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ObjectiveNursing homes’ residents and staff constitute the largest proportion of the fatalities associated with COVID-19 epidemic. Although there is a significant variation in COVID-19 outbreaks among the US nursing homes, we still do not know why such outbreaks are larger and more likely in some nursing homes than others. This research aims to understand why some nursing homes are more susceptible to larger COVID-19 outbreaks.DesignObservational study of all nursing homes in the state of California until 1 May 2020.SettingThe state of California.Participants713 long-term care facilities in the state of California that participate in public reporting of COVID-19 infections as of 1 May 2020 and their infections data could be matched with data on ratings and governance features of nursing homes provided by Centers for Medicare & Medicaid Services (CMS).Main outcome measureThe number of reported COVID-19 infections among staff and residents.ResultsStudy sample included 713 nursing homes. The size of outbreaks among residents in for-profit nursing homes is 12.7 times larger than their non-profit counterparts (log count=2.54; 95% CI, 1.97 to 3.11; p<0.001). Higher ratings in CMS-reported health inspections are associated with lower number of infections among both staff (log count=−0.19; 95% CI, −0.37 to −0.01; p=0.05) and residents (log count=−0.20; 95% CI, −0.27 to −0.14; p<0.001). Nursing homes with higher discrepancy between their CMS-reported and self-reported ratings have higher number of infections among their staff (log count=0.41; 95% CI, 0.31 to 0.51; p<0.001) and residents (log count=0.13; 95% CI, 0.08 to 0.18; p<0.001).ConclusionsThe size of COVID-19 outbreaks in nursing homes is associated with their ratings and governance features. To prepare for the possible next waves of COVID-19 epidemic, policy makers should use these insights to identify the nursing homes who are more likely to experience large outbreaks.

T he Nursing Home Compare system administrated by the Centers for Medicare & Medicaid Services (CMS) is widely used by patients, medical providers and payers. We argue that the rating system is prone to inflation in self-reported measures, which leads to biased and misleading ratings. We use the CMS rating data over 2009-2013 and the corresponding financial data reported by Office of Statewide Health Planning and Development and patients' complaints data reported by California Department of Public Health for 1219 nursing homes in California to empirically examine the key factors affecting the star rating of a nursing home. We find a significant association between the changes in a nursing home's star rating and its profits, which points to a financial incentive for nursing homes to improve the ratings. We then demonstrate that this association does not always lead to legitimate efforts to improve service quality, but instead can induce inflation in self-reporting in the rating procedure. A prediction model is then developed to evaluate the extensiveness of inflation among the suspect population based on which 6% to 8.5% of the nursing homes are identified as likely inflators. We also summarize the key characteristics of likely inflators, which can be useful for future audit.

This paper formulates and studies the distributed formation problems of multi-agent systems (MAS) with randomly switching topologies and time-varying delays. The nonlinear dynamic of each agent at different time-interval corresponds to different switching mode which reflects the changing of traveling path in practical systems. The communication topology of the system is switching among finite modes which are governed by a finite-state Markov process. On the basis of artificial potential functions (APFs), a formation controller is designed in a general form. Sufficient conditions for stochastic formation stability of the multi-agent system are obtained in terms of Lyapunov functional approach and linear matrix inequalities (LMIs). Some heuristic rules to design a formation controller for the MAS are then presented. Finally, specific potential functions are discussed and corresponding simulation results are provided to demonstrate the effectiveness of the proposed approach.

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We release a dataset of over 2,100 COVID-19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites. We include an additional 24, 000 questions pulled from online sources that have been aligned by experts with existing answered questions from our dataset. This paper describes our efforts in collecting the dataset and summarizes the resulting data. Our dataset is automatically updated daily and available at https://github.com/JHU-COVID-QA/ scraping-qas. So far, this data has been used to develop a chatbot providing users information about COVID-19. We encourage others to build analytics and tools upon this dataset as well.

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