Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network

Hu, Hongping; Wang, Haiyan; Wang, Feng; Langley, Daniel; Avram, Adrian; Liu, Maoxing

doi:10.1038/s41598-018-23075-1

Cited by 50 publications

(43 citation statements)

References 28 publications

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“…Given the complex transmission cycle of the virus, exposures and RRV incidence would not be expected to have a simple linear relationship. Non-linear models such as generalised additive mixed models and machine learning approaches are more likely to provide a more sophisticated representation of the transmission system than linear regression [ 87 – 89 ]. Analytical methods that encompass climate, environmental exposures, socio-economic factors and spatio-temporal aspects for forecasting RRV incidence are also worthy of consideration.…”

Section: Discussionmentioning

confidence: 99%

Epidemiological models for predicting Ross River virus in Australia: A systematic review

et al. 2020

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Ross River virus (RRV) is the most common and widespread arbovirus in Australia. Epidemiological models of RRV increase understanding of RRV transmission and help provide early warning of outbreaks to reduce incidence. However, RRV predictive models have not been systematically reviewed, analysed, and compared. The hypothesis of this systematic review was that summarising the epidemiological models applied to predict RRV disease and analysing model performance could elucidate drivers of RRV incidence and transmission patterns. We performed a systematic literature search in PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus for studies of RRV using population-based data, incorporating at least one epidemiological model and analysing the association between exposures and RRV disease. Forty-three articles, all of high or medium quality, were included. Twenty-two (51.2%) used generalised linear models and 11 (25.6%) used timeseries models. Climate and weather data were used in 27 (62.8%) and mosquito abundance or related data were used in 14 (32.6%) articles as model covariates. A total of 140 models were included across the articles. Rainfall (69 models, 49.3%), temperature (66, 47.1%) and tide height (45, 32.1%) were the three most commonly used exposures. Ten (23.3%) studies published data related to model performance. This review summarises current knowledge of RRV modelling and reveals a research gap in comparing predictive methods. To improve predictive accuracy, new methods for forecasting, such as non-linear mixed models and machine learning approaches, warrant investigation.

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Section: Discussionmentioning

confidence: 99%

Epidemiological models for predicting Ross River virus in Australia: A systematic review

et al. 2020

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“…The previous studies for prediction models for seasonal influenza have focused on social networking service data, search engine query data, and environmental factors [23–25]. These predictors are correlated with present influenza cases with a relatively short-term gap, of about one to four weeks.…”

Section: Discussionmentioning

confidence: 99%

Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries

Choi

Kim

Ahn

2019

Preprint

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17To identify countries that have seasonal patterns similar to the time series of influenza 18 surveillance data in the United States and other countries, and to forecast the 2018-2019 19 seasonal influenza outbreak in the U.S. using linear regression, auto regressive integrated 20 moving average, and deep learning. We collected the surveillance data of 164 countries from 21 2010 to 2018 using the FluNet database. Data for influenza-like illness (ILI) in the U.S. were 22 collected from the Fluview database. This cross-correlation study identified the time lag 23 between the two time-series. Deep learning was performed to forecast ILI, total influenza, A, 24 and B viruses after 26 weeks in the U.S. The seasonal influenza patterns in Australia and Chile 25showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. 26The R 2 score of DNN models for ILI for validation set in 2015-2019 was 0.722 despite how 27 hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. 28in 2018-2019 may be later and less severe than those in 2017-2018, judging from the influenza 29 activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and 30 type-specific influenza activities for next season at 40 th week. The correlation for seasonal 31 influenza among Australia, Chile, and the U.S. could be used to decide on influenza vaccine 32 strategy six months ahead in the U.S. 33 34

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“…Detection and surveillance of infectious diseases provide epidemiological intelligence to assist health practitioners in managing disease outbreaks [24]. Although digital surveillance cannot replace traditional surveillance of infectious diseases, they are useful in filling the critical gaps.…”

Section: Education Versus Spread and Researchmentioning

confidence: 99%

Research on Prediction of Infectious Diseases, their spread via Social Media and their link to Education

Aduragba

Cristea

2019

Proceedings of the 2019 4th International Conference on Information and Education Innovations - ICIEI 2019

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTInfectious diseases are a great plague, especially in low and middle income countries. Beyond the actual treatment, an important role is played by early prevention mechanisms, and education of society at large, about existing risks. This paper tackles these two important challenges, describing the current state of the art in this area, and pointing towards the need for both further, more inclusive research, as well as better education in affected countries on infectious diseases.

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Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network

Cited by 50 publications

References 28 publications

Epidemiological models for predicting Ross River virus in Australia: A systematic review

Epidemiological models for predicting Ross River virus in Australia: A systematic review

Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries

Research on Prediction of Infectious Diseases, their spread via Social Media and their link to Education

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