Modeling transmission of pathogens in healthcare settings.

PURPOSE OF REVIEW: Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. RECENT FINDINGS: The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. SUMMARY: As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Hospital and ICU Admission Risk Associated With Comorbidities Among Children With COVID-19 Ancestral Strains.

A large proportion of children have been affected by COVID-19; we evaluated the association between comorbidities and hospitalization/ICU (intensive care unit) admission among 4097 children under age 21 years with symptomatic COVID-19 (not just polymerase chain reaction [PCR]-positive or multisystem inflammatory syndrome in children associated with COVID-19 [MIS-C]) from 2 large health systems from March 2020 to September 2021. Significant comorbidities and demographic factors identified by univariable analysis were included in a multivariable logistic regression compared with children ages 6 to 11 without comorbidities. In all, 475 children (11.6%) were hospitalized, of whom 25.5% required ICU admission. Children under 1 year had high hospitalization risk, but low risk of ICU admission. Presence of at least 1 comorbidity was associated with hospitalization and ICU admission (odds ratio [OR] > 4). Asthma, obesity, chronic kidney disease, sickle cell disease, bone marrow transplantation, and neurologic disorders were associated with hospitalization (adjusted odds ratio [AOR] > 2). Malignancy, intellectual disability, and prematurity were associated with ICU admission (AOR > 4). Comorbidities are significantly associated with hospitalization/ICU admission among children with COVID-19.

Impact of Prenatal SARS-CoV-2 Infection on Infant Emergency Department Visits and Hospitalization.

To better understand the impact of prenatal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on infants, this study sought to compare the risk of hospital visits and of postnatal SARS-CoV-2 infection between infants born to mothers with and without prenatal SARS-CoV-2 infection. In this retrospective observational cohort study of 6871 mothers and their infants, overall rates of emergency department (ED) visits and hospital admissions in the first 90 days of life were similar for infants born to mothers with and without prenatal SARS-CoV-2 infection. Infants born to negative mothers were more likely than infants of positive mothers to be hospitalized after ED visit (relative risk: 3.76; 95% confidence interval: 1.27-11.13, P = .003). Five infants tested positive; all were born to negative mothers, suggesting that maternal prenatal SARS-CoV-2 infection may protect infants from postnatal infection. The lower acuity ED visits for infants born to mothers with prenatal SARS-CoV-2 infection may reflect a heightened level of concern among these mothers.

Development and validation of a machine learning model to predict mortality risk in patients with COVID-19.

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients was needed due to a sudden and massive increase in patients during the COVID-19 pandemic as healthcare providers incurred an exponential increase in workload,which created a strain on the staff and limited resources. Further, methods to better understand and characterise the predictors of morbidity and mortality was needed. METHODS: We developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3395 hospital admissions with COVID-19. Multiple methods were applied, and final model was selected based on performance. A variable importance algorithm was used for interpretability, and understanding of performance and predictors was applied to the best model. We built a model with an area under the receiver operating characteristic curve of 83-97 to identify predictors and patients with high risk of mortality due to COVID-19. Oximetry, respirations, blood urea nitrogen, lymphocyte per cent, calcium, troponin and neutrophil percentage were important features, and key ranges were identified that contributed to a 50% increase in patients' mortality prediction score. With an increasing negative predictive value starting 0.90 after the second day of admission suggests we might be able to more confidently identify likely survivors DISCUSSION: This study serves as a use case of a machine learning methods with visualisations to aide clinicians with a better understanding of the model and predictors of mortality. CONCLUSION: As we continue to understand COVID-19, computer assisted algorithms might be able to improve the care of patients.

SARS-CoV-2 antibodies: IgA correlates with severity of disease in early COVID-19 infection.

Timing of detection of immunoglobulin G (IgG), immunoglobulin A (IgA), and immunoglobulin M (IgM) antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and their use to support the diagnosis are of increasing interest. We used the Gold Standard Diagnostics ELISA to evaluate the kinetics of SARS-CoV-2 IgG, IgA, and IgM antibodies in sera of 82 hospitalized patients with polymerase chain reaction (PCR)-confirmed coronavirus disease 2019 (COVID-19). Serum samples were collected 1-59 days post-onset of symptoms (PoS) and we examined the association of age, sex, disease severity, and symptoms' duration with antibody levels. We also tested sera of 100 ambulatory hospital employees with PCR-confirmed COVID-19 and samples collected during convalescence, 35-57 days PoS. All but four of the admitted patients (95.1%) developed antibodies to SARS-CoV-2. Antibodies were detected within 7 days PoS; IgA in 60.0%, IgM in 53.3%, and IgG in 46.7% of samples. IgG positivity increased to 100% on Day 21. We did not observe significant differences in the rate of antibody development in regard to age and sex. IgA levels were highest in patients with a severe and critical illness. In multiple regression analyses, only IgA levels were statistically significantly correlated with critical disease (p = .05) regardless of age, sex, and duration of symptoms. Among 100 ambulatory hospital employees who had antibody testing after 4 weeks PoS only 10% had positive IgA antibodies. The most frequently isolated isotype in sera of employees after 30 days PoS was IgG (88%). IgA was the predominant immunoglobulin in early disease and correlated independently with a critical illness. IgG antibodies remained detectable in almost 90% of samples collected up to two months after infection.