1,895 research outputs found

    A weather-driven model of malaria transmission

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    BACKGROUND: Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. METHODS: This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. RESULTS: Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. CONCLUSION: A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts

    Environmental change and Rift Valley fever in eastern Africa: projecting beyond HEALTHY FUTURES

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    Outbreaks of Rift Valley fever (RVF), a relatively recently emerged zoonosis endemic to large parts of sub-Saharan Africa that has the potential to spread beyond the continent, have profound health and socio-economic impacts, particularly in communities where resilience is already low. Here output from a new, dynamic disease model [the Liverpool RVF (LRVF) model], driven by downscaled, bias-corrected climate change data from an ensemble of global circulation models from the Inter-Sectoral Impact Model Intercomparison Project run according to two radiative forcing scenarios [representative concentration pathway (RCP)4.5 and RCP8.5], is combined with results of a spatial assessment of social vulnerability to the disease in eastern Africa. The combined approach allowed for analyses of spatial and temporal variations in the risk of RVF to the end of the current century. Results for both scenarios highlight the high-risk of future RVF outbreaks, including in parts of eastern Africa to date unaffected by the disease. The results also highlight the risk of spread from/to countries adjacent to the study area, and possibly farther afield, and the value of considering the geography of future projections of disease risk. Based on the results, there is a clear need to remain vigilant and to invest not only in surveillance and early warning systems, but also in addressing the socio-economic factors that underpin social vulnerability in order to mitigate, effectively, future impacts

    Direct and indirect health impacts of climate change on the vulnerable elderly population in East China

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    The latest scientific advances on the impacts of climate change on the health of the elderly in East China were reviewed consulting peer-reviewed publications from 2000-2017. The direct impacts of climate change result from rising temperatures, heatwaves, and increases in the frequency of complex extreme weather events such as windstorms, floods, and droughts. The health and social consequences of these events are far-reaching, ranging from reduced labour productivity and heat-related deaths, through to direct physical injury during extreme weather events, the spread of infectious diseases, and mental health effects following widespread flooding or prolonged drought. Research has indicated that climate change will have the greatest impact on vulnerable groups of people, including the elderly population. However, there is a dearth of empirical evidence, a lack of focus on vulnerable segments of the population (especially elderly), limited understanding of how health status will change in the future, and lack of acknowledgement of how different regions in China vary in terms of the consequences of climate change. The main risk in East China that climate change may exacerbate is flooding (sea level rise, coastal and riverine, flood risk). However in some regions of East China such as in the provinces of Anhui, Jiangsu, Hebei and Shandong the biggest climate change risk is considered to be drought. Main health risks linked to climate change are evident as cardiovascular and respiratory diseases (heat stroke, exhaustion, and asthma), often caused by interactions between heatwave episodes and concurrent poor air quality

    (Dodecafluorosubphthalocyaninato)(4-methylphenolato)boron(III)

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    In the title compound, C31H7BF12N6O, mol­ecules are arranged into one-dimensional columns with an inter­molecular B⋯B distance of 5.3176 (8) Å. Bowl-shaped mol­ecules are arranged within the columns in a concave bowl-to-ligand arrangement separated by a ring centroid distance of 3.532 (2) Å between the benzene ring of the 4-methyl­phen­oxy ligand and one of the three five-membered rings of a symmetry-related mol­ecule

    A Quantitative Prioritisation of Human and Domestic Animal Pathogens in Europe

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    Disease or pathogen risk prioritisations aid understanding of infectious agent impact within surveillance or mitigation and biosecurity work, but take significant development. Previous work has shown the H-(Hirsch-)index as an alternative proxy. We present a weighted risk analysis describing infectious pathogen impact for human health (human pathogens) and well-being (domestic animal pathogens) using an objective, evidence-based, repeatable approach; the H-index. This study established the highest H-index European pathogens. Commonalities amongst pathogens not included in previous surveillance or risk analyses were examined. Differences between host types (humans/animals/zoonotic) in pathogen H-indices were explored as a One Health impact indicator. Finally, the acceptability of the H-index proxy for animal pathogen impact was examined by comparison with other measures. 57 pathogens appeared solely in the top 100 highest H-indices (1) human or (2) animal pathogens list, and 43 occurred in both. Of human pathogens, 66 were zoonotic and 67 were emerging, compared to 67 and 57 for animals. There were statistically significant differences between H-indices for host types (humans, animal, zoonotic), and there was limited evidence that H-indices are a reasonable proxy for animal pathogen impact. This work addresses measures outlined by the European Commission to strengthen climate change resilience and biosecurity for infectious diseases. The results include a quantitative evaluation of infectious pathogen impact, and suggest greater impacts of human-only compared to zoonotic pathogens or scientific under-representation of zoonoses. The outputs separate high and low impact pathogens, and should be combined with other risk assessment methods relying on expert opinion or qualitative data for priority setting, or could be used to prioritise diseases for which formal risk assessments are not possible because of data gaps

    Climate prediction of El Niño malaria epidemics in north-west Tanzania

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    Malaria is a significant public health problem in Tanzania. Approximately 16 million malaria cases are reported every year and 100,000 to 125,000 deaths occur. Although most of Tanzania is endemic to malaria, epidemics occur in the highlands, notably in Kagera, a region that was subject to widespread malaria epidemics in 1997 and 1998. This study examined the relationship between climate and malaria incidence in Kagera with the aim of determining whether seasonal forecasts may assist in predicting malaria epidemics. A regression analysis was performed on retrospective malaria and climatic data during each of the two annual malaria seasons to determine the climatic factors influencing malaria incidence. The ability of the DEMETER seasonal forecasting system in predicting the climatic anomalies associated with malaria epidemics was then assessed for each malaria season. It was found that malaria incidence is positively correlated with rainfall during the first season (Oct-Mar) (R-squared = 0.73, p < 0.01). For the second season (Apr-Sep), high malaria incidence was associated with increased rainfall, but also with high maximum temperature during the first rainy season (multiple R-squared = 0.79, p < 0.01). The robustness of these statistical models was tested by excluding the two epidemic years from the regression analysis. DEMETER would have been unable to predict the heavy El Niño rains associated with the 1998 epidemic. Nevertheless, this epidemic could still have been predicted using the temperature forecasts alone. The 1997 epidemic could have been predicted from observed temperatures in the preceding season, but the consideration of the rainfall forecasts would have improved the temperature-only forecasts over the remaining years. These results demonstrate the potential of a seasonal forecasting system in the development of a malaria early warning system in Kagera region

    Suitability of European climate for the Asian tiger mosquito Aedes albopictus: recent trends and future scenarios

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    The Asian tiger mosquito (Aedes albopictus) is an invasive species that has the potential to transmit infectious diseases such as dengue and chikungunya fever. Using high-resolution observations and regional climate model scenarios for the future, we investigated the suitability of Europe for A. albopictus using both recent climate and future climate conditions. The results show that southern France, northern Italy, the northern coast of Spain, the eastern coast of the Adriatic Sea and western Turkey were climatically suitable areas for the establishment of the mosquito during the 1960–1980s. Over the last two decades, climate conditions have become more suitable for the mosquito over central northwestern Europe (Benelux, western Germany) and the Balkans, while they have become less suitable over southern Spain. Similar trends are likely in the future, with an increased risk simulated over northern Europe and slightly decreased risk over southern Europe. These distribution shifts are related to wetter and warmer conditions favouring the overwintering of A. albopictus in the north, and drier and warmer summers that might limit its southward expansion

    Modelling hotspots of the two dominant Rift Valley fever vectors (Aedes vexans and Culex poicilipes) in Barkedji, Senegal

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    BACKGROUND: Climatic and environmental variables were used successfully by using models to predict Rift Valley fever (RVF) virus outbreaks in East Africa. However, these models are not replicable in the West African context due to a likely difference of the dynamic of the virus emergence. For these reasons specific models mainly oriented to the risk mapping have been developed. Hence, the areas of high vector pressure or virus activity are commonly predicted. However, the factors impacting their occurrence are poorly investigated and still unknown. In this study, we examine the impact of climate and environmental factors on the likelihood of occurrence of the two main vectors of RVF in West Africa (Aedes vexans and Culex poicilipes) hotspots. METHODS: We used generalized linear mixed models taking into account spatial autocorrelation, in order to overcome the default threshold for areas with high mosquito abundance identified by these models. Getis’ Gi*(d) index was used to define local adult mosquito abundance clusters (hotspot). RESULTS: For Culex poicilipes, a decrease of the minimum temperature promotes the occurrence of hotspots, whereas, for Aedes vexans, the likelihood of hotspot occurrence is negatively correlated with relative humidity, maximum and minimum temperatures. However, for the two vectors, proximity to ponds would increase the risk of being in an hotspot area. CONCLUSIONS: These results may be useful in the improvement of RVF monitoring and vector control management in the Barkedji area. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13071-016-1399-3) contains supplementary material, which is available to authorized users
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