endemic area

Background

Identifying and targeting hyper-endemic communities within meso-endemic areas constitutes an important challenge in malaria control in endemic countries such like Angola. Recent national and global predictive maps of malaria allow the identification and quantification of the population at risk of malaria infection in Angola, but their small-scale accuracy is surrounded by large uncertainties. To observe the need to develop higher resolution malaria endemicity maps a predictive risk map of malaria infection for the municipality of Dande (a malaria endemic area in Northern Angola) was developed and compared to existing national and global maps, the role of individual, household and environmental risk factors for malaria endemicity was quantified and the spatial variation in the number of children at-risk of malaria was estimated.

Methods

Bayesian geostatistical models were developed to predict small-scale spatial variation using data collected during a parasitological survey conducted from May to August 2010. Maps of the posterior distributions of predicted prevalence were constructed in a geographical information system.

Results

Malaria infection was significantly associated with maternal malaria awareness, households with canvas roofing, distance to health care centre and distance to rivers. The predictive map showed remarkable spatial heterogeneity in malaria risk across the Dande municipality in contrast to previous national and global spatial risk models; large high-risk areas of malaria infection (prevalence >50%) were found in the northern and most eastern areas of the municipality, in line with the observed prevalence.

Conclusions

There is remarkable spatial heterogeneity of malaria burden which previous national and global spatial modelling studies failed to identify suggesting that the identification of malaria hot-spots within seemingly mesoendemic areas may require the generation of high resolution malaria maps. Individual, household and hydrological factors play an important role in the small-scale geographical variation of malaria risk in northern Angola. The results presented in this study can be used by provincial malaria control programme managers to help target the delivery of malaria control resources to priority areas in the Dande municipality.

Epidemiology of malaria in endemic areas

Malaria infection is still to be considered a major public health problem in those 106 countries where the risk of contracting the infection with one or more of the Plasmodium species exists. According to estimates from the World Health Organization, over 200 million cases and about 655.000 deaths have occurred in 2010. Estimating the real health and social burden of the disease is a difficult task, because many of the malaria endemic countries have limited diagnostic resources, especially in rural settings where conditions with similar clinical picture may coexist in the same geographical areas. Moreover, asymptomatic parasitaemia may occur in high transmission areas after childhood, when anti-malaria semi-immunity occurs. Malaria endemicity and control activities are very complex issues, that are influenced by factors related to the host, to the parasite, to the vector, to the environment and to the health system capacity to fully implement available anti-malaria weapons such as rapid diagnostic tests, artemisinin-based combination treatment, impregnated bed-nets and insecticide residual spraying while waiting for an effective vaccine to be made available.

Mapping the receptivity of malaria risk to plan the future of control in Somalia

OBJECTIVES

To measure the receptive risks of malaria in Somalia and compare decisions on intervention scale-up based on this map and the more widely used contemporary risk maps.

DESIGN

Cross-sectional community Plasmodium falciparum parasite rate (PfPR) data for the period 2007-2010 corrected to a standard age range of 2 to <10 years (PfPR(2-10)) and used within a Bayesian space-time geostatistical framework to predict the contemporary (2010) mean PfPR(2-10) and the maximum annual mean PfPR(2-10) (receptive) from the highest predicted PfPR(2-10) value over the study period as an estimate of receptivity.

SETTING

Randomly sampled communities in Somalia.

PARTICIPANTS

Randomly sampled individuals of all ages.

MAIN OUTCOME MEASURE

Cartographic descriptions of malaria receptivity and contemporary risks in Somalia at the district level.

RESULTS

The contemporary annual PfPR(2-10) map estimated that all districts (n=74) and population (n=8.4 million) in Somalia were under hypoendemic transmission (≤10% PfPR(2-10)). Of these, 23% of the districts, home to 13% of the population, were under transmission of <1% PfPR(2-10). About 58% of the districts and 55% of the population were in the risk class of 1% to <5% PfPR(2-10). In contrast, the receptivity map estimated 65% of the districts and 69% of the population were under mesoendemic transmission (>10%-50% PfPR(2-10)) and the rest as hypoendemic.

CONCLUSION

Compared with maps of receptive risks, contemporary maps of transmission mask disparities of malaria risk necessary to prioritise and sustain future control. As malaria risk declines across Africa, efforts must be invested in measuring receptivity for efficient control planning.

Leptospirosis in American Samoa--estimating and mapping risk using environmental data

Background

The recent emergence of leptospirosis has been linked to many environmental drivers of disease transmission. Accurate epidemiological data are lacking because of under-diagnosis, poor laboratory capacity, and inadequate surveillance. Predictive risk maps have been produced for many diseases to identify high-risk areas for infection and guide allocation of public health resources, and are particularly useful where disease surveillance is poor. To date, no predictive risk maps have been produced for leptospirosis. The objectives of this study were to estimate leptospirosis seroprevalence at geographic locations based on environmental factors, produce a predictive disease risk map for American Samoa, and assess the accuracy of the maps in predicting infection risk.

Methodology and Principal Findings

Data on seroprevalence and risk factors were obtained from a recent study of leptospirosis in American Samoa. Data on environmental variables were obtained from local sources, and included rainfall, altitude, vegetation, soil type, and location of backyard piggeries. Multivariable logistic regression was performed to investigate associations between seropositivity and risk factors. Using the multivariable models, seroprevalence at geographic locations was predicted based on environmental variables. Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive. Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house. Models had acceptable goodness of fit, and correctly classified ∼84% of cases.

Conclusions and Significance

Environmental variables could be used to identify high-risk areas for leptospirosis. Environmental monitoring could potentially be a valuable strategy for leptospirosis control, and allow us to move from disease surveillance to environmental health hazard surveillance as a more cost-effective tool for directing public health interventions.

Leptospirosis is the most common bacterial infection transmitted from animals to humans. Infected animals excrete the bacteria in their urine, and humans can become infected through contact with animals or a contaminated environment such as water and soil. Environmental factors are important in determining the risk of human infection, and differ between ecological settings. The wide range of risk factors include high rainfall and flooding; poor sanitation and hygiene; urbanisation and overcrowding; contact with animals (including rodents, livestock, pets, and wildlife); outdoor recreation and ecotourism; and environmental degradation. Predictive risk maps have been produced for many infectious diseases to identify high-risk areas for transmission and guide allocation of public health resources. Maps are particularly useful where disease surveillance and epidemiological data are poor. The objectives of this study were to estimate leptospirosis seroprevalence at geographic locations based on environmental factors, produce a predictive disease risk map for American Samoa, and assess the accuracy of the maps in predicting infection risk. This study demonstrated the value of geographic information systems and disease mapping for identifying environmental risk factors for leptospirosis, and enhancing our understanding of disease transmission. Similar principles could be used to investigate the epidemiology of leptospirosis in other areas.

Characterizing the spatial and temporal variation of malaria incidence in Bangladesh, 2007

Background

Malaria remains a significant health problem in Bangladesh affecting 13 of 64 districts. The risk of malaria is variable across the endemic areas and throughout the year. A better understanding of the spatial and temporal patterns in malaria risk and the determinants driving the variation are crucial for the appropriate targeting of interventions under the National Malaria Control and Prevention Programme.

Methods

Numbers of Plasmodium falciparum and Plasmodium vivax malaria cases reported by month in 2007, across the 70 endemic thanas (sub-districts) in Bangladesh, were assembled from health centre surveillance reports. Bayesian Poisson regression models of incidence were constructed, with fixed effects for monthly rainfall, maximum temperature and elevation, and random effects for thanas, with a conditional autoregressive prior spatial structure.

Results

The annual incidence of reported cases was 34.0 and 9.6 cases/10,000 population for P. falciparum and P. vivax respectively and the population of the 70 malaria-endemic thanas was approximately 13.5 million in 2007. Incidence of reported cases for both types of malaria was highest in the mountainous south-east of the country (the Chittagong Hill Tracts). Models revealed statistically significant positive associations between the incidence of reported P. vivax and P. falciparum cases and rainfall and maximum temperature.

Conclusions

The risk of P. falciparum and P. vivax was spatially variable across the endemic thanas of Bangladesh and also highly seasonal, suggesting that interventions should be targeted and timed according to the risk profile of the endemic areas. Rainfall, temperature and elevation are major factors driving the spatiotemporal patterns of malaria in Bangladesh.

Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal

Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the model's ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.

Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co-endemicity

Background

In Uganda, malaria and lymphatic filariasis (causative agent Wuchereria bancrofti) are transmitted by the same vector species of Anopheles mosquitoes, and thus are likely to share common environmental risk factors and overlap in geographical space. In a comprehensive nationwide survey in 2000-2003 the geographical distribution of W. bancrofti was assessed by screening school-aged children for circulating filarial antigens (CFA). Concurrently, blood smears were examined for malaria parasites. In this study, the resultant malariological data are analysed for the first time and the CFA data re-analysed in order to identify risk factors, produce age-stratified prevalence maps for each infection, and to define the geographical patterns of Plasmodium sp. and W. bancrofti co-endemicity.

Methods

Logistic regression models were fitted separately for Plasmodium sp. and W. bancrofti within a Bayesian framework. Models contained covariates representing individual-level demographic effects, school-level environmental effects and location-based random effects. Several models were fitted assuming different random effects to allow for spatial structuring and to capture potential non-linearity in the malaria- and filariasis-environment relation. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting models. Maps of predicted hyper-endemic malaria and filariasis were furthermore overlaid in order to define areas of co-endemicity.

Results

Plasmodium sp. parasitaemia was found to be highly endemic in most of Uganda, with an overall population adjusted parasitaemia risk of 47.2% in the highest risk age-sex group (boys 5-9 years). High W. bancrofti prevalence was predicted for a much more confined area in northern Uganda, with an overall population adjusted infection risk of 7.2% in the highest risk age-group (14-19 year olds). Observed overall prevalence of individual co-infection was 1.1%, and the two infections overlap geographically with an estimated number of 212,975 children aged 5 - 9 years living in hyper-co-endemic transmission areas.

Conclusions

The empirical map of malaria parasitaemia risk for Uganda presented in this paper is the first based on coherent, national survey data, and can serve as a baseline to guide and evaluate the continuous implementation of control activities. Furthermore, geographical areas of overlap with hyper-endemic W. bancrofti transmission have been identified to help provide a better informed platform for integrated control.

Plasmodium falciparum malaria endemicity in Indonesia in 2010

Background

Malaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010.

Methods

Plasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006–2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985–2009). After quality control, 2,516 were included into a national database of age-standardized 2–10 year old PfPR data (PfPR_2–10) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR_2–10 endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface.

Results

We estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas.

Conclusion

While further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching pre-elimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies.

Bayesian geostatistics in health cartography: the perspective of malaria

Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision.

Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax

Background

Temperature is a key determinant of environmental suitability for transmission of human malaria, modulating endemicity in some regions and preventing transmission in others. The spatial modelling of malaria endemicity has become increasingly sophisticated and is now central to the global scale planning, implementation, and monitoring of disease control and regional efforts towards elimination, but existing efforts to model the constraints of temperature on the malaria landscape at these scales have been simplistic. Here, we define an analytical framework to model these constraints appropriately at fine spatial and temporal resolutions, providing a detailed dynamic description that can enhance large scale malaria cartography as a decision-support tool in public health.

Results

We defined a dynamic biological model that incorporated the principal mechanisms of temperature dependency in the malaria transmission cycle and used it with fine spatial and temporal resolution temperature data to evaluate time-series of temperature suitability for transmission of Plasmodium falciparum and P. vivax throughout an average year, quantified using an index proportional to the basic reproductive number. Time-series were calculated for all 1 km resolution land pixels globally and were summarised to create high-resolution maps for each species delineating those regions where temperature precludes transmission throughout the year. Within suitable zones we mapped for each pixel the number of days in which transmission is possible and an integrated measure of the intensity of suitability across the year. The detailed evaluation of temporal suitability dynamics provided by the model is visualised in a series of accompanying animations.

Conclusions

These modelled products, made available freely in the public domain, can support the refined delineation of populations at risk; enhance endemicity mapping by offering a detailed, dynamic, and biologically driven alternative to the ubiquitous empirical incorporation of raw temperature data in geospatial models; and provide a rich spatial and temporal platform for future biological modelling studies.

The applications of model-based geostatistics in helminth epidemiology and control

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes.

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.

Social and environmental determinants of malaria in space and time in Viet Nam

The malaria burden in Viet Nam has been in decline in recent decades, but localised areas of high transmission remain. We used spatiotemporal analytical tools to determine the social and environmental drivers of malaria risk and to identify residual high-risk areas where control and surveillance resources can be targeted. Counts of reported Plasmodium falciparum and Plasmodium vivax malaria cases by month (January 2007-December 2008) and by district were assembled. Zero-inflated Poisson regression models were developed in a bayesian framework. Models had the percentage of the district's population living below the poverty line, percent of the district covered by forest, median elevation, median long-term average precipitation, and minimum temperature included as fixed effects, and terms for temporal trend and residual district-level spatial autocorrelation. Strong temporal and spatial heterogeneity in counts of malaria cases was apparent. Poverty and forest cover were significantly associated with an increased count of malaria cases but the magnitude and direction of associations between climate and malaria varied by socio-ecological zone. There was a declining trend in counts of malaria cases during the study period. After accounting for the social and environmental fixed effects, substantial spatial heterogeneity was still evident. Unmeasured factors which may contribute to this residual variation include malaria control activities, population migration and accessibility to health care. Forest-related activities and factors encompassed by poverty indicators are major drivers of malaria incidence in Viet Nam.

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.

Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya

BACKGROUND

Infectious diseases often demonstrate heterogeneity of transmission among host populations. This heterogeneity reduces the efficacy of control strategies, but also implies that focusing control strategies on "hotspots" of transmission could be highly effective.

METHODS AND FINDINGS

In order to identify hotspots of malaria transmission, we analysed longitudinal data on febrile malaria episodes, asymptomatic parasitaemia, and antibody titres over 12 y from 256 homesteads in three study areas in Kilifi District on the Kenyan coast. We examined heterogeneity by homestead, and identified groups of homesteads that formed hotspots using a spatial scan statistic. Two types of statistically significant hotspots were detected; stable hotspots of asymptomatic parasitaemia and unstable hotspots of febrile malaria. The stable hotspots were associated with higher average AMA-1 antibody titres than the unstable clusters (optical density [OD] = 1.24, 95% confidence interval [CI] 1.02-1.47 versus OD = 1.1, 95% CI 0.88-1.33) and lower mean ages of febrile malaria episodes (5.8 y, 95% CI 5.6-6.0 versus 5.91 y, 95% CI 5.7-6.1). A falling gradient of febrile malaria incidence was identified in the penumbrae of both hotspots. Hotspots were associated with AMA-1 titres, but not seroconversion rates. In order to target control measures, homesteads at risk of febrile malaria could be predicted by identifying the 20% of homesteads that experienced an episode of febrile malaria during one month in the dry season. That 20% subsequently experienced 65% of all febrile malaria episodes during the following year. A definition based on remote sensing data was 81% sensitive and 63% specific for the stable hotspots of asymptomatic malaria.

CONCLUSIONS

Hotspots of asymptomatic parasitaemia are stable over time, but hotspots of febrile malaria are unstable. This finding may be because immunity offsets the high rate of febrile malaria that might otherwise result in stable hotspots, whereas unstable hotspots necessarily affect a population with less prior exposure to malaria.

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.

Serologic markers for detecting malaria in areas of low endemicity, Somalia, 2008

Areas in which malaria is not highly endemic are suitable for malaria elimination, but assessing transmission is difficult because of lack of sensitivity of commonly used methods. We evaluated serologic markers for detecting variation in malaria exposure in Somalia. Plasmodium falciparum or P. vivax was not detected by microscopy in cross-sectional surveys of samples from persons during the dry (0/1,178) and wet (0/1,128) seasons. Antibody responses against P. falciparum or P. vivax were detected in 17.9% (179/1,001) and 19.3% (202/1,044) of persons tested. Reactivity against P. falciparum was significantly different between 3 villages (p<0.001); clusters of seroreactivity were present. Distance to the nearest seasonal river was negatively associated with P. falciparum (p = 0.028) and P. vivax seroreactivity (p = 0.016). Serologic markers are a promising tool for detecting spatial variation in malaria exposure and evaluating malaria control efforts in areas where transmission has decreased to levels below the detection limit of microscopy.

Climate change and health research in the Eastern Mediterranean Region

Anthropologically induced climate change, caused by an increased concentration of greenhouse gases in the atmosphere, is an emerging threat to human health. Consequences of climate change may affect the prevalence of various diseases and environmental and social maladies that affect population health. In this article, we reviewed the literature on climate change and health in the Eastern Mediterranean Region. This region already faces numerous humanitarian crises, from conflicts to natural hazards and a high burden of disease. Climate change is likely to aggravate these emergencies, necessitating a strengthening of health systems and capacities in the region. However, the existing literature on climate change from the region is sparse and informational gaps stand in the way of regional preparedness and adaptation. Further research is needed to assess climatic changes and related health impacts in the Eastern Mediterranean Region. Such knowledge will allow countries to identify preparedness vulnerabilities, evaluate capacity to adapt to climate change, and develop adaptation strategies to allay the health impacts of climate change.