Use of Geospatial Surveillance and Response Systems for Vector-Borne Diseases in the Elimination Phase

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

BACKGROUND

In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies.

RESULTS

We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance.

CONCLUSIONS

Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Community-Led Data Collection: Enhancing Local-Level Scabies Surveillance in Remote Aboriginal Communities in Australia

Novel approaches to geohealth data analysis offer major benefits to neglected tropical disease control by identifying how social, economic and environmental elements of place interact to influence disease outcomes. However, a lack of timely and accurate geohealth data poses substantial risks to the accuracy of risk identification and challenges to the development of suitably targeted disease control programs. Scabies is one of many skin-related NTDs that is nominated as a priority for global disease control by the World Health Organization, but for which there remains a lack of baseline geospatial data on disease distribution. In this opinion paper, we consider lessons on impediments to geohealth data availability for other skin-related NTDs before outlining challenges specific to the collection of scabies-related geohealth data. We illustrate the importance of a community-centred approach in this context using a recent initiative to develop a community-led model of scabies surveillance in remote Aboriginal communities in Australia.

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.

Distribution and Risk of Cutaneous Leishmaniasis in Khyber Pakhtunkhwa, Pakistan

Cutaneous leishmaniasis (CL) is a zoonotic infection caused by obligate intracellular protozoa of the genus Leishmania. This study aimed to investigate CL in Khyber Pakhtunkhwa, Pakistan and to estimate the risk of epidemics. Clinico-epidemiological data of 3188 CL patients were collected from health facilities in 2021. Risk factors were analyzed using the chi-square test. ArcGIS V.10.7.1 was applied for spatial analysis. The association between CL occurrence and climatic variables was examined by Bayesian geostatistical analysis. The clinical data revealed males or individuals younger than 20 years old were more affected. Most patients presented with a single lesion, and the face was the most attacked body part. CL was prevalent in the southern region in winter. A proportional symbol map, a choropleth map, and a digital elevation model map were built to show the distribution of CL. Focal transmission was predicted by inverse distance weighting interpolation. Cluster and outlier analysis identified clusters in Bannu, Dir Lower, and Mardan, and hotspot analysis suggested Bannu as a high-risk foci. Bayesian geostatistical analysis indicated that increasing precipitation and temperature as well as low altitudes were associated with CL infection. The study has provided important information for public health sectors to develop intervention strategies for future CL epidemics.

Analysis of epidemiological changes and elimination effects for malaria in Handan city, the north China

The elimination of malaria requires high-quality surveillance data to quickly detect and respond to individual cases. This study aims to analyze the epidemiological characteristics of malaria and ascertain the long-term epidemic trends of malaria by 2020 in Handan China. Case-level data for the period 2011 to 2020 were extracted from Chinese Information System for Disease Control and Prevention. The lamp trap method was used to capture mosquitoes so that the characteristics of mosquitoes can be analyzed. The incidence, accuracy, and timeliness of malaria case diagnosis, reporting and investigation were evaluated at the elimination stage (2011-2020) in Handan City, China. Between 2011 and 2020, 94 malaria cases were reported in Handan City, of which 93 malaria cases were male and all of which were imported from abroad. The annual average incidence decreased from 622.33/100,000 to 0.11/100,000 in the elimination stage. Since the initiation of the National Malaria Elimination Program in 2010, malaria cases have been consistent with the increase in overseas export channels and labor personnel service. There is a need to strengthen malaria surveillance of returning workers from Africa and to conduct timely blood tests to diagnose and treat imported infections. Local authorities ensure that imported malaria cases can be timely diagnosed, reported, treated and investigated at local level.

Comparing Satellite and Ground-Based Measurements of Environmental Suitability for Vector Mosquitoes in an Urban Landscape

Exposure to mosquito-borne diseases is influenced by landscape patterns and microclimates associated with land cover. These influences can be particularly strong in heterogeneous urban landscapes where human populations are concentrated. We investigated how land cover and climate influenced abundances of Ae. albopictus (Skuse) (Diptera: Culicidae) and Cx. quinquefasciatus (Say) (Diptera: Culicidae) in Norman, Oklahoma (United States). From June-October 2019 and May-October 2020 we sampled mosquitoes along an urban-rural gradient using CO2 baited BG Sentinel traps. Microclimate sensors at these sites measured temperature and humidity. We mapped environmental variables using satellite images from Landsat, Sentinel-2, and VIIRS, and the CHIRPS rainfall dataset. We also obtained meteorological data from the closest weather station. We compared statistical models of mosquito abundance based on microclimate, satellite, weather station, and land cover data. Mosquitoes were more abundant on trap days with higher temperature and relative humidity. Rainfall 2 wk prior to the trap day negatively affected mosquito abundances. Impervious surface cover was positively associated with Cx. quinquefasciatus and tree cover was negatively associated with Ae. albopictus. Among the data sources, models based on satellite variables and land cover data had the best fits for Ae. albopictus (R2 = 0.7) and Cx. quinquefasciatus (R2 = 0.51). Models based on weather station or microclimate data had weaker fits (R2 between 0.09 and 0.17) but were improved by adding land cover variables (R2 between 0.44 and 0.61). These results demonstrate the potential for using satellite remote sensing for mosquito habitat analyses in urban areas.

Emerging computational technologies in human leishmaniasis: where are we?

Human leishmaniasis is a neglected tropical disease (NTD) with high morbidity and is endemic in low- to middle-income countries. Its diagnosis, treatment and epidemiological control methods are outdated and obsolete, which has become a challenge for health practitioners in controlling the disease. Computational methods have proven to be beneficial and have become popular in many fields of medicine, especially in affluent countries. However, they have not been widely used for NTDs. To date, few computational technologies have been employed for leishmaniasis. Although new technologies in leishmaniasis are theorized, they have only been minimally applied and have not been updated, even in other infections. Research and development on NTDs suffers from the inherent difficulties of the demographic regions the diseases afflict. In this narrative review we described the e-tools available in managing leishmaniasis, ranging from drug discovery to treatment.

Incentivizing optimal risk map use for Triatoma infestans surveillance in urban environments

In Arequipa, Peru, a large-scale vector control campaign has successfully reduced urban infestations of the Chagas disease vector, Triatoma infestans. In addition to preventing new infections with Trypanosoma cruzi (etiological agent of Chagas disease), the campaign produced a wealth of information about the distribution and density of vector infestations. We used these data to create vector infestation risk maps for the city in order to target the last few remaining infestations, which are unevenly distributed and difficult to pinpoint. Our maps, which are provided on a mobile app, display color-coded, individual house-level estimates of T. infestans infestation risk. Entomologic surveillance personnel can use the maps to select homes to inspect based on estimated risk of infestation, as well as keep track of which parts of a given neighborhood they have inspected to ensure even surveillance throughout the zone. However, the question then becomes, how do we encourage surveillance personnel to actually use these two functionalities of the risk map? As such, we carried out a series of rolling trials to test different incentive schemes designed to encourage the following two behaviors by entomologic surveillance personnel in Arequipa: (i) preferential inspections of homes shown as high risk on the maps, and (ii) even surveillance across the geographical distribution of a given area, which we term, 'spatial coverage.' These two behaviors together constituted what we termed, 'optimal map use.' We found that several incentives resulted in one of the two target behaviors, but just one incentive scheme based on the game of poker resulted in optimal map use. This poker-based incentive structure may be well-suited to improve entomological surveillance activities and other complex multi-objective tasks.

Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.

Spatial patterns and climate drivers of malaria in three border areas of Brazil, Venezuela and Guyana, 2016-2018

In 2020, 77% of malaria cases in the Americas were concentrated in Venezuela, Brazil, and Colombia. These countries are characterized by a heterogeneous malaria landscape and malaria hotspots. Furthermore, the political unrest in Venezuela has led to significant cross-border population movement. Hence, the aim of this study was to describe spatial patterns and identify significant climatic drivers of malaria transmission along the Venezuela-Brazil-Guyana border, focusing on Bolivar state, Venezuela and Roraima state, Brazil. Malaria case data, stratified by species from 2016 to 2018, were obtained from the Brazilian Malaria Epidemiology Surveillance Information System, the Guyana Vector Borne Diseases Program, the Venezuelan Ministry of Health, and civil society organizations. Spatial autocorrelation in malaria incidence was explored using Getis-Ord (Gi*) statistics. A Poisson regression model was developed with a conditional autoregressive prior structure and posterior parameters were estimated using the Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. There were 685,498 malaria cases during the study period. Plasmodium vivax was the predominant species (71.7%, 490,861). Malaria hotspots were located in eight municipalities along the Venezuela and Guyana international borders with Brazil. Plasmodium falciparum increased by 2.6% (95% credible interval [CrI] 2.1%, 2.8%) for one meter increase in altitude, decreased by 1.6% (95% CrI 1.5%, 2.3%) and 0.9% (95% CrI 0.7%, 2.4%) per 1 cm increase in 6-month lagged precipitation and each 1 °C increase of minimum temperature without lag. Each 1 °C increase of 1-month lagged maximum temperature increased P. falciparum by 0.6% (95% CrI 0.4%, 1.9%). P. vivax cases increased by 1.5% (95% CrI 1.3%, 1.6%) for one meter increase in altitude and decreased by  1.1% (95% CrI 1.0%, 1.2%) and 7.3% (95% CrI 6.7%, 9.7%) for each 1 cm increase of precipitation lagged at 6-months and 1 °C increase in minimum temperature lagged at 6-months. Each 1°C increase of two-month lagged maximum temperature increased P. vivax by 1.5% (95% CrI 0.6%, 7.1%). There was no significant residual spatial clustering after accounting for climatic covariates. Malaria hotspots were located along the Venezuela and Guyana international border with Roraima state, Brazil. In addition to population movement, climatic variables were important drivers of malaria transmission in these areas.

Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil

Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.

There is more to satellite imagery than meets the eye

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Potential risk of colonization of Bulinus globosus in the mainland of China under climate change

Background

Bulinus globosus, the main intermediate snail host of Schistosoma haematobium. The increased contacts between Africa and China could even lead to large-scale dissemination of B. globosus in China. Temperature is the key factor affecting fresh-water snail transmission. This study predicted potential risk of colonization of B. globosus in the mainland of China under climate change.

Methods

We investigated minimum and maximum temperatures for B. globosus eggs, juveniles and adult snails kept under laboratory conditions to find the most suitable range by pinpointing the median effective temperatures (ET50). We also assessed the influence of temperature on spawning and estimated the accumulated temperature (AT). The average air temperatures between 1955 and 2019 in January and July, the coldest and hottest months in China, respectively, were collected from national meteorological monitoring stations and investigated in a geographic information system (GIS) using empirical Bayesian Kriging to evaluate the theoretical possibility for distribution of B. globosus in southern China based on temperature.

Results

The effective minimum temperature (ET50_min) for eggs, juveniles, adult snails and spawning were 8.5, 7.0, 7.0, 14.9 °C, respectively, with the corresponding maximum values (ET50_max) of 36.6, 40.5, 40.2 and 38.1 °C. The AT was calculated at 712.1 ± 64.9 °C·d. In 1955, the potential B. globosus distribution would have had a northern boundary stretching from the coastal areas of Guangdong Province and Guangxi Autonomous Region to southern Yunnan Province. Since then, this line has gradually moved northward.

Conclusions

Annual regeneration of B. globosus can be supported by the current climate conditions in the mainland of China, and a gradual expansion trend from south to north is shown in the study from 2015 to 2019. Thus, there is a potential risk of colonization of B. globosus in the mainland of China under climate change.

Graphical Abstract

Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future

The Sustainable Development Goals (SDG) framework aims to end poverty, improve health and education, reduce inequality, design sustainable cities, support economic growth, tackle climate change and leave no one behind. To monitor and report the progress on the 231 unique SDGs indicators in all signatory countries, data play a key role. Here, we reviewed the data challenges and costs associated with obtaining traditional data and satellite data (particularly for developing countries), emphasizing the benefits of using satellite data, alongside their portal and platforms in data access. We then assessed, under the maturity matrix framework (MMF 2.0), the current potential of satellite data applications on the SDG indicators that were classified into the sustainability pillars. Despite the SDG framework having more focus on socio-economic aspects of sustainability, there has been a rapidly growing literature in the last few years giving practical examples in using earth observation (EO) to monitor both environmental and socio-economic SDG indicators; there is a potential to populate 108 indicators by using EO data. EO also has a wider potential to support the SDGs beyond the existing indicators.

The changing risk of vector-borne diseases: Global satellite remote sensing and geospatial surveillance at the forefront

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Modeling dengue vector population with earth observation data and a generalized linear model

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population. Field campaigns are expensive, and data from meteorological sites that feed models with the required environmental data often lack detail. As a consequence, we explore temporal modeling of the population of Ae. aegypti mosquito vector species and environmental conditions- temperature, moisture, precipitation, and vegetation- have been shown to have significant effects. We use earth observation (EO) data as our source for estimating these biotic and abiotic environmental variables based on proxy features, namely: Normalized difference vegetation index, Normalized difference water index, Precipitation, and Land surface temperature. We obtained our response variable from field-collected mosquito population measured weekly using 791 mosquito traps in Vila Velha city, Brazil, for 36 weeks in 2017, and 40 weeks in 2018. Recent similar studies have used machine learning (ML) techniques for this task. However, these techniques are neither intuitive nor explainable from an operational point of view. As a result, we use a Generalized Linear Model (GLM) to model this relationship due to its fitness for count response variable modeling, its interpretability, and the ability to visualize the confidence intervals for all inferences. Also, to improve our model, we use the Akaike Information Criterion to select the most informative environmental features. Finally, we show how to improve the quality of the model by weighting our GLM. Our resulting weighted GLM compares well in quality with ML techniques: Random Forest and Support Vector Machines. These results provide an advancement with regards to qualitative and explainable epidemiological risk modeling in urban environments.

Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017

Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.

Core components, concepts and strategies for parasitic and vector-borne disease elimination with a focus on schistosomiasis: A landscape analysis

Efforts to control and eliminate human schistosomiasis have accelerated over the past decade. In a number of endemic countries and settings, interruption of schistosome transmission has been achieved. In others, Schistosoma infections continue to challenge program managers at different levels, from the complexity of the transmission cycle, over limited treatment options and lack of field-friendly accurate diagnostics, to controversy around adequate intervention strategies. We conducted a landscape analysis on parasitic and vector-borne disease elimination approaches with the aim to identify evidence-based strategies, core components and key concepts for achieving and sustaining schistosomiasis control and for progressing elimination efforts towards interruption of transmission in sub-Saharan Africa. A total of 118 relevant publications were identified from Web of Science, Pubmed and the grey literature and reviewed for their content. In addition, we conducted in-depth interviews with 23 epidemiologists, program managers, policymakers, donors and field researchers. Available evidence emphasizes the need for comprehensive, multipronged and long-term strategies consisting of multiple complementary interventions that must be sustained over time by political commitment and adequate funding in order to reach interruption of transmission. Based on the findings of this landscape analysis, we propose a comprehensive set of intervention strategies for schistosomiasis control and elimination. Before deployment, the proposed interventions will require review, evaluation and validation in the frame of an expert consultation as a step towards adaptation to specific contexts, conditions and settings. Field testing to ensure local relevance and effectiveness is paramount given the diversity of socio-ecological and epidemiological contexts.

This landscape analysis explored successful concepts, approaches and interventions of past and ongoing parasitic and vector-borne disease elimination efforts and programs with regard to relevance for progress in the elimination of human schistosome infections. Schistosomiasis is a disabling, water borne parasitic disease of public health concern with an estimated 250 million people infected worldwide. The long-term morbidity of this neglected tropical disease significantly impacts growth, cognition and socioeconomic development at all ages. Despite increased global efforts to control morbidity and advance elimination, challenges in view of the complex life cycle which involves freshwater sources, intermediate snail hosts and humans, remain. This calls for targeted interventions and concerted programs. According to the evidence from the literature and as proposed by a wide range of key informants, comprehensive, multipronged and long-term strategies supported by strong political commitment and adequate funding are required in order to achieve and sustain the set goals. Based on the findings, we propose here a comprehensive set of intervention strategies for schistosomiasis control and elimination for review and evaluation to inform implementation research needs and elimination program design.

The typical cestodes of the red fox in eastern areas of the Iberian Peninsula have a grouped distribution

The red fox (Vulpes vulpes) is the most abundant wild canid in the Iberian Peninsula, and can act as a carrier of a wide range of parasite species shared with dogs and cats and which are sometimes zoonoses. Despite this, few studies are available describing the distribution and epidemiology of the cestode community of this opportunistic carnivore in areas of Mediterranean climate. In this study, prevalence, intensity, abundance and aggregation coefficient of cestodes found in 286 red foxes of the Valencian Community (East Spain) were calculated. For the most prevalent species, presence data were combined with environmental variables in order to build spatial models and predict the limiting factors associated with the decrease of suitable habitat for these parasites. The overall prevalence of cestodes was 83.22 % (238/286). Specifically, foxes harboured Mesocestoides spp. (75.87 %), Joyeuxiella echinorhynchoides (27.62 %), Taenia pisiformis (13.29 %), Dypilidium caninum (3.15 %), Taenia polyacantha (1.05 %), Taenia hydatigena (0.70 %), Taenia crassiceps (0.70 %) and Taenia spp. (0.35 %). Tetrathyridia larvae were found both in thoracic and abdominal cavity of three foxes (1.05 %), being the first time that this metacestode stage is described in the thoracic cavity of the red fox. Models on Mesocestoides spp. (Area Under the Curve, AUC = 0.80) and J. echinorhynchoides (AUC = 0.83) indicate that anthropogenic, climatic and environmental factors efficiently describe the habitat shape of parasites. This study emphasizes that foxes in Mediterranean areas are hosts of cestodes of veterinary and public health concern. Furthermore, our results demonstrate the usefulness of Ecological Niche Modeling applications for mapping the distribution of fox parasites and identifying areas of higher epidemiological risk for this wild canid, as well as for dogs and cats and, in the case of Mesocestoides spp., for humans.

Importance of microenvironment to arbovirus vector distribution in an urban area, São Paulo, Brazil

INTRODUCTION:

The study of the landscape ecology, biological microhabitat, and epidemiological implications for the distribution of the main vectors Aedes aegypti and Aedes albopictus contribute to the prevention and control actions regarding the diseases they transmit.

METHODS:

This study sought to assess data on positive properties of the vector control program activities from 1998 to 2010. An entomological survey was also carried out on a sample of buildings collecting larvae and pupae from containers between October and April (spring / summer) from 2002 to 2005. We assessed the physico-chemical data of the water in 20% of positive containers. The vegetation and urbanization were assessed with the aid of satellite images and microenvironments were classified as urbanized, woods, and shrubs. The data were analyzed using statistical and geoprocessing software.

RESULTS:

Ae. aegypti and Ae. albopictus colonized all types of microhabitats and microenvironments, predominantly in the urbanized area, in isolation and in coexistence. The microhabitat of Ae. aegypti showed a temperature gradient greater than that of Ae. albopictus, and there was an association with urbanized areas for the first species and wooded areas for the last species.

CONCLUSIONS:

Landscape ecology and intra-urban differences favor different microclimates, which contribute to the coexistence of species in the urban environment in an area close to the forest, raising the risk of other arbovirus infections in urban areas. The ecological niche should be considered for Ae. albopictus. Entomological and virologic monitoring are suggested as arbovirus surveillance actions in urban infested centers near preserved forests.

From inspiration to translation: Closing the gap between research and control of helminth zoonoses in Southeast Asia

Poverty magnifies limitations resulting from traditional biases and environmental risks in endemic areas. Any approach towards disease control needs to recognise that socially embedded vulnerabilities can be as powerful as externally imposed infections. Important for RNAS was networking across borders, not just on schistosomiasis but on the whole spectrum of endemic helminthiases, and this bore fruit in the form of the expansion of RNAS into the 'Regional Network on Asian Schistosomiasis and other Helminth Zoonoses (RNAS⁺)', which focuses on technical standardization, supporting the growth of research capacity and the further development of networking. Administration is lean and largely virtual with the focus on connecting members via the Internet, providing databases and administrative back-up. The strategy emphasizes ways and means to alleviate the spectre of disease and poverty from the endemic areas through boosting research on target diseases and supporting collaboration between basic and operational research on the one hand and control/elimination activities on the other. RNAS⁺ also benefits from continuing input from outside research institutions in areas outside Southeast Asia. This paper is aiming to identify the priority actions to close the gap between researcher and policy makers.

Ecological niche models for sand fly species and predicted distribution of Lutzomyia longipalpis (Diptera: Psychodidae) and visceral leishmaniasis in Bahia state, Brazil

Visceral leishmaniasis is a public health problem in Brazil. This disease is endemic in most of Bahia state, with increasing reports of cases in new areas. Ecological niche models (ENM) can be used as a tool for predicting potential distribution for disease, vectors, and to identify risk factors associated with their distribution. In this study, ecological niche models (ENMs) were developed for visceral leishmaniasis (VL) cases and 12 sand fly species captured in Bahia state. Sand fly data was collected monthly by CDC light traps from July 2009 to December 2012. MODIS satellite imagery was used to calculate NDVI, NDMI, and NDWI vegetation indices, MODIS day and night land surface temperature (LST), enhanced vegetation index (EVI), and 19 Bioclim variables were used to develop the ENM using the maximum entropy approach (Maxent). Mean diurnal range was the variable that most contributed to all the models for sand flies, followed by precipitation in wettest month. For Lutzomyia longipalpis (L. longipalpis), annual precipitation, precipitation in wettest quarter, precipitation in wettest month, and NDVI were the most contributing variables. For the VL model, the variables that contributed most were precipitation in wettest month, annual precipitation, LST day, and temperature seasonality. L. longipalpis was the species with the widest potential distribution in the state. The identification of risk areas and factors associated with this distribution is fundamental to prioritize resource allocation and to improve the efficacy of the state's program for surveillance and control of VL.

Schistosomiasis Elimination: Beginning of the End or a Continued March on a Trodden Path

In spite of spectacular progress towards the goal of elimination of schistosomiasis, particularly in China but also in other areas, research gaps and outstanding issues remain. Although expectations of achieving elimination of this disease have never been greater, all constraints have not been swept aside. Indeed, there are some formidable obstacles, such as insufficient amounts of drugs to treat everybody and still limited use of high-sensitive diagnostic techniques, both for the definitive and the intermediate hosts, which indicate that prevalence is considerably underrated in well-controlled areas. Elimination will be difficult to achieve without a broad approach, including a stronger focus on transmission, better diagnostics and the establishment of a reliable survey system activating a rapid response when called for. Importantly, awareness of the crucial importance of transmission has been revived resulting in renewed interest in snail control together with more emphasis on health education and sanitation. The papers collected in this special issue entitled ‘Prospects for Schistosomiasis Elimination’ reflect these issues and we are particularly pleased to note that some also discuss the crucial question when to declare a country free of schistosomiasis and present techniques that together create an approach that can show unequivocally when interruption of transmission has been achieved.

Elimination of Schistosomiasis Mekongi from Endemic Areas in Cambodia and the Lao People's Democratic Republic: Current Status and Plans

The areas endemic for schistosomiasis in the Lao People’s Democratic Republic and in Cambodia were first reported 50 and 60 years ago, respectively. However, the causative parasite Schistosoma mekongi was not recognized as a separate species until 1978. The infection is distributed along a limited part of the Mekong River, regulated by the focal distribution of the intermediate snail host Neotricula aperta. Although more sensitive diagnostics imply a higher figure, the current use of stool examinations suggests that only about 1500 people are presently infected. This well-characterized setting should offer an exemplary potential for the elimination of the disease from its endemic areas; yet, the local topography, reservoir animals, and a dearth of safe water sources make transmission control a challenge. Control activities based on mass drug administration resulted in strong advances, and prevalence was reduced to less than 5% according to stool microscopy. Even so, transmission continues unabated, and the true number of infected people could be as much as 10 times higher than reported. On-going control activities are discussed together with plans for the future.