Nowcasting the 2022 mpox outbreak in England

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.