Dynamic models for Coronavirus Disease 2019 and data analysis

Shao, Nan; Zhong, Min; Yi, Yan; Pan, Han Shuang; Cheng, Jin; Chen, Wenbin

doi:10.1002/mma.6345

Cited by 39 publications

(38 citation statements)

References 15 publications

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“…Although there have been a number of studies on PCE, their focus has been on prior risk assessment, clinical and epidemiological investigation, viral genome analysis, vaccine development, establishment of evolution and transmission models, and epidemic management mechanisms ( Ahn et al, 2020 , Alhazzani et al, 2020 , Chen et al, 2020 , Phua et al, 2020 , Shao et al, 2020 , Wu et al, 2020 , Zhang, 2020 , Zhao et al, 2020a , Zhou et al, 2020 ). Conversely, regarding the pervasive and significant HOFs in the public sectors with regard to PCE, there have only been qualitative analyses of understaffing, lack of medical and emergency supplies, lack of emergency drills, improper safety protection operation, improper administration of epidemic areas, improper surveillance of imported cases of infection, concealed report on or release of epidemic information, poor technical ability of MI and CDC, insufficient public intervention, and imperfect management and response mechanisms for emergencies ( de Bruin et al, 2020 , Elavarasan and Pugazhendhi, 2020 , Gasmi et al, 2020 , Lancet, 2020 , Lau et al, 2004 , Law et al, 2020 , Liu et al, 2020 , Nicola et al, 2020 , Peng et al, 2020 , Rutayisire et al, 2020 , Wang and Wang, 2020 , WHO, 2020b , WHO, 2020c , Zhang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Human and organizational factors within the public sectors for the prevention and control of epidemic

Wang

et al. 2020

Safety Science

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Highlights Human factors in public sectors for epidemic prevention need systematic analysis. The Human Factors Analysis and Classiﬁcation System is suitable for this purpose. Bayesian Network theory enables probability prediction and key factors diagnosis. Hybrid model’s feasibility is tested by analyzing the COVID-19 outbreak in China.

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Section: Introductionmentioning

confidence: 99%

Human and organizational factors within the public sectors for the prevention and control of epidemic

Wang

et al. 2020

Safety Science

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“…It is important to note that it might still be early to predict overall outcome of the pandemic due to the complexity and variety of the biomedical and social factors involved [37], however mathematical models can be used as intelligent input during the decision-making process. These models can help predict the health infrastructure and material needs that patients will need in these countries in near future [38].…”

Section: Discussionmentioning

confidence: 99%

Resetting the Initial Conditions for Calculating Epidemic Spread: COVID-19 Outbreak in Italy

Babac

Mornar

2020

IEEE Access

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Confirmed cases of the disease COVID-19 have spread to more than 200 countries and regions of the world within a few months. Although the authorities report the number of new cases on daily basis, there remains a gap between the number of reported cases and actual number of cases in a population. One way to bridge this gap is to gain more in-depth understanding of the disease. In this paper, we have used the recent findings about the clinical courses of inpatients with COVID-19 to reset the initial conditions of the epidemic process in order to estimate more realistic number of cases in the population. By translating the reported cases certain number of days earlier with regard to an average clinical course of the disease, we have obtained much higher number of cases, which suggests that the actual number of infected cases and death rate might have been higher than reported. Based on the outbreak of COVID-19 in Italy, this paper shows an estimate of the number of infected cases based on infection and removal rates from data during the pandemic.

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“…Arguments and results are comparable to ours, though our integral formulation is more general and more robust, when extracting parameters from available data to feed the prognostic model. But delay models [8,14] can also be described with several variables and many parameters, which again makes them difficult to set up as forecasting model.…”

Section: Introductionmentioning

confidence: 99%

A novel deterministic forecast model for COVID-19 epidemic based on a single ordinary integro-differential equation

Köhler-Rieper

Röhl

Micheli

2020

Preprint

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In this paper we present a new approach to deterministic modelling of COVID-19 epidemic. Our model dynamics is expressed by a single prognostic variable which satisfies an integro-differential equation. All unknown parameters are described with a single, time-dependent variable R(t). We show that our model has similarities to classic compartmental models, such as SIR, and that the variable R(t) can be interpreted as a generalized effective reproduction number. The advantages of our approach are the simplicity of having only one equation, the numerical stability due to an integral formulation and the reliability since the model is formulated in terms of the most trustable statistical data variable: the number of cumulative diagnosed positive cases of Covid-19. Once this dynamic variable is calculated, other non-dynamic variables, such as the number of heavy cases (hospital beds), the number of intensive-care cases (ICUs) and the fatalities, can be derived from it using a similarly stable, integral approach. The formulation with a single equation allows us to calculate from real data the values of the sample effective reproduction number, which can then be fitted. Extrapolated values of R(t) can be used in the model to make reliable forecasts, though under the assumption that measures for reducing infections are maintained. We have applied our model to more than 20 countries and the ongoing results are available on a web-based platform [1]. In this paper, we focus on the data for two exemplary countries, Italy and Germany, and show that the model is capable of reproducing the course of the epidemic in the past and forecasting its course for a period of four to five weeks with a reasonable numerical stability.

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Dynamic models for Coronavirus Disease 2019 and data analysis

Cited by 39 publications

References 15 publications

Human and organizational factors within the public sectors for the prevention and control of epidemic

Human and organizational factors within the public sectors for the prevention and control of epidemic

Resetting the Initial Conditions for Calculating Epidemic Spread: COVID-19 Outbreak in Italy

A novel deterministic forecast model for COVID-19 epidemic based on a single ordinary integro-differential equation

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