Imen Hammami (MAP5)

Statistical properties of parasite density estimators in malaria and field applications

jeudi 20 juin 2013, 13h45 - 14h45

Salle de réunion, espace Turing

{{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.

L’exposé sera donné en anglais.