Saharan sand and dust storms and neonatal mortality: Evidence from Burkina Faso

West African populations are exposed to the longest and harshest dust storms on the planet, the Saharan sand and dust storms (SDS). Nonetheless, little is known about the effects of the severe storms on early-life health in West Africa. This study investigated the association of the risk of neonatal mortality, an indicator of the population's early-life health, with potential prenatal and neonatal exposure to the Saharan SDS. Data on 30,552 under-five children from Burkina Faso's 1993, 2003, and 2010 demographic and health surveys were matched to the particulate matters (PM) and terrestrial air temperature and precipitation forecasts. Exposure to dust events was measured by the number of days with average PM10 and PM2.5 concentrations above a series of threshold. Intensity-dependent patterns of associations between neonatal mortality and both prenatal and birth month exposure to dust events were identified. There was no association if average daily PM10 and PM2.5 levels were <60 and 30 μg/m3, respectively. However, strong associations, which increase almost linearly with the intensity of exposure, were identified when daily PM10 and PM2.5 levels ranged from 70 to 150 and from 40 to 70 μg/m3, respectively. At the higher PM levels, the association for the gestation period decreased, but that for the birth month remained mostly unresponsive to changes in the PM levels. Larger associations were identified when siblings were compared. © 2020 Elsevier B.V

Saharan sand and dust storms and neonatal mortality: Evidence from Burkina Faso

This is an accepted manuscript of an article published by Elsevier in Science of the Total Environment on 29/04/2020, available online: https://doi.org/10.1016/j.scitotenv.2020.139053 The accepted version of the publication may differ from the final published version.West African populations are exposed to the longest and harshest dust storms on the planet, the Saharan sand and dust storms (SDS). Nonetheless, little is known about the effects of the severe storms on early-life health in West Africa. This study investigated the association of the risk of neonatal mortality, an indicator of the population's early-life health, with potential prenatal and neonatal exposure to the Saharan SDS. Data on 30,552 under-five children from Burkina Faso's 1993, 2003, and 2010 demographic and health surveys were matched to the particulate matters (PM) and terrestrial air temperature and precipitation forecasts. Exposure to dust events was measured by the number of days with average PM10 and PM2.5 concentrations above a series of threshold. Intensity-dependent patterns of associations between neonatal mortality and both prenatal and birth month exposure to dust events were identified. There was no association if average daily PM10 and PM2.5 levels were <60 and 30 μg/m3, respectively. However, strong associations, which increase almost linearly with the intensity of exposure, were identified when daily PM10 and PM2.5 levels ranged between 70 and 150 and 40–70 μg/m3, respectively. At the higher PM levels, the association for the gestation period decreased, but that for the birth month remained mostly unresponsive to changes in the PM levels. Larger associations were identified when siblings were compared.Published versio

Prediction optimization of diffusion paths in social networks using integration of ant colony and densest subgraph algorithms

© 2020 - IOS Press and the authors. All rights reserved. One of the most important challenges of social networks is to predict information diffusion paths. Studying and modeling the propagation routes is important in optimizing social network-based platforms. In this paper, a new method is proposed to increase the prediction accuracy of diffusion paths using the integration of the ant colony and densest subgraph algorithms. The proposed method consists of 3 steps; clustering nodes, creating propagation paths based on ant colony algorithm and predicting information diffusion on the created paths. The densest subgraph algorithm creates a subset of maximum independent nodes as clusters from the input graph. It also determines the centers of clusters. When clusters are identified, the final information diffusion paths are predicted using the ant colony algorithm in the network. After the implementation of the proposed method, 4 real social network datasets were used to evaluate the performance. The evaluation results of all methods showed a better outcome for our method