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Pattern Recognit Lett ; 158: 133-140, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1804964


The outbreak of the SARS-CoV-2 novel coronavirus has caused a health crisis of immeasurable magnitude. Signals from heterogeneous public data sources could serve as early predictors for infection waves of the pandemic, particularly in its early phases, when infection data was scarce. In this article, we characterize temporal pandemic indicators by leveraging an integrated set of public data and apply them to a Prophet model to predict COVID-19 trends. An effective natural language processing pipeline was first built to extract time-series signals of specific articles from a news corpus. Bursts of these temporal signals were further identified with Kleinberg's burst detection algorithm. Across different US states, correlations for Google Trends of COVID-19 related terms, COVID-19 news volume, and publicly available wastewater SARS-CoV-2 measurements with weekly COVID-19 case numbers were generally high with lags ranging from 0 to 3 weeks, indicating them as strong predictors of viral spread. Incorporating time-series signals of these effective predictors significantly improved the performance of the Prophet model, which was able to predict the COVID-19 case numbers between one and two weeks with average mean absolute error rates of 0.38 and 0.46 respectively across different states.

Ann Glob Health ; 86(1): 70, 2020 06 29.
Article in English | MEDLINE | ID: covidwho-648198


Background: In December 2019, early cases of COVID-19 were identified in Wuhan, China. By late January 2020, it was evident that COVID-19 was rapidly spreading and represented a national health emergency. In order to contain the spread of COVID-19, China adopted a centralized treatment plan by appointing designated hospitals in each region. Shantou Central Hospital is a Grade A Class A general hospital in Guangdong Province. It was appointed as a provincial COVID-19 designated treatment hospital on January 21, 2020, to provide all COVID-19-related treatments for the city of Shantou. The nursing department at Shantou Central Hospital is fully responsible for hospital nursing administration, nursing human resource management, nursing quality management, and all nursing tasks related to hospital medical care, nursing, teaching, scientific research, preventive healthcare, and so on. Objective: To summarize the role of nursing management in transforming a general hospital into a designated hospital for treatment of COVID-19 patients. Methods: We undertook a series of nursing management measures in the strategic phase and the implementation phase. Findings: Through a series of nursing management measures, all COVID-19 patients admitted to our hospital were cured and discharged. All non-COVID-19 patients and staff hospitalized during the same period were not infected with the virus. During this period, our hospital completed 7,466 operations. Hence, our nursing management measures were effective. Conclusions: Our efficient nursing management system, first of all, effectively mobilized all available manpower; secondly, up-skilled and trained personnel within a very short period of time; thirdly, provided reliable logistical support for front-line protection equipments; and finally, motivated nurses during this very difficult time to make a significant positive contribution to the fight against COVID-19 pandemic.

Communicable Disease Control/organization & administration , Coronavirus Infections/epidemiology , Coronavirus Infections/nursing , Hospitals, General/organization & administration , Nursing Staff, Hospital/organization & administration , Pneumonia, Viral/epidemiology , Pneumonia, Viral/nursing , Betacoronavirus , COVID-19 , China/epidemiology , Efficiency, Organizational , Humans , Pandemics , SARS-CoV-2
Elife ; 92020 05 28.
Article in English | MEDLINE | ID: covidwho-401507


The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and deep omics insights is unavailable. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations from unstructured text, and triangulation with insights from single-cell RNA-sequencing, bulk RNA-seq and proteomics from diverse tissue types. A hypothesis-free profiling of ACE2 suggests tongue keratinocytes, olfactory epithelial cells, airway club cells and respiratory ciliated cells as potential reservoirs of the SARS-CoV-2 receptor. We find the gut as the putative hotspot of COVID-19, where a maturation correlated transcriptional signature is shared in small intestine enterocytes among coronavirus receptors (ACE2, DPP4, ANPEP). A holistic data science platform triangulating insights from structured and unstructured data holds potential for accelerating the generation of impactful biological insights and hypotheses.

Coronavirus Infections/virology , Libraries, Medical , Pneumonia, Viral/virology , Receptors, Virus/metabolism , Animals , Betacoronavirus/genetics , Betacoronavirus/metabolism , COVID-19 , Coronavirus Infections/metabolism , Coronavirus Infections/pathology , Gene Expression Profiling , Humans , Knowledge Discovery , Mice , Pandemics , Pneumonia, Viral/metabolism , Pneumonia, Viral/pathology , Receptors, Coronavirus , Receptors, Virus/chemistry , Receptors, Virus/genetics , SARS-CoV-2