Reducing Hospital Lengths of Stay in the Epidemic

The need for efficiency has been a major challenge for hospitals in the United States. The efficiency of these providers is directly related to their inpatient lengths of stay. The coronavirus epidemic has challenged the ability of hospitals in the United States to reduce stays and provide efficient care. This study described the impact of the epidemic on inpatient lengths of stay in the hospitals of Syracuse NY between March-November 2020 compared with the same periods in previous years. It demonstrated that, during this period, adult medicine lengths of stay increased by 4.5 percent and adult surgery stays increased by 5 - 6 percent. These increases were not large;however, they challenged the ability of hospitals to provide efficient care at a time when additional capacity was needed to deal with the epidemic. The results of the study suggested that the coronavirus epidemic should not limit the effectiveness of hospital programs that support efficiency and protect needed health care resources at the community level.

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Coronavirus(COVID-19)epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide.This newly recognized virus is highly transmissible,and no clinically approved vaccine or antiviral medicine is currently available.Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus.Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup.Here,a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray(CX-R)images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation.First,Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images,respectively.Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused.Parallel architecture,which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people,was considered.The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%,sensitivity of 99.90%,specificity of 100%,precision of 100%,F1-score of 99.93%,MSE of 0.021%,and RMSE of 0.016%in a large-scale dataset.This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

Association between population migration and epidemic control of coronavirus disease 2019

Dear Editor,In January 2020,a widespread outbreak of coronavirus disease 2019(COVID-19)occurred after the beginning of the largest annual migration in China,which is known as the Spring Festival migration.Starting in January 20,2020,the Chinese government took a series of unprecedented measures to contain the spread of COVID-19.Because of the role of Wuhan as a central transportation hub.

Taiwan’s Community Epidemic Prevention Based on Health Building and Collaborative Network

The outbreak of the novel coronavirus epidemic in early 2020 triggered off worldwide concerns for epidemic prevention as a coping strategy against public health crises.Facing the epidemic which has been characterized by wide spreading range and a long incubation period,communities have become the frontier in the war against it.At the same time,various problems have been exposed at the community level,including the lack of epidemic prevention planning,the high workload of community staff,and the insufficient public awareness on public health,etc.Focusing on the construction of Taiwan’s community epidemic prevention system,this paper elaborates the signifcance and necessity of the community’s participation in epidemic prevention based on a systematic literature review and policy study.It analyzes the restructuring and transition of a community epidemic prevention system and the primary strategies of community epidemic prevention planning,community health building,community health resources networking,community epidemic prevention practice,and the emergency planning in response to the novel coronavirus epidemic.It fnally summarizes the methods of building a sustainable community epidemic prevention system.