Estimation method and device for propagation risk of Japanese encephalitis virus and electronic equipment

Abstract

The invention provides a Japanese encephalitis virus transmission risk estimation method and device and electronic equipment, and relates to the technical field of disease transmission, and the method comprises the steps: storing main infection dynamics data and host data based on mosquito-borne data corresponding to a first time interval, determining a first infection model, a second infection model, a first disease overflow rate model and a second disease overflow rate model; estimating the contribution coefficient of the vertical propagation basic infectivity and the mosquito-borne egg trap index to the infectivity of mosquito-borne propagation to the main storage body based on the first infectivity model and the second infectivity model; estimating an intensity coefficient based on the first disease overflow rate model and the second disease overflow rate model; estimating host data in a second time interval based on the vertical propagation basic infectivity, the contribution coefficient of the mosquito-borne egg trap index to the infectivity and the strength coefficient; therefore, full-chain quantitative evaluation from mosquito vector to storage host and from storage host to host can be realized, and the risk evaluation precision of host virus infection in a real scene is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of disease transmission, and particularly relates to a Japanese encephalitis virus transmission risk estimation method, device and electronic equipment. BACKGROUND

[0002] In the monitoring and risk assessment of mosquito-borne diseases, the commonly used method is to infer the transmission level of the pathogen by inputting the reservoir host infection rate. When the infection rate increases, it indicates the risk of disease spread, and when it decreases, it means that the epidemic is under control. Therefore, the infection rate is often used as an important basis for formulating prevention and control measures. However, relying solely on host infection data has obvious shortcomings. It has high collection cost, limited monitoring capacity, and often has delays and omissions. More importantly, it does not combine the synergistic effect of reservoir hosts and mosquito vectors. This indicator cannot depict the direct impact of mosquito population changes on transmission risk, nor can it assess the impact of external factors on transmission risk in a timely manner. Therefore, a new method is needed that can bypass the limitations of host data and use more easily accessible and timely indicators to conduct risk assessment. SUMMARY

[0003] The present disclosure provides a Japanese encephalitis virus transmission risk estimation method, device and electronic equipment to at least solve the above technical problems in the prior art.

[0004] According to a first aspect of the present disclosure, a Japanese encephalitis virus transmission risk estimation method is provided, the method comprising:

[0005] obtaining mosquito vector data, reservoir host infection dynamics data and host data corresponding to a first time interval; determining a first infection force model of mosquito vectors to reservoir hosts based on mosquito biting rate, mosquito hit rate, number of infected mosquito vectors and total number of reservoir hosts in the mosquito vector data; determining a second infection force model based on mosquito ovitraps index, vertical transmission maintenance base infection force of mosquito vectors, and contribution coefficient of mosquito ovitraps index to infection force of mosquito vectors to reservoir hosts; estimating vertical transmission base infection force of mosquito vectors and contribution coefficient of mosquito ovitraps index to infection force of mosquito vectors to reservoir hosts based on the first infection force model and the second infection force model; determining a first incidence overflow rate model based on the number of cases, the number of infected reservoir hosts, the reservoir host infection period to recovery period disease carrying rate, and the incidence overflow rate; determining a second incidence overflow rate model based on the incidence overflow rate, the intensity coefficient and the mosquito ovitraps index; estimating the intensity coefficient based on the first incidence overflow rate model and the second incidence overflow rate model; the intensity coefficient includes a calibration coefficient for converting the mosquito ovitraps index to the incidence overflow rate; estimating the host data of the second time interval based on the mosquito data, the reservoir infection kinetics data, the estimated vertical transmission basic infectivity of the mosquito vector, a contribution coefficient of the mosquito ovitraps index to the infectivity of the mosquito vector to the reservoir, and a strength coefficient.

[0006] In the above scheme, the mosquito data includes at least one of the mosquito ovitraps index, the mosquito biting rate, the mosquito hit rate, and the number of infected mosquitoes. The reservoir infection kinetics data includes at least one of the number of susceptible reservoirs, the number of latent reservoirs, the number of infected reservoirs, the number of infectious reservoirs, the number of recovered reservoirs, the infection rate of input reservoirs, the daily replenishment amount of reservoirs, the daily mortality rate of reservoirs, the direct transmission coefficient between reservoirs, the probability of latent reservoirs to infected reservoirs, the probability of infected reservoirs to virus-carrying recovery reservoirs, and the probability of virus-carrying recovery reservoirs to complete recovery reservoirs. The host data includes the number of infected hosts.

[0007] In the above scheme, the first infectivity model of the mosquito vector to the reservoir is determined based on the mosquito biting rate, the mosquito hit rate, the number of infected mosquitoes, and the total number of reservoirs in the mosquito data, including:

[0008] wherein, is the mosquito biting rate, is the mosquito hit rate, representing the virus transmission probability of each mosquito bite, is the number of infected mosquitoes, is the total number of reservoirs, is the infectivity of the mosquito vector to the reservoir, representing the probability of infection of the reservoir caused by a single bite of the infected mosquito.

[0009] In the above scheme, the total number of reservoirs is determined based on the number of susceptible reservoirs , the number of latent reservoirs , the number of infected reservoirs , the number of infectious reservoirs , and the number of recovered reservoirs , wherein the susceptible reservoir variable , the latent reservoir variable , the infected reservoir variable , the infectious reservoir variable , and the recovered reservoir variable include:

[0010] wherein, is the infection rate of input reservoirs, is the daily replenishment amount of reservoirs; is the daily mortality rate of reservoirs, is a direct transmission coefficient between reservoirs; is a probability of reservoirs from latent phase to infectious phase; is a probability of reservoirs from infectious phase to recovery phase with virus; is a probability of reservoirs from recovery phase with virus to complete recovery phase.

[0011] In the above solution, the second infection force model is determined based on the mosquito vector ovipositor index, the basic infection force of mosquito vector vertical transmission, and the contribution coefficient of the mosquito vector ovipositor index to the infection force of the mosquito vector transmission to the reservoir, and includes:

[0012] wherein, is a mosquito vector ovipositor index value, is a contribution coefficient of the mosquito vector ovipositor index to the infection force of the mosquito vector transmission to the reservoir, is a basic infection force of mosquito vector vertical transmission.

[0013] In the above solution, the first incidence overflow rate model is determined based on the number of cases, the number of reservoirs in the infectious phase, the virus carrying rate of reservoirs from the infectious phase to the recovery phase, and the incidence overflow rate, and includes:

[0014] wherein, characterizes the incidence overflow rate of the first week, characterizes the probability of reservoirs from the infectious phase to the recovery phase with virus, is the number of reservoirs in the infectious phase, is the number of cases.

[0015] In the above solution, the second incidence overflow rate model is determined based on the incidence overflow rate, the intensity coefficient, and the mosquito vector ovipositor index, and includes:

[0016] wherein, is the incidence overflow rate of reservoirs to hosts, is an intensity coefficient, is a mosquito vector ovipositor index at the time t, is a transmission delay.

[0017] In the above solution, the host data of the second time interval is estimated based on the mosquito vector data of the second time interval, the reservoir infection dynamics data, the estimated basic infection force of mosquito vector vertical transmission, the contribution coefficient of the mosquito vector ovipositor index to the infection force of the mosquito vector transmission to the reservoir, and the intensity coefficient, and includes: ​​Based on the mosquito ovipositor index in the second time interval, the estimated basic infectivity of mosquito vertical transmission, and the contribution coefficient of the mosquito ovipositor index to the infectivity of mosquito transmission to the storage host, the infectivity of mosquito transmission to the storage host is estimated. Based on the estimated intensity coefficient and the mosquito ovipositor index for the second time interval, the disease spillover rate is estimated. The number of storage households in the second time interval is determined based on the estimated infectivity of mosquito vectors to the storage households and the infection dynamics data of the storage households. Based on the number of infected hosts, the morbidity spillover rate, and the probability of hosts carrying the virus from the infection period to the recovery period corresponding to the second time interval, host data for the second time interval are estimated.

[0018] According to a second aspect of this disclosure, a device for estimating the risk of transmission of Japanese encephalitis virus is provided, comprising: The data acquisition unit is used to acquire mosquito vector data, reservoir infection dynamics data, and host data corresponding to the first time interval. The first model determination unit is used to determine the first infectivity model from mosquito vector to reservoir owner based on mosquito vector biting rate, mosquito vector hit rate, number of infected mosquito vectors and total number of reservoir owners in mosquito vector data. The second model determination unit is used to determine the second infectivity model based on the mosquito ovipositor index, the basic infectivity maintained by vertical mosquito transmission, and the contribution coefficient of the mosquito ovipositor index to the infectivity of mosquito transmission to the reservoir host. The first estimation unit is used to estimate the contribution coefficients of the basic infectivity of mosquito vertical transmission and the mosquito oviduct index to the infectivity of mosquito transmission to the reservoir owner based on the first infectivity model and the second infectivity model. The third model determination unit is used to determine the first disease spillover rate model based on the number of cases, the number of infected patients, the rate of patients carrying the disease from the infected period to the recovery period, and the disease spillover rate. The fourth model determination unit is used to determine the second disease spillover rate model based on the disease spillover rate, intensity coefficient, and mosquito ovipositor index. The second estimation unit is used to estimate the intensity coefficient based on the first disease spillover rate model and the second disease spillover rate model; the intensity coefficient includes a calibration coefficient that converts the mosquito oviduct index into the disease spillover rate. The host data estimation unit is used to estimate the host data for the second time interval based on mosquito vector data, host infection dynamics data, estimated basic infectivity of mosquito vertical transmission, contribution coefficient and intensity coefficient of mosquito oviduct index to the infectivity of mosquito transmission to the host.

[0019] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods of this disclosure.

[0020] The disclosed method for estimating the transmission risk of Japanese encephalitis virus (JEV) involves acquiring mosquito vector data, reservoir host infection dynamics data, and host data corresponding to a first time interval. Based on the mosquito vector data, including mosquito bite rate, mosquito hit rate, number of infected mosquitoes, and total number of reservoir hosts, a first infectivity model from mosquito vector to reservoir host is determined. A second infectivity model is determined based on the ovipositor index, the baseline infectivity maintained by vertical mosquito transmission, and the contribution coefficient of the ovipositor index to the infectivity of mosquito vector transmission to reservoir hosts. Finally, the method considers the baseline infectivity of vertical mosquito transmission and the contribution coefficient of the ovipositor index to the infectivity of mosquito vector transmission to reservoir hosts based on the first and second infectivity models. The contribution coefficient of the disease to the transmission of mosquitoes to the host is estimated. A first disease spillover rate model is determined based on the number of cases, the number of host mosquitoes during the infectious period, the carrier rate from the infectious period to the recovery period, and the disease spillover rate. A second disease spillover rate model is determined based on the disease spillover rate, intensity coefficient, and ovitrap index. The intensity coefficient is estimated based on the first and second disease spillover rate models. Host data for the second time interval is estimated based on mosquito vector data, host infection dynamics data, the estimated basic infectivity of vertical transmission of mosquitoes, the contribution coefficient of the ovitrap index to the infectivity of mosquito transmission to the host, and the intensity coefficient. In this way, a connection between mosquito vectors, host mosquitoes, and hosts can be established, integrating easily accessible mosquito vector and host data to achieve a full-chain quantitative assessment from mosquito density detection to host infection risk, and from host mosquitoes to hosts, thus improving the accuracy of host infection risk assessment in real-world scenarios.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0023] Figure 1 This illustration shows a first optional flowchart of the method for estimating the transmission risk of Japanese encephalitis virus provided in this embodiment of the present disclosure; Figure 2 This illustration shows a second optional flowchart of the method for estimating the transmission risk of Japanese encephalitis virus provided in this embodiment of the present disclosure; Figure 3 The time series of the mosquito ovipositor index is shown; Figure 4 The curves showing the local pig population and daily consumption are displayed. Figure 5 A schematic diagram of the fitting results of the algorithm disclosed herein is shown; Figure 6 A schematic diagram of an optional structure of the Japanese encephalitis virus transmission risk estimation device provided in an embodiment of this disclosure is shown; Figure 7 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0024] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0025] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0026] In the following description, the terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0027] Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used in this disclosure is for the purpose of describing embodiments of this disclosure only and is not intended to be limiting of this disclosure.

[0028] It should be understood that in the various embodiments of this disclosure, the sequence number of each implementation process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0029] Before providing a further detailed description of the embodiments of this disclosure, the nouns and terms involved in the embodiments of this disclosure will be explained, and the nouns and terms involved in the embodiments of this disclosure shall be interpreted as follows.

[0030] Mosquito vectors: Mosquitoes are insect species that can carry and transmit pathogens (such as viruses and parasites). They serve as intermediate transmission vectors for pathogens between different hosts. Mosquito vectors themselves can be infected by pathogens, but usually do not show obvious symptoms. The pathogens in their bodies can be transferred to new hosts during bites.

[0031] A reservoir host, also known as a pathogen reservoir, is an animal host capable of carrying pathogens for an extended period without exhibiting obvious clinical symptoms. Reservoirs act as "repositories" of pathogens in nature, maintaining their survival and reproduction for a long time, even without a human host. For example, pigs and birds are primary reservoirs of Japanese encephalitis virus. The virus replicates extensively in pigs; when mosquitoes bite infected pigs, they carry the virus and can then infect humans through bites. Infected pigs may exhibit symptoms such as abortion and stillbirth, but in most cases, they survive and continue to carry the virus. Birds are primary reservoirs of West Nile virus. The virus circulates within birds; mosquitoes bite infected birds and then bite humans or other animals, completing the transmission. Birds generally show no obvious symptoms after infection and can serve as long-term viral reservoirs.

[0032] Host: The definitive host or susceptible host refers to the host in which a pathogen can complete key stages of its life cycle (such as reproduction and development) or cause disease symptoms. For pathogens transmitted by mosquitoes, humans are usually the definitive host—the pathogen can multiply in the human body and cause disease, but humans, as hosts, generally will not transmit the virus to mosquitoes again (or the transmission efficiency is extremely low), and cannot become long-term storage vectors for pathogens.

[0033] Taking Japanese encephalitis virus as an example, its transmission chain usually includes virus reservoir (pig or bird) - mosquito bite - mosquito carrying the virus - mosquito carrying the virus bites the host - host infection and disease.

[0034] Japanese encephalitis virus (JEV) is usually transmitted by mosquitoes, primarily pigs and birds. It can be transmitted to humans via mosquitoes, and infected humans are the "terminal host" of the virus, meaning that infected humans can no longer transmit the virus via mosquitoes.

[0035] Vector-free transmission refers to the transmission of viruses between hosts through contact or other routes in the absence of mosquito vectors, such as direct pig-to-pig transmission. This transmission route is particularly important when mosquito density is low.

[0036] Mosquito oviposition index: A commonly used core indicator for assessing mosquito population density, with the advantages of easy acquisition and real-time availability. Meanwhile, the number of pigs in stock (storage owner data) can directly reflect the scale of Japanese encephalitis virus amplification in storage owners. Establishing a transmission risk modeling method based on dual data of storage owners and mosquitoes can not only make up for the lack of host data, but also identify potential epidemic threats earlier, thereby providing public health departments with more timely and effective decision-making basis.

[0037] In related technologies, mosquito-borne infectious disease transmission risk analysis techniques have significant limitations in real-world scenarios: First, they heavily rely on host infection data, failing to utilize the readily available data on both pig farm owners and mosquito vectors; second, they do not adapt to the interactive effects of "fluctuations in pig farm size + changes in mosquito vector density," failing to characterize the dynamic transmission driven by the synergistic effect of both; and third, they ignore the impact of external factors (such as adjustments in pig farming) on ​​pig farm data, making it impossible to accurately assess the transmission pattern under the synergistic effect of "pig farm owners and mosquito vectors." This disclosure uses pig farm (pig inventory) and mosquito vector data (oviposition index) as core inputs, combining a method for calculating the transmission risk of Japanese encephalitis virus from both pig farm and mosquito vector data. It integrates two types of readily available non-host data to construct a dynamic parameter correlation model, achieving a full-chain quantitative assessment from "mosquito vector density monitoring to pig farm (pig herd) infection risk, and then to human transmission risk." This eliminates the need to rely on difficult-to-obtain host infection data, quantifies the interaction between the two, and improves the accuracy of risk assessment in real-world scenarios.

[0038] This disclosure uses a "mosquito-storage owner (pig)-host (human)-environment system" as its core, establishing a dynamic correlation mechanism between the storage owner and mosquito vector data: the infectivity of mosquito vectors transmitted to the storage owner (pig)... The dynamic correlation between the mosquito ovipositor index (mosquito data) and the disease spillover rate from the host (pig) to the human population is analyzed. The data were correlated with the data of the storage owners (infection level in pig herds) with a lag, revealing the interaction pattern of "storage owners-mosquito vectors-environment"; and it was confirmed that the existing "simplified mosquito-host model" and "fixed mosquito density chamber model" are special cases of this framework (because they do not combine data of both storage owners and mosquito vectors).

[0039] Based on the multi-species compartment model equations, the infection status of the storage owners (pig herd) was further subdivided. S p 、E p 、I p 、C p 、R p ), introducing mosquito ovipositor index-driven and By combining the data of pig owners (number of pigs in stock), an integrated "observation-inference" model can be constructed that can directly utilize the two types of core data and case data.

[0040] Finally, combining pig inventory data (number of pigs) and mosquito vector data (oviposition index) from the Japanese encephalitis virus surveillance data of a certain region from 2003 to 2017, parameter estimation was performed based on a partially observed Markov process framework and sequential Monte Carlo method. The results show that the model provided in this embodiment can completely cover 17 actual cases in 95% of the simulation interval.

[0041] Figure 1 A schematic diagram of a first optional process for estimating the risk of Japanese encephalitis virus transmission provided in this embodiment of the present disclosure is shown, and the steps will be described accordingly.

[0042] Step S101: Obtain mosquito vector data, reservoir infection dynamics data, and host data corresponding to the first time interval.

[0043] In some embodiments, the first time interval is a historical time interval, and the vector (hereinafter referred to as the vector) implementing the Japanese encephalitis virus transmission risk estimation method acquires mosquito vector data, reservoir infection dynamics data and host data of the first time interval.

[0044] In specific implementation, the mosquito vector data includes at least one of the following: mosquito ovipositor index, mosquito bite rate, mosquito hit rate, and number of infected mosquitoes; the host infection dynamics data includes at least one of the following: number of hosts in the susceptible period, number of hosts in the incubation period, number of hosts in the infectious period, number of hosts in the infectious period, number of recovered hosts, infection rate of imported hosts, daily replenishment of hosts, daily mortality rate of hosts, direct transmission coefficient between hosts, probability of hosts transitioning from the incubation period to the infectious period, probability of hosts transitioning from the infectious period to the recovery period with the virus, and probability of hosts transitioning from the recovery period with the virus to the complete recovery period; the host data includes the number of infected hosts.

[0045] The carrier can be computer programs, electronic circuits, databases, mobile applications, electronic devices, cloud computing platforms, distributed systems, artificial intelligence frameworks, mathematical models, automation tools, and microcontrollers, etc., which are software or hardware capable of implementing algorithms and methods.

[0046] Step S102: Based on the mosquito bite rate, mosquito hit rate, number of infected mosquitoes, and total number of reservoir owners in the mosquito vector data, determine the first infectivity model from mosquito vector to reservoir owner.

[0047] In some embodiments, the first infectivity model includes the infectivity of mosquito transmission to the reservoir owner. The relationship between these factors and mosquito bite rate, mosquito hit rate, number of infected mosquitoes, and total number of reservoir owners includes: (1) in, Mosquito bite rate, Mosquito hit rate represents the probability of virus transmission per mosquito bite. The number of infected mosquitoes, The total number of depositors, This characterizes the probability that a single bite from an infected mosquito will lead to infection in the storage unit.

[0048] In some embodiments, the total number of storage owners Based on the number of depositors in the susceptible period Number of latent reserve holders Number of reservoir owners during the infection period Number of storage owners during the infectious period and number of rehabilitation and storage owners The sum is determined, including: (2) In some optional embodiments, the number of vulnerable reservoir owners is counted for the first time. Number of latent reserve holders Number of reservoir owners during the infection period Number of storage owners during the infectious period and number of rehabilitation and storage owners Afterwards, the number of vulnerable storage owners can be recounted each time it is needed. Number of latent reserve holders Number of reservoir owners during the infection period Number of storage owners during the infectious period and number of rehabilitation and storage owners Alternatively, it can be based on the number of vulnerable depositors from the previous statistical analysis. Number of latent reserve holders Number of reservoir owners during the infection period Number of storage owners during the infectious period and number of rehabilitation and storage owners The variables for the depositors at different stages are calculated, and the latest data is obtained by adding the variables to the data from the previous statistical analysis. The specific variable calculation methods include: (3) in, To input the infection rate of the storage unit, This is the amount to be replenished by the main storage unit on the main day; The daily mortality rate of the depositors. The direct propagation coefficient between store owners; The probability of the incubation period turning into the infection period for the storage owner; The probability of a patient carrying the virus during the recovery period after infection; The probability of a patient recovering from the virus during the recovery period progressing to full recovery.

[0049] Furthermore, among other auxiliary parameters, the infection rate of the storage unit ( The calculation formula is as follows: ,in, The incidence of Japanese encephalitis during the average lifespan of the depositors. For the storage owner's infection cycle, For the recovery period of the depositor, For the average lifespan of depositors, , and It can be used as a fixed input parameter for the model.

[0050] In some embodiments, the vulnerable period is stored as a master variable. Based on the number of uninfected individuals among the supplementary storage owners ( The most recently confirmed The number of deceased depositors ( ) and the most recently determined The number of bankers who transitioned from the susceptible period to the latent period ( )Sure.

[0051] In some embodiments, the latency period is the main variable. Based on the most recent determination The number of bankers who transitioned from the susceptible period to the latent period ( The most recently confirmed The number of deceased depositors ( ) and the most recently determined The number of bank owners who transitioned from the incubation period to the infectious period ( )Sure.

[0052] In some embodiments, the infection period is stored as a master variable. Based on the number of infected individuals among the supplementary storage owners ( The most recently confirmed The number of bank owners who transitioned from the incubation period to the infectious period ( The most recently confirmed The number of deceased depositors ( ) and the most recently determined The number of bank owners who transitioned from the infectious phase to the transmissible phase ( )Sure.

[0053] In some embodiments, the infectious period storage variable Based on the most recent determination The number of bank owners who transitioned from the infectious phase to the transmissible phase ( The most recently confirmed The number of deceased depositors ( ) and the most recently determined The number of bank depositors who transitioned from the infectious period to the recovery period ( )Sure In some embodiments, the rehabilitation storage variable Based on the most recent determination The number of bank depositors who transitioned from the infectious period to the recovery period ( ) and the most recently determined The number of deceased depositors ( )Sure.

[0054] Furthermore, the carrier can be based on For the most recently determined Update the database to obtain the latest number of vulnerable storage owners. ;based on For the most recently determined Update to obtain the latest number of latent holders. ;based on For the most recently determined Update to obtain the latest number of infected individuals. ;based on For the most recently determined Update to obtain the latest number of storage owners during the infectious period. ;based on For the most recently determined Update to obtain the latest number of rehabilitation reservoir owners. .

[0055] Step S103: Based on the mosquito ovipositor index, the basic infectivity maintained by vertical mosquito transmission, and the contribution coefficient of the mosquito ovipositor index to the infectivity of mosquito transmission to the reservoir host, determine the second infectivity model.

[0056] In some embodiments, to achieve a dynamic correlation between mosquito density and mosquito ovitrap index monitoring data, a correlation formula is introduced between the infectivity of mosquitoes transmitted to the storage owner (pig) and the mosquito ovitrap index, specifically including: (4) in, This refers to the mosquito ovipositor index. The contribution coefficient of the ovitrap index to the infectivity of mosquitoes transmitted to the reservoir host. This provides the basic infectivity for vertical transmission of mosquitoes.

[0057] In some alternative embodiments, The mosquito ovipositor index value can eliminate daily sampling fluctuations and retain the seasonal trend of a high mosquito ovipositor index in the rainy season and a low mosquito ovipositor index in the dry season. The contribution coefficient of the mosquito ovitrap index to the infectivity of mosquitoes transmitted to the storage owner (pig); To maintain the basic infectivity for vertical transmission of mosquitoes, ensuring that Japanese encephalitis virus can still maintain the lowest level of transmission when the mosquito ovitrap index is close to 0 during the dry season.

[0058] In some optional embodiments, the vector can establish different secondary infectivity models based on the virus's invasion status in a certain region. Specifically, if a new viral strain (or a mutated viral strain) invades, a second infectivity model is created before and after the invasion, using the invasion time as a node, specifically including:

[0059] in, This refers to the time point when the virus strain invades. Characterizing the virus strain before invasion, After the virus strain invades, then Or, if Characterizing the invasion of viral strains, Characterizing the virus strain before invasion, .

[0060] Step S104: Based on the first infectivity model and the second infectivity model, estimate the contribution coefficients of the basic infectivity of mosquito vertical transmission and the mosquito ovipositor index to the infectivity of mosquito transmission to the reservoir host.

[0061] In some embodiments, combining equations (1) and (4), the time intervals corresponding to both are the same, namely the first time interval. Therefore, the infectivity of mosquito transmission to the reservoir owner in the first infectivity model is... The infectivity of mosquitoes transmitted to the reservoir owner in the second infectivity model Similarly, all parameters on the right side of the equation in equation (1) are equal. All of these are known, and in equation (4) It is known that Based on equation (1), the basic infectivity of mosquito vertical transmission in equation (4) can be determined. The contribution coefficient of the ovipositor index to the infectivity of mosquitoes transmitted to the reservoir host. Make an estimate.

[0062] Step S105: Based on the number of cases, the number of infected individuals, the rate of carriers transitioning from the infected period to the recovery period, and the morbidity spillover rate, determine the first morbidity spillover rate model.

[0063] In some embodiments, the first morbidity spillover rate model includes: (5) in, Characterizing the first Weekly incidence spillover rate This characterizes the probability of a storage subject carrying the virus during the recovery period from the infection phase. The number of reservoir owners during the infection period. For the first Weekly case count (i.e., the number of infected hosts).

[0064] Step S106: Determine the second disease spillover rate model based on the disease spillover rate, intensity coefficient, and mosquito ovipositor index.

[0065] In some embodiments, the second morbidity spillover rate model includes: (6) in, The disease spillover rate from the storage host to the host. The strength coefficient, for Mosquito oviposition index at any given time This is due to the delay in transmission.

[0066] In some optional embodiments, the vector can establish different secondary morbidity spillover rate models based on the virus's invasion of a certain region. Specifically, if a new viral strain (or a mutated viral strain) invades, a secondary morbidity spillover rate model is created before and after the invasion, using the invasion time as a node. This includes:

[0067] in, This refers to the time point when the virus strain invades. Characterizing the virus strain before invasion, After the virus strain invades, then Or, if Characterizing the invasion of viral strains, Characterizing the virus strain before invasion, .

[0068] Step S107: Estimate the intensity coefficient based on the first incidence spillover rate model and the second incidence spillover rate model.

[0069] In some embodiments, , and All of these are exact values ​​that can be obtained, as shown in equation (5). , and It can be determined that the first Weekly incidence spillover rate Furthermore, based on Equation 6 and right Make an estimate.

[0070] The intensity coefficient is a calibration coefficient that converts mosquito density (ovipositor index) into actual disease transmission risk (incidence spillover rate). This intensity coefficient directly quantifies the effective transmission efficiency of mosquitoes carrying viruses and causing human infection, while also reflecting the differences in transmission capacity under different virus strains (or environmental conditions).

[0071] Step S108: Based on mosquito vector data, host infection dynamics data, estimated basic infectivity of vertical mosquito transmission, contribution coefficient and intensity coefficient of mosquito ovipositor index to infectivity of mosquito transmission to host, host data for the second time interval are estimated.

[0072] In some embodiments, the host data for the second time interval can be estimated based on equation (5). The second time interval is after the first time interval and can be the current time interval or a future time interval.

[0073] In some embodiments, the carrier may be based on the mosquito ovipositor index in a second time interval. The estimated baseline infectivity of mosquitoes during vertical transmission. The contribution coefficient of the ovipositor index to the infectivity of mosquitoes transmitted to the reservoir host. Estimate the infectivity of mosquito-borne transmission to the store owner .

[0074] In some embodiments, the disease spillover rate is estimated based on the estimated intensity coefficient and the mosquito ovipositor index in the second time interval; specifically, in equation (5) Given a value The mosquito ovipositor index can be obtained based on equation (6). and the estimated strength coefficient Sure.

[0075] In some embodiments, the vector determines the number of storage households in the infection period corresponding to the second time interval based on the estimated infectivity of mosquito vectors to storage households and storage household infection dynamics data; specifically, the vector determines the number of storage households in the infection period corresponding to the estimated infectivity of mosquito vectors to storage households based on the estimated infectivity of mosquito vectors to storage households. Formula (3) determines the main variable for the susceptible period. This allows for the determination of the number of vulnerable reservoir owners corresponding to the second time interval. Furthermore, based on the estimated infectivity of mosquitoes transmitted to the storage site... Number of vulnerable depositors corresponding to the second time interval Determine the latent period of the master variable corresponding to the second time interval. Then, based on the latent period of the master variable corresponding to the second time interval Determine the main variables for the infection period corresponding to the second time interval. Based on the main variables of the infection period corresponding to the second time interval Determine the number of reservoir owners during the infection period corresponding to the second time interval. .

[0076] Furthermore, the vector is based on the number of reservoir hosts during the infection period corresponding to the second time interval. , incidence spillover rate And the probability of the storage owner carrying the virus during the infection period and the recovery period. Equation (5) is used to estimate the host data for the second time interval. .

[0077] Thus, the method for estimating the transmission risk of Japanese encephalitis virus provided in this embodiment can establish a connection between mosquito vectors, reservoirs, and hosts, integrate easily accessible mosquito vector data and reservoir data, and achieve full-chain quantitative assessment from mosquito vector density detection to reservoir infection risk, and from reservoir to host, thereby improving the accuracy of host infection risk assessment in real-world scenarios.

[0078] In some embodiments, the mosquito-borne infectious disease-vector-host-environment complex system provided in this disclosure involves a virus transmission process coupled with three parts: mosquito population dynamics (mosquito data: density fluctuations reflected by the ovitrap index), pig infection dynamics (pig data: pig herd timeline of "susceptibility-latency-infection-recovery with the virus-complete recovery" and changes in stock size), and environmental and policy-driven factors. The core is the quantitative correlation between pig data and mosquito data. In this system, the quantitative correlation between core transmission parameters and observed variables needs to be determined based on empirical data. The infectivity of mosquito-borne diseases transmitted to the storage owner (pig) As a key parameter connecting the medium and the storage owner, it reflects the probability of a single mosquito bite leading to infection in pigs. Its correlation formula with the ovitrap index is as follows: (4) In equation (4), The value of the mosquito ovipositor index is obtained by local weighted regression smoothing (eliminating daily random fluctuations and preserving seasonal trends). The mosquito ovipositor index versus λ vp The contribution coefficient (quantifying the driving force of mosquito density fluctuations on transmission). This indicates the basic infectivity of vertical mosquito transmission (ensuring the minimum probability of transmission even when the mosquito ovipositor index is close to 0 during the dry season).

[0079] The spillover rate of disease from pigs (stork owners) to humans ( As a key parameter bridging mosquito hosts and the human population, it reflects the probability of indirect transmission from infected mosquito hosts to the host via mosquito bites. Due to the time lag between the mosquito incubation period and the human incubation period, its correlation formula with the delayed ovitrap index is as follows:

[0080] In the formula The spillover transmission intensity coefficient is 15 days, which is the transmission delay period (matching the actual cycle of 6-12 days for mosquito incubation and 5-13 days for human incubation). The value is the mosquito ovipositor index with a 15-day lag; meanwhile, the total number of pigs (as the amplification host) is... ) is the sum of the number of each infection state, and the formula is: In the formula , , , , These represent the number of pigs in the susceptible, latent, infected, recovered and carrier, and fully recovered states, respectively.

[0081] From a stochastic process perspective, mosquito biting behavior and the shift in infection status of pigs (such as pigs changing from "susceptible" to "latent") are both random events. Observable nodes include the mosquito ovitrap index (quantifying mosquito density) and the number of pigs. (Site size), number of new human cases C ( t These variables can be directly obtained through actual monitoring; however, "hidden processes" such as the duration of viral replication in mosquito vectors (3-5 days) and the incubation period in pigs (1.5 days) cannot be directly observed and must be inferred indirectly through observable data such as mosquito ovipositor index and case count. , Key propagation parameters are used to ensure that the model parameters match the actual propagation patterns.

[0082] From an epidemiological perspective, this framework can quantify the impact of "fluctuations in mosquito-borne data (ovipositor index) on..." Changes, in turn, led to an increase in infection rates in pig herds, driving... The chain link of "increased incidence, ultimately leading to an increase in human cases" precisely explains the unique transmission pattern of mosquito-borne infectious diseases.

[0083] Existing models generally fail to incorporate both mosquito host and vector data, resulting in two major drawbacks: first, they only simulate mosquito-to-human transmission, ignoring the amplification effect of pigs (storage hosts); second, they assume a fixed mosquito density, neglecting the dynamic fluctuations in vector data. The "mosquito-pig-human coupled compartment model" proposed in this technology is a generalized extension and core correction to existing technologies. If the amplification host of the "pig population" (i.e., the mosquito population) is ignored... S p =E p = I p = C p = R p If the value is 0, then the spillover rate from the pig (storage animal) to the human population via mosquito vectors is 0. p =0, the model degenerates into the existing mosquito-human binary transmission model that only simulates direct mosquito-to-human transmission; if "vertical transmission of mosquitoes" (i.e., l vp in the formula b =0) and "dynamic fluctuation of mosquito oviduct index" (i.e., w ( t )= w 0 (fixed as the annual average), then l vp Simplified to k · w The model degenerates into the existing susceptible-exposure-infection-recovery model based on the "uniform mosquito density assumption," indicating that existing technical models are all special cases of this technical model, and this technical framework has compatibility and scalability with existing technologies.

[0084] The core improvement of this technology is the integration of "multi-species status compartments + dynamic parameter association" with mosquito data from storage owners and vectors: by adding a recovery period for pig herds carrying the virus (… C p ), and the associated mosquito ovipositor index (mosquito data) and l vp Adjusting the size of the reserve holders ( N p The changes in mosquito density (ovipositor index) are unified into the same framework.

[0085] To address the limitations of traditional models that rely on host data, this embodiment of the disclosure constructs an adaptive model using host data (pig inventory) and mosquito vector data (mosquito ovipositor index) as core inputs: monitoring data can provide mosquito vector data (directly reflecting mosquito density) and pig numbers, and the two types of data together support a clear reconstruction of the "mosquito-pig-human" transmission chain; the core of the model adopts a "mosquito-pig-human multi-species compartmental differential equation system", which establishes the mathematical relationship of the entire transmission chain of "mosquito vector transmission to pigs, and then indirect transmission from pigs to humans" by quantifying the dynamics of mosquito vector populations, changes in the infection status of pig herds, the occurrence patterns of human cases, and the transmission coefficients among the three groups.

[0086] Figure 2 A second optional flowchart of the method for estimating the transmission risk of Japanese encephalitis virus provided in this embodiment of the present disclosure is shown, and the steps will be described accordingly.

[0087] Step S201: Determine the infection dynamics data of the storage host.

[0088] In some embodiments, considering the transmission characteristics of Japanese encephalitis virus, the pig herd needs to be divided into five different states (compartments): susceptible ( ), infiltration ( ),Infect( ), recovered but still infectious ( ), complete recovery ( Meanwhile, the dual impact of Japanese encephalitis virus transmission via direct pig-to-pig and mosquito-borne transmission is also considered. The transmission of Japanese encephalitis virus can be described by formula (3).

[0089] in, The local daily pig replenishment rate, which includes the number of newborn and imported pigs, is dynamically adjusted according to breeding policies and is a core parameter connecting mosquito-borne and pig-herd transmission. The direct transmission coefficient between pigs quantifies the ability of Japanese encephalitis virus to spread in a pig herd without mosquito vectors, with a value range of 0.0-0.4 per day. The incubation period to infection rate in pigs corresponds to an incubation period of 1.5 days after infection with Japanese encephalitis virus. ; The rate of recovery and viral carriage in pigs corresponds to a 3-day infection period. ; The rate of recovery from viral carriage to complete recovery in pigs corresponds to a recovery period of 2.5 days, i.e. ; This represents the daily mortality rate of pigs, including both natural deaths and slaughter, corresponding to a pig's average lifespan of 234 days. Among other auxiliary parameters, the infection rate of the storage unit is input ( The calculation formula is as follows: ,in, The incidence of Japanese encephalitis during the average lifespan of pigs, in days. For the incubation period of pigs, days The day is the infection cycle of pigs, when In the range of 25%-85%, Within the range of 0.43% to 1.45%, these values ​​are used as fixed input parameters for the model.

[0090] The total number of local pigs must meet the population conservation relationship, i.e., Equation (2). In Equation (2), the time-dependent parameter is in line with the actual fluctuation of pig farming scale.

[0091] Step S202: Determine the first morbidity spillover rate model.

[0092] In some embodiments, humans are the definitive host of Japanese encephalitis virus and cannot produce sufficient viremia to infect mosquito vectors after infection; therefore, a variable spillover rate is used. Simulate human cases, i.e., formula (5).

[0093] Step S203: Determine the second infectivity model.

[0094] In some embodiments, in order to achieve dynamic correlation between mosquito density and mosquito ovipositor index monitoring data, a correlation formula is introduced between the infectivity of mosquitoes transmitted to the storage owner (pig) and the mosquito ovipositor index, namely formula (4).

[0095] In equation (4), The mosquito ovipositor index value can eliminate daily sampling fluctuations and retain the seasonal trend of a high mosquito ovipositor index in the rainy season and a low mosquito ovipositor index in the dry season. as The contribution coefficient of the mosquito ovitrap index to the infectivity of mosquitoes transmitted to the storage owner (pig); To maintain the basic infectivity for vertical transmission of mosquitoes, ensuring that Japanese encephalitis virus can still maintain the lowest level of transmission when the mosquito ovitrap index is close to 0 during the dry season.

[0096] Step S204: Estimate the contribution coefficient and intensity coefficient of the basic infectivity of mosquito vertical transmission and the mosquito ovipositor index to the infectivity of mosquito transmission to the reservoir host.

[0097] In some embodiments, by combining the differential equations of the pig herd associated with the storage owner data (Equation (3)) with the formula for the total number of pigs (Equation (2)), the human case simulation (Equation (5)), and the formula for the correlation between the infectivity of mosquito vectors transmitted to the storage owner (pig) and the mosquito ovitrap index (Equation (5)), the model can simultaneously fit three types of core data: "storage owner data (number of pigs in stock, infection status of pig herd), mosquito vector data (mosquito ovitrap index), and case data"; and calibrate with the time series data of the mosquito ovitrap index (mosquito data). The fluctuation range was analyzed, and combined with the data on pig owners (changes in the number of pigs at various stages) to ensure that the contribution of mosquito vector transmission was synchronized with the actual mosquito density and the dynamics of infection in pig owners. When verifying the case tracing information, the matching degree between the data on pig owners (time distribution of infection in pig herds) and the data on mosquito vectors (time series of ovitrap index) was simultaneously referenced to ensure the accuracy of the verification results. Finally, through the collaborative fitting of the data on pig owners and mosquito vectors, a precise estimate was obtained. , By analyzing key transmission parameters, the study comprehensively depicts the transmission dynamics of Japanese encephalitis virus in the "mosquito-pig-human" system, relying on the collaborative data of both the reservoir owner and the mosquito vector, thus providing quantitative support for regional mosquito-borne infectious disease risk early warning.

[0098] Furthermore, the method for estimating the transmission risk of Japanese encephalitis virus provided in this embodiment is verified by combining real data with simulation.

[0099] A Japanese encephalitis surveillance dataset from 2003 to 2017 was selected from a certain region. The core data included mosquito vector data, reservoir infection dynamics data, and host data. Mosquito vector data includes monthly mosquito ovipositor indices from 2003 to 2016; The infection dynamics data for pigs stored include the number of pigs and daily pig consumption from 2003 to 2016; The host data includes the number of new cases of Japanese encephalitis each month from 2004 to 2016.

[0100] Figure 3 The time series of the mosquito ovipositor index is shown.

[0101] Figure 3 The average monthly mosquito ovipositor index for a certain region is represented by a "dot" in the graph from 2003 to 2016. The mosquito ovipositor index for each month of each year is smoothed to obtain the smoothed data (lighter lines) for each year. Figure 3 The darker lines represent the annual average.

[0102] Figure 4 The curves showing the local pig population and daily consumption are displayed.

[0103] like Figure 4 The figure shows the number of local pigs and their daily consumption in the region from January 2004 to May 2017. The triangular simulation lines represent the number of pigs, connected by annual reports and estimated numbers, and are represented by solid and hollow triangles respectively. N p Solid dots represent the local daily pork consumption. The vertical dashed line indicates the point in time when the total number of local pigs in the region declined.

[0104] Centered on the "mosquito-pig-human" cross-species compartment model, the computer simulation of the model uses the Euler-polynomial integration method and sets a fixed time step of 1 day.

[0105] Assuming the number of new cases observed each month is ( ,in (total number of months), which follows a Poisson distribution, i.e. ,in For the model in the first The average monthly output of potential Japanese encephalitis virus cases was derived using a mosquito-pig-human coupling equation and correlated with key transmission parameters. Based on this observational hypothesis, the overall log-likelihood function... Defined as In the formula Let be the vector of key propagation parameters to be estimated. For a given Monthly Cases and Parameters Time Monthly observation cases The posterior probability measurement function.

[0106] In the parameterized expression of the key transmission parameters, the infectivity of the medium to the host (pig) is... ) expressed from a biological mechanism as ,in Mosquito bite rate, The probability of Japanese encephalitis virus transmission per mosquito bite. This represents the number of mosquitoes that cause infection.

[0107] The infectivity can be simplified as a function of the mosquito ovipositor index. ,in , For the parameter to be estimated, The vertical transmission contribution of adult mosquitoes to eggs is represented, with mosquito vertical transmission rates ranging from 12% to 100%. Considering the invasion of a new variant of Japanese encephalitis virus in 2011, the piecewise expression for infectivity is: And satisfy The spillover rate from host (pig) to human ( Let ) be a lag function of the mosquito ovipositor index. ,in The strength parameter to be estimated, The spread is delayed; considering the invasion of new strains, the piecewise expression for the spillover ratio is: And satisfy .

[0108] The parameter estimation adopts a statistical inference framework based on the likelihood function to obtain point estimates and 95% confidence intervals for key parameters.

[0109] In some embodiments, a risk assessment was conducted based on Japanese encephalitis virus (JE) surveillance data from a certain region from 2003 to 2017, focusing on the synergistic effect between pig stockpilers and mosquito vectors. The core assessment results showed that after the invasion of a new JE virus variant in 2011, the spillover rate of JE incidence from pig stockpilers to human hosts... The infectivity of mosquito-borne pathogens increased from 0.0002 to 0.0013, becoming a key driver of the resurgence of local cases, while the infectivity of mosquito-borne pathogens transmitted to pigs (stork owners) was also a key factor. The model remains stable throughout the entire process, accurately reproducing the dynamic changes during propagation. The algorithm's fitting results to the data are as follows: Figure 5 As shown, the 95% confidence interval of the output results of this embodiment can explain all observed local cases of Japanese encephalitis.

[0110] Figure 5 A schematic diagram of the fitting results of the algorithm of this disclosure is shown.

[0111] like Figure 5 As shown, under the scenario of invasion by a new Japanese encephalitis virus strain, the variable The model fitting results for local Japanese encephalitis virus cases in a certain region from 2004 to 2016 during the period of change. Figure 5 (a) represents the simulation results after standardization based on the number of pigs. Figure 5 (b) represents the trend change in the annual average standardized simulation results. Figure 5 (a) and Figure 5 (b) In the figure, the thicker curve represents the simulation results, the solid dots represent reported (i.e., observed) local cases of Japanese encephalitis virus, and the thinner curve represents Japanese encephalitis virus cases processed using a locally weighted regression smoothing method. The vertical, lighter dashed line marks the time points when the total number of local pigs in the region declined (compared to...). Figure 4 (The time points are consistent with those in the figure), with the darker vertical dashed lines marking the time points when the new Japanese encephalitis virus strain was introduced into the pig herd. The inset shows the estimated results of the model parameters.

[0112] It does not rely on difficult-to-obtain host data such as swine serology; model calibration can be completed using only three data sets: mosquito vector data, pig stock, and case time series. At the same time, it can simulate the dynamic changes in transmission risk over time.

[0113] Thus, the method for estimating the transmission risk of Japanese encephalitis virus (JEV) provided in this disclosure innovatively proposes to use the mosquito ovitrap index (mosquito vector data) as the core, combined with the number of pigs in stock (stockholder data), to replace the traditional reliance on host infection data, and construct a JEV transmission risk prediction model. Through dynamic parameter correlation, it achieves a quantitative assessment of JEV transmission risk, the core of which lies in the synergistic coupling of stockholder and mosquito vector data. By introducing stockholder variables and mosquito vector factors, combined with transmission dynamics theory, a modeling framework integrating multi-source non-host data is constructed. This method not only simulates the driving effect of mosquito vectors on stockholder (pig herd) infection, but also takes into account the supporting role of stockholder size in transmission, achieving an accurate characterization of JEV transmission patterns. The key lies in the full-chain integration of stockholder and mosquito vector data.

[0114] Not only is it applicable to Japanese encephalitis virus outbreaks, but it can also be extended to other mosquito-borne infectious diseases that require the combination of reservoir and mosquito vector data (such as West Nile virus disease). By replacing reservoir data (such as bird population size) and mosquito vector data (corresponding to the oviposition index), it can quickly adapt to the transmission patterns of different diseases. This flexibility further enhances the practicality of the model, enabling it to play a role in different regions and different mosquito-borne infectious disease prevention and control scenarios.

[0115] By combining data from both mosquito hosts and mosquito vectors, an assessment model that better reflects the "host-vector" transmission logic of mosquito-borne infectious diseases was constructed. This not only completely solves the problem of dependence on host infection data, but also enables data completion through the interaction and correlation of the two types of data when host or vector data is partially missing. At the same time, it significantly reduces data collection costs and effectively compensates for the shortcomings of traditional methods that rely too heavily on host data.

[0116] By monitoring both mosquito host and vector data in real time, timely and accurate transmission predictions and early warnings can be provided even when host data is missing or delayed. This is achieved through the complete chain of "rising mosquito index leading to prediction of infection risk in mosquito hosts, and then to early warning of risk in the population." In particular, when the size of mosquito hosts changes or mosquito density suddenly increases, the risk changes brought about by the synergy between the two can be quickly assessed. This innovation significantly enhances the emergency response capability for disease prevention and control, helping public health policymakers to take action more quickly and control the spread of epidemics more effectively.

[0117] Figure 6 A schematic diagram of an optional structure of the Japanese encephalitis virus transmission risk estimation device provided in an embodiment of this disclosure is shown, and the details will be explained based on each part.

[0118] In some embodiments, the Japanese encephalitis virus transmission risk estimation device includes a data acquisition unit, a first model determination unit, a second model determination unit, a first estimation unit, a third model determination unit, a fourth model determination unit, a second estimation unit, and a host data estimation unit.

[0119] The data acquisition unit is used to acquire mosquito vector data, reservoir infection dynamics data, and host data corresponding to the first time interval. The first model determination unit is used to determine the first infectivity model from mosquito vector to reservoir owner based on mosquito vector biting rate, mosquito vector hit rate, number of infected mosquito vectors and total number of reservoir owners in mosquito vector data. The second model determination unit is used to determine the second infectivity model based on the mosquito ovipositor index, the basic infectivity maintained by vertical mosquito transmission, and the contribution coefficient of the mosquito ovipositor index to the infectivity of mosquito transmission to the reservoir host. The first estimation unit is used to estimate the contribution coefficients of the basic infectivity of mosquito vertical transmission and the mosquito oviduct index to the infectivity of mosquito transmission to the reservoir owner based on the first infectivity model and the second infectivity model. The third model determination unit is used to determine the first disease spillover rate model based on the number of cases, the number of infected patients, the rate of patients carrying the disease from the infected period to the recovery period, and the disease spillover rate. The fourth model determination unit is used to determine the second disease spillover rate model based on the disease spillover rate, intensity coefficient, and mosquito ovipositor index. The second estimation unit is used to estimate the intensity coefficient based on the first morbidity spillover rate model and the second morbidity spillover rate model; The host data estimation unit is used to estimate the host data for the second time interval based on mosquito vector data, host infection dynamics data, estimated basic infectivity of mosquito vertical transmission, contribution coefficient and intensity coefficient of mosquito oviduct index to the infectivity of mosquito transmission to the host.

[0120] In some embodiments, the mosquito vector data includes at least one of the following: mosquito ovipositor index, mosquito bite rate, mosquito hit rate, and number of infected mosquitoes; The infection dynamics data of the storage subjects include at least one of the following: the number of storage subjects in the susceptible period, the number of storage subjects in the incubation period, the number of storage subjects in the infectious period, the number of storage subjects in the infectious period, the number of recovered storage subjects, the infection rate of imported storage subjects, the daily replenishment of storage subjects, the daily mortality rate of storage subjects, the direct transmission coefficient between storage subjects, the probability of storage subjects progressing from the incubation period to the infectious period, the probability of storage subjects progressing from the infectious period to the recovery period with the virus, and the probability of storage subjects progressing from the recovery period with the virus to the complete recovery period. The host data includes the number of infected hosts.

[0121] The first model determination unit is specifically used for:

[0122] in, Mosquito bite rate, Mosquito hit rate represents the probability of virus transmission per mosquito bite. The number of infected mosquitoes, The total number of depositors, The infectivity of mosquito vectors transmitted to the storage site represents the probability that a single bite from an infected mosquito will lead to infection in the storage site owner.

[0123] In some embodiments, the total number of depositors is based on the number of depositors during the vulnerable period. Number of latent reserve holders Number of reservoir owners during the infection period Number of storage owners during the infectious period and number of rehabilitation and storage owners The sum is determined, and the susceptible period is the main variable. Latency period main variable Infection period main variables Main variables during the infectious period and rehabilitation main variables include:

[0124] in, To input the infection rate of the storage unit, This is the amount to be replenished by the main storage unit on the main day; The daily mortality rate of the depositors. The direct propagation coefficient between store owners; The probability of the incubation period turning into the infection period for the storage owner; The probability of a patient carrying the virus during the recovery period after infection; The probability of a patient recovering from the virus during the recovery period progressing to full recovery.

[0125] The second model determining unit is specifically used for:

[0126] in, This refers to the mosquito ovipositor index. The contribution coefficient of the ovitrap index to the infectivity of mosquitoes transmitted to the reservoir host. This provides the basic infectivity for vertical transmission of mosquitoes.

[0127] The third model determination unit is specifically used for:

[0128] in, Characterizing the first Weekly incidence spillover rate This characterizes the probability of a storage subject carrying the virus during the recovery period from the infection phase. The number of reservoir owners during the infection period. This refers to the number of cases.

[0129] The fourth model determination unit is specifically used for:

[0130] in, The disease spillover rate from the storage host to the host. The strength coefficient, for Mosquito oviposition index at any given time This is due to the delay in transmission.

[0131] The host data estimation unit is specifically used to estimate the infectivity of mosquitoes transmitted to the host based on the mosquito ovipositor index in the second time interval, the estimated basic infectivity of mosquito vertical transmission, and the contribution coefficient of the mosquito ovipositor index to the infectivity of mosquito transmission to the host. Based on the estimated intensity coefficient and the mosquito ovipositor index for the second time interval, the disease spillover rate is estimated. The number of storage households in the second time interval is determined based on the estimated infectivity of mosquito vectors to the storage households and the infection dynamics data of the storage households. Based on the number of infected hosts, the morbidity spillover rate, and the probability of hosts carrying the virus from the infection period to the recovery period corresponding to the second time interval, host data for the second time interval are estimated.

[0132] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0133] Figure 7 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0134] like Figure 7 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0135] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of displays, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0136] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the Japanese encephalitis virus transmission risk estimation method. For example, in some embodiments, the Japanese encephalitis virus transmission risk estimation method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the Japanese encephalitis virus transmission risk estimation method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform a Japanese encephalitis virus transmission risk estimation method by any other suitable means (e.g., by means of firmware).

[0137] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0139] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0141] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0142] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0143] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0144] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0145] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for estimating the risk of transmission of Japanese encephalitis virus, characterized in that, The method includes: Acquire mosquito vector data, reservoir infection dynamics data, and host data corresponding to the first time interval; Based on mosquito vector data, including mosquito bite rate, mosquito vector hit rate, number of infected mosquitoes, and total number of reservoir owners, a first infectivity model from mosquito vector to reservoir owner is determined. The second infectivity model was determined based on the ovipositor index, the basic infectivity maintained by vertical mosquito transmission, and the contribution coefficient of the ovipositor index to the infectivity of mosquito transmission to the reservoir host. Based on the first infectivity model and the second infectivity model, the contribution coefficients of the basic infectivity of mosquito vertical transmission and the mosquito ovipositor index to the infectivity of mosquito transmission to the reservoir owner are estimated; Based on the number of cases, the number of infected individuals, the rate of carriers transitioning from the infected period to the recovery period, and the incidence spillover rate, a first incidence spillover rate model is determined. The second disease spillover rate model was determined based on the disease spillover rate, intensity coefficient, and mosquito ovipositor index. The intensity coefficient is estimated based on the first disease spillover rate model and the second disease spillover rate model; the intensity coefficient includes a calibration coefficient that converts the mosquito ovipositor index into the disease spillover rate. Based on mosquito vector data, host infection dynamics data, estimated basic infectivity of vertical mosquito transmission, contribution coefficient and intensity coefficient of mosquito ovipositor index to infectivity of mosquito transmission to host, host data for the second time interval are estimated.

2. The method according to claim 1, characterized in that, The mosquito vector data includes at least one of the following: mosquito ovipositor index, mosquito bite rate, mosquito hit rate, and number of infected mosquitoes; The infection dynamics data of the storage subjects include at least one of the following: the number of storage subjects in the susceptible period, the number of storage subjects in the incubation period, the number of storage subjects in the infectious period, the number of storage subjects in the infectious period, the number of recovered storage subjects, the infection rate of imported storage subjects, the daily replenishment of storage subjects, the daily mortality rate of storage subjects, the direct transmission coefficient between storage subjects, the probability of storage subjects progressing from the incubation period to the infectious period, the probability of storage subjects progressing from the infectious period to the recovery period with the virus, and the probability of storage subjects progressing from the recovery period with the virus to the complete recovery period. The host data includes the number of infected hosts.

3. The method according to claim 1, characterized in that, The method for determining the first infectivity model from mosquito vector to reservoir owner based on mosquito vector biting rate, mosquito vector hit rate, number of infected mosquitoes, and total number of reservoir owners includes: in, Mosquito bite rate, Mosquito hit rate represents the probability of virus transmission per mosquito bite. The number of mosquitoes that cause infection. The total number of depositors, The infectivity of mosquito vectors transmitted to the storage site represents the probability that a single bite from an infected mosquito will lead to infection in the storage site owner.

4. The method according to claim 3, characterized in that, The total number of depositors is based on the number of depositors during the vulnerable period. Number of latent reserve holders Number of reservoir owners during the infection period Number of storage owners during the infectious period and number of rehabilitation and storage owners The sum is determined, and the susceptible period is the main variable. Latency period main variable Infection period main variables Main variables during the infectious period and rehabilitation main variables include: in, To input the infection rate of the storage unit, This is the amount to be replenished by the main storage unit on the main day; The daily mortality rate of the depositors. The direct propagation coefficient between store owners; The probability of the incubation period turning into the infection period for the storage owner; The probability of a patient carrying the virus during the recovery period after infection; The probability of a patient recovering from the virus during the recovery period progressing to full recovery.

5. The method according to claim 1, characterized in that, The second infectivity model, determined based on the ovitrap index, the baseline infectivity maintained by vertical mosquito transmission, and the contribution coefficient of the ovitrap index to the infectivity of mosquito transmission to the reservoir host, includes: in, for t Mosquito ovipositor index value at any given time. The contribution coefficient of the ovitrap index to the infectivity of mosquitoes transmitted to the reservoir host. This provides the basic infectivity for vertical transmission of mosquitoes.

6. The method according to claim 1, characterized in that, The model for determining the first morbidity spillover rate, based on the number of cases, the number of infected individuals, the carrier rate from the infected period to the recovery period, and the morbidity spillover rate, includes: in, Characterizing the first Weekly incidence spillover rate This characterizes the probability of a storage subject carrying the virus during the recovery period from the infection phase. The number of reservoir owners during the infection period. This refers to the number of cases.

7. The method according to claim 1, characterized in that, The second disease spillover rate model, based on disease spillover rate, intensity coefficient, and mosquito ovipositor index, includes: in, The disease spillover rate from the storage host to the host. The strength coefficient, for Mosquito oviposition index at any given time This is due to a delay in transmission.

8. The method according to claim 1, characterized in that, The mosquito vector data, host infection dynamics data, estimated basic infectivity of mosquito vertical transmission, contribution coefficient and intensity coefficient of the mosquito ovitrap index to the infectivity of mosquito transmission to the host, and estimated host data for the second time interval, include: Based on the mosquito ovipositor index in the second time interval, the estimated basic infectivity of mosquito vertical transmission, and the contribution coefficient of the mosquito ovipositor index to the infectivity of mosquito transmission to the storage host, the infectivity of mosquito transmission to the storage host is estimated. Based on the estimated intensity coefficient and the mosquito ovipositor index for the second time interval, the disease spillover rate is estimated. The number of storage households in the second time interval is determined based on the estimated infectivity of mosquito vectors to the storage households and the infection dynamics data of the storage households. Based on the number of infected hosts, the morbidity spillover rate, and the probability of hosts carrying the virus from the infection period to the recovery period corresponding to the second time interval, host data for the second time interval are estimated.

9. A device for estimating the risk of Japanese encephalitis virus transmission, characterized in that, The device includes: The data acquisition unit is used to acquire mosquito vector data, reservoir infection dynamics data, and host data corresponding to the first time interval. The first model determination unit is used to determine the first infectivity model from mosquito vector to reservoir owner based on mosquito vector biting rate, mosquito vector hit rate, number of infected mosquito vectors and total number of reservoir owners in mosquito vector data. The second model determination unit is used to determine the second infectivity model based on the mosquito ovipositor index, the basic infectivity maintained by vertical mosquito transmission, and the contribution coefficient of the mosquito ovipositor index to the infectivity of mosquito transmission to the reservoir host. The first estimation unit is used to estimate the contribution coefficients of the basic infectivity of mosquito vertical transmission and the mosquito oviduct index to the infectivity of mosquito transmission to the reservoir owner based on the first infectivity model and the second infectivity model. The third model determination unit is used to determine the first disease spillover rate model based on the number of cases, the number of infected patients, the rate of patients carrying the disease from the infected period to the recovery period, and the disease spillover rate. The fourth model determination unit is used to determine the second disease spillover rate model based on the disease spillover rate, intensity coefficient, and mosquito ovipositor index. The second estimation unit is used to estimate the intensity coefficient based on the first disease spillover rate model and the second disease spillover rate model; the intensity coefficient includes a calibration coefficient that converts the mosquito oviduct index into the disease spillover rate. The host data estimation unit is used to estimate the host data for the second time interval based on mosquito vector data, host infection dynamics data, estimated basic infectivity of mosquito vertical transmission, contribution coefficient and intensity coefficient of mosquito oviduct index to the infectivity of mosquito transmission to the host.

10. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

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