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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315415

ABSTRACT

Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In order to reflect the real-time trend of the epidemic in the process of infection for different areas, different policies and different epidemic diseases, a general adapted time- window based SIR model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the Basic reproduction number R0 and the exponential growth rate of the epidemic. Multiple data sets of epidemic diseases are analyzed, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%

2.
J Int Med Res ; 49(12): 3000605211062783, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1571589

ABSTRACT

OBJECTIVE: Secondary infection, especially bloodstream infection, is an important cause of death in critically ill patients with COVID-19. We aimed to describe secondary bloodstream infection (SBI) in critically ill adults with COVID-19 in the intensive care unit (ICU) and to explore risk factors related to SBI. METHODS: We reviewed all SBI cases among critically ill patients with COVID-19 from 12 February 2020 to 24 March 2020 in the COVID-19 ICU of Jingmen First People's Hospital. We compared risk factors associated with bloodstream infection in this study. All SBIs were confirmed by blood culture. RESULTS: We identified five cases of SBI among the 32 patients: three with Enterococcus faecium, one mixed septicemia (E. faecium and Candida albicans), and one C. parapsilosis. There were no significant differences between the SBI group and non-SBI group. Significant risk factors for SBI were extracorporeal membrane oxygenation, central venous catheter, indwelling urethral catheter, and nasogastric tube. CONCLUSIONS: Our findings confirmed that the incidence of secondary infection, particularly SBI, and mortality are high among critically ill patients with COVID-19. We showed that long-term hospitalization and invasive procedures such as tracheotomy, central venous catheter, indwelling urethral catheter, and nasogastric tube are risk factors for SBI and other complications.


Subject(s)
COVID-19 , Coinfection , Sepsis , Adult , Critical Illness , Humans , Intensive Care Units , SARS-CoV-2
3.
Comput Biol Med ; 138: 104868, 2021 11.
Article in English | MEDLINE | ID: covidwho-1401386

ABSTRACT

COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries - India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust.


Subject(s)
COVID-19 , Deep Learning , Artificial Intelligence , Humans , Neural Networks, Computer , SARS-CoV-2
4.
Sci Rep ; 10(1): 22454, 2020 12 31.
Article in English | MEDLINE | ID: covidwho-1003317

ABSTRACT

Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries---China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.


Subject(s)
COVID-19/epidemiology , Epidemiological Monitoring , Forecasting/methods , Models, Statistical , Basic Reproduction Number/statistics & numerical data , Brazil/epidemiology , China/epidemiology , France/epidemiology , Germany/epidemiology , Humans , Italy/epidemiology , Machine Learning , Republic of Korea/epidemiology , SARS-CoV-2 , Spain/epidemiology
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