Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

by   Alexander Rodriguez, et al.

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts is affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. We term the ILI values observed when it is potentially affected as COVID-ILI. Under the current pandemic, historical influenza models carry valuable expertise about the disease dynamics but face difficulties adapting. Therefore, we propose CALI-NET, a neural transfer learning architecture which allows us to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our framework enables this adaptation by automatically learning when it is should emphasize learning from COVID-related signals and when from the historical model. In such way, we exploit representations learned from historical ILI data as well as the limited COVID-related signals. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic. In addition, we show that success in our primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.



page 7

page 11

page 12

page 13


COVID-19 and Influenza Joint Forecasts Using Internet Search Information in the United States

As COVID-19 pandemic progresses, severe flu seasons may happen alongside...

Modeling the Dynamics of the COVID-19 Population in Australia: A Probabilistic Analysis

The novel Corona Virus COVID-19 arrived on Australian shores around 25 J...

AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates

The COVID-19 pandemic has profound global consequences on health, econom...

Knowledge Infused Policy Gradients for Adaptive Pandemic Control

COVID-19 has impacted nations differently based on their policy implemen...

Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model

Forecasting the number of Olympic medals for each nation is highly relev...

Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future

In real-time forecasting in public health, data collection is a non-triv...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.