# Can Self Reported Symptoms Predict Daily COVID-19 Cases?

arXiv: https://arxiv.org/abs/2105.08321 · HTML: https://mlsanity.com/paper/2105.08321/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings](https://mlsanity.com/paper/2101.01693/) (2021)
- [Measurement-Based Evaluation Of Google/Apple Exposure Notification API For Proximity Detection in a Commuter Bus](https://mlsanity.com/paper/2006.08543/) (2020)
- [Tracking disease outbreaks from sparse data with Bayesian inference](https://mlsanity.com/paper/2009.05863/) (2020)
- [COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms](https://mlsanity.com/paper/2101.10266/) (2020)

## Built on by (most cited first, 1 of 1)

- [DFW-PP: Dynamic Feature Weighting based Popularity Prediction for Social Media Content](https://mlsanity.com/paper/2110.08510/) (2021)

## Abstract (arXiv)

The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths. While vaccination is an important intervention, its roll-out is slow and unequal across the globe. Therefore, extensive testing still remains one of the key methods to monitor and contain the virus. Testing on a large scale is expensive and arduous. Hence, we need alternate methods to estimate the number of cases. Online surveys have been shown to be an effective method for data collection amidst the pandemic. In this work, we develop machine learning models to estimate the prevalence of COVID-19 using self-reported symptoms. Our best model predicts the daily cases with a mean absolute error (MAE) of 226.30 (normalized MAE of 27.09%) per state, which demonstrates the possibility of predicting the actual number of confirmed cases by utilizing self-reported symptoms. The models are developed at two levels of data granularity - local models, which are trained at the state level, and a single global model which is trained on the combined data aggregated across all states. Our results indicate a lower error on the local models as opposed to the global model. In addition, we also show that the most important symptoms (features) vary considerably from state to state. 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. The code is publicly available at https://github.com/parthpatwa/Can-Self-Reported-Symptoms-Predict-Daily-COVID-19-Cases.