Synthetic Data Generation for Improved COVID-19 Epidemic Forecasting
Bannur, N.; Shah, V.; Raval, A.; White, J.
Show abstract
During an epidemic, accurate long term forecasts are crucial for decision-makers to adopt appropriate policies and to prevent medical resources from being overwhelmed. This came to the forefront during the covid-19 pandemic, during which there were numerous efforts to predict the number of new infections. Various classes of models were employed for forecasting including compartmental models and curve-fitting approaches. Curve fitting models often have accurate short term forecasts. Their parameters, however, can be difficult to associate with actual disease dynamics. Compartmental models take these dynamics into account, allowing for more flexible and interpretable models that facilitate qualitative comparison of scenarios. This paper proposes a method of strengthening the forecasts from compartmental models by using short term predictions from a curve fitting approach as synthetic data. We discuss the method of fitting this hybrid model in a generalized manner without reliance on region specific data, making this approach easy to adapt. The model is compared to a standard approach; differences in performance are analyzed for a diverse set of covid-19 case counts.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Fast and Accurate Influenza Forecasting in the United States with Inferno 98%
- Improving Probabilistic Infectious Disease Forecasting Through Coherence 97%
- An ensemble n -sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA 96%
Similar papers in this journal
- Simple discrete-time self-exciting models can describe complex dynamic processes: a case study of COVID-19 97%
- Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling 96%
- A Stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments 96%
Similar papers in this journal
- A prospective real-time transfer learning approach to estimate Influenza hospitalizations with limited data 96%
- Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer 95%
- Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States 95%
Similar papers in this journal
- Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy 96%
- Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro 95%
- Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.