Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model
TL;DR: The temporal evolution of the pandemic Sars-Cov-2 in Italy by means of dynamic population models and suggests that a different analysis, region by region, would be more sensible than one on the whole Italy, because the region Lombardy has a behaviour very fast compared to the other ones.
Abstract: In this article we study the temporal evolution of the pandemic Sars-Cov-2 in Italy by means of dynamic population models. The time window of the available population data is between February 24, and March 25. After we upgrade the data until April 1. We perform the analysis with 4 different models and we think that the best candidate to correctly described the italian situation is a generalized Logistic equation. We use two coupled differential equations that model the evolution of the severe infected and the dead. This choice is due to the fact that in Italy the pharyngeal swabs are made only to severe infected, therefore we have no information about asymptomatic people. Moreover, an important observation is that the virus spreads between Regions with some delay. Indeed, we suggest that a different analysis, region by region, would be more sensible than one on the whole Italy. In particular the region Lombardy has a behaviour very fast compared to the other ones. We show the fit and forecast of the dead and total severe infected for Italy and five regions: Lombardy, Piedmont, Emilia-Romagna, Veneto and Tuscany. Finally we perform an analysis of the peak (intended, in our study, as the maximum of the daily total severe infected) and an estimation of how many lives have been saved by means of the LockDown.
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TL;DR: This article proposes an alternative method to a classical SIRD model for the evaluation of the Sars-Cov-2 epidemic, and studies the behavior of the ratio infected over swabs for Italy, Germany and USA, to recover the generalized Logistic model used in [1].
Abstract: In a previous article [1] we have described the temporal evolution of the Sars-Cov-2 in Italy in the time window February 24-April 1. As we can see in [1] a generalized logistic equation captures both the peaks of the total infected and the deaths. In this article our goal is to study the missing peak, i.e. the currently infected one (or total currently positive). After the April 7, the large increase in the number of swabs meant that the logistical behavior of the infected curve no longer worked. So we decided to generalize the model, introducing new parameters. Moreover, we adopt a similar approach used in [1] (for the estimation of deaths) in order to evaluate the recoveries. In this way, introducing a simple conservation law, we define a model with 4 populations: total infected, currently positives, recoveries and deaths. Therefore, we propose an alternative method to a classical SIRD model for the evaluation of the Sars-Cov-2 epidemic. However, the method is general and thus applicable to other diseases. Finally we study the behavior of the ratio infected over swabs for Italy, Germany and USA, and we show as studying this parameter we recover the generalized Logistic model used in [1] for these three countries. We think that this trend could be useful for a future epidemic of this coronavirus.
19 citations
Cites background or methods from "Analysis of the evolution of the Sa..."
...In [1] we described two different peaks, the peak of the infected and the deaths one....
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...3 million of infected at April 17, with 172,434 detected; • 4) time delay between hospitalization and death t d 4 days, parameter extrapolated also in [1] ; • 5) time delay between the onset of symptoms and healing t r 14 − 42 days, a very oscillating parameter; • 6) two different LockDown (LD) data, March 10, and March 22, with different restriction....
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...1 that represents the inuence of asymptomatic; δ, a correction of the quadratic term of ogistic, and γ are the constant parameters considering the influnce of the government measures 1 , K f is a proportionality constant etween deaths and total number of infected, while t d and t r are he delays of deaths and recoveries respect to infected respectively; he constant A represents the contribution of asymptomatic people s introduced in [1] and finally t 0 is the time of LD start....
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...Moreover, we adopt a similar approach used in [1] (for the estimation of deaths) in order to evaluate the recoveries....
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...As we can see in [1] a generalized logistic equation captures both the peaks of the total infected and the deaths....
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TL;DR: To predict the spread of the new coronavirus infection COVID-19, the critical values of spread indicators have been determined and two models based on machine learning methods are proposed: a recurrent neural network with two layers of long short-term memory (LSTM) blocks and a 1-D convolutional neuralnetwork with a description of the choice of an optimization algorithm.
Abstract: To predict the spread of the new coronavirus infection COVID-19, the critical values of spread indicators have been determined for deciding on the introduction of restrictive measures using the city of Moscow as an example. A model was developed using classical methods of mathematical modeling based on exponential regression, the accuracy of the forecast was estimated, and the shortcomings of mathematical methods for predicting the spread of infection for more than two weeks. As a solution to the problem of the accuracy of long-term forecasts for more than two weeks, two models based on machine learning methods are proposed: a recurrent neural network with two layers of long short-term memory (LSTM) blocks and a 1-D convolutional neural network with a description of the choice of an optimization algorithm. The forecast accuracy of ML models was evaluated in comparison with the exponential regression model and one another using the example of data on the number of COVID-19 cases in the city of Moscow.
10 citations
TL;DR: In this article, a phenomenological epidemiological model for the description and prediction of the time trends of COVID-19 deaths worldwide is presented, where a bimodal distribution function is assumed to model the time distribution of deaths in a country.
Abstract: The paper presents a phenomenological epidemiological model for the description and prediction of the time trends of COVID-19 deaths worldwide. A bimodal distribution function—defined as the mixture of two lognormal distributions—is assumed to model the time distribution of deaths in a country. The asymmetric lognormal distribution enables better data fitting with respect to symmetric distribution functions. Besides, the presence of a second mode allows the model to also describe second waves of the epidemic. For each country, the model has six parameters, which are determined by fitting the available data through a nonlinear least-squares procedure. The fitted curves can then be extrapolated to predict the future trends of the total and daily number of deaths. Results for the six continents and the World are obtained by summing those computed for the 210 countries in the Our World in Data (OWID) dataset. To assess the accuracy of predictions, a validation study is first conducted. Then, based on data available as of 30 September 2020, the future trends are extrapolated until the end of year 2020.
9 citations
TL;DR: In this article, the authors developed a mathematical model of the coronavirus propagation in different countries (Brazil, India, US, Japan, Israel, Spain, Sweden), in the city of Moscow, and across the world.
Abstract: We develop a mathematical model of the coronavirus propagation in different countries (Brazil, India, US, Japan, Israel, Spain, Sweden), in the city of Moscow, and across the world. The pandemic spreads by a highly complex dynamics because it occurs in open nonhomogeneous systems where new infection foci erupt from time to time, triggering new transmission chains from infected to susceptible people. In general, statistical data collected as cumulative and epidemic curves are a superposition of many distinct local pandemic waves. In our modeling, we use the system of Feigenbaum’s discrete logistic equations (a logistic map) that describes the variation of the total number of infected over time. We show that this is the optimal model for the description of pandemic propagation in open nonhomogeneous systems with large errors in statistical data. We develop a procedure for isolating local waves, determining their model parameters, and predicting further evolution of each wave. We show that this model provides a good description of the statistical data and makes realistic forecasts. The forecast horizon depends on the degree of system closure and homogeneity. We calculate the start and end times of each wave, the peak, and the total number of infected in the current wave.
6 citations
TL;DR: In this paper, a generalized logistic growth model (GLM) approach was adopted to make prediction of growth of cases according to each state in Malaysia, which can assist government in devising short and long-term plan to tackle the ongoing pandemic.
4 citations
References
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TL;DR: The frequent opportunities I have had of receiving pleasure from your writings and conversation, have induced me to prefer offering to the Royal Society through your medium, this Paper on Life Contingencies, which forms part of a continuation of my original paper on the same subject, published among the valuable papers of the Society, as by passing through your hands it may receive the advantage of your judgment.
Abstract: Dear Sir, The frequent opportunities I have had of receiving pleasure from your writings and conversation, have induced me to prefer offering to the Royal Society through your medium, this Paper on Life Contingencies, which forms part of a continuation of my original paper on the same subject, published among the valuable papers of the Society, as by passing through your hands it may receive the advantage of your judgment.
3,257 citations
TL;DR: This review will help understand the biology and potential risk of CoVs that exist in richness in wildlife such as bats and describe diseases caused by different CoVs in humans and animals.
Abstract: The recent emergence of a novel coronavirus (2019-nCoV), which is causing an outbreak of unusual viral pneumonia in patients in Wuhan, a central city in China, is another warning of the risk of CoVs posed to public health. In this minireview, we provide a brief introduction of the general features of CoVs and describe diseases caused by different CoVs in humans and animals. This review will help understand the biology and potential risk of CoVs that exist in richness in wildlife such as bats.
2,480 citations
"Analysis of the evolution of the Sa..." refers methods in this paper
...Many growth models have been very recently applied to study the Covid-19 infection [2] [3] [4] [5] [6] [7] [8] [9] ....
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01 Jan 1838
1,918 citations
"Analysis of the evolution of the Sa..." refers background in this paper
...(10) As in the logistic study we compute the limit or t− >∞ and we obtain I(∞) = Kg, (11) this represents the asymptotic term of the population, defined by the resources available in the environment, in some sense also in this model we can encoded the human external action....
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TL;DR: COVID-19 cases in the United States that occurred during February 12-March 16, 2020 and severity of disease (hospitalization, admission to intensive care unit [ICU], and death) were analyzed by age group, suggesting that the risk for serious disease and death from CO VID-19 is higher in older age groups.
Abstract: Globally, approximately 170,000 confirmed cases of coronavirus disease 2019 (COVID-19) caused by the 2019 novel coronavirus (SARS-CoV-2) have been reported, including an estimated 7,000 deaths in approximately 150 countries (1). On March 11, 2020, the World Health Organization declared the COVID-19 outbreak a pandemic (2). Data from China have indicated that older adults, particularly those with serious underlying health conditions, are at higher risk for severe COVID-19-associated illness and death than are younger persons (3). Although the majority of reported COVID-19 cases in China were mild (81%), approximately 80% of deaths occurred among adults aged ≥60 years; only one (0.1%) death occurred in a person aged ≤19 years (3). In this report, COVID-19 cases in the United States that occurred during February 12-March 16, 2020 and severity of disease (hospitalization, admission to intensive care unit [ICU], and death) were analyzed by age group. As of March 16, a total of 4,226 COVID-19 cases in the United States had been reported to CDC, with multiple cases reported among older adults living in long-term care facilities (4). Overall, 31% of cases, 45% of hospitalizations, 53% of ICU admissions, and 80% of deaths associated with COVID-19 were among adults aged ≥65 years with the highest percentage of severe outcomes among persons aged ≥85 years. In contrast, no ICU admissions or deaths were reported among persons aged ≤19 years. Similar to reports from other countries, this finding suggests that the risk for serious disease and death from COVID-19 is higher in older age groups.
1,896 citations
"Analysis of the evolution of the Sa..." refers methods in this paper
...Many mathematical models have been recently applied to study the Covid-19 infection[2, 3, 4, 5, 6, 7, 8, 9]....
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TL;DR: This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections even as the epidemic is growing by reconstructing the evolutionary dynamics of the 2019 novel‐coronavirus recently causing an outbreak in Wuhan, China.
Abstract: To reconstruct the evolutionary dynamics of the 2019 novel coronavirus recently causing an outbreak in Wuhan, China, 52 SARS-CoV-2 genomes available on 04 February 2020 at GISAID were analysed The two models used to estimate the reproduction number (coalescent-based exponential growth and a birth-death skyline method) indicated an estimated mean evolutionary rate of 7 8 x 10(-4) subs/site/year (range 1 1x10(-4) -15x10(-4) ) and a mean tMRCA of the tree root of 73 days The estimated R value was 2 6 (range 2 1-5 1), and increased from 0 8 to 2 4 in December 2019 The estimated mean doubling time of the epidemic was between 3 6 and 4 1 days This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections even as the epidemic is growing This article is protected by copyright All rights reserved
187 citations