What factors are contributing to the unexpected 30% increase in processing time for Institute for Health Metrics and Evaluation's COVID-19 mortality forecasting model in the last month?

Introduction

The unexpected 30% increase in processing time for the Institute for Health Metrics and Evaluation's COVID-19 mortality forecasting model is a critical issue that demands immediate attention. This surge in processing time could significantly impact the timeliness and effectiveness of crucial public health decisions. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Given the sudden increase, I'm wondering about recent changes. Have there been any updates to the model's algorithms or data sources in the past month?

Why it matters: Recent changes could directly correlate with the performance issue. Expected answer: Yes, there was a recent update to incorporate new data sources. Impact on approach: If confirmed, we'd focus on the new data integration process.

• Considering the scale of the increase, I'm curious about the data volume. Has there been a significant increase in the amount of data being processed?

Why it matters: Data volume changes could explain processing time increases. Expected answer: Data volume has increased by 20% due to expanded global coverage. Impact on approach: We'd investigate data handling and processing optimizations.

• Thinking about infrastructure, I'm wondering about any recent changes. Have there been any modifications to the computing environment or resources allocated to the model?

Why it matters: Infrastructure changes could impact processing efficiency. Expected answer: No significant changes to the computing environment. Impact on approach: We'd shift focus to software and data-related factors.

• Reflecting on the model's complexity, I'm curious about its evolution. Has the model's complexity increased recently to improve accuracy or incorporate new variables?

Why it matters: Increased complexity could explain longer processing times. Expected answer: The model has been expanded to include more granular regional data. Impact on approach: We'd examine the trade-offs between model complexity and performance.