Seasonal dynamics of Anopheles stephensi and its implications for mosquito detection and emergent malaria control in the Horn of Africa.

Invasion of the malaria vector Anopheles stephensi across the Horn of Africa threatens control efforts across the continent, particularly in urban settings where the vector is able to proliferate. Malaria transmission is primarily determined by the abundance of dominant vectors, which often varies seasonally with rainfall. However, it remains unclear how An. stephensi abundance changes throughout the year, despite this being a crucial input to surveillance and control activities. We collate longitudinal catch data from across its endemic range to better understand the vector's seasonal dynamics and explore the implications of this seasonality for malaria surveillance and control across the Horn of Africa. Our analyses reveal pronounced variation in seasonal dynamics, the timing and nature of which are poorly predicted by rainfall patterns. Instead, they are associated with temperature and patterns of land use; frequently differing between rural and urban settings. Our results show that timing entomological surveys to coincide with rainy periods is unlikely to improve the likelihood of detecting An. stephensi. Integrating these results into a malaria transmission model, we show that timing indoor residual spraying campaigns to coincide with peak rainfall offers little improvement in reducing disease burden compared to starting in a random month. Our results suggest that unlike other malaria vectors in Africa, rainfall may be a poor guide to predicting the timing of peaks in An. stephensi-driven malaria transmission. This highlights the urgent need for longitudinal entomological monitoring of the vector in its new environments given recent invasion and potential spread across the continent.

AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact.

In recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel. AnophelesModel is an online, open-access R package that quantifies the impact of vector control interventions depending on mosquito species and location-specific characteristics. In addition, it includes a previously published, comprehensive, curated database of field entomological data from over 50 Anopheles species, field data on mosquito and human behaviour, and estimates of vector control effectiveness. Using the input data, the package parameterizes a discrete-time, state transition model of the mosquito oviposition cycle and infers species-specific impacts of various interventions on vectorial capacity. In addition, it offers formatted outputs ready to use in downstream analyses and by other models of malaria transmission for accurate representation of the vector-specific components. Using AnophelesModel, we show how the key implications for intervention impact change for various vectors and locations. The package facilitates quantitative comparisons of likely intervention impacts in different geographical settings varying in vector compositions, and can thus guide towards more robust and efficient malaria control recommendations. The AnophelesModel R package is available under a GPL-3.0 license at https://github.com/SwissTPH/AnophelesModel.

Evaluating insecticide resistance across African districts to aid malaria control decisions.

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.

The potential impact of Anopheles stephensi establishment on the transmission of Plasmodium falciparum in Ethiopia and prospective control measures.

BACKGROUND: Sub-Saharan Africa has seen substantial reductions in cases and deaths due to malaria over the past two decades. While this reduction is primarily due to an increasing expansion of interventions, urbanisation has played its part as urban areas typically experience substantially less malaria transmission than rural areas. However, this may be partially lost with the invasion and establishment of Anopheles stephensi. A. stephensi, the primary urban malaria vector in Asia, was first detected in Africa in 2012 in Djibouti and was subsequently identified in Ethiopia in 2016, and later in Sudan and Somalia. In Djibouti, malaria cases have increased 30-fold from 2012 to 2019 though the impact in the wider region remains unclear. METHODS: Here, we have adapted an existing model of mechanistic malaria transmission to estimate the increase in vector density required to explain the trends in malaria cases seen in Djibouti. To account for the observed plasticity in An. stephensi behaviour, and the unknowns of how it will establish in a novel environment, we sample behavioural parameters in order to account for a wide range of uncertainty. This quantification is then applied to Ethiopia, considering temperature-dependent extrinsic incubation periods, pre-existing vector-control interventions and Plasmodium falciparum prevalence in order to assess the potential impact of An. stephensi establishment on P. falciparum transmission. Following this, we estimate the potential impact of scaling up ITN (insecticide-treated nets)/IRS (indoor residual spraying) and implementing piperonyl butoxide (PBO) ITNs and larval source management, as well as their economic costs. RESULTS: We estimate that annual P. falciparum malaria cases could increase by 50% (95% CI 14-90) if no additional interventions are implemented. The implementation of sufficient control measures to reduce malaria transmission to pre-stephensi levels will cost hundreds of millions of USD. CONCLUSIONS: Substantial heterogeneity across the country is predicted and large increases in vector control interventions could be needed to prevent a major public health emergency.

A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations.

Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species-questions rarely answerable from individual entomological studies (that typically focus on a single location or species). We develop a novel statistical framework enabling identification and classification of time series with similar temporal properties, and use this framework to systematically explore variation in population dynamics and seasonality in anopheline mosquito time series catch data spanning seven species, 40 years and 117 locations across mainland India. Our analyses reveal pronounced variation in dynamics across locations and between species in the extent of seasonality and timing of seasonal peaks. However, we show that these diverse dynamics can be clustered into four 'dynamical archetypes', each characterized by distinct temporal properties and associated with a largely unique set of environmental factors. Our results highlight that a range of environmental factors including rainfall, temperature, proximity to static water bodies and patterns of land use (particularly urbanicity) shape the dynamics and seasonality of mosquito populations, and provide a generically applicable framework to better identify and understand patterns of seasonal variation in vectors relevant to public health.

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.

Modelling the relative abundance of the primary African vectors of malaria before and after the implementation of indoor, insecticide-based vector control.

BACKGROUND: Malaria remains a heavy burden across sub-Saharan Africa where transmission is maintained by some of the world's most efficient vectors. Indoor insecticide-based control measures have significantly reduced transmission, yet elimination remains a distant target. Knowing the relative abundance of the primary vector species can provide transmission models with much needed information to guide targeted control measures. Moreover, understanding how existing interventions are impacting on these relative abundances highlights where alternative control (e.g., larval source management) is needed. METHODS: Using the habitat suitability probabilities generated by predictive species distribution models combined with data collated from the literature, a multinomial generalized additive model was applied to produce relative abundance estimates for Anopheles arabiensis, Anopheles funestus and Anopheles gambiae/Anopheles coluzzii. Using pre- and post-intervention abundance data, estimates of the effect of indoor insecticide-based interventions on these relative abundances were made and are illustrated in post-intervention maps. RESULTS: Conditional effect plots and relative abundance maps illustrate the individual species' predicted habitat suitability and how they interact when in sympatry. Anopheles arabiensis and An. funestus show an affinity in habitat preference at the expense of An. gambiae/An. coluzzii, whereas increasing habitat suitability for An. gambiae/An. coluzzii is conversely less suitable for An. arabiensis but has little effect on An. funestus. Indoor insecticide-based interventions had a negative impact on the relative abundance of An. funestus, and a lesser effect on An. arabiensis. Indoor residual spraying had the greatest impact on the relative abundance of An. funestus, and a lesser effect on An. gambiae/An. coluzzii. Insecticide-treated bed nets reduced the relative abundance of both species equally. These results do not indicate changes in the absolute abundance of these species, which may be reduced for all species overall. CONCLUSIONS: The maps presented here highlight the interactions between the primary vector species in sub-Saharan Africa and demonstrate that An. funestus is more susceptible to certain indoor-based insecticide interventions than An. gambiae/An. coluzzii, which in turn, is more susceptible than An. arabiensis. This may provide An. arabiensis with a competitive advantage where it is found in sympatry with other more endophilic vectors, and potentially increase the need for outdoor-based vector interventions to deal with any residual transmission barring the way to malaria elimination.

Bridging Biodiversity and Health: The Global Biodiversity Information Facility's initiative on open data on vectors of human diseases.

There is an increased awareness of the importance of data publication, data sharing, and open science to support research, monitoring and control of vector-borne disease (VBD). Here we describe the efforts of the Global Biodiversity Information Facility (GBIF) as well as the World Health Special Programme on Research and Training in Diseases of Poverty (TDR) to promote publication of data related to vectors of diseases. In 2020, a GBIF task group of experts was formed to provide advice and support efforts aimed at enhancing the coverage and accessibility of data on vectors of human diseases within GBIF. Various strategies, such as organizing training courses and publishing data papers, were used to increase this content. This editorial introduces the outcome of a second call for data papers partnered by the TDR, GBIF and GigaScience Press in the journal GigaByte. Biodiversity and infectious diseases are linked in complex ways. These links can involve changes from the microorganism level to that of the habitat, and there are many ways in which these factors interact to affect human health. One way to tackle disease control and possibly elimination, is to provide stakeholders with access to a wide range of data shared under the FAIR principles, so it is possible to support early detection, analyses and evaluation, and to promote policy improvements and/or development.

Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi.

Anopheles stephensi, an invasive malaria vector native to South Asia and the Arabian Peninsula, was detected in Djibouti's seaport, followed by Ethiopia, Sudan, Somalia, and Nigeria. If An. stephensi introduction is facilitated through seatrade, similar to other invasive mosquitoes, the identification of at-risk countries are needed to increase surveillance and response efforts. Bilateral maritime trade data is used to (1) identify coastal African countries which were highly connected to select An. stephensi endemic countries, (2) develop a prioritization list of countries based on the likelihood of An. stephensi introduction through maritime trade index (LASIMTI), and (3) use network analysis of intracontinental maritime trade to determine likely introduction pathways. Sudan and Djibouti were ranked as the top two countries with LASIMTI in 2011, which were the first two coastal African countries where An. stephensi was detected. With Djibouti and Sudan included as source populations, 2020 data identify Egypt, Kenya, Mauritius, Tanzania, and Morocco as the top countries with LASIMTI. Network analysis highlight South Africa, Mauritius, Ghana, and Togo. These tools can prioritize efforts for An. stephensi surveillance and control in Africa. Surveillance in seaports of identified countries may limit further expansion of An. stephensi by serving as an early warning system.

Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities.

The unprecedented generation of large volumes of biodiversity data is consistently contributing to a wide range of disciplines, including disease ecology. Emerging infectious diseases are usually zoonoses caused by multi-host pathogens. Therefore, their understanding may require the access to biodiversity data related to the ecology and the occurrence of the species involved. Nevertheless, despite several data-mobilization initiatives, the usage of biodiversity data for research into disease dynamics has not yet been fully leveraged. To explore current contribution, trends, and to identify limitations, we characterized biodiversity data usage in scientific publications related to human health, contrasting patterns of studies citing the Global Biodiversity Information Facility (GBIF) with those obtaining data from other sources. We found that the studies mainly obtained data from scientific literature and other not aggregated or standardized sources. Most of the studies explored pathogen species and, particularly those with GBIF-mediated data, tended to explore and reuse data of multiple species (>2). Data sources varied according to the taxa and epidemiological roles of the species involved. Biodiversity data repositories were mainly used for species related to hosts, reservoirs, and vectors, and barely used as a source of pathogens data, which was usually obtained from human and animal-health related institutions. While both GBIF- and not GBIF-mediated data studies explored similar diseases and topics, they presented discipline biases and different analytical approaches. Research on emerging infectious diseases may require the access to geographical and ecological data of multiple species. The One Health challenge requires interdisciplinary collaboration and data sharing, which is facilitated by aggregated repositories and platforms. The contribution of biodiversity data to understand infectious disease dynamics should be acknowledged, strengthened, and promoted.

What incentives encourage local communities to collect and upload mosquito sound data by using smartphones? A mixed methods study in Tanzania.

BACKGROUND: To detect and identify mosquitoes using their characteristic high-pitched sound, we have developed a smartphone application, known as the 'HumBug sensor', that records the acoustic signature of this sound, along with the time and location. This data is then sent remotely to a server where algorithms identify the species according to their distinctive acoustic signature. Whilst this system works well, a key question that remains is what mechanisms will lead to effective uptake and use of this mosquito survey tool? We addressed this question by working with local communities in rural Tanzania and providing three alternative incentives: money only, short message service (SMS) reminders and money, and SMS reminders only. We also had a control group with no incentive. METHODS: A multi-site, quantitative empirical study was conducted in four villages in Tanzania from April to August 2021. Consenting participants (n = 148) were recruited and placed into one of the three intervention arms: monetary incentives only; SMS reminders with monetary incentives; and SMS reminders only. There was also a control group (no intervention). To test effectiveness of the mechanisms, the number of audio uploads to the server of the four trial groups on their specific dates were compared. Qualitative focus group discussions and feedback surveys were also conducted to explore participants' perspectives on their participation in the study and to capture their experiences of using the HumBug sensor. RESULTS: Qualitative data analysis revealed that for many participants (37 out of 81), the main motivation expressed was to learn more about the types of mosquitoes present in their houses. Results from the quantitative empirical study indicate that the participants in the 'control' group switched on their HumBug sensors more over the 14-week period (8 out of 14 weeks) when compared to those belonging to the 'SMS reminders and monetary incentives' trial group. These findings are statistically significant (p < 0.05 or p > 0.95 under a two-sided z-test), revealing that the provision of monetary incentives and sending SMS reminders did not appear to encourage greater number of audio uploads when compared to the control. CONCLUSIONS: Knowledge on the presence of harmful mosquitoes was the strongest motive for local communities to collect and upload mosquito sound data via the HumBug sensor in rural Tanzania. This finding suggests that most efforts should be made to improve flow of real-time information back to the communities on types and risks associated with mosquitoes present in their houses.

Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data.

The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model resulting in maps of the potential geographical distribution of three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria.

A global assessment of surveillance methods for dominant malaria vectors.

The epidemiology of human malaria differs considerably between and within geographic regions due, in part, to variability in mosquito species behaviours. Recently, the WHO emphasised stratifying interventions using local surveillance data to reduce malaria. The usefulness of vector surveillance is entirely dependent on the biases inherent in the sampling methods deployed to monitor mosquito populations. To understand and interpret mosquito surveillance data, the frequency of use of malaria vector collection methods was analysed from a georeferenced vector dataset (> 10,000 data records), extracted from 875 manuscripts across Africa, the Americas and the Asia-Pacific region. Commonly deployed mosquito collection methods tend to target anticipated vector behaviours in a region to maximise sample size (and by default, ignoring other behaviours). Mosquito collection methods targeting both host-seeking and resting behaviours were seldomly deployed concurrently at the same site. A balanced sampling design using multiple methods would improve the understanding of the range of vector behaviours, leading to improved surveillance and more effective vector control.

Distribution of the main malaria vectors in Kenya.

BACKGROUND: A detailed knowledge of the distribution of the main Anopheles malaria vectors in Kenya should guide national vector control strategies. However, contemporary spatial distributions of the locally dominant Anopheles vectors including Anopheles gambiae, Anopheles arabiensis, Anopheles merus, Anopheles funestus, Anopheles pharoensis and Anopheles nili are lacking. The methods and approaches used to assemble contemporary available data on the present distribution of the dominant malaria vectors in Kenya are presented here. METHOD: Primary empirical data from published and unpublished sources were identified for the period 1990 to 2009. Details recorded for each source included the first author, year of publication, report type, survey location name, month and year of survey, the main Anopheles species reported as present and the sampling and identification methods used. Survey locations were geo-positioned using national digital place name archives and on-line geo-referencing resources. The geo-located species-presence data were displayed and described administratively, using first-level administrative units (province), and biologically, based on the predicted spatial margins of Plasmodium falciparum transmission intensity in Kenya for the year 2009. Each geo-located survey site was assigned an urban or rural classification and attributed an altitude value. RESULTS: A total of 498 spatially unique descriptions of Anopheles vector species across Kenya sampled between 1990 and 2009 were identified, 53% were obtained from published sources and further communications with authors. More than half (54%) of the sites surveyed were investigated since 2005. A total of 174 sites reported the presence of An. gambiae complex without identification of sibling species. Anopheles arabiensis and An. funestus were the most widely reported at 244 and 265 spatially unique sites respectively with the former showing the most ubiquitous distribution nationally. Anopheles gambiae, An. arabiensis, An. funestus and An. pharoensis were reported at sites located in all the transmission intensity classes with more reports of An. gambiae in the highest transmission intensity areas than the very low transmission areas. CONCLUSION: A contemporary, spatially defined database of the main malaria vectors in Kenya provides a baseline for future compilations of data and helps identify areas where information is currently lacking. The data collated here are published alongside this paper where it may help guide future sampling location decisions, help with the planning of vector control suites nationally and encourage broader research inquiry into vector species niche modeling.

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.

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.

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.

Potential for reduction of burden and local elimination of malaria by reducing Plasmodium falciparum malaria transmission: a mathematical modelling study.

BACKGROUND: Rapid declines in malaria prevalence, cases, and deaths have been achieved globally during the past 15 years because of improved access to first-line treatment and vector control. We aimed to assess the intervention coverage needed to achieve further gains over the next 15 years. METHODS: We used a mathematical model of the transmission of Plasmodium falciparum malaria to explore the potential effect on case incidence and malaria mortality rates from 2015 to 2030 of five different intervention scenarios: remaining at the intervention coverage levels of 2011-13 (Sustain), for which coverage comprises vector control and access to treatment; two scenarios of increased coverage to 80% (Accelerate 1) and 90% (Accelerate 2), with a switch from quinine to injectable artesunate for management of severe disease and seasonal malaria chemoprevention where recommended for both Accelerate scenarios, and rectal artesunate for pre-referral treatment at the community level added to Accelerate 2; a near-term innovation scenario (Innovate), which included longer-lasting insecticidal nets and expansion of seasonal malaria chemoprevention; and a reduction in coverage to 2006-08 levels (Reverse). We did the model simulations at the first administrative level (ie, state or province) for the 80 countries with sustained stable malaria transmission in 2010, accounting for variations in baseline endemicity, seasonality in transmission, vector species, and existing intervention coverage. To calculate the cases and deaths averted, we compared the total number of each under the five scenarios between 2015 and 2030 with the predicted number in 2015, accounting for population growth. FINDINGS: With an increase to 80% coverage, we predicted a reduction in case incidence of 21% (95% credible intervals [CrI] 19-29) and a reduction in mortality rates of 40% (27-61) by 2030 compared with 2015 levels. Acceleration to 90% coverage and expansion of treatment at the community level was predicted to reduce case incidence by 59% (Crl 56-64) and mortality rates by 74% (67-82); with additional near-term innovation, incidence was predicted to decline by 74% (70-77) and mortality rates by 81% (76-87). These scenarios were predicted to lead to local elimination in 13 countries under the Accelerate 1 scenario, 20 under Accelerate 2, and 22 under Innovate by 2030, reducing the proportion of the population living in at-risk areas by 36% if elimination is defined at the first administrative unit. However, failing to maintain coverage levels of 2011-13 is predicted to raise case incidence by 76% (Crl 71-80) and mortality rates by 46% (39-51) by 2020. INTERPRETATION: Our findings show that decreases in malaria transmission and burden can be accelerated over the next 15 years if the coverage of key interventions is increased. FUNDING: UK Medical Research Council, UK Department for International Development, the Bill & Melinda Gates Foundation, the Swiss Development Agency, and the US Agency for International Development.

Vectorial capacity and vector control: reconsidering sensitivity to parameters for malaria elimination.

BACKGROUND: Major gains have been made in reducing malaria transmission in many parts of the world, principally by scaling-up coverage with long-lasting insecticidal nets and indoor residual spraying. Historically, choice of vector control intervention has been largely guided by a parameter sensitivity analysis of George Macdonald's theory of vectorial capacity that suggested prioritizing methods that kill adult mosquitoes. While this advice has been highly successful for transmission suppression, there is a need to revisit these arguments as policymakers in certain areas consider which combinations of interventions are required to eliminate malaria. METHODS AND RESULTS: Using analytical solutions to updated equations for vectorial capacity we build on previous work to show that, while adult killing methods can be highly effective under many circumstances, other vector control methods are frequently required to fill effective coverage gaps. These can arise due to pre-existing or developing mosquito physiological and behavioral refractoriness but also due to additive changes in the relative importance of different vector species for transmission. Furthermore, the optimal combination of interventions will depend on the operational constraints and costs associated with reaching high coverage levels with each intervention. CONCLUSIONS: Reaching specific policy goals, such as elimination, in defined contexts requires increasingly non-generic advice from modelling. Our results emphasize the importance of measuring baseline epidemiology, intervention coverage, vector ecology and program operational constraints in predicting expected outcomes with different combinations of interventions.

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.