Method and device for estimating risk of transmission of japanese encephalitis virus, and electronic device

Abstract

The disclosure provides a Japanese encephalitis virus transmission risk estimation method and device and electronic equipment, relating to the technical field of disease transmission, the method comprises the steps of: determining a first infection force model, a second infection force model, a first incidence overflow rate model and a second incidence overflow rate model based on mosquito vector data, host infection kinetics data and host data corresponding to a first time interval; estimating the contribution coefficient of vertical transmission basic infection force and mosquito ovitraps index to the infection force of mosquito vector transmission to the host based on the first infection force model and the second infection force model; estimating the intensity coefficient based on the first incidence overflow rate model and the second incidence overflow rate model; estimating the host data of the second time interval based on the vertical transmission basic infection force, the contribution coefficient of the mosquito ovitraps index to the infection force and the intensity coefficient; in this way, the whole chain quantitative evaluation from mosquito vector to host and from host to host can be realized, and the risk evaluation accuracy of host infection virus 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 and 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;

[0006] 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;

[0007] determining a second infection force model based on mosquito ovitraps index, vertical transmission maintenance of mosquito vectors, and contribution coefficient of mosquito ovitraps index to infection force of mosquito vectors to reservoir hosts;

[0008] 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;

[0009] 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 infection rate, and the incidence overflow rate;

[0010] determining a second incidence overflow rate model based on the incidence overflow rate, the intensity coefficient and the mosquito ovitraps index;

[0011] estimating an intensity coefficient based on the first incidence spill-over rate model and the second incidence spill-over rate model; the intensity coefficient comprises a calibration coefficient that converts a mosquito-borne ovitraps index to an incidence spill-over rate;

[0012] estimating host data for the second time interval based on mosquito data for the second time interval, reservoir infection kinetics data, the estimated mosquito vertical transmission force of infection, a contribution coefficient of the mosquito-borne ovitraps index to the infection force of infection of the mosquito to the reservoir, and the intensity coefficient.

[0013] In the above scheme, the mosquito data comprises at least one of a mosquito-borne ovitraps index, a mosquito biting rate, a mosquito hit rate, and a number of infected mosquitoes;

[0014] The reservoir infection kinetics data comprises at least one of a number of susceptible reservoirs, a number of latent reservoirs, a number of infectious reservoirs, a number of infectious reservoirs, a number of recovered reservoirs, an input reservoir infection rate, a daily reservoir replenishment amount, a daily reservoir mortality rate, a direct transmission coefficient between reservoirs, a probability of a latent reservoir turning into an infectious reservoir, a probability of an infectious reservoir turning into a virus-carrying recovered reservoir, and a probability of a virus-carrying recovered reservoir turning into a fully recovered reservoir.

[0015] The host data comprises a number of infected hosts.

[0016] In the above scheme, the first infection force model of the mosquito 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, comprising:

[0017]

[0018] wherein, is the mosquito biting rate, is the mosquito hit rate, representing the probability of virus transmission per mosquito bite, is the number of infected mosquitoes, is the total number of reservoirs, is the infection force of the mosquito to the reservoir, representing the probability of infection of the reservoir caused by a single bite of the infected mosquito.

[0019] 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 infectious reservoirs , the number of infectious reservoirs , and the number of recovered reservoirs , wherein the susceptible reservoir variable , the latent reservoir variable , the infectious reservoir variable , the infectious reservoir variable , and the recovered reservoir variable include:

[0020]

[0021] 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.

[0022] In the above scheme, the determination of the second infectivity model 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:

[0023]

[0024] 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.

[0025] In the above scheme, 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:

[0026]

[0027] 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.

[0028] In the above scheme, the method for determining the second disease spillover rate model based on the disease spillover rate, intensity coefficient, and mosquito ovipositor index includes:

[0029]

[0030] 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.

[0031] In the above scheme, the mosquito vector data based on the second time interval, the reservoir host infection kinetics data, the estimated vertical transmission basic infectivity of the mosquito vector, the contribution coefficient of the mosquito ovitrap index to the infectivity of the mosquito vector to the reservoir host, and the intensity coefficient, the host data of the second time interval are estimated, comprising:

[0032] The infectivity of the mosquito vector to the reservoir host is estimated based on the mosquito ovitrap index of the second time interval, the estimated vertical transmission basic infectivity of the mosquito vector, and the contribution coefficient of the mosquito ovitrap index to the infectivity of the mosquito vector to the reservoir host.

[0033] The incidence overflow rate is estimated based on the estimated intensity coefficient and the mosquito ovitrap index of the second time interval.

[0034] The number of reservoir hosts in the infectious period corresponding to the second time interval is determined based on the estimated infectivity of the mosquito vector to the reservoir host and the reservoir host infection kinetics data.

[0035] The host data of the second time interval is estimated based on the number of reservoir hosts in the infectious period corresponding to the second time interval, the incidence overflow rate, and the probability of reservoir hosts carrying the virus from the infectious period to the convalescent period.

[0036] According to a second aspect of the present disclosure, a Japanese B encephalitis virus transmission risk estimation device is provided, comprising:

[0037] A data acquisition unit is configured to acquire mosquito vector data corresponding to a first time interval, reservoir host infection kinetics data, and host data.

[0038] A first model determination unit is configured to determine a first infectivity model of the mosquito vector to the reservoir host based on the mosquito bite rate, the mosquito hit rate, the number of infected mosquito vectors, and the total number of reservoir hosts in the mosquito vector data.

[0039] A second model determination unit is configured to determine a second infectivity model based on the mosquito ovitrap index, the vertical transmission basic infectivity of the mosquito vector, and the contribution coefficient of the mosquito ovitrap index to the infectivity of the mosquito vector to the reservoir host.

[0040] A first estimation unit is configured to estimate the vertical transmission basic infectivity of the mosquito vector and the contribution coefficient of the mosquito ovitrap index to the infectivity of the mosquito vector to the reservoir host based on the first infectivity model and the second infectivity model.

[0041] A third model determination unit is configured to determine a first incidence overflow rate model based on the number of cases, the number of reservoir hosts in the infectious period, the reservoir host infection period to the convalescent period, and the incidence overflow rate.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0046] 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.

[0047] 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.

[0048] It should be understood that the contents described in this part are not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0049] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will be more apparent from the following detailed description read in conjunction with the accompanying drawings, in which:

[0050] In the drawings, identical or corresponding reference signs refer to identical or corresponding parts.

[0051] Figure 1 A first optional flowchart of a method for estimating the transmission risk of Japanese encephalitis virus according to an embodiment of the present disclosure is shown;

[0052] Figure 2 A second optional flowchart of a method for estimating the transmission risk of Japanese encephalitis virus according to an embodiment of the present disclosure is shown;

[0053] Figure 3 A time series of the mosquito ovitrap index is shown;

[0054] Figure 4 A curve of the local pig population and daily consumption is shown;

[0055] Figure 5 A fitting result of the algorithm according to the present disclosure is shown;

[0056] Figure 6 An optional structural diagram of a device for estimating the transmission risk of Japanese encephalitis virus according to an embodiment of the present disclosure is shown;

[0057] Figure 7 A structural diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0058] In order to make the purposes, features and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present disclosure.

[0059] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but which can be understood as a same or different subset of all possible embodiments, and which can be combined with each other, without conflict, in the following description.

[0060] In the following description, the terms "first\second" are merely used to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that the "first\second" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0061] Unless otherwise defined, all technical and scientific terms used in the present disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. The terms used in the present disclosure are merely for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0062] It should be understood that in various embodiments of the present disclosure, the size of the serial number of each implementation process does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0063] Before the embodiments of the present disclosure are further described in detail, the terms and phrases involved in the embodiments of the present disclosure are explained, and the terms and phrases involved in the embodiments of the present disclosure are applicable to the following explanations.

[0064] Mosquito vector: mosquito vector refers to the species of mosquitoes that can carry and transmit pathogens (such as viruses and parasites), and is an intermediate transmission carrier of pathogens between different hosts. Mosquitoes themselves are infected with pathogens, but usually do not show obvious symptoms of disease, and the pathogens in their bodies can be transferred to new hosts during biting.

[0065] Reservoir host: reservoir host or insect host refers to an animal host that can carry pathogens for a long time and usually does not show obvious clinical symptoms. Reservoir host is a "storage" of pathogens in nature, which can maintain the survival and reproduction of pathogens for a long time, even without human hosts, pathogens can persist in the reservoir host. For example, pigs or birds are the main reservoir hosts of Japanese encephalitis virus, the virus replicates in pigs, and mosquitoes bite infected pigs and carry the virus, which can cause human infection when they bite humans. Pigs may have symptoms such as abortion and stillbirth after infection, but most cases can survive and continue to carry the virus. Birds are the main reservoir hosts of West Nile virus, which circulates in birds, and mosquitoes bite infected birds and then bite humans or other animals to complete the transmission. Birds generally have no obvious symptoms after infection and can serve as a long-term storage carrier for the virus.

[0066] Host: final host or susceptible host, refers to a host in which a pathogen can complete the key stages of its life cycle (such as reproduction, development), or can trigger disease symptoms. For pathogen transmitted by mosquitoes, humans are usually the final host - the pathogen can reproduce in the human body and cause disease, but humans as hosts generally cannot transmit the virus to mosquitoes (or have very low transmission efficiency) and cannot become long-term storage carriers of the pathogen.

[0067] For example, Japanese encephalitis virus, the transmission chain usually includes virus reservoir (pig or bird) - mosquito bites - mosquito carrying virus - mosquito carrying virus bites host - host infection and disease.

[0068] Japanese encephalitis virus: (Japanese Encephalitis Virus, JEV), usually transmitted by mosquitoes, the main reservoir is pigs and birds, and can be transmitted to humans through mosquitoes. Humans infected with the virus are "final hosts" and cannot be transmitted by mosquitoes even after infection.

[0069] Vector-free transmission: refers to the transmission of viruses between hosts through contact or other means without the participation of mosquitoes, for example, pig-to-pig direct transmission, which is particularly important when the density of mosquitoes is low.

[0070] Mosquito ovitraps index: a commonly used core indicator for assessing the density of mosquito populations, which has the advantages of easy acquisition and strong real-time performance. The number of live pigs (reservoir data) can directly reflect the scale of the Japanese encephalitis virus amplification reservoir. The transmission risk modeling method based on the dual data of reservoirs and mosquitoes not only makes up for the lack of host data, but also can identify potential epidemic threats earlier, thereby providing more timely and effective decision-making basis for public health departments.

[0071] In related technologies, the mosquito-borne infectious disease transmission risk analysis technology has significant limitations in real-world scenarios: first, it is highly dependent on host infection data and does not take advantage of the easy availability of dual data of reservoirs and mosquitoes; second, it does not adapt to the interactive influence of "reservoir scale fluctuation + mosquito density change", and cannot depict the transmission dynamics driven by the two; third, it ignores the influence of external factors (such as pig breeding adjustment) on reservoir data, and cannot accurately evaluate the transmission pattern under the synergistic action of "reservoir - mosquito". The embodiments of the present disclosure take the reservoir (number of live pigs) and mosquito data (mosquito ovitraps index) as the core input, combine the Japanese encephalitis virus transmission risk calculation method based on the dual data of reservoirs and mosquitoes, integrate two types of easily accessible non-host data, construct a dynamic parameter correlation model, and realize the whole-chain quantitative evaluation from "mosquito density monitoring to reservoir (pig herd) infection risk, and then to human population transmission risk". Without relying on difficult-to-obtain host infection data, the interaction between the two is quantified, and the risk assessment accuracy in real-world scenarios is improved.

[0072] The embodiment of the disclosure takes the "mosquito-reservoir (pig)-host (human)-environment system" as the core, establishes a dynamic correlation mechanism between the reservoir and the mosquito vector data: the infectivity of the mosquito vector spreading to the reservoir (pig) ) is dynamically correlated with the mosquito vector ovitraps index (mosquito vector data), and the incidence overflow rate of the reservoir (pig) spreading to the crowd ) is lagged correlated with the reservoir data (pig population infection level), which reveals the interaction law of "reservoir-mosquito vector-environment"; and it is proved that the "mosquito-host simplified model" and the "fixed mosquito density chamber model" are special cases of the framework (because the reservoir and mosquito vector double data are not combined).

[0073] Based on the multi-species chamber model equation set, the reservoir (pig population) infection state S p 、E p 、I p 、C p 、R p ), the mosquito vector ovitraps index driven and are introduced, and the reservoir data (live pig inventory) are combined to construct an "observation-inference" integrated model that can directly use two types of core data and case data.

[0074] Finally, combined with the reservoir data (live pig inventory) and mosquito vector data (mosquito vector ovitraps index) in the 2003-2017 Japanese B encephalitis virus monitoring data of a certain region, parameter estimation is carried out based on the partial observation Markov process framework and the sequential Monte Carlo method. The results show that the 95% simulation interval of the model provided by the embodiment of the disclosure can completely cover 17 actual cases.

[0075] Figure 1 A first optional flowchart of the Japanese B encephalitis virus transmission risk estimation method provided by the embodiment of the disclosure is shown, which will be described according to each step.

[0076] In step S101, the mosquito vector data, reservoir infection kinetics data and host data corresponding to the first time interval are obtained.

[0077] In some embodiments, the first time interval is a historical time interval, and the carrier of the Japanese B encephalitis virus transmission risk estimation method (hereinafter referred to as the carrier) obtains the mosquito vector data, reservoir infection kinetics data and host data of the first time interval.

[0078] In specific implementation, the mosquito vector data includes at least one of mosquito vector ovitraps index, mosquito vector biting rate, mosquito vector hit rate and infected mosquito vector quantity; the reservoir host infection kinetics data includes at least one of susceptible period reservoir host quantity, latent period reservoir host quantity, infectious period reservoir host quantity, infectious period reservoir host quantity, recovered reservoir host quantity, input reservoir host infection rate, reservoir host daily replenishment amount, reservoir host daily mortality rate, direct transmission coefficient between reservoir hosts, reservoir host latent period to infectious period probability, reservoir host infectious period to virus-carrying recovery period probability and reservoir host virus-carrying recovery period to complete recovery period probability; and the host data includes infected host quantity.

[0079] The carrier can be a computer program, an electronic circuit, a database, a mobile application, an electronic device, a cloud computing platform, a distributed system, an artificial intelligence framework, a mathematical model, an automation tool and a microcontroller, etc., and can be software or hardware capable of realizing an algorithm and a method flow.

[0080] In step S102, based on the mosquito vector biting rate, the mosquito vector hit rate, the infected mosquito vector quantity and the total reservoir host quantity in the mosquito vector data, a first infection force model of the mosquito vector to the reservoir host is determined.

[0081] In some embodiments, the first infection force model includes the infection force of the mosquito vector transmission to the reservoir host The relationship between the mosquito vector biting rate, the mosquito vector hit rate, the infected mosquito vector quantity and the total reservoir host quantity specifically includes:

[0082] (1)

[0083] Wherein, is the mosquito vector biting rate, is the mosquito vector hit rate, representing the virus transmission probability of each mosquito vector bite, is the infected mosquito vector quantity, is the total reservoir host quantity, represents the probability of reservoir host infection caused by a single bite of the infected mosquito vector.

[0084] In some embodiments, the total reservoir host quantity is determined based on the sum of the susceptible period reservoir host quantity , the latent period reservoir host quantity , the infectious period reservoir host quantity , the infectious period reservoir host quantity and the recovered reservoir host quantity , including:

[0085] (2)

[0086] In some optional embodiments, the susceptible period reservoir host quantity , the latent period reservoir host quantity , the infectious period reservoir host quantity , infectious period reservoir quantity , and convalescent reservoir quantity Afterwards, susceptible period reservoir quantity can be recalculated each time it is needed , latent period reservoir quantity , infectious period reservoir quantity , infectious period reservoir quantity , and convalescent reservoir quantity , latent period reservoir quantity , infectious period reservoir quantity , infectious period reservoir quantity , infectious period reservoir quantity , and convalescent reservoir quantity , and convalescent reservoir quantity

[0087] (3)

[0088] wherein, is the input reservoir infection rate, is the daily reservoir replenishment amount; is the daily reservoir mortality rate, is the direct transmission coefficient between reservoirs; is the reservoir latent period to infectious period probability; is the reservoir infectious period to virus-carrying convalescent period probability; is the reservoir virus-carrying convalescent period to complete convalescent period probability.

[0089] Further, among other auxiliary parameters, the input reservoir infection rate ( ) calculation formula is wherein, is the average life span of the reservoir, is the reservoir infection period, is the reservoir recovery period, is the average life span of the reservoir, , and can be used as model fixed input parameters.

[0090] In some embodiments, the susceptible period reservoir variable is determined based on the number of uninfected reservoirs in the replenished reservoirs ( ), the number of reservoirs that died in determined in the last time, and the number of reservoirs that transitioned from the susceptible period to the latent period in determined in the last time. ​

[0091] In some embodiments, the latent reservoir variable is determined based on the number of reservoirs in the most recent determination of reservoirs in the susceptible phase that transitioned to the latent phase , the number of reservoirs in the most recent determination of reservoirs that died , and the number of reservoirs in the most recent determination of reservoirs in the latent phase that transitioned to the infectious phase .

[0092] In some embodiments, the infectious reservoir variable is determined based on the number of reservoirs in the replenished reservoirs that were infected , the number of reservoirs in the most recent determination of reservoirs in the latent phase that transitioned to the infectious phase , the number of reservoirs in the most recent determination of reservoirs that died , and the number of reservoirs in the most recent determination of reservoirs in the infectious phase that transitioned to the infectious phase

[0093] In some embodiments, the infectious reservoir variable is determined based on the number of reservoirs in the most recent determination of reservoirs in the infectious phase that transitioned to the infectious phase , the number of reservoirs in the most recent determination of reservoirs that died , and the number of reservoirs in the most recent determination of reservoirs in the infectious phase that transitioned to the infectious phase

[0094] In some embodiments, the recovered reservoir variable is determined based on the number of reservoirs in the most recent determination of reservoirs in the infectious phase that transitioned to the recovered phase , and the number of reservoirs in the most recent determination of reservoirs that died .

[0095] Further, the carrier can update the most recent determination of susceptible reservoirs based on the number of reservoirs in the susceptible phase ; update the most recent determination of latent reservoirs based on the number of reservoirs in the latent phase ; update the most recent determination of infectious reservoirs based on the number of reservoirs in the infectious phaseUpdate the latest infectious period reservoir quantity ; based on the last time the Update the latest infectious period reservoir quantity ; based on the last time the Update the latest recovery reservoir quantity .

[0096] Step S103, based on the mosquito vector ovipositor index, the basic infection force of mosquito vector vertical transmission, and the contribution coefficient of mosquito vector ovipositor index to the infection force of mosquito vector transmission to reservoir, determine the second infection force model.

[0097] In some embodiments, to realize the dynamic correlation of mosquito density and mosquito ovipositor index monitoring data, the correlation formula of the infection force of mosquito vector transmission to reservoir (pig) and mosquito ovipositor index is introduced, which specifically includes:

[0098] (4)

[0099] Wherein, is the mosquito ovipositor index value, is the contribution coefficient of mosquito ovipositor index to the infection force of mosquito vector transmission to reservoir, is the basic infection force of mosquito vector vertical transmission.

[0100] In some optional embodiments, is the mosquito ovipositor index value, which can eliminate single-day sampling fluctuations and retain the seasonal trend of high mosquito ovipositor index in the rainy season and low mosquito ovipositor index in the dry season; is the contribution coefficient of mosquito ovipositor index to the infection force of mosquito vector transmission to reservoir (pig); is the basic infection force maintained by mosquito vector vertical transmission, which ensures that when the mosquito ovipositor index in the dry season is close to 0, the Japanese encephalitis virus can still maintain the minimum transmission level.

[0101] In some optional embodiments, the carrier can establish different second infection force models based on the invasion of the virus in a certain area. Specifically, if there is a new virus strain invasion (or a virus variant strain invasion), create the second infection force model before and after the invasion with the invasion time as the node, which specifically includes:

[0102]

[0103] Wherein, is the virus strain invasion time node, if indicates before the virus strain invasion, indicates after the virus strain invasion, then ; or, if Characterizing the invasion of viral strains, Characterizing the virus strain before invasion, .

[0104] 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.

[0105] 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 host 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 the above 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.

[0106] 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.

[0107] In some embodiments, the first morbidity spillover rate model includes:

[0108] (5)

[0109] 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).

[0110] Step S106: Determine the second disease spillover rate model based on the disease spillover rate, intensity coefficient, and mosquito ovipositor index.

[0111] In some embodiments, the second morbidity spillover rate model includes:

[0112] (6)

[0113] 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.

[0114] 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:

[0115]

[0116] 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, .

[0117] Step S107: Estimate the intensity coefficient based on the first incidence spillover rate model and the second incidence spillover rate model.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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 .

[0123] 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.

[0124] 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. .

[0125] 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. .

[0126] Therefore, by using the Japanese encephalitis virus transmission risk estimation method provided by the embodiments of the present disclosure, the connection between the mosquito vector, the reservoir host and the host can be established, the mosquito vector data and the reservoir host data that are easy to obtain can be integrated, the whole-chain quantitative evaluation from mosquito vector density detection to reservoir host infection risk and from the reservoir host to the host can be realized, and the risk evaluation accuracy of the host infection with the virus in a real scene can be improved.

[0127] In some embodiments, the mosquito-borne infectious disease "vector-host-environment" complex system provided by the embodiments of the present disclosure is coupled by the mosquito vector population dynamics (mosquito vector data: density fluctuation reflected by the ovitrap index), the reservoir host (pig) infection dynamics (reservoir host data: pig population "susceptible-latent-infected-recovered with virus-completely recovered" time sequence, and the change of the number of pigs in stock), and the environment and policy driving, and the core is the quantitative correlation of the reservoir host and the mosquito vector data. In the system, the quantitative correlation of the core transmission parameter and the observation variable needs to be determined based on the empirical data:

[0128] The infection force of the mosquito vector to the reservoir host (pig) As a key parameter connecting the vector and the reservoir host, it reflects the probability of pig infection caused by a single bite of the infected mosquito vector. The correlation formula between the mosquito vector ovitrap index and the infection force is:

[0129] (4)

[0130] In formula (4), is the mosquito vector ovitrap index value after local weighted regression smoothing (eliminating single-day random fluctuations and retaining seasonal trends), is the contribution coefficient of the mosquito vector ovitrap index to λ vp (quantifying the driving strength of mosquito density fluctuation on transmission), represents the basic infection force of vertical transmission of the mosquito vector (ensuring that there is still a minimum transmission probability when the mosquito vector ovitrap index in the dry season is close to 0).

[0131] The incidence overflow rate of the reservoir host (pig) to the human population As a key parameter connecting the reservoir host and the human population, it reflects the probability of indirect transmission from the reservoir host to the host through mosquito bites. Due to the time delay of "mosquito incubation period + human latent period" in transmission, the correlation formula between the lagged mosquito vector ovitrap index and the incidence overflow rate is:

[0132]

[0133] In formula (5), is the overflow transmission strength coefficient, and 15 days is the transmission delay (matching the actual period of mosquito incubation for 6-12 days and human latent period for 5-13 days), is the mosquito vector ovitrap index value lagged by 15 days; meanwhile, as an amplification host, the total number of pigs in stock ) 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The core improvement of this technology is the integration of "multi-species status compartments + dynamic parameter association" with data from storage owners and mosquito 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.

[0138] 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.

[0139] 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.

[0140] Step S201: Determine the infection dynamics data of the storage host.

[0141] 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).

[0142] in, The local live pig daily supplement rate covers the number of newborn and imported live pigs and is dynamically adjusted according to the breeding policy, and is a core parameter for connecting the mosquito vector and pig population transmission. The direct transmission coefficient between pigs is a quantitative index of the transmission ability of the Japanese encephalitis virus in the pig population without mosquito vectors, and the value range is 0.0-0.4 per day. The latent infection rate of pigs corresponds to the latent period of 1.5 days after the pigs are infected with the Japanese encephalitis virus, that is, ; The infection-recovery-virus-carrying rate of pigs corresponds to the infection period of 3 days of the pigs, that is, ; The recovery-virus-carrying-to-complete-recovery rate of pigs corresponds to the recovery-virus-carrying period of 2.5 days of the pigs, that is, ; The daily mortality rate of pigs includes natural death and slaughter, and corresponds to the average life span of 234 days of the pigs, that is, . Among other auxiliary parameters, the input reservoir infection rate ( ) is calculated by formula (3) , wherein is the Japanese encephalitis incidence rate during the average life span of the pigs, days is the latent period of the pigs, days is the infection period of the pigs, and when is in the range of 25%-85%, is in the range of 0.43%-1.45%, and both are fixed input parameters of the model.

[0143] The total number of local live pigs needs to meet the population conservation relationship, that is, formula (2), wherein t is a time-dependent parameter, and formula (2) is consistent with the actual fluctuation of the live pig breeding scale.

[0144] In step S202, a first incidence overflow rate model is determined.

[0145] In some embodiments, humans are the terminal host of the Japanese encephalitis virus, and after infection, they cannot produce enough viremia to infect mosquitoes, so a variable overflow rate ( ) is used to simulate human cases, that is, formula (5).

[0146] In step S203, a second infection force model is determined.

[0147] In some embodiments, to realize the dynamic correlation of mosquito density and mosquito ovitrap index monitoring data, the correlation formula between the infection force of the mosquito vector to the reservoir (pig) and the mosquito ovitrap index is introduced, that is, formula (4).

[0148] In formula (4), is the mosquito ovitrap index value, which can eliminate the sampling fluctuation of a single day and retain the seasonal trend that the mosquito ovitrap index is high in the rainy season and low in the dry season; as the contribution coefficient of the mosquito vector ovitraps index to the infectivity of the mosquito vector to the reservoir (pig); the basic infectivity maintained for the vertical transmission of the mosquito vector, ensuring that when the mosquito vector ovitraps index is close to 0 in the dry season, the Japanese encephalitis virus can still maintain the minimum transmission level.

[0149] In step S204, the basic infectivity of the vertical transmission of the mosquito vector, the contribution coefficient of the mosquito vector ovitraps index to the infectivity of the mosquito vector to the reservoir, and the intensity coefficient are estimated.

[0150] In some embodiments, by simultaneously correlating the pig population differential equation set (formula (3)) of the reservoir data, the total inventory formula (formula (2)), the human case simulation (formula (5)), and the infectivity of the mosquito vector to the reservoir (pig) and the mosquito vector ovitraps index correlation formula (formula (5)), the model can simultaneously fit the three types of core data: “reservoir data (live pig inventory, pig population infection status), mosquito data (mosquito ovitraps index), and case data”; the fluctuation range of the mosquito ovitraps index time series data (mosquito data) is calibrated , and the mosquito vector transmission contribution is synchronized with the actual mosquito density and reservoir infection dynamics by combining the reservoir data (quantity changes of each state of the pig population); when the case tracing information is verified, the matching degree of the reservoir data (pig population infection time distribution) and the mosquito data (ovitraps index time series) is simultaneously referenced to ensure the accuracy of the verification result. Finally, by cooperatively fitting the reservoir and mosquito vector data, the key transmission parameters such as , are accurately estimated, and the transmission dynamics of the Japanese encephalitis virus in the “mosquito-pig-human” system driven by the cooperative driving of the reservoir and mosquito vector data are completely characterized, thereby providing quantitative support for regional mosquito vector infectious disease risk warning.

[0151] Further, the Japanese encephalitis virus transmission risk estimation method provided in the embodiments of the present disclosure is simulated and verified in combination with real data.

[0152] The Japanese encephalitis monitoring data set of a certain region from 2003 to 2017 is selected, wherein the core data includes mosquito data, reservoir infection dynamics data, and host data, and wherein:

[0153] The mosquito data includes the mosquito ovitraps index every month from 2003 to 2016.

[0154] The reservoir infection dynamics data includes the number of live pigs and the daily consumption of live pigs from 2003 to 2016.

[0155] The host data includes the number of new Japanese encephalitis cases every month from 2004 to 2016.

[0156] Figure 3 The time series of the mosquito ovitraps index is shown.

[0157] 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.

[0158] Figure 4 The curves showing the local pig population and daily consumption are displayed.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The infectivity is simplified as a function of the index of mosquito ovitraps where , is the parameter to be estimated, represents the contribution of adult mosquitoes to the vertical transmission of eggs, and the vertical transmission rate of mosquitoes ranges from 12% to 100%; considering the invasion of new strains of Japanese encephalitis virus in 2011, the piecewise expression of the infectivity is , and satisfies . The spillover ratio of the host (pig) to humans ( ) is set as a lag function of the index of mosquito ovitraps where is the intensity parameter to be estimated, days is the delay of transmission; considering the invasion of new strains, the piecewise expression of the spillover ratio is , and satisfies .

[0164] Parameter estimation adopts a statistical inference framework based on the likelihood function, and the point estimates and 95% confidence intervals of the key parameters are obtained.

[0165] In some embodiments, based on the monitoring data of Japanese encephalitis virus in a certain region from 2003 to 2017, risk assessment is carried out around the “reservoir-mosquito” synergy. The core evaluation result shows that after the invasion of new Japanese encephalitis virus strains in 2011, the spillover rate of Japanese encephalitis from pig reservoirs to human hosts rose from 0.0002 to 0.0013, becoming the core driving factor for the reappearance of local cases, while the infectivity of mosquito transmission to the reservoir (pig) maintained stable throughout the process. The model accurately reproduces the dynamic change process in the transmission process. The fitting results of the algorithm to the data are shown in Figure 5 , and the 95% confidence interval of the output results of the embodiments of the present disclosure can explain all observed local Japanese encephalitis cases.

[0166] Figure 5 The fitting result schematic diagram of the algorithm of the present disclosure is shown.

[0167] As shown in Figure 5 , under the scenario of the invasion of new Japanese encephalitis virus strains, the variable changes, and the model fitting results of local Japanese encephalitis virus cases in a certain region from 2004 to 2016. Figure 5 (a) represents the simulation results normalized according to the number of live pigs. Figure 5 (b) represents the trend change in the average annual normalized simulation results. In Figure 5 (a) and Figure 5(b) In the figure, the thicker curve represents the simulation results, the solid dots represent the reported (i.e., observed) Japanese encephalitis virus indigenous cases, and the thinner curve represents the Japanese encephalitis virus cases processed by the local weighted regression smoothing method. The vertical lighter dashed line marks the time point of the total reduction of the native pig population in the region (consistent with the time point in Figure 4

[0168] Without relying on difficult-to-obtain host data such as pig serology, the model calibration can be completed only by using three data: mosquito vector data, pig inventory, and case time series; and the dynamic changes of the transmission risk over time can be simulated.

[0169] Thus, the Japanese encephalitis virus transmission risk estimation method provided by the embodiments of the present disclosure innovatively proposes to take the mosquito vector ovitrap index (mosquito vector data) as the core, combine the pig inventory (host data), replace the traditional dependence on host infection data, and construct a Japanese encephalitis virus transmission risk prediction model; through dynamic parameter correlation, the quantitative evaluation of the Japanese encephalitis virus transmission risk is realized, and the core lies in the synergistic coupling of host and mosquito vector data. By introducing host variables and mosquito vector factors, combining transmission dynamics theory, and constructing a modeling framework integrating multi-source non-host data. This method not only simulates the driving effect of mosquito vectors on host (pig population) infection, but also takes into account the supporting effect of host size on transmission, realizes the accurate characterization of the Japanese encephalitis virus transmission mode, and the key lies in the whole-chain integration of host and mosquito vector data.

[0170] Not only suitable for Japanese encephalitis virus epidemic, but also can be extended to other mosquito-borne diseases (such as West Nile virus disease) that need to combine host and mosquito vector data, by replacing host data (such as bird population) and mosquito vector data (corresponding to mosquito vector ovitrap index), the transmission rules of different diseases can be quickly adapted; this flexibility further enhances the practicality of the model, making it play a role in different regions and different mosquito-borne disease prevention and control situations.

[0171] By combining host and mosquito vector data, an evaluation model that better fits the "host-mosquito vector" transmission logic of mosquito-borne diseases is constructed. Not only does it completely solve the problem of dependence on host infection data, but also it can realize data completion through the interaction of the two types of data when the host data or mosquito vector data is partially missing, while greatly reducing the data collection cost, effectively making up for the shortcomings of traditional methods that rely too much on host data.

[0172] ​By monitoring the host and mosquito vector data in real time, the complete chain of "mosquito vector index rising driving host infection risk prediction, and human risk early warning" can still be provided timely and accurately even if the host data is missing or delayed, especially when the host scale is adjusted or the mosquito vector density suddenly rises, the risk changes brought by the cooperation of the two can be quickly evaluated, which significantly enhances the emergency response capability of disease prevention and control, and helps public health decision-makers to take action faster and control the spread of the epidemic more effectively.

[0173] Figure 6 An optional structural schematic diagram of the Japanese encephalitis virus transmission risk estimation device provided by the embodiments of the present disclosure is shown, which will be described according to various parts.

[0174] In some embodiments, the Japanese encephalitis virus transmission risk estimation device comprises 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.

[0175] The data acquisition unit is configured to acquire mosquito vector data, host infection dynamics data and host data corresponding to a first time interval.

[0176] The first model determination unit is configured to determine a first infection force model of mosquito vector to host based on mosquito biting rate, mosquito hit rate, number of infected mosquito and total number of host in the mosquito vector data.

[0177] The second model determination unit is configured to determine a second infection force model based on mosquito ovitraps index, vertical transmission maintenance of mosquito basic infection force and contribution coefficient of mosquito ovitraps index to infection force of mosquito transmission to host.

[0178] The first estimation unit is configured to estimate vertical transmission basic infection force of mosquito and contribution coefficient of mosquito ovitraps index to infection force of mosquito transmission to host based on the first infection force model and the second infection force model.

[0179] The third model determination unit is configured to determine a first incidence overflow rate model based on number of cases, number of infected hosts, host infection period to recovery period rate and incidence overflow rate.

[0180] The fourth model determination unit is configured to determine a second incidence overflow rate model based on incidence overflow rate, intensity coefficient and mosquito ovitraps index.

[0181] The second estimation unit is configured to estimate the intensity coefficient based on the first incidence overflow rate model and the second incidence overflow rate model.

[0182] a host data estimation unit configured to estimate host data of a second time interval based on the mosquito data, the host infection kinetics data, an estimated vertical transmission basic infectivity of the mosquito, a contribution coefficient of the mosquito ovitrap index to the infectivity of the mosquito to the host, and a strength coefficient.

[0183] In some embodiments, the mosquito data comprises at least one of a mosquito ovitrap index, a mosquito biting rate, a mosquito hit rate, and a number of infected mosquitoes;

[0184] The host infection kinetics data comprises at least one of a number of susceptible hosts, a number of latent hosts, a number of infected hosts, a number of infectious hosts, a number of recovered hosts, an input host infection rate, a daily replenishment of hosts, a daily mortality rate of hosts, a direct transmission coefficient between hosts, a probability of a latent host turning into an infected host, a probability of an infected host turning into a virus-carrying recovered host, and a probability of a virus-carrying recovered host turning into a fully recovered host.

[0185] The host data comprises a number of infected hosts.

[0186] The first model determination unit is specifically configured to:

[0187]

[0188] wherein, is a mosquito biting rate, is a mosquito hit rate, representing a virus transmission probability per mosquito bite, is a number of infected mosquitoes, is a total number of hosts, is an infectivity of the mosquito to the host, representing a probability of a single bite of an infected mosquito leading to an infection of the host.

[0189] In some embodiments, the total number of hosts is determined based on a number of susceptible hosts , a number of latent hosts , a number of infected hosts , a number of infectious hosts , and a number of recovered hosts , wherein the number of susceptible hosts , the number of latent hosts , the number of infected hosts , the number of infectious hosts , and the number of recovered hosts comprise:

[0190]

[0191] wherein, is an input host infection rate, is a daily replenishment of hosts; is a daily mortality rate of the reservoir, is a direct transmission coefficient between reservoirs; is a probability of reservoirs from the latent period to the infectious period; is a probability of reservoirs from the infectious period to the virus-carrying recovery period; is a probability of reservoirs from the virus-carrying recovery period to the complete recovery period.

[0192] The second model determination unit is specifically configured to:

[0193]

[0194] wherein, is an ovitrap index value of the mosquito vector, is a contribution coefficient of the ovitrap index of the mosquito vector to the infectious force of the mosquito vector to the reservoir, is a vertical transmission basic infectious force of the mosquito vector.

[0195] The third model determination unit is specifically configured to:

[0196]

[0197] wherein, characterizes a weekly incidence overflow rate, characterizes a probability of reservoirs from the infectious period to the virus-carrying recovery period, is a number of reservoirs in the infectious period, is a number of cases. The fourth model determination unit is specifically configured to:

[0198]

[0199]

[0200] wherein, is an incidence overflow rate of the reservoir to the host, is an intensity coefficient, is is an ovitrap index of the mosquito vector at the moment, is a transmission delay.

[0201] The host data estimation unit is specifically configured to estimate the infectious force of the mosquito vector to the reservoir based on the ovitrap index of the mosquito vector in the second time interval, the estimated vertical transmission basic infectious force of the mosquito vector, and the contribution coefficient of the ovitrap index of the mosquito vector to the infectious force of the mosquito vector to the reservoir.

[0202] The incidence overflow rate is estimated based on the estimated intensity coefficient and the ovitrap index of the mosquito vector in the second time interval.

[0203] ​determine the reservoir quantity in the infectious period corresponding to the second time interval based on the estimated infectiousness of the mosquito vector to the reservoir and the reservoir infection kinetics data;

[0204] estimate the host data of the second time interval based on the reservoir quantity in the infectious period corresponding to the second time interval, the incidence overflow rate, and the probability that the reservoir is infected in the infectious period and recovers in the convalescent period.

[0205] According to embodiments of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0206] 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 laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0207] As shown in Figure 7 The electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with 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. Various programs and data required for the operation of the electronic device 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0208] Various components in the electronic device 800 are connected to the I / O interface 805, including an input unit 806, such as a keyboard, a mouse, etc., an output unit 807, such as various types of displays, a speaker, etc., a storage unit 808, such as a magnetic disk, an optical disk, etc., and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0209] The computing unit 801 can be various general 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 specialized 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 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 tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded onto the 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 can be configured to perform the Japanese encephalitis virus transmission risk estimation method by any other suitable means, such as by means of firmware.

[0210] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0211] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0212] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0213] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; 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 acoustic, speech, or tactile input.

[0214] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0215] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0216] It should be understood that the various forms of flow shown above can be used to reorder, add, or delete steps. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which are not limited herein.

[0217] In addition, the terms "first", "second", are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0218] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present disclosure, which should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A method of estimating the risk of transmission of a Japanese encephalitis virus, characterized by, The method comprises: 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 the mosquito vector to the reservoir host based on mosquito bite 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, basic infection force maintained by vertical transmission of the mosquito vector, and contribution coefficient of the mosquito ovitraps index to the infection force of the mosquito vector transmitted to the reservoir host; estimating the basic infection force of vertical transmission of the mosquito vector and the contribution coefficient of the mosquito ovitraps index to the infection force of the mosquito vector transmitted to the reservoir host 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 reservoir hosts in the infection period, the reservoir host virus carrying rate from the infection period to the recovery period, 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 into the incidence overflow rate; estimating the host data of the second time interval based on the mosquito vector data, the reservoir host infection dynamics data, the estimated basic infection force of vertical transmission of the mosquito vector, the contribution coefficient of the mosquito ovitraps index to the infection force of the mosquito vector transmitted to the reservoir host, and the intensity coefficient of the second time interval.

2. The method of claim 1, wherein the mosquito vector data includes at least one of mosquito ovitraps index, mosquito bite rate, mosquito hit rate and number of infected mosquito vectors; the reservoir host infection dynamics data includes at least one of susceptible reservoir host number, latent reservoir host number, infected reservoir host number, infectious reservoir host number, recovered reservoir host number, input reservoir host infection rate, daily reservoir host supplement, daily reservoir host mortality rate, direct transmission coefficient between reservoir hosts, reservoir host latent period to infection period probability, reservoir host infection period to virus carrying recovery period probability, and reservoir host virus carrying recovery period to complete recovery period probability; the host data includes the number of infected hosts.

3. The method of claim 1, wherein, the determination of the first infection force model of the mosquito vector to the reservoir host based on the mosquito bite rate, the mosquito hit rate, the number of infected mosquito vectors and the total number of reservoir hosts in the mosquito vector data comprises: wherein, is the mosquito bite rate, is the mosquito hit rate, representing the probability of virus transmission per mosquito bite, is the number of infected mosquitoes, is the total reservoir number, is the infectivity of mosquito transmission to reservoir, representing the probability of reservoir infection caused by a single bite of infected mosquito.

4. The method of claim 3, wherein, The total reservoir quantity is determined based on the sum of susceptible reservoir quantity , latent reservoir quantity , infectious reservoir quantity , infectious reservoir quantity , and recovered reservoir quantity , susceptible reservoir variable , latent reservoir variable , infectious reservoir variable , infectious reservoir variable , and recovered reservoir variable include: wherein, is the input reservoir infection rate, is the reservoir daily replenishment rate; is the reservoir daily mortality rate, is the reservoir direct transmission coefficient between reservoirs; is the reservoir probability of moving from the latent phase to the infectious phase; is the reservoir probability of moving from the infectious phase to the virus-carrying recovery phase; is the reservoir probability of moving from the virus-carrying recovery phase to the full recovery phase.

5. The method of claim 1, wherein, the determination of the second infection force model based on the mosquito ovitraps index, the basic infection force maintained by vertical transmission of the mosquito vector, and the contribution coefficient of the mosquito ovitraps index to the infection force of the mosquito vector transmitted to the reservoir host comprises: wherein, is t the mosquito-borne ovitraps index value at the time, is the contribution coefficient of the mosquito-borne ovitraps index to the infectiousness of the mosquito-borne transmission to the reservoir, is the vertical transmission basic infectiousness of the mosquito-borne.

6. The method of claim 1, wherein, the determination of the first incidence overflow rate model based on the number of cases, the number of reservoir hosts in the infection period, the reservoir host virus carrying rate from the infection period to the recovery period, and the incidence overflow rate comprises: wherein, characterizing the number of cases, the weekly incidence spill-over rate, characterizing the probability of a reservoir host to be infected during the incubation period and to recover with virus during the recovery period, the number of reservoir hosts during the incubation period, the number of cases.

7. The method of claim 1, wherein, the determination of the second incidence overflow rate model based on the incidence overflow rate, the intensity coefficient and the mosquito ovitraps index comprises: wherein, is the host-to-reservoir incidence rate, is the strength coefficient, is is the mosquito-borne ovitrap index at time, is the transmission delay.

8. The method of claim 1, wherein, the estimation of the host data of the second time interval based on the mosquito vector data, the reservoir host infection dynamics data, the estimated basic infection force of vertical transmission of the mosquito vector, the contribution coefficient of the mosquito ovitraps index to the infection force of the mosquito vector transmitted to the reservoir host, and the intensity coefficient of the second time interval comprises: estimating the infection force of the mosquito to the reservoir based on the mosquito ovitrap index of the second time interval, the estimated vertical transmission basic infection force of the mosquito, and the contribution coefficient of the mosquito ovitrap index to the infection force of the mosquito to the reservoir; estimating the incidence spill rate based on the estimated intensity coefficient and the mosquito ovitrap index of the second time interval; determining the number of reservoirs in the infectious period corresponding to the second time interval based on the estimated infection force of the mosquito to the reservoir and the reservoir infection kinetics data; estimating the host data of the second time interval based on the number of reservoirs in the infectious period corresponding to the second time interval, the incidence spill rate, and the probability of reservoirs carrying the virus from the infectious period to the convalescent period.

9. A Japanese encephalitis virus transmission risk estimation device characterized by comprising: The device comprises: a data acquisition unit configured to acquire mosquito data corresponding to a first time interval, reservoir infection kinetics data, and host data; a first model determination unit configured to determine a first infection force model of the mosquito to the reservoir based on the mosquito bite rate, the mosquito hit rate, the number of infected mosquitoes, and the total number of reservoirs in the mosquito data; a second model determination unit configured to determine a second infection force model based on the mosquito ovitrap index, the vertical transmission basic infection force maintained by the mosquito, and the contribution coefficient of the mosquito ovitrap index to the infection force of the mosquito to the reservoir; a first estimation unit configured to estimate the vertical transmission basic infection force of the mosquito and the contribution coefficient of the mosquito ovitrap index to the infection force of the mosquito to the reservoir based on the first infection force model and the second infection force model; a third model determination unit configured to determine a first incidence spill rate model based on the number of cases, the number of reservoirs in the infectious period, the reservoirs' rate of carrying the virus from the infectious period to the convalescent period, and the incidence spill rate; a fourth model determination unit configured to determine a second incidence spill rate model based on the incidence spill rate, the intensity coefficient, and the mosquito ovitrap index; a second estimation unit configured to estimate the intensity coefficient based on the first incidence spill rate model and the second incidence spill rate model; the intensity coefficient includes a calibration coefficient for converting the mosquito ovitrap index to the incidence spill rate; a host data estimation unit configured to estimate the host data of the second time interval based on the mosquito data of the second time interval, the reservoir infection kinetics data, the estimated vertical transmission basic infection force of the mosquito, the contribution coefficient of the mosquito ovitrap index to the infection force of the mosquito to the reservoir, and the intensity coefficient.

10. An electronic device, comprising: comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed 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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