tracking potential covid-19 outbreaks with influenza-like ... · 7/31/2020 · the cdc’s...

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©2020 American Academy of Pediatrics

Tracking Potential COVID-19 Outbreaks With Influenza-Like Symptoms Urgent Care Visits

Brian Muchmore, BS, Patrick Muchmore, MA, MS, PhD, Chi Wing Lee, BS, Marta E. Alarcón-Riquelme, MD, PhD, Andrew Muchmore, MD

DOI: 10.1542/peds.2020-1798

Journal: Pediatrics

Article Type: Research Brief

Citation: Muchmore B, Muchmore P, Lee CW, Alarcón-Riquelme ME, Muchmore A. Tracking potential COVID-19 outbreaks with influenza-like symptoms urgent care visits. Pediatrics. 2020; doi: 10.1542/peds.2020-1798

This is a prepublication version of an article that has undergone peer review and been accepted for publication but is not the final version of record. This paper may be cited using the DOI and date of access. This paper may contain information that has errors in facts, figures, and statements, and will be corrected in the final published version. The journal is providing an early version of this article to expedite access to this information. The American Academy of Pediatrics, the editors, and authors are not responsible for inaccurate information and data described in this version.

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Tracking Potential COVID-19 Outbreaks With Influenza-Like Symptoms Urgent Care Visits

Brian Muchmore, BSa,b,c, Patrick Muchmore, MA, MS, PhDa, Chi Wing Lee, BSa, Marta E Alarcón-Riquelme, MD, PhDb,d, Andrew Muchmore, MDa

Affiliations: aCodoniX Electronic Health Record Software, Rockville, Maryland; bMedical Genomics, GENYO. Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, Granada, Spain; cRobert Larner College of Medicine, University of Vermont, Burlington, Vermont; and dUnit of Inflammatory Chronic Diseases, Karolinska Institutet, Solna, Sweden Address correspondence to: Brian Muchmore, Data Intelligence Division, CodoniX Electronic Health Record Software, 390 Colchester Avenue, Burlington, VT 05401, [email protected] Funding Source: This study was supported in part by the Innovative Medicines Initiative Joint Undertaking funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contributions, grant number 115565. Conflict of Interest Disclosures: Dr Andrew Muchmore is founder and CEO of CodoniX Inc. Chi Wing Lee is a CodoniX employee. Brian Muchmore and Dr Patrick Muchmore consult for CodoniX. Dr Andrew Muchmore and Dr Patrick Muchmore own stock in CodoniX Inc. Abbreviations: COVID-19 — Coronavirus disease 2019 SARS-CoV-2 — Severe acute respiratory syndrome coronavirus 2 ILI — Influenza-like illness CDC — Centers for Disease Control and Prevention Contributors’ Statement Brian Muchmore conceptualized and designed the study, collected data, carried out initial and final analyses, drafted the initial manuscript and reviewed and revised the manuscript. Dr Patrick Muchmore, Chi Wing Lee and Dr Alarcon-Riquelme carried out the initial analyses, collected data, and reviewed and revised the manuscript. Dr Andrew Muchmore designed the data collection platform, coordinated and supervised data collection, and reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.


mailto:[email protected]



Introduction

The 2019-2020 influenza season has had elevated influenza-confirmed hospitalization rates.1

Simultaneously, the novel coronavirus (SARS-CoV-2) has infected millions worldwide.2 There

are open questions about COVID-19 related to its prevalence3 and seasonality.4 Many

individuals who develop COVID-19 present with influenza-like illness (ILI) symptoms including

fever, cough, or sore throat.5 This symptom overlap makes it difficult to differentiate COVID-19

from influenza or other related illnesses without testing, which has limited availability in the

United States6. Understanding the degree to which ILI in a community is not due to influenza

could help clinicians estimate the risk of COVID-19. The proportion of local current cases of

ILI that are influenza negative could inform clinical care and provide epidemiological insight

into the ongoing pandemic.

Methods

CodoniX is an electronic health record that captures data from approximately 3,000 patients

daily from >100 urgent care clinics in 15 states. We evaluated data for patients ≤21 years seen

during the months of January, February, and March from 2018-2020. We also evaluated

COVID-19 data from the same period, however, because there were fewer recorded cases all

ages were used.

We evaluated ICD-10 codes for discharge diagnoses of fever, cough, sore throat, influenza,

streptococcal pharyngitis, COVID-19, mononucleosis, and respiratory syncytial diagnoses, and

we evaluated Logical Observation Identifiers Names and Codes (LOINC) for positive test

results. The diagnosis of influenza was based on either a positive test or the discharge diagnosis.




There is no ICD-10 code for ILI, so it was defined as an ICD-10 diagnosis of fever with cough

and/or sore throat without another known cause such as mononucleosis or respiratory syncytial

viral. Supplementary Table 1 presents the distribution of these diagnoses by age range.

To validate the CodoniX data findings, publicly available ILI, influenza, and COVID-19 data

from the Center for Disease Control (CDC) were collected. ILI diagnoses were collected from

the CDC’s Outpatient Influenza-like Illness Surveillance Network, and CDC COVID-19 data

were acquired from the Johns Hopkins University Center for Systems Science and Engineering

Coronavirus repository. For both of these datasets, all ages were used for analysis. However,

influenza data collected from public health laboratories that report as World Health Organization

Collaborating Laboratories are stratified by age, so only ages 0-4 years and 5-24 years were used

in analysis.

Results

In both the CodoniX and CDC data, an increase in the ratio of ILI diagnoses to confirmed

influenza cases was observed in late February/early March 2020, which was not evident during

the same period in 2018 or 2019. Figures 1A-D illustrate for each year the ILI to influenza ratio

and number of COVID-19 cases. Figures 1A-B suggest an increasing trend in March 2020 that

was absent in March of 2018 and 2019, and these temporal patterns match the absolute weekly

COVID-19 case incidence seen in both the CodoniX (Figure 1C) and CDC COVID-19 data

(Figure 1D). In 2020 the ILI-influenza ratio rose in February, and Buishand’s U test indicates a

change-point during the last week of February (P = 0.01). An adjusted Mann-Kendall trend test

for the same time-series indicates a statistically significant trend (P <0.001). Figure 1E shows


https://github.com/CSSEGISandData/COVID-19



the ratio of ILI cases to streptococcal pharyngitis cases, and Supplementary Figure 1 illustrates a

heatmap and the clustering of these time series based on a statistical measure of similarity.

Discussion

Our results indicate that, beginning in February 2020, a significant number of patients receiving

ILI diagnoses were infected with a virus other than influenza. This suggests that monitoring the

ratio of influenza-negative ILI cases to influenza-positive cases could potentially be used as an

early warning system for influenza-negative viral syndromes with features of ILI.

A limitation of our study is that many diseases, including COVID-19, do not always present as

ILI. For example, while influenza patients typically present with ILI symptoms, streptococcal

pharyngitis patients often do not, so we would not expect the ratio between the two to contain a

discernible pattern, which is supported by Figure 1E. However, while describing the clinical

presentation of COVID-19 is an ongoing topic of study, recent data indicate it may often present

as ILI8. Additionally, although the CDC and CodoniX data exhibit similar patterns, neither

constitute a random sample of US residents, and the extent to which they are representative of

the entire population is unknown.

Acknowledgments We would like to thank Dr. James D. Michelson, Dr. Jacob Shaw and Kristen Koeller for their feedback and support throughout the project.




References 1. Dawood FS, Chung JR, Kim SS, et al. Interim Estimates of 2019-20 Seasonal Influenza Vaccine Effectiveness - United States, February 2020. MMWR Morb Mortal Wkly Rep. 2020;69:177-182. 2. Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019 - United States, February 12-March 28, 2020. MMWR Morb Mortal Wkly Rep. 2020;69:382-386. 3. Kissler SM, Tedijanto C, Goldstein E, et al. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020;368(6493):860-868. 4. Monto AS, DeJonge PM, Callear AP, et al. Coronavirus Occurrence and Transmission Over 8 Years in the HIVE Cohort of Households in Michigan. J Infect Dis. 2020;222(1):9-16. 5. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507-513. 6. Cheng MP, Papenburg J, Desjardins M, et al. Diagnostic Testing for Severe Acute Respiratory Syndrome-Related Coronavirus 2: A Narrative Review. Ann Intern Med. 2020;172(11):726-734 7. Mummert A, Weiss H, Long LP, et al. A perspective on multiple waves of influenza pandemics. PLoS One. 2013;8(4):e60343. 8. Guan WJ, Ni ZY, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382(18):1708-1720.


FIGURE 1

0.0

21.5

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ILI :

Influ

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Year 2018 2019 2020

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I : In

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ILI :

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CDC − Centers for Disease Control and Prevention ILI − Influenza−like illness

FIGURE 1Time series of disease ratios along with weekly SARS-CoV-2 positive test results.A. The ratio of weekly CodoniX ILI cases to influenza cases. B. The ratio ofweekly CDC ILI cases to CDC influenza-confirmed hospitalizations. C. WeeklyCodoniX positive SARS-CoV-2 tests. D. Weekly CDC positive SARS-CoV-2tests. E. The ratio of weekly CodoniX ILI cases to streptococcal pharyngitiscases.






Supplemental Information

SUPPLEMENTARY TABLE 1 Cases of non-specific influenza-like Illness (ILI), influenza, streptococcal pharyngitis, mononucleosis, and respiratory syncytial virus diagnoses by age from 2018-2020 between January 1st and March 31st.

Disease Age: 0-2 N = 5,2721

Age: 3-5 N = 8,2511

Age: 6-10 N = 14,6391

Age: 11-14 N = 12,9831

Age: 15-17 N = 11,9191

Age: 18-21 N = 15,8291

Influenza-like Illness

1,610 (11.8%)

2,188 (16.0%)

3,243 (23.8%)

2,461 (18.0%)

1,919 (14.1%)

2,214 (16.2%)

Influenza 499 (5.8%)

1,353 (15.9%)

2,564 (30.1%)

1,799 (21.1%)

1,199 (14.1%)

1,118 (13.1%)

Streptococcal pharyngitis

100 (3.5%)

430 (15.3%)

1,146 (40.7%)

517 (18.4%)

281 (10.0%)

343 (12.2%)

Respiratory syncytial virus

97 (70.8%)

28 (20.4%)

6 (4.4%)

3 (2.2%)

2 (1.5%)

1 (0.7%)

Mononucleosis 0 (0.0%)

2 (1.5%)

4 (2.9%)

9 (6.6%)

48 (35.0%)

74 (54.0%)

1Statistics presented: n (%)




SUPPLEMENTARY TABLE 2 Significance values of the change point detection increase of the ILI to influenza ratio. The adjusted Mann-Kendall p-value and Buishand U tests for change-point detection were used.

Time Series Adjusted Mann-Kendall P Value Buishand U Change-Point Detection*

COVID-19 (CDC 2020) <.001 Week 10 (P = 0.07)

ILI : influenza (CDC 2020) <.001 Week 10 (P = 0.04)

ILI : influenza (CodoniX 2020) <.001 Week 9 (P = 0.01)

ILI : influenza (CDC 2018) 0.01 Week 9 (P = 0.02)

ILI : influenza (CodoniX 2018) 0.03 Week 8 (P <.001)

COVID-19 (CodoniX 2020) 0.04 Week 10 (P = 0.05)

ILI : streptococcal pharyngitis (CodoniX 2018) 0.04 Week 6 (P = 0.01)



ILI : influenza (CodoniX 2019) 0.64 Week 3 (P = 0.06)

ILI : influenza (CDC 2019) 0.76 Week 2 (P = 0.05)

CDC — Centers for Disease Control

COVID-19 — Coronavirus Disease 2019

ILI — Influenza-like illness

* - The week since January 1st




SUPPLEMENTARY METHODS CodoniX case definitions of disease COVID-19, streptococcal pharyngitis, mononucleosis, and respiratory syncytial cases were pulled from the CodoniX database either using their ICD-10 diagnosis codes or the appropriate associated LOINC test code with a positive result. CodoniX ILI cases are filtered for positive COVID-19, streptococcal pharyngitis, mononucleosis, and respiratory syncytial cases. A CodoniX diagnosis of influenza requires that a patient have been tested for influenza and that the patient has either a positive influenza test determined by LOINC code and/or an ICD-10 diagnosis of influenza. Although we could filter for only positive tests, many of our clinics use rapid influenza diagnostic tests that have sensitivities reported to be between 50-70%, thus the CDC does not recommend that a negative test exclude the diagnosis of influenza if it is clinically justified. The above cases are then filtered against each other to ensure that, for example, an influenza positive case was not diagnosed with streptococcal pharyngitis. While co-infection is a possibility, it is rare in the urgent care setting, and although uncommon, a mismatched disease diagnosis with positive test is more often due to human error. Statistical Analyses All statistical analyses were performed using R version 3.6.3. An adjusted Mann-Kendall statistic for each time-series was used for trend analysis because of evidence that there is positive autocorrelation in many of the time-series. For example, if infection rate grows faster than average in one period then it will often grow faster than average in the following periods. The adjusted Mann-Kendall statistics were calculated with the modifiedmk package in R using the Mann-Kendall test of pre-whitened time series data in presence of serial correlation using the von Storch approach. The Buishand U test for change-point detection was calculated using the trend package in R. The Euclidean distance between time-series was calculated using the R package proxy and then converted to a similarity via 1 − abs(x) and normalized to a range of [0, 1]. The dendrogram in Figure 2 was created using the similarity matrix as input to the hclust R package. The agglomeration method used was the Weighted Pair Group Method with Arithmetic Mean. The appropriate agglomeration method was determined by using the R dendextend package, which has a function to compare the cophenetic correlation between the input similarity matrix and the cophenetic distance of the clustering tree. Data and Code Availability Source code necessary to recreate the figures and tables can be obtained upon request. The CodoniX data needed to recreate the figures and tables can be obtained after signing a data use agreement. The CDC data used in the study is freely available upon request. Alternatively, influenza data from public health laboratories that report as World Health Organization Collaborating Laboratories can be downloaded from https://gis.cdc.gov/grasp/fluview/flu_by_age_virus.html, the ILI data can be downloaded using the R package ilinet or from https://www.cdc.gov/flu/weekly/fluviewinteractive.htm, and the COVID-19 data can be downloaded using the R package coronavirus or from https://github.com/CSSEGISandData/COVID-19.


https://github.com/CSSEGISandData/COVID-19

0.6 0.6 0.5 0.3 0.3 0 0.1 0 0 0.1

0.7 0.7 0.2 0.2 0 0.1 0 0 0.1

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ILI : Influenza (CodoniX 2020)

COVID−19 (CodoniX 2020)

ILI : Influenza (CDC 2020)

COVID−19 (CDC 2020)


ILI : Influenza (CDC 2018)

ILI : Streptococcal Pharyngitis (CodoniX 2019)




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Similarity

Supplementary Figure 1A measure of similarity between time series. Similarity was defined to be 1-|d|, where d is the normalized Euclidean distance between series. Pairwise similarities are shown here as a similarity matrix, and the dendrogram at the top of the figure represents the hierarchical clustering of the similarity matrix.

CDC − Centers for Disease Control and Prevention COVID−19 − Coronavirus Disease 2019 ILI − Influenza−like illness




2020; originally published online July 22, 2020; originally published online July 22,Pediatrics

Andrew MuchmoreBrian Muchmore, Patrick Muchmore, Chi Wing Lee, Marta E Alarcón-Riquelme and

Care VisitsTracking Potential COVID-19 Outbreaks With Influenza-Like Symptoms Urgent

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2020; originally published online July 22, 2020; originally published online July 22,Pediatrics

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