Method and system for recommending seasonal influenza virus vaccine strains based on variant fitness

Abstract

The application provides a seasonal influenza virus vaccine strain recommendation method and system based on variant adaptability, comprising: obtaining influenza virus hemagglutinin protein HA sequence data and influenza positive rate data from the current influenza season to the last three influenza seasons; identifying amino acid mutations at each site of the HA protein sequence, screening risk mutations for the next influenza season; searching the HA protein sequence of the current influenza season for sequences containing risk mutations, and grouping sequences carrying completely consistent risk mutations into the same risk variant; calculating the product of the conditional probability of the independent component branching ability, immune escape ability and transmission ability of the risk variant as the risk variant adaptability; taking the risk variant with the highest adaptability score as the dominant variant for the next influenza season; determining the HA protein sequence corresponding to the dominant variant, determining the consensus sequence, and selecting the strain corresponding to the HA protein sequence with the highest similarity to the consensus sequence as the candidate vaccine strain for the next influenza season.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological information, in particular to a seasonal influenza virus vaccine strain recommendation method and system based on variant fitness. BACKGROUND

[0002] Seasonal influenza is a global public health problem. Vaccination is the most effective means of preventing influenza, and vaccine strain recommendation requires a manual decision-making process of "sample collection-PCR / sequencing-antigenicity experiment-WHO expert review". This process is long, costly, and highly dependent on expert experience. Because the decision-making lags behind the evolution of the virus, the accuracy of the recommended results is not ideal, and the vaccine protection rate is low and volatile. Therefore, it is necessary to predict the virus strain that may dominate the next influenza season in advance to provide a basis for vaccine strain recommendation.

[0003] Existing prediction methods track the development dynamics of branches, strains or mutations to predict the evolution direction of influenza viruses. However, the impact of viral sequence variation on antigenicity has not been fully considered, making the accuracy of predicting future dominant variants based only on genetic information not ideal. On the other hand, existing antigenicity prediction models cannot be directly applied to actual vaccine strain recommendations.

[0004] Therefore, it is urgent to develop a highly automated, fast and accurate seasonal influenza virus dominant variant prediction and vaccine strain recommendation system that adapts to the vaccine strain recommendation scenario. In this way, the matching of vaccine strains and actual epidemic dominant strains can be improved, the vaccine efficacy can be improved, and the people's life and health can be protected. SUMMARY

[0005] The present application provides a seasonal influenza virus vaccine strain recommendation method and system based on variant fitness prediction. The present application considers the independent branch ability, immune escape ability and transmission ability of the variant, comprehensively evaluates its fitness, selects the vaccine strain from the variant with the highest fitness, and realizes the automatic, fast and accurate recommendation of the seasonal influenza vaccine strain.

[0006] Specifically, the present application is realized by the following technical solutions:

[0007] The present application provides a seasonal influenza virus vaccine strain recommendation method based on variant fitness prediction, comprising:

[0008] Obtaining influenza virus hemagglutinin protein sequence data and influenza positive rate data from the current influenza season to the last three influenza seasons;

[0009] Identifying amino acid mutations at each site of the influenza virus hemagglutinin protein sequence, and screening risk mutations for the next influenza season;

[0010] finding sequences containing risk mutations of the next influenza season in the hemagglutinin protein sequences of the influenza viruses in the current influenza season, grouping sequences carrying identical risk mutations in the sequences containing risk mutations of the next influenza season into the same risk variant;

[0011] calculating the product of the conditional probability of independent component branch ability, the conditional probability of immune escape ability, and the conditional probability of transmission ability of the risk variant as the risk variant fitness;

[0012] ranking the risk variants according to the risk variant fitness, and selecting the risk variant with the highest risk variant fitness as the dominant variant that can be widely transmitted in the next influenza season; and determining the hemagglutinin protein sequence corresponding to the dominant variant, determining the consensus sequence by sequence comparison, and selecting the influenza virus strain corresponding to the hemagglutinin protein sequence with the highest similarity to the consensus sequence as the candidate vaccine strain for the next influenza season.

[0013] In one embodiment, the identification of amino acid mutations at each site of the hemagglutinin protein sequence of the influenza virus and the screening of risk mutations for the next influenza season include:

[0014] calculating the predicted prevalence of the amino acid type at any site of the hemagglutinin protein sequence of the influenza virus in the next influenza season, and if the predicted prevalence ≥ risk threshold and there is a prevalence < risk threshold in the last three influenza seasons from the current influenza season, the amino acid at the site is considered a risk mutation; wherein the risk threshold is obtained according to the linear fitting of the site prevalence and the influenza positive rate.

[0015] In one embodiment, the predicted prevalence of the amino acid at any site in the next influenza season is calculated according to Formulas 1-3:

[0016]

[0017] : the prevalence of amino acid a at site i in influenza season s;

[0018] : the number of sequences with amino acid a at site i in influenza season s;

[0019] : the total number of sequences in influenza season s;

[0020] : the rate of change of the prevalence of amino acid a at site i;

[0021] : the prevalence of amino acid a at site i in the current influenza season;

[0022] : prevalence of amino acid a at site i in the previous flu season;

[0023] : predicted prevalence of amino acid a at site i in the next flu season.

[0024] In one embodiment, the risk threshold is calculated according to Formula 4-9 :

[0025]

[0026] : three flu season sets for fitting;

[0027] : candidate risk threshold sets;

[0028] : index of amino acid site on influenza virus hemagglutinin protein sequence;

[0029] : possible amino acid types at this site, together constituting a "mutation combination" index ;

[0030] : proportion of sequences with site = a in all submitted sequences in flu season ;

[0031] I (·): indicator function, taking 1 if the condition in the bracket is true, and 0 otherwise;

[0032] : "total prevalence measure" at threshold , i.e. sum of prevalence of only mutation combinations with frequency ≥ ;

[0033] : influenza positivity rate in flu season , with its average value ;

[0034] : size of set ;

[0035] : coefficient of determination for linear fit of vs. , reflecting the degree of explaining .

[0036] ​In one embodiment, the independent component branching ability of the risk variant is calculated according to Equation 11-14, and the conditional probability of the independent component branching ability of the risk variant is calculated according to Equation 15:

[0037]

[0038] : the number of risk mutations contained in the risk variant;

[0039] : the number of times the risk mutation independently appears in the current influenza season sequence;

[0040] : the total number of times the risk mutation appears in the current influenza season sequence;

[0041] : the mutation pattern, used to represent the combined state of the risk mutations;

[0042] : the state value of the j risk mutation in the mutation pattern;

[0043] : the number of times a certain specific risk mutation combination pattern appears in the current influenza season sequence;

[0044] : the actually observed mutation pattern probability;

[0045] : the theoretical probability assuming that each risk mutation independently appears;

[0046] : the number of sequences of the current influenza season;

[0047] : the state of the sample sequence at the j risk mutation;

[0048] : the marginal probability of the j risk mutation.

[0049] In one embodiment, the immune escape ability of the risk variant is calculated according to Equation 16, and the conditional probability of the immune escape ability of the risk variant is calculated according to Equation 17:

[0050]

[0051] : the immune escape score of the j risk mutation in the risk variant.

[0052] In one implementation, the transmissibility of the risk variant is calculated according to Formula 18, and the conditional probability of the transmissibility of the risk variant is calculated according to Formula 19:

[0053]

[0054] The number of risky mutations contained in a risky variant;

[0055] : The first in the risk variant j The predicted prevalence of a risk mutation in the next flu season.

[0056] This application also provides a seasonal influenza virus vaccine strain recommendation system based on variant adaptability prediction, the system comprising:

[0057] The data acquisition module is used to acquire influenza virus hemagglutinin protein sequence data and influenza positivity rate data for the past three influenza seasons since the current influenza season.

[0058] The risk variant adaptation module is used to identify amino acid mutations at each site of the influenza virus hemagglutinin protein sequence and screen for risk mutations in the next influenza season; it searches for sequences containing risk mutations in the hemagglutinin protein sequence of the influenza virus in the current influenza season, and classifies sequences containing risk mutations in the next influenza season that are completely identical to the risk mutations into the same risk variant; it calculates the product of the conditional probability of the risk variant's independent branching ability, the conditional probability of immune escape ability, and the conditional probability of transmissibility as the risk variant adaptation;

[0059] The candidate vaccine strain recommendation module is used to identify the risk variant with the highest fitness score as the dominant variant that is likely to be widely prevalent in the next influenza season; and to determine the hemagglutinin protein sequence corresponding to the dominant variant, determine the consensus sequence through sequence comparison, and select the influenza virus strain corresponding to the hemagglutinin protein sequence with the highest similarity to the consensus sequence as the candidate vaccine strain for the next influenza season.

[0060] In another aspect, this application provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method.

[0061] In another aspect, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-described method when executing the program.

[0062] The technical solution provided in this application can achieve the following beneficial effects:

[0063] This method can rapidly, accurately, and with a high degree of automation predict dominant variants of seasonal influenza viruses and recommend vaccine strains, significantly improving the matching accuracy between vaccine strains and actual prevalent dominant strains. This method is not only applicable to seasonal influenza viruses but can also be extended to the prediction of variants and the recommendation of vaccine strains for other rapidly mutating pathogens, providing crucial technical support for the formulation of global infectious disease control strategies and the deployment of regional control measures, and has broad application prospects. Attached Figure Description

[0064] Figure 1 This document illustrates a flowchart of the method for recommending seasonal influenza virus vaccine strains based on variant adaptability prediction, as provided in this specification.

[0065] Figure 2 This document illustrates a schematic diagram of a seasonal influenza virus vaccine strain recommendation system based on variant adaptability prediction, as provided in this specification.

[0066] Figure 3 This specification illustrates a corresponding method. Figure 1 A schematic diagram of an electronic device. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0068] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0069] One aspect of this specification provides a method for recommending seasonal influenza virus vaccine strains based on variant adaptability prediction. For example... Figure 1 As shown, the method includes steps S101-S106.

[0070] S101: Obtain influenza virus hemagglutinin (HA) protein sequence data and influenza positivity rate data for the past three influenza seasons since the current flu season.

[0071] Seasonal influenza viruses circulate from February 1st to September 1st each year in the Southern Hemisphere, and from September 1st to February 1st of the following year in the Northern Hemisphere. Taking the recommended vaccine strain for the next influenza season (T+1) in the Northern Hemisphere as an example, the following two types of data are required:

[0072] Influenza virus HA protein sequence data: download seasonal influenza virus hemagglutinin protein sequences from GISAID database, which were sampled and submitted in the northern hemisphere in the past three years (current influenza season (T), previous influenza season (T-1), and two years ago influenza season (T-2)). Align the sequences of these strains with the preset virus reference genome to determine the specific numbering of each amino acid position, and then extract the amino acid sequence of the HA fragment.

[0073] Influenza positive rate data: download influenza virus monitoring data from FluNet database, and calculate the positive rate of T, T-1, and T-2 historical influenza seasons in the northern hemisphere, i.e., the ratio of the number of influenza-positive samples to the total number of influenza samples.

[0074] S102: Identify amino acid mutations at each site of the influenza virus hemagglutinin protein sequence, and screen risk mutations for the next influenza season.

[0075] According to the linear extrapolation method, calculate the predicted prevalence of the amino acid type at any site of the influenza virus HA protein in the next influenza season (T+1). If the predicted prevalence ≥ risk threshold , and there is a prevalence < in T, T-1, T-2,..., then the amino acid at this site is considered a risk mutation; wherein the risk threshold is obtained according to the linear fitting of the site prevalence and the influenza positive rate.

[0076] Specifically, calculate the predicted prevalence of the amino acid at any site in the T+1 influenza season according to formulas 1-3:

[0077]

[0078] : the prevalence of amino acid a at site i in influenza season s;

[0079] : the number of sequences with amino acid a at site i in influenza season s;

[0080] : the total number of sequences in influenza season s;

[0081] : the change rate of the prevalence of amino acid a at site i;

[0082] : the prevalence of amino acid a at site i in the current influenza season (T);

[0083] : the prevalence of amino acid a at site i in the previous influenza season (T-1);

[0084] : Predicted prevalence of amino acid a at site i in the next flu season (T+1).

[0085] In particular, the risk threshold is calculated as in Equation 4-9 :

[0086]

[0087] : Set of three flu seasons used for fitting;

[0088] : Set of candidate risk thresholds;

[0089] : Index of amino acid site on HA protein;

[0090] : Possible amino acid types at this site, together constituting a "mutation combination" index ;

[0091] : Fraction of sequences at site in flu season T among all submitted sequences in flu season

[0092] I (·): Indicator function, taking 1 if the condition in the parentheses is true, and 0 otherwise;

[0093] : "Total prevalence measure" at threshold , i.e. sum of prevalence of only mutation combinations with frequency ≥ ;

[0094] : Flu positivity rate in flu season , averaged over ;

[0095] : Size of set , in this case ;

[0096] : Coefficient of determination for linear fit of vs. , reflecting the degree of explained by .

[0097] S103: Find sequences containing risk mutations of next influenza season in the current influenza season's influenza virus hemagglutinin protein sequences, and classify sequences carrying the same risk mutations as the same risk variant.

[0098] Specifically, find pre-screened risk mutations of next influenza season in the current influenza season's influenza virus HA protein sequences, and pick out sequences containing any risk mutation; then, classify sequences carrying the same combination of risk mutations as the same "risk variant".

[0099] S104: Calculate the product of the conditional probability of independent branching ability, the conditional probability of immune escape ability, and the conditional probability of transmission ability of the risk variant as the risk variant fitness.

[0100] The risk variant fitness is calculated according to formula 10:

[0101]

[0102] Specifically, the conditional probability of independent branching ability of the risk variant is calculated as follows.

[0103] First, calculate the independent branching ability according to formulas 11-14, and then calculate the conditional probability conversion of the independent branching ability according to formula 15:

[0104]

[0105] : The number of risk mutations contained in the risk variant;

[0106] : The number of independent occurrences of the risk mutation in T influenza season sequences;

[0107] : The total number of occurrences of the risk mutation in T influenza season sequences;

[0108] : Mutation pattern, used to represent the combination state of risk mutations. Specific meaning: for each risk mutation , if the sequence contains the risk mutation, the state is 1, otherwise 0; one represents the combination of the presence state of all risk mutations;

[0109] : The state value of the j risk mutation in the mutation pattern (1 indicates that the sequence contains this risk mutation, 0 indicates that it does not);

[0110] : Number of times a particular combination of risk mutations appears in the sequence of T-flu seasons;

[0111] : Observed probability of the mutation pattern;

[0112] : Theoretical probability assuming each risk mutation appears independently;

[0113] : Number of sequences of T-flu seasons;

[0114] : State of the i th sample sequence at the j th risk mutation;

[0115] : Marginal probability of the j th risk mutation.

[0116] Specifically, the conditional probability of the immune escape ability of the risk variant is calculated as follows.

[0117] First, the immune escape ability is calculated according to formula 16, and then the probability of the calculated immune escape ability is converted according to formula 17:

[0118]

[0119] : Immune escape score of the th risk mutation in the risk variant.

[0120] Calculated by the EVEscape model, which was published by Nicole et al. in Nature in 2023, titled Learning from prepandemic data to forecast viral escape.

[0121] Specifically, the conditional probability of the transmission ability of the risk variant is calculated as follows.

[0122] First, the transmission ability is calculated according to formula 18, and then the probability of the transmission ability is converted according to formula 19:

[0123]

[0124] : Number of risk mutations contained in the risk variant;

[0125] : the number of risk mutations in the risk variant j in the prediction of the prevalence of the T+1 flu season.

[0126] S105: The risk variant with the highest risk variant adaptability score is taken as the dominant variant that may spread widely in the next flu season.

[0127] Specifically, the risk variants are ranked according to the risk variant adaptability scores from high to low, and the risk variant with the highest risk variant adaptability score is taken as the dominant variant that may spread widely in the T+1 flu season.

[0128] S106: Determine the hemagglutinin protein sequence corresponding to the dominant variant, determine the consensus sequence through sequence comparison, and select the influenza virus strain corresponding to the hemagglutinin protein sequence with the highest similarity to the consensus sequence as the candidate vaccine strain for the next flu season.

[0129] Specifically, first, multiple sequence alignment is performed on the N HA protein sequences corresponding to the dominant variant to ensure that they are consistent at each amino acid site on the same length L; then, the number of occurrences of 20 standard amino acids is counted at each position i (i=1…L) of the aligned sequence, and the amino acid with the highest frequency is selected as the consensus residue at that position. The consensus residues from position 1 to position L are concatenated in turn to obtain the HA consensus sequence C. Finally, the homology similarity (i.e., the percentage of identical sites to L) between the HA sequence of each dominant variant and the consensus sequence C is calculated, and the strain with the highest similarity is selected as the candidate vaccine strain for the T+1 flu season.

[0130] The method for recommending seasonal influenza virus vaccine strains based on variant adaptability prediction of the present application innovatively introduces a comprehensive evaluation mechanism for variant adaptability, quantifies the adaptability of the variant through the product of the conditional probabilities of three dimensions of independent component branch ability, immune escape ability and transmission ability, breaks through the limitations of traditional methods that rely only on a single indicator, and improves the accuracy and reliability of the prediction. In the retrospective test of multiple flu seasons, the matching accuracy of the candidate vaccine strain recommended by the method for recommending seasonal influenza virus vaccine strains based on variant adaptability prediction of the present application and the actual dominant strain is about 20% higher than that of the traditional method.

[0131] The variant adaptability prediction-based seasonal influenza virus vaccine strain recommendation method of the present application has good portability: it can be used not only for predicting the dominant variant of seasonal influenza virus and recommending the vaccine strain, but also directly applied to other rapidly mutating pathogens (such as norovirus, etc.). The present inventors conducted a retrospective analysis on norovirus, and the results showed that the system could successfully predict and warn the New Orleans 2009 variant that widely prevailed in 2009 in 2008, and accurately predict and warn the Sydney 2012 variant that prevailed in 2012 in 2011. The above results fully prove that the variant adaptability prediction-based seasonal influenza virus vaccine strain recommendation method provided by the present application can be popularized to various pathogens, and provides reliable technical support for public health prevention and control.

[0132] An aspect of the present specification provides a variant adaptability prediction-based seasonal influenza virus vaccine strain recommendation system, as shown in Figure 2 , comprising:

[0133] A data acquisition module 201 is configured to acquire influenza virus hemagglutinin protein sequence data and influenza positive rate data of the past three influenza seasons from the current influenza season;

[0134] A risk variant adaptability module 202 is configured to identify amino acid mutations at each site of the influenza virus hemagglutinin protein sequence, screen risk mutations in the next influenza season, find sequences containing risk mutations in the next influenza season in the influenza virus HA protein sequence of the current influenza season, and classify sequences carrying the same risk mutations as the same risk variant; and calculate the product of the conditional probability of the independent component branching ability, the conditional probability of the immune escape ability and the conditional probability of the transmission ability of the risk variant as the risk variant adaptability.

[0135] A candidate vaccine strain recommendation module 203 is configured to select the risk variant with the highest risk variant adaptability score as the dominant variant that may widely spread in the next influenza season, and determine the HA protein sequence corresponding to the dominant variant, determine the consensus sequence through sequence comparison, and select the influenza virus strain corresponding to the HA protein sequence with the highest similarity to the consensus sequence as the candidate vaccine strain for the next influenza season.

[0136] The present specification also provides a computer readable storage medium storing a computer program, and the computer program can be used to execute the variant adaptability prediction-based seasonal influenza virus vaccine strain recommendation method provided above. Figure 1 The present specification also provides a computer readable storage medium storing a computer program, and the computer program can be used to execute the variant adaptability prediction-based seasonal influenza virus vaccine strain recommendation method provided above.

[0137] The present specification also provides a computer readable storage medium storing a computer program, and the computer program can be used to execute the variant adaptability prediction-based seasonal influenza virus vaccine strain recommendation method provided above. Figure 3 , a method for recommending a vaccine strain corresponding to a variant adaptability prediction-based seasonal influenza virus, as shown in Figure 1A schematic structural diagram of an electronic device. As shown in Figure 3 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and can also include other hardware required by the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement the above Figure 1 The method for recommending a seasonal influenza virus vaccine strain based on variant adaptability prediction. Of course, in addition to the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or a logic device.

[0138] For a technical improvement, it can be clearly distinguished whether it is a hardware improvement (for example, improvement of circuit structures such as diodes, transistors, switches, etc.) or a software improvement (improvement of method processes). However, with the development of technology, many improvements of method processes today can be considered as direct improvements of hardware circuit structures. Designers almost always get the corresponding hardware circuit structures by programming the improved method processes into hardware circuits. Therefore, it cannot be said that the improvement of a method process cannot be implemented by a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A digital system is "integrated" on a PLD by the designer programming it himself, without having to ask a chip manufacturer to design and manufacture a special integrated circuit chip. Moreover, instead of manually manufacturing integrated circuit chips, this programming is now mostly implemented using "logic compiler" software, which is similar to the software compiler used when developing programs, and the original code to be compiled must also be written in a specific programming language, which is called a hardware description language (HDL), and there are many HDLs, such as ABEL, AHDL, Confluence, CUPL, HDCal, JHDL, Lava, Lola, MyHDL, PALASM, RHDL, etc., and the most commonly used is VHDL (and Verilog). Those skilled in the art should also be clear that only the method process needs to be logically programmed and programmed into an integrated circuit using the above-mentioned several hardware description languages to easily obtain a hardware circuit that implements the logical method process.

[0139] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the microprocessor, logic gates, switches, application specific integrated circuits (ASICs), programmable logic controllers and embedded microcontrollers, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to being implemented in pure computer readable program code form, the controller can equally well be implemented by means of logic programmed into logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. to perform the same functions. The controller can thus be considered as a hardware component, and the means comprised therein for performing various functions can be considered as structures within the hardware component. Alternatively, or even additionally, the means for performing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0140] The systems, apparatuses, modules or units illustrated by the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0141] For the sake of description, the above apparatuses are described in functional division and are described respectively as various units. Of course, the functions of the units can be implemented in the same or multiple software and / or hardware in implementing the present specification.

[0142] Those skilled in the art will understand that the embodiments of the present specification can be provided as a method, a system or a computer program product. Therefore, the present specification can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] The specification is presented with reference to flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flow or multiple flows and / or blocks Figure 1 one or more flow or multiple flows and / or blocks

[0144] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flow or multiple flows and / or blocks Figure 1 one or more flow or multiple flows and / or blocks

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flow or multiple flows and / or blocks ​ one or more flow or multiple flows and / or blocks

[0146] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0147] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as a read only memory (ROM) device, a floppy disk, a hard disk, or a flash memory. The memory can store an operating system including procedures and data used to manage the computer's operation, as well as programs for implementing the methods of the embodiments of the specification. The memory can also store the data generated by the methods of the embodiments of the specification.

[0148] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carriers.

[0149] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0150] Those skilled in the art will appreciate that embodiments of the present specification can be provided as methods, systems or computer program products. Therefore, the present specification can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0151] The present specification can be described in the general context of computer-executable instructions, such as program modules, executed by computers. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including storage devices.

[0152] The various embodiments described in this specification are described using a numbering of embodiments approach: these are each individually integrated contributions pertaining to different aspects of the description. For each embodiment, the description focuses on the differences from the other embodiments. In particular, the description of the system embodiments is relatively brief, as the system embodiments are largely analogous to the method embodiments. The relevant parts of the description of the method embodiments are therefore referred to.

[0153] The above only describes the embodiments of the present specification and is not intended to limit the present specification. The present specification can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of claims of the present specification.

Claims

1. A method for recommending seasonal influenza virus vaccine strains based on variant adaptability prediction, characterized in that, The method comprises: obtaining influenza virus hemagglutinin protein sequence data and influenza positive rate data of the past three influenza seasons since the current influenza season; identifying amino acid mutations at each site of the influenza virus hemagglutinin protein sequence, and screening risk mutations in the next influenza season; finding sequences containing risk mutations in the next influenza season in the influenza virus hemagglutinin protein sequence of the current influenza season, and grouping sequences carrying risk mutations completely consistent in the sequences containing risk mutations in the next influenza season into the same risk variant; calculating the product of the conditional probability of the independent branching ability, the conditional probability of the immune escape ability, and the conditional probability of the transmission ability of the risk variant as the risk variant fitness; ranking the risk variant with the highest score of risk variant fitness as the dominant variant that may be widely prevalent in the next influenza season; and determining the hemagglutinin protein sequence corresponding to the dominant variant, determining the consensus sequence through sequence comparison, and selecting the influenza virus strain corresponding to the hemagglutinin protein sequence with the highest similarity to the consensus sequence as the candidate vaccine strain for the next influenza season; wherein the independent branching ability of the risk variant is calculated according to formulas 11-14, and the conditional probability of the independent branching ability of the risk variant is calculated according to formula 15: : number of risk mutations included in the risk variant; : number of independent occurrences of risk mutations in the current influenza season sequence; RiskMut is the total number of occurrences of the risk mutation in the current influenza season sequence; : mutation pattern, for representing the combined state of risk mutations; : the state value of the nth risk mutation in the mutation pattern j : the state value of the nth risk mutation in the mutation pattern : the number of times a certain combination of risk mutations has occurred in the current flu season sequence; : probability of the observed mutation pattern; : Theoretical probability assuming independent occurrence of each risk mutation; : number of sequences for current flu season; : the first sample sequence is in a state of the first risk mutation; and : the second sample sequence is in a state of the second risk mutation. j : the first sample sequence is in a state of the first risk mutation; and : No. j The marginal probability of a risky mutation; the immune escape ability of the risk variant is calculated according to formula 16, and the conditional probability of the immune escape ability of the risk variant is calculated according to formula 17: : immune escape score of the i-th risk mutation in the risk variant th risk mutation in the risk variant the transmission ability of the risk variant is calculated according to formula 18, and the conditional probability of the transmission ability of the risk variant is calculated according to formula 19: : number of risk mutations included in the risk variant; : the number of risk mutations in the risk variant j predicting the prevalence of the next influenza season; wherein the identification of amino acid mutations at each site of the influenza virus hemagglutinin protein sequence and the screening of risk mutations in the next influenza season comprise: predicting the prevalence of the influenza virus hemagglutinin protein sequence at any site in the next influenza season, if the predicted prevalence ≥ risk threshold and the condition that there is a prevalence < risk threshold in the last three influenza seasons from the current influenza season, the amino acid at the site is considered a risk mutation; wherein the risk threshold is obtained according to the linear fitting of the site prevalence and the influenza positive rate. calculating the predicted prevalence of the amino acid at any site in the next influenza season according to formulas 1-3: : prevalence of amino acid a at position i in the flu season s; : Number of sequences with amino acid a at median position i in flu season : total number of sequences in flu season s; : rate of change in prevalence of amino acid a at site i; : prevalence of amino acid a at site i in the current flu season; : prevalence of amino acid a at site i in the previous flu season; : Predicted prevalence of amino acid a at site i in the next flu season.

2. The method of recommending a seasonal influenza virus vaccine strain according to claim 1, wherein, Risk threshold is calculated according to equation 4-9 : : Three influenza season sets for fitting a set of candidate risk thresholds; : index of amino acid positions on the influenza virus hemagglutinin protein sequence; : possible amino acid types for the site, both constituting a "mutation combination" index ; : In the flu season , the proportion of sequences with site at ; I (·): indicator function, which takes 1 when the condition in the parentheses is true, and 0 otherwise; : threshold value "total prevalence metric" under the "total prevalence metric" is the sum of the prevalence of only the mutation combinations with statistical frequency ≥ ; : the flu season the positive rate of influenza, is the average value; : set of sizes; : the coefficient of determination of the linear fit, reflecting the degree of explanation of by .​ 3. A variant adaptive prediction-based seasonal influenza vaccine strain recommendation system, characterized by, The system comprises: a data acquisition module for acquiring influenza virus hemagglutinin protein sequence data and influenza positive rate data of the past three influenza seasons since the current influenza season; a risk variant fitness module for identifying amino acid mutations at each site of the influenza virus hemagglutinin protein sequence, screening risk mutations in the next influenza season, finding sequences containing risk mutations in the next influenza season in the influenza virus hemagglutinin protein sequence of the current influenza season, grouping sequences carrying risk mutations completely consistent in the sequences containing risk mutations in the next influenza season into the same risk variant, and calculating the product of the conditional probability of the independent branching ability, the conditional probability of the immune escape ability, and the conditional probability of the transmission ability of the risk variant as the risk variant fitness; a candidate vaccine strain recommendation module for ranking the risk variant with the highest score of risk variant fitness as the dominant variant that may be widely prevalent in the next influenza season, and determining the hemagglutinin protein sequence corresponding to the dominant variant, determining the consensus sequence through sequence comparison, and selecting the influenza virus strain corresponding to the hemagglutinin protein sequence with the highest similarity to the consensus sequence as the candidate vaccine strain for the next influenza season; wherein the independent component branching ability of the risk variant is calculated according to formula 11-14, and the conditional probability of the independent component branching ability of the risk variant is calculated according to formula 15: : number of risk mutations included in the risk variant; : number of independent occurrences of risk mutations in the current influenza season sequence; RiskMut is the total number of occurrences of the risk mutation in the current influenza season sequence; : mutation pattern, for representing the combined state of risk mutations; : the state value of the i-th risk mutation in the mutation pattern j ; : the number of times a certain combination of risk mutations has occurred in the current flu season sequence; : probability of the observed mutation pattern; : Theoretical probability assuming independent occurrence of each risk mutation; : number of sequences for current flu season; : the first sample sequence is in a state of the first risk mutation; and the second sample sequence is in a state of the second risk mutation. j the first sample sequence is in a state of the first risk mutation; and : No. j The marginal probability of a risky mutation; wherein the immune escape ability of the risk variant is calculated according to formula 16, and the conditional probability of the immune escape ability of the risk variant is calculated according to formula 17: : immune escape score of the i-th risk mutation in the risk variant; : immune escape score of the i-th risk mutation in the risk variant; wherein the transmission ability of the risk variant is calculated according to formula 18, and the conditional probability of the transmission ability of the risk variant is calculated according to formula 19: : number of risk mutations included in the risk variant; : the number of risk mutations in the risk variant j predicting the prevalence of the next influenza season; wherein the identifying the amino acid mutation at each site of the influenza virus hemagglutinin protein sequence, screening the risk mutation of the next influenza season comprises: predicting the prevalence of the influenza virus hemagglutinin protein sequence at any site in the next influenza season, if the predicted prevalence ≥ risk threshold and the condition that there is a prevalence < risk threshold in the last three influenza seasons from the current influenza season, then the amino acid at the site is considered a risk mutation; wherein the risk threshold is obtained according to the linear fitting of the site prevalence and the influenza positive rate; the predicted prevalence of the amino acid at any site in the next influenza season is calculated according to formula 1-3: : prevalence of amino acid a at position i in the flu season s; : Number of sequences with amino acid a at median position i in flu season : total number of sequences for flu season s; : rate of change in prevalence of amino acid a at site i; : prevalence of amino acid a at site i in the current flu season; : prevalence of amino acid a at site i in the previous flu season; : Predicted prevalence of amino acid a at site i in the next flu season.

4. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed by the processor to realize the method of any one of claims 1-2.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1-2.

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