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Nowcasting the 2022 mpox outbreak in England.

Christopher E Overton1,2,3, Sam Abbott4, Rachel Christie2

  • 1Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom.

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A new model accurately corrects the mpox epidemic curve in England by addressing reporting delays. This allows for real-time tracking of the mpox outbreak, crucial for public health policy.

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Area of Science:

  • Epidemiology
  • Public Health
  • Mathematical Modeling

Background:

  • The 2022 mpox (monkeypox) outbreak in the UK presented challenges for public health surveillance due to significant reporting delays.
  • Delays in symptom onset and specimen collection dates led to data backfilling, complicating real-time interpretation of the epidemic curve.
  • Existing nowcasting models lacked the flexibility to effectively address these specific data challenges.

Purpose of the Study:

  • To develop and validate a novel nowcasting model for the mpox epidemic in England.
  • To correct for data backfilling and provide real-time estimates of the epidemic's trajectory, including its growth rate.
  • To improve the robustness of real-time epidemiological data for public health decision-making.

Main Methods:

  • Development of a nowcasting model utilizing generalized additive models.
  • Incorporation of individual-level patient data to refine epidemic curve estimations.
  • Collaboration with data collection and processing teams to account for temporal changes in reporting structures.

Main Results:

  • The developed model accurately corrected for backfilling in the mpox epidemic curve.
  • The model provided reliable real-time characteristics of the epidemic, including the growth rate.
  • The nowcasts generated demonstrated improved robustness due to the model's adaptability.

Conclusions:

  • The novel nowcasting model effectively addresses data limitations in real-time mpox surveillance.
  • Accurate, real-time epidemic curve data is essential for effective public health management of outbreaks.
  • Close collaboration and flexible modeling approaches enhance the reliability of epidemiological surveillance.