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 Methods Resistance data are obtained from published articles, by contacting authors and custodians of unpublished data sets.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

Associated patterns of insecticide resistance in field populations of malaria vectors across Africa.

The development of insecticide resistance in African malaria vectors threatens the continued efficacy of important vector control methods that rely on a limited set of insecticides. To understand the operational significance of resistance we require quantitative information about levels of resistance in field populations to the suite of vector control insecticides. Estimation of resistance is complicated by the sparsity of observations in field populations, variation in resistance over time and space at local and regional scales, and cross-resistance between different insecticide types. Using observations of the prevalence of resistance in mosquito species from the complex sampled from 1,183 locations throughout Africa, we applied Bayesian geostatistical models to quantify patterns of covariation in resistance phenotypes across different insecticides. For resistance to the three pyrethroids tested, deltamethrin, permethrin, and λ-cyhalothrin, we found consistent forms of covariation across sub-Saharan Africa and covariation between resistance to these pyrethroids and resistance to DDT. We found no evidence of resistance interactions between carbamate and organophosphate insecticides or between these insecticides and those from other classes. For pyrethroids and DDT we found significant associations between predicted mean resistance and the observed frequency of mutations in the gene in field mosquito samples, with DDT showing the strongest association. These results improve our capacity to understand and predict resistance patterns throughout Africa and can guide the development of monitoring strategies

Modelling spatiotemporal trends in the frequency of genetic mutations conferring insecticide target-site resistance in African mosquito malaria vector species

Background Resistance in malaria vectors to pyrethroids, the most widely used class of insecticides for malaria vector control, threatens the continued efficacy of vector control tools. Target-site resistance is an important genetic resistance mechanism caused by mutations in the voltage-gated sodium channel (Vgsc) gene that encodes the pyrethroid target-site. Understanding the geographic distribution of target-site resistance, and temporal trends across different vector species, can inform strategic deployment of vector control tools. Results We develop a Bayesian statistical spatiotemporal model to interpret species-specific trends in the frequency of the most common resistance mutations, Vgsc-995S and Vgsc- 995F, in three major malaria vector species Anopheles gambiae, An. coluzzii, and An. arabiensis over the period 2005-2017. The models are informed by 2418 observations of the frequency of each mutation in field sampled mosquitoes collected from 27 countries spanning western and eastern regions of Africa. For nine selected countries, we develop annual predictive maps which reveal geographically-structured patterns of spread of each mutation at regional and continental scales. The results show associations, as well as stark differences, in spread dynamics of the two mutations across the three vector species. The coverage of ITNs was an influential predictor of Vgsc allele frequencies, with modelled relationships between ITN coverage and allele frequencies varying across species and geographic regions. We found that our mapped Vgsc allele frequencies are a significant partial predictor of phenotypic resistance to the pyrethroid deltamethrin in An. gambiae complex populations. Conclusions Our predictive maps show how spatiotemporal trends in insecticide target-site resistance mechanisms in African An. gambiae vary across individual vector species and geographic regions. Molecular surveillance of resistance mechanisms will help to predict resistance phenotypes and track their spread

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

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

Associated patterns of insecticide resistance in field populations of malaria vectors across Africa.

The development of insecticide resistance in African malaria vectors threatens the continued efficacy of important vector control methods that rely on a limited set of insecticides. To understand the operational significance of resistance we require quantitative information about levels of resistance in field populations to the suite of vector control insecticides. Estimation of resistance is complicated by the sparsity of observations in field populations, variation in resistance over time and space at local and regional scales, and cross-resistance between different insecticide types. Using observations of the prevalence of resistance in mosquito species from the Anopheles gambiae complex sampled from 1,183 locations throughout Africa, we applied Bayesian geostatistical models to quantify patterns of covariation in resistance phenotypes across different insecticides. For resistance to the three pyrethroids tested, deltamethrin, permethrin, and λ-cyhalothrin, we found consistent forms of covariation across sub-Saharan Africa and covariation between resistance to these pyrethroids and resistance to DDT. We found no evidence of resistance interactions between carbamate and organophosphate insecticides or between these insecticides and those from other classes. For pyrethroids and DDT we found significant associations between predicted mean resistance and the observed frequency of kdr mutations in the Vgsc gene in field mosquito samples, with DDT showing the strongest association. These results improve our capacity to understand and predict resistance patterns throughout Africa and can guide the development of monitoring strategies

Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria

Plasmodium knowlesi is a malaria parasite found in wild monkey populations and transmitted from this animal reservoir to humans via infected mosquitoes. It causes severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. The geographical distribution of this disease is largely unknown because it is often misdiagnosed as one of the human malarias. Human malaria parasites are primarily transmitted between humans via mosquitoes and are not frequently transmitted from other animals to humans. Many countries in Southeast Asia, where P. knowlesi infections have been reported, are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated. In locations that have high volumes of P. knowlesi infection data, we modelled patterns of variation in the data linked to environmental predictors, and used this to estimate P. knowlesi infection risk in locations where data is lacking. The resulting map represents an initial evidence-base for identifying areas of human disease risk that should be prioritized for surveillance, particularly in the context of malaria elimination in the region

Bionomic database for the dominant vectors of malaria in the Americas

Compilation of bionomics data for the dominant vectors of malaria in the Americas from published sources reporting data collected from 1985-2014

Predicting the geographical distributions of the macaque hosts and mosquito vectors of Plasmodium knowlesi malaria in forested and non-forested areas

Background: Plasmodium knowlesi is a zoonotic pathogen, transmitted among macaques and to humans by anopheline mosquitoes. Information on P. knowlesi malaria is lacking in most regions so the first step to understand the geographical distribution of disease risk is to define the distributions of the reservoir and vector species. Methods: We used macaque and mosquito species presence data, background data that captured sampling bias in the presence data, a boosted regression tree model and environmental datasets, including annual data for land classes, to predict the distributions of each vector and host species. We then compared the predicted distribution of each species with cover of each land class. Results: Fine-scale distribution maps were generated for three macaque host species (Macaca fascicularis, M. nemestrina and M. leonina) and two mosquito vector complexes (the Dirus Complex and the Leucosphyrus Complex). The Leucosphyrus Complex was predicted to occur in areas with disturbed, but not intact, forest cover (> 60 % tree cover) whereas the Dirus Complex was predicted to occur in areas with 10-100 % tree cover as well as vegetation mosaics and cropland. Of the macaque species, M. nemestrina was mainly predicted to occur in forested areas whereas M. fascicularis was predicted to occur in vegetation mosaics, cropland, wetland and urban areas in addition to forested areas. Conclusions: The predicted M. fascicularis distribution encompassed a wide range of habitats where humans are found. This is of most significance in the northern part of its range where members of the Dirus Complex are the main P. knowlesi vectors because these mosquitoes were also predicted to occur in a wider range of habitats. Our results support the hypothesis that conversion of intact forest into disturbed forest (for example plantations or timber concessions), or the creation of vegetation mosaics, will increase the probability that members of the Leucosphyrus Complex occur at these locations, as well as bringing humans into these areas. An explicit analysis of disease risk itself using infection data is required to explore this further. The species distributions generated here can now be included in future analyses of P. knowlesi infection risk