The Retail Market for Bednets in Kenya: How Well Is It Working?

Background Achieving the goals set by Roll Back Malaria and the Government of Kenya for use of insecticide treated bednets (ITNs) will require that the private retail market for nets and insecticide treatments grow substantially. This paper applies some basic concepts of market structure and pricing to a set of recently-collected retail price data from Kenya in order to answer the question, “How well are Kenyan retail markets for ITNs working?” Methods Data on the availability and prices of ITNs at a wide range of retail outlets throughout Kenya were collected in January 2002, and vendors and manufacturers were interviewed regarding market structure. Findings Untreated nets are manufactured in Kenya by a number of companies and are widely available in large and medium-sized towns. Availability in smaller villages is limited. There is relatively little geographic price variation, and nets can be found at competitive prices in towns and cities. Marketing margins on prices appear to be within normal ranges. No finished nets are imported. Few pre-treated nets or net+treatment combinations are available, with the exception of the subsidized Supanet/Power Tab combination marketed by a donor-funded social marketing project. Conclusions Retail markets for untreated nets in Kenya are well established and appear to be competitive. Markets for treated nets and insecticide treatment kits are not well established. The role of subsidized ITN marketing projects should be monitored to ensure that these projects support, rather than hinder, the development of retail markets.

Nearest-neighbor Queries in Probabilistic Graphs

Large probabilistic graphs arise in various domains spanning from social networks to biological and communication networks. An important query in these graphs is the k nearest-neighbor query, which involves finding and reporting the k closest nodes to a specific node. This query assumes the existence of a measure of the "proximity" or the "distance" between any two nodes in the graph. To that end, we propose various novel distance functions that extend well known notions of classical graph theory, such as shortest paths and random walks. We argue that many meaningful distance functions are computationally intractable to compute exactly. Thus, in order to process nearest-neighbor queries, we resort to Monte Carlo sampling and exploit novel graph-transformation ideas and pruning opportunities. In our extensive experimental analysis, we explore the trade-offs of our approximation algorithms and demonstrate that they scale well on real-world probabilistic graphs with tens of millions of edges.