Outbreak detection is critical to contain potential threats before they escalate into full-blown crises. However, training models to predict outbreaks when case numbers are low can be challenging due to the small sample size problem. To address this issue, transfer learning has emerged as a promising approach in machine learning.
In simple terms, transfer learning is like borrowing knowledge from related fields to improve our understanding of the target problem. In outbreak detection, we use historical data from similar situations to train models that can detect potential outbreaks early on. By leveraging this proxy data, we can create more accurate models that are less prone to overfitting when case numbers are low.
To apply transfer learning in outbreak detection, we first partition the data into two-day "data blocks." We then normalize these blocks by subtracting the difference between the dependent variable (ln(It,c)) and the independent variable (t – (t-1)) for each combination of time and location. This process helps us feed a more manageable dataset to the GRF algorithm, which generates GRF weights.
Finally, we combine these weights with the preprocessed data to create the TLGRF estimator: rT LGRF(Xt,c) = (γt,c (Xt,c))(ln(It,c) – ln(It-1,c)). This estimator can detect potential outbreaks by analyzing the GRF weights and identifying anomalies that may indicate an emerging threat.
In conclusion, transfer learning offers a practical solution to the small sample size problem in outbreak detection. By leveraging historical data from similar situations, we can create more accurate models that are better equipped to detect potential threats early on. This approach has important implications for public health officials seeking to contain outbreaks before they spread uncontrollably.
Data-Driven Approach to COVID-19 Forecasting: A Survey of Recent Methods
