Aid quality and donor rankings

This paper offers new measures of aid quality covering 38 bilateral and multilateral donors, as well as new insights about the robustness and usefulness of such measures. The 2005 Paris Declaration on Aid Effectiveness and the follow-up 2008 Accra Agenda for Action have focused attention on common donor practices that reduce the development impact of aid. Using 18 underlying indicators that capture these practices -- derived from the OECD-DAC's Survey for Monitoring the Paris Declaration, the new AidData database, and the DAC aid tables -- the authors construct an overall aid quality index and four coherently defined sub-indexes on aid selectivity, alignment, harmonization, and specialization. Compared with earlier indicators used in donor rankings, this indicator set is more comprehensive and representative of the range of donor practices addressed in the Paris Declaration, improving the validity, reliability, and robustness of rankings. One of the innovations is to increase the validity of the aid quality indicators by adjusting for recipient characteristics, donor aid volumes, and other factors. Despite these improvements in data and methodology, the authors caution against overinterpretation on overall indexes such as these. Alternative plausible assumptions regarding weights or the inclusion of additional indicators can still produce marked shifts in the ranking of some donors, so that small differences in overall rankings are not meaningful. Moreover, because the performance of some donors varies considerably across the four sub-indexes, these sub-indexes may be more useful than the overall index in identifying donors’ relative strengths and weaknesses.Gender and Health,Development Economics&Aid Effectiveness,Economic Adjustment and Lending,Disability,Poverty Monitoring&Analysis

Don't bleach chaotic data

A common first step in time series signal analysis involves digitally filtering the data to remove linear correlations. The residual data is spectrally white (it is ``bleached''), but in principle retains the nonlinear structure of the original time series. It is well known that simple linear autocorrelation can give rise to spurious results in algorithms for estimating nonlinear invariants, such as fractal dimension and Lyapunov exponents. In theory, bleached data avoids these pitfalls. But in practice, bleaching obscures the underlying deterministic structure of a low-dimensional chaotic process. This appears to be a property of the chaos itself, since nonchaotic data are not similarly affected. The adverse effects of bleaching are demonstrated in a series of numerical experiments on known chaotic data. Some theoretical aspects are also discussed.Comment: 12 dense pages (82K) of ordinary LaTeX; uses macro psfig.tex for inclusion of figures in text; figures are uufile'd into a single file of size 306K; the final dvips'd postscript file is about 1.3mb Replaced 9/30/93 to incorporate final changes in the proofs and to make the LaTeX more portable; the paper will appear in CHAOS 4 (Dec, 1993

in silico Surveillance: evaluating outbreak detection with simulation models

Background Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols. Methods A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years ofin silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection. Results Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection. Conclusions Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection

Scaling laws for the movement of people between locations in a large city

Large scale simulations of the movements of people in a ‘‘virtual’’ city and their analyses are used to generate insights into understanding the dynamic processes that depend on the interactions between people. Models, based on these interactions, can be used in optimizing traffic flow, slowing the spread of infectious diseases, or predicting the change in cell phone usage in a disaster. We analyzed cumulative and aggregated data generated from the simulated movements of 1.63106 individuals in a computer ~pseudo-agent-based! model during a typical day in Portland, Oregon. This city is mapped into a graph with 181 206 nodes representing physical locations such as buildings. Connecting edges model individual’s flow between nodes. Edge weights are constructed from the daily traffic of individuals moving between locations. The number of edges leaving a node ~out-degree!, the edge weights ~out-traffic!, and the edge weights per location ~total out-traffic! are fitted well by power-law distributions. The power-law distributions also fit subgraphs based on work, school, and social/recreational activities. The resulting weighted graph is a ‘‘small world’’ and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes. We observe a strong linear correlation between the out-degree and total out-traffic distributions and significant levels of clustering. We discuss how these network features can be used to characterize social networks and their relationship to dynamic processes