CN-121999860-A - Aedes albopictus drug resistance prediction and dynamic prevention and control decision system and method

Abstract

The invention discloses an aedes albopictus drug resistance prediction and dynamic prevention and control decision system and method, comprising the following steps of S1, obtaining multisource space-time data of a target monitoring area, S2, constructing an aedes albopictus population generation replacement deduction model based on real-time meteorological data and aedes albopictus population characteristics, calculating a reproduction algebra, S3, constructing a drug resistance gene frequency evolution model introducing fitness cost based on a population genetics principle, and calculating the frequency of a resistance allele in a current population, and S4, adopting a multi-objective optimization algorithm based on model prediction control, carrying out deduction simulation with the aim of minimizing resistance increase and prevention and control cost and maximizing killing efficiency, and selecting a strategy with the highest comprehensive score as a prevention and control method. The invention can precisely quantify the resistance evolution law and dynamically adapt to the prevention and control requirements, effectively solves the problem of drug resistance surge caused by traditional empirical prevention and control, remarkably prolongs the service life of the pesticide, and realizes precise and low-consumption long-term sustainable prevention and control.

Inventors

• LI QIAN
• NI TINGTING
• LIAO YINGYING
• NIE JILIANG
• Jia Zhongtang
• ZHANG YING
• BI JUN
• HUANG LIYE
• LIANG LI
• LIU XUE
• WANG QIN
• LI ZIQING
• ZHAO WEI
• LIU HAIHUA

Assignees

• 徐州市疾病预防控制中心(徐州市健康教育所)

Dates

Publication Date

20260508

Application Date

20260129

Claims (5)

1. 1. A method for predicting and dynamically preventing and controlling drug resistance of Aedes albopictus is characterized by comprising the following steps: S1, acquiring multi-source space-time data of a target monitoring area, wherein the multi-source space-time data comprises real-time meteorological data, vector population monitoring data and medication mosquito killing records; S2, constructing an aedes albopictus population generation replacement deduction model by utilizing an effective accumulation temperature rule based on the real-time meteorological data and aedes albopictus population characteristics, and calculating theoretical reproduction algebra in a target time period; S3, constructing a drug resistance gene frequency evolution model introducing fitness cost based on a population genetics principle and combining data of the theoretical reproduction algebra and drug administration mosquito killing records, and calculating the frequency of resistance alleles in the current population; and S4, adopting a multi-target optimization algorithm based on model predictive control, taking the minimization of resistance increase, prevention and control cost and the maximization of killing efficiency as targets, traversing candidate prevention and control strategies, carrying out deduction simulation, and selecting the strategy with the highest comprehensive score as a prevention and control method.
2. 2. The method for predicting and dynamically controlling the drug resistance of Aedes albopictus according to claim 1, wherein in the step S2, the aedes albopictus population generation replacement deduction model satisfies the following expression: ; ; Wherein, the For the accumulated effective accumulation temperature of the target time period, T i is the daily average temperature of the i day, C is the development starting point temperature of the aedes albopictus, K total is the effective accumulation temperature constant required by the aedes albopictus to complete a complete generation, and N g is the generation algebra of the aedes albopictus in the target time period.
3. 3. The method for predicting and dynamically controlling drug resistance of Aedes albopictus according to claim 2, wherein in step S3, the construction and calculation process of the drug resistance gene frequency evolution model is as follows: defining a resistance allele R and a sensitive isothermal gene S, setting the relative fitness of genotype resistance homozygote RR, heterozygote RS and sensitive homozygote SS under specific medicament concentration to be respectively called W RR 、W RS 、W SS , wherein when medicament pressure is applied, the calculation formula of the next generation resistance gene frequency p t+1 is as follows: ; ; Wherein W RR 、W RS and W SS are the relative fitness of resistant homozygote, heterozygote and sensitive homozygote respectively, and p t is the current generation resistance gene frequency; Average fitness for the population; When the medication is stopped, a fitness cost parameter f c is introduced, and the relative fitness of the resistant homozygote is corrected to W RR =1-f c so as to simulate the competitive disadvantage of the resistant individual in a non-medication environment.
4. 4. The method for predicting and dynamically controlling drug resistance of Aedes albopictus according to claim 3, wherein in step S4, the decision scoring function S formula of the multi-objective optimization algorithm is: ; Wherein alpha, beta and gamma are weight coefficients, The predicted increment of the resistance gene frequency is predicted by utilizing the drug resistance gene frequency evolution model in the step S3, and C is normalized prevention and control cost; Wherein E is the estimated killing efficiency, and the calculation adopts a Logistic dose-response attenuation model ; Wherein E max is the maximum killing rate, p t is the current resistance gene frequency, p th is the half-threshold of drug effect, k is the sensitivity coefficient, and the system selects the strategy with the highest S as the optimal solution.
5. 5. The aedes albopictus drug resistance prediction and dynamic prevention and control decision system is characterized by comprising a data acquisition module, a calculation processing module, a decision output module and a storage module, wherein the data acquisition module is used for acquiring temperature and humidity data and mosquito density indexes, the calculation processing module is used for executing the aedes albopictus drug resistance prediction and dynamic prevention and control method according to any one of claims 1 to 4, the decision output module is used for visually displaying a resistance evolution trend graph and a recommended medicament rotation scheme, and the storage module is used for storing historical data, a medicament record, model preset parameters and an attribute database.

Description

Aedes albopictus drug resistance prediction and dynamic prevention and control decision system and method Technical Field The invention relates to the technical field of disease medium biological monitoring and prevention and control, in particular to an aedes albopictus drug resistance prediction and dynamic prevention and control decision system and method. Background Currently, the prevention and control of infectious diseases such as dengue fever and the like using Aedes albopictus as a transmission medium mainly depend on chemical pesticides. However, the existing aedes albopictus prevention and control system has a plurality of technical defects, and is difficult to meet the precise and long-acting prevention and control requirements. In the aspect of drug resistance monitoring, the traditional method mainly relies on WHO bioassay or molecular biology detection technology, and needs on-site mosquito sample collection, laboratory feeding and a series of experimental analysis flows, so that the method is complex in operation and long in period, and essentially belongs to 'post-detection', and can only confirm after drug resistance is generated, and early warning of resistance evolution trend cannot be performed in advance, so that prevention and control measures lack perspective. In terms of prevention and control strategy formulation, the prior proposal usually ignores the key influence of meteorological factors on the growth and development of the aedes albopictus, namely the aedes albopictus is used as a temperature-changing animal, the breeding period of the aedes albopictus is closely related to temperature, the generation replacement speed is accelerated in a high-temperature environment, the screening pressure of the resistance genes is in nonlinear growth, and the rapid outbreak of high-resistance mosquito populations is extremely easy to be caused only by an empirical medication mode. In addition, the first-line prevention and control personnel lack of scientific guidance, and a certain type of chemical agent is singly used for a long time, so that the generation of mosquito drug resistance can be accelerated, the agent is fast invalid, the environmental pollution is serious, and the prevention and control cost is high. Therefore, aiming at the defects of the existing aedes albopictus prevention and control technology in monitoring timeliness, strategy scientificity and prevention and control sustainability, a method for predicting and dynamically preventing and controlling the drug resistance of aedes albopictus, which can precisely quantify the evolution rule of resistance and dynamically adapt to the prevention and control requirements, is needed. Disclosure of Invention In order to solve the technical problems, the invention provides a system and a method for predicting the drug resistance of Aedes albopictus and dynamically controlling the drug resistance, which are used for realizing the drug resistance accurate prediction and the optimal drug rotation strategy output by constructing a accumulated temperature genetic coupling model to quantify the relation of temperature-generation-heredity-resistance, solving the problem of drug resistance rapid increase caused by traditional empirical control, remarkably prolonging the service life of the pesticide and realizing accurate and low-consumption long-term sustainable control. The technical scheme adopted for solving the technical problems is that the method for predicting and dynamically preventing and controlling the drug resistance of the aedes albopictus comprises the following steps: S1, acquiring multi-source space-time data of a target monitoring area, wherein the multi-source space-time data comprises real-time meteorological data, vector population monitoring data and medication mosquito killing records; S2, constructing an aedes albopictus population generation replacement deduction model by utilizing an effective accumulation temperature rule based on the real-time meteorological data and aedes albopictus population characteristics, and calculating theoretical reproduction algebra in a target time period; S3, constructing a drug resistance gene frequency evolution model introducing fitness cost based on a population genetics principle and combining data of the theoretical reproduction algebra and drug administration mosquito killing records, and calculating the frequency of resistance alleles in the current population; and S4, adopting a multi-target optimization algorithm based on model predictive control, taking the minimization of resistance increase, prevention and control cost and the maximization of killing efficiency as targets, traversing candidate prevention and control strategies, carrying out deduction simulation, and selecting the strategy with the highest comprehensive score as a prevention and control method. Further, in step S2, the aedes albopictus population generation replacement deduction model satisfies the foll