Prediction and control of COVID-19 infection based on a hybrid intelligent model

Zhang, Gengpei; Liu, Xiongding

doi:10.1101/2020.10.22.20218032

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…This model has achieved superior performance in predicting vector-borne infectious diseases like dengue fever33 and is one of the potential deep learning predictive models for childhood infectious diseases. It recently has been applied as one of the state-of-the-art deep neural networks in forecasting COVID-19 34–36. We developed a two-layer LSTM model that includes 128 and 32 memory cells and uses a batch size of 32 and a diagnostic of 1000 epochs.…”

Section: Methodsmentioning

confidence: 99%

Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study

2022

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ObjectivesMalaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. This study aims to investigate the impact of climatic factors on the re-emergence of malaria in mainland China.DesignA modelling study.Setting and participantsMonthly malaria cases for four Plasmodium species (P. falciparum, P. malariae, P. vivax and other Plasmodium) and monthly climate data were collected for 31 provinces; malaria cases from 2004 to 2016 were obtained from the Chinese centre for disease control and prevention and climate parameters from China meteorological data service centre. We conducted analyses at the aggregate level, and there was no involvement of confidential information.Primary and secondary outcome measuresThe long short-term memory sequence-to-sequence (LSTMSeq2Seq) deep neural network model was used to predict the re-emergence of malaria cases from 2004 to 2016, based on the influence of climatic factors. We trained and tested the extreme gradient boosting (XGBoost), gated recurrent unit, LSTM, LSTMSeq2Seq models using monthly malaria cases and corresponding meteorological data in 31 provinces of China. Then we compared the predictive performance of models using root mean squared error (RMSE) and mean absolute error evaluation measures.ResultsThe proposed LSTMSeq2Seq model reduced the mean RMSE of the predictions by 19.05% to 33.93%, 18.4% to 33.59%, 17.6% to 26.67% and 13.28% to 21.34%, for P. falciparum, P. vivax, P. malariae, and other plasmodia, respectively, as compared with other candidate models. The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%.ConclusionsThe LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. Therefore, the LSTMSeq2Seq model can be effectively applied in the malaria re-emergence prediction.

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Section: Methodsmentioning

confidence: 99%

Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study

2022

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An Exploratory Analysis and Predictive SIR Model for the Early Onset of COVID-19 in Tamil Nadu, India

Mandapati

2021

Advances in Medical Technologies and Clinical Practice

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The growth of COVID-19 (SARS-CoV-2) in India has been rampant. Despite having a relatively small value of R0, the spread of disease increases exponentially every consecutive day. This chapter aims to analyze and conduct a concise study for the southern state of Tamil Nadu in India and build non-linear predictive models that evaluate the transmission of coronavirus amongst locals. A logistic regression and SIR model are deployed to understand the potential spread of disease. Through descriptive analysis on theoretical segmented portions, districts in Tamil Nadu with a higher number of confirmed cases are identified. Computation of crude mortality rate, infection fatality rate, predictive models, illustrations, and their results are discussed analytically.

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Prediction and control of COVID-19 infection based on a hybrid intelligent model

Cited by 2 publications

References 30 publications

Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study

Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study

An Exploratory Analysis and Predictive SIR Model for the Early Onset of COVID-19 in Tamil Nadu, India

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