Kelly Charniga - Nowcasting and Forecasting the 2022 U.S. Mpox Outbreak: Support for Public Health Decision Making and Lessons Learned

On Wednesday the 3rd of May at 2pm UK time Kelly Charniga will be walking through their experience of nowcasting and forecasting the 2022 US. Mpox outbreak and highlighting some lessons learnt.

Kelly is a Prevention Effectiveness Fellow at the US CDC. Prior to this they conducted a PhD focussed on the 2014-2017 Zika and Chikungunya epidemics in Columbia using spatial interaction models to explore the spatial and temporal invasion dynamics of epidemics. Read more of Kelly’s work here.

A recording of this talk will be posted to our community site a week after it is scheduled to take place. You can also ask questions ahead of time and asynchronously there.

Abstract

In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tool for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.

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More details about this seminar series are available here.