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J Clin Transl Sci ; 6(1): e59, 2022.
Article in English | MEDLINE | ID: covidwho-1821561


Introduction: COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Methods: Here we present a method called COVIDNearTerm to "forecast" hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). Results: We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%. Conclusion: COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations.

J Clin Oncol ; 39(2): 155-169, 2021 01 10.
Article in English | MEDLINE | ID: covidwho-1013168


This report presents the American Society of Clinical Oncology's (ASCO's) evaluation of the adaptations in care delivery, research operations, and regulatory oversight made in response to the coronavirus pandemic and presents recommendations for moving forward as the pandemic recedes. ASCO organized its recommendations for clinical research around five goals to ensure lessons learned from the COVID-19 experience are used to craft a more equitable, accessible, and efficient clinical research system that protects patient safety, ensures scientific integrity, and maintains data quality. The specific goals are: (1) ensure that clinical research is accessible, affordable, and equitable; (2) design more pragmatic and efficient clinical trials; (3) minimize administrative and regulatory burdens on research sites; (4) recruit, retain, and support a well-trained clinical research workforce; and (5) promote appropriate oversight and review of clinical trial conduct and results. Similarly, ASCO also organized its recommendations regarding cancer care delivery around five goals: (1) promote and protect equitable access to high-quality cancer care; (2) support safe delivery of high-quality cancer care; (3) advance policies to ensure oncology providers have sufficient resources to provide high-quality patient care; (4) recognize and address threats to clinician, provider, and patient well-being; and (5) improve patient access to high-quality cancer care via telemedicine. ASCO will work at all levels to advance the recommendations made in this report.

Biomedical Research , COVID-19/therapy , Medical Oncology , Neoplasms/therapy , SARS-CoV-2 , Clinical Trials as Topic , Delivery of Health Care , Humans , Research Design , Societies, Medical