Dataset Information

Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China.

ABSTRACT: Background: Coronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the countries suffering the most with the COVID-19 epidemic. It is important to predict the epidemic trend of the COVID-19 epidemic in Italy to help develop public health strategies. Methods: We used time-series data of COVID-19 from Jan 22 2020 to Apr 02 2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with a similar total population number to Italy, was used as a comparative item. Results: In the eSIR model, we estimated that the mean of basic reproductive number for COVID-19 was 4.34 (95% CI, 3.04-6.00) in Italy and 3.16 (95% CI, 1.73-5.25) in Hunan. There would be a total of 182 051 infected cases (95%CI:116 114-274 378) under the current country blockade and the endpoint would be Aug 05 in Italy. Conclusion: Italy's current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures should be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.

SUBMITTER: Wangping J

PROVIDER: S-EPMC7218168 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China.

Wangping Jia J Ke Han H Yang Song S Wenzhe Cao C Shengshu Wang W Shanshan Yang Y Jianwei Wang W Fuyin Kou K Penggang Tai T Jing Li L Miao Liu L Yao He H

Frontiers in medicine 20200506

<b>Background:</b> Coronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the countries suffering the most with the COVID-19 epidemic. It is important to predict the epidemic trend of the COVID-19 epidemic in Italy to help develop public health strategies. <b>Methods:</b> We used time-series data of COVID-19 from Jan 22 2020 to Apr 02 2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the ...[more]

PMID: 32435645

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Project description:BackgroundCoronavirus disease 2019 (COVID-19) has developed into a worldwide pandemic. This study aimed to retrospectively describe the use of corticosteroids in treating COVID-19.MethodsFor this multicenter retrospective study, medical records from 488 symptomatic COVID-19 patients were reviewed. Patients were divided into severe and nonsevere groups. Baseline characteristics, signs and symptoms, laboratory findings, treatments, and disease outcomes were compared. Specific data for corticosteroid treatment were further analyzed.ResultsFour hundred fifty COVID-19 patients were included in this study, including 82 severe patients and 368 nonsevere cases. Out of the 450 patients, 126 (28.0%) received corticosteroid treatment. In the 126 patients treated with corticosteroids, the median daily dose of corticosteroid therapy was 56.6 [interquartile range (IQR): 40.0-78.4] mg and median corticosteroid therapy duration was 5.0 (IQR: 3.0-7.0) days. Among nonsevere cases, patients treated with corticosteroids were significantly older in comparison with patients who did not receive corticosteroid treatment (p<0.01); the proportion of patients receiving antibiotic therapy in the corticosteroid group was significantly higher than that in the noncorticosteroid group (p<0.001); hospitalization length and duration of viral shedding were significantly longer in patients in the corticosteroid group than in the noncorticosteroid group after adjusting for age, sex, and comorbidities (p<0.05). In severe cases, patients treated with corticosteroids were significantly older and comorbidities at admission were significantly more common in patients receiving corticosteroid treatment (p<0.05); the proportion of patients receiving antibiotic therapy in the corticosteroid group was significantly higher than that in the noncorticosteroid group (p<0.001); no significant difference in hospitalization length or viral shedding duration was found between two groups after adjusting for age, sex, and comorbidities (p>0.05).ConclusionOur study indicates that corticosteroids are regarded as one of treatments in the general clinical practice of COVID-19. However, corticosteroid use may be accompanied by increased use of antibiotics, longer hospitalization, and prolonged viral shedding.

Project description:The presence of a large number of infected individuals with few or no symptoms is an important epidemiological difficulty and the main mathematical feature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible-Infected-Removed) model with a compartment for infected individuals with no symptoms or few symptoms was proposed by Giuseppe Gaeta, Mathematics in Engineering, 3(2): 1–39 (2020). In this paper we investigate a slightly generalized version of the same model and propose a scheme for fitting the parameters of the model to real data using the time series only of the deceased individuals. The scheme is applied to the concrete cases of Lombardy, Italy and São Paulo state, Brazil, showing different aspects of the epidemics. In both cases we see strong evidence that the adoption of social distancing measures contributed to a slower increase in the number of deceased individuals when compared to the baseline of no reduction in the infection rate. Both for Lombardy and São Paulo we show that we may have good fits to the data up to the present, but with very large differences in the future behavior. The reasons behind such disparate outcomes are the uncertainty on the value of a key parameter, the probability that an infected individual is fully symptomatic, and on the intensity of the social distancing measures adopted. This conclusion enforces the necessity of trying to determine the real number of infected individuals in a population, symptomatic or asymptomatic. Highlights • COVID-19 epidemic previsions must take into account a large class of asymptomatic cases.• Disparate outcomes in previsions are consequence of the uncertainty in the number of asymptomatic individuals.• Necessity of determining the real number of infected individuals, symptomatic or asymptomatic.• Parameters of the model fitted to real data using only time series of deceased individuals.• Social distancing measures contributed to slower increase in the number of deceased individuals.

Dataset Information

Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China.

Publications

Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China.

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