Background and Challenges
In the throes of a global pandemic, a leading biopharmaceutical company faced unprecedented challenges in forecasting the demand for COVID-19-related products across seven major markets. Traditional models struggled to adapt to the rapidly evolving virus variants and their unpredictable impacts. The client needed a robust yet flexible forecasting model that could incorporate the complex interplay of epidemiological factors, variant evolution and non-epidemiological influences such as vaccination rates and public health policies.
Approach
L.E.K. Consulting took on the challenge with a two-phase approach to build and implement a state-of-the-art predictive epidemiology model.
Phase 1: Model Development
Epidemiological modeling: Our team developed a sophisticated short-term model that parameterized COVID-19 variants with specific epidemiological properties, such as the basic reproduction number, enabling predictions of their population-level impacts.
Variant and scenario analysis: To explore hypothetical future scenarios, we created a variant library, integrating external drivers such as vaccination, outpatient therapies and testing in order to forecast variant spread.
Custom user interface: To enhance client interaction with the model, our developers built a user-friendly interface that allows easy adjustments based on new data or emerging trends.
Phase 2: Implementation and Maintenance
Model calibration and testing: Using historical data from previous COVID-19 waves, our team calibrated the model to refine its accuracy. We also evaluated various machine learning models to optimize the forecasting, selecting the enhanced stochastic SEIRS (susceptible, exposed, infectious, recovered, susceptible) model as the top performer.
Deployment and training: We rolled out the models in the client’s Amazon Web Services environment, transferred over 1,200 hours of Python development code and provided comprehensive training to ensure seamless integration and ongoing utilization.
Results
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