Providing open access data online to advance malaria research and control

The Planetary Child Health & Enterics Observatory (Plan-EO): A protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

BACKGROUND

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

METHODS

The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

DISCUSSION

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

STUDY REGISTRATION

PROSPERO protocol #CRD42023384709.

The Planetary Child Health & Enterics Observatory (Plan-EO): a protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

Background

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

Methods

The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

Discussion

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

Study registration

PROSPERO protocol #CRD42023384709.

The Planetary Child Health & Enterics Observatory (Plan-EO): a protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

Background

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

Methods

The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

Discussion

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

Study registration

PROSPERO protocol #CRD42023384709.

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.

Temperature impacts the environmental suitability for malaria transmission by Anopheles gambiae and Anopheles stephensi

Extrinsic environmental factors influence the spatiotemporal dynamics of many organisms, including insects that transmit the pathogens responsible for vector-borne diseases (VBDs). Temperature is an especially important constraint on the fitness of a wide variety of ectothermic insects. A mechanistic understanding of how temperature impacts traits of ectotherms, and thus the distribution of ectotherms and vector-borne infections, is key to predicting the consequences of climate change on transmission of VBDs like malaria. However, the response of transmission to temperature and other drivers is complex, as thermal traits of ectotherms are typically nonlinear, and they interact to determine transmission constraints. In this study, we assess and compare the effect of temperature on the transmission of two malaria parasites, Plasmodium falciparum and Plasmodium vivax, by two malaria vector species, Anopheles gambiae and Anopheles stephensi. We model the nonlinear responses of temperature dependent mosquito and parasite traits (mosquito development rate, bite rate, fecundity, proportion of eggs surviving to adulthood, vector competence, mortality rate, and parasite development rate) and incorporate these traits into a suitability metric based on a model for the basic reproductive number across temperatures. Our model predicts that the optimum temperature for transmission suitability is similar for the four mosquito-parasite combinations assessed in this study, but may differ at the thermal limits. More specifically, we found significant differences in the upper thermal limit between parasites spread by the same mosquito (A. stephensi) and between mosquitoes carrying P. falciparum. In contrast, at the lower thermal limit the significant differences were primarily between the mosquito species that both carried the same pathogen (e.g., A. stephensi and A. gambiae both with P. falciparum). Using prevalence data, we show that the transmission suitability metric S T $$ S(T) $$ calculated from our mechanistic model is consistent with observed P. falciparum prevalence in Africa and Asia but is equivocal for P. vivax prevalence in Asia, and inconsistent with P. vivax prevalence in Africa. We mapped risk to illustrate the number of months various areas in Africa and Asia predicted to be suitable for malaria transmission based on this suitability metric. This mapping provides spatially explicit predictions for suitability and transmission risk.

Evaluating insecticide resistance across African districts to aid malaria control decisions

Malaria control in Africa largely relies on the use of insecticides to prevent mosquitoes from transmitting the malaria parasite to humans; however, these mosquitoes have evolved resistance to these insecticides. To manage this threat to malaria control, it is vital that we map locations where the prevalence of resistance exceeds thresholds defined by insecticide resistance management plans. A geospatial model and data from Africa are used to predict locations where thresholds of resistance linked to specific recommended actions are exceeded. This model is shown to provide more accurate next-year predictions than two simpler approaches. The model is used to generate maps that aid insecticide resistance management planning and that allow targeted deployment of interventions that counter specific mechanisms of resistance.

Malaria vector control may be compromised by resistance to insecticides in vector populations. Actions to mitigate against resistance rely on surveillance using standard susceptibility tests, but there are large gaps in the monitoring data across Africa. Using a published geostatistical ensemble model, we have generated maps that bridge these gaps and consider the likelihood that resistance exceeds recommended thresholds. Our results show that this model provides more accurate next-year predictions than two simpler approaches. We have used the model to generate district-level maps for the probability that pyrethroid resistance in Anopheles gambiae s.l. exceeds the World Health Organization thresholds for susceptibility and confirmed resistance. In addition, we have mapped the three criteria for the deployment of piperonyl butoxide-treated nets that mitigate against the effects of metabolic resistance to pyrethroids. This includes a critical review of the evidence for presence of cytochrome P450-mediated metabolic resistance mechanisms across Africa. The maps for pyrethroid resistance are available on the IR Mapper website, where they can be viewed alongside the latest survey data.

Malaria Data by District: An open-source web application for increasing access to malaria information

Preventable diseases still cause huge mortality in low- and middle-income countries. Research in spatial epidemiology and earth observation is helping academics to understand and prioritise how mortality could be reduced and generates spatial data that are used at a global and national level, to inform disease control policy. These data could also inform operational decision making at a more local level, for example to help officials target efforts at a local/regional level. To be usable for local decision-making, data needs to be presented in a way that is relevant to and understandable by local decision makers. We demonstrate an approach and prototype web application to make spatial outputs from disease modelling more useful for local decision making. Key to our approach is: (1) we focus on a handful of important data layers to maintain simplicity; (2) data are summarised at scales relevant to decision making (administrative units); (3) the application has the ability to rank and compare administrative units; (4) open-source code that can be modified and re-used by others, to target specific user-needs. Our prototype application allows visualisation of a handful of key layers from the Malaria Atlas Project. Data can be summarised by administrative unit for any malaria endemic African country, ranked and compared; e.g. to answer questions such as, ‘does the district with the highest malaria prevalence also have the lowest coverage of insecticide treated nets?’. The application is developed in R and the code is open-source. It would be relatively easy for others to change the source code to incorporate different data layers, administrative boundaries or other data visualisations. We suggest such open-source web application development can facilitate the use of data for public health decision making in low resource settings.

Malaria Data by District: An open-source web application for increasing access to malaria information

Preventable diseases still cause huge mortality in low- and middle-income countries. Research in spatial epidemiology and earth observation is helping academics to understand and prioritise how mortality could be reduced and generates spatial data that are used at a global and national level, to inform disease control policy. These data could also inform operational decision making at a more local level, for example to help officials target efforts at a local/regional level. To be usable for local decision-making, data needs to be presented in a way that is relevant to and understandable by local decision makers. We demonstrate an approach and prototype web application to make spatial outputs from disease modelling more useful for local decision making. Key to our approach is: (1) we focus on a handful of important data layers to maintain simplicity; (2) data are summarised at scales relevant to decision making (administrative units); (3) the application has the ability to rank and compare administrative units; (4) open-source code that can be modified and re-used by others, to target specific user-needs. Our prototype application allows visualisation of a handful of key layers from the Malaria Atlas Project. Data can be summarised by administrative unit for any malaria endemic African country, ranked and compared; e.g. to answer questions such as, 'does the district with the highest malaria prevalence also have the lowest coverage of insecticide treated nets?'. The application is developed in R and the code is open-source. It would be relatively easy for others to change the source code to incorporate different data layers, administrative boundaries or other data visualisations. We suggest such open-source web application development can facilitate the use of data for public health decision making in low resource settings.

Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors

The impact of insecticide resistance in malaria vectors is poorly understood and quantified. Here a series of geospatial datasets for insecticide resistance in malaria vectors are provided, so that trends in resistance in time and space can be quantified, and the impact of resistance found in wild populations on malaria transmission in Africa can be assessed. Specifically, data have been collated and geopositioned for the prevalence of insecticide resistance, as measured by standard bioassays, in representative samples of individual species or species complexes. Data are provided for the Anopheles gambiae species complex, the Anopheles funestus subgroup, and for nine individual vector species. Data are also given for common genetic markers of resistance to support analyses of whether these markers can improve the ability to monitor resistance in low resource settings. Allele frequencies for known resistance-associated markers in the Voltage-gated sodium channel (Vgsc) are provided. In total, eight analysis-ready, standardised, geopositioned datasets encompassing over 20,000 African mosquito collections between 1957 and 2017 are released.

Mapping the potential use of endectocide-treated cattle to reduce malaria transmission

Treating cattle with endectocide is a longstanding veterinary practice to reduce the load of endo and ectoparasites, but has the potential to be added to the malaria control and elimination toolbox, as it also kills malaria mosquitoes feeding on the animals. Here we used openly available data to map the areas of the African continent where high malaria prevalence in 2-10 year old children coincides with a high density of cattle and high density of the partly zoophilic malaria vector Anopheles arabiensis. That is, mapping the areas where treating cattle with endectocide would potentially have the greatest impact on reducing malaria transmission. In regions of Africa that are not dominated by rainforest nor desert, the map shows a scatter of areas in several countries where this intervention shows potential, including central and eastern sub-Saharan Africa. The savanna region underneath the Sahel in West Africa appears as the climatic block that would benefit to the largest extent from this intervention, encompassing several countries. West Africa currently presents the highest under-10 malaria prevalence and elimination within the next twenty years cannot be contemplated there with currently available interventions alone, making the use of endectocide treated cattle as a complementary intervention highly appealing.

Integrated Serologic Surveillance of Population Immunity and Disease Transmission

Antibodies are unique among biomarkers in their ability to identify persons with protective immunity to vaccine-preventable diseases and to measure past exposure to diverse pathogens. Most infectious disease surveillance maintains a single-disease focus, but broader testing of existing serologic surveys with multiplex antibody assays would create new opportunities for integrated surveillance. In this perspective, we highlight multiple areas for potential synergy where integrated surveillance could add more value to public health efforts than the current trend of independent disease monitoring through vertical programs. We describe innovations in laboratory and data science that should accelerate integration and identify remaining challenges with respect to specimen collection, testing, and analysis. Throughout, we illustrate how information generated through integrated surveillance platforms can create new opportunities to more quickly and precisely identify global health program gaps that range from undervaccination to emerging pathogens to multilayered health disparities that span diverse communicable diseases.

Growing evidence of Plasmodium vivax across malaria-endemic Africa

Effective malaria control strategies require an accurate understanding of the epidemiology of locally transmitted Plasmodium species. Compared to Plasmodium falciparum infection, Plasmodium vivax has a lower asexual parasitaemia, forms dormant liver-stages (hypnozoites), and is more transmissible. Hence, treatment and diagnostic policies aimed exclusively at P. falciparum are far less efficient against endemic P. vivax. Within sub-Saharan Africa, malaria control programmes justly focus on reducing the morbidity and mortality associated with P. falciparum. However, the recent emphasis on malaria elimination and increased accessibility of more sensitive diagnostic tools have revealed greater intricacies in malaria epidemiology across the continent. Since 2010, the number of studies identifying P. vivax endemic to Africa has expanded considerably, with 88 new scientific reports published since a review of evidence in 2015, approximately doubling the available data. There is evidence of P. vivax in all regions of Africa, apparent from infected vectors, clinical cases, serological indicators, parasite prevalence, exported infections, and P. vivax-infected Duffy-negative individuals. Where the prevalence of microscopic parasitaemia is low, a greater proportion of P. vivax infections were observed relative to P. falciparum. This evidence highlights an underlying widespread presence of P. vivax across all malaria-endemic regions of Africa, further complicating the current practical understanding of malaria epidemiology in this region. Thus, ultimate elimination of malaria in Africa will require national malaria control programmes to adopt policy and practice aimed at all human species of malaria.

Artificial Intelligence for infectious disease Big Data Analytics

BACKGROUND

Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the advancement of information and communications technology and the data collection systems now in place.

METHODS

This paper aims to highlight the opportunities gained through the use of Artificial Intelligence (AI) methods to enable reliable disease-oriented monitoring and projection in this information age.

RESULTS AND CONCLUSION

It is foreseeable that together with reliable data management platforms AI methods will enable analysis of massive infectious disease and surveillance data effectively to support government agencies, healthcare service providers, and medical professionals to response to disease in the future.

malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project

Background

The Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing the distribution of malaria and associated illnesses, blood disorders, and intervention coverage. MAP has recently released malariaAtlas, an R package providing a direct interface to MAP’s routinely-updated malariometric databases and research outputs.

Methods and results

The current paper reviews the functionality available in malariaAtlas and highlights its utility for spatial epidemiological analysis of malaria. malariaAtlas enables users to freely download, visualise and analyse global malariometric data within R. Currently available data types include: malaria parasite rate and vector occurrence point data; subnational administrative boundary shapefiles; and a large suite of rasters covering a diverse range of metrics related to malaria research. malariaAtlas is here used in two mock analyses to illustrate how this data may be incorporated into a standard R workflow for spatial analysis.

Conclusions

malariaAtlas is the first open-access R-interface to malariometric data, providing a new and reproducible means of accessing such data within a freely available and commonly used statistical software environment. In this way, the malariaAtlas package aims to contribute to the environment of data-sharing within the malaria research community.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2500-5) contains supplementary material, which is available to authorized users.

From global action against malaria to local issues: state of the art and perspectives of web platforms dealing with malaria information

Background

Since prehistory to present times and despite a rough combat against it, malaria remains a concern for human beings. While evolutions of science and technology through times allowed for some infectious diseases eradication in the 20th century, malaria resists.

Objectives

This review aims at assessing how Internet and web technologies are used in fighting malaria. Precisely, how do malaria fighting actors profit from these developments, how do they deal with ensuing phenomena, such as the increase of data volume, and did these technologies bring new opportunities for fighting malaria?

Methods

Eleven web platforms linked to spatio-temporal malaria information are reviewed, focusing on data, metadata, web services and categories of users.

Results

Though the web platforms are highly heterogeneous the review reveals that the latest advances in web technologies are underused. Information are rarely updated dynamically, metadata catalogues are absent, web services are more and more used, but rarely standardized, and websites are mainly dedicated to scientific communities, essentially researchers.

Conclusion

Improvement of systems interoperability, through standardization, is an opportunity to be seized in order to allow real time information exchange and online multisource data analysis. To facilitate multidisciplinary/multiscale studies, the web of linked data and the semantic web innovations can be used in order to formalize the different view points of actors involved in the combat against malaria. By doing so, new malaria fighting strategies could take place, to tackle the bottlenecks listed in the United Nation Millennium Development Goals reports, but also specific issues highlighted by the World Health Organization such as malaria elimination in international borders.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2270-0) contains supplementary material, which is available to authorized users.

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate 'packages' of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes.

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia

Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations.

Methodological Considerations for Use of Routine Health Information System Data to Evaluate Malaria Program Impact in an Era of Declining Malaria Transmission

Coverage of malaria control interventions is increasing dramatically across endemic countries. Evaluating the impact of malaria control programs and specific interventions on health indicators is essential to enable countries to select the most effective and appropriate combination of tools to accelerate progress or proceed toward malaria elimination. When key malaria interventions have been proven effective under controlled settings, further evaluations of the impact of the intervention using randomized approaches may not be appropriate or ethical. Alternatives to randomized controlled trials are therefore required for rigorous evaluation under conditions of routine program delivery. Routine health management information system (HMIS) data are a potentially rich source of data for impact evaluation, but have been underused in impact evaluation due to concerns over internal validity, completeness, and potential bias in estimates of program or intervention impact. A range of methodologies were identified that have been used for impact evaluations with malaria outcome indicators generated from HMIS data. Methods used to maximize internal validity of HMIS data are presented, together with recommendations on reducing bias in impact estimates. Interrupted time series and dose-response analyses are proposed as the strongest quasi-experimental impact evaluation designs for analysis of malaria outcome indicators from routine HMIS data. Interrupted time series analysis compares the outcome trend and level before and after the introduction of an intervention, set of interventions or program. The dose-response national platform approach explores associations between intervention coverage or program intensity and the outcome at a subnational (district or health facility catchment) level.

Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans

Both Aedes aegytpi and Ae. albopictus are major vectors of 5 important arboviruses (namely chikungunya virus, dengue virus, Rift Valley fever virus, yellow fever virus, and Zika virus), making these mosquitoes an important factor in the worldwide burden of infectious disease. Vector control using insecticides coupled with larval source reduction is critical to control the transmission of these viruses to humans but is threatened by the emergence of insecticide resistance. Here, we review the available evidence for the geographical distribution of insecticide resistance in these 2 major vectors worldwide and map the data collated for the 4 main classes of neurotoxic insecticide (carbamates, organochlorines, organophosphates, and pyrethroids). Emerging resistance to all 4 of these insecticide classes has been detected in the Americas, Africa, and Asia. Target-site mutations and increased insecticide detoxification have both been linked to resistance in Ae. aegypti and Ae. albopictus but more work is required to further elucidate metabolic mechanisms and develop robust diagnostic assays. Geographical distributions are provided for the mechanisms that have been shown to be important to date. Estimating insecticide resistance in unsampled locations is hampered by a lack of standardisation in the diagnostic tools used and by a lack of data in a number of regions for both resistance phenotypes and genotypes. The need for increased sampling using standard methods is critical to tackle the issue of emerging insecticide resistance threatening human health. Specifically, diagnostic doses and well-characterised susceptible strains are needed for the full range of insecticides used to control Ae. aegypti and Ae. albopictus to standardise measurement of the resistant phenotype, and calibrated diagnostic assays are needed for the major mechanisms of resistance.

Going beyond personal protection against mosquito bites to eliminate malaria transmission: population suppression of malaria vectors that exploit both human and animal blood

Protecting individuals and households against mosquito bites with long-lasting insecticidal nets (LLINs) or indoor residual spraying (IRS) can suppress entire populations of unusually efficient malaria vector species that predominantly feed indoors on humans. Mosquitoes which usually feed on animals are less reliant on human blood, so they are far less vulnerable to population suppression effects of such human-targeted insecticidal measures. Fortunately, the dozens of mosquito species which primarily feed on animals are also relatively inefficient vectors of malaria, so personal protection against mosquito bites may be sufficient to eliminate transmission. However, a handful of mosquito species are particularly problematic vectors of residual malaria transmission, because they feed readily on both humans and animals. These unusual vectors feed often enough on humans to be potent malaria vectors, but also often enough on animals to evade population control with LLINs, IRS or any other insecticidal personal protection measure targeted only to humans. Anopheles arabiensis and A. coluzzii in Africa, A. darlingi in South America and A. farauti in Oceania, as well as A. culicifacies species E, A. fluviatilis species S, A. lesteri and A. minimus in Asia, all feed readily on either humans or animals and collectively mediate residual malaria transmission across most of the tropics. Eliminating malaria transmission by vectors exhibiting such dual host preferences will require aggressive mosquito population abatement, rather than just personal protection of humans. Population suppression of even these particularly troublesome vectors is achievable with a variety of existing vector control technologies that remain underdeveloped or underexploited.

Mapping the spatial distribution of the Japanese encephalitis vector, Culex tritaeniorhynchus Giles, 1901 (Diptera: Culicidae) within areas of Japanese encephalitis risk

BACKGROUND

Japanese encephalitis (JE) is one of the most significant aetiological agents of viral encephalitis in Asia. This medically important arbovirus is primarily spread from vertebrate hosts to humans by the mosquito vector Culex tritaeniorhynchus. Knowledge of the contemporary distribution of this vector species is lacking, and efforts to define areas of disease risk greatly depend on a thorough understanding of the variation in this mosquito's geographical distribution.

RESULTS

We assembled a contemporary database of Cx. tritaeniorhynchus presence records within Japanese encephalitis risk areas from formal literature and other relevant resources, resulting in 1,045 geo-referenced, spatially and temporally unique presence records spanning from 1928 to 2014 (71.9% of records obtained between 2001 and 2014). These presence data were combined with a background dataset capturing sample bias in our presence dataset, along with environmental and socio-economic covariates, to inform a boosted regression tree model predicting environmental suitability for Cx. tritaeniorhynchus at each 5 × 5 km gridded cell within areas of JE risk. The resulting fine-scale map highlights areas of high environmental suitability for this species across India, Nepal and China that coincide with areas of high JE incidence, emphasising the role of this vector in disease transmission and the utility of the map generated.

CONCLUSIONS

Our map contributes towards efforts determining the spatial heterogeneity in Cx. tritaeniorhynchus distribution within the limits of JE transmission. Specifically, this map can be used to inform vector control programs and can be used to identify key areas where the prevention of Cx. tritaeniorhynchus establishment should be a priority.

Developing global maps of insecticide resistance risk to improve vector control

Background

Significant reductions in malaria transmission have been achieved over the last 15 years with elimination occurring in a small number of countries, however, increasing drug and insecticide resistance threatens these gains. Insecticide resistance has decreased the observed mortality to the most commonly used insecticide class, the pyrethroids, and the number of alternative classes approved for use in public health is limited. Disease prevention and elimination relies on operational control of Anopheles malaria vectors, which requires the deployment of effective insecticides. Resistance is a rapidly evolving phenomena and the resources and human capacity to continuously monitor vast numbers of mosquito populations in numerous locations simultaneously are not available.

Methods

Resistance data are obtained from published articles, by contacting authors and custodians of unpublished data sets. Where possible data is disaggregated to single sites and collection periods to give a fine spatial resolution.

Results

Currently the data set includes data from 1955 to October 2016 from 71 malaria endemic countries and 74 anopheline species. This includes data for all four classes of insecticides and associated resistance mechanisms.

Conclusions

Resistance is a rapidly evolving phenomena and the resources and human capacity to continuously monitor vast numbers of mosquito populations in numerous locations simultaneously are not available. The Malaria Atlas Project-Insecticide Resistance (MAP-IR) venture has been established to develop tools that will use available data to provide best estimates of the spatial distribution of insecticide resistance and help guide control programmes on this serious issue.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1733-z) contains supplementary material, which is available to authorized users.

Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance

Background

Many of the mosquito species responsible for malaria transmission belong to a sibling complex; a taxonomic group of morphologically identical, closely related species. Sibling species often differ in several important factors that have the potential to impact malaria control, including their geographical distribution, resistance to insecticides, biting and resting locations, and host preference. The aim of this study was to define the geographical distributions of dominant malaria vector sibling species in Africa so these distributions can be coupled with data on key factors such as insecticide resistance to aid more focussed, species-selective vector control.

Results

Within the Anopheles gambiae species complex and the Anopheles funestus subgroup, predicted geographical distributions for Anopheles coluzzii, An. gambiae (as now defined) and An. funestus (distinct from the subgroup) have been produced for the first time. Improved predicted geographical distributions for Anopheles arabiensis, Anopheles melas and Anopheles merus have been generated based on records that were confirmed using molecular identification methods and a model that addresses issues of sampling bias and past changes to the environment. The data available for insecticide resistance has been evaluated and differences between sibling species are apparent although further analysis is required to elucidate trends in resistance.

Conclusions

Sibling species display important variability in their geographical distributions and the most important malaria vector sibling species in Africa have been mapped here for the first time. This will allow geographical occurrence data to be coupled with species-specific data on important factors for vector control including insecticide resistance. Species-specific data on insecticide resistance is available for the most important malaria vectors in Africa, namely An. arabiensis, An. coluzzii, An. gambiae and An. funestus. Future work to combine these data with the geographical distributions mapped here will allow more focussed and resource-efficient vector control and provide information to greatly improve and inform existing malaria transmission models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1734-y) contains supplementary material, which is available to authorized users.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R² = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity.

Web-based GIS for spatial pattern detection: application to malaria incidence in Vietnam

Introduction

There is a great concern on how to build up an interoperable health information system of public health and health information technology within the development of public information and health surveillance programme. Technically, some major issues remain regarding to health data visualization, spatial processing of health data, health information dissemination, data sharing and the access of local communities to health information. In combination with GIS, we propose a technical framework for web-based health data visualization and spatial analysis.

Methods

Data was collected from open map-servers and geocoded by open data kit package and data geocoding tools. The Web-based system is designed based on Open-source frameworks and libraries. The system provides Web-based analyst tool for pattern detection through three spatial tests: Nearest neighbour, K function, and Spatial Autocorrelation.

Results

The result is a web-based GIS, through which end users can detect disease patterns via selecting area, spatial test parameters and contribute to managers and decision makers. The end users can be health practitioners, educators, local communities, health sector authorities and decision makers. This web-based system allows for the improvement of health related services to public sector users as well as citizens in a secure manner.

Conclusions

The combination of spatial statistics and web-based GIS can be a solution that helps empower health practitioners in direct and specific intersectional actions, thus provide for better analysis, control and decision-making.

A global bionomic database for the dominant vectors of human malaria

Anopheles mosquitoes were first recognised as the transmitters of human malaria in the late 19th Century and have been subject to a huge amount of research ever since. Yet there is still much that is unknown regarding the ecology, behaviour (collectively 'bionomics') and sometimes even the identity of many of the world's most prominent disease vectors, much less the within-species variation in their bionomics. Whilst malaria elimination remains an ambitious goal, it is becoming increasingly clear that knowledge of vector behaviour is needed to effectively target control measures. A database of bionomics data for the dominant vector species of malaria worldwide has been compiled from published peer-reviewed literature. The data identification and collation processes are described, together with the geo-positioning and quality control methods. This is the only such dataset in existence and provides a valuable resource to researchers and policy makers in this field.

A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997-2010

BACKGROUND

The absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries.

METHODS

In this analysis an explicit geostatistical, autoregressive, mixed model is employed to quantitatively assess the association between conflicts and variations in Plasmodium falciparum parasite prevalence across a 13-year period in sub-Saharan Africa.

RESULTS

Analyses of geolocated, malaria prevalence survey variations against armed conflict data in general showed a wide, but short-lived impact of conflict events geographically. The number of countries with decreased P. falciparum parasite prevalence (17) is larger than the number of countries with increased transmission (12), and notably, some of the countries with the highest transmission pre-conflict were still found with lower transmission post-conflict. For four countries, there were no significant changes in parasite prevalence. Finally, distance from conflicts, duration of conflicts, violence of conflict, and number of conflicts were significant components in the model explaining the changes in P. falciparum parasite rate.

CONCLUSIONS

The results suggest that the maintenance of intervention coverage and provision of healthcare in conflict situations to protect vulnerable populations can maintain gains in even the most difficult of circumstances, and that conflict does not represent a substantial barrier to elimination goals.

A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models

Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is being used to validate the data that our system uses to produce updated predictive disease maps on a weekly basis.

A Framework for Global Collaborative Data Management for Malaria Research

Data generated during the course of research activities carried out by the International Centers of Excellence for Malaria Research (ICEMR) is heterogeneous, large, and multi-scaled. The complexity of federated and global data operations and the diverse uses planned for the data pose tremendous challenges and opportunities for collaborative research. In this article, we present the foundational principles for data management across the ICEMR Program, the logistics associated with multiple aspects of the data life cycle, and describe a pilot centralized web information system created in PlasmoDB to query a subset of this data. The paradigm proposed as a solution for the data operations in the ICEMR Program is widely applicable to large, multifaceted research projects, and could be reproduced in other contexts that require sophisticated data management.

Prioritising Infectious Disease Mapping

Background

Increasing volumes of data and computational capacity afford unprecedented opportunities to scale up infectious disease (ID) mapping for public health uses. Whilst a large number of IDs show global spatial variation, comprehensive knowledge of these geographic patterns is poor. Here we use an objective method to prioritise mapping efforts to begin to address the large deficit in global disease maps currently available.

Methodology/Principal Findings

Automation of ID mapping requires bespoke methodological adjustments tailored to the epidemiological characteristics of different types of diseases. Diseases were therefore grouped into 33 clusters based upon taxonomic divisions and shared epidemiological characteristics. Disability-adjusted life years, derived from the Global Burden of Disease 2013 study, were used as a globally consistent metric of disease burden. A review of global health stakeholders, existing literature and national health priorities was undertaken to assess relative interest in the diseases. The clusters were ranked by combining both metrics, which identified 44 diseases of main concern within 15 principle clusters. Whilst malaria, HIV and tuberculosis were the highest priority due to their considerable burden, the high priority clusters were dominated by neglected tropical diseases and vector-borne parasites.

Conclusions/Significance

A quantitative, easily-updated and flexible framework for prioritising diseases is presented here. The study identifies a possible future strategy for those diseases where significant knowledge gaps remain, as well as recognising those where global mapping programs have already made significant progress. For many conditions, potential shared epidemiological information has yet to be exploited.

Maps have long been used to not only visualise, but also to inform infectious disease control efforts, identify and predict areas of greatest risk of specific diseases, and better understand the epidemiology of disease over various spatial scales. In spite of the utilities of such outputs, globally comprehensive maps have been produced for only a handful of infectious diseases. Due to limited resources, it is necessary to define a framework to prioritise which diseases to consider mapping globally. This paper outlines a framework which compares each disease’s global burden with its associated interest from the policy community in a data-driven manner which can be used to determine the relative priority of each condition. Malaria, HIV and TB are, unsurprisingly, ranked highest due to their considerable health burden, while the other priority diseases are dominated by neglected tropical diseases and vector-borne diseases. For some conditions, global mapping efforts are already in place, however, for many neglected conditions there still remains a need for high resolution spatial surveys.

Defining the relationship between Plasmodium vivax parasite rate and clinical disease

Background

Though essential to the development and evaluation of national malaria control programmes, precise enumeration of the clinical illness burden of malaria in endemic countries remains challenging where local surveillance systems are incomplete. Strategies to infer annual incidence rates from parasite prevalence survey compilations have proven effective in the specific case of Plasmodium falciparum, but have yet to be developed for Plasmodium vivax. Moreover, defining the relationship between P. vivax prevalence and clinical incidence may also allow levels of endemicity to be inferred for areas where the information balance is reversed, that is, incident case numbers are more widely gathered than parasite surveys; both applications ultimately facilitating cartographic estimates of P. vivax transmission intensity and its ensuring disease burden.

Methods

A search for active case detection surveys was conducted and the recorded incidence values were matched to local, contemporary parasite rate measures and classified to geographic zones of differing relapse phenotypes. A hierarchical Bayesian model was fitted to these data to quantify the relationship between prevalence and incidence while accounting for variation among relapse zones.

Results

The model, fitted with 176 concurrently measured P. vivax incidence and prevalence records, was a linear regression of the logarithm of incidence against the logarithm of age-standardized prevalence. Specific relationships for the six relapse zones where data were available were drawn, as well as a pooled overall relationship. The slope of the curves varied among relapse zones; zones with short predicted time to relapse had steeper slopes than those observed to contain long-latency relapse phenotypes.

Conclusions

The fitted relationships, along with appropriate uncertainty metrics, allow for estimates of clinical incidence of known confidence to be made from wherever P. vivax prevalence data are available. This is a prerequisite for cartographic-based inferences about the global burden of morbidity due to P. vivax, which will be used to inform control efforts.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0706-3) contains supplementary material, which is available to authorized users.

Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach

Background

Malaria risk maps play an increasingly important role in disease control planning, implementation, and evaluation. The construction of these maps using modern geospatial techniques relies on covariate grids: continuous surfaces quantifying environmental factors that partially explain spatial heterogeneity in malaria endemicity. Although crucial, past variable selection processes for this purpose have often been subjective and ad-hoc, with many covariates used in modeling with little quantitative justification.

Methods

This research consists of an extensive covariate construction and selection process for predicting Plasmodium falciparum parasite rates (PfPR) in Africa for years 2000-2012. First, a literature review was conducted to establish a comprehensive list of covariates used for malaria mapping. Second, a library of covariate data was assembled to reflect this list, a process that included the construction of multiple, temporally dynamic datasets. Third, the resulting set of covariates was leveraged to create more than 50 million possible covariate terms via factorial combinations of different spatial and temporal aggregations, transformations, and pairwise interactions. Fourth, the expanded set of covariates was reduced via successive selection criteria to yield a robust covariate subset that was assessed using an out-of-sample validation approach.

Results

The final covariate subset included predominately dynamic covariates and it substantially out-performed earlier sets used by the Malaria Atlas Project (MAP) for creating global malaria risk maps, with the pseudo-R² value for the out-of-sample validation increasing from 0.43 to 0.52. Dynamic covariates improved the model, with 17 of the 20 new covariates consisting of monthly or annual products, but the selected covariates were typically interaction terms that included both dynamic and synoptic datasets. Thus the interplay between normal (i.e., long-term averages) and immediate conditions may be key for characterizing environmental controls on parasite rate.

Conclusions

This analysis represents the first effort to systematically audit covariate utility for malaria mapping and then derive an objective, empirically based set of environmental covariates for modeling PfPR. The new covariates produce more reliable representations of malaria risk patterns and how they are changing through time, and these covariates will be used to characterize spatially and temporally varying environmental conditions affecting PfPR within a geostatistical-modeling framework, thus building upon previous research by MAP that produced global malaria maps for 2007 and 2010.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0574-x) contains supplementary material, which is available to authorized users.

Vitamin A supplements, routine immunization, and the subsequent risk of Plasmodium infection among children under 5 years in sub-Saharan Africa

Recent studies, partly based on murine models, suggest childhood immunization and vitamin A supplements may confer protection against malaria infection, although strong evidence to support these theories in humans has so far been lacking. We analyzed national survey data from children aged 6–59 months in four sub-Saharan African countries over an 18-month time period, to determine the risk of Plasmodium spp. parasitemia (n=8390) and Plasmodium falciparum HRP-2 (PfHRP-2)-related antigenemia (n=6121) following vitamin A supplementation and standard vaccination. Bacille Calmette Guerin-vaccinated children were more likely to be PfHRP-2 positive (relative risk [RR]=4.06, 95% confidence interval [CI]=2.00–8.28). No association was identified with parasitemia. Measles and polio vaccination were not associated with malaria. Children receiving vitamin A were less likely to present with parasitemia (RR=0.46, 95% CI=0.39–0.54) and antigenemia (RR=0.23, 95% CI=0.17–0.29). Future studies focusing on climate seasonality, placental malaria and HIV are needed to characterize better the association between vitamin A and malaria infection in different settings.

DOI:http://dx.doi.org/10.7554/eLife.03925.001

More than half of the world's population is at risk of malaria, with an estimated 198 million clinical cases each year. A vaccine that fully prevents it has not yet been discovered. Most cases of malaria occur among children living in sub-Saharan Africa, a region where many receive routine vaccinations designed to prevent other diseases; for example, 75% of children in sub-Saharan Africa receive measles vaccines. Many also receive vitamin A supplements, which have been linked not only to the protection of a child's vision, but also to a lower risk of death and an improved ability to fight off infections.

Some researchers have suggested that vitamin A supplements and routine childhood vaccinations for other diseases may also provide some protection against malaria. For example, some studies performed in mice have shown that a commonly used tuberculosis vaccine may eliminate Plasmodium parasites that cause malaria infections. However, this effect depended on several factors, including how the vaccine was administered and whether the vaccination was given before or after the mouse developed malaria.

It is less clear whether vaccines or vitamin A have antimalarial effects in humans. To address this, Hollm-Delgado et al. analyzed national survey data collected from thousands of children aged between 6 months and 5 years old who lived in four different countries in sub-Saharan Africa. The surveys contained information about the vaccines and supplements the children received, and whether their blood showed signs of infection with malaria-causing Plasmodium parasites.

Hollm-Delgado et al. found that routine vaccinations did not affect the likelihood of malaria parasites being detected in the child's blood. However, children vaccinated against tuberculosis were more likely to have a specific type of protein released when malaria infects the blood. Hollm-Delgado et al. suspect that the tests may actually have inadvertently detected other parasitic infections in the children, such as Schistosoma, producing false-positive results for malaria.

In contrast, Hollm-Delgado et al. found that children who received vitamin A supplements were less likely to become infected with malaria. The benefits of the supplements appeared to be affected by several conditions, including the time of year when the children received their supplements or when they were tested for malaria, and whether their mother had malaria when pregnant. Clinical trials are now needed to confirm these results and investigate how effectively vitamin A prevents malaria.

DOI:http://dx.doi.org/10.7554/eLife.03925.002

Ethical, Political and Societal Implications of the Open Access Journal Movement in the Era of Economic Crisis, with Emphasis on Public Health Pharmacogenomics

Publication of the research outputs is a vital step of the research processes and a gateway between the laboratory and the global society. Open Access is revolutionizing the dissemination of scientific ideas, particularly in the field of public health pharmacogenomics that examines the ways in which pharmacogenomics impacts health systems and services at a societal level, rather than a narrow bench to bedside model of translation science. This manuscript argues that despite some limitations and drawbacks, open access has profound ethical, political and societal implications especially on underdeveloped and developing countries, and that it provides opportunities for science to grow in these resource-limited countries, particularly in the era of a severe economic and financial crisis that is imposing cuts and restrictions to research.

A global assembly of adult female mosquito mark-release-recapture data to inform the control of mosquito-borne pathogens

Background

Pathogen transmission by mosquitos is known to be highly sensitive to mosquito bionomic parameters. Mosquito mark-release-recapture (MMRR) experiments are a standard method for estimating such parameters including dispersal, population size and density, survival, blood feeding frequency and blood meal host preferences.

Methods

We assembled a comprehensive database describing adult female MMRR experiments. Bibliographic searches were used to build a digital library of MMRR studies and selected data describing the reported outcomes were extracted.

Results

The resulting database contained 774 unique adult female MMRR experiments involving 58 vector mosquito species from the three main genera of importance to human health: Aedes, Anopheles and Culex. Crude examination of these data revealed patterns associated with geography as well as mosquito genus, consistent with bionomics varying by species-specific life history and ecological context. Recapture success varied considerably and was significantly different amongst genera, with 8, 4 and 1% of adult females recaptured for Aedes, Anopheles and Culex species, respectively. A large proportion of experiments (59%) investigated dispersal and survival and many allowed disaggregation of the release and recapture data. Geographic coverage was limited to just 143 localities around the world.

Conclusions

This MMRR database is a substantial contribution to the compilation of global data that can be used to better inform basic research and public health interventions, to identify and fill knowledge gaps and to enrich theory and evidence-based ecological and epidemiological studies of mosquito vectors, pathogen transmission and disease prevention. The database revealed limited geographic coverage and a relative scarcity of information for vector species of substantial public health relevance. It represents, however, a wealth of entomological information not previously compiled and of particular interest for mosquito-borne pathogen transmission models.

Declining malaria in Africa: improving the measurement of progress

The dramatic escalation of malaria control activities in Africa since the year 2000 has increased the importance of accurate measurements of impact on malaria epidemiology and burden. This study presents a systematic review of the emerging published evidence base on trends in malaria risk in Africa and argues that more systematic, timely, and empirically-based approaches are urgently needed to track the rapidly evolving landscape of transmission.