Predicting the short-term success of human influenza A variants with machine learning

Abstract

Seasonal influenza viruses are constantly changing, and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system from recognizing those viruses. Due to rapid mutations, in particular in the hemagglutinin gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines' benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper we use longitudinally sampled phylogenetic trees based on hemagglutinin sequences from human influenza viruses, together with counts of epitope site polymorphisms in hemagglutinin, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier 'area under the curve' (AUC) 0.75-0.9.

Polynomial Phylogenetic Analysis of Tree Shapes

“…We used several influenza virus clades, described in (Hayati, 2020). In that work we downloaded all human H3N2 full-length HA sequences with dates between 1980 and May 2018 and created a large, timed phylogeny of H3N2…”

12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context

“…Previously, ML techniques have been commonly applied to predict changes in seasonal diseases, such as influenza ( 46 ), to further allow hospitals to appropriately prepare for medical supply needs, such as bed capacity, and to appropriately update both vaccine developments and citizens themselves of prevalent circulating strains. This is because many viruses commonly mutate and produce a variety of strains each year, yet vaccines can only account for a number of the most prevalent strains.…”

“…This is because many viruses commonly mutate and produce a variety of strains each year, yet vaccines can only account for a number of the most prevalent strains. In such paradigm, ML tools can be applied to estimate which strains will be most common in upcoming seasons with high accuracy to be included in upcoming seasonal vaccines ( 46 ). However, unexpected changes can occur in the environment, such as a new pandemic, which drastically alters the environmental landscape and therefore changes the way two variables may be modeled based on new environmental parameters.…”