Considerable interest exists in confirming that meteorological variables may play determinant roles in dengue vector abundance. The principle vector for dengue is 'Aedes aegypti'. Dengue Fever has been considered the most important vector-borne viral disease in Jeddah, Saudi Arabia, and is susceptible to climate variability. The aim of this study is to describe the association between adult female 'Aedes aegypti' mosquitoes and meteorological variables and to develop models for predicting the mosquito abundance using Pearson's correlation and regression analyses. Our results show that mosquitoes have the highest correlation with temperature at lag 0 time and relative humidity at lag 5 weeks. The highest two correlations were found between the mosquitoes and minimum temperature (r=-0.57) and maximum relative humidity (r=0.46). Two models were created based on the regression analysis results. The first model shows that 86% of mosquito values were within the upper and lower limits of agreement. The second model shows that 94% of the values were within the limits of agreement. The study findings could contribute to the forecasting of mosquito abundance peaks and subsequently guide a plan for mosquito control operations ahead of time that would help to minimize the outbreak of dengue occurrence and prevent the spread of dengue infections.

Increasing anthropogenic impacts on biodiversity has been a cause for concern in Australia in recent years. Areas that hold high levels of endemic species and also face exceptional threats of destruction have been described as biodiversity hotspots. Ecological research focused on biodiversity hotspots will provide a better understanding of the flora and fauna of these regions and thus inform conservation strategies. Consequently, it is important to understand where biodiversity hotspots are located and how well they have been researched in the past. However, the choice of ecological research sites may be influenced by a variety of factors such as proximity to research institutions. This study utilized a geographic information system to investigate the spatial distribution of ecological research field sites in Australia and its territorial waters, the hotspots of the field sites around research institutions and the proximity of ecological research field sites from the main campus of the research institutions. Furthermore, these hotspots of ecological research were linked to biodiversity hotspots to identify the regions that were commonly depicted in the ecological literature and to identify others that may need more attention. We demonstrated that hotspots of ecological research were concentrated around research institutions, with a large number of field sites being located between 0 km and 500 km from the nearest institution, especially along the eastern coast. This study highlighted areas that have been the focus of much ecological research as well as areas that need more attention from ecologists to add new knowledge to Australian ecological science.

We used the Model for Interdisciplinary Research on Climate-H climate model with the A2 Special Report on Emissions Scenarios for the years 2050 and 2100 and CLIMEX software for projections to illustrate the potential impact of climate change on the spatial distributions of malaria in China, India, Indochina, Indonesia, and The Philippines based on climate variables such as temperature, moisture, heat, cold and dryness. The model was calibrated using data from several knowledge domains, including geographical distribution records. The areas in which malaria has currently been detected are consistent with those showing high values of the ecoclimatic index in the CLIMEX model. The match between prediction and reality was found to be high. More than 90% of the observed malaria distribution points were associated with the currently known suitable climate conditions. Climate suitability for malaria is projected to decrease in India, southern Myanmar, southern Thailand, eastern Borneo, and the region bordering Cambodia, Malaysia and the Indonesian islands, while it is expected to increase in southern and south-eastern China and Taiwan. The climatic models for Anopheles mosquitoes presented here should be useful for malaria control, monitoring, and management, particularly considering these future climate scenarios.

House-to-house surveys of larval population of 'Aedes aegypti' were conducted to determine the importance of house index for each habitat in Jeddah governorate. In this study, we aimed to survey and monitor mosquito population and potential breeding sites by using House index (HI), Container index (CI), and Breteau index (BI). The statistical analysis showed that the presence of larval stages of 'Ae. aegypti' reported throughout the year inside houses in the studied locations (Ghuleel, Al-Balad, Al-Jameiah, Al-Nazlah Al-Yamaneyyah, and Al-Safa) with some significant differences among investigated areas showed that Ghuleel had highest and Al-Safa lowest in density of larvae, respectively. House indices of each study area compared with the highest ratio of standard WHO (5-10%) were as follows: 8.7, 7.6, 6.6, 6.22 and 4%, respectively, for the above sites (P<0.05). The results showed that there were significant differences among types of containers of water in the inspected houses. Large containers were most significant compared with medium and small containers. Container index (CI) was 12% (Ghuleel), 13% (Al-Balad) and 14% (Al-Jameiah), 12% (Al-Nazlah Al- Yamaneyyah) and 9% (Al-Safa), whereas Breteau index (BI) was 8, 6.6, 4.7, 4.5 and 1.43%, respectively. Significant increase in the density of larvae was found in November, March, June and January due to the effect of the environmental factors including temperature and humidity.

This book of modelling interactions between vector-borne diseases and the environment using geographic information system (GIS) methods fills many literature gaps. The book shows how GIS-based approaches provide innovative geographical methods with the capability of mapping and modelling such interactions with high accuracy. It shows how GISs can be used to merge satellite images with ground observations of vector demographics and disease incidence more accurately. It comes with the hope of increasing the ability of controlling the global prevalence of vector-borne diseases, such as dengue fever, malaria, and Rift Valley fever, which have increased dramatically in recent times, causing medical, environmental, and economic issues for most of the tropical and subtropical countries. Modelling interaction between vector-borne diseases and the environment using GISs increases understanding of the distribution of vector-borne disease incidence and vectors such as mosquitoes in time and space, which can be a major foundation for control and management programs for vector-borne diseases. The geographical methods used in this book show how knowledge of when and where disease cases and vectors occur enables the formulation of disease causation hypotheses for vectors and cases with unknown or poorly characterized aetiology, identification of areas at risk for disease, and design of efficient surveillance and control programs. These methods for modelling risks of diseases and vectors can also be implemented at local, country, and regional levels by vector-borne disease program managers, health officers and workers, and policy makers to ensure their optimal contribution to prevention, control, acceptance, and sustainability of programs. In addition, the book shows a variety of GIS implications in the planning of health interventions that can be used to enhance disease surveillance systems. It is useful for undergraduate and postgraduate students and postdoctoral researchers involved in epidemiological studies, particularly of vector-borne diseases, especially when they require the use of geographical modelling techniques in a GIS environment. The geographical modelling and analytical techniques described in this book are also valuable for researchers, workers, and students dealing with geographical data in the areas of entomology, environmental health, ecology, environmental science, public health, crime, geography, parasitological, and statistics. There is no doubt that GIS-based approaches will play a more significant role in such applications.

We examined the potential added risk posed by global climate change on the dengue vector Aedes aegypti abundance using CLIMEX, a powerful tool for exploring the relationship between the fundamental and realised niche of any species. After calibrating the model using data from several knowledge domains, including geographical distribution records, we estimated potential distributions of the mosquito under current and future potential scenarios. The impact of climate change on its potential distribution was assessed with two global climate models, the CSIRO-Mk3.0 and the MIROC-H, run with two potential, future emission scenarios (A1B and A2) published by the Intergovernmental Panel on Climate Change. We compared today's climate situation with two arbitrarily chosen future time points (2030 and 2070) to see the impact on the worldwide distribution of 'A. aegypti'. The model for the current global climate indicated favourable areas for the mosquito within its known distribution in tropical and subtropical areas. However, even if much of the tropics and subtropics will continue to be suitable, the climatically favourable areas for 'A. aegypti' globally are projected to contract under the future scenarios produced by these models, while currently unfavourable areas, such as inland Australia, the Arabian Peninsula, southern Iran and some parts of North America may become climatically favourable for this mosquito species. The climate models for the Aedes dengue vector presented here should be useful for management purposes as they can be adapted for decision/making regarding allocation of resources for dengue risk toward areas where risk infection remains and away from areas where climatic suitability is likely to decrease in the future.