Multinomial Logistic Model for Coinfection Diagnosis Between Arbovirus and Malaria in Kedougou

Loum Mor Absa (), Poursat Marie-Anne (), Sow Abdourahmane (), Sall Amadou Alpha (), Loucoubar Cheikh () and Gassiat Elisabeth ()
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Loum Mor Absa: Laboratoire de Mathématiques d'Orsay, Univ. Paris-Sud, CNRS, Université Paris-Saclay, 91405Orsay, France
Poursat Marie-Anne: Laboratoire de Mathématiques d'Orsay, Univ. Paris-Sud, CNRS, Université Paris-Saclay, 91405Orsay, France
Sow Abdourahmane: Arboviruses and Viral Hemorrhagic Fevers Unit, Institut Pasteur de Dakar, 36 Avenue Pasteur, Dakar, Senegal
Sall Amadou Alpha: Arboviruses and Viral Hemorrhagic Fevers Unit, Institut Pasteur de Dakar, 36 Avenue Pasteur, Dakar, Senegal
Loucoubar Cheikh: Biostatistics, Bioinformatics and Modeling Group, Institut Pasteur de Dakar, 36 Avenue Pasteur, Dakar, Senegal
Gassiat Elisabeth: Laboratoire de Mathématiques d'Orsay, Univ. Paris-Sud, CNRS, Université Paris-Saclay, 91405Orsay, France

The International Journal of Biostatistics, 2019, vol. 15, issue 2, 11

Abstract: In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Thus, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, long duration of illness and age are mostly indicative of arboviral disease while high body temperature and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease.

Keywords: arbovirus; coinfection; malaria; multinomial logistic regression; random forest classification; variable selection (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/ijb-2017-0015

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