Evolving consensus on managing vitreo-retina and uvea practice in post-COVID-19 pandemic era

The COVID-19 pandemic has brought new challenges to the health care community. Many of the super-speciality practices are planning to re-open after the lockdown is lifted. However there is lot of apprehension in everyone's mind about conforming practices that would safeguard the patients, ophthalmologists, healthcare workers as well as taking adequate care of the equipment to minimize the damage. The aim of this article is to develop preferred practice patterns, by developing a consensus amongst the lead experts, that would help the institutes as well as individual vitreo-retina and uveitis experts to restart their practices with confidence. As the situation remains volatile, we would like to mention that these suggestions are evolving and likely to change as our understanding and experience gets better. Further, the suggestions are for routine patients as COVID-19 positive patients may be managed in designated hospitals as per local protocols. Also these suggestions have to be implemented keeping in compliance with local rules and regulations.

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

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 are affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population. 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 should emphasize learning from COVID-related signals and when it should learn from the historical model. Thus, 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.