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

Division of Entomology, Walter Reed Army Institute of Research, 503 Robert Grant Avenue, Silver Spring, MD 20910, USA.
International Journal of Health Geographics (Impact Factor: 2.62). 02/2010; 9(1):11. DOI: 10.1186/1476-072X-9-11
Source: PubMed


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.
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.
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.

Download full-text


Available from: Leopoldo M Rueda
  • Source
    • "To compensate for this under-representation, specific databases for epidemiological records or vector groups have also been developed, most notably in the VectorMap project (, encompassing the MosquitoMap, SandflyMap and TickMap projects (Foley et al., 2010, 2012). Alongside the vector occurrence data compiled from wide ranging survey reports and literature, VectorMap provides some prediction maps of vectors, pathogen and ecto-parasite data from vertebrate hosts and expert opinion maps of disease occurrence (Foley et al., 2012), fostering broadscale understanding of the ecological context of transmission. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Species distribution modelling is widely used in epidemiology for mapping spatial patterns and the risk of introduction of diseases and vectors and also for predicting how exposure may alter given future environmental change, motivated by the high societal impact and the multiple environmental drivers of disease outbreaks. Although pathogens and vectors have historically been sparsely recorded, monitoring systems and media sources are generating novel, online data sources on occurrence. Moreover, increasing ecological realism is being incorporated into distribution modelling techniques, focussing on dispersal, biotic interactions and evolutionary constraints that shape species distributions alongside abiotic factors and biases in recording effort, common to pathogens and vectors and wildlife species. Considering pathogens and arthropod vector systems with high impact on plant, animal and human health, the present review describes how biological records for vectors and pathogens arise, introduces the concepts behind distribution models and illustrates the potential for ecologically realistic distribution models to yield insight into the establishment and spread of pathogens. Because distribution modellers aim to provide policy makers with evidence and maps for planning and evaluation of disease mitigation measures, we highlight factors that currently constrain direct translation of models to policy. Disease distributions will be better understood and mapped in the future given improved occurrence data access and integration and combined (correlative and mechanistic) modelling approaches that are developed iteratively in concert with stakeholders.
    Full-text · Article · Jul 2015 · Biological Journal of the Linnean Society
  • Source
    • "Highfield et al. (2011) designed Community Health Information System (CHIS), an online mapping system using a Google mapping interface to facilitate the dissemination of health-related geospatial data. Foley et al. (2010) introduced MosquitoMap, a web-based spatial database of mosquito collection records and distribution models, which can integrate geographical data from different sources at various scales. Moncrieff et al. (2013) design and implement an open-source server-side web mapping framework for the analysis of health data, relying on Open Geospatial Consortium (OGC) web map service standard. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The rapid propagation of vector-borne diseases, such as dengue fever, poses a threat to vulnerable populations, especially those in tropical regions. Prompt space-time analyses are critical elements for accurate outbreak detection and mitigation purposes. Open access web-based geospatial tools are particularly critical in developing countries lacking GIS software and expertise. Currently, online geospatial tools for the monitoring of surveillance data are confined to the mapping of aggregated data. In this paper, we present a web-based geospatial toolkit with a user-friendly interactive interface for the monitoring of dengue fever outbreaks, in space and time. Our geospatial toolkit is designed around the integration of (1) a spatial data management module in which epidemiologists upload spatio-temporal explicit data, (2) an analytical module running an accelerated Kernel Density Estimation (KDE) to map the outbreaks of dengue fever, (3) a spatial database module to extract pairs of disease events close in space and time and (4) a GIS mapping module to visualize space-time linkages of pairs of disease events. We illustrate our approach on a set of dengue fever cases which occurred in Cali (659 geocoded cases), an urban environment in Colombia. Results indicate that dengue fever cases are significantly clustered, but the degree of intensity varies across the city. The design and implementation of the on-line toolkit underscores the benefits of the approach to monitor vector-borne disease outbreaks in a timely manner and at different scales, facilitating the appropriate allocation of resources. The toolkit is designed collaboratively with health epidemiologists and is portable for other surveillance data at the individual level such as crime or traffic accidents.
    Full-text · Article · Aug 2014 · Applied Geography
  • Source
    • "Resources for infection prevention and control on the World Wide Web were abundant and easily available [15]. Web GIS (Geographic Information System) have always shared many of the foundational data and enable remixing and repurposing those data, many scientists have utilized this technology for infectious disease surveillance and data collection [16]–[18]. Intelligent mobile-phone services and WebGIS have been accessible and inexpensive tool in our lives. "
    [Show abstract] [Hide abstract]
    ABSTRACT: For years, emerging infectious diseases have appeared worldwide and threatened the health of people. The emergence and spread of an infectious-disease outbreak are usually unforeseen, and have the features of suddenness and uncertainty. Timely understanding of basic information in the field, and the collection and analysis of epidemiological information, is helpful in making rapid decisions and responding to an infectious-disease emergency. Therefore, it is necessary to have an unobstructed channel and convenient tool for the collection and analysis of epidemiologic information in the field.
    Full-text · Article · Jan 2013 · PLoS ONE
Show more