Calibration of a SEIR–SEI epidemic model to describe the Zika virus outbreak in Brazil

Dantas, Eber; Tosin, Michel; Cunha, Americo

doi:10.1016/j.amc.2018.06.024

Cited by 61 publications

(54 citation statements)

References 53 publications

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“…It can carry and spread viruses to humans and animals causes many of deaths every year. A great efforts has been paid to develop and analyze mathematical models that describe the population dynamics of mosquito-borne diseases such as Zika [2,4,7], dengue [1,27,43,48], malaria [3,5,6,36], yellow fever [40] and chikungunya [8-10, 34, 37-39, 46]. Chikungunya virus (CHIKV) is transmitted to humans by infected Aedes albopictus and Aedes agypti mosquito.…”

Section: Introductionmentioning

confidence: 99%

Global dynamics of humoral immunity Chikungunya virus with two routes of infection and Holling type-II

Ełaiw¹,

Almalki²,

Hobiny³

2019

J. Math. Computer Sci.

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In this work, we analyze the global dynamics of within-host Chikungunya virus (CHIKV) infection model with humoral immune response. We incorporate two modes of infections, attaching a CHIKV to a host monocyte, and contacting an infected monocyte with an uninfected monocyte. The infection incident rate is given by Holling type-II. The basic reproduction number R 0 is used to prove that the CHIKV-free equilibrium E 0 is globally asymptotically stable when R 0 1 and the infected equilibrium E 1 is globally asymptotically stable when R 0 > 1. Numerical simulations have been performed to confirm the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Global dynamics of humoral immunity Chikungunya virus with two routes of infection and Holling type-II

Ełaiw¹,

Almalki²,

Hobiny³

2019

J. Math. Computer Sci.

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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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“…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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Calibration of a SEIR–SEI epidemic model to describe the Zika virus outbreak in Brazil

Cited by 61 publications

References 53 publications

Global dynamics of humoral immunity Chikungunya virus with two routes of infection and Holling type-II

Global dynamics of humoral immunity Chikungunya virus with two routes of infection and Holling type-II

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

COVID-19 Outbreak Prediction with Machine Learning

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