Hospital Bed Allocation Strategy Based on Queuing Theory during the COVID-19 Epidemic

Abstract

During the current epidemic, it is necessary to ensure the rehabilitation treatment of children with serious illness. At the same time, however, it is essential to effectively prevent cross-infection and prevent infections from occurring within the hospital setting. To resolve this contradiction, the rehabilitation department of Nanjing Children's Hospital adjusted its bed allocation based on the queuing model, with reference to the regional source and classification of the children's conditions in the rehabilitation department ward. The original triple rooms were transformed into a double room to enable the treatment of severely sick children coming from other places. A M/G/2 queuing model with priority was also applied to analyze the state of patient admissions. Moreover, patients in Nanjing were also classified into mild and severe cases. The M/M/1 queuing model with priority was used for analysis of this situation, so that severely ill children could be treated in time while patients with mild symptoms could be treated at home. This approach not only eases the bed tension in the ward, but also provides suitable conditions for controlling cross-infection.

Process Optimization Method for Day Ward Based on Bayesian Decision-Tree

“…They are good at using scientific research and management tools, and have achieved great achievements in definition, acquisition, nursing, patient satisfaction, therapeutic effect, anesthesia, economic benefits, postoperative recovery and other aspects of day surgery [8][9][10]. Domestic studies on day surgery mainly focus on the definition, development direction, existing problems [11][12][13], effect evaluation [14][15][16], and access of nurses and physicians [17][18][19], etc., while there are few studies on patient awareness and satisfaction [3][4][5], as well as the application of professional quality management tools.…”

“…Bai et al [23,24] suggested that the operation process of day surgery [17] should be started to strengthen the process management. Hu et al [18] emphasized the change of the concept of preoperative fasting and proposed that free intake of clear fluids before anesthesia during day surgery could reduce the incidence of postoperative nausea and vomiting and promote postoperative recovery of patients during daytime. Gong et al conducted a randomized controlled experiment on daytime and non-daytime patients from the perspective of publicity and education, and the study proved that the full use of preoperative publicity and education could reduce patients' anxiety.…”

Preventive control strategy on second wave of Covid-19 pandemic model incorporating lock-down effect

“…Mathematical models and projections in relation to climate and geographical change, environment, humanitarian disasters, and global health play a critical role in producing evidence in response to every viral outbreak such as Ebola, MERS, SARS, and currently SARS CoV-2, and in eliminating chronic infections such as viral Hepatitis, HIV, and Tuberculosis. Many researchers have recently investigated the current scenario of SARS CoV-2 in greater detail using the effects of fractional order models, reaction diffusion systems, and complex networks [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , effect of lock-down [45] , [46] and predicted different control strategies [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] . Impact of isolation disobedience and movement restrictions on CoV-19 pandemic has been studied by Stipic et al [55] and mathematical model of CoV-19 disease on the basis of latency and age structure has been considered by Blyuss and Kyrychko [56] .…”

Towards Public Integrity Audition for Cloud-IoT Data Based on Blockchain

“…With the fast development of IoT technique [5][6][7], the data generated by IoT systems increases significantly so that traditional methods of data storage cannot match the requirements of data management for IoT systems. Therefore, many enterprises have to outsource their huge IoT data to cloud server [8,9]. By renting the cloud storage service, the IoT data owner's burden of data storage and supervision is reduced greatly.…”

“…However, this simple solution is not practical because the volume of IoT data is normally very large. To address the problem, lots of data integrity auditing schemes for cloud-IoT data have been proposed [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. However, these traditional schemes have two main problems: (1) TPA is assumed to be trustful, but in real application scenarios, TPA is not completely trusted.…”