Models for predicting the evolution of influenza to inform vaccine strain selection

Agor, Joseph; Özaltın, Osman Y.

doi:10.1080/21645515.2017.1423152

Cited by 69 publications

(52 citation statements)

References 46 publications

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“…The Influenza virus has a high morbidity and mortality rate, leading to about 3-5 million cases of severe illnesses, up to half a million deaths around the world annually, and accordingly, high economic losses [1]. The World Health Organization (WHO) continuously monitors the viral acts, including both epidemic and pandemic, and decides the strategies to fight with the virus.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Forghani

Khachay

2020

Viruses

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Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between two strains is calculated on the basis of HI assays. Usually, such distances are visualized by using some kind of antigenic cartography method. The known drawback of the HI assay is that it is rather time-consuming and expensive. In this paper, we propose a novel approach for antigenic distance approximation based on deep learning in the feature spaces induced by hemagglutinin protein sequences and Convolutional Neural Networks (CNNs). To apply a CNN to compare the protein sequences, we utilize the encoding based on the physical and chemical characteristics of amino acids. By varying (hyper)parameters of the CNN architecture design, we find the most robust network. Further, we provide insight into the relationship between approximated antigenic distance and antigenicity by evaluating the network on the HI assay database for the H1N1 subtype. The results indicate that the best-trained network gives a high-precision approximation for the ground-truth antigenic distances, and can be used as a good exploratory tool in practical tasks.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Forghani

Khachay

2020

Viruses

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“…Periodic adjustment of the Influenza vaccine components is necessary to account for antigenic drift 24 . The flu shot is annually prepared at least six months in advance, and comprises a cocktail of historical strains determined by the WHO via global surveillance 25 , hoping to match the circulating strain(s) in the upcomimg flu season. A variety of hard-to-model effects hinders this prediction, and has limited vaccine effectiveness in recent years 26 .…”

Section: Resultsmentioning

confidence: 99%

Preparing For the Next Pandemic: Learning Wild Mutational Patterns At Scale For Analyzing Sequence Divergence In Novel Pathogens

Jin

Chattopadhyay

2020

Preprint

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As we begin to recover from the COVID-19 pandemic, a key question is if we can avert such disasters in future. Current surveillance protocols generally focus on qualitative impact assessments of viral diversity. These efforts are primarliy aimed at ecosystem and human impact monitoring, and do not help to precisely quantify emergence. Currently, the similarity of biological strains is measured by the edit distance or the number of mutations that separate their genomic sequences, e.g. the number of mutations that make an avian flu strain human-adapted. However, ignoring the odds of those mutations in the wild keeps us blind to the true jump risk, and gives us little indication of which strains are more risky. In this study, we develop a more meaningful metric for comparison of genomic sequences. Our metric, the q-distance, precisely quantifies the probability of spontaneous jump by random chance. Learning from patterns of mutations from large sequence databases, the q-distance adapts to the specific organism, the background population, and realistic selection pressures; demonstrably improving inference of ancestral relationships and future trajectories. As important application, we show that the q-distance predicts future strains for seasonal Influenza, outperforming World Health Organization (WHO) recommended flu-shot composition almost consistently over two decades. Such performance is demonstrated separately for Northern and Southern hemisphere for different subtypes, and key capsidic proteins. Additionally, we investigate the SARS-CoV2 origin problem, and precisely quantify the likelihood of different animal species that hosted an immediate progenitor, producing a list of related species of bats that have a quantifiably high likelihood of being the source. Additionally, we identify specific rodents with a credible likelihood of hosting a SARS-CoV2 ancestor. Combining machine learning and large deviation theory, the analysis reported here may open the door to actionable predictions of future pandemics.

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“…Forecasting seasonal influenza after 26 weeks was defined as forecasting influenza pattern after six months (26 weeks) with data available only at the current point (S2 Fig). Because of the time-consuming nature of vaccine production, vaccine strategy needs to be prepared at least six months in advance of the upcoming flu season [14]. Three seasons for 2015–2018 were selected to validate the prediction models for seasonal influenza patterns because the four seasonal surveillance data for Australia and Chile from 2011 to 2014 for the training set accounted for 50% of eight seasons in whole data.…”

Section: Methodsmentioning

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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Models for predicting the evolution of influenza to inform vaccine strain selection

Cited by 69 publications

References 46 publications

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Preparing For the Next Pandemic: Learning Wild Mutational Patterns At Scale For Analyzing Sequence Divergence In Novel Pathogens

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

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