COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

• URL: http://arxiv.org/abs/2101.10266v1
• Date: Mon, 21 Dec 2020 00:37:24 GMT
• Title: COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms
• Authors: Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy and Ramesh Raskar
• Abstract summary: We use self-reported symptoms survey data to understand trends in the spread of COVID-19. From our studies, we try to predict the likely % of the population that tested positive for COVID-19 based on self-reported symptoms. We forecast that % of the population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively.
• Score: 12.864257751458712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The COVID-19 pandemic has challenged scientists and policy-makers internationally to develop novel approaches to public health policy. Furthermore, it has also been observed that the prevalence and spread of COVID-19 vary across different spatial, temporal, and demographics. Despite ramping up testing, we still are not at the required level in most parts of the globe. Therefore, we utilize self-reported symptoms survey data to understand trends in the spread of COVID-19. The aim of this study is to segment populations that are highly susceptible. In order to understand such populations, we perform exploratory data analysis, outbreak prediction, and time-series forecasting using public health and policy datasets. From our studies, we try to predict the likely % of the population that tested positive for COVID-19 based on self-reported symptoms. Our findings reaffirm the predictive value of symptoms, such as anosmia and ageusia. And we forecast that % of the population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively. These findings could help aid faster development of the public health policy, particularly in areas with low levels of testing and having a greater reliance on self-reported symptoms. Our analysis sheds light on identifying clinical attributes of interest across different demographics. We also provide insights into the effects of various policy enactments on COVID-19 prevalence.

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