Modeling lesion transition dynamics to clinically characterize mpox patients in the Democratic Republic of the Congo

Abstract

AbstractCoinciding with the global outbreak of clade IIb mpox virus (MPXV), the Democratic Republic of the Congo (DRC) recently experienced a rapid surge in mpox cases with clade I MPXV. Clade I MPXV is known to be more fatal, but its clinical characteristics and prognosis differ between patients. Here, we used mathematical modelling to quantify disease progression in a large cohort of mpox patients in the DRC from 2007-2011, particularly focusing on lesion transition dynamics. We further analyzed individuals’ clinical data to find predictive biomarkers of severity of symptoms. Our analysis shows that mpox patients can be stratified into three groups according to symptom severity, and that viral load at symptom onset may serve as a predictor to distinguish groups with the most severe or mild symptoms after progression. Understanding the severity and duration of symptoms in different patients, as characterized by our approach, allows treatment strategies to be improved and individual-specific control measures (e.g isolation strategies based on disease progression) to be developed.