COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

  title={COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms},
  author={Rohan Sukumaran and Parth Patwa and TV Sethuraman and Sheshank Shankar and Rishank Kanaparti and Joseph Bae and Y. K. Mathur and Abhishek Singh and Ayush Chopra and Myungsun Kang and Priya Ramaswamy and Ramesh Raskar},
It is crucial for policymakers to understand the community prevalence of COVID-19 so combative resources can be effectively allocated and prioritized during the COVID-19 pandemic. Traditionally, community prevalence has been assessed through diagnostic and antibody testing data. However, despite the increasing availability of COVID-19 testing, the required level has not been met in parts of the globe, introducing a need for an alternative method for communities to determine disease prevalence… 

Trend and prediction of COVID-19 outbreak in Iran: SEIR and ANFIS model

Combining parametrized SEIR model and ANFIS is effective in predicting the trend of COVID-19 new cases in Iran and model predictions indicate that the corona epidemic in Iran may recur as an immortal oscillation and Iran may undergo a recurrence of the third peak.

Can Self Reported Symptoms Predict Daily COVID-19 Cases?

This work demonstrates that the models developed on crowd-sourced data, curated via online platforms, can complement the existing epidemiological surveillance infrastructure in a cost-effective manner.



Estimating the COVID-19 Prevalence in Spain With Indirect Reporting via Open Surveys

The results of the CoronaSurveys project strongly support the idea of using open surveys with indirect reporting as a method to broadly sense the progress of a pandemic.

DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

DeepCOVID, an operational deep learning framework designed for real-time COVID-19 forecasting, works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast.

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

CALI-Net is proposed, a neural transfer learning architecture which allows to 'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist, and shows that success in the primary goal, adaptation, does not sacrifice overall performance as compared with state-of-the-art influenza forecasting approaches.

Immune life history, vaccination, and the dynamics of SARS-CoV-2 over the next 5 years

It is found that variations in the immune response to primary SARS-CoV-2 infections and a potential vaccine can lead to markedly different immune landscapes and burdens of critically severe cases, ranging from sustained epidemics to near elimination.

Tracking disease outbreaks from sparse data with Bayesian inference

An efficient stochastic variational inference method which relies on a novel gradient estimator for the variational objective and a Bayesian framework which accommodates partial observability in a principled manner is proposed.

Oxford COVID-19 Government Response Tracker

Type I interferon sensing unlocks dormant adipocyte inflammatory potential

A capacity for the type I IFN/IFNAR axis to regulate unifying inflammatory features in both myeloid cells and adipocytes is revealed and hint at an underappreciated contribution of adipocyte inflammation in disease pathogenesis.

Real-time tracking of self-reported symptoms to predict potential COVID-19

Analysis of data from a smartphone-based app designed for large-scale tracking of potential COVID-19 symptoms, used by over 2.5 million participants in the United Kingdom and United States, shows that loss of taste and smell sensations is predictive of potential SARS-CoV-2 infection.

More than smell – COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

The results show that COVID-19-associated chemosensory impairment is not limited to smell, but also affects taste and chemesthesis, and suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms.

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.