Multinomial logistic model for coinfection diagnosis between arbovirus and malaria in Kedougou
Mor Absa Loum (LM-Orsay), Marie-Anne Poursat (LM-Orsay), Abdourahmane, Sow, Amadou Sall, Cheikh Loucoubar (G4-IPD), Elisabeth Gassiat (LM-Orsay)

TL;DR
This study develops a multinomial logistic model using patient data from Kedougou, Senegal, to improve diagnosis of coinfection between malaria and arboviruses, considering symptom overlap and immunity factors.
Contribution
It introduces a novel multinomial logistic model that identifies key variables for diagnosing coinfection and differentiating between malaria and arboviral diseases in an endemic region.
Findings
Duration of illness >3 days and age >10 years suggest arboviral infection.
High body temperature and nausea/vomiting indicate malaria during the rainy season.
Model helps estimate coinfection probabilities based on clinical variables.
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. Then, 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…
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