Open Access
Public Health and Primary Care  |   February 2021
Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs
Author Affiliations & Notes
  • Gabrielle Bruzda, DO
    Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA
  • Fred Rawlins, DO
    Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA
    Simulation and Technology Center, Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA
  • Cameron Sumpter, BS
    Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA
    Gibbs Cancer Center and Research Institute, Spartanburg, SC, USA
    Center for Bioinformatics and Genetics, Primary Care Research Network, Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA
  • Harold R. Garner, PhD
    Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA
    Gibbs Cancer Center and Research Institute, Spartanburg, SC, USA
    Center for Bioinformatics and Genetics, Primary Care Research Network, Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA
  • Corresponding author: Harold R. Garner, PhD, Gibbs Cancer Center and Research Institute, Spartanburg, SC, USA; and Center for Bioinformatics and Genetics, Primary Care Research Network, Edward Via College of Osteopathic Medicine, 24060, Blacksburg, VA, USA, E-mail: skipgarner@gmail.com  
Article Information
Disaster Medicine / Medical Education / Preventive Medicine / Pulmonary Disorders
Public Health and Primary Care   |   February 2021
Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs
The Journal of the American Osteopathic Association, February 2021, Vol. 121, 211-220. doi:https://doi.org/10.1515/jom-2020-0129
The Journal of the American Osteopathic Association, February 2021, Vol. 121, 211-220. doi:https://doi.org/10.1515/jom-2020-0129
Abstract

Context: While the data generated by medical students at schools that require electronic patient encounter logs is primarily used to monitor their training progress, it can also be a great source of public health data. Specifically, it can be used for syndromic surveillance, a method used to analyze instantaneous health data for early detection of disease outbreaks.

Objective: To analyze how the International Classification of Diseases, 10th Revision (ICD-10) codes input by medical students at the Edward Via College of Osteopathic Medicine into the Clinical Rotation Evaluation and Documentation Organizer (CREDO) patient encounter logging system could act as a new syndromic surveillance tool.

Methods: A CREDO database query was conducted for ICD-10 codes entered between November 1, 2019 and March 13, 2020 using the World Health Organization’s 2011 revised case definitions for Influenza Like Illness (ILI). During that period, medical students had an approximated mean of 3,000 patient encounters per day from over 1,500 clinical sites. A cumulative sum technique was applied to the data to generate alert thresholds. Breast cancer, a disease with a stable incidence during the specified timeframe, was used as a control.

Results: Total ILI daily ICD-10 counts that exceeded alert thresholds represented unusual levels of disease occurred 11 times from November 20, 2020 through February 28, 2020. This analysis is consistent with the COVID-19 pandemic timeline. The first statistically significant ILI increase occurred nine days prior to the first laboratory confirmed case in the country.

Conclusion: Syndromic surveillance can be timelier than traditional surveillance methods, which require laboratory testing to confirm disease. As a result of this study, we are installing a real-time alert for ILI into CREDO, so rates can be monitored continuously as an indicator of possible future new infectious disease outbreaks.

Syndromic surveillance is a method used to analyze instantaneous health data for early detection of disease outbreaks. 1 Using real-time analysis, illness clusters can be identified before confirmed diagnoses, and epidemiologists can be alerted to potential outbreaks. 2, 3 Syndromic surveillance provides the most accurate assessment when data is tracked continuously in a setting with universal access. 4, 5 This approach places emergency medicine at the frontline of an emerging disease, and the data (i.e., diagnostic codes) retrieved from emergency visit records are an invaluable source for surveillance. 5 Therefore, those charged with investigating outbreaks classically rely on electronic health records obtained from emergency department visits. 
Syndromic surveillance does not replace traditional surveillance methods, which require laboratory testing to confirm disease and physician reporting of the disease activity. 2 Syndromic surveillance was developed in response to the bioterrorism-related cases of anthrax in 2001, 6 which motivated the United States to pass the Public Health Security and Bioterrorism Preparedness and Response Act (2002 Bioterrorism Act). 3 The objective of the 2002 Bioterrorism Act was early and rapid response to the intentional release of biologic agents. 7 To fulfill this objective, government, academic, and private sectors formed a partnership titled The National Syndromic Surveillance Program. 3 This collaboration between the Center for Disease Control (CDC), state and local health departments, and private facilities established a system whereby electronic health data could be collected, shared, and analyzed rapidly to respond to public health concerns. 3 In 2003, the CDC launched a program to integrate electronic health data from participating health facilities called BioSense, a shared platform program with tools to analyze health data received within 24 h. 3, 7  
The goals of syndromic surveillance extend beyond bioterrorism and include early warning systems for seasonal flu or influenza-like illnesses (ILI) such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2), notable outbreaks like the Ebola and Zika viruses, and food-borne illnesses such as Escherichia coli (E. coli). 8 Additionally, noninfectious public health concerns like heart disease and opioid overdoses can be monitored, 7 though syndromic surveillance is most useful for conditions that spread throughout the population, like acute respiratory illnesses. 9  
As an example of syndromic surveillance utility, 403,000 Milwaukee-area residents became infected in 1993 by cryptosporidium after a water treatment plant was contaminated. 10 After the outbreak, the Milwaukee Health Department created SurvNet, a communicable disease reporting system. This public health surveillance tool helped identify an outbreak of E. coli, alerted public health officials to a heatwave (a warning they used to take measures that reduced the overall mortality by 50%), and diagnosed the first outbreak of monkeypox in the Western hemisphere. 10, 11, 12  
Syndromic surveillance is also a useful tool during public health events of international concern. For example, in March 2003, the SARS Surveillance Project demonstrated the ability to rapidly establish multiregional syndromic surveillance during a global epidemic. 13, 14, 15 Similarly, by tracking discharge diagnoses during the 2009 flu pandemic (hemagglutinin 1 neuroaminidases 1 [H1N1]), an alert was triggered five days before the CDC press release and 18 days before the first laboratory confirmed H1N1 case in the country. 1, 16  
In December 2019, a cluster of mysterious pneumonia cases manifested in Wuhan, China. These cases were caused by novel coronavirus disease 2019 (COVID-19), an infectious disease caused by SARS-CoV-2. 17 Rapid transmission and high mortality rates quickly made this a public health emergency of international concern, and the World Health Organization (WHO) declared COVID-19 a pandemic on March 11, 2020. 18 The COVID-19 symptom complex continues to evolve, and current indications of possible infection include elevated temperature, fatigue, headache, cough, loss of taste or smell, muscle aches, nausea, sore throat or vomiting. 19  
The 2003 SARS outbreak, which resulted in approximately 8,000 cases and 800 deaths worldwide, was eradicated by interrupting all human-to-human transmission. This eradication was accomplished by means of syndromic surveillance with aggressive measures including prompt isolation, strict quarantine procedures, and law enforcement lockdown of communities most affected by the virus. 20 Whole genome analysis conducted in January 2020 demonstrated an 86% similarity between SARS-CoV and SARS-CoV-2. Both have similar incubation periods, pattern of progression to severe disease, and basic reproduction numbers. 20 The trajectory of the COVID-19 epidemic, however, is much different. On December 14, 2020, 12 months after the “beginning” of the COVID-19 pandemic, WHO reported 70,835,171 confirmed cases with 1,605,243 deaths across 220 countries, territories, or areas. 21 With such widespread disease, the worldwide pandemic is past the point of containment and the focus has shifted to broad mitigation strategies through social distancing and other hygienic techniques. 20  
We hypothesized that in the setting of community spread of COVID-19, syndromic surveillance data could have been analyzed to detect and alert public health professionals of early transmission because there would have been an increase in the number of ILI codes entered in electronic tracking systems. As a demonstration of this potential, we retrospectively analyzed diagnostic data collected from medical student patient encounters completed during core clinical and international rotations at Edward Via College of Osteopathic Medicine (VCOM)-affiliated hospitals and clinics during the pandemic period. 
Methods
This study was reviewed and was approved by the VCOM institutional review board (#1172780, effective until 1/15/2021). We retrospectively analyzed International Classification of Diseases, 10th Revision (ICD-10) codes logged by medical students completing core clinical (US-based) and international rotations at VCOM-affiliated hospitals and clinics during the COVID-19 pandemic period. Data was accumulated throughout the Appalachian and Delta regions of the US and in developing communities across Central America; it had been entered into the CREDO system, in which ICD-10 codes were recorded in real-time for each patient encounter. 22 The CREDO logging system collects only basic patient demographics (e.g., age range, sex); unique identifiers that conflict with the Health Insurance Portability and Accountability Act regulations are not captured by the system. 
Data from November 1, 2019, through March 13, 2020 was gathered; on March 13, 2020, VCOM was closed because of the COVID-19 pandemic and students were no longer able to participate in clinical rotations. During the study period, approximately 900 third- and fourth-year medical students from VCOM on clinical rotations logged approximately 3,000 patient encounters per day from over 1,500 clinical sites. A CREDO database query was conducted for ICD-10 codes using WHO’s 2011 revised case definitions for ILI and severe acute respiratory infections (SARI); these codes are presented in Table 1 and included J00, J01, J06, J09, J10, J11, J12, J18, J20, J22, J40, R05. 23, 24 Each patient interaction (encounter) can generate multiple ICD-10 codes; each diagnostic, procedure, and drug entry are independently tracked and counted. 
Table 1:
International Classification of Diseases, 10th Revision (ICD-10) codes for influenza-like illnesses (ILI) and severe acute respiratory infections (SARI).
J00 Acute nasopharyngitis [common cold]
J01 Acute sinusitis
J06 Acute upper respiratory infections of multiple and unspecified site
J09 Influenza due to certain identified influenza viruses
J10 Influenza due to other identified influenza viruses
J11 Influenza due to unidentified influenza virus
J12 Viral pneumonia, not elsewhere classified 646 entries
J18 Pneumonia, unspecified organism
J20 Acute bronchitis
J22 Unspecified acute lower respiratory infection
J40 Bronchitis, not specified as acute or chronic
R05 Cough
Table 1:
International Classification of Diseases, 10th Revision (ICD-10) codes for influenza-like illnesses (ILI) and severe acute respiratory infections (SARI).
J00 Acute nasopharyngitis [common cold]
J01 Acute sinusitis
J06 Acute upper respiratory infections of multiple and unspecified site
J09 Influenza due to certain identified influenza viruses
J10 Influenza due to other identified influenza viruses
J11 Influenza due to unidentified influenza virus
J12 Viral pneumonia, not elsewhere classified 646 entries
J18 Pneumonia, unspecified organism
J20 Acute bronchitis
J22 Unspecified acute lower respiratory infection
J40 Bronchitis, not specified as acute or chronic
R05 Cough
×
We calculated the sum for each ICD-10 code reported each day, then entered the data into a Microsoft Excel spreadsheet (_). The total daily syndrome count (the sum of all reported instances of all relevant ICD-10 codes per day) was also recorded (Table 2). A modified cumulative sum (CUSUM) algorithm was applied to the syndromic data to detect temporal aberrant events (Figure 1). The CUSUM algorithm is used in statistical quality control to monitor variance over time. The algorithm uses sequential analysis to display the cumulative sums of deviation from a target value. The CUSUM control chart provides an objective measure to monitor changes in disease activity and has been proven useful in monitoring respiratory syndromic data. 1, 25, 26  
Table 2:
Sample calculation for total syndrome count on January 31.
Date ICD-10 diagnosis code Total counts
J00 J01 J06 J09 J10 J11 J12 J18 J20 J22 J40 R05
Day 102 (N102) 1/31/2020 15 75 74 166 2 10 0 70 34 6 0 49 501
Total syndrome count = J00 + J01 + J06 + J09 + J10 + J11 + J12 + J18 + J20 + J22 + J40 + R05
Table 2:
Sample calculation for total syndrome count on January 31.
Date ICD-10 diagnosis code Total counts
J00 J01 J06 J09 J10 J11 J12 J18 J20 J22 J40 R05
Day 102 (N102) 1/31/2020 15 75 74 166 2 10 0 70 34 6 0 49 501
Total syndrome count = J00 + J01 + J06 + J09 + J10 + J11 + J12 + J18 + J20 + J22 + J40 + R05
×
Figure 1:
Equation for syndrome mean and alert threshold using a modified cumulative sum technique.
Figure 1:
Equation for syndrome mean and alert threshold using a modified cumulative sum technique.
The mean of daily syndrome counts from days three through nine before the day of interest (“moving mean”) was calculated. The alert threshold, or the upper limit, was calculated as the moving mean plus 2.5× the standard deviation during the same time period: 
 
Total daily counts that exceeded this threshold represented unusual levels of disease and indicated a potential outbreak. A sample calculation for the alert threshold on interest day January 31, 2020 is shown in Table 3. In this example, the total daily count (501) exceeded the alert threshold (32.6951), indicating a notable change in disease activity. Total syndrome counts were also obtained for the 2018–2019 flu season using the same case definitions. Lastly, total daily counts for breast malignancy—a disease with a stable incidence during the specified time frame—were used as a control.
Table 3:
Sample calculation for alert threshold on interest day January 31.
Date ICD-10 diagnosis code Total counts Alert threshold J00 J01 J06 J09 J10 J11 J12 J18 J20 J22 J40 R05 N93 1/22/2020 7 21 42 32 1 10 1 40 15 0 0 33 202 239.9814 N94 1/23/2020 11 15 27 33 2 2 0 35 7 0 0 21 153 248.3825 N95 1/24/2020 4 27 13 42 1 2 0 11 5 0 0 18 123 267.2127 N96 1/25/2020 6 5 5 7 0 0 0 7 3 0 0 2 35 287.1224 N97 1/26/2020 14 4 10 25 3 5 0 29 4 0 0 6 100 288.0848 N98 1/27/2020 13 23 32 47 2 10 1 16 10 0 0 15 169 284.6464 N99 1/28/2020 7 39 53 70 4 6 0 33 21 0 0 30 263 308.0681 N100 1/29/2020 18 47 52 73 3 10 0 27 17 0 0 26 273 300.8173 N101 1/30/2020 11 56 39 78 2 12 0 44 7 1 0 18 268 293.5103 N102 1/31/2020 15 75 74 166 2 10 0 70 34 6 0 49 501 332.6951 Alert threshold = (MEAN(N93:N99) + (2.5 × (STDEVA(N93:N99))))
Table 3:
Sample calculation for alert threshold on interest day January 31.
Date ICD-10 diagnosis code Total counts Alert threshold J00 J01 J06 J09 J10 J11 J12 J18 J20 J22 J40 R05 N93 1/22/2020 7 21 42 32 1 10 1 40 15 0 0 33 202 239.9814 N94 1/23/2020 11 15 27 33 2 2 0 35 7 0 0 21 153 248.3825 N95 1/24/2020 4 27 13 42 1 2 0 11 5 0 0 18 123 267.2127 N96 1/25/2020 6 5 5 7 0 0 0 7 3 0 0 2 35 287.1224 N97 1/26/2020 14 4 10 25 3 5 0 29 4 0 0 6 100 288.0848 N98 1/27/2020 13 23 32 47 2 10 1 16 10 0 0 15 169 284.6464 N99 1/28/2020 7 39 53 70 4 6 0 33 21 0 0 30 263 308.0681 N100 1/29/2020 18 47 52 73 3 10 0 27 17 0 0 26 273 300.8173 N101 1/30/2020 11 56 39 78 2 12 0 44 7 1 0 18 268 293.5103 N102 1/31/2020 15 75 74 166 2 10 0 70 34 6 0 49 501 332.6951 Alert threshold = (MEAN(N93:N99) + (2.5 × (STDEVA(N93:N99))))
×
 
Results
Daily total counts that exceeded alert thresholds for ILI/SARI are depicted in red in Figure 2. The first significant alert would have occurred on January 6 (daily count 120/alert threshold 53), which was three days before the Chinese Center for Disease Control and Prevention linked the mysterious cluster of pneumonia cases to COVID-19, 27 nine days before the first confirmed case in the United States, and 24 days before WHO declared the novel virus a Public Health Emergency of International Concern (PHEIC). 28 These alerts were tracked along a timeline of critical moments in the COVID-19 pandemic (Figure 3). 
Figure 2:
This figure shows total daily influenza-like illness (ILI) and severe acute respiratory infections (SARI) counts from November 1, 2019 through March 13, 2020 (blue), the cumulative sum (CUSUM) + 2.5 standard deviation (SD) alert threshold (broken line). Exceeded alert threshold dates are depicted in red. Breast malignancy acted as a control and is shown in black.
Figure 2:
This figure shows total daily influenza-like illness (ILI) and severe acute respiratory infections (SARI) counts from November 1, 2019 through March 13, 2020 (blue), the cumulative sum (CUSUM) + 2.5 standard deviation (SD) alert threshold (broken line). Exceeded alert threshold dates are depicted in red. Breast malignancy acted as a control and is shown in black.
Figure 3:
A timeline of the coronavirus outbreak may be explained by the statistically significant increases in influenza-like illnessactivity, shown here as an exceeded alert threshold.
Figure 3:
A timeline of the coronavirus outbreak may be explained by the statistically significant increases in influenza-like illnessactivity, shown here as an exceeded alert threshold.
The second alert would have been triggered on January 31 (daily count 501/alert threshold 333), two days before the US suspended entry for people who had traveled to China 29 in the prior 14 and five days before a briefing on Capitol Hill where Senators urged administration officials to take the threat more seriously. 30 The third important alert would have been triggered for February 24 (daily count 281/alert threshold 280), five days prior to the first confirmed COVID related death in the United States, 31 16 days before WHO declared a pandemic, 32 and 18 days before the US declared a national emergency. 33  
Additional data from the 2018–2019 flu season was included for comparison, with an adjusted timeframe to overlay ILI/SARI activity for both seasons (Figure 4). The total monthly counts of the designated ICD-10 codes were higher in 2019–2020 compared with 2018–19 (17,738 vs. 6,598) 
Figure 4:
Influenza-like illness (ILI) and severe acute respiratory infections (SARI) total daily counts comparing 2018–2019 flu season (black) to 2019–2020 season (blue). Data was accumulated from November 1 through March 13 for both seasons. Note, the diminished data from approximately December 20 to January 1 in each year is a consequence of students on holiday and thus not reporting patient encounters.
Figure 4:
Influenza-like illness (ILI) and severe acute respiratory infections (SARI) total daily counts comparing 2018–2019 flu season (black) to 2019–2020 season (blue). Data was accumulated from November 1 through March 13 for both seasons. Note, the diminished data from approximately December 20 to January 1 in each year is a consequence of students on holiday and thus not reporting patient encounters.
The CUSUM control chart provides an objective measure to monitor changes in disease activity and has been proven useful in monitoring respiratory syndromic data. 34 This method was effective in identifying statistically significant spikes in ILI activity. These alerts were overlaid along the pandemic timeline in Figure 3 as an indicator of the potential clinical significance and likelihood of a true outbreak. Further, the overall ILI activity level as illustrated in Figure 4 was significantly larger than the previous year (17,738 vs. 6,598). 
Discussion
Diagnostic codes entered by medical students to document each patient encounter across all rotations as a standard component of their clinical training were used for surveillance in this study. For respiratory syndromes, prior studies have shown the superiority of diagnosis codes over chief complaint alone for surveillance. 5, 35, 36 A study 5 by George Washington University compared surveillance between complaint and diagnostic codes and found despite a potential delay in detection using diagnostic codes, the additional information obtained is more reliable and beneficial than surveillance using chief complaint. Children’s Hospital of Boston also demonstrated that diagnosis was superior to chief complaint for respiratory illness in the pediatric population. 35 The use of diagnosis codes also demonstrates good sensitivity and specificity. A study by the University of Pittsburgh Center for Biomedical Informatics showed the sensitivity of detection of acute respiratory illness using ICD-9 coded diagnoses was 44% with a 97% specificity. 36  
The results of our syndromic surveillance COVID-19 validation demonstration are consistent with recent reported studies. 37, 38 In one study, 37 a group at University of California-Los Angeles Health System performed a retrospective observational EMR inpatient and outpatient chart review across three hospitals for patients who presented with a “cough” and for patients hospitalized with acute respiratory failure. The researchers found a statistically-significant excess in symptoms as early as December 22, 2019, and concluded these were most likely early COVID-19 infections. In another recent study, a group from the CDC and the American Red Cross, among others, tested blood bank samples for COVID-19 reactive antibodies. 38 They found samples with reactive antibodies from California, Oregon, and Washington as early as December 13–16, 2019. 
Traditional surveillance methods rely on confirmed laboratory testing and physician reporting. This has important health implications, as traditional surveillance may delay the time to response in an evolving outbreak; for example, our results indicated a lag of nine days. Today, approximately 70% of emergency departments and 4,000 health care facilities across the nation transmit electronic health data to the CDC’s BioSense Platform daily. Therefore, this method can be used to monitor infectious disease activity across much larger populations enhancing detection of evolving disease outbreaks. Our results have potential instructional value for medical students, as they illustrate a unique approach that utilizes student generated data for entirely new purposes, as well as a relevant statistical approach for time-dependent surveillance data analysis. Given the annual influenza season and the potential for new and emerging infectious diseases to surface from anywhere on the planet and affect health in America, it is important for future clinicians to be familiar with approaches like the one illustrated here, because it is likely to be valuable for future epidemics and pandemics. 
A limitation of this study is that the increase in ILI/SARI activity could have resulted from any number of possible infectious diseases, limiting our ability to conclude causation; however, it is consistent with the timeline of the COVID-19 pandemic and other similar reports. 37, 38 Additionally, surveillance data is subject to confounding factors. Another limitation is the data is representative of the VCOM associated hospitals and clinics throughout Appalachia and Central America (Dominican Republic, Honduras, and El Salvador) and does not represent the entire US population. A strength of the study was that data was accumulated from thousands of patients at many hospitals/clinics. Another advantage was the accuracy of discharge diagnosis codes as compared with chief complaints as this eliminates variability and results in higher outbreak detection sensitivity. Finally, the study was quick and easy to conduct with few resources and can easily be reproduced and enhance collaboration among public health, academic investigators, and industry. 
As a result of this study, we are installing a real-time alert for ILI into CREDO, so it can be added to those diseases that are currently monitored by VCOM faculty. This significant, high quality data is amenable to automated computational analysis for generating automated alerts for identifying and tracking the progress of future pandemics. There are a number of advantages to using CREDO data. First, it is real-time, as medical students are continuously logging their clinical training patient encounters. Second, data are collected over a very large number of distributed rural and urban sites, from large, urban metropolitan hospitals to small clinics and individual practices in socioeconomically disadvantaged areas, and thus provides a method for surveilling a large region of the US and the Caribbean area without the need to extract and merge data from a variety of EMR systems. Third, the data is uniformly collected by clinically trained professionals simultaneously across a plurality of indications, thus providing well matched controls. Last, the CREDO system is extensible and if used by most or all health care trainees (medical, physician assistants, nursing, pharmacy students), could ensure coverage across the entire US at even higher data rates, thus improving surveillance performance. 
Conclusion
One of the tenets of public health is prevention, and we deduce that syndromic surveillance is timelier than traditional surveillance methods and can be used in conjunction with them to enhance outbreak detection. Earlier detection can lead to more efficient laboratory testing and prompt infection control measures to interrupt human-to-human transmission during disease outbreaks. This study demonstrates the potential to use medical student clinical experience logs and the VCOM CREDO medical student patient encounter logging system as a real-time surveillance and rate-triggered alert tool. 
  Research funding: This work was supported by New Initiative internal funding provided by the Edward Via College of Osteopathic Medicine. This funding was used for programmer salaries and for cloud computing charges to create CREDO, the software system that acquired the data for this project.
 
  Author contributions: Drs Rawlins and Garner provided substantial contributions to conception and design, acquisition of data, analysis and interpretation of data; all authors drafted the article or revised it critically for important intellectual content; all authors gave final approval of the version of the article to be published; and all authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
 
  Competing interests: Authors state no conflict of interest.
 
  Ethical approval: This study was reviewed and was approved by the Institutional Review Board at Edward Via College of Osteopathic Medicine (#1172780, effective until 1/15/2021).
 
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Basavaraju, SV, Patton, ME, Grimm, K, et al.. Serologic Testing of U.S. Blood Donations to Identify SARS-CoV-2-Reactive Antibodies: December 2019-January 2020. Clinical Infectious Diseases. Published Ahead of Print (November 30, 2020). https://doi.org/10.1093/cid/ciaa1785
Raw data for total daily diagnostic code counts and calculated alert thresholds
Date J09 J00 J18 J01 J06 J10 R05 J20 J11 J12 J22 J40 Counts/day Alert threshold 10/23/2019 5 9 10 29 50 1 47 6 6 0 0 10 173 10/24/2019 0 7 6 19 23 0 8 3 3 1 0 3 73 10/25/2019 3 6 7 31 23 0 24 0 3 0 0 1 98 10/26/2019 0 1 0 0 2 0 7 0 0 0 0 0 10 10/27/2019 0 0 0 4 16 0 1 1 0 0 0 0 22 10/28/2019 1 9 8 6 39 0 20 3 2 0 0 1 89 10/29/2019 4 3 34 18 29 1 19 10 2 0 0 1 121 10/30/2019 6 6 14 16 31 0 25 11 2 0 0 0 111 10/31/2019 0 8 17 15 39 1 17 18 0 0 0 0 115 11/1/2019 1 2 30 11 20 0 20 10 2 0 1 0 97 224.2 11/2/2019 5 0 6 2 20 0 3 4 2 0 0 0 42 182.7 11/3/2019 1 11 6 7 9 0 10 7 6 0 0 0 57 195.1 11/4/2019 6 13 26 23 25 2 28 13 2 2 0 0 140 194.8 11/5/2019 3 13 23 25 50 0 31 13 4 0 0 1 163 181.3 11/6/2019 7 8 32 25 33 0 31 11 3 1 0 1 152 165.8 11/7/2019 2 7 32 16 23 0 18 13 0 0 0 1 112 186.4 11/8/2019 3 8 13 18 11 1 16 11 5 0 0 1 87 210.8 11/9/2019 1 5 16 4 17 1 8 6 1 1 1 0 61 226.2 11/10/2019 2 3 5 3 9 0 3 4 0 0 0 0 29 225.7 11/11/2019 5 6 26 22 54 0 15 17 1 0 0 0 146 225.7 11/12/2019 11 5 35 28 52 0 28 23 0 1 0 1 184 218.4 11/13/2019 3 14 24 16 25 1 38 9 1 0 0 2 133 230.7 11/14/2019 13 11 18 19 43 0 21 9 3 0 1 0 138 233.4 11/15/2019 6 3 14 22 25 0 7 13 1 0 0 0 91 247.2 11/16/2019 1 5 9 20 21 0 8 4 1 0 0 0 69 239.5 11/17/2019 7 1 10 20 21 0 13 7 0 1 0 0 80 246.4 11/18/2019 7 19 31 34 71 1 50 13 1 1 0 1 229 246.3 11/19/2019 10 10 26 34 35 0 24 15 5 0 0 0 159 244.5 11/20/2019 11 4 37 33 97 0 49 18 4 1 0 1 255 223.8 11/21/2019 13 8 68 42 59 1 32 25 3 0 0 0 251 278.1 11/22/2019 8 9 56 28 48 0 12 24 3 1 1 0 190 267.0 11/23/2019 0 4 19 7 11 0 16 0 1 0 0 0 58 329.3 11/24/2019 1 0 15 4 2 0 0 1 0 0 0 0 23 369.9 11/25/2019 2 6 9 34 21 1 20 2 0 0 0 0 95 369.3 11/26/2019 8 22 29 28 47 1 25 14 4 1 1 1 181 374.3 11/27/2019 7 5 20 19 23 0 6 3 3 0 0 0 86 398.8 11/28/2019 0 0 9 3 3 0 0 1 0 0 0 0 16 376.6 11/29/2019 1 0 10 4 8 1 2 2 4 0 0 0 32 381.8 11/30/2019 1 0 8 26 5 0 0 2 1 0 0 0 43 331.8 12/1/2019 2 0 7 5 4 0 5 6 0 0 0 0 29 267.3 12/2/2019 4 19 21 29 28 1 22 14 5 0 1 1 145 214.2 12/3/2019 8 20 21 48 34 3 27 13 3 1 1 0 179 214.0 12/4/2019 7 33 34 50 67 9 51 16 6 1 1 1 276 213.1 12/5/2019 5 9 19 51 24 0 18 15 0 0 2 0 143 236.5 12/6/2019 9 14 40 33 27 1 15 22 3 1 0 0 165 234.8 12/7/2019 2 4 4 10 8 1 1 1 2 0 0 0 33 351.1 12/8/2019 9 5 5 13 6 0 3 1 0 0 0 0 42 351.3 12/9/2019 10 22 57 64 43 0 23 12 7 2 0 1 241 350.2 12/10/2019 17 25 24 51 36 3 13 14 7 0 0 0 190 353.7 12/11/2019 10 11 33 28 17 3 13 6 3 0 0 0 124 348.9 12/12/2019 18 9 19 18 30 0 10 10 2 0 0 1 117 383.5 12/13/2019 12 16 18 43 23 0 23 10 3 2 0 0 150 386.6 12/14/2019 21 16 9 6 6 1 8 4 1 0 0 0 72 323.3 12/15/2019 3 4 6 13 8 0 1 1 0 0 0 0 36 319.9 12/16/2019 29 31 21 60 84 2 23 12 9 0 1 0 272 315.4 12/17/2019 27 16 32 37 76 1 26 14 0 0 1 0 230 303.1 12/18/2019 29 14 26 42 27 0 21 16 8 2 0 0 185 305.7 12/19/2019 28 10 33 70 48 1 18 32 3 1 0 0 244 331.5 12/20/2019 57 48 64 56 52 4 38 25 5 1 0 0 350 351.6 12/21/2019 2 1 6 5 2 0 6 1 0 0 0 0 23 362.5 12/22/2019 1 13 6 8 4 0 7 0 0 1 0 3 43 392.7 12/23/2019 1 7 7 1 4 0 0 2 5 0 0 0 27 476.1 12/24/2019 0 4 1 1 0 0 1 3 0 0 0 0 10 495.0 12/25/2019 0 0 1 3 0 0 0 0 0 0 0 0 4 492.3 12/26/2019 6 0 3 0 3 0 2 0 0 0 0 0 14 478.2 12/27/2019 1 0 2 1 0 0 1 1 1 1 0 0 8 461.9 12/28/2019 7 1 3 2 0 0 1 0 0 0 0 0 14 446.3 12/29/2019 1 1 4 3 8 0 0 0 4 0 0 0 21 380.6 12/30/2019 3 2 30 0 2 0 1 0 0 0 0 0 38 52.4 12/31/2019 0 1 3 0 1 0 0 1 0 0 0 0 6 50.9 1/1/2020 0 0 9 0 2 0 0 1 0 0 0 0 12 33.6 1/2/2020 5 0 10 1 4 15 1 1 0 0 0 0 37 43.7 1/3/2020 1 0 3 0 0 0 0 1 0 0 0 0 5 44.2 1/4/2020 2 0 8 0 2 0 0 1 0 0 0 0 13 43.1 1/5/2020 7 0 0 1 4 0 2 3 0 0 0 0 17 52.5 1/6/2020 21 4 27 24 13 1 20 7 2 0 1 0 120 53.3 1/7/2020 15 8 21 27 30 0 34 17 1 0 0 0 153 53.3 1/8/2020 28 14 26 33 40 1 27 11 4 2 0 0 186 52.7 1/9/2020 19 4 22 31 35 3 31 15 3 0 0 0 163 132.7 1/10/2020 20 1 32 17 20 0 12 17 2 0 1 1 123 201.0 1/11/2020 9 2 7 4 10 2 1 5 0 0 0 0 40 264.0 1/12/2020 13 0 12 7 3 0 7 2 3 0 0 0 47 292.3 1/13/2020 24 4 31 45 29 0 16 21 4 0 0 0 174 283.8 1/14/2020 25 8 23 24 43 3 26 8 16 1 0 0 177 273.0 1/15/2020 12 5 34 23 30 0 31 13 7 1 1 0 157 259.6 1/16/2020 22 8 31 33 15 0 23 11 4 1 0 0 148 276.7 1/17/2020 11 1 27 22 9 1 21 4 0 0 0 0 96 286.2 1/18/2020 33 7 16 5 18 0 7 4 3 1 0 0 94 273.3 1/19/2020 19 2 18 4 11 2 3 6 3 0 0 0 68 267.9 1/20/2020 38 15 29 28 43 0 18 12 5 0 0 0 188 266.4 1/21/2020 38 40 41 24 28 1 16 14 4 0 1 0 207 250.5 /22/2020 32 7 40 21 42 1 33 15 10 1 0 0 202 240.0 1/23/2020 33 11 35 15 27 2 21 7 2 0 0 0 153 248.4 1/24/2020 42 4 11 27 13 1 18 5 2 0 0 0 123 267.2 1/25/2020 7 6 7 5 5 0 2 3 0 0 0 0 35 287.1 1/26/2020 25 14 29 4 10 3 6 4 5 0 0 0 100 288.1 1/27/2020 47 13 16 23 32 2 15 10 10 1 0 0 169 284.6 1/28/2020 70 7 33 39 53 4 30 21 6 0 0 0 263 308.1 1/29/2020 73 18 27 47 52 3 26 17 10 0 0 0 273 300.8 1/30/2020 78 11 44 56 39 2 18 7 12 0 1 0 268 293.5 1/31/2020 166 15 70 75 74 2 49 34 10 0 6 0 501 332.7 2/1/2020 32 3 11 3 5 0 1 4 1 0 0 0 60 373.8 2/2/2020 5 2 6 13 7 0 5 0 2 0 0 0 40 412.9 2/3/2020 67 9 38 29 33 6 23 7 4 0 0 0 216 606.9 2/4/2020 64 4 20 24 29 9 28 11 10 0 0 0 199 597.5 2/5/2020 44 1 17 19 38 4 14 4 1 0 0 0 142 615.3 2/6/2020 50 3 25 14 33 0 12 5 5 1 0 0 148 617.5 2/7/2020 44 0 23 30 29 0 19 5 2 0 0 0 152 607.7 2/8/2020 8 0 5 1 23 0 6 1 0 1 0 0 45 590.9 2/9/2020 11 0 8 2 9 0 0 1 1 0 0 0 32 569.6 2/10/2020 70 20 22 29 40 0 19 12 15 0 0 0 227 300.6 2/11/2020 80 6 22 30 46 6 26 5 6 0 0 0 227 306.2 2/12/2020 50 2 24 18 23 4 14 6 7 1 0 0 149 309.8 2/13/2020 38 2 27 12 30 4 19 12 6 2 0 2 154 316.9 2/14/2020 57 3 26 32 26 6 19 10 7 0 0 0 186 332.7 2/15/2020 13 1 13 1 5 0 8 0 0 0 0 0 41 334.0 2/16/2020 51 10 13 15 38 2 11 9 17 0 0 0 166 335.2 2/17/2020 41 3 29 16 46 5 19 12 3 1 0 0 175 344.6 2/18/2020 51 12 31 30 33 3 23 10 9 1 1 0 204 346.2 2/19/2020 62 1 20 31 29 1 13 14 6 0 0 0 177 322.0 2/20/2020 41 2 10 14 28 0 14 6 4 0 0 0 119 300.0 2/21/2020 28 1 21 20 40 1 12 10 4 0 0 0 137 286.3 2/22/2020 3 0 7 1 4 0 1 2 2 0 0 0 20 291.9 2/23/2020 15 1 5 2 2 2 1 2 1 0 0 0 31 291.8 2/24/2020 70 8 89 36 34 12 13 14 5 0 0 0 281 280.2 2/25/2020 89 8 50 54 63 5 19 12 2 0 0 0 302 294.5 2/26/2020 99 17 44 41 71 1 24 13 15 1 1 1 328 304.3 2/27/2020 46 19 42 46 46 33 14 3 5 0 0 2 256 371.3 2/28/2020 214 29 91 83 133 0 45 21 13 8 9 0 646 428.5 2/29/2020 14 6 15 1 10 1 5 9 1 0 0 0 62 496.9 3/1/2020 16 0 10 4 21 1 0 0 0 1 0 0 53 518.1 3/2/2020 36 24 27 22 22 1 21 7 0 0 0 0 160 792.3 3/3/2020 32 3 36 14 22 1 14 14 11 0 0 0 147 778.9 3/4/2020 46 9 26 14 23 3 10 14 8 0 0 0 153 771.5 3/5/2020 51 10 21 16 43 2 25 16 2 0 0 0 186 765.8 3/6/2020 39 5 21 14 23 0 11 10 2 1 0 0 126 750.8 3/7/2020 7 5 9 5 6 1 3 1 1 0 0 0 38 719.8 3/8/2020 3 3 2 6 11 0 3 0 1 0 0 0 29 707.6 3/9/2020 47 14 21 11 43 0 15 8 3 0 0 0 162 253.2 3/10/2020 27 13 23 16 42 1 51 9 4 0 0 0 186 263.8 3/11/2020 26 10 25 15 35 1 9 8 4 1 1 1 136 274.0 3/12/2020 18 7 14 18 32 1 23 7 2 2 0 0 124 274.9 3/13/2020 36 24 24 12 37 0 19 13 9 0 0 0 174 291.5 Total 3251 1087 2973 2807 3609 206 2057 1170 467 52 35 24 17738
Date J09 J00 J18 J01 J06 J10 R05 J20 J11 J12 J22 J40 Counts/day Alert threshold 10/23/2019 5 9 10 29 50 1 47 6 6 0 0 10 173 10/24/2019 0 7 6 19 23 0 8 3 3 1 0 3 73 10/25/2019 3 6 7 31 23 0 24 0 3 0 0 1 98 10/26/2019 0 1 0 0 2 0 7 0 0 0 0 0 10 10/27/2019 0 0 0 4 16 0 1 1 0 0 0 0 22 10/28/2019 1 9 8 6 39 0 20 3 2 0 0 1 89 10/29/2019 4 3 34 18 29 1 19 10 2 0 0 1 121 10/30/2019 6 6 14 16 31 0 25 11 2 0 0 0 111 10/31/2019 0 8 17 15 39 1 17 18 0 0 0 0 115 11/1/2019 1 2 30 11 20 0 20 10 2 0 1 0 97 224.2 11/2/2019 5 0 6 2 20 0 3 4 2 0 0 0 42 182.7 11/3/2019 1 11 6 7 9 0 10 7 6 0 0 0 57 195.1 11/4/2019 6 13 26 23 25 2 28 13 2 2 0 0 140 194.8 11/5/2019 3 13 23 25 50 0 31 13 4 0 0 1 163 181.3 11/6/2019 7 8 32 25 33 0 31 11 3 1 0 1 152 165.8 11/7/2019 2 7 32 16 23 0 18 13 0 0 0 1 112 186.4 11/8/2019 3 8 13 18 11 1 16 11 5 0 0 1 87 210.8 11/9/2019 1 5 16 4 17 1 8 6 1 1 1 0 61 226.2 11/10/2019 2 3 5 3 9 0 3 4 0 0 0 0 29 225.7 11/11/2019 5 6 26 22 54 0 15 17 1 0 0 0 146 225.7 11/12/2019 11 5 35 28 52 0 28 23 0 1 0 1 184 218.4 11/13/2019 3 14 24 16 25 1 38 9 1 0 0 2 133 230.7 11/14/2019 13 11 18 19 43 0 21 9 3 0 1 0 138 233.4 11/15/2019 6 3 14 22 25 0 7 13 1 0 0 0 91 247.2 11/16/2019 1 5 9 20 21 0 8 4 1 0 0 0 69 239.5 11/17/2019 7 1 10 20 21 0 13 7 0 1 0 0 80 246.4 11/18/2019 7 19 31 34 71 1 50 13 1 1 0 1 229 246.3 11/19/2019 10 10 26 34 35 0 24 15 5 0 0 0 159 244.5 11/20/2019 11 4 37 33 97 0 49 18 4 1 0 1 255 223.8 11/21/2019 13 8 68 42 59 1 32 25 3 0 0 0 251 278.1 11/22/2019 8 9 56 28 48 0 12 24 3 1 1 0 190 267.0 11/23/2019 0 4 19 7 11 0 16 0 1 0 0 0 58 329.3 11/24/2019 1 0 15 4 2 0 0 1 0 0 0 0 23 369.9 11/25/2019 2 6 9 34 21 1 20 2 0 0 0 0 95 369.3 11/26/2019 8 22 29 28 47 1 25 14 4 1 1 1 181 374.3 11/27/2019 7 5 20 19 23 0 6 3 3 0 0 0 86 398.8 11/28/2019 0 0 9 3 3 0 0 1 0 0 0 0 16 376.6 11/29/2019 1 0 10 4 8 1 2 2 4 0 0 0 32 381.8 11/30/2019 1 0 8 26 5 0 0 2 1 0 0 0 43 331.8 12/1/2019 2 0 7 5 4 0 5 6 0 0 0 0 29 267.3 12/2/2019 4 19 21 29 28 1 22 14 5 0 1 1 145 214.2 12/3/2019 8 20 21 48 34 3 27 13 3 1 1 0 179 214.0 12/4/2019 7 33 34 50 67 9 51 16 6 1 1 1 276 213.1 12/5/2019 5 9 19 51 24 0 18 15 0 0 2 0 143 236.5 12/6/2019 9 14 40 33 27 1 15 22 3 1 0 0 165 234.8 12/7/2019 2 4 4 10 8 1 1 1 2 0 0 0 33 351.1 12/8/2019 9 5 5 13 6 0 3 1 0 0 0 0 42 351.3 12/9/2019 10 22 57 64 43 0 23 12 7 2 0 1 241 350.2 12/10/2019 17 25 24 51 36 3 13 14 7 0 0 0 190 353.7 12/11/2019 10 11 33 28 17 3 13 6 3 0 0 0 124 348.9 12/12/2019 18 9 19 18 30 0 10 10 2 0 0 1 117 383.5 12/13/2019 12 16 18 43 23 0 23 10 3 2 0 0 150 386.6 12/14/2019 21 16 9 6 6 1 8 4 1 0 0 0 72 323.3 12/15/2019 3 4 6 13 8 0 1 1 0 0 0 0 36 319.9 12/16/2019 29 31 21 60 84 2 23 12 9 0 1 0 272 315.4 12/17/2019 27 16 32 37 76 1 26 14 0 0 1 0 230 303.1 12/18/2019 29 14 26 42 27 0 21 16 8 2 0 0 185 305.7 12/19/2019 28 10 33 70 48 1 18 32 3 1 0 0 244 331.5 12/20/2019 57 48 64 56 52 4 38 25 5 1 0 0 350 351.6 12/21/2019 2 1 6 5 2 0 6 1 0 0 0 0 23 362.5 12/22/2019 1 13 6 8 4 0 7 0 0 1 0 3 43 392.7 12/23/2019 1 7 7 1 4 0 0 2 5 0 0 0 27 476.1 12/24/2019 0 4 1 1 0 0 1 3 0 0 0 0 10 495.0 12/25/2019 0 0 1 3 0 0 0 0 0 0 0 0 4 492.3 12/26/2019 6 0 3 0 3 0 2 0 0 0 0 0 14 478.2 12/27/2019 1 0 2 1 0 0 1 1 1 1 0 0 8 461.9 12/28/2019 7 1 3 2 0 0 1 0 0 0 0 0 14 446.3 12/29/2019 1 1 4 3 8 0 0 0 4 0 0 0 21 380.6 12/30/2019 3 2 30 0 2 0 1 0 0 0 0 0 38 52.4 12/31/2019 0 1 3 0 1 0 0 1 0 0 0 0 6 50.9 1/1/2020 0 0 9 0 2 0 0 1 0 0 0 0 12 33.6 1/2/2020 5 0 10 1 4 15 1 1 0 0 0 0 37 43.7 1/3/2020 1 0 3 0 0 0 0 1 0 0 0 0 5 44.2 1/4/2020 2 0 8 0 2 0 0 1 0 0 0 0 13 43.1 1/5/2020 7 0 0 1 4 0 2 3 0 0 0 0 17 52.5 1/6/2020 21 4 27 24 13 1 20 7 2 0 1 0 120 53.3 1/7/2020 15 8 21 27 30 0 34 17 1 0 0 0 153 53.3 1/8/2020 28 14 26 33 40 1 27 11 4 2 0 0 186 52.7 1/9/2020 19 4 22 31 35 3 31 15 3 0 0 0 163 132.7 1/10/2020 20 1 32 17 20 0 12 17 2 0 1 1 123 201.0 1/11/2020 9 2 7 4 10 2 1 5 0 0 0 0 40 264.0 1/12/2020 13 0 12 7 3 0 7 2 3 0 0 0 47 292.3 1/13/2020 24 4 31 45 29 0 16 21 4 0 0 0 174 283.8 1/14/2020 25 8 23 24 43 3 26 8 16 1 0 0 177 273.0 1/15/2020 12 5 34 23 30 0 31 13 7 1 1 0 157 259.6 1/16/2020 22 8 31 33 15 0 23 11 4 1 0 0 148 276.7 1/17/2020 11 1 27 22 9 1 21 4 0 0 0 0 96 286.2 1/18/2020 33 7 16 5 18 0 7 4 3 1 0 0 94 273.3 1/19/2020 19 2 18 4 11 2 3 6 3 0 0 0 68 267.9 1/20/2020 38 15 29 28 43 0 18 12 5 0 0 0 188 266.4 1/21/2020 38 40 41 24 28 1 16 14 4 0 1 0 207 250.5 /22/2020 32 7 40 21 42 1 33 15 10 1 0 0 202 240.0 1/23/2020 33 11 35 15 27 2 21 7 2 0 0 0 153 248.4 1/24/2020 42 4 11 27 13 1 18 5 2 0 0 0 123 267.2 1/25/2020 7 6 7 5 5 0 2 3 0 0 0 0 35 287.1 1/26/2020 25 14 29 4 10 3 6 4 5 0 0 0 100 288.1 1/27/2020 47 13 16 23 32 2 15 10 10 1 0 0 169 284.6 1/28/2020 70 7 33 39 53 4 30 21 6 0 0 0 263 308.1 1/29/2020 73 18 27 47 52 3 26 17 10 0 0 0 273 300.8 1/30/2020 78 11 44 56 39 2 18 7 12 0 1 0 268 293.5 1/31/2020 166 15 70 75 74 2 49 34 10 0 6 0 501 332.7 2/1/2020 32 3 11 3 5 0 1 4 1 0 0 0 60 373.8 2/2/2020 5 2 6 13 7 0 5 0 2 0 0 0 40 412.9 2/3/2020 67 9 38 29 33 6 23 7 4 0 0 0 216 606.9 2/4/2020 64 4 20 24 29 9 28 11 10 0 0 0 199 597.5 2/5/2020 44 1 17 19 38 4 14 4 1 0 0 0 142 615.3 2/6/2020 50 3 25 14 33 0 12 5 5 1 0 0 148 617.5 2/7/2020 44 0 23 30 29 0 19 5 2 0 0 0 152 607.7 2/8/2020 8 0 5 1 23 0 6 1 0 1 0 0 45 590.9 2/9/2020 11 0 8 2 9 0 0 1 1 0 0 0 32 569.6 2/10/2020 70 20 22 29 40 0 19 12 15 0 0 0 227 300.6 2/11/2020 80 6 22 30 46 6 26 5 6 0 0 0 227 306.2 2/12/2020 50 2 24 18 23 4 14 6 7 1 0 0 149 309.8 2/13/2020 38 2 27 12 30 4 19 12 6 2 0 2 154 316.9 2/14/2020 57 3 26 32 26 6 19 10 7 0 0 0 186 332.7 2/15/2020 13 1 13 1 5 0 8 0 0 0 0 0 41 334.0 2/16/2020 51 10 13 15 38 2 11 9 17 0 0 0 166 335.2 2/17/2020 41 3 29 16 46 5 19 12 3 1 0 0 175 344.6 2/18/2020 51 12 31 30 33 3 23 10 9 1 1 0 204 346.2 2/19/2020 62 1 20 31 29 1 13 14 6 0 0 0 177 322.0 2/20/2020 41 2 10 14 28 0 14 6 4 0 0 0 119 300.0 2/21/2020 28 1 21 20 40 1 12 10 4 0 0 0 137 286.3 2/22/2020 3 0 7 1 4 0 1 2 2 0 0 0 20 291.9 2/23/2020 15 1 5 2 2 2 1 2 1 0 0 0 31 291.8 2/24/2020 70 8 89 36 34 12 13 14 5 0 0 0 281 280.2 2/25/2020 89 8 50 54 63 5 19 12 2 0 0 0 302 294.5 2/26/2020 99 17 44 41 71 1 24 13 15 1 1 1 328 304.3 2/27/2020 46 19 42 46 46 33 14 3 5 0 0 2 256 371.3 2/28/2020 214 29 91 83 133 0 45 21 13 8 9 0 646 428.5 2/29/2020 14 6 15 1 10 1 5 9 1 0 0 0 62 496.9 3/1/2020 16 0 10 4 21 1 0 0 0 1 0 0 53 518.1 3/2/2020 36 24 27 22 22 1 21 7 0 0 0 0 160 792.3 3/3/2020 32 3 36 14 22 1 14 14 11 0 0 0 147 778.9 3/4/2020 46 9 26 14 23 3 10 14 8 0 0 0 153 771.5 3/5/2020 51 10 21 16 43 2 25 16 2 0 0 0 186 765.8 3/6/2020 39 5 21 14 23 0 11 10 2 1 0 0 126 750.8 3/7/2020 7 5 9 5 6 1 3 1 1 0 0 0 38 719.8 3/8/2020 3 3 2 6 11 0 3 0 1 0 0 0 29 707.6 3/9/2020 47 14 21 11 43 0 15 8 3 0 0 0 162 253.2 3/10/2020 27 13 23 16 42 1 51 9 4 0 0 0 186 263.8 3/11/2020 26 10 25 15 35 1 9 8 4 1 1 1 136 274.0 3/12/2020 18 7 14 18 32 1 23 7 2 2 0 0 124 274.9 3/13/2020 36 24 24 12 37 0 19 13 9 0 0 0 174 291.5 Total 3251 1087 2973 2807 3609 206 2057 1170 467 52 35 24 17738
×
 
Figure 1:
Equation for syndrome mean and alert threshold using a modified cumulative sum technique.
Figure 1:
Equation for syndrome mean and alert threshold using a modified cumulative sum technique.
Figure 2:
This figure shows total daily influenza-like illness (ILI) and severe acute respiratory infections (SARI) counts from November 1, 2019 through March 13, 2020 (blue), the cumulative sum (CUSUM) + 2.5 standard deviation (SD) alert threshold (broken line). Exceeded alert threshold dates are depicted in red. Breast malignancy acted as a control and is shown in black.
Figure 2:
This figure shows total daily influenza-like illness (ILI) and severe acute respiratory infections (SARI) counts from November 1, 2019 through March 13, 2020 (blue), the cumulative sum (CUSUM) + 2.5 standard deviation (SD) alert threshold (broken line). Exceeded alert threshold dates are depicted in red. Breast malignancy acted as a control and is shown in black.
Figure 3:
A timeline of the coronavirus outbreak may be explained by the statistically significant increases in influenza-like illnessactivity, shown here as an exceeded alert threshold.
Figure 3:
A timeline of the coronavirus outbreak may be explained by the statistically significant increases in influenza-like illnessactivity, shown here as an exceeded alert threshold.
Figure 4:
Influenza-like illness (ILI) and severe acute respiratory infections (SARI) total daily counts comparing 2018–2019 flu season (black) to 2019–2020 season (blue). Data was accumulated from November 1 through March 13 for both seasons. Note, the diminished data from approximately December 20 to January 1 in each year is a consequence of students on holiday and thus not reporting patient encounters.
Figure 4:
Influenza-like illness (ILI) and severe acute respiratory infections (SARI) total daily counts comparing 2018–2019 flu season (black) to 2019–2020 season (blue). Data was accumulated from November 1 through March 13 for both seasons. Note, the diminished data from approximately December 20 to January 1 in each year is a consequence of students on holiday and thus not reporting patient encounters.
Table 1:
International Classification of Diseases, 10th Revision (ICD-10) codes for influenza-like illnesses (ILI) and severe acute respiratory infections (SARI).
J00 Acute nasopharyngitis [common cold]
J01 Acute sinusitis
J06 Acute upper respiratory infections of multiple and unspecified site
J09 Influenza due to certain identified influenza viruses
J10 Influenza due to other identified influenza viruses
J11 Influenza due to unidentified influenza virus
J12 Viral pneumonia, not elsewhere classified 646 entries
J18 Pneumonia, unspecified organism
J20 Acute bronchitis
J22 Unspecified acute lower respiratory infection
J40 Bronchitis, not specified as acute or chronic
R05 Cough
Table 1:
International Classification of Diseases, 10th Revision (ICD-10) codes for influenza-like illnesses (ILI) and severe acute respiratory infections (SARI).
J00 Acute nasopharyngitis [common cold]
J01 Acute sinusitis
J06 Acute upper respiratory infections of multiple and unspecified site
J09 Influenza due to certain identified influenza viruses
J10 Influenza due to other identified influenza viruses
J11 Influenza due to unidentified influenza virus
J12 Viral pneumonia, not elsewhere classified 646 entries
J18 Pneumonia, unspecified organism
J20 Acute bronchitis
J22 Unspecified acute lower respiratory infection
J40 Bronchitis, not specified as acute or chronic
R05 Cough
×
Table 2:
Sample calculation for total syndrome count on January 31.
Date ICD-10 diagnosis code Total counts
J00 J01 J06 J09 J10 J11 J12 J18 J20 J22 J40 R05
Day 102 (N102) 1/31/2020 15 75 74 166 2 10 0 70 34 6 0 49 501
Total syndrome count = J00 + J01 + J06 + J09 + J10 + J11 + J12 + J18 + J20 + J22 + J40 + R05
Table 2:
Sample calculation for total syndrome count on January 31.
Date ICD-10 diagnosis code Total counts
J00 J01 J06 J09 J10 J11 J12 J18 J20 J22 J40 R05
Day 102 (N102) 1/31/2020 15 75 74 166 2 10 0 70 34 6 0 49 501
Total syndrome count = J00 + J01 + J06 + J09 + J10 + J11 + J12 + J18 + J20 + J22 + J40 + R05
×