COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

Zoabi, Yazeed; Shomron, Noam

doi:10.1101/2020.05.07.20093948

Cited by 14 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Myers et al analyzed the COVID-19 positive patients in California to investigate its prognosis in the higher age groups and individuals with preexisting conditions [24]. Zoabi et al applied ML on 51,831 COVID-19 positive patients to understand the effect of gender, age and contact to show that close social interaction is a strong feature for COVID-19 transmissibility [25]. Khan et al applied regression tree, cluster analysis and principal component analysis on Worldometer infection count data to study the variability and effect of testing in prediction of confirmed cases [26].…”

Section: Introductionmentioning

confidence: 99%

Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking

Roy

Ghosh

2020

PLoS ONE

View full text Add to dashboard Cite

Background After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave—or the extension of the first wave—it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures. Methods We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making. Findings Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance. Interpretation States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.

show abstract

Section: Introductionmentioning

confidence: 99%

Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking

Roy

Ghosh

2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Similarly, a simple model constructed from the rate of social media posts can be used as a reliable prediction model when analyzing the uncertainties surrounding the pandemic [48]. Hence, accurate prediction models can be useful tools to model the outbreak of the pandemic as well as in diagnosis prediction [49,50]. As the fight against the SAR-CoV-2 virus continues, more studies are needed to uncover useful information hidden as a result of the uncertainties surrounding the pandemic.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Number of COVID-19 Infections and Deaths in USA

Felix

Ozigbu

Conserve

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Uncertainties surrounding the 2019 novel coronavirus (COVID-19) remain a major global health challenge and requires attention. Researchers and medical experts have made remarkable efforts to reduce the number of cases and prevent future outbreaks through vaccines and other measures. However, there is little evidence on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection entropy can be applied in predicting the possible number of infections and deaths. In addition, more studies on how the COVID-19 infection density contributes to the rise in infections are needed. This study demonstrates how the SARS-COV-2 daily infection entropy can be applied in predicting the number of infections within a given period. In addition, the infection density within a given population attributes to an increase in the number of COVID-19 cases and, consequently, the new variants. Result: Using the COVID-19 initial data reported by Johns Hopkins University, World Health Organization and Global Initiative on Sharing All Influenza Data, the result shows that the original SAR-COV-2 strain has R0<1 with an initial infection growth rate entropy of 9.11 bits for the United States. At close proximity the average infection time for an infected individual to infect others within a susceptible population is approximately 7 minutes. Assuming no vaccines were available, in the United States, the number of infections could range between 41,220,199 and 82,440,398 in late March 2022 with approximately, 1,211,036 deaths. However, with the available vaccines, nearly 48 Million COVID-19 cases and 706, 437 deaths have been prevented. Conclusion: The proposed technique will contribute to the ongoing investigation of the COVID-19 pandemic and a blueprint to address the uncertainties surrounding the pandemic.

show abstract

“…In [62] , machine learning algorithms are used to process symptoms of patients to diagnose covid-19 patients. The symptoms are assessed by asking basic questions from the patients.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

160

View full text Add to dashboard Cite

show abstract

COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

Cited by 14 publications

References 11 publications

Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking

Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking

Predicting the Number of COVID-19 Infections and Deaths in USA

Applications of artificial intelligence in battling against covid-19: A literature review

Contact Info

Product

Resources

About