Prediction Methods for Airborne Infectious Diseases Based on Population Movement and Contact Intensity

Abstract

This invention proposes a method for predicting airborne infectious diseases based on population movement and contact intensity, belonging to the field of infectious disease transmission prediction. The method includes: S1: Preprocessing population movement trajectory data to obtain preprocessed trajectory data; S2: Based on the preprocessed trajectory data, identifying contact events between people within a set time window and distance threshold, and constructing a contact matrix; S3: Establishing a RealSEIR model based on basic data on airborne infectious diseases in the target area and prediction requirements; S4: Establishing a disease model differential equation system based on the RealSEIR model; establishing disease model compartment transmission rules based on the disease model differential equation system and the contact matrix; S5: Calculating the population size in each state based on infectious disease prediction requirements, the contact matrix, and the disease model compartment transmission rules, and predicting the epidemic trend of airborne infectious diseases. This invention's method can accurately predict the transmission trend of infectious diseases.

Description

Technical Field

[0001] This invention belongs to the field of public health and infectious disease transmission prediction, and specifically relates to a method for predicting airborne infectious diseases based on population movement and contact intensity. Background Technology

[0002] Airborne infectious diseases are those transmitted among people through airborne pathogens such as bacteria, viruses, or fungi. These pathogens typically spread via droplets, aerosols, or dust particles, and can be released into the air through everyday activities such as breathing, talking, coughing, or sneezing, allowing infected individuals to transmit the pathogens to susceptible individuals. Common airborne infectious diseases include influenza, measles, and tuberculosis. The characteristics of airborne transmission make this type of infectious disease spread extremely rapidly in enclosed or densely populated environments, and due to the wide range of airborne media, controlling outbreaks presents significant challenges.

[0003] However, current methods for predicting airborne infectious diseases have significant limitations: they generally assume uniform mixing of contacts, meaning everyone has an equal probability of contacting others, ignoring the unevenness of contact caused by factors such as geography and social networks in real life. Furthermore, they generally struggle to accurately simulate the effects of dynamic interventions, such as testing, isolation, and vaccination. Moreover, they generally fail to consider spatial population mobility, which plays a crucial role in the speed of infectious disease transmission, rendering the models inadequate in predicting complex infectious disease spreads. Therefore, accurately simulating actual contact patterns, population mobility, and the impact of dynamic interventions to accurately predict the spread trends of airborne infectious diseases has become an urgent problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for predicting airborne infectious diseases based on population movement and contact intensity, comprising the following steps:

[0005] Step S1: Perform preliminary processing on the personnel movement trajectory data, filter out the data within the target area that starts from the start date and has a length of the prediction period; perform sparsification processing on the filtered data based on the set time interval to obtain the preprocessed trajectory data;

[0006] Step S2: Based on the preprocessed trajectory data, identify contact events between people within a set time window and distance threshold; based on the contact events, construct a contact matrix using a hash table;

[0007] Step S3: Based on the basic data of airborne infectious diseases in the target area and combined with the needs of infectious disease prediction, establish a RealSEIR model, including: basic compartment structure, compartment transmission links and compartment transformation parameters;

[0008] Step S4: Based on the RealSEIR model, establish a set of differential equations for the disease model; based on the set of differential equations for the disease model and the contact matrix, establish the disease model compartment transmission rules;

[0009] Step S5: Based on the infectious disease prediction demand, the contact matrix, and the disease model cabin transmission rules, calculate the population size in each state in the target area, predict the epidemic trend of airborne infectious diseases in the target area, and formulate a medical resource adjustment strategy for the target area.

[0010] Beneficial effects:

[0011] 1. This invention provides a method for predicting airborne infectious diseases based on population movement and contact intensity, which shows significant advantages over existing technologies. By finely processing population movement trajectory data and accurately identifying contact events, and using a contact matrix constructed with a hash table, it accurately captures the contact patterns and spatial behavior dynamics between individuals, overcoming the limitations of traditional models that assume uniform contact distribution. By using an efficient hash table data structure, the construction of the contact matrix is ​​limited to actual contact events, greatly reducing the storage of invalid data and improving the system's efficiency in large-scale data processing.

[0012] 2. This invention innovatively proposes the RealSEIR (Test & Isolation) model, which successfully achieves dynamic simulation of isolation measures by introducing detection and isolation mechanisms. By incorporating parameters such as detection rate and detection sensitivity, the model can flexibly respond to changes in public health intervention measures, thereby more accurately predicting the development trend of the epidemic. Based on a system of differential equations and transmission rules, the model can effectively predict key data such as the scale of infectious disease transmission, infection peak, and end time, providing a scientific basis for the formulation of epidemic prevention and control strategies. These technological innovations not only enhance the model's predictive ability for actual epidemics but also provide solid technical support for public health decision-making. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of a method for predicting airborne infectious diseases based on population movement and contact intensity according to the present invention.

[0014] Figure 2 This is a schematic diagram of the RealSEIR (Test & Isolation) model. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0016] Example 1

[0017] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for predicting airborne infectious diseases based on personnel movement and contact intensity, comprising the following steps:

[0018] Step S1: Perform preliminary processing on the personnel movement trajectory data, filter out the data within the target area that starts from the start date and has a length of the prediction period; perform sparsification processing on the filtered data based on the set time interval to obtain the preprocessed trajectory data;

[0019] Step S2: Based on the preprocessed trajectory data, identify contact events between people within a set time window and distance threshold; based on the contact events, construct a contact matrix using a hash table;

[0020] Step S3: Based on the basic data of airborne infectious diseases in the target area and combined with the needs of infectious disease prediction, establish a RealSEIR model, including: basic compartment structure, compartment transmission links and compartment transformation parameters;

[0021] Step S4: Based on the RealSEIR model, establish a set of differential equations for the disease model; based on the set of differential equations for the disease model and the contact matrix, establish the disease model compartment transmission rules;

[0022] Step S5: Based on the infectious disease prediction demand, contact matrix and disease model compartment transmission rules, calculate the population size of each state in the target area, predict the epidemic trend of airborne infectious diseases in the target area, and formulate medical resource adjustment strategies for the target area.

[0023] In one embodiment, step S1 above involves: performing preliminary processing on the movement trajectory data of personnel to filter out data within the target area that starts from the start date and has a length equal to the prediction period; and performing sparsification processing on the filtered data based on a set time interval to obtain preprocessed trajectory data, specifically including:

[0024] Step S11: Obtain the movement trajectory data of the personnel, including: personnel ID (Id), latitude, longitude, and time; delete trajectory data containing null values;

[0025] After obtaining the personnel movement trajectory data, in order to prevent null values ​​from affecting the stability and accuracy of the algorithm, it is necessary to perform null value filtering on the data corresponding to the above four fields, filtering out data containing null values.

[0026] Step S12: Obtain trajectory data within the target area based on the latitude and longitude range of the target area;

[0027] This step extracts trajectory data that is only located within the target area, thereby ensuring the geographical relevance of subsequent analysis and prediction and avoiding interference from irrelevant data.

[0028] Step S13: Based on the start date start_date and the prediction period T, obtain the trajectory data within the target time period from the trajectory data of the target area;

[0029] This step extracts trajectory data that is only within the target time period, thereby ensuring the temporal consistency of subsequent analysis and prediction and avoiding interference from irrelevant data.

[0030] In this embodiment of the invention, start_date is selected as a certain day (e.g., Monday) within the time covered by the trajectory data, and T is set to 7 days to filter out the trajectory data for the entire week within the target area.

[0031] Step S14: Sample the trajectory data within the target time period according to the set time interval time_interval to obtain the preprocessed trajectory data.

[0032] The specific steps of the sampling process are as follows: First, arrange the trajectory data of each person in chronological order. Then, within each time interval, select the trajectory point closest to the start time of that time interval and retain it as a representative point. Other trajectory points within that time interval are discarded. Finally, the preprocessed trajectory data is obtained.

[0033] This step effectively reduces the amount of data while retaining crucial information about people's movement trajectories. The sparsified trajectory data reduces storage and computational complexity while ensuring the accuracy of subsequent contact event identification and prediction of airborne infectious disease transmission trends.

[0034] In this embodiment of the invention, time_interval is set to 1 minute, that is, at each minute's time node, the person's trajectory point that is closest to the current time node within 1 minute is retained, and the person's other trajectory points within 1 minute are discarded.

[0035] In one embodiment, step S2 above involves: identifying contact events between people within a set time window and distance threshold based on the preprocessed trajectory data; and constructing a contact matrix using a hash table based on the contact events, specifically including:

[0036] Step S21: Identify and record contact events between people based on the set time window (time_window) and distance threshold (distance_threshold); a contact event is defined as follows: within the time window, if the geographical distance between person i and person j is less than or equal to distance_threshold, then contact is considered to have occurred, generating a contact event (contact_event); contact_event includes {User i User j , Date, Distance, Center_Latitude, Center_Longitude}, where Date is the day t in which the contact occurs during the prediction period T ( ); Distance is for two users. i and User j The actual distance between them, where Center_Longitude and Center_Latitude are the latitude and longitude of the center point of the contact event;

[0037] In this embodiment of the invention, the time window is set to 5 minutes and the distance threshold is set to 50 meters. The 7-day period is divided into 2016 time intervals of 5 minutes each. Within each time interval, all personnel are analyzed one by one to identify all other personnel j whose distance from the current personnel i is less than or equal to the distance threshold, and these contact events are recorded. Finally, all contact events are generated.

[0038] Step S22: Define the frequency of contact events as the contact intensity between personnel i and j. Using hash tables, based on Construct a contact matrix H, where the keys are combinations of time and personnel number pairs. The value is the contact strength between person i and person j. .

[0039] Because hash tables only store pairs of people who have actually been in contact, they avoid storing large amounts of invalid data, significantly reducing memory usage. Furthermore, hash tables can be dynamically expanded without requiring large pre-allocation of memory, exhibiting high memory efficiency and ensuring the system can still operate efficiently in infectious disease prediction tasks involving large numbers of people.

[0040] In this embodiment of the invention, if on day t within the prediction period, the contact intensity between personnel i and j is... The IDs of the personnel are respectively and Then H The value is .

[0041] In one embodiment, step S3 above: Based on the basic data of airborne infectious diseases in the target area and combined with the needs of infectious disease prediction, a RealSEIR model is established, including: basic compartment structure, compartment transmission links, and compartment transformation parameters, specifically including:

[0042] Step S31: Obtain basic data on airborne infectious diseases in the target area, including: population data, case data, and hospital bed data of the target area;

[0043] In this embodiment of the invention, the case data comes from the statistical data of the Health Commission of the target region, and the population data and hospital bed data come from the China Urban Statistical Yearbook published by the National Bureau of Statistics.

[0044] Step S32: Based on basic data on airborne infectious diseases and combined with the needs of infectious disease prediction, establish the basic compartment structure of the model; the basic compartment structure includes multiple population compartments, including: susceptible population compartment, exposed population compartment, exposed isolation population compartment, infected population compartment, infected isolation population compartment, recovered population compartment and deceased population compartment;

[0045] The characteristics of the people in each cabin are as follows:

[0046] Susceptible population compartment: Healthy individuals who are susceptible to airborne infectious diseases;

[0047] Exposed group compartment: A group of people who are infected with the pathogen but are not yet infectious, in the incubation period;

[0048] Quarantined Exposed: Individuals infected with the pathogen but not yet infectious, who have been isolated. They are in the incubation period and are kept separate from the general population through isolation measures.

[0049] Infected: A compartment for people who are infected and infectious.

[0050] Quarantined: Infected and infectious individuals are isolated, limiting the scope of transmission and significantly reducing the risk of spread.

[0051] Recovered: People who have recovered from infection, are no longer infectious, and have permanent immunity;

[0052] Deceased wards: These are wards where individuals who have died after infection are no longer involved in the transmission of the disease.

[0053] In this embodiment of the invention, based on the basic data of airborne infectious disease A in the target area and combined with the prediction requirements, the basic cabin structure selected for building the model includes: susceptible population cabin, exposed population cabin, exposed and isolated population cabin, infected population cabin, infected and isolated population cabin, recovered population cabin, and deceased population cabin.

[0054] Step S33: Based on the basic compartment structure, establish compartment transmission links, including multiple personnel transfer paths between compartments:

[0055] The transfer path in this embodiment of the invention includes:

[0056] The first ward transfer route: susceptible individuals - exposed individuals - infected individuals - recovered individuals;

[0057] The second compartment transfer route: susceptible individuals - exposed individuals - infected individuals - deceased individuals;

[0058] The transfer route in the third compartment is: susceptible person - exposed person - infected person - infected person in isolation - recovered person;

[0059] Fourth compartment transfer route: Susceptible person - Exposed person - Infected person - Infected person in isolation - Deceased person;

[0060] Transfer route in the fifth compartment: susceptible person - exposed person - exposed person in isolation - infected person in isolation - recovered person;

[0061] Transfer route in the sixth compartment: susceptible person - exposed person - exposed person in isolation - infected person in isolation - deceased person;

[0062] In this embodiment of the invention, based on the established basic cabin structure of the model, all the above-mentioned transmission links between the model cabins are selected and constructed according to the possible disease transmission process.

[0063] Step S34: Based on the basic compartment structure and compartment transmission links, and combined with basic data on airborne infectious diseases and the needs of infectious disease prediction, set the compartment transformation parameters of the RealSEIR model, including: infection rate. Isolation infection rate The rate at which exposed individuals become infected σ The rate at which individuals in exposure isolation become infected individuals in isolation. The rate of recovery of infected individuals The recovery rate of infected individuals in isolation Infectious disease detection rate among asymptomatic individuals Infectious disease detection rate among infected individuals Sensitivity of latent agent detection Sensitivity of infection detection Mortality rate of infected individuals Mortality rate among those infected and isolated Isolation contact frequency adjustment factor .

[0064] In this embodiment of the invention, based on the established basic compartment structure and compartment transmission links of the model, and combined with the basic data and prediction requirements of airborne infectious disease A in the target area, all the aforementioned compartment transformation parameters are selected. At this point, the RealSEIR (Test & Isolation) model is complete.

[0065] Figure 2 A schematic diagram of the RealSEIR (Test & Isolation) model is shown.

[0066] In one embodiment, step S4 above: establishing a set of differential equations for the disease model based on the RealSEIR model; establishing disease model compartment transmission rules based on the set of differential equations for the disease model and the contact matrix, specifically including:

[0067] Step S41: Based on the RealSEIR model, establish a set of differential equations for the disease model, including:

[0068] (1)

[0069] (2)

[0070] (3)

[0071] (4)

[0072] (5)

[0073] (6)

[0074] (7)

[0075] (8)

[0076] Where S(t) is the number of susceptible individuals at time t, E(t) is the number of exposed individuals at time t, and Q... E I(t) represents the number of people exposed and isolated at time t, I(t) represents the number of people infected at time t, and Q(t) represents the number of people infected at time t. I R(t) represents the number of infected and isolated individuals at time t, R(t) represents the number of recovered individuals at time t, and F(t) represents the number of deaths at time t; N(t) represents the total number of people at time t.

[0077] In this embodiment of the invention, based on the established RealSEIR (Test & Isolation) model, corresponding differential equations are established to represent the model. Taking into account the basic compartment structure, compartment transmission links and compartment transformation parameters of the model, all equations in the above-mentioned disease model differential equation set are selected.

[0078] Step S42: Based on the differential equations and contact matrix of the disease model, establish the disease model compartment transmission rules, including:

[0079] 1) Susceptible S LurkerE

[0080] When in contact with infected person I: (9)

[0081] Q, an infected person in isolation I Upon contact: (10)

[0082] in, Personnel and infected persons / infected persons in isolation The contact intensity on day t within the prediction period; if personnel The individual is in a susceptible state (S) and has been in contact with any infected person / infected person in isolation. Contact strength If > 0, then use The probability of personnel Transmission to exposed individual E;

[0083] 2) Lurker E Infected Person I

[0084] (11)

[0085] If personnel If the state is lurking E, then with a fixed probability σ Transmission to infected person I;

[0086] 3) Lurker E Isolate the latent Q E

[0087] (12)

[0088] If personnel If the state is lurking agent E, then the detection rate is... and detection sensitivity The product probability propagates to the exposed isolated person Q E ;

[0089] 4) Infected Person I Q, an infected person in isolation I

[0090] (13)

[0091] If personnel If the status is infected person I, then the detection rate will be used. and detection sensitivity The product probability of transmission to infected and isolated individuals Q I ;

[0092] 5) Infected Person I Recovered patient R

[0093] (14)

[0094] If personnel If the state is infected person I, then with a fixed probability Transmission to recovered patients R;

[0095] 6) Infected Person I The deceased F

[0096] (15)

[0097] If personnel If the state is infected person I, then with a fixed probability Transmission to deceased person F;

[0098] 7) Isolate the latent Q E Q, an infected person in isolation I

[0099] (16)

[0100] If personnel The status is that of the infiltrator Q who is in isolation. E Then with a fixed probability Transmission to infected and isolated individuals Q I ;

[0101] 8) Infected person in isolation Q I Recovered patient R

[0102] (17)

[0103] If personnel The status is infected and isolated person Q I Then with a fixed probability Transmission to recovered patients R;

[0104] 9) Infected person in isolation Q I The deceased F

[0105] (18)

[0106] If personnel The status is infected and isolated person Q I Then with a fixed probability It spread to the deceased F.

[0107] In this embodiment of the invention, all of the above-mentioned compartment transmission rules are selected based on the disease model differential equation set and the contact matrix.

[0108] In one embodiment, step S5 above, based on infectious disease prediction demand, contact matrix, and disease model compartment transmission rules, calculates the population size in each state in the target area, predicts the prevalence trend of airborne infectious diseases in the target area, and formulates a medical resource adjustment strategy for the target area, specifically including:

[0109] Step S51: Based on the needs of infectious disease prediction, set the total number of days for the prediction period. The proportion of infected individuals in the initial state The proportion of infected individuals to the total number of survivors at the end of the infection period. Based on the contact matrix, the total number of people is obtained. ;

[0110] In this embodiment of the invention, the total number of days for forecasting is set based on forecasting requirements. The proportion of infected individuals in the initial state The percentage of infected individuals out of the total number of survivors at the end of the infection period (as a criterion for determining the end of the infection). Based on the contact matrix, the total number of people is obtained. .

[0111] Step S52: Initialize personnel categories: S, E, Q E I, Q I , R, F, generate Individuals, in proportion Randomly select a group of people and initialize their status as infected person I, while the remaining unselected people are initialized as susceptible person S;

[0112] In this embodiment of the invention, the generated Individuals in proportion Randomly select a group of people and initialize their status as infected person I, while the remaining unselected people are initialized as susceptible person S;

[0113] Step S53: Substitute the compartment transformation parameters into the compartment transmission rules of the disease model, and execute the STEPS rounds sequentially. In the nth round of the loop, the parameters from the hash table need to be substituted. Finally, the number of different categories of people in the target area within STEPS days is calculated, including: the number of susceptible people, the number of exposed people, the number of people in isolation after exposure, the number of infected people, the number of people in isolation after infection, the number of recovered people, and the number of deaths.

[0114] In this embodiment of the invention, the cabin conversion parameters are set as follows:

[0115] Table 1 Cabin Conversion Parameters

[0116]

[0117] Substitute the aforementioned compartment transformation parameters into the compartment transmission rules of the disease model, and execute a loop for 250 rounds. In the nth round, the parameters from the hash table need to be substituted into the hash table. For example, if the prediction period T = 7 days, and we are currently in the 10th cycle (n = 10), then the calculation process for t is as follows: This means that in the 10th iteration, the data from day 4 of the corresponding prediction period needs to be used in the hash table. Following this calculation method, data from the prediction period is used cyclically throughout the entire prediction duration to represent the contact intensity of the periodic changes over the entire prediction duration.

[0118] The final calculation yielded the number of different population categories in the target area over 250 days, of which we selected the number of all the different population categories mentioned above.

[0119] Step S54: Based on the number of different categories of people in the target area within a specific day (STEPS), calculate key data indicators reflecting the scale of infectious disease transmission, including:

[0120] Basic reproduction number: It introduces the intensity of interpersonal contact into the traditional basic reproduction number to more accurately measure the transmissibility of infectious diseases without intervention;

[0121] Peak number of infections and time: The maximum number of infected persons I within the predicted time period and the corresponding time point, to assess the peak number of infections under isolation measures;

[0122] Peak number of infected individuals in isolation and duration: Q number of infected individuals in isolation within the predicted duration I The maximum number and its corresponding time point are used to assess the peak number of infected people in isolation under quarantine measures.

[0123] Total peak number of infections: peak number of infections + peak number of infections in isolation, reflecting the highest infection burden during the epidemic;

[0124] Average doubling time: The average time required for the number of infected individuals to double reflects the rate of increase in the number of infected individuals throughout the entire forecast period.

[0125] Time to establish herd immunity: The number of recovered individuals (R) reaches the total surviving population, i.e., NF, at a ratio of [missing value]. The time point represents the time when herd immunity is established; if the time of herd immunity establishment is greater than the time of infection end, then it has no practical significance and its value is set to empty.

[0126] End of infection time: Infected person I and infected person in isolation Q I The total number of people reached the proportion of the total surviving population. The point in time indicates that the epidemic is basically over;

[0127] In this embodiment of the invention, key data indicators reflecting the scale of infectious disease transmission are calculated based on the number of different population groups in the target area over 250 days. All of the aforementioned data indicators are selected. The calculation results are as follows:

[0128] Table 2. Calculation Results of Key Data Indicators Reflecting the Scale of Infectious Disease Transmission

[0129]

[0130] Step S55: Based on the number of different categories of people in the target area within STEPS days, and combined with key data indicators reflecting the scale of infectious disease transmission, predict the epidemic trend of airborne infectious diseases in the target area;

[0131] Step S56: Based on the prevalence trend of airborne infectious diseases in the target area, formulate a medical resource adjustment strategy for the target area, including: increasing hospital beds and increasing the frequency of airborne infectious disease testing.

[0132] The above embodiments are provided merely for the purpose of describing the present invention and are not intended to limit the scope of the invention. The scope of the invention is defined by the appended claims. Various equivalent substitutions and modifications made without departing from the spirit and principles of the invention should be covered within the scope of the invention.

Claims

1. A method for predicting airborne infectious diseases based on population movement and contact intensity, characterized in that, include: Step S1: Perform preliminary processing on the movement trajectory data of personnel, and filter out the data within the target area that starts from the start date and has a length of the prediction period; Based on a set time interval, the filtered data is sparsified to obtain preprocessed trajectory data. Step S2: Based on the preprocessed trajectory data, identify contact events between people within a set time window and distance threshold; Based on the contact events, a contact matrix is ​​constructed using a hash table; Step S3: Based on the basic data of airborne infectious diseases in the target area and combined with the needs of infectious disease prediction, establish a RealSEIR model, including: basic compartment structure, compartment transmission links and compartment transformation parameters; Step S4: Based on the RealSEIR model, establish a set of differential equations for the disease model; based on the set of differential equations for the disease model and the contact matrix, establish the disease model compartment transmission rules, specifically including: Step S41: Based on the RealSEIR model, establish a set of differential equations for the disease model, including: (1) (2) (3) (4) (5) (6) (7) (8) Where S(t) is the number of susceptible individuals at time t, E(t) is the number of exposed individuals at time t, and Q... E I(t) represents the number of people exposed and isolated at time t, I(t) represents the number of people infected at time t, and Q(t) represents the number of people infected at time t. I R(t) represents the number of infected and isolated individuals at time t, R(t) represents the number of recovered individuals at time t, and F(t) represents the number of deaths at time t; N(t) represents the total number of people at time t. Step S42: Based on the differential equations of the disease model and the contact matrix, establish the disease model compartment transmission rules, including: 1) Susceptible S LurkerE When in contact with infected person I: (9) Q, an infected person in isolation I Upon contact: (10) in, Personnel and infected persons / infected persons in isolation The contact intensity on day t within the prediction period; if personnel The individual is in a susceptible state (S) and has been in contact with any infected person / infected person in isolation. Contact strength If > 0, then use The probability of personnel Transmission to exposed individual E; 2) Lurker E Infected person I (11) If personnel If the state is lurking E, then with a fixed probability Transmission to infected person I; 3) Lurker E Exposed and isolated person Q E (12) If personnel If the state is lurking agent E, then the detection rate is... and detection sensitivity The product probability propagates to the exposed isolated person Q E ; 4) Infected Person I Q, an infected person in isolation I (13) If personnel If the status is infected person I, then the detection rate will be used. and detection sensitivity The product probability of transmission to infected and isolated individuals Q I ; 5) Infected Person I Recovered patient R (14) If personnel If the state is infected person I, then with a fixed probability Transmission to recovered patients R; 6) Infected Person I The deceased F (15) If personnel If the state is infected person I, then with a fixed probability Transmission to deceased person F; 7) Exposed and isolated individuals Q E Q, an infected person in isolation I (16) If personnel Status: Exposed and in isolation Q E Then with a fixed probability Transmission to infected and isolated individuals Q I ; 8) Infected person in isolation Q I Recovered patient R (17) If personnel The status is infected and isolated person Q I Then with a fixed probability Transmission to recovered patients R; 9) Infected person in isolation Q I The deceased F (18) If personnel The status is infected and isolated person Q I Then with a fixed probability Transmission to deceased person F; Among them, the compartment conversion parameters of the RealSEIR model include: infection rate Isolation infection rate The rate at which exposed individuals become infected The rate at which individuals in exposure isolation become infected individuals in isolation. The rate of recovery of infected individuals The recovery rate of infected individuals in isolation Infectious disease detection rate among asymptomatic individuals Infectious disease detection rate among infected individuals Sensitivity of latent agent detection Sensitivity of infection detection Mortality rate of infected individuals Mortality rate among those infected and isolated Isolation contact frequency adjustment factor ; Step S5: Based on the infectious disease prediction demand, the contact matrix, and the disease model compartment transmission rules, calculate the population size in each state in the target area, predict the epidemic trend of airborne infectious diseases in the target area, and formulate a medical resource adjustment strategy for the target area, specifically including: Step S51: Based on the infectious disease prediction requirements, set the total number of days for the prediction period. The proportion of infected individuals in the initial state The proportion of infected individuals to the total number of survivors at the end of the infection period. Based on the contact matrix, the total number of people is obtained. ; Step S52: Initialize personnel categories: S, E, Q E I, Q I , R, F, generate Individuals, in proportion Randomly select a group of people and initialize their status as infected person I, while the remaining unselected people are initialized as susceptible person S; Step S53: Substitute the compartment transformation parameters into the disease model compartment transmission rules, and execute the STEPS rounds sequentially. In the nth round of the loop, the parameters from the hash table need to be substituted. Finally, the number of different categories of people in the target area within a specific day is calculated. Step S54: Based on the number of different categories of people in the target area within a specific day (STEPS), calculate key data indicators reflecting the scale of infectious disease transmission, including: Basic reproduction number: It introduces the intensity of interpersonal contact into the traditional basic reproduction number to more accurately measure the transmissibility of infectious diseases without intervention; Peak number of infections and time: The maximum number of infected persons I within the predicted time period and the corresponding time point, to assess the peak number of infections under isolation measures; Peak number of infected individuals in isolation and duration: Q number of infected individuals in isolation within the predicted duration I The maximum number and its corresponding time point are used to assess the peak number of infected people in isolation under quarantine measures. Total peak number of infections: peak number of infections + peak number of infections in isolation, reflecting the highest infection burden during the epidemic; Average doubling time: The average time required for the number of infected individuals to double reflects the rate of increase in the number of infected individuals throughout the entire forecast period. Time to establish herd immunity: The number of recovered individuals (R) reaches the total surviving population, i.e., NF, at a ratio of [missing value]. The time point represents the time when herd immunity is established; if the time of herd immunity establishment is greater than the time of infection end, then it has no practical significance and its value is set to empty. End of infection time: Infected person I and infected person in isolation Q I The total number of people reached the proportion of the total surviving population. The point in time indicates that the epidemic is basically over; Step S55: Based on the number of different categories of people in the target area within STEPS days, and combined with the key data indicators reflecting the scale of infectious disease transmission, predict the epidemic trend of airborne infectious diseases in the target area; Step S56: Based on the prevalence trend of airborne infectious diseases in the target area, formulate a medical resource adjustment strategy for the target area, including: increasing hospital beds and increasing the frequency of airborne infectious disease testing.

2. The method for predicting airborne infectious diseases based on population movement and contact intensity according to claim 1, characterized in that, Step S1: Perform preliminary processing on the movement trajectory data of personnel to filter out data within the target area that starts from the start date and has a length of the prediction period; Based on a set time interval, the filtered data is sparsified to obtain preprocessed trajectory data, specifically including: Step S11: Obtain the movement trajectory data of the personnel, including: personnel ID (Id), latitude, longitude, and time; delete trajectory data containing null values; Step S12: Obtain trajectory data within the target area based on the latitude and longitude range of the target area; Step S13: Based on the start date start_date and the prediction period T, obtain the trajectory data within the target time period from the trajectory data of the target area; Step S14: Sample the trajectory data within the target time period according to the set time interval time_interval to obtain the preprocessed trajectory data.

3. The method for predicting airborne infectious diseases based on population movement and contact intensity according to claim 2, characterized in that, Step S2: Based on the preprocessed trajectory data, identify contact events between people within a set time window and distance threshold; Based on the contact events, a contact matrix is ​​constructed using a hash table, specifically including: Step S21: Identify and record contact events between personnel based on the set time window (time_window) and distance threshold (distance_threshold); wherein the contact event is defined as follows: within the time window, if the geographical distance between personnel i and personnel j is less than or equal to distance_threshold, then it is determined that the two have made contact, and a contact event (contact_event) is generated; contact_event includes {User i User j , Date, Distance, Center_Latitude, Center_Longitude}, where Date is the day t in which the contact occurs during the prediction period T ( ); Distance is for two users. i and User j The actual distance between them, where Center_Longitude and Center_Latitude are the latitude and longitude of the center point of the contact event; Step S22: Define the frequency of contact events as the contact intensity between personnel i and j. Using hash tables, based on Construct a contact matrix H, where the keys are combinations of time and personnel number pairs. The value is the contact strength between person i and person j. .

4. The method for predicting airborne infectious diseases based on population movement and contact intensity according to claim 3, characterized in that, Step S3: Based on the basic data of airborne infectious diseases in the target area and combined with the needs of infectious disease prediction, establish a RealSEIR model, including: basic compartment structure, compartment transmission links, and compartment transformation parameters, specifically including: Step S31: Obtain basic data on airborne infectious diseases in the target area, including: population data, case data, and hospital bed data of the target area; Step S32: Based on the basic data of airborne infectious diseases and combined with the needs of infectious disease prediction, establish the basic cabin structure of the model; wherein the basic cabin structure includes multiple population cabins, the population cabins include: susceptible population cabins, exposed population cabins, exposed isolation population cabins, infected population cabins, infected isolation population cabins, recovered population cabins and deceased population cabins; Step S33: Based on the basic compartment structure, establish compartment transmission links, including multiple personnel transfer paths between compartments: Step S34: Based on the basic compartment structure and the compartment transmission link, combined with the basic data on airborne infectious diseases and the infectious disease prediction requirements, set the compartment transformation parameters of the RealSEIR model, including: infection rate. Isolation infection rate The rate at which exposed individuals become infected The rate at which individuals in exposure isolation become infected individuals in isolation. The rate of recovery of infected individuals The recovery rate of infected individuals in isolation Infectious disease detection rate among asymptomatic individuals Infectious disease detection rate among infected individuals Sensitivity of latent agent detection Sensitivity of infection detection Mortality rate of infected individuals Mortality rate among those infected and isolated Isolation contact frequency adjustment factor .

Images