en fr Statistical properties of parasite density estimators in malaria and field applications Propriétés statistiques des estimateurs de la densité parasitaire dans les études portant sur le paludisme et applications opératiReportar como inadecuado




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1 MAP5 - MAP5 - Mathématiques Appliquées à Paris 5

Abstract : Malaria is a devastating global health problem that affected 219 million people and caused 660,000 deaths in 2010. Inaccurate estimation of the level of infection may have adverse clinical and therapeutic implications for patients, and for epidemiological endpoint measurements. The level of infection, expressed as the parasite density PD, is classically defined as the number of asexual parasites relative to a microliter of blood. Microscopy of Giemsa-stained thick blood smears TBSs is the gold standard for parasite enumeration. Parasites are counted in a predetermined number of high-power fields HPFs or against a fixed number of leukocytes. PD estimation methods usually involve threshold values; either the number of leukocytes counted or the number of HPFs read. Most of these methods assume that 1 the distribution of the thickness of the TBS, and hence the distribution of parasites and leukocytes within the TBS, is homogeneous; and that 2 parasites and leukocytes are evenly distributed in TBSs, and thus can be modeled through a Poisson-distribution. The violation of these assumptions commonly results in overdispersion. Firstly, we studied the statistical properties mean error, coefficient of variation, false negative rates of PD estimators of commonly used threshold-based counting techniques and assessed the influence of the thresholds on the cost-effectiveness of these methods. Secondly, we constituted and published the first dataset on parasite and leukocyte counts per HPF. Two sources of overdispersion in data were investigated: latent heterogeneity and spatial dependence. We accounted for unobserved heterogeneity in data by considering more flexible models that allow for overdispersion. Of particular interest were the negative binomial model NB and mixture models. The dependent structure in data was modeled with hidden Markov models HMMs. We found evidence that assumptions 1 and 2 are inconsistent with parasite and leukocyte distributions. The NB-HMM is the closest model to the unknown distribution that generates the data. Finally, we devised a reduced reading procedure of the PD that aims to a better operational optimization and a practical assessing of the heterogeneity in the distribution of parasites and leukocytes in TBSs. A patent application process has been launched and a prototype development of the counter is in process.

Résumé : Pas de résumé en français

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Keywords : Patent HMMs Mixture models Negative binomial distribution Heterogeneity Overdispersion Poisson distribution Malaria epidemiology Threshold-based counting techniques Parasite density estimators Mean error Coefficient of variation False-negative rates Cost-effectiveness Parasite and leukocyte counts per high-power field

Mots-clés : Paludisme





Autor: Imen Hammami -

Fuente: https://hal.archives-ouvertes.fr/



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