Surveillance and Control of Malaria Transmission Using Remotely Sensed Meteorological and Environmental Parameters

Meteorological and environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters can most conveniently be obtained using remote sensing. Selected provinces and districts in Thailand and Indonesia are used to illustrate how remotely sensed meteorological and environmental parameters may enhance the capabilities for malaria surveillance and control. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records

Toward Malaria Risk Prediction in Afghanistan Using Remote Sensing

Malaria causes more than one million deaths every year worldwide, with most of the mortality in Sub-Saharan Africa. It is also a significant public health concern in Afghanistan, with approximately 60% of the population, or nearly 14 million people, living in a malaria-endemic area. Malaria transmission has been shown to be dependent on a number of environmental and meteorological variables. For countries in the tropics and the subtropics, rainfall is normally the most important variable, except for regions with high altitude where temperature may also be important. Afghanistan s diverse landscape contributes to the heterogeneous malaria distribution. Understanding the environmental effects on malaria transmission is essential to the effective control of malaria in Afghanistan. Provincial malaria data gathered by Health Posts in 23 provinces during 2004-2007 are used in this study. Remotely sensed geophysical parameters, including precipitation from TRMM, and surface temperature and vegetation index from MODIS are used to derive the empirical relationship between malaria cases and these geophysical parameters. Both neural network methods and regression analyses are used to examine the environmental dependency of malaria transmission. And the trained models are used for predicting future transmission. While neural network methods are intrinsically more adaptive for nonlinear relationship, the regression approach lends itself in providing statistical significance measures. Our results indicate that NDVI is the strongest predictor. This reflects the role of irrigation, instead of precipitation, in Afghanistan for agricultural production. The second strongest prediction is surface temperature. Precipitation is not shown as a significant predictor, contrary to other malarious countries in the tropics or subtropics. With the regression approach, the malaria time series are modelled well, with average R2 of 0.845. For cumulative 6-month prediction of malaria cases, the average provincial accuracy reaches 91%. The developed predictive and early warning capabilities support the Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan

First observation of 54Zn and its decay by two-proton emission

The nucleus 54Zn has been observed for the first time in an experiment at the SISSI/LISE3 facility of GANIL in the quasi-fragmentation of a 58Ni beam at 74.5 MeV/nucleon in a natNi target. The fragments were analysed by means of the ALPHA-LISE3 separator and implanted in a silicon-strip detector where correlations in space and time between implantation and subsequent decay events allowed us to generate almost background free decay spectra for about 25 different nuclei at the same time. Eight 54Zn implantation events were observed. From the correlated decay events, the half-life of 54Zn is determined to be 3.2 +1.8/-0.8 ms. Seven of the eight implantations are followed by two-proton emission with a decay energy of 1.48(2) MeV. The decay energy and the partial half-life are compared to model predictions and allow for a test of these two-proton decay models.Comment: 4 pages, 4 figures, accepted for publication in PR

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050

© 2016 The Author(s). Background: Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Methods: Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Results: Average malaria incidence was 0.107 per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R2 = 0.825) and 17.102 % for test data (R2 = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. Conclusions: The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas

Chemical composition and antifungal activity of essential oil from Eucalyptus smithii against dermatophytes

ABSTRACT INTRODUCTION: In this study, we evaluated the chemical composition of a commercial sample of essential oil from Eucalyptus smithii R.T. Baker and its antifungal activity against Microsporum canis ATCC 32903, Microsporum gypseum ATCC 14683, Trichophyton mentagrophytes ATCC 9533, T. mentagrophytes ATCC 11480, T. mentagrophytes ATCC 11481, and Trichophyton rubrum CCT 5507. METHODS: Morphological changes in these fungi after treatment with the oil were determined by scanning electron microscopy (SEM). The antifungal activity of the oil was determined on the basis of minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) values. RESULTS: The compound 1,8-cineole was found to be the predominant component (72.2%) of the essential oil. The MIC values of the oil ranged from 62.5μg·mL−1 to >1,000μg·mL−1, and the MFC values of the oil ranged from 125μg·mL−1 to >1,000μg·mL−1. SEM analysis showed physical damage and morphological alterations in the fungi exposed to this oil. CONCLUSIONS: We demonstrated the potential of Eucalyptus smithii essential oil as a natural therapeutic agent for the treatment of dermatophytosis

Assessing Malaria Risks in Greater Mekong Subregion based on Environmental Parameters

The Greater Mekong Subregion, which consists of Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China, is the world's epicenter of falciparum malaria. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. We have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls using remote sensing data and technology. The data which we have used in this study include AVHRR Pathfinder, MODIS, TRMM, Ikonos, and SIESIP. The objectives are: 1) identification of potential larval habitats; 2) identification of the key factors that promote malaria transmission; 3) estimation of malaria transmission intensity based on environmental parameters. Preliminary results associated with these objectives are discussed