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The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view. To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COVID-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy. Here, we present a novel approach to model COVID-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling. Our process will serve as a methodology for analysis of the data being collected in the ASAIO database and other data sources worldwide.

This paper presents a targeted, machine learning based solution to model the phenomenon known as the ‘cytokine storm,’ which is suspected to play a major role in explaining the highly variable severity of COVID-19 among patients. It describes how a Natural Language Processing (NLP) approach, augmented by biomedical knowledge databases, can extract pre-existing conditions and relevant clinical markers from Electronic Health Records (EHRs). These extracted variables can be modeled to demonstrate correlation with the severity of infection outcomes, the building blocks of a comprehensive risk assessment and stratification strategy to predict which patients have higher or lower risks in terms of the disease severity and likelihood of hospitalization, exclusively from insights taken from the natural language data. The model has been applied to a cohort of patients from a large database of real, anonymized patients and has displayed demonstrable results.

Two regions of cauliflower mosaic virus DNA were designed as markers to study pathogen transmission in a child care home (CCH) and child care center (CCC) and in homes of CCC children. The DNA markers were stable for 1 month in the environment. The DNA markers were introduced into the environment through sensitized objects, and spread in the environment was traced by detection of the markers with polymerase chain reaction. The DNA markers spread rapidly in both the CCH and CCC after introduction and spread more rapidly in the toddler room than in the infant room of the CCC. Hand touching of contaminated areas was the major factor leading to spread of the markers. Hand washing and surface wiping decreased spread of the markers. The markers spread minimally from room to room in the CCC but were detected in the children's homes after introduction of markers in the CCC.

Objective. This study describes use of specifically designed DNA markers as surrogate indicators of transmission of enteric pathogens in child care settings and from the settings to homes of children. Methods. Two regions of cauliflower CaMV 35S promoter DNA were designed as markers and were introduced into the environment through sensitized objects. Spread of markers in the environment was traced by detection of the markers using PCR technique. Studies of marker transmission were conducted in a child care home, in a child care center (CCC), and in homes of children attending CCC.Results. The DNA markers were stable for at least one month on a dried surface at room temperature. In the child care home, DNA markers spread rapidly in the environment following introduction. Hand touching of the contaminated areas was the major factor leading to spread of the markers. Hand washing and wiping of contaminated surfaces decreased spread of the markers. In the CCC infant room, 62% of 13 infant's hands, 70% of 10 surfaces and 100% of 10 toys were positive at 24 hours. In the toddler room, 73% of 15 toddler's hands and 100% of 10 toys and 10 surfaces were positive at 24 hours. Two weeks later