Using viral load tests to help predict mpox severity when skin lesions first appear
July 2, 2025
A new study led by Nagoya University offers promising insights into mpox clinical care. Researchers found that measuring viral loads in blood at the onset of skin lesions can predict disease severity in clade I mpox patients. Using data from the Democratic Republic of the Congo, the team identified a threshold—40,000 viral copies per milliliter—beyond which patients are more likely to experience severe, long-lasting symptoms. These findings could help health workers triage patients early and better allocate resources. As the clade I outbreak spreads across Africa, this method may enable faster, more personalized care and improve containment efforts.
As Africa contends with an escalating outbreak of clade I mpox—now classified as a Public Health Emergency of International Concern—researchers at Nagoya University have introduced a critical clinical tool: a predictive model for determining mpox disease severity. Published in Science Translational Medicine, the study analyzes patient data from the Democratic Republic of the Congo and finds that viral load in blood at the onset of lesions is a strong predictor of clinical trajectory.
By applying mathematical modeling and machine learning, the research team identified that patients with viral loads exceeding 40,000 copies per milliliter are more likely to suffer severe, prolonged symptoms. These cases may also correlate with longer infectious periods, intensifying the need for early intervention and strict isolation. In contrast, individuals below that threshold tend to recover more quickly and exhibit milder disease progression.
The significance of this research cannot be overstated. With clade I strains exhibiting mortality rates near 10%, in stark contrast to clade IIb’s 1% rate during the 2022 outbreak, early identification of high-risk patients could reduce morbidity and mortality while easing the burden on overwhelmed healthcare systems. This approach aligns with global efforts to introduce data-driven, personalized care to outbreak response.
As the current outbreak expands into neighboring countries and involves both clade Ia and Ib variants, validating this method across strains will be vital. If successful, it would mark a step forward in precision epidemic management, enabling more efficient triaging, targeted monitoring, and optimized resource deployment. The model also sets a precedent for integrating predictive analytics into infectious disease control—especially critical in under-resourced settings where timely diagnostics can make the difference between containment and catastrophe.
Professor Shingo Iwami and his team are paving the way for smarter, more equitable responses to emerging global health threats.