MosquitoMap and the Mal-area calculator: new web tools to relate mosquito species distribution with vector borne disease

<p>Abstract</p> <p>Background</p> <p>Mosquitoes are important vectors of diseases but, in spite of various mosquito faunistic surveys globally, there is a need for a spatial online database of mosquito collection data and distribution summaries. Such a resource could provide entomologists with the results of previous mosquito surveys, and vector disease control workers, preventative medicine practitioners, and health planners with information relating mosquito distribution to vector-borne disease risk.</p> <p>Results</p> <p>A web application called MosquitoMap was constructed comprising mosquito collection point data stored in an ArcGIS 9.3 Server/SQL geodatabase that includes administrative area and vector species x country lookup tables. In addition to the layer containing mosquito collection points, other map layers were made available including environmental, and vector and pathogen/disease distribution layers. An application within MosquitoMap called the Mal-area calculator (MAC) was constructed to quantify the area of overlap, for any area of interest, of vector, human, and disease distribution models. Data standards for mosquito records were developed for MosquitoMap.</p> <p>Conclusion</p> <p>MosquitoMap is a public domain web resource that maps and compares georeferenced mosquito collection points to other spatial information, in a geographical information system setting. The MAC quantifies the Mal-area, i.e. the area where it is theoretically possible for vector-borne disease transmission to occur, thus providing a useful decision tool where other disease information is limited. The Mal-area approach emphasizes the independent but cumulative contribution to disease risk of the vector species predicted present. MosquitoMap adds value to, and makes accessible, the results of past collecting efforts, as well as providing a template for other arthropod spatial databases.</p

SandflyMap: leveraging spatial data on sand fly vector distribution for disease risk assessments

We feature SandflyMap (www.sandflymap.org), a new map service within VectorMap (www.vectormap.org) that allows free public online access to global sand fly, tick and mosquito collection records and habitat suitability models. Given the short home range of sand flies, combining remote sensing and collection point data give a powerful insight into the environmental determinants of sand fly distribution. SandflyMap is aimed at medical entomologists, vector disease control workers, public health officials and health planners. Data are checked for geographical and taxonomic errors, and are comprised of vouchered specimen information, and both published and unpublished observation data. SandflyMap uses Microsoft Silverlight and ESRI’s ArcGIS Server 10 software platform to present disease vector data and relevant remote sensing layers in an online geographical information system format. Users can view the locations of past vector collections and the results of models that predict the geographic extent of individual species. Collection records are searchable and downloadable, and Excel collection forms with drop down lists, and Excel charts to country, are available for data contributors to map and quality control their data. SandflyMap makes accessible, and adds value to, the results of past sand fly collecting efforts. We detail the workflow for entering occurrence data from the literature to SandflyMap, using an example for sand flies from South America. We discuss the utility of SandflyMap as a focal point to increase collaboration and to explore the nexus between geography and vector-borne disease transmission

Species composition, larval habitats, seasonal occurrence and distribution of potential malaria vectors and associated species of Anopheles (Diptera: Culicidae) from the Republic of Korea

<p>Abstract</p> <p>Background</p> <p>Larval mosquito habitats of potential malaria vectors and related species of <it>Anopheles </it>from three provinces (Gyeonggi, Gyeongsangbuk, Chungcheongbuk Provinces) of the Republic of Korea were surveyed in 2007. This study aimed to determine the species composition, seasonal occurrence and distributions of <it>Anopheles </it>mosquitoes. Satellite derived normalized difference vegetation index data (NDVI) was also used to study the seasonal abundance patterns of <it>Anopheles </it>mosquitoes.</p> <p>Methods</p> <p>Mosquito larvae from various habitats were collected using a standard larval dipper or a white plastic larval tray, placed in plastic bags, and were preserved in 100% ethyl alcohol for species identification by PCR and DNA sequencing. The habitats in the monthly larval surveys included artificial containers, ground depressions, irrigation ditches, drainage ditches, ground pools, ponds, rice paddies, stream margins, inlets and pools, swamps, and uncultivated fields. All field-collected specimens were identified to species, and relationships among habitats and locations based on species composition were determined using cluster statistical analysis.</p> <p>Results</p> <p>In about 10,000 specimens collected, eight species of <it>Anopheles </it>belonging to three groups were identified: Hyrcanus Group - <it>Anopheles sinensis</it>, <it>Anopheles kleini</it>, <it>Anopheles belenrae</it>, <it>Anopheles pullus</it>, <it>Anopheles lesteri</it>, <it>Anopheles sineroides</it>; Barbirostris Group - <it>Anopheles koreicus</it>; and Lindesayi Group - <it>Anopheles lindesayi japonicus</it>. Only <it>An. sinensis </it>was collected from all habitats groups, while <it>An. kleini, An. pullus </it>and <it>An. sineroides </it>were sampled from all, except artificial containers. The highest number of <it>Anopheles </it>larvae was found in the rice paddies (34.8%), followed by irrigation ditches (23.4%), ponds (17.0%), and stream margins, inlets and pools (12.0%). <it>Anopheles sinensis </it>was the dominant species, followed by <it>An. kleini, An. pullus </it>and <it>An. sineroides</it>. The monthly abundance data of the <it>Anopheles </it>species from three locations (Munsan, Jinbo and Hayang) were compared against NDVI and NDVI anomalies.</p> <p>Conclusion</p> <p>The species composition of <it>Anopheles </it>larvae varied in different habitats at various locations. <it>Anopheles </it>populations fluctuated with the seasonal dynamics of vegetation for 2007. Multi-year data of mosquito collections are required to provide a better characterization of the abundance of these insects from year to year, which can potentially provide predictive capability of their population density based on remotely sensed ecological measurements.</p

The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks

The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program’s ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia

A collaboratively derived environmental research agenda for Galapagos

Galápagos is one of the most pristine archipelagos in the world and its conservation relies upon research and sensible management. In recent decades both the interest in, and the needs of, the islands have increased, yet the funds and capacity for necessary research have remained limited. It has become, therefore, increasingly important to identify areas of priority research to assist decision-making in Galápagos conservation. This study identified 50 questions considered priorities for future research and management. The exercise involved the collaboration of policy makers, practitioners and researchers from more than 30 different organisations. Initially, 360 people were consulted to generate 781 questions. An established process of preworkshop voting and three rounds to reduce and reword the questions, followed by a two-day workshop, was used to produce the final 50 questions. The most common issues raised by this list of questions were human population growth, climate change and the impact of invasive alien species. These results have already been used by a range of organisations and politicians and are expected to provide the basis for future research on the islands so that its sustainability may be enhanced. </jats:p

in the Afrotropical Region (Diptera: Culicidae)

www.mapress.com/zootaxa

Comatacarus dolosus

Comatacarus dolosus (Gould, 1956): NEA Leeuwenhoekia (Comatacarus) dolosa Gould, 1956Published as part of Nielsen, David H., Robbins, Richard G. & Rueda, Leopoldo M., 2021, Annotated world checklist of the Trombiculidae and Leeuwenhoekiidae (1758 - 2021) (Acari: Trombiculoidea), with notes on nomenclature, taxonomy, and distribution, pp. 1-243 in Zootaxa 4967 (1) on page 11, DOI: 10.11646/zootaxa.4967.1.1, http://zenodo.org/record/474551