Can Self Reported Symptoms Predict Daily COVID-19 Cases?

TL;DR

This study develops machine learning models using self-reported symptoms from online surveys to estimate daily COVID-19 cases at state levels, showing potential for cost-effective epidemiological monitoring.

Contribution

It introduces localized and global machine learning models that predict COVID-19 cases from symptom data, highlighting the importance of state-specific symptom features.

Findings

Local models outperform global models in accuracy.

Self-reported symptoms can predict daily cases with a mean absolute error of 226.30.

Important symptoms vary significantly across states.

Abstract

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.