Combined Symptom Screening and At-Home Tests for COVID-19

Graph data science and machine learning for the detection of COVID-19 infection from symptoms

Background

COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms.

Methods

The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted.

Results

We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy.

Conclusion

The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.

Testing the Acceptability and Usability of an AI-Enabled COVID-19 Diagnostic Tool Among Diverse Adult Populations in the United States

BACKGROUND AND OBJECTIVES

Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States.

METHODS

A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool.

RESULTS

Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing.

CONCLUSION

Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.

Guidelines for Triage of COVID-19 Patients Presenting With Multisystemic Symptoms

BACKGROUND AND OBJECTIVES

This article describes how multisystemic symptoms, both respiratory and nonrespiratory, can be used to differentiate coronavirus disease-2019 (COVID-19) from other diseases at the point of patient triage in the community. The article also shows how combinations of symptoms could be used to predict the probability of a patient having COVID-19.

METHODS

We first used a scoping literature review to identify symptoms of COVID-19 reported during the first year of the global pandemic. We then surveyed individuals with reported symptoms and recent reverse transcription polymerase chain reaction (RT-PCR) test results to assess the accuracy of diagnosing COVID-19 from reported symptoms. The scoping literature review, which included 81 scientific articles published by February 2021, identified 7 respiratory, 9 neurological, 4 gastrointestinal, 4 inflammatory, and 5 general symptoms associated with COVID-19 diagnosis. The likelihood ratio associated with each symptom was estimated from sensitivity and specificity of symptoms reported in the literature. A total of 483 individuals were then surveyed to validate the accuracy of predicting COVID-19 diagnosis based on patient symptoms using the likelihood ratios calculated from the literature review. Survey results were weighted to reflect age, gender, and race of the US population. The accuracy of predicting COVID-19 diagnosis from patient-reported symptoms was assessed using area under the receiver operating curve (AROC).

RESULTS

In the community, cough, sore throat, runny nose, dyspnea, and hypoxia, by themselves, were not good predictors of COVID-19 diagnosis. A combination of cough and fever was also a poor predictor of COVID-19 diagnosis (AROC = 0.56). The accuracy of diagnosing COVID-19 based on symptoms was highest when individuals presented with symptoms from different body systems (AROC of 0.74-0.81); the lowest accuracy was when individuals presented with only respiratory symptoms (AROC = 0.48).

CONCLUSIONS

There are no simple rules that clinicians can use to diagnose COVID-19 in the community when diagnostic tests are unavailable or untimely. However, triage of patients to appropriate care and treatment can be improved by reviewing the combinations of certain types of symptoms across body systems.

Order of Occurrence of COVID-19 Symptoms

BACKGROUND AND OBJECTIVES

COVID-19 symptoms change after onset-some show early, others later. This article examines whether the order of occurrence of symptoms can improve diagnosis of COVID-19 before test results are available.

METHODS

In total, 483 individuals who completed a COVID-19 test were recruited through Listservs. Participants then completed an online survey regarding their symptoms and test results. The order of symptoms was set according to (a) whether the participant had a "history of the symptom" due to a prior condition; and (b) whether the symptom "occurred first," or prior to, other symptoms of COVID-19. Two LASSO (Least Absolute Shrinkage and Selection Operator) regression models were developed. The first model, referred to as "time-invariant," used demographics and symptoms but not the order of symptom occurrence. The second model, referred to as "time-sensitive," used the same data set but included the order of symptom occurrence.

RESULTS

The average cross-validated area under the receiver operating characteristic (AROC) curve for the time-invariant model was 0.784. The time-sensitive model had an AROC curve of 0.799. The difference between the 2 accuracy levels was statistically significant (α < .05).

CONCLUSION

The order of symptom occurrence made a statistically significant, but small, improvement in the accuracy of the diagnosis of COVID-19.

The Role of Symptom Clusters in Triage of COVID-19 Patients

BACKGROUND AND OBJECTIVE

COVID-19 manifests with a broad range of symptoms. This study investigates whether clusters of respiratory, gastrointestinal, or neurological symptoms can be used to diagnose COVID-19.

METHODS

We surveyed symptoms of 483 subjects who had completed COVID-19 laboratory tests in the last 30 days. The survey collected data on demographic characteristics, self-reported symptoms for different types of infections within 14 days of onset of illness, and self-reported COVID-19 test results. Robust LASSO regression was used to create 3 nested models. In all 3 models, the response variable was the COVID-19 test result. In the first model, referred to as the "main effect model," the independent variables were demographic characteristics, history of chronic symptoms, and current symptoms. The second model, referred to as the "hierarchical clustering model," added clusters of variables to the list of independent variables. These clusters were established through hierarchical clustering. The third model, referred to as the "interaction-terms model," also added clusters of variables to the list of independent variables; this time clusters were established through pairwise and triple-way interaction terms. Models were constructed on a randomly selected 80% of the data and accuracy was cross-validated on the remaining 20% of the data. The process was bootstrapped 30 times. Accuracy of the 3 models was measured using the average of the cross-validated area under the receiver operating characteristic curves (AUROCs).

RESULTS

In 30 bootstrap samples, the main effect model had an AUROC of 0.78. The hierarchical clustering model had an AUROC of 0.80. The interaction-terms model had an AUROC of 0.81. Both the hierarchical cluster model and the interaction model were significantly different from the main effect model (α = .04). Patients with different races/ethnicities, genders, and ages presented with different symptom clusters.

CONCLUSIONS

Using clusters of symptoms, it is possible to more accurately diagnose COVID-19 among symptomatic patients.