Managing patients with COVID-19 infections: a first-hand experience from the Wuhan Mobile Cabin Hospital

Sun, Chenyu; Wu, Qibing; Zhang, Chengyuan

doi:10.3399/bjgp20x709529

Cited by 15 publications

(16 citation statements)

References 3 publications

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“…Thus, given the current outbreak patterns of COVID-19 in Brazil, the government should continue to implement strict preventive and control strategies, and even more strict interventions, such as continued lockdown, keeping social distancing, an optimisation of the current tools, increasing the numbers of the mobile cabin hospitals, avoiding hospital-related infections, increasing medical personnel, increasing ICU availability, preparing isolation wards, enhancing the awareness of the general public etc. [4,30,[47][48][49][50]. Similar prophylactic measures are also expected to be carried out in Peru and Chile because the daily confirmed cases and deaths have still been noticeably rising in these two countries with daily 3676 and 4321 confirmed cases, coupled with daily 196 and 144 deaths, respectively, and seemingly they required more days to reach the plateau.…”

Section: Discussionmentioning

confidence: 97%

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Wang

Yao

et al. 2020

Epidemiol. Infect.

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Forecasting the epidemics of the diseases is very valuable in planning and supplying resources effectively. This study aims to estimate the epidemiological trends of the coronavirus disease 2019 (COVID-19) prevalence and mortality using the advanced α-Sutte Indicator, and its prediction accuracy level was compared with the most frequently adopted autoregressive integrated moving average (ARIMA) method. Time-series analysis was performed based on the total confirmed cases and deaths of COVID-19 in the world, Brazil, Peru, Canada and Chile between 27 February 2020 and 30 June 2020. By comparing the prediction reliability indices, including the root mean square error, mean absolute error, mean error rate, mean absolute percentage error and root mean square percentage error, the α-Sutte Indicator was found to produce lower forecasting error rates than the ARIMA model in all data apart from the prevalence testing set globally. The α-Sutte Indicator can be recommended as a useful tool to nowcast and forecast the COVID-19 prevalence and mortality of these regions except for the prevalence around the globe in the near future, which will help policymakers to plan and prepare health resources effectively. Also, the findings of our study may have managerial implications for the outbreak in other countries.

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Section: Discussionmentioning

confidence: 97%

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Wang

Yao

et al. 2020

Epidemiol. Infect.

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“…The ETS models can also play a pivotal role in estimating the effects of current and future prevention and control measures taken for the COVID-19 pandemic (eg, lockdown, an optimization of the present tools, an introduction of the available vaccine, increasing intensive care unit availability, an increase in the number of mobile cabin hospitals, and/or other intervention strategies). 1,7,20,48,49 If our ETS models eventually gave an overestimation for the epidemiological trends of the prevalence and mortality of the COVID-19 pandemic, meaning that the current countermeasures play a role; otherwise, additional prevention and control measures are required to prepare for and response to the COVID-19 outbreak. To our best knowledge, this is the only study to perform the prevalence and mortality time series forecasting of the COVID-19 pandemic using the advanced ETS models in the USA, the UK, Russia, and India, and our findings from different prevalence and mortality datasets confirmed the flexibility and usefulness of the ETS models in forecasting the outbreak of COVID-19.…”

Section: Discussionmentioning

confidence: 98%

<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

Wang¹,

Xu²,

Yao³

et al. 2020

IDR

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The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model. Materials and Methods: The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models. Results: Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD, A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A, N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets. Conclusion: The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.

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“…To reduce the impact of this type of shortage, public health systems should be flexible in increasing their medical capacity. Increased recruitment of retired doctors and nurses and the establishment of mobile field hospitals (17)(18)(19) have been implemented to compensate for deficiencies in medical capacity and slow the spread of COVID-19. We believe it is still critical to manage the medical capacity in the coming second wave of COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

Medical Capacity Shortages Facilitated the Rapid Dissemination of COVID-19 in Wuhan, New York State, and Italy

Zhang

Qian

2021

Preprint

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Background: During the coronavirus disease 2019 (COVID-19) outbreak, every public health system faced the potential challenge of medical capacity shortages. Infections without timely diagnosis or treatment may facilitate the stealth transmission and spread of the virus. Important as the influence of capacity shortages on the epidemic, it is still unclear how they could intensify the spread of the epidemic qualitatively under different circumstances. Our study aims to throw light on this influence.Methods: Using infection and medical capacity information reported in Wuhan in China, New York State in the United States, and Italy, we developed a dynamic susceptible–exposed–infected–recovered (SEIR) model to estimate the impact of medical capacity shortages during the COVID-19 outbreak at the city, state, and country levels.Results: The proposed model can fit data well (R-square > 0.9). Through sensitivity analysis, we found that doubled capacity would lead to a 39% lower peak infected number in Wuhan. Italy and New York State have similar results.Conclusions: The less shortages in medical capacity, the faster decline in the daily infection numbers and the fewer deaths, and more shortage would lead to steepen infection curve. This study provides a method for estimating potential shortages and explains how they may dynamically facilitate disease spreading during future pandemics such as COVID-19. Based on this, policy makers may figure out some way to explore more medical capacity and control the epidemic better.

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Managing patients with COVID-19 infections: a first-hand experience from the Wuhan Mobile Cabin Hospital

Cited by 15 publications

References 3 publications

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

Medical Capacity Shortages Facilitated the Rapid Dissemination of COVID-19 in Wuhan, New York State, and Italy

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