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.

Factorization in the Cloud: Integer Factorization Using F# and Windows Azure

Implementations are presented of two common algorithms for integer factorization, Pollard’s “p – 1” method and the SQUFOF method. The algorithms are implemented in the F# language, a functional programming language developed by Microsoft and officially released for the first time in 2010. The algorithms are thoroughly tested on a set of large integers (up to 64 bits in size), running both on a physical machine and a Windows Azure machine instance. Analysis of the relative performance between the two environments indicates comparable performance when taking into account the difference in computing power. Further analysis reveals that the relative performance of the Azure implementation tends to improve as the magnitudes of the integers increase, indicating that such an approach may be suitable for larger, more complex factorization tasks. Finally, several questions are presented for future research, including the performance of F# and related languages for more efficient, parallelizable algorithms, and the relative cost and performance of factorization algorithms in various environments, including physical hardware and commercial cloud computing offerings from the various vendors in the industry.