Malaria in Africa: vector species' niche models and relative risk maps

Insights and challenges of insecticide resistance modelling in malaria vectors: a review

BACKGROUND

Malaria is one of the most devastating tropical diseases, resulting in loss of lives each year, especially in children under the age of 5 years. Malaria burden, related deaths and stall in the progress against malaria transmission is evident, particularly in countries that have moderate or high malaria transmission. Hence, mitigating malaria spread requires information on the distribution of vectors and the drivers of insecticide resistance (IR). However, owing to the impracticality in establishing the critical need for real-world information at every location, modelling provides an informed best guess for such information. Therefore, this review examines the various methodologies used to model spatial, temporal and spatio-temporal patterns of IR within populations of malaria vectors, incorporating pest-biology parameters, adopted ecological principles, and the associated modelling challenges.

METHODS

The review focused on the period ending March 2023 without imposing restrictions on the initial year of publication, and included articles sourced from PubMed, Web of Science, and Scopus. It was also limited to publications that deal with modelling of IR distribution across spatial and temporal dimensions and excluded articles solely focusing on insecticide susceptibility tests or articles not published in English. After rigorous selection, 33 articles met the review's elibility criteria and were subjected to full-text screening.

RESULTS

Results show the popularity of Bayesian geostatistical approaches, and logistic and static models, with limited adoption of dynamic modelling approaches for spatial and temporal IR modelling. Furthermore, our review identifies the availability of surveillance data and scarcity of comprehensive information on the potential drivers of IR as major impediments to developing holistic models of IR evolution.

CONCLUSIONS

The review notes that incorporating pest-biology parameters, and ecological principles into IR models, in tandem with fundamental ecological concepts, potentially offers crucial insights into the evolution of IR. The results extend our knowledge of IR models that provide potentially accurate results, which can be translated into policy recommendations to combat the challenge of IR in malaria control.

Impacts of Deforestation on Childhood Malaria Depend on Wealth and Vector Biology

Ecosystem change can profoundly affect human well-being and health, including through changes in exposure to vector-borne diseases. Deforestation has increased human exposure to mosquito vectors and malaria risk in Africa, but there is little understanding of how socioeconomic and ecological factors influence the relationship between deforestation and malaria risk. We examined these interrelationships in six sub-Saharan African countries using demographic and health survey data linked to remotely sensed environmental variables for 11,746 children under 5 years old. We found that the relationship between deforestation and malaria prevalence varies by wealth levels. Deforestation is associated with increased malaria prevalence in the poorest households, but there was not significantly increased malaria prevalence in the richest households, suggesting that deforestation has disproportionate negative health impacts on the poor. In poorer households, malaria prevalence was 27%-33% larger for one standard deviation increase in deforestation across urban and rural populations. Deforestation is also associated with increased malaria prevalence in regions where Anopheles gambiae and Anopheles funestus are dominant vectors, but not in areas of Anopheles arabiensis. These findings indicate that deforestation is an important driver of malaria risk among the world's most vulnerable children, and its impact depends critically on often-overlooked social and biological factors. An in-depth understanding of the links between ecosystems and human health is crucial in designing conservation policies that benefit people and the environment.

Revolutionizing Malaria Vector Control: The Importance of Accurate Species Identification through Enhanced Molecular Capacity

Many factors, such as the resistance to pesticides and a lack of knowledge of the morphology and molecular structure of malaria vectors, have made it more challenging to eradicate malaria in numerous malaria-endemic areas of the globe. The primary goal of this review is to discuss malaria vector control methods and the significance of identifying species in vector control initiatives. This was accomplished by reviewing methods of molecular identification of malaria vectors and genetic marker classification in relation to their use for species identification. Due to its specificity and consistency, molecular identification is preferred over morphological identification of malaria vectors. Enhanced molecular capacity for species identification will improve mosquito characterization, leading to accurate control strategies/treatment targeting specific mosquito species, and thus will contribute to malaria eradication. It is crucial for disease epidemiology and surveillance to accurately identify the Plasmodium spp. that are causing malaria in patients. The capacity for disease surveillance will be significantly increased by the development of more accurate, precise, automated, and high-throughput diagnostic techniques. In conclusion, although morphological identification is quick and achievable at a reduced cost, molecular identification is preferred for specificity and sensitivity. To achieve the targeted malaria elimination goal, proper identification of vectors using accurate techniques for effective control measures should be prioritized.

Trends in mosquito species distribution modeling: insights for vector surveillance and disease control

Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.

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 rare sugar, allose, inhibits the development of Plasmodium parasites in the Anopheles mosquito independently of midgut microbiota

A rare sugar, allose, was reported to inhibit the development of Plasmodium parasites in Anopheles mosquitoes; however, the mechanism remains unknown. The present study addressed the inhibitory mechanism of allose on the development of the Plasmodium parasite by connecting it with bacteria involvement in the midgut. In addition, further inhibitory sugars against Plasmodium infection in mosquitoes were explored. Antibiotic-treated and antibiotic-untreated Anopheles stephensi were fed fructose with or without allose. The mosquitoes were infected with luciferase-expressing Plasmodium berghei, and parasite development was evaluated by luciferase activity. Bacterial composition analysis in gut of their mosquitoes was performed with comprehensive 16S ribosomal RNA sequencing. As the result, allose inhibited the development of oocysts in mosquitoes regardless of prior antibiotic treatment. Microbiome analysis showed that the midgut bacterial composition in mosquitoes before and after blood feeding was not affected by allose. Although allose inhibited transient growth of the midgut microbiota of mosquitoes after blood feeding, neither toxic nor inhibitory effects of allose on the dominant midgut bacteria were observed. Ookinete development in the mosquito midgut was also not affected by allose feeding. Additional 15 sugars including six monosaccharides, four polyols, and five polysaccharides were tested; however, no inhibitory effect against Plasmodium development in mosquitoes was observed. These results indicated that allose inhibits parasite development in midgut stage of the mosquito independently of midgut microbiota. Although further studies are needed, our results suggest that allose may be a useful material for the vector control of malaria as a "transmission-blocking sugar."

Predicting Suitable Areas for African Swine Fever Outbreaks in Wild Boars in South Korea and Their Implications for Managing High-Risk Pig Farms

African swine fever (ASF) is a highly contagious disease affecting domestic pigs and wild boars, with no effective vaccine or treatment available. In South Korea, extensive measures have been implemented to prevent ASF transmission between wild boars and ASF spillover from wild boars to pig farm sectors, including the search for ASF-infected carcasses in mountainous forests and the installation of fences across wide areas of these forests. To determine the priority search range for infected carcasses and establish pig farm-centered quarantine measures, it is necessary to predict the specific path of ASF outbreaks in wild boars and identify pig farms at high risk of ASF spillover from wild boars. Here, we aimed to predict suitable areas and geographical paths for ASF outbreaks in wild boars using the MaxEnt model and shortest-path betweenness centrality analysis. The analysis identified a high frequency of ASF outbreaks in areas with a suitability value ≥0.4 on the suitability map and in areas within a 1.8 km range from the path on the shortest-path map, indicating these areas were high-risk zones for ASF outbreaks. Among the 5063 pig farms analyzed, 37 were in the high-risk zone on the suitability map, 499 were in the high-risk zone on the shortest-path map, and 9 were in both risk zones. Of the 51 pig farm sectors with a dense distribution of pig farms (kernel density ≥ 8), 25 sectors were in contact with or partially overlapped the high risk zone on the suitability map, 18 sectors were located within the high risk zone on the shortest-path map, and 14 sectors were located within both risk zones. These findings aided in determining the priority range for searches for wild boar carcasses and enabled the establishment of preemptive ASF prevention measures around the pig farming sectors that are at risk of ASF spillover from wild boars.

Current and future distribution of a parasite with complex life cycle under global change scenarios: Echinococcus multilocularis in Europe

Global change is expected to have complex effects on the distribution and transmission patterns of zoonotic parasites. Modelling habitat suitability for parasites with complex life cycles is essential to further our understanding of how disease systems respond to environmental changes, and to make spatial predictions of their future distributions. However, the limited availability of high quality occurrence data with high spatial resolution often constrains these investigations. Using 449 reliable occurrence records for Echinococcus multilocularis from across Europe published over the last 35 years, we modelled habitat suitability for this parasite, the aetiological agent of alveolar echinococcosis, in order to describe its environmental niche, predict its current and future distribution under three global change scenarios, and quantify the probability of occurrence for each European country. Using a machine learning approach, we developed large-scale (25 × 25 km) species distribution models based on seven sets of predictors, each set representing a distinct biological hypothesis supported by current knowledge of the autecology of the parasite. The best-supported hypothesis included climatic, orographic and land-use/land-cover variables such as the temperature of the coldest quarter, forest cover, urban cover and the precipitation seasonality. Future projections suggested the appearance of highly suitable areas for E. multilocularis towards northern latitudes and in the whole Alpine region under all scenarios, while decreases in habitat suitability were predicted for central Europe. Our spatially explicit predictions of habitat suitability shed light on the complex responses of parasites to ongoing global changes.

Malaria transmission in Nepal under climate change: anticipated shifts in extent and season, and comparison with risk definitions for intervention

BACKGROUND

Climate and climate change affect the spatial pattern and seasonality of malaria risk. Season lengths and spatial extents of mapped current and future malaria transmission suitability predictions for Nepal were assessed for a combination of malaria vector and parasites: Anopheles stephensi and Plasmodium falciparum (ASPF) and An. stephensi and Plasmodium vivax (ASPV) and compared with observed estimates of malaria risk in Nepal.

METHODS

Thermal bounds of malaria transmission suitability for baseline (1960-1990) and future climate projections (RCP 4.5 and RCP 8.5 in 2030 and 2050) were extracted from global climate models and mapped for Nepal. Season length and spatial extent of suitability between baseline and future climate scenarios for ASPF and ASPV were compared using the Warren's I metric. Official 2010 DoHS risk districts (DRDs) and 2021 DoHS risk wards (DRWs), and spatiotemporal incidence trend clusters (ITCs) were overlaid on suitability season length and extent maps to assess agreement, and potential mismatches.

RESULTS

Shifts in season length and extent of malaria transmission suitability in Nepal are anticipated under both RCP 4.5 and RCP 8.5 scenarios in 2030 and 2050, compared to baseline climate. The changes are broadly consistent across both future climate scenarios for ASPF and ASPV. There will be emergence of suitability and increasing length of season for both ASPF and ASPV and decreasing length of season for ASPV by 2050. The emergence of suitability will occur in low and no-risk DRDs and outside of high and moderate-risk DRWs, season length increase will occur across all DRD categories, and outside of high and moderate-risk DRWs. The high and moderate risk DRWs of 2021 fall into ITCs with decreasing trend.

CONCLUSIONS

The study identified areas of Nepal where malaria transmission suitability will emerge, disappear, increase, and decrease in the future. However, most of these areas are anticipated outside of the government's current and previously designated high and moderate-risk areas, and thus outside the focus of vector control interventions. Public health officials could use these anticipated changing areas of malaria risk to inform vector control interventions for eliminating malaria from the country, and to prevent malaria resurgence.

Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches

BACKGROUND

Tunisia has experienced several West Nile virus (WNV) outbreaks since 1997. Yet, there is limited information on the spatial distribution of the main WNV mosquito vector Culex pipiens suitability at the national level.

OBJECTIVES

In the present study, our aim was to predict and evaluate the potential and current distribution of Cx. pipiens in Tunisia.

METHODS

To this end, two species distribution models were used, i.e. MaxEnt and Random Forest. Occurrence records for Cx. pipiens were obtained from adult and larvae sampled in Tunisia from 2014 to 2017. Climatic and human factors were used as predictors to model the Cx. pipiens geographical distribution. Mean decrease accuracy and mean decrease Gini indices were calculated to evaluate the importance of the impact of different environmental and human variables on the probability distribution of Cx. pipiens.

RESULTS

Suitable habitats were mainly distributed next to oases, in the north and eastern part of the country. The most important predictor was the population density in both models. The study found out that the governorates of Monastir, Nabeul, Manouba, Ariana, Bizerte, Gabes, Medenine and Kairouan are at highest epidemic risk.

CONCLUSIONS

The potential distribution of Cx. pipiens coincides geographically with the observed distribution of the disease in humans in Tunisia. Our study has the potential for driving control effort in the fight against West Nile vector in Tunisia.

Global assessment of the distribution and conservation status of a key medicinal plant (Artemisia annua L.): The roles of climate and anthropogenic activities

As a medicinal plant, Artemisia annua L. is the main source of artemisinin in malaria drugs, but the lack of understanding of its distribution, environmental conditions and protection status limits the mass acquisition of artemisinin. Therefore, we used the ensemble forecast method to model the current and future global distribution areas of A. annua, evaluated the changes in suitable distribution areas on each continent under impacts of human activities and climate change, and its protection status on each continent in the corresponding period. The results showed that the main distribution areas of A. annua were concentrated in mid-latitudes in western and central Europe, southeastern Asia, southeastern North America and southeastern South America. Under the current climate scenario, human modifications have greatly reduced the suitable distribution area of A. annua, which was projected to expand inland with climate change and human socioeconomic impacts of CMIP6 in the future, but the effects of increasing temperature were different in different periods. Among all continents, the suitable distribution area in Europe was the most affected. However, at present and in the future, A. annua needs high priority protection on all continents. Asia and Europe have slightly better protection status scores than other continents, but the protection status scores of all continents are still very low. Our findings can be useful to guide development of protective measures for medicinal plants such as A. annua to further support drug production and disease treatment.

Of Mice and Fungi: Coccidioides spp. Distribution Models

The continuous increase of Coccidioidomycosis cases requires reliable detection methods of the causal agent, Coccidioides spp., in its natural environment. This has proven challenging because of our limited knowledge on the distribution of this soil-dwelling fungus. Knowing the pathogen’s geographic distribution and its relationship with the environment is crucial to identify potential areas of risk and to prevent disease outbreaks. The maximum entropy (Maxent) algorithm, Geographic Information System (GIS) and bioclimatic variables were combined to obtain current and future potential distribution models (DMs) of Coccidioides and its putative rodent reservoirs for Arizona, California and Baja California. We revealed that Coccidioides DMs constructed with presence records from one state are not well suited to predict distribution in another state, supporting the existence of distinct phylogeographic populations of Coccidioides. A great correlation between Coccidioides DMs and United States counties with high Coccidioidomycosis incidence was found. Remarkably, under future scenarios of climate change and high concentration of greenhouse gases, the probability of habitat suitability for Coccidioides increased. Overlap analysis between the DMs of rodents and Coccidioides, identified Neotoma lepida as one of the predominant co-occurring species in all three states. Considering rodents DMs would allow to implement better surveillance programs to monitor disease spread.

Where have all the parasites gone? Unusual Plasmodium falciparum monoparasitaemia in a cross-sectional malariometric survey in northern Nigeria

Background: Malaria is caused by one of five currently known Plasmodium parasite species causing disease in humans. While modelling has provided information of the vector, the same is not entirely the case for the parasite. The World Malaria reports of 2014 to 2016 reported 100% of confirmed cases from Nigeria being due to Plasmodium falciparum. Generally, about 98% of cases of uncomplicated malaria in most regions surveyed in Nigeria recently is due to P. falciparum, with the remainder being due to P. malariae. This study aimed to determine the proportions of Plasmodium parasites causing uncomplicated malaria in Wamakko Local Government Area of Sokoto State, north-western Nigeria.

Methods: The study was a descriptive, cross-sectional study conducted during the rainy season and dry season in north-western Nigeria. The area has a ‘local steppe’ climate and Sudanian Savannah vegetation. Sampling was via multistage cluster sampling. Selected participants were examined for pallor, palpable splenomegaly and signs of complicated malaria. Blood samples were also taken for rapid diagnosis of malaria and thick and thin films to identify parasitaemia and the parasite species. Participants found to have malaria were treated with Artemether/Lumefantrine and those with complicated malaria were referred to the nearest hospital.

 Results: We found a parasite prevalence of 34.8% overall, which was higher in the rainy season (49.3%) than in the dry season (20.2%). There was monoparasitaemia of Plasmodium falciparum throughout the study area, irrespective of the clinical status of the participant. Mapping of the parasite was extended throughout the Local Government Area and the State.

Conclusions: Despite the intermediate endemicity in the area. P. falciparum monoparasitaemia affirms theories of disappearance of other parasite species, either due to faltering control of P. falciparum or more efficient control of other species.

Risk Factors for Household Vector Abundance Using Indoor CDC Light Traps in a High Malaria Transmission Area of Northern Zambia

Malaria transmission is dependent on the density and distribution of mosquito vectors, but drivers of vector abundance have not been adequately studied across a range of transmission settings. To inform intervention strategies for high-burden areas, further investigation is needed to identify predictors of vector abundance. Active household (HH) surveillance was conducted in Nchelenge district, Luapula Province, northern Zambia, a high-transmission setting with limited impact of malaria control. Between April 2012 and July 2017, mosquitoes were collected indoors during HH visits using CDC light traps. Demographic, environmental, and climatological correlates of vector abundance were identified using log-binomial regression models with robust standard errors. The primary malaria vectors in this setting were Anopheles funestus sensu stricto (s.s.) and Anopheles gambiae s.s. Anopheles funestus predominated in both seasons, with a peak in the dry season. Anopheles gambiae peaked at lower numbers in the rainy season. Environmental, climatic, and demographic factors were correlated with HH vector abundance. Higher vector counts were found in rural areas with low population density and among HHs close to roads and small streams. Vector counts were lower with increasing elevation and slope. Anopheles funestus was negatively associated with rainfall at lags of 2-6 weeks, and An. gambiae was positively associated with rainfall at lags of 3-10 weeks. Both vectors had varying relationships with temperature. These results suggest that malaria vector control in Nchelenge district should occur throughout the year, with an increased focus on dry-season transmission and rural areas.

Ecological niche modelling to estimate the distribution of Culicoides, potential vectors of bluetongue virus in Senegal

BACKGROUND

Vector-borne diseases are among the leading causes of morbidity and mortality in humans and animals. In the Afrotropical region, some are transmitted by Culicoides, such as Akabane, bluetongue, epizootic haemorrhagic fever and African horse sickness viruses. Bluetongue virus infection has an enormous impact on ruminant production, due to its high morbidity and mortality rates.

METHODS

A nationwide Culicoides trapping campaign was organized at the end of the 2012 rainy season in Senegal. A Maximum Entropy approach (MaxEnt), Boosted Regression Tree (BRT) method and Ecological Niche Factor Analysis (ENFA) were used to develop a predictive spatial model for the distribution of Culicoides, using bio-climatic variables, livestock densities and altitude.

RESULTS

The altitude, maximum temperature of the warmest month, precipitation of the warmest quarter, mean temperature of the wettest quarter, temperature seasonality, precipitation of the wettest quarter and livestock density were among the most important factors to predict suitable habitats of Culicoides. Culicoides occurrences were, in most of the cases, positively correlated to precipitation variables and livestock densities; and negatively correlated to the altitude and temperature indices. The Niayes area and the Groundnut basin were the most suitable habitats predicted.

CONCLUSION

We present ecological niche models for different Culicoides species, namely C. imicola, C. oxystoma, C. enderleini and C. miombo, potential vectors of bluetongue virus, on a nationwide scale in Senegal. Through our modelling approach, we were able to determine the effect of bioclimatic variables on Culicoides habitats and were able to generate maps for the occurrence of Culicoides species. This information will be helpful in developing risk maps for disease outbreaks.

Potential distribution of dominant malaria vector species in tropical region under climate change scenarios

Risk assessment regarding the distribution of malaria vectors and environmental variables underpinning their distribution under changing climates is crucial towards malaria control and eradication. On this basis, we used Maximum Entropy (MaxEnt) Model to estimate the potential future distribution of major transmitters of malaria in Nigeria-Anopheles gambiae sensu lato and its siblings: Anopheles gambiae sensu stricto, and Anopheles arabiensis under low and high emissions scenarios. In the model, we used mosquito occurrence data sampled from 1900 to 2010 alongside land use and terrain variables, and bioclimatic variables for baseline climate 1960-1990 and future climates of 2050s (2041-2060) and 2070s (2061-2080) that follow RCP2.6 and RCP8.5 scenarios. The Anopheles gambiae species are projected to experience large shift in potential range and population with increased distribution density, higher under high emissions scenario (RCP8.5) and 2070s than low emission scenario (RCP2.6) and 2050s. Anopheles gambiae sensu stricto and Anopheles arabiensis are projected to have highest invasion with 47-70% and 10-14% percentage increase, respectively in Sahel and Sudan savannas within northern states in 2041-2080 under RCP8.5. Highest prevalence is predicted for Humid forest and Derived savanna in southern and North Central states in 2041-2080; 91-96% and 97-99% for Anopheles gambiae sensu stricto, and 67-71% and 72-75% for Anopheles arabiensis under RCP2.6 and RCP8.5, respectively. The higher magnitude of change in species prevalence predicted for the later part of the 21st century under high emission scenario, driven mainly by increasing and fluctuating temperature, alongside longer seasonal tropical rainfall accompanied by drier phases and inherent influence of rapid land use change, may lead to more significant increase in malaria burden when compared with other periods and scenarios during the century; especially in Humid forest, Derived savanna, Sahel and Sudan savannas.

Mapping access to domestic water supplies from incomplete data in developing countries: An illustrative assessment for Kenya

Water point mapping databases, generated through surveys of water sources such as wells and boreholes, are now available in many low and middle income countries, but often suffer from incomplete coverage. To address the partial coverage in such databases and gain insights into spatial patterns of water resource use, this study investigated the use of a maximum entropy (MaxEnt) approach to predict the geospatial distribution of drinking-water sources, using two types of unimproved sources in Kenya as illustration. Geographic locations of unprotected dug wells and surface water sources derived from the Water Point Data Exchange (WPDx) database were used as inputs to the MaxEnt model alongside geological/hydrogeological and socio-economic covariates. Predictive performance of the MaxEnt models was high (all > 0.9) based on Area Under the Receiver Operator Curve (AUC), and the predicted spatial distribution of water point was broadly consistent with household use of these unimproved drinking-water sources reported in household survey and census data. In developing countries where geospatial datasets concerning drinking-water sources often have necessarily limited resolution or incomplete spatial coverage, the modelled surface can provide an initial indication of the geography of unimproved drinking-water sources to target unserved populations and assess water source vulnerability to contamination and hazards.

Dominant malaria vector species in Nigeria: Modelling potential distribution of Anopheles gambiae sensu lato and its siblings with MaxEnt

Malaria is a major infectious disease that still affects nearly half of the world's population. Information on spatial distribution of malaria vector species is needed to improve malaria control efforts. In this study we used Maximum Entropy Model (MaxEnt) to estimate the potential distribution of Anopheles gambiae sensu lato and its siblings: Anopheles gambiae sensu stricto, and Anopheles arabiensis in Nigeria. Species occurrence data collected during the period 1900-2010 was used together with 19 bioclimatic, landuse and terrain variables. Results show that these species are currently widespread across all ecological zones. Temperature fluctuation from mean diurnal temperature range, extreme temperature and precipitation conditions, high humidity in dry season from precipitation during warm months, and land use and land cover dynamics have the greatest influence on the current seasonal distribution of the Anopheles species. MaxEnt performed statistically significantly better than random with AUC approximately 0.7 for estimation of the Anopheles species environmental suitability, distribution and variable importance. This model result can contribute to surveillance efforts and control strategies for malaria eradication.

Inferring the risk factors behind the geographical spread and transmission of Zika in the Americas

BACKGROUND

An unprecedented Zika virus epidemic occurred in the Americas during 2015-2016. The size of the epidemic in conjunction with newly recognized health risks associated with the virus attracted significant attention across the research community. Our study complements several recent studies which have mapped epidemiological elements of Zika, by introducing a newly proposed methodology to simultaneously estimate the contribution of various risk factors for geographic spread resulting in local transmission and to compute the risk of spread (or re-introductions) between each pair of regions. The focus of our analysis is on the Americas, where the set of regions includes all countries, overseas territories, and the states of the US.

METHODOLOGY/PRINCIPAL FINDINGS

We present a novel application of the Generalized Inverse Infection Model (GIIM). The GIIM model uses real observations from the outbreak and seeks to estimate the risk factors driving transmission. The observations are derived from the dates of reported local transmission of Zika virus in each region, the network structure is defined by the passenger air travel movements between all pairs of regions, and the risk factors considered include regional socioeconomic factors, vector habitat suitability, travel volumes, and epidemiological data. The GIIM relies on a multi-agent based optimization method to estimate the parameters, and utilizes a data driven stochastic-dynamic epidemic model for evaluation. As expected, we found that mosquito abundance, incidence rate at the origin region, and human population density are risk factors for Zika virus transmission and spread. Surprisingly, air passenger volume was less impactful, and the most significant factor was (a negative relationship with) the regional gross domestic product (GDP) per capita.

CONCLUSIONS/SIGNIFICANCE

Our model generates country level exportation and importation risk profiles over the course of the epidemic and provides quantitative estimates for the likelihood of introduced Zika virus resulting in local transmission, between all origin-destination travel pairs in the Americas. Our findings indicate that local vector control, rather than travel restrictions, will be more effective at reducing the risks of Zika virus transmission and establishment. Moreover, the inverse relationship between Zika virus transmission and GDP suggests that Zika cases are more likely to occur in regions where people cannot afford to protect themselves from mosquitoes. The modeling framework is not specific for Zika virus, and could easily be employed for other vector-borne pathogens with sufficient epidemiological and entomological data.

Global hotspots and correlates of emerging zoonotic diseases

Zoonoses originating from wildlife represent a significant threat to global health, security and economic growth, and combatting their emergence is a public health priority. However, our understanding of the mechanisms underlying their emergence remains rudimentary. Here we update a global database of emerging infectious disease (EID) events, create a novel measure of reporting effort, and fit boosted regression tree models to analyze the demographic, environmental and biological correlates of their occurrence. After accounting for reporting effort, we show that zoonotic EID risk is elevated in forested tropical regions experiencing land-use changes and where wildlife biodiversity (mammal species richness) is high. We present a new global hotspot map of spatial variation in our zoonotic EID risk index, and partial dependence plots illustrating relationships between events and predictors. Our results may help to improve surveillance and long-term EID monitoring programs, and design field experiments to test underlying mechanisms of zoonotic disease emergence.

A geo-coded inventory of anophelines in the Afrotropical Region south of the Sahara: 1898-2016

Background: Understanding the distribution of anopheline vectors of malaria is an important prelude to the design of national malaria control and elimination programmes. A single, geo-coded continental inventory of anophelines using all available published and unpublished data has not been undertaken since the 1960s. Methods: We have searched African, European and World Health Organization archives to identify unpublished reports on anopheline surveys in 48 sub-Saharan Africa countries. This search was supplemented by identification of reports that formed part of post-graduate theses, conference abstracts, regional insecticide resistance databases and more traditional bibliographic searches of peer-reviewed literature. Finally, a check was made against two recent repositories of dominant malaria vector species locations ( circa 2,500). Each report was used to extract information on the survey dates, village locations (geo-coded to provide a longitude and latitude), sampling methods, species identification methods and all anopheline species found present during the survey. Survey records were collapsed to a single site over time.    Results: The search strategy took years and resulted in 13,331 unique, geo-coded survey locations of anopheline vector occurrence between 1898 and 2016. A total of 12,204 (92%) sites reported the presence of 10 dominant vector species/sibling species; 4,473 (37%) of these sites were sampled since 2005. 4,442 (33%) sites reported at least one of 13 possible secondary vector species; 1,107 (25%) of these sites were sampled since 2005. Distributions of dominant and secondary vectors conform to previous descriptions of the ecological ranges of these vectors. Conclusion: We have assembled the largest ever geo-coded database of anophelines in Africa, representing a legacy dataset for future updating and identification of knowledge gaps at national levels. The geo-coded database is available on Harvard Dataverse as a reference source for African national malaria control programmes planning their future control and elimination strategies.

Influenza A H5N1 and H7N9 in China: A spatial risk analysis

Background

Zoonotic avian influenza poses a major risk to China, and other parts of the world. H5N1 has remained endemic in China and globally for nearly two decades, and in 2013, a novel zoonotic influenza A subtype H7N9 emerged in China. This study aimed to improve upon our current understanding of the spreading mechanisms of H7N9 and H5N1 by generating spatial risk profiles for each of the two virus subtypes across mainland China.

Methods and findings

In this study, we (i) developed a refined data set of H5N1 and H7N9 locations with consideration of animal/animal environment case data, as well as spatial accuracy and precision; (ii) used this data set along with environmental variables to build species distribution models (SDMs) for each virus subtype in high resolution spatial units of 1km² cells using Maxent; (iii) developed a risk modelling framework which integrated the results from the SDMs with human and chicken population variables, which was done to quantify the risk of zoonotic transmission; and (iv) identified areas at high risk of H5N1 and H7N9 transmission. We produced high performing SDMs (6 of 8 models with AUC > 0.9) for both H5N1 and H7N9. In all our SDMs, H7N9 consistently showed higher AUC results compared to H5N1, suggesting H7N9 suitability could be better explained by environmental variables. For both subtypes, high risk areas were primarily located in south-eastern China, with H5N1 distributions found to be more diffuse and extending more inland compared to H7N9.

Conclusions

We provide projections of our risk models to public health policy makers so that specific high risk areas can be targeted for control measures. We recommend comparing H5N1 and H7N9 prevalence rates and survivability in the natural environment to better understand the role of animal and environmental transmission in human infections.

Vector status of Aedes species determines geographical risk of autochthonous Zika virus establishment

Background

The 2015-16 Zika virus pandemic originating in Latin America led to predictions of a catastrophic global spread of the disease. Since the current outbreak began in Brazil in May 2015 local transmission of Zika has been reported in over 60 countries and territories, with over 750 thousand confirmed and suspected cases. As a result of its range expansion attention has focused on possible modes of transmission, of which the arthropod vector-based disease spread cycle involving Aedes species is believed to be the most important. Additional causes of concern are the emerging new links between Zika disease and Guillain-Barre Syndrome (GBS), and a once rare congenital disease, microcephaly.

Methodology/principal findings

Like dengue and chikungunya, the geographic establishment of Zika is thought to be limited by the occurrence of its principal vector mosquito species, Ae. aegypti and, possibly, Ae. albopictus. While Ae. albopictus populations are more widely established than those of Ae. aegypti, the relative competence of these species as a Zika vector is unknown. The analysis reported here presents a global risk model that considers the role of each vector species independently, and quantifies the potential spreading risk of Zika into new regions. Six scenarios are evaluated which vary in the weight assigned to Ae. albopictus as a possible spreading vector. The scenarios are bounded by the extreme assumptions that spread is driven by air travel and Ae. aegypti presence alone and spread driven equally by both species. For each scenario destination cities at highest risk of Zika outbreaks are prioritized, as are source cities in affected regions. Finally, intercontinental air travel routes that pose the highest risk for Zika spread are also ranked. The results are compared between scenarios.

Conclusions/significance

Results from the analysis reveal that if Ae. aegypti is the only competent Zika vector, then risk is geographically limited; in North America mainly to Florida and Texas. However, if Ae. albopictus proves to be a competent vector of Zika, which does not yet appear to be the case, then there is risk of local establishment in all American regions including Canada and Chile, much of Western Europe, Australia, New Zealand, as well as South and East Asia, with a substantial increase in risk to Asia due to the more recent local establishment of Zika in Singapore.

Between 1952, when the Zika virus was first found in humans, and 2007 Zika disease outbreaks were limited to small isolated epidemics in equatorial Africa and tropical Asia. However, the recent outbreak, which began in Brazil in May 2015, resulted over 750 thousand estimated cases and confirmed local transmission in more than 60 countries by October, 2016. Like dengue and chikungunya, Zika is spread by Aedes aegypti mosquitoes and possibly, other species including Aedes albopictus. Geographic spread of the virus occurs when infected travelers travel from affected regions to ones without an established local Zika disease cycle, but in which the known and potential vector species have established populations. We estimate the risk of Zika importation and establishment into new regions using air travel data and ecological vector habitat suitability models for Ae. aegypti and Ae. albopictus. Given the uncertainties surrounding the vectorial competence of Aedes mosquitoes, we compare the geographic risk profiles when spread is driven by air travel and Ae. aegypti presence alone, with spread driven by air travel and both species. We conclude that there is a much higher global risk of Zika spread under the latter scenario, although it is the least likely.

Chromosome inversions and ecological plasticity in the main African malaria mosquitoes

Chromosome inversions have fascinated the scientific community, mainly because of their role in the rapid adaption of different taxa to changing environments. However, the ecological traits linked to chromosome inversions have been poorly studied. Here, we investigated the roles played by 23 chromosome inversions in the adaptation of the four major African malaria mosquitoes to local environments in Africa. We studied their distribution patterns by using spatially explicit modeling and characterized the ecogeographical determinants of each inversion range. We then performed hierarchical clustering and constrained ordination analyses to assess the spatial and ecological similarities among inversions. Our results show that most inversions are environmentally structured, suggesting that they are actively involved in processes of local adaptation. Some inversions exhibited similar geographical patterns and ecological requirements among the four mosquito species, providing evidence for parallel evolution. Conversely, common inversion polymorphisms between sibling species displayed divergent ecological patterns, suggesting that they might have a different adaptive role in each species. These results are in agreement with the finding that chromosomal inversions play a role in Anopheles ecotypic adaptation. This study establishes a strong ecological basis for future genome-based analyses to elucidate the genetic mechanisms of local adaptation in these four mosquitoes.

Modeling of the ecological niches of the anopheles spp in Ecuador by the use of geo-informatic tools

Ecuador in the northwestern edge of South America is struggling by vector-borne diseases with an endemic-epidemic behavior leading to an enormous public health problem. Malaria, which has a cyclicality in its dynamics, is closely related to climatic, ecological and socio-economic phenomena. The main objective of this research has been to compare three different prediction species models, the so-called Maxent, logistic regression and multi criteria evaluation with fuzzy logic, in order to determine the model which best describes the ecological niche of the Anopheles spp species, which transmits malaria within Ecuador. After performing a detailed data collection and data processing, we applied the mentioned models and validated them with a statistical analysis in order to discover that the Maxent model has been the model that best defines the distribution of Anopheles spp within the territory. The determined sites, which are of high strategic value and important for the increasing national development, will now be able to initiate preventive countermeasures based on this study.

Implicating Cryptic and Novel Anophelines as Malaria Vectors in Africa

Entomological indices and bionomic descriptions of malaria vectors are essential to accurately describe and understand malaria transmission and for the design and evaluation of appropriate control interventions. In order to correctly assign spatio-temporal distributions, behaviors and responses to interventions to particular anopheline species, identification of mosquitoes must be accurately made. This paper reviews the current methods and their limitations in correctly identifying anopheline mosquitoes in sub-Saharan Africa, and highlights the importance of molecular methods to discriminate cryptic species and identify lesser known anophelines. The increasing number of reports of Plasmodium infections in assumed "minor", non-vector, and cryptic and novel species is reviewed. Their importance in terms of evading current control and elimination strategies and therefore maintaining malaria transmission is emphasized.

Spatial Heterogeneity of Habitat Suitability for Rift Valley Fever Occurrence in Tanzania: An Ecological Niche Modelling Approach

BACKGROUND

Despite the long history of Rift Valley fever (RVF) in Tanzania, extent of its suitable habitat in the country remains unclear. In this study we investigated potential effects of temperature, precipitation, elevation, soil type, livestock density, rainfall pattern, proximity to wild animals, protected areas and forest on the habitat suitability for RVF occurrence in Tanzania.

MATERIALS AND METHODS

Presence-only records of 193 RVF outbreak locations from 1930 to 2007 together with potential predictor variables were used to model and map the suitable habitats for RVF occurrence using ecological niche modelling. Ground-truthing of the model outputs was conducted by comparing the levels of RVF virus specific antibodies in cattle, sheep and goats sampled from locations in Tanzania that presented different predicted habitat suitability values.

PRINCIPAL FINDINGS

Habitat suitability values for RVF occurrence were higher in the northern and central-eastern regions of Tanzania than the rest of the regions in the country. Soil type and precipitation of the wettest quarter contributed equally to habitat suitability (32.4% each), followed by livestock density (25.9%) and rainfall pattern (9.3%). Ground-truthing of model outputs revealed that the odds of an animal being seropositive for RVFV when sampled from areas predicted to be most suitable for RVF occurrence were twice the odds of an animal sampled from areas least suitable for RVF occurrence (95% CI: 1.43, 2.76, p < 0.001).

CONCLUSION/SIGNIFICANCE

The regions in the northern and central-eastern Tanzania were more suitable for RVF occurrence than the rest of the regions in the country. The modelled suitable habitat is characterised by impermeable soils, moderate precipitation in the wettest quarter, high livestock density and a bimodal rainfall pattern. The findings of this study should provide guidance for the design of appropriate RVF surveillance, prevention and control strategies which target areas with these characteristics.

Malaria and large dams in sub-Saharan Africa: future impacts in a changing climate

BACKGROUND

Sub-Saharan Africa (SSA) has embarked on a new era of dam building to improve food security and promote economic development. Nonetheless, the future impacts of dams on malaria transmission are poorly understood and seldom investigated in the context of climate and demographic change.

METHODS

The distribution of malaria in the vicinity of 1268 existing dams in SSA was mapped under the Intergovernmental Panel on Climate Change (IPCC) representative concentration pathways (RCP) 2.6 and 8.5. Population projections and malaria incidence estimates were used to compute population at risk of malaria in both RCPs. Assuming no change in socio-economic interventions that may mitigate impacts, the change in malaria stability and malaria burden in the vicinity of the dams was calculated for the two RCPs through to the 2080s. Results were compared against the 2010 baseline. The annual number of malaria cases associated with dams and climate change was determined for each of the RCPs.

RESULTS

The number of dams located in malarious areas is projected to increase in both RCPs. Population growth will add to the risk of transmission. The population at risk of malaria around existing dams and associated reservoirs, is estimated to increase from 15 million in 2010 to 21-23 million in the 2020s, 25-26 million in the 2050s and 28-29 million in the 2080s, depending on RCP. The number of malaria cases associated with dams in malarious areas is expected to increase from 1.1 million in 2010 to 1.2-1.6 million in the 2020s, 2.1-3.0 million in the 2050s and 2.4-3.0 million in the 2080s depending on RCP. The number of cases will always be higher in RCP 8.5 than RCP 2.6.

CONCLUSION

In the absence of changes in other factors that affect transmission (e.g., socio-economic), the impact of dams on malaria in SSA will be significantly exacerbated by climate change and increases in population. Areas without malaria transmission at present, which will transition to regions of unstable transmission, may be worst affected. Modifying conventional water management frameworks to improve malaria control, holds the potential to mitigate some of this increase and should be more actively implemented.

Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools)

BACKGROUND

Risk maps identifying suitable locations for infection transmission are important for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur.

METHODS

A newly available add-in to Esri's ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus.

RESULTS

A more accurate, robust, and localized (1 km × 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous.

CONCLUSION

This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change.

Environmental Factors Related to Fungal Wound Contamination after Combat Trauma in Afghanistan, 2009-2011

Environmental characteristics, along with known risk factors, may help predict likelihood of mold contamination after injury.

During the recent war in Afghanistan (2001–2014), invasive fungal wound infections (IFIs) among US combat casualties were associated with risk factors related to the mechanism and pattern of injury. Although previous studies recognized that IFI patients primarily sustained injuries in southern Afghanistan, environmental data were not examined. We compared environmental conditions of this region with those of an area in eastern Afghanistan that was not associated with observed IFIs after injury. A larger proportion of personnel injured in the south (61%) grew mold from wound cultures than those injured in the east (20%). In a multivariable analysis, the southern location, characterized by lower elevation, warmer temperatures, and greater isothermality, was independently associated with mold contamination of wounds. These environmental characteristics, along with known risk factors related to injury characteristics, may be useful in modeling the risk for IFIs after traumatic injury in other regions.

Spatio-temporal surveillance of water based infectious disease (malaria) in Rawalpindi, Pakistan using geostatistical modeling techniques

One of the features of medical geography that has made it so useful in health research is statistical spatial analysis, which enables the quantification and qualification of health events. The main objective of this research was to study the spatial distribution patterns of malaria in Rawalpindi district using spatial statistical techniques to identify the hot spots and the possible risk factor. Spatial statistical analyses were done in ArcGIS, and satellite images for land use classification were processed in ERDAS Imagine. Four hundred and fifty water samples were also collected from the study area to identify the presence or absence of any microbial contamination. The results of this study indicated that malaria incidence varied according to geographical location, with eco-climatic condition and showing significant positive spatial autocorrelation. Hotspots or location of clusters were identified using Getis-Ord Gi* statistic. Significant clustering of malaria incidence occurred in rural central part of the study area including Gujar Khan, Kaller Syedan, and some part of Kahuta and Rawalpindi Tehsil. Ordinary least square (OLS) regression analysis was conducted to analyze the relationship of risk factors with the disease cases. Relationship of different land cover with the disease cases indicated that malaria was more related with agriculture, low vegetation, and water class. Temporal variation of malaria cases showed significant positive association with the meteorological variables including average monthly rainfall and temperature. The results of the study further suggested that water supply and sewage system and solid waste collection system needs a serious attention to prevent any outbreak in the study area.

Where have all the mosquito nets gone? Spatial modelling reveals mosquito net distributions across Tanzania do not target optimal Anopheles mosquito habitats

Background

Malaria remains the deadliest vector-borne disease despite long-term, costly control efforts. The United Republic of Tanzania has implemented countrywide anti-malarial interventions over more than a decade, including national insecticide-treated net (ITN) rollouts and subsequent monitoring. While previous analyses have compared spatial variation in malaria endemicity with ITN distributions, no study has yet compared Anopheles habitat suitability to determine proper allocation of ITNs. This study assesses where mosquitoes were most likely to thrive before implementation of large-scale ITN interventions in Tanzania and determine if ITN distributions successfully targeted those areas.

Methods

Using Maxent, a species distribution model was constructed relating anopheline mosquito occurrences for 1999–2003 to high resolution environmental observations. A 2011–2012 layer of mosquito net ownership was created using georeferenced data across Tanzania from the Demographic and Health Surveys. The baseline mosquito habitat suitability was compared to subsequent ITN ownership using (1) the average ITN numbers per house and (2) the proportion of households with ≥1 net to test whether national ITN ownership targets have been met and have tracked malaria risk.

Results

Elevation, land cover, and human population distribution outperformed variants of temperature and Normalized Difference Vegetation Index (NDVI) in anopheline distribution models. The spatial distribution of ITN ownership across Tanzania was near-random spatially (Moran’s I = 0.07). Householders reported owning 2.488 ITNs on average and 93.41 % of households had ≥1 ITN. Mosquito habitat suitability was statistically unrelated to reported ITN ownership and very weakly to the proportion of households with ≥1 ITN (R² = 0.051). Proportional ITN ownership/household varied relative to mosquito habitat suitability (Levene’s test F = 3.0037). Quantile regression was used to assess trends in ITN ownership among households with the highest and lowest 10 % of ITN ownership. ITN ownership declined significantly toward areas with the highest vector habitat suitability among households with lowest ITN ownership (t = −3.38). In areas with lowest habitat suitability, ITN ownership was consistently higher.

Conclusions

Insecticide-treated net ownership is critical for malaria control. While Tanzania-wide efforts to distribute ITNs has reduced malaria impacts, gaps and variance in ITN ownership are unexpectedly large in areas where malaria risk is highest. Supplemental ITN distributions targeting prime Anopheles habitats are likely to have disproportionate human health benefits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0841-x) contains supplementary material, which is available to authorized users.

Habitat segregation and ecological character displacement in cryptic African malaria mosquitoes

Understanding how divergent selection generates adaptive phenotypic and population diversification provides a mechanistic explanation of speciation in recently separated species pairs. Towards this goal, we sought ecological gradients of divergence between the cryptic malaria vectors Anopheles coluzzii and An. gambiae and then looked for a physiological trait that may underlie such divergence. Using a large set of occurrence records and eco-geographic information, we built a distribution model to predict the predominance of the two species across their range of sympatry. Our model predicts two novel gradients along which the species segregate: distance from the coastline and altitude. Anopheles coluzzii showed a ‘bimodal’ distribution, predominating in xeric West African savannas and along the western coastal fringe of Africa. To test whether differences in salinity tolerance underlie this habitat segregation, we assessed the acute dose–mortality response to salinity of thirty-two larval populations from Central Africa. In agreement with its coastal predominance, Anopheles coluzzii was overall more tolerant than An. gambiae. Salinity tolerance of both species, however, converged in urban localities, presumably reflecting an adaptive response to osmotic stress from anthropogenic pollutants. When comparing degree of tolerance in conjunction with levels of syntopy, we found evidence of character displacement in this trait.

Human brucellosis occurrences in inner mongolia, China: a spatio-temporal distribution and ecological niche modeling approach

BACKGROUND

Brucellosis is a common zoonotic disease and remains a major burden in both human and domesticated animal populations worldwide. Few geographic studies of human Brucellosis have been conducted, especially in China. Inner Mongolia of China is considered an appropriate area for the study of human Brucellosis due to its provision of a suitable environment for animals most responsible for human Brucellosis outbreaks.

METHODS

The aggregated numbers of human Brucellosis cases from 1951 to 2005 at the municipality level, and the yearly numbers and incidence rates of human Brucellosis cases from 2006 to 2010 at the county level were collected. Geographic Information Systems (GIS), remote sensing (RS) and ecological niche modeling (ENM) were integrated to study the distribution of human Brucellosis cases over 1951-2010.

RESULTS

Results indicate that areas of central and eastern Inner Mongolia provide a long-term suitable environment where human Brucellosis outbreaks have occurred and can be expected to persist. Other areas of northeast China and central Mongolia also contain similar environments.

CONCLUSIONS

This study is the first to combine advanced spatial statistical analysis with environmental modeling techniques when examining human Brucellosis outbreaks and will help to inform decision-making in the field of public health.

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

Background

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

Methods

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

Results

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

Conclusions

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

A global set of Fourier-transformed remotely sensed covariates for the description of abiotic niche in epidemiological studies of tick vector species

BACKGROUND

Correlative modelling combines observations of species occurrence with environmental variables to capture the niche of organisms. It has been argued for the use of predictors that are ecologically relevant to the target species, instead of the automatic selection of variables. Without such biological background, the forced inclusion of numerous variables can produce models that are highly inflated and biologically irrelevant. The tendency in correlative modelling is to use environmental variables that are interpolated from climate stations, or monthly estimates of remotely sensed features.

METHODS

We produced a global dataset of abiotic variables based on the transformation by harmonic regression (time series Fourier transform) of monthly data derived from the MODIS series of satellites at a nominal resolution of 0.1°. The dataset includes variables, such as day and night temperature or vegetation and water availability, which potentially could affect physiological processes and therefore are surrogates in tracking the abiotic niche. We tested the capacities of the dataset to describe the abiotic niche of parasitic organisms, applying it to discriminate five species of the globally distributed tick subgenus Boophilus and using more than 9,500 published records.

RESULTS

With an average reliability of 82%, the Fourier-transformed dataset outperformed the raw MODIS-derived monthly data for temperature and vegetation stress (62% of reliability) and other popular interpolated climate datasets, which had variable reliability (56%-65%). The transformed abiotic variables always had a collinearity of less than 3 (as measured by the variance inflation factor), in contrast with interpolated datasets, which had values as high as 300.

CONCLUSIONS

The new dataset of transformed covariates could address the tracking of abiotic niches without inflation of the models arising from internal issues with the descriptive variables, which appear when variance inflation is higher than 10. The coefficients of the harmonic regressions can also be used to reconstruct the complete original time series, being an adequate complement for ecological, epidemiological, or phylogenetic studies. We provide the dataset as a free download under the GNU general public license as well as the scripts necessary to integrate other time series of data into the calculations of the harmonic coefficients.

Ecological niche and potential distribution of Anopheles arabiensis in Africa in 2050

Background

The future distribution of malaria in Africa is likely to be much more dependent on environmental conditions than the current distribution due to the effectiveness of indoor and therapeutic anti-malarial interventions, such as insecticide-treated nets (ITNs), indoor residual spraying for mosquitoes (IRS), artemisinin-combination therapy (ACT), and intermittent presumptive treatment (IPT). Future malaria epidemiology is therefore expected to be increasingly dominated by Anopheles arabiensis, which is the most abundant exophagic mosquito competent to transmit Plasmodium falciparum and exhibits a wide geographic range.

Methods

To map the potential distribution of An. arabiensis in Africa, ecological niche models were fit to 20th century collection records. Many common species distribution modelling techniques aim to discriminate species habitat from the background distribution of environments. Since these methods arguably result in unnecessarily large Type I and Type II errors, LOBAG-OC was used to identify the niche boundary using only data on An. arabiensis occurrences. The future distribution of An. arabiensis in Africa was forecasted by projecting the fit model onto maps of simulated climate change following three climate change scenarios.

Results

Ecological niche modelling revealed An. arabiensis to be a climate generalist in the sense that it can occur in most of Africa’s contemporary environmental range. Under three climate change scenarios, the future distribution of An. arabiensis is expected to be reduced by 48%-61%. Map differences between baseline and projected climate suggest that habitat reductions will be especially extensive in Western and Central Africa; portions of Botswana, Namibia, and Angola in Southern Africa; and portions of Sudan, South Sudan, Somalia, and Kenya in East Africa. The East African Rift Valley and Eastern Coast of Africa are expected to remain habitable. Some modest gains in habitat are predicted at the margins of the current range in South Sudan, South Africa, and Angola.

Conclusion

In summary, these results suggest that the future potential distribution of An. arabiensis in Africa is likely to be smaller than the contemporary distribution by approximately half as a result of climate change. Agreement among the three modelling scenarios suggests that this outcome is robust to a wide range of potential climate futures.

An online tool for mapping insecticide resistance in major Anopheles vectors of human malaria parasites and review of resistance status for the Afrotropical region

BACKGROUND

Malaria control programmes across Africa and beyond are facing increasing insecticide resistance in the major anopheline vectors. In order to preserve or prolong the effectiveness of the main malaria vector interventions, up-to-date and easily accessible insecticide resistance data that are interpretable at operationally-relevant scales are critical. Herein we introduce and demonstrate the usefulness of an online mapping tool, IR Mapper.

METHODS

A systematic search of published, peer-reviewed literature was performed and Anopheles insecticide susceptibility and resistance mechanisms data were extracted and added to a database after a two-level verification process. IR Mapper ( http://www.irmapper.com) was developed using the ArcGIS for JavaScript Application Programming Interface and ArcGIS Online platform for exploration and projection of these data.

RESULTS

Literature searches yielded a total of 4,084 susceptibility data points for 1,505 populations, and 2,097 resistance mechanisms data points for 1,000 populations of Anopheles spp. tested via recommended WHO methods from 54 countries between 1954 and 2012. For the Afrotropical region, data were most abundant for populations of An. gambiae, and pyrethroids and DDT were more often used in susceptibility assays (51.1 and 26.8% of all reports, respectively) than carbamates and organophosphates. Between 2001 and 2012, there was a clear increase in prevalence and distribution of confirmed resistance of An. gambiae s.l. to pyrethroids (from 41 to 87% of the mosquito populations tested) and DDT (from 64 to 91%) throughout the Afrotropical region. Metabolic resistance mechanisms were detected in western and eastern African populations and the two kdr mutations (L1014S and L1014F) were widespread. For An. funestus s.l., relatively few populations were tested, although in 2010-2012 resistance was reported in 50% of 10 populations tested. Maps are provided to illustrate the use of IR Mapper and the distribution of insecticide resistance in malaria vectors in Africa.

CONCLUSIONS

The increasing pyrethroid and DDT resistance in Anopheles in the Afrotropical region is alarming. Urgent attention should be afforded to testing An. funestus populations especially for metabolic resistance mechanisms. IR Mapper is a useful tool for investigating temporal and spatial trends in Anopheles resistance to support the pragmatic use of insecticidal interventions.

Ecological niche modelling of potential West Nile virus vector mosquito species and their geographical association with equine epizootics in Italy

In Italy, West Nile virus (WNV) equine outbreaks have occurred annually since 2008. Characterizing WNV vector habitat requirements allows for the identification of areas at risk of viral amplification and transmission. Maxent-based ecological niche models were developed using literature records of 13 potential WNV Italian vector mosquito species to predict their habitat suitability range and to investigate possible geographical associations with WNV equine outbreak occurrence in Italy from 2008 to 2010. The contribution of different environmental variables to the niche models was also assessed. Suitable habitats for Culex pipiens, Aedes albopictus, and Anopheles maculipennis were widely distributed; Culex modestus, Ochlerotatus geniculatus, Ochlerotatus caspius, Coquillettidia richiardii, Aedes vexans, and Anopheles plumbeus were concentrated in north-central Italy; Aedes cinereus, Culex theileri, Ochlerotatus dorsalis, and Culiseta longiareolata were restricted to coastal/southern areas. Elevation, temperature, and precipitation variables showed the highest predictive power. Host population and landscape variables provided minor contributions. WNV equine outbreaks had a significantly higher probability to occur in habitats suitable for Cx. modestus and Cx. pipiens, providing circumstantial evidence that the potential distribution of these two species coincides geographically with the observed distribution of the disease in equines.

Predicting shifts in parasite distribution with climate change: a multitrophic level approach

Climate change likely will lead to increasingly favourable environmental conditions for many parasites. However, predictions regarding parasitism's impacts often fail to account for the likely variability in host distribution and how this may alter parasite occurrence. Here, we investigate potential distributional shifts in the meningeal worm, Parelaphostrongylosis tenuis, a protostrongylid nematode commonly found in white-tailed deer in North America, whose life cycle also involves a free-living stage and a gastropod intermediate host. We modelled the distribution of the hosts and free-living larva as a complete assemblage to assess whether a complex trophic system will lead to an overall increase in parasite distribution with climate change, or whether divergent environmental niches may promote an ecological mismatch. Using an ensemble approach to climate modelling under two different carbon emission scenarios, we show that whereas the overall trend is for an increase in niche breadth for each species, mismatches arise in habitat suitability of the free-living larva vs. the definitive and intermediate hosts. By incorporating these projected mismatches into a combined model, we project a shift in parasite distribution accounting for all steps in the transmission cycle, and identify that overall habitat suitability of the parasite will decline in the Great Plains and southeastern USA, but will increase in the Boreal Forest ecoregion, particularly in Alberta. These results have important implications for wildlife conservation and management due to the known pathogenicity of parelaphostrongylosis to alternate hosts including moose, caribou and elk. Our results suggest that disease risk forecasts which fail to consider biotic interactions may be overly simplistic, and that accounting for each of the parasite's life stages is key to refining predicted responses to climate change.

A global airport-based risk model for the spread of dengue infection via the air transport network

The number of travel-acquired dengue infections has seen a consistent global rise over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue has contributed to a rise in the number of dengue cases in both areas of endemicity and elsewhere. This paper reports results from a network-based risk assessment model which uses international passenger travel volumes, travel routes, travel distances, regional populations, and predictive species distribution models (for the two vector species, Aedes aegypti and Aedes albopictus) to quantify the relative risk posed by each airport in importing passengers with travel-acquired dengue infections. Two risk attributes are evaluated: (i) the risk posed by through traffic at each stopover airport and (ii) the risk posed by incoming travelers to each destination airport. The model results prioritize optimal locations (i.e., airports) for targeted dengue surveillance. The model is easily extendible to other vector-borne diseases.

Abundance and diversity of Anopheles species (Diptera: Culicidae) associated with malaria transmission in human dwellings in rural and urban communities in Oyo State, Southwestern Nigeria

Mosquito samples were collected from rural and urban communities in three selected major towns in Southwestern Nigeria to determine the impact of urbanization on the diversity and abundance of Anopheles species associated with malaria transmission in human habitations. A total of ten Anopheles species were identified in the rural communities, while eight Anopheles species were identified in the urban communities. Out of the ten Anopheles species identified, only four species, Anopheles gambiae (Giles), Anopheles funestus (Giles), Anopheles moucheti (Evans), and Anopheles nili (Theobald), were established to be vectors of malaria occurring in greater than 50% of the rural communities. Only A. gambiae occurred in all the urban communities, while the other three major vectors occurred in not more than 20% of the urban communities. Margalef's and Shannon-Wiener indices showed that diversity and species richness were higher in the rural compared to the urban. Comprehensive information on malaria vector abundance and diversity in rapidly changing communities is an important tool in planning and implementing successful vector control programs.

A global model of avian influenza prediction in wild birds: the importance of northern regions

Avian influenza virus (AIV) is enzootic to wild birds, which are its natural reservoir. The virus exhibits a large degree of genetic diversity and most of the isolated strains are of low pathogenicity to poultry. Although AIV is nearly ubiquitous in wild bird populations, highly pathogenic H5N1 subtypes in poultry have been the focus of most modeling efforts. To better understand viral ecology of AIV, a predictive model should 1) include wild birds, 2) include all isolated subtypes, and 3) cover the host’s natural range, unbounded by artificial country borders. As of this writing, there are few large-scale predictive models of AIV in wild birds. We used the Random Forests algorithm, an ensemble data-mining machine-learning method, to develop a global-scale predictive map of AIV, identify important predictors, and describe the environmental niche of AIV in wild bird populations. The model has an accuracy of 0.79 and identified northern areas as having the highest relative predicted risk of outbreak. The primary niche was described as regions of low annual rainfall and low temperatures. This study is the first global-scale model of low-pathogenicity avian influenza in wild birds and underscores the importance of largely unstudied northern regions in the persistence of AIV.

Stable and fluctuating temperature effects on the development rate and survival of two malaria vectors, Anopheles arabiensis and Anopheles funestus

Background

Understanding the biology of malaria vector mosquitoes is crucial to understanding many aspects of the disease, including control and future outcomes. The development rates and survival of two Afrotropical malaria vectors, Anopheles arabiensis and Anopheles funestus, are investigated here under conditions of constant and fluctuating temperatures. These data can provide a good starting point for modelling population level consequences of temperature change associated with climate change. For comparative purposes, these data were considered explicitly in the context of those available for the third African malaria vector, Anopheles gambiae.

Methods

Twenty five replicates of 20–30 eggs were placed at nine constant and two fluctuating temperatures for development rate experiments and survival estimates. Various developmental parameters were estimated from the data, using standard approaches.

Results

Lower development threshold (LDT) for both species was estimated at 13-14°C. Anopheles arabiensis developed consistently faster than An. funestus. Optimum temperature (T_opt) and development rate at this temperature (μ_max) differed significantly between species for overall development and larval development. However, T_opt and μ_max for pupal development did not differ significantly between species. Development rate and survival of An. funestus was negatively influenced by fluctuating temperatures. By contrast, development rate of An. arabiensis at fluctuating temperatures either did not differ from constant temperatures or was significantly faster. Survival of this species declined by c. 10% at the 15°C to 35°C fluctuating temperature regime, but was not significantly different between the constant 25°C and the fluctuating 20°C to 30°C treatment. By comparison, previous data for An. gambiae indicated fastest development at a constant temperature of 28°C and highest survival at 24°C.

Conclusions

The three most important African malaria vectors all differ significantly in development rates and survival under different temperature treatments, in keeping with known distribution data, though differences among M and S molecular forms of An. gambiae likely complicate the picture. Increasing temperatures associated with climate change favour all three species, but fluctuations in temperatures are detrimental to An. funestus and may also be for An. gambiae. This may have significant implications for disease burden in areas where each species is the main malaria vector.

A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region. II. Validation of species distribution and seasonal variations

BACKGROUND

The first part of this study aimed to develop a model for Anopheles gambiae s.l. with separate parametrization schemes for Anopheles gambiae s.s. and Anopheles arabiensis. The characterizations were constructed based on literature from the past decades. This part of the study is focusing on the model's ability to separate the mean state of the two species of the An. gambiae complex in Africa. The model is also evaluated with respect to capturing the temporal variability of An. arabiensis in Ethiopia. Before conclusions and guidance based on models can be made, models need to be validated.

METHODS

The model used in this paper is described in part one (Malaria Journal 2013, 12:28). For the validation of the model, a data base of 5,935 points on the presence of An. gambiae s.s. and An. arabiensis was constructed. An additional 992 points were collected on the presence An. gambiae s.l.. These data were used to assess if the model could recreate the spatial distribution of the two species. The dataset is made available in the public domain. This is followed by a case study from Madagascar where the model's ability to recreate the relative fraction of each species is investigated. In the last section the model's ability to reproduce the temporal variability of An. arabiensis in Ethiopia is tested. The model was compared with data from four papers, and one field survey covering two years.

RESULTS

Overall, the model has a realistic representation of seasonal and year to year variability in mosquito densities in Ethiopia. The model is also able to describe the distribution of An. gambiae s.s. and An. arabiensis in sub-Saharan Africa. This implies this model can be used for seasonal and long term predictions of changes in the burden of malaria. Before models can be used to improving human health, or guide which interventions are to be applied where, there is a need to understand the system of interest. Validation is an important part of this process. It is also found that one of the main mechanisms separating An. gambiae s.s. and An. arabiensis is the availability of hosts; humans and cattle. Climate play a secondary, but still important, role.

Predicting the potential distribution ofVexillata(Nematoda: Ornithostrongylidae) and its hosts (Mammalia: Rodentia) within America

Species distribution modelling has been a powerful tool to explore the potential distribution of parasites in wildlife, being the basis of studies on biogeography.Vexillataspp. are intestinal nematodes found in several species of mammalian hosts, such as rodents (Geomyoidea) and hares (Leporidae) in the Nearctic and northern Neotropical regions. In the present study, we modelled the potential distribution ofVexillataspp. and their hosts, using exclusively species from the Geomyidae and Heteromyidae families, in order to identify their distributional patterns. Bioclimatic and topographic variables were used to identify and predict suitable habitats forVexillataand its hosts. Using these models, we identified that temperature seasonality is a significant environmental factor that influences the distribution of the parasite genus and its host. In particular, the geographical distribution is estimated to be larger than that predicted for its hosts. This suggests that the nematode has the potential to extend its geographical range and also its spectrum of host species. Increasing sample size and geographical coverage will contribute to recommendations for conservation of this host–parasite system.