BackgroundMany studies have found associations between climatic conditions and dengue transmission. However, there is a debate about the future impacts of climate change on dengue transmission. This paper reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative methods for assessing the potential impacts of climate change on global dengue transmission.MethodsA literature search was conducted in October 2012, using the electronic databases PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in English from January 1991 through October 2012.ResultsSixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the studies. Several key methodological issues and current knowledge gaps were identified through this review.ConclusionsIt is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with climate change.
This report assesses the impact of the variability in environmental and vector factors on the transmission of Ross River virus (RRV) in Brisbane, Australia. Poisson time series regression analyses were conducted using monthly data on the counts of RRV cases, climate variables (Southern Oscillation Index and rainfall), high tides and mosquito density for the period of 1998-2001. The results indicate that increases in the high tide (relative risk (RR): 1.65; 95% confidence interval (CI): 1.20-2.26), rainfall (RR: 1.45; 95% CI: 1.21-1.73), mosquito density (RR: 1.17; 95% CI: 1.09-1.27), the density of Culex annulirostris (RR: 1.25; 95% CI: 1.13-1.37) and the density of Ochlerotatus vigilax (RR: 2.39; 95% CI: 2.30-2.48), each at a lag of 1 month, were statistically significantly associated with the rise of monthly RRV incidence. The results of the present study might facilitate the development of early warning systems for reducing the incidence of this wide-spread disease in Australia and other Pacific island nations.
Background Malaria is an increasing concern in Indonesia. Socio-demographic factors were found to strongly influence malaria prevalence. This research aimed to explore the associations between socio-demographic factors and malaria prevalence in Indonesia. Methods The study used a cross-sectional design and analysed relationships among the explanatory variables of malaria prevalence in five endemic provinces using multivariable logistic regression. Results The analysis of baseline socio-demographic data revealed the following independent risk variables related to malaria prevalence: gender, age, occupation, knowledge of the availability of healthcare services, measures taken to protect from mosquito bites, and housing condition of study participants. Multivariable analysis showed that participants who were unaware of the availability of health facilities were 4.2 times more likely to have malaria than those who were aware of the health facilities (adjusted odds ratio = 4.18; 95% CI 1.52–11.45; P = 0.005). Conclusions Factors that can be managed and would favour malaria elimination include a range of prevention behaviours at the individual level and using the networks at the community level of primary healthcare centres. This study suggests that improving the availability of a variety of health facilities in endemic areas, information about their services, and access to these is essential. Electronic supplementary material The online version of this article (10.1186/s12936-019-2760-8) contains supplementary material, which is available to authorized users.
Dengue dynamics are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors. Several studies examined the role of El Niño Southern Oscillation (ENSO) in dengue incidence. However, the role of Indian Ocean Dipole (IOD), a coupled ocean atmosphere phenomenon in the Indian Ocean, which controls the summer monsoon rainfall in the Indian region, remains unexplored. Here, we examined the effects of ENSO and IOD on dengue incidence in Bangladesh. According to the wavelet coherence analysis, there was a very weak association between ENSO, IOD and dengue incidence, but a highly significant coherence between dengue incidence and local climate variables (temperature and rainfall). However, a distributed lag nonlinear model (DLNM) revealed that the association between dengue incidence and ENSO or IOD were comparatively stronger after adjustment for local climate variables, seasonality and trend. The estimated effects were nonlinear for both ENSO and IOD with higher relative risks at higher ENSO and IOD. The weak association between ENSO, IOD and dengue incidence might be driven by the stronger effects of local climate variables such as temperature and rainfall. Further research is required to disentangle these effects.
This study aimed to identify the major mosquito vectors of Ross River virus (family Togaviridae, genus Alphavirus, RRV) and to explore the threshold of mosquito abundance necessary for RRV transmission in Brisbane, Australia. Data on the monthly counts of RRV cases by statistical local areas from the Queensland Health and the monthly mosquito abundance in Brisbane between November 1998 and December 2001 from the Brisbane City Council were used to assess the pairwise relationship between mosquito abundance and the incidence of RRV disease over a range of time lags using cross-correlations. We used time series Poisson regression models to identify major mosquito species associated with incidence of RRV after adjusting for overdispersion, maximum temperature, autocorrelation, and seasonality. Our results show that Aedes vigilax (Skuse) (relative risk [RR] = 1.32; 95% CI = 1.01-1.74 per 100 mosquitoes per trap) and Culex annulirostris (Skuse) (RR = 1.14, 95% CI = 1.04-1.24 per 100 mosquitoes per trap) were most strongly associated with RRV transmission at a lag of 1 mo. Classification and regression tree (CART) analyses indicate that the occurrence of RRV was associated with an average monthly mosquito abundance ofAedes vigilax above 72 and Cx. annulirostris above 52. The validation analyses indicate that the crude agreement between predicted values and actual observations was 76% (sensitivity, 61%; specificity, 80%). The results may have applications as a decision support tool in planning disease control and risk-management programs.
14 Roads are important basic urban geography phenomena, and the automatic recognition and 15 accurate extraction of such features from remote sensing images is useful for many applications. 16 However, automated road extraction from high-resolution remote sensing imagery is very difficult. 17 In recent years, many approaches have been explored for automatic road extraction and detecting 18 road edges is an important aspect of this. The traditional edge detection operators (e.g., Canny 19 operator, Sobel operator, etc.) are often used, but problems of over-or under-detection are serious, 20 and heavy and complicated post-processing work is often needed. In this paper, a new Revised 21 Parallel-beam Radon Transform (RPRT) approach is proposed. The traditional Parallel-beam 22 Radon Transform can have a problem with step values, which may result in false edge detection. 23 In order to overcome this problemዊRPRT is introduced, using the harmonic average of the pixel 24 value in every strip of the Radon slice. An algorithm suitable for straight edge detection of road in 25 high-resolution remote sensing imagery is designed based on ridgelet transform with RPRT. The 26 experimental results show that our algorithm can detect straight road edges efficiently and 27 accurately, and avoid cumbersome and complicated post-processing work.
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