Background COVID-19 is the most informative pandemic in history. These unprecedented recorded data give rise to some novel concepts, discussions, and models. Macroscopic modeling of the period of hospitalization is one of these new issues. Methods Modeling of the lag between diagnosis and death is done by using two classes of macroscopic analytical methods: the correlation-based methods based on Pearson, Spearman, and Kendall correlation coefficients, and the logarithmic methods of two types. Also, we apply eight weighted average methods to smooth the time series before calculating the distance. We consider five lags with the least distance. All the computations are conducted on Matlab R2015b. Results The length of hospitalization for the fatal cases in the USA, Italy, and Germany are 2–10, 1–6, and 5–19 days, respectively. Overall, this length in the USA is two days more than in Italy and five days less than in Germany. Conclusion We take the distance between the diagnosis and death as the length of hospitalization. There is a negative association between the length of hospitalization and the case fatality rate. Therefore, the estimation of the length of hospitalization by using these macroscopic mathematical methods can be introduced as an indicator to scale the success of the countries fighting the ongoing pandemic.
IntroductionTime series models are one of the frequently used methods to describe the pattern of spreading an epidemic.MethodsWe presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples.ResultsWe estimated the parameters of two samples of the novel family to model the spreading of COVID-19 in China.DiscussionOur model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.
In epidemiology, the modeling of epicenters is important both conceptually and mathematically. This paper is an attempt to model epicenters mathematically. We present an algorithm to find new epicenters. Applying our model for the data related to COVID-19 pandemic, we obtain epicenters in China, South Korea, Iran, Italy, France, Germany, Spain, the USA, and Switzerland, on the days 1, 35, 42, 42, 49, 50, 50, 50, and 56, respectively. Although the number of these epicenters is less than 5% of all contaminated countries across the globe, as of March 22, 2020, they make up 74% of new cases and over 80% of total confirmed cases. Finally, we conclude that we expect to face three new epicenters between March 22 and April 1, 2020.
COVID-19 was first identified in December 2019 in Wuhan, China. From the beginning, this disease has been the subject of various scientific studies. Due to the large amount of data related to the spread of COVID-19 and the high speed of changes, particularly in modeling and forecasting works, it is required to update the predictions and assess the goodness of performance or the accuracy of the models. In this regard, we aim at evaluating the performance of the model introduced by Jamshidi et al [1] to describe the first wave of infectious diseases. Since about the propagation of COVID-19 in the UK, until early July 2020, we had encountered the first wave of the disease, it is possible to examine the performance of the model to describe the trend of the disease up to early July. Therefore, in this letter, we want to evaluate the performance of the model in two periods: - The time studied by Jamshidi et al [2] (April 15 to May 30, 2020), and - A one-month period thereafter (May 31 to July 1).
In the present paper, our objective is to forecast the spread of the pandemic of COVID-19 in terms of the number of confirmed cases and deaths. The paper is based on a two-part to model the time series of the daily relative increments whose second part solely models the pattern of the death rate. All the simulations and calculations have been done in MatLab R2015b, and the average curves and confidence intervals are calculated based on 100 simulations of the fitted models. Our results establish that the cumulative number of confirmed cases reach 1464729 cases on 21 May 2020, with 80% confidence interval of [1375362 1540424], and the number of new confirmed cases decreases to the interval [12801 22578] with the probability of 80% (the point prediction is equal to 17551) on 21 May 2020. Finally, we forecast that the cumulative number of deaths from 18747 cases on 11 April increases to around 47000 cases on 21 May.
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