Back

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

Nishiyama, T.; Miura, F.; Jeong, Y. D.; Nakamura, N.; Park, H.; Ishikane, M.; Yamamoto, S.; Iwamoto, N.; Suzuki, M.; Sakurai, A.; Aihara, K.; Watashi, K.; Hart, W. S.; Thompson, R. N.; Yasutomi, Y.; Ohmagari, N.; Mbala Kingebeni, P.; Huggins, J. W.; Pittman, P. R.; Iwami, S.

2024-01-29 infectious diseases
10.1101/2024.01.28.24301907 medRxiv
Show abstract

Coinciding 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.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.