Fitting the SEIR model of seasonal influenza outbreak to the incidence data for Russian cities

Leonenko, Vasiliy N.; Ivanov, Sergey V.

doi:10.1515/rnam-2016-0026

Cited by 27 publications

(24 citation statements)

References 14 publications

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“…Figures 8 and 9 present total cases and total mortality rate, respectively, and Figure 10 Table 7. represents the validation of the MPL-ICA and ANFIS models for the period of [20][21][22][23][24][25][26][27][28] April. The proposed model of MPL-ICA presented promising values for RMSE and determination coefficient for prediction of both outbreak and total mortality.…”

Section: Training Resultsmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér

Felde

Mosavi

et al. 2020

Preprint

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Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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Section: Training Resultsmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér

Felde

Mosavi

et al. 2020

Preprint

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“…The SEIR models through considering the significant incubation period during which individuals have been infected showed progress in improving the model accuracy for Varicella and Zika outbreak [13,14]. SEIR models assume that the incubation period is a random variable and similarly to the SIR model, there would be a disease-free-equilibrium [15,16]. It is worth mentioning that SEIR model will not work well where the parameters are non-stationary through time [17].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Outbreak Prediction with Machine Learning

Ardabili

Mosavi

Ghamisi

et al. 2020

Preprint

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Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak.

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“…In particular, due to the growing influence of collective immunity on the dynamics of morbidity, the prognostic model of the spread of influenza in the USSR, which was used in 1970s at the Research Institute of Influenza [10], showed a decline in the accuracy of predictions and due to that reason was put out of service [11]. The calibration of influenza dynamics models to contemporary Russian ARI data [12] confirmed that in case when ARI incidence is the only type of data used for the calibration procedure, the forecasting accuracy becomes low [13], [14]. Also, the population immunity level might not be accurately assessed by such model calibration when it is treated as a free parameter [15].…”

Section: Introductionmentioning

confidence: 99%

Modeling the dynamics of population immunity to influenza in Russian cities

Leonenko

Danilenko

2020

ITM Web Conf.

Self Cite

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Prediction models which will explicitly include the immunity levels of the population are required to plan effective measures for the containment of seasonal epidemics of influenza. The aim of the current work is to develop an approach to herd immunity dynamics modeling, with the long-term goal of employing it as a part of multicomponent model of influenza incidence dynamics. Based on serological studies performed for 52 Russian cities and 2 virus strains (A(H1N1)pdm09, A(H3N2)) in 11 years period, we propose statistical models which allow to analyze and predict the strain-specific immunity dynamics. *

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Fitting the SEIR model of seasonal influenza outbreak to the incidence data for Russian cities

Cited by 27 publications

References 14 publications

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

COVID-19 Outbreak Prediction with Machine Learning

Modeling the dynamics of population immunity to influenza in Russian cities

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