Case report:Evidence of rise in rabies cases in southern Malawi--better preventative measures are urgently required

We describe five children who died of clinical rabies in a three month period (September to November 2011) in the Queen Elizabeth Central Hospital. From previous experience and hospital records, this number of cases is higher than expected. We are concerned that difficulty in accessing post-exposure prophylaxis (PEP) rabies vaccine may be partly responsible for this rise. We advocate: (a) prompt course of active immunisation for all patients with significant exposure to proven or suspected rabid animals. (b) the use of an intradermal immunisation regime that requires a smaller quantity of the vaccine than the intramuscular regime and gives a better antibody response. (c) improved dog rabies control measures.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.