Identifying Risk Factors That Distinguish Symptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infection From Common Upper Respiratory Infections in Children.

Background Demographic and clinical risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children presenting with respiratory viral symptoms are not well defined. An understanding of risk factors for SARS-CoV-2 infection can help prioritize testing. Methodology We evaluated potential demographic and clinical factors in children who had respiratory viral symptoms and were tested by polymerase chain reaction (PCR) for SARS-CoV-2 and other respiratory viral infections. Results Among the 263 symptomatic children tested for routine seasonal respiratory viruses by PCR, 18 (6.8%) tested positive for SARS-CoV-2. Overall, 22.2% of SARS-CoV-2-infected children and 37.1% of SARS-CoV-2-uninfected children had infection with one or more non-SARS-CoV-2 pathogens (p = 0.31). Higher proportions of children with compared to without SARS-CoV-2 infection were male (77.8 vs. 51.8%, p = 0.05), Hispanic (44.4% vs. 9.8%, p < 0.001), or had the symptoms of fatigue (22.2% vs. 2.5%, p = 0.003) or anosmia/ageusia (11.1% vs. 0%, p = 0.004). History of hypoxic-ischemic encephalopathy (HIE) and obesity were more common in children with versus without SARS-CoV-2 infection (11.1% vs. 1.2%, p = 0.04, and 11.1% vs. 0%, p = 0.004, respectively). In a multivariate analysis, Hispanic ethnicity, symptoms of fatigue or anosmia/ageusia, and presence of obesity (as noted on physical examination) or HIE were independently associated with SARS-CoV-2 infection. Numbers in each category were small, and these preliminary associations require confirmation in future studies. Conclusions In this area of the United States, infection with other viruses did not rule out infection with SARS-CoV-2. Additionally, children with respiratory viral symptoms who were of Hispanic ethnicity, had symptoms of weakness/fatigue, or had obesity or HIE were at an increased risk for SARS-CoV-2 infection. Future studies should assess if these factors are associated with risk in populations in other areas of the United States.

Synthea™ Novel coronavirus (COVID-19) model and synthetic data set.

March through May 2020, a model of novel coronavirus (COVID-19) disease progression and treatment was constructed for the open-source Synthea patient simulation. The model was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies. For this simulation, we generated 124,150 synthetic patients, with 88,166 infections and 18,177 hospitalized patients. Patient symptoms, disease severity, and morbidity outcomes were calibrated using clinical data from the peer-reviewed publications. 4.1% of all simulated infected patients died and 20.6% were hospitalized. At peak observation, 548 dialysis machines and 209 mechanical ventilators were needed. This simulation and the resulting data have been used for the development of algorithms and prototypes designed to address the current or future pandemics, and the model can continue to be refined to incorporate emerging COVID-19 knowledge, variations in patterns of care, and improvement in clinical outcomes. The resulting model, data, and analysis are available as open-source code on GitHub and an open-access data set is available for download.

Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record.

OBJECTIVE: Our objective is to create a source of synthetic electronic health records that is readily available; suited to industrial, innovation, research, and educational uses; and free of legal, privacy, security, and intellectual property restrictions. MATERIALS AND METHODS: We developed Synthea, an open-source software package that simulates the lifespans of synthetic patients, modeling the 10 most frequent reasons for primary care encounters and the 10 chronic conditions with the highest morbidity in the United States. RESULTS: Synthea adheres to a previously developed conceptual framework, scales via open-source deployment on the Internet, and may be extended with additional disease and treatment modules developed by its user community. One million synthetic patient records are now freely available online, encoded in standard formats (eg, Health Level-7 [HL7] Fast Healthcare Interoperability Resources [FHIR] and Consolidated-Clinical Document Architecture), and accessible through an HL7 FHIR application program interface. DISCUSSION: Health care lags other industries in information technology, data exchange, and interoperability. The lack of freely distributable health records has long hindered innovation in health care. Approaches and tools are available to inexpensively generate synthetic health records at scale without accidental disclosure risk, lowering current barriers to entry for promising early-stage developments. By engaging a growing community of users, the synthetic data generated will become increasingly comprehensive, detailed, and realistic over time. CONCLUSION: Synthetic patients can be simulated with models of disease progression and corresponding standards of care to produce risk-free realistic synthetic health care records at scale.