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Nonparametric comparison of epidemic time trends: The case of COVID-19.

ABSTRACT: The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following: Does the virus spread in the same way in different countries? Or are there significant differences in the development of the epidemic? In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. In our empirical study, we use the methods to compare the outbreak patterns of the epidemic in a number of European countries.

SUBMITTER: Khismatullina M

PROVIDER: S-EPMC8141781 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Nonparametric comparison of epidemic time trends: The case of COVID-19.

Khismatullina Marina M Vogt Michael M

Journal of econometrics 20210524 1

The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following: Does the virus spread in the same way in different countries? Or are there significant differences in the development of the epidemic? In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. In our empirical study, we ...[more]

PMID: 34054197

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Project description:Highlights • A predicting model for the long-term epidemic trend of COVID-19 by using LSTM with rolling update mechanism is proposed.• The 150-days ahead epidemic trend of COVID-19 in Russia, Peru and Iran are estimated by our proposed model.• The results provide that the epidemic of Peru will end in early December.• The number of daily cases in Russia and Iran is expected to fall below 2000 and 1000 by mid-November and early December.• By introducing diffusion index, the effectiveness of preventive measures taken by the government are analyzed. The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19.

Dataset Information

Nonparametric comparison of epidemic time trends: The case of COVID-19.

Publications

Nonparametric comparison of epidemic time trends: The case of COVID-19.

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