Spatio-temporal distribution of vector borne diseases in Australia and Papua New Guinea vis-à-vis climatic factors

Background & objectives: Weather and climate are directly linked to human health including the distribution and occurrence of vector-borne diseases which are of significant concern for public health. Methods: In this review, studies on spatiotemporal distribution of dengue, Barmah Forest Virus (BFV) and Ross River Virus (RRV) in Australia and malaria in Papua New Guinea (PNG) under the influence of climate change and/ or human society conducted in the past two decades were analysed and summarised. Environmental factors such as temperature, rainfall, relative humidity and tides were the main contributors from climate. Results: The Socio-Economic Indexes for Areas (SEIFA) index (a product from the Australian Bureau of Statistics that ranks areas in Australia according to relative socio-economic advantage and disadvantage) was important in evaluating contribution from human society. Interpretation & conclusion: For future studies, more emphasis on evaluation of impact of the El Niño-Southern Oscillation (ENSO) and human society on spatio-temporal distribution of vector borne diseases is recommended to highlight importance of the environmental factors in spreading mosquito-borne diseases in Australia and PNG.

Some entomological observations on temporal and spatial distribution of malaria vectors in three villages in northwestern Thailand using a geographic information system.

This spatial and temporal heterogeneity in the distribution of Anopheles mosquitos were studied during August 2001 to December 2002 in three villages Ban Khun Huay, Ban Pa Dae, and Ban Tham Seau, in northwestern Thailand in Mae Sot district, Tak Province. The three Karen villages are located about 20 km east of the city of Mae Sot near the Myanmar border. Twenty-one species were collected on human collections during 68 nights of 17 months. Anopheles minimus comprised of 86% of the specimens biting man. An. minimus was implicated as a vector based on the detection of sporozoite infections using enzyme-linked immunosorbent assays for Plasmodium falciparum and P. vivax. Seasonal comparison of vectorial capacity and entomological inoculation rate was calculated. An. dirus was rarely encountered and probably played little part in transmission in these three villages during the period of study. Information is provided on nightly biting activity, parity rate, infectivity, and adult and larval bionomics. Spatial and temporal comparisons among the collections were displayed on different satellite images including the Normalized Difference Vegetation Index data from on the National Oceanographic and Atmospheric Administration satellites (NOAA/NDVI), the LANDSAT satellite Thematic Mapper (spatial resolution 30x30 m) and the IKONOS satellite (spatial resolution 1x1 m) in a Geographical Information System (GIS).

Use of GIS-based spatial modeling approach to characterize the spatial patterns of malaria mosquito vector breeding habitats in northwestern Thailand.

We sampled 291 bodies of water for Anopheles larvae around three malaria-endemic villages of Ban Khun Huay, Ban Pa Dae, and Ban Tham Seau, Mae Sot district, Tak Province, Thailand during August 2001-December 2002 and collected 4,387 larvae from 12 categories of breeding habitat types. We modeled surface slope and wetness indices to identify the extent and spatial pattern of potential mosquito breeding habitats by digitizing base topographical maps of the study site and overlaying them with coordinates for each larval habitat. Topographical contours and streamlines were incorporated into the Geographical Information System (GIS). We used Global Positioning System (GPS) instruments to locate accurately each field observed breeding habitat, and produced a 30-m spatial resolution Digital Elevation Model (DEM). The slope (of less than 12 degrees) and wetness (more than 8 units) derived from spatial modeling were positively associated with the abundance of major malaria vectors An. dirus, An. maculatus, An. minimus, and An. sawadwongporni. These associations permit real-time monitoring and possibly forecasting of the distributions of these four species, enabling public health agencies to institute control measures before the mosquitos emerge as adults and transmit disease.