Guidelines for Triage of COVID-19 Patients Presenting With Multisystemic Symptoms : Quality Management in Healthcare

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

Alemi, Farrokh PhD; Guralnik, Elina MPH; Vang, Jee PhD; Wojtusiak, Janusz PhD; Peterson, Rachele MS, MBA; Roess, Amira PhD; Jain, Praduman MS

Author Information
Quality Management in Health Care 32(Supplement 1):p S3-S10, January/March 2023. | DOI: 10.1097/QMH.0000000000000398


Coronavirus disease-2019 (COVID-19) is a systemic disease with respiratory, neurological, gastrointestinal, and other symptom manifestations.1–4 A thorough symptom screening, which goes beyond respiratory symptoms (eg, cough and fever), can improve clinicians' triage decisions.5–12 Presently, primary care providers continue to screen patients via phone and online screening questionnaires prior to in-person visits to medical offices. Many clinics continue to screen patients, repeatedly, at the entrance to their facilities. These screening procedures rely on a limited list of symptoms, per the guidelines from the US Centers for Disease Control and Prevention (CDC), and often result in triaging patients who present with symptoms similar to COVID-19 to the COVID-19-restricted areas. Incorrectly identifying COVID-19 in healthy individuals bears a number of adverse consequences. First, it puts healthy individuals at risk of acquiring the disease as a result of being misidentified and placed in quarantine wards with actual COVID-19 patients. Second, it prevents healthy individuals from access to timely medical care, which delays proper diagnosis and treatment, at times lifesaving. Third, misidentification of healthy individuals for highly infectious ones puts further pressure on the health system by misallocating and wasting valuable resources.

This article reviews the literature for multisystemic symptoms reported by COVID-19 patients and develops a model to predict the probability of COVID-19 diagnosis from both respiratory and nonrespiratory symptoms. We report the accuracy of symptom screening for COVID-19 based on the likelihood ratios (LRs) estimated from symptom prevalence among COVID-19 and non-COVID-19 patients. A comprehensive and accurate symptom screening can (1) improve quarantine protocols for return to work or school; (2) improve timely access to needed medical care; and (3) make better use of already strained resources within health care ecosystem. Furthermore, improved screening protocols can better guide surveillance initiatives, the interpretations of at-home rapid tests, and clinicians' triage decisions.


Data were obtained from 2 sources: a scoping literature review and a survey. In the scoping literature review, we first identified all symptoms reported in the literature that were associated with COVID-19. We then identified the same symptoms that were reported in the literature that were not associated with COVID-19. Having identified the prevalence of symptoms in COVID-19 and non-COVID-19 patients, we generated LRs of symptoms as predictors of COVID-19. This determined the diagnostic value associated with each symptom. We then tested those LRs, applying them to a new cohort of survey participants to learn (a) whether they could predict the odds of a symptomatic individual having COVID and (b) the accuracy of diagnosing patients based on their symptoms.

We searched PubMed, between October 2020 and February 2021, for studies reporting all known symptoms of COVID-19. This study also required a purposeful literature search to find most recent publications that provided the prevalence of symptoms associated with COVID-19 known in non-COVID-19 patients, prior to 2019. Thus, the literature review identified most recent articles on a variety of diagnoses and was organized based on the need to calculate the LRs for each of the known symptoms associated with COVID-19. The search for symptoms was not restricted by population, age, comorbidities, or viral strains. Instead, we took the most recent related publications with the purpose of estimating the LRs. A total of 81 studies were identified. Most studies included in the review were from years 2020-2021 with the exception of one publication from 2009, which was used to obtain the prevalence of seizures prior to the beginning of the pandemic. We searched for “COVID-19” and “symptoms.” Once all known symptoms of COVID-19 to date were identified in the literature, we searched for the studies that reported the prevalence of those symptoms prior to the emergence of COVID-19. The symptoms identified from the reviewed studies were classified into 5 categories: respiratory, neurological, gastrointestinal, inflammatory, and general. The following formulas (see below) were used to estimate the LRs associated with the presence or absence of a symptom—from either the reported sensitivity or specificity of the symptom or the reported prevalence of the symptom in COVID-19 patients. Sensitivity defines the percentage of individuals screened as positive for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, among those who actually have the infection. Specificity defines the percentage of individuals who were screened negative for SARS-CoV-2 infection, among those who do not have the infection.


In above formula, LR+ indicates the likelihood ratio associated with the presence of the symptom and LR indicates the same for the absence of the symptom. The independent form of Bayes formula was used to examine the impact of multiple symptoms on odds of having COVID-19 diagnosis: Posteriorodds=Priorodds×SLRs

To test the accuracy of the model, 483 study participants were recruited and surveyed between November 2020 and January 2021. Study participants were recruited through online advertisement and neighborhood listservs, with permission from moderators. Participants were eligible if they were adults, 18 years or older, and had tested for COVID-19 within 30 days prior to the survey. At the time of recruitment, no at-home antigen tests were readily available. Reverse transcription polymerase chain reaction (RT-PCR) tests were done at a variety of laboratories while rapid antigen tests were available primarily at point-of-care. Survey participants self-reported their test results. Twenty-two study participants were excluded from the final analysis because their test results were inconclusive or not available in time. The survey was confidential and did not include any participation incentive information. Subjects had to complete a separate survey for receiving a gift card for participation, where their names and phone numbers were recorded. The survey was designed such that participants could not take the survey multiple times. The survey instrument is available in the Supplemental Digital Content file (available at:

Data were collected prior to vaccination being made widely available, and when the alpha variant of the novel coronavirus was predominant in the United States. In addition to COVID-19 test results, the survey captured participants' COVID-19 symptoms and exposures within 30 days prior to the survey, as well as general health status and sociodemographic characteristics. At the time of data collection, the national COVID-19 prevalence was 5%. The sample size of 400 was calculated to include at least 50 individuals who tested positive for an infection causing COVID-19. The subject's age, gender, and race were weighted to reflect those in the United States' population. The accuracy of symptoms in predicting COVID-19 diagnosis was examined using area under the receiver operating curve (AROC). Since the LR associated with each symptom was calculated based on the data obtained from the literature, the reported accuracy rate is a cross-validated AROC on an independent sample of patients.

This study was approved by George Mason University IRB (number 1668273-8).


In total, 483 participants completed the survey. Twenty-two respondents did not know their test results at the time of participation in the study or had an inconclusive test result. Analysis was done on the remaining 461 patients. Table 1 shows the demographic characteristics of the population recruited for the study. These cases were weighted to reflect the distribution of age, gender, and race in the United States.

Table 1. - Characteristics of the Study Sample
Characteristics Number of Cases (%)
COVID-19 test results
Negative 330 (68.32%)
Positive 131 (27.12%)
Results pending 15 (3.11%)
Inconclusive 7 (1.45%)
18-24 84 (17.39%)
25-34 210 (43.48%)
35-44 156 (32.30%)
45-54 20 (4.43%)
55-84 13 (2.69%)
Female 279 (57.76%)
Male 203 (42.03%)
Hispanic Latino 60 (12.42%)
non-Hispanic Latino 401 (83.02%)
Unknown 22 (4.55%)
Other 18 (3.75%)
Asian 25 (5.18%)
Black or African American 60 (12.42%)
White 380 (78.67%)
Essential workers
No 282 (58.39%)
Yes 201 (41.61%)
Health care workers
No 281 (58.18%)
Yes 202 (41.82%)

Table 2 provides the symptoms of COVID-19, as identified from the literature review. Table 2 also reports the LRs associated with each symptom, estimated from sensitivity, specificity, or the prevalence of the symptom reported in the literature. An LR above 1 indicates how many times the symptom increased the odds of COVID-19 diagnosis. A ratio below 1 indicates symptoms useful for ruling out COVID-19 diagnosis; the smaller the value the more useful it is. LRs near 1 indicate symptoms that are neither useful as predictors nor rule out COVID-19 diagnosis. It is noteworthy to report that cough, by itself, was not predictive of COVID-19 diagnosis (LR = 0.93); fever was predictive (LR = 1.62). Cough and fever were 2 of the early symptoms used to triage patients at the onset of the pandemic. Later studies also identified loss of smell as a symptom of COVID-19, and its LR (LR = 5.5) suggests that it is a strong predictor of COVID-19 diagnosis.

Table 2. - Likelihood Ratios (LR) Associated With Symptoms of COVID-19
Nonrespiratory Symptoms Number of Patients Sensitivity Specificity LR+ LR
Fever or feeling feverish14–23 5 484 0.44 0.73 1.62 0.77
Muscle aches/myalgia12,20,22 1 427 0.30 0.83 1.74 0.85
Pinkeye/conjunctivitis1 30 494 0.01 1.00 3.46 0.99
Fatigue (more than normal)12,14,16,17 273 0.41 0.70 1.37 0.84
Chills12,16,20 1 443 0.09 0.89 0.84 1.02
Headaches12,14,16,17,20,22 1 700 0.16 0.87 1.24 0.96
Loss of balance17 88 0.38 0.78 1.73 0.79
New confusion24–26 a 3 848 0.42 1.06
Unusual shivering or shaking12,16 132 0.14 0.86 1.00 1.00
Loss of smell12,27 262 0.22 0.96 5.50 0.81
Loss of taste12,26 262 0.20 0.95 4.00 0.84
Seizures24,28–30 a 518 0.12 1.04
Diarrhea12,14–17,20,22 1 733 0.05 0.95 0.88 1.01
Stomach/abdominal pain12,14,16 185 0.01 0.97 0.50 1.01
Change in or loss of appetite31 2 763 0.47 0.64 1.31 0.83
Nausea or vomiting12,14,16,20 489 0.03 0.97 1.13 1.00
Joint/other unexplained pain (myalgia/arthralgia)12,14–18 339 0.57 0.67 1.74 0.64
Red/purple rash or lesions on toes32 318 0.87 0.84 5.58 0.15
Unexplained rashes33 30 494 0.02 0.97 0.68 1.01
Excessive sweating33 30 494 0.06 0.97 1.68 0.98
Respiratory symptoms
Cough12,14–18,20,23,34 2 607 0.57 0.39 0.93 1.1
Sore throat12,14–18,20,34 5 045 0.09 0.33 0.13 2.73
Difficulty breathing (dyspnea)12,14–18,20,32,34 2 554 0.17 0.84 1.08 0.99
Shortness of breath (hypoxia)19 2 929 0.15 0.83 0.88 1.02
Runny nose (rhinorrhea/nasal symptoms) 12,14–18,20,34 5 334 0.04 0.37 0.06 2.57
Chest pain (chest tightness)12,35 34 0.05 1.00 ... 0.95
aCalculated from prevalence of symptoms.

Combinations of symptoms

Table 3 reports the accuracy of symptoms and combinations of symptoms in predicting COVID-19 diagnosis. For patients with nonrespiratory symptoms, the AROC ranged from 0.62 to 0.81, a moderate to high level of accuracy. The more nonrespiratory symptoms were present, the more accurate were the predictions. When 2 or more sets of nonrespiratory symptoms were present, the AROC ranged from 0.72 to 0.81.

Table 3. - Accuracy of Symptom Screening for Patients With Different Clinical Presentationsa
Nonrespiratory Presentations Respiratory Presentation
Category Cases With at Least 1 Symptom in Listed Categories (%) AROC Cases With at Least 1 Symptom in Category (%) AROC
None 388 (84%) 0.46
Neurological 266 (58%) 0.64 248 (54%) 0.50
Gastrointestinal 234 (51%) 0.62 221 (48%) 0.47
Inflammatory 134 (29%) 0.67 127 (28%) 0.40
General 378 (82%) 0.73 357 (77%) 0.56
Neurological and gastrointestinal 189 (41%) 0.74 180 (39%) 0.50
Neurological and inflammatory 121 (26%) 0.72 116 (25%) 0.48
Neurological and general 241 (52%) 0.73 231 (50%) 0.57
Gastrointestinal and inflammatory 120 (26%) 0.73 115 (25%) 0.48
Gastrointestinal and general 233 (51%) 0.71 215 (47%) 0.51
Inflammatory and general 127 (28%) 0.72 122 (26%) 0.46
Neurological, gastrointestinal, and inflammatory 114 (25%) 0.78 110 (24%) 0.52
Neurological, gastrointestinal, and general 180 (39%) 0.77 175 (38%) 0.57
Gastrointestinal, inflammatory, and general 119 (26%) 0.77 114 (25%) 0.50
All 113 (25%) 0.81 109 (24%) 0.60
Abbreviation: AROC, area under the receiver operating curve.
aRed/purple toes and sore throat were dropped from the analysis, as it reduced accuracy of predictions.

Patients often present with multiple symptoms. We assume that each symptom has an independent impact on the diagnosis of COVID-19. Under this assumption, the odds of COVID-19 diagnosis are the product of the LRs associated with each of the symptoms, including symptoms that are absent. If a patient does not have any of the symptoms in the category, then the LRs associated with absence of the symptoms in that category are ignored.

Table 4 shows an example of a patient, presenting with symptoms in different categories. This table shows a patient who has no neurological symptoms. When all symptoms in a category are absent, no prediction is made in that symptom category. When one or more symptoms in the category are present, then a prediction is made for the combination of symptoms by multiplying the LRs associated with the symptom. For the patient in Table 4, the odds of COVID-19 diagnosis are highest in the general category. The odds of COVID-19 diagnosis for this patient were 0.11 (vs prior odds of 0.03), which corresponds to the probability of 0.10 (vs population prevalence of 0.02). The patient is at an elevated risk of having COVID-19. LRs modify prior odds of COVID-19 to estimate the patients' odds of having COVID-19. The estimation of prior odds requires access to local odds of COVID-19. This information is not always available. When unknown, the prior odds of 1 can be assumed, and the posterior odds can be interpreted to the extent of the evidence, which supports the presence of COVID-19.36

Table 4. - An Example Patient With Multiple Symptoms
Nonrespiratory Symptoms One Patient's Symptoms Associated Likelihood Ratio Impact of Relevant Symptoms Odds of COVID-19
Fever/feeling feverish Present 1.62 4.08 0.11
Muscle aches/myalgia Absent 0.85
Pinkeye/conjunctivitis Present 3.46
Fatigue (more than normal) Absent 0.84
Chills Absent 1.02
Headaches Absent 0.96 None calculated as all symptoms are absent
Loss of balance Absent 0.79
Slurred speech Absent 1
New confusion Absent 1
Unusual shivering or shaking Absent 1
Loss of smell Absent 0.81
Loss of taste Absent 0.84
Tingling/numbness/swelling in hands/feet Absent 1
Seizures Absent 1
Diarrhea Absent 0.88 0.83 0.02
Stomach/abdominal pain Absent 1.01
Change in/loss of appetite Absent 0.83
Nausea/vomiting Present 1.13
Joint/other unexplained pain (myalgia/arthralgia) Absent 0.64 0.10 0.00
Red/purple rash/lesions on toes Absent 0.15
Unexplained rashes Absent 1.01
Excessive sweating Absent 0.98
Prior odds
Weighted sample 0.03
Sample 0.40


Our analyses showed that cough, sore throat, runny nose, dyspnea, and hypoxia are not good predictors of COVID-19 diagnosis. The CDC lists cough as a common symptom of COVID-19 that can be used to diagnose COVID-19. One possible explanation for divergence in findings between the CDC and our analyses may be the role that seasonality plays on the diagnostic value of cough. Our scoping literature review identified 29 symptoms that can be used in diagnosing COVID-19, including respiratory, neurological, gastrointestinal, inflammatory, and general symptoms. There was a wide range in values of the LRs, suggesting that symptoms are not equally important. Some of the symptoms that are commonly used for COVID-19 screening (eg, cough) showed not to be the best predictors. The literature review showed that individuals with cough were less likely to have COVID-19 than other diseases. This does not imply that cough is uncommon among COVID-19 patients but emphasizes that cough is also common in people with seasonal allergies, influenza, and other diseases caused by respiratory pathogens.

The estimated LRs suggest a group of symptoms that increases the odds of COVID-19 (eg, loss of smell), another group of symptoms that decreases the odds (eg, runny nose), and yet another set of symptoms that does neither (eg, difficulty breathing). This suggests that combinations of different symptoms could play an important role in suggesting and ruling out COVID-19 diagnosis.

Relying solely on the respiratory symptoms (eg, cough, runny nose, but not fever) to diagnose COVID-19 had low accuracy (AROC = 0.48), which is no better than random guessing. Relying on respiratory and general symptoms (eg, fever) improved the accuracy to 0.56, but this is too low to be clinically significant. Our analyses demonstrate that patients who present with nonrespiratory symptoms can be more accurately evaluated (average AROC = 0.73). For those patients, clinicians could base their triage decisions on the reported symptoms. Even when diagnosing patients with combined nonrespiratory and respiratory symptoms, the model accuracy was moderate (AROC = 0.60). In a subset of 25% of patients who presented with symptoms in multiple nonrespiratory categories and reported no respiratory symptoms, our predictions were highly accurate (AROC = 0.81). Therefore, clinicians can be more confident in their triage of COVID-19 patients that present with nonrespiratory or multisystemic symptoms, which do not include respiratory symptoms. If patients present with only respiratory symptoms, other information is needed. For those patients, triage could be delayed until diagnostic testing is performed to either confirm or rule out the infection or COVID-19 diagnosis.

In the community, there are 2 ways to assess the presence of COVID-19 in a patient. One is based on clinical presentation of the disease and the other is based on diagnostic laboratory testing (ie, RT-PCR) or at-home testing (ie, antigen test). Of course, one can do both and many approved antigen tests for COVID-19 require the test results to be assessed in the context of the patient's symptoms and the clinical presentation of the disease. Few studies have compared the performance of symptom screening and antigen at-home testing, as part of the same study. In our additional work, we presented a study that compares the accuracy of antigen tests with symptom screening.37


This study had a number of limitations. First, in examining the impact of the combination of symptoms, we assumed that symptoms were independent. This assumption is obviously false as certain symptoms may co-occur. It may have been more reasonable to diagnose COVID-19 based on the clusters of symptoms, rather than individual symptoms as some of our additional work suggested.38 This would have allowed us to examine dependencies among the symptoms. Unfortunately, due to the examined symptoms having been obtained from different studies as part of the literature review, we could not estimate the LRs associated with symptoms across different studies.

Second, our study did not look at how seasons affect the diagnostic value of symptoms. The diagnostic value of symptoms may vary during and outside of flu season, further complicating the accuracy of COVID-19 diagnosis.39 Flu and allergies are seasonal diseases40 and, depending on the prevalence of those diseases in a given year, cough may play a different role in the diagnosis of COVID-19.

Third, other factors such as age, exposure history, virus variants and vaccination status can also affect clinical presentation of COVID-19, as reported in the literature.41,42 Most of the literature cited in Table 2 reported on data collected in the first 9 months of 2020 whereas our survey data were collected in the last 3 months of 2020, when a new viral strain, delta variant, was beginning to spread. While no distinction in symptoms were known between different viral strains in the first year of the pandemic, we have learned, overtime, that omicron had symptoms affecting upper airways more than the earlier strains of the virus.43 Hence, our findings show a way to establish a screening tool but the tool itself would need to be calibrated based on new data on prevalence of symptoms and the resulting LRs.


Nearly 3 years into the pandemic, no strides have been made on how to differentiate COVID-19 from other diseases with overlapping symptoms. Unlike other studies that used symptoms for diagnostic and prognostic models of COVID-19,44,45 this article provides analysis on how to differentiate COVID-19 from other diseases based on the symptoms and the LRs derived from the prevalence of those symptoms in patients with and without COVID-19. This study also examines the combinations of symptoms that are most predictive of COVID-19 diagnosis. Our data suggest that the types of symptoms that are present, both nonrespiratory and respiratory, and particular combinations of those symptoms, are important to consider during screening for and diagnosing COVID-19. Patients differ considerably in their clinical presentation. However, when presenting with any symptoms, especially multisystemic symptoms, rather than only respiratory ones, the accuracy of predictions improves. Our findings suggest that a symptom screening tool, which accounts for diagnostic values of symptoms and various combinations of symptoms, could have important clinical implications and utility.


1. Lai CC, Ko WC, Lee PI, Jean SS, Hsueh PR. Extra-respiratory manifestations of COVID-19. Int J Antimicrob Agents. 2020;56(2):106024. doi:10.1016/j.ijantimicag.2020.
2. Aluko OM, Lawal SA, Reuben CS, Jeje SO, Ijomone OM. Understanding the systemic effects of COVID-19: possible clues to potential therapeutic approaches. Int J Trop Dis. 2022;5(1):057. doi:10.23937/2643-461X/1710057.
3. Bongiovanni M, Marra AM. COVID-19 infection as a systemic disease. Clin Res Trials. 2021;7:1–6. doi:10.15761/CRT.1000332.
4. Konturek PC, Harsch IA, Neurath MF, Zopf Y. COVID-19—more than respiratory disease: a gastroenterologist's perspective. J Physiol Pharmacol. 2020;71(2). doi:10.26402/jpp.2020.2.02.
5. Bai Y, Yao L, Wei T, et al. Presumed asymptomatic carrier transmission of COVID-19. JAMA. 2020;323(14):1406–1407.
6. Ferretti L, Wymant C, Kendall M, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491):eabb6936.
7. Furukawa NW, Brooks JT, Sobel J. Evidence supporting transmission of severe acute respiratory syndrome coronavirus 2 while presymptomatic or asymptomatic. Emerg Infect Dis. 2020;26(7):e201595.
8. He X, Lau EHY, Wu P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020; 26(5):672–675.
9. Hu Z, Song C, Xu C, et al. Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China. Sci China Life Sci. 2020;63(5):706–711.
10. Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science. 2020;368(6490):489–493.
11. Rothe C, Schunk M, Sothmann P, et al. Transmission of 2019-NCOV infection from an asymptomatic contact in Germany. N Engl J Med. 2020;382(10):970–971.
12. Zhang J, Tian S, Lou J, Chen Y. Familial cluster of COVID-19 infection from an asymptomatic. Crit Care. 2020;24(1):119.
13. Online Appendix. Survey Instrument, Phase 1 COVID-19 Symptom and Exposure Screening. George Mason University–Vibrent Health, Inc; 2020.
14. Struyf T, Deeks JJ, Dinnes J, et al. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease. Cochrane Database Syst Rev. 2020;7(7):CD013665. doi:10.1002/14651858.CD013665.
15. Ai J-W, Zhang H-C, Xu T, et al. Optimizing diagnostic strategy for novel coronavirus pneumonia, a multi-center study in Eastern China [published online ahead of print February 17, 2020]. medRxiv. Fdoi:10.1101/2020.02.13.20022673.
16. Cheng Z, Lu Y, Cao Q, et al. Clinical features and chest CT manifestations of coronavirus disease 2019 (COVID-19) in a single-center study in Shanghai, China. AJR Am J Roentgenol. 2020;215(1):121–126. doi:10.2214/AJR.20.22959.
17. Feng C, Huang Z, Wang L, et al. A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics [published online ahead of print March 20, 2020]. SSRN Electron J. doi:10.1101/2020.03.19.20039099.
18. Liang Y, Liang J, Zhou Q, et al. Prevalence and clinical features of 2019 novel coronavirus disease (COVID-19) in the Fever Clinic of a teaching hospital in Beijing: a single-center, retrospective study [published online ahead of print February 28, 2020]. medRxiv. doi:10.1101/2020.02.25.20027763.
19. Peng L, Liu KY, Xue F, et al. Improved early recognition of coronavirus disease-2019 (COVID-19): single-center data from a Shanghai screening hospital. Arch Iran Med. 2020;23(4):272–276. doi:10.34172/aim.2020.10.
20. Rentsch C, Kidwai-Khan F, Tate J, et al. COVID-19 testing, hospital admission, and intensive care among 2,026,227 United States Veterans aged 54-75 years [published online ahead of print April 14, 2020]. medRxiv. doi:10.1101/2020.04.09.20059964.
21. Song C-Y, Xu J, He J-Q, Lu Y-Q. COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients [published online ahead of print March 8, 2020]. medRxiv. doi:10.1101/2020.03.05.20031906.
22. Tolia VM, Chan TC, Castillo EM. Preliminary results of initial testing for coronavirus (COVID-19) in the emergency department. West J Emerg Med. 2020;21(3):503–506. doi:10.5811/westjem.2020.3.47348.
23. Zhu W, Xie K, Lu H, Xu L, Zhou S, Fang S. Initial clinical features of suspected coronavirus disease 2019 in two emergency departments outside of Hubei, China. J Med Virol. 2020;92(9):1525–1532. doi:10.1002/jmv.25763.
24. Pinzon RT, Wijaya VO, Buana RB, Al Jody A, Nunsio PN. Neurologic characteristics in coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Front Neurol. 2020;11:565. doi:10.3389/fneur.2020.00565.
25. Mao L, Jin H, Wang M, et al. Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurol. 2020;77(6):683–690. doi:10.1001/jamaneurol.2020.1127.
26. Guan W-J, Liang W-H, Zhao Y, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J. 2020;55(5):2000547. doi:10.1183/13993003.00547-2020.
27. Yan CH, Faraji F, Prajapati DP, Boone CE, DeConde AS. Association of chemosensory dysfunction and COVID-19 in patients presenting with influenza-like symptoms. Int Forum Allergy Rhinol. 2020;10(7):806–813. doi:10.1002/alr.22579.
28. Romoli M, Jelcic I, Bernard-Valnet R, et al. A systematic review of neurological manifestations of SARS-CoV-2 infection: the devil is hidden in the details. Eur J Neurol. 2020;27(9):1712–1726. doi:10.1111/ene.14382.
29. Lu L, Xiong W, Liu D, et al. New onset acute symptomatic seizure and risk factors in coronavirus disease 2019: a retrospective multicenter study. Epilepsia. 2020;61(6):e49–e53. doi:10.1111/epi.16524.
30. Kelley BJ, Rodriguez M. Seizures in patients with multiple sclerosis: epidemiology, pathophysiology and management. CNS Drugs. 2009;23(10):805–815. doi:10.2165/11310900-000000000-00000.
31. Menni C, Valdes AM, Freidin MB, et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat Med. 2020;26(7):1037–1040. doi:10.1038/s41591-020-0916-2.
32. Freeman EE, McMahon DE, Lipoff JB, et al. Pernio-like skin lesions associated with COVID-19: a case series of 318 patients from 8 countries. J Am Acad Dermatol. 2020;83(2):486–492. doi:10.1016/j.jaad. 2020.05.109.
33. Shweta F, Murugadoss K, Awasthi S, et al. Augmented curation of unstructured clinical notes from a massive EHR system reveals specific phenotypic signature of impending COVID-19 diagnosis [published online ahead of print April 30, 2020]. medRxiv. doi:10.1101/2020.04.19.20067660.
34. Sun Y, Koh V, Marimuthu K, et al. Epidemiological and clinical predictors of COVID-19. Clin Infect Dis. 2020;71(15):786–792. doi:10.1093/cid/ciaa322.
35. Zhao D, Yao F, Wang L, et al. A comparative study on the clinical features of coronavirus 2019 (COVID-19) pneumonia with other pneumonias. Clin Infect Dis. 2020;71(15):756–761. doi:10.1093/cid/ciaa247.
36. Thompson WC, Vuille J, Biedermann A, Taroni F. The role of prior probability in forensic assessments. Front Genet. 2013;4:220. doi:10.3389/fgene.2013.00220.
37. Alemi F, Vang J, Bagais WH, et al. Combined symptom screening and at-home tests for COVID-19. Qual Manag Health Care. 2023;32(1 suppl):S11–S20. doi: 10.1097/QMH.0000000000000404.
38. Wojtusiak J, Bagais WH, Vang J, Guralnik E, Roess A, Alemi F. The role of symptom clusters in triage of COVID-19 patients. Qual Manag Health Care. 2023;32(1 suppl):S21–S28. doi: 10.1097/QMH.0000000000000399.
39. Alemi F, Vang J, Wojtusiak J, et al. Differential diagnosis of COVID-19 and influenza. PLOS Global Public Health. 2022;2(7):e0000221. doi:10.1371/journal.pgph.0000221.
40. Wallace DV, Dykewicz MS. Seasonal allergic rhinitis: a focused systematic review and practice parameter update. Curr Opin Allergy Clin Immunol. 2017;17(4):286–294.
41. Unim B, Palmieri L, Lo Noce C, Brusaferro S, Onder G. Prevalence of COVID-19-related symptoms by age group. Aging Clin Exp Res. 2021;33(4):1145–1147. doi:10.1007/s40520-021-01809-y.
42. Mehta OP, Bhandari P, Raut A, Kacimi SEO, Huy NT. Coronavirus disease (COVID-19): comprehensive review of clinical presentation. Front Public Health. 2021;8:582932. doi:10.3389/fpubh.2020.582932.
43. Menni C, Valdes AM, Polidori L, et al. Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance: a prospective observational study from the ZOE COVID Study. Lancet. 2022;399(10335):1618–1624. doi:10.1016/S0140-6736(22)00327-0.
44. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi:10.1136/bmj.m1328.
45. Dardenne N, Locquet M, Diep AN, et al. Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study. BMC Infect Dis. 2022;22(1):464. doi:10.1186/s12879-022-07420-4.

combinations of symptoms; COVID-19 diagnosis; nonrespiratory symptoms; patient triage; respiratory systems

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