Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction.

Background: U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe. Methods: We developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions. Results: The model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education. Conclusions: Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

Modeling infection dynamics and mitigation strategies to support K-6 in-person instruction during the COVID-19 pandemic.

OBJECTIVE: To support safer in-person K-6 instruction during the coronavirus disease 2019 (COVID- 19) pandemic by providing public health authorities and school districts with a practical model of transmission dynamics and mitigation strategies. METHODS: We developed an agent-based model of infection dynamics and preventive mitigation strategies such as distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. The model parameters can be updated as the science evolves and are adjustable via an online user interface, enabling users to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions. RESULTS: Under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education. CONCLUSIONS: Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model's parameters can be immediately updated in response to changes in epidemiological conditions, science of COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis.

BACKGROUND: Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. OBJECTIVE: The aim of this study is to investigate whether excess visits where the word "cough" was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. METHODS: A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. RESULTS: The percentage of patients presenting with an EHR reason for visit containing the word "cough" to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. CONCLUSIONS: A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

Identification and validation of a multi-assay algorithm for cross-sectional HIV incidence estimation in populations with subtype C infection.

INTRODUCTION: Cross-sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi-assay algorithms (MAAs) for incidence estimation in subtype C settings. METHODS: We analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1-4 of the following assays: Limiting Antigen Avidity assay (LAg-Avidity), BioRad-Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was <1 year. This MAA was compared to the LAg-Avidity and BioRad-Avidity assays alone, a widely used LAg algorithm (LAg-Avidity <1.5 OD-n + VL >1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross-sectional incidence estimates to observed incidence in an independent longitudinal cohort. RESULTS: The optimal MAA was LAg-Avidity <2.8 OD-n  +  BioRad-Avidity <95% + VL >400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort. CONCLUSIONS: An optimal MAA was identified for cross-sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance.

Cataract Surgery Complications and Revisit Rates Among Three States.

PURPOSE: To characterize population-based 30-day procedure-related readmissions (revisits) following cataract surgery. SETTING: Ambulatory cataract surgery performed in California, Florida, or New York. DESIGN: Retrospective cohort study. METHODS: This study used all-capture state administrative datasets. Cataract procedures from California, Florida, and New York state ambulatory surgery settings were identified using ICD-9-CM and CPT codes. Thirty-day readmissions (revisits) were identified in inpatient, ambulatory, and emergency department settings across each state. RESULTS: Across the 3 states, the all-cause 30-day readmission rate was 6.0% and the procedure-related readmission (revisit) rate was 1.0%. Procedure-related revisits were highest for patients aged 20-29 (2.9%) and 30-39 (2.3%) and lowest for patients aged 70-79 (0.9%). Multivariate associations between clinical characteristics and 30-day procedure-related revisits included age 20-29 (odds ratio [OR]: 3.13; 95% confidence intervals [CI]: 2.33-4.20) and age 30-39 (OR: 2.35; CI: 1.91-2.89) compared with age 70-79, male sex (OR: 1.29; CI: 1.24-1.34), races black (OR: 1.37; CI: 1.27-1.48) and Hispanic (OR: 1.16; CI: 1.08-1.24) compared with white, and Medicaid insurance (OR: 1.18, CI: 1.07-1.30) compared with Medicare. Diabetes was also associated with increased 30-day procedure-related revisits (OR: 1.093, CI: 1.024-1.168). CONCLUSIONS: Cataract surgery is a common and, in aggregate, expensive procedure. Complication-related revisits follow a similar trend as surgical complications in large-scale population data, and may be useful as a preliminary, screening outcome measure. Our results highlight the importance of age as a risk factor for cataract surgery readmissions, and suggest a relationship between black or Hispanic race, Medicaid insurance, and diabetes associated with higher risk for cataract surgery complications.