Showing papers by "Abraham D. Flaxman published in 2010"

Rapid scaling up of insecticide-treated bed net coverage in Africa and its relationship with development assistance for health: a systematic synthesis of supply, distribution, and household survey data.

Abstract: Background Development assistance for health (DAH) targeted at malaria has risen exponentially over the last 10 years, with a large fraction of these resources directed toward the distribution of insecticide-treated bed nets (ITNs). Identifying countries that have been successful in scaling up ITN coverage and understanding the role of DAH is critical for making progress in countries where coverage remains low. Sparse and inconsistent sources of data have prevented robust estimates of the coverage of ITNs over time. Methods and Principal Findings We combined data from manufacturer reports of ITN deliveries to countries, National Malaria Control Program (NMCP) reports of ITNs distributed to health facilities and operational partners, and household survey data using Bayesian inference on a deterministic compartmental model of ITN distribution. For 44 countries in Africa, we calculated (1) ITN ownership coverage, defined as the proportion of households that own at least one ITN, and (2) ITN use in children under 5 coverage, defined as the proportion of children under the age of 5 years who slept under an ITN. Using regression, we examined the relationship between cumulative DAH targeted at malaria between 2000 and 2008 and the change in national-level ITN coverage over the same time period. In 1999, assuming that all ITNs are owned and used in populations at risk of malaria, mean coverage of ITN ownership and use in children under 5 among populations at risk of malaria were 2.2% and 1.5%, respectively, and were uniformly low across all 44 countries. In 2003, coverage of ITN ownership and use in children under 5 was 5.1% (95% uncertainty interval 4.6% to 5.7%) and 3.7% (2.9% to 4.9%); in 2006 it was 17.5% (16.4% to 18.8%) and 12.9% (10.8% to 15.4%); and by 2008 it was 32.8% (31.4% to 34.4%) and 26.6% (22.3% to 30.9%), respectively. In 2008, four countries had ITN ownership coverage of 80% or greater; six countries were between 60% and 80%; nine countries were between 40% and 60%; 12 countries were between 20% and 40%; and 13 countries had coverage below 20%. Excluding four outlier countries, each US$1 per capita in malaria DAH was associated with a significant increase in ITN household coverage and ITN use in children under 5 coverage of 5.3 percentage points (3.7 to 6.9) and 4.6 percentage points (2.5 to 6.7), respectively. Conclusions Rapid increases in ITN coverage have occurred in some of the poorest countries, but coverage remains low in large populations at risk. DAH targeted at malaria can lead to improvements in ITN coverage; inadequate financing may be a reason for lack of progress in some countries. Please see later in the article for the Editors' Summary

Tracking the random surfer: empirically measured teleportation parameters in PageRank

Abstract: PageRank computes the importance of each node in a directed graph under a random surfer model governed by a teleportation parameter. Commonly denoted alpha, this parameter models the probability of following an edge inside the graph or, when the graph comes from a network of web pages and links, clicking a link on a web page. We empirically measure the teleportation parameter based on browser toolbar logs and a click trail analysis. For a particular user or machine, such analysis produces a value of alpha. We find that these values nicely fit a Beta distribution with mean edge-following probability between 0.3 and 0.7, depending on the site. Using these distributions, we compute PageRank scores where PageRank is computed with respect to a distribution as the teleportation parameter, rather than a constant teleportation parameter. These new metrics are evaluated on the graph of pages in Wikipedia.

Machine learning methods for verbal autopsy in developing countries

Abstract: Many resource-poor countries lack the capacity to accurately track vital registration data, such as cause of death, which are crucial inputs to health and development decision making. Verbal autopsy provides a means to ascertain cause of death in the poorest countries through the means of a standard questionnaire, but because doctors are scarce and their time is better spent treating the ill, methods of classifying deaths based on questionnaire input have become increasingly important. In this paper we present preliminary work on the use of machine learning algorithms to classify cause of death in developing countries.