WO2019196281A1 - Epidemic grading and prediction method and apparatus, computer apparatus, and readable storage medium - Google Patents

Abstract

An epidemic grading and prediction method, the method comprising: training and testing an epidemic prediction model to obtain an optimised epidemic prediction model and using the optimised epidemic prediction model to implement prediction on epidemic monitoring data before a time point to be tested; on the basis of the epidemic prediction model, determining the size of a grading time window; and, on the basis of the prediction results of each time point before the time point to be tested and the size of the grading time window, determining the epidemic risk level of the time point to be tested. Also provided in the present application are an epidemic grading and prediction apparatus, a computer apparatus, and a readable storage medium. The present application can improve the accuracy of determining epidemic risk level.

Description

Epidemic grading prediction method and device, computer device and readable storage medium

The present application claims the priority of the Chinese Patent Application entitled "Population Classification Prediction Method and Apparatus, Computer Apparatus, and Readable Storage Medium" by the Chinese Patent Office on April 11, 2018, the entire disclosure of which is incorporated herein by reference. This is incorporated herein by reference.

Technical field

The present application relates to the field of disease prediction technologies, and in particular, to a method and device for predicting epidemic grading, a computer device, and a non-volatile readable storage medium.

Background technique

The epidemic prediction and warning is based on the collected epidemiological epidemic reports and epidemic monitoring data, comprehensive assessment and prediction of the area and scale of the epidemic, and then, within a certain range, adopt appropriate methods to pre-release event threat warnings, and then timely Found outbreaks and epidemics. At present, epidemic epidemic prediction has become an important part of the disease surveillance information system.

However, the existing epidemiological grading prediction methods cannot obtain better grading prediction results.

Summary of the invention

In view of the above, it is necessary to propose an epidemiological grading prediction method and apparatus, a computer apparatus and a non-volatile readable storage medium, which can improve the accuracy of the epidemiological risk level determination.

A first aspect of the present application provides a method for predicting an epidemic grading, the method comprising:

(1) Establish an epidemiological prediction model;

(2) training the epidemic prediction model with the first training data;

(3) using the epidemic prediction model to predict test data, determining whether the prediction result of the test data meets a preset condition, and if the predicted result of the test data satisfies a preset condition, executing (5);

(4) if the predicted result of the test data does not satisfy the preset condition, fine-tuning the epidemic prediction model, and then executing (5);

(5) determining, by using the second training data, a grading time window size for determining an epidemic risk level based on the epidemic prediction model, so that the time point based on the epidemic prediction model is determined to be a medium risk level and a medium risk level or higher During the epidemic period of the real epidemic, the time point determined as the low risk level and the medium risk level based on the epidemiological prediction model is within the real epidemic non-population period;

(6) using the epidemic prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic period within the grading time window size time before the time point to be measured Epidemic period and non-epidemic period of epidemic disease;

(7) calculating a mean value and a standard deviation of the epidemiological monitoring data of the epidemic non-epidemic period within the size time window size period before the time point to be measured;

(8) calculating a threshold value of the epidemic risk level according to the mean value and the standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period before the time point of the grading time window;

(9) determining an epidemic risk level of the time point to be tested according to the threshold value of the epidemic risk level.

A second aspect of the present application provides an epidemic grading prediction apparatus, the apparatus comprising:

Establish a unit for establishing an epidemiological prediction model;

a training unit, configured to train the epidemic prediction model with the first training data;

a test unit, configured to use the epidemiological prediction model to predict test data, determine whether the prediction result of the test data meets a preset condition, and if the predicted result of the test data does not satisfy a preset condition, Fine-tuning the epidemiological prediction model;

a determining unit, configured to determine, by using the second training data, a hierarchical time window size based on the epidemic risk prediction model for determining an epidemic risk level, and determining the time based on the epidemic prediction model as a medium risk level and a medium risk level or higher Point in the real epidemic epidemic period, based on the epidemiological prediction model, the time point determined as the low risk level and the medium risk level is within the real epidemic non-population period;

a prediction unit, configured to use the epidemiological prediction model to predict each time point in the grading time window size time before the time point to be measured, and divide the time in the grading time window size before the time point to be tested The epidemic epidemic period and the epidemic non-population period; calculating the mean and standard deviation of the epidemiological monitoring data of the epidemic non-epidemic period within the grading time window size time before the time point to be measured; according to the time point to be tested Calculating an epidemic risk level dividing threshold by using the mean and standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period in the classification time window size; determining the prevalence of the time point to be tested according to the threshold of the epidemic risk level Disease risk level.

A third aspect of the present application provides a computer apparatus comprising a memory and a processor, the memory for storing at least one computer readable instruction, the processor for executing the at least one computer readable instruction The above-mentioned epidemic grading prediction method is implemented.

A fourth aspect of the present application provides a non-volatile readable storage medium storing at least one computer readable instruction when executed by a processor Implement epidemiological grading prediction methods.

The present application trains and tests the epidemic prediction model, obtains an optimized epidemiological prediction model, and uses the optimized epidemiological prediction model to predict the epidemiological monitoring data before the measurement time point, and determines based on the epidemic prediction model. The size of the time window is determined according to the prediction result at each time point before the time point to be measured and the size of the classification time window to determine the epidemic risk level of the time point to be measured. Since the time window size used for the epidemic risk level determination is determined based on the epidemiological prediction model, the present application can improve the accuracy of the epidemic risk level determination.

DRAWINGS

FIG. 1 is a flowchart of a method for predicting epidemic grading according to Embodiment 1 of the present application.

2 is a detailed flow chart of step 105 of FIG. 1.

FIG. 3 is a structural diagram of an epidemic grading prediction apparatus according to Embodiment 2 of the present application.

4 is a schematic diagram of a computer device according to Embodiment 3 of the present application.

detailed description

Preferably, the epidemic grading prediction method of the present application is applied to one or more computer devices.

Embodiment 1

FIG. 1 is a flowchart of a method for predicting epidemic grading according to Embodiment 1 of the present application. The epidemic grading prediction method can predict an epidemic risk level at a time point to be tested according to epidemiological monitoring data before the time point to be measured.

As shown in FIG. 1 , the epidemic grading prediction method specifically includes the following steps:

In step 101, an epidemic prediction model is established.

The epidemiological prediction model is used to predict epidemic epidemics and epidemic non-epidemic periods based on epidemiological surveillance data.

The epidemiological monitoring data is time series data. The epidemiological monitoring data may include epidemiological data such as the number of visits to the epidemic, the rate of visits, the number of cases, and the incidence rate. For example, the number of daily visits to an epidemic (eg, flu) can be obtained from a medical institution (eg, a hospital), and the number of daily visits to an epidemic (eg, flu) can be used as epidemiological surveillance data. As another example, the number of daily epidemics of a student's epidemic (eg, flu) can be obtained from the school, and the number of daily epidemics of an epidemic (eg, flu) can be used as epidemiological surveillance data. For example, the number of daily visits to an epidemic (eg, flu) can be obtained from a medical institution (eg, a hospital), and the number of daily visits to an epidemic (eg, flu) can be used as epidemiological surveillance data.

An epidemiological monitoring network composed of a plurality of monitoring points may be established in a preset area (for example, a province, a city, a region), and the epidemiological monitoring data is obtained from the monitoring points. Medical institutions, schools and child care institutions, pharmacies, etc. can be selected as monitoring points to conduct epidemiological surveillance and data collection for the corresponding target population. A place that meets the preset conditions can be selected as the monitoring point. The preset condition may include a number of people, a scale, and the like. For example, select a school with a predetermined number of schools and child care institutions as monitoring points. Another example is to select a pharmacy that has reached the preset size (for example, by daily turnover) as a monitoring point. For another example, select a hospital (for example, the number of people who seek medical treatment in Japan) to reach a preset size as a monitoring point.

Epidemiological data at different time points constitute the epidemiological surveillance data (ie, time series data). For example, epidemiological data collected on a daily basis can be used as epidemiological surveillance data. Alternatively, epidemiological disease data collected on a weekly basis can be used as epidemiological surveillance data.

Medical institutions (mainly including hospitals) are the best place to capture early warning signs of epidemics and are the first choice for epidemiological surveillance. Epidemiological surveillance data can be obtained based on patient visits.

Some prevalent patients go to the pharmacy to buy medicines to relieve early symptoms. Therefore, epidemiological surveillance data can be obtained based on the sales of drugs in pharmacies.

Children and adolescents are at high risk of epidemics and an important link in the spread of epidemics, and monitoring of this population should also be strengthened. Schools and child care institutions are better places to monitor the incidence of epidemics in children and adolescents. Epidemiological surveillance data can be obtained based on the leave of children and adolescents in schools and child care institutions.

Therefore, in this application, medical institutions, schools, child care institutions, and pharmacies are mainly selected to collect epidemiological surveillance data. Of course, the above selection of data sources does not limit the addition or replacement of other focused populations or sites in other embodiments as a source of data for monitoring. For example, hotels can be included in the epidemiological surveillance area to obtain epidemiological surveillance data for hotel residents.

According to the needs, only epidemiological surveillance data collected by any type of monitoring point (such as a medical institution) can be taken. For example, only epidemiological surveillance data collected by the hospital can be taken. Alternatively, epidemiological surveillance data collected from multiple types of monitoring points can be combined. For example, epidemiological surveillance data collected by hospitals can be used as a supplement, supplemented by epidemiological surveillance data from pharmacies.

The epidemic prediction model may include a CUSUM (Cumulative Sum) prediction model, an EWMA (Exponentially Weighted Moving-Average) prediction model, and a mobile percentile prediction model. The three models are introduced separately below.

(1) CUSUM prediction model

The CUSUM prediction model achieves the amplification effect by accumulating small deviations between the actual values (ie epidemiological monitoring data) and the reference values, and improves the sensitivity of small deviations in the prediction process. When the deviations accumulate to a certain extent and exceed the threshold, then It is believed that there has been a turning point, that is, the epidemic has shifted from the non-popular period to the epidemic period.

Let the epidemiological surveillance data X obey the normal distribution, w is the time window size of the CUSUM prediction model, and the initial CUSUM value is C ₀ =0, then the CUSUM value C _t and the threshold H _t of the time point _t are:

C _t =max{0,X _t -(μ _w +k ₁ σ _w )+C _t-1 }

H _t =hσ _t

w is the time window size of the CUSUM prediction model. For example, when the epidemiological surveillance data X is daily epidemiology data, w can take 7 for 7 days (ie, one week). μ _w is the mean of time point tw to t-1 (length w) X, and σ _w is the standard deviation of time point tw to t-1 (length is w) X. K ₁ is a tunable parameter and is generally taken in (0, 3).

If C _{t is} greater than the threshold H _t =hσ _t , it is considered to enter the epidemic epidemic period. h is an adjustable parameter, generally taking 1, 2, and 3. σ _t is the standard deviation of the historical data at time point t.

C _t is the value of the next time point generated based on the value of the previous time point. According to C _t and H _t , the epidemic epidemic period and the beginning of the epidemic non-population period can be quickly determined. If the CUSUM value C _{t is} greater than the threshold H _t , then the epidemic epidemic period is entered. If the CUSUM value C _{t is} less than or equal to the threshold H _t , it enters the epidemic non-population period.

(2) EWMA prediction model

EWMA is an exponentially decreasing weighted moving average. The weighted influence of each value decreases exponentially with time. The more recent the data weighting influence is, the older the data also gives a certain weight value.

Let the epidemiological surveillance data X obey the normal distribution X _t ~ N(μ, σ ² ), and the initial value Z ₀ = X ₀ , then the EWMA value at time point t is:

Z _t =λ×X _t +(1−λ)×Z _t-1

If the EWMA value Z _{t at time t is} greater than UCL, it is considered to enter the epidemic epidemic period.

The constant λ is a weight coefficient and is generally taken within (0, 1).

K ₂ is a tunable parameter and is generally taken in (0, 3).

Z _t is based on the value of the previous time point to generate the value of the next time point. According to Z _t and UCL, the start of the epidemic period and the non-popular period can be quickly determined. If Z _{t is} greater than UCL, it enters the epidemic period. If the CUSUM value Z _{t is} less than or equal to UCL, it enters the epidemic non-population period.

(3) Moving percentile prediction model

The moving percentile prediction model is based on the epidemiological monitoring data of the observation week of the previous year and the pre-set weeks (for example, 2 weeks before and after) as the baseline data, and the specified percentiles are calculated (for example, P5, P10,... , P90, P95, P100) as a candidate early warning threshold, establish an early warning model.

For example, the P80 value of the influenza ILI index for the third week of 2014 represents the 80th percentile of the 10-week morbidity rate for the 1-5th week of 2012-2013, which is taken as the early warning threshold for the third week of 2014. The P75 value for the 20th week of 2014 represents the 75th percentile of the 10-week morbidity rate for the 18th to 22nd week of 2012-2013, which is taken as the warning threshold for the 3rd week of 2014.

Compare epidemiological surveillance data with early warning thresholds and enter epidemic epidemic if epidemiological surveillance data is greater than the pre-warning threshold. If the epidemiological surveillance data is less than or equal to the warning threshold, it enters the epidemic non-population period.

Step 102: Train the epidemic prediction model with the first training data.

The first training data is epidemiological surveillance data. The training of the epidemic prediction model is performed by using the first training data, that is, the first training data is predicted by using the epidemic prediction model, and the parameters of the epidemic prediction model are adjusted or selected according to the prediction result of the first training data. .

For example, the first training data is subjected to epidemiological prediction using the CUSUM prediction model, and the time window size w, parameters k ₁ and h of the CUSUM prediction model are adjusted according to the prediction result of the first training data.

For another example, the EWMA prediction model is used to perform epidemiological prediction on the first training data, and the weight coefficient λ and the parameter k _{2 of} the EWMA prediction model are adjusted according to the prediction result of the first training data.

For another example, the first training data is subjected to epidemiological prediction by using the moving percentile prediction model, and the appropriate percentile (for example, the 80th percentile) is selected as the moving percentage according to the prediction result of the first training data. The warning threshold for the bit prediction model.

Specifically, the first training data may be predicted by using the epidemic prediction model, and the prediction result of the first training data is compared with a real epidemic/non-period segmentation result, and the comparison result is adjusted or selected according to the comparison result. The parameters of the epidemic prediction model.

The real epidemic/non-epidemic period is defined by medical methods. The preset indicator of the prediction result of the epidemic prediction model on the first training data may be calculated, and the parameters of the epidemic prediction model are adjusted or selected according to the preset index. For example, three indicators of accuracy, specificity, and timeliness of the prediction result of the epidemic prediction model for the first training data may be calculated, and parameters of the epidemic prediction model are adjusted or selected based on the three indicators.

The calculation methods of accuracy, specificity and timeliness can be as follows:

Accuracy = number of days of effective warning / total number of days of true epidemic epidemic x 100%;

Specificity = number of days without warning / total number of days of true epidemic non-population period x 100%;

Timeliness (ie lag period) = start date of the epidemic period of effective early warning - the start date of the real epidemic epidemic period.

Using the epidemiological prediction model to predict the epidemic epidemic period for the first training data, if it is predicted that the epidemiological epidemic period is a certain day, and it falls within the range of the real epidemic epidemic period, it is recorded as an effective warning. .

Step 103: The test data is predicted by using the epidemic prediction model, and it is determined whether the prediction result of the test data meets a preset condition. If the prediction result of the test data satisfies a preset condition, step 105 is performed.

The test data is epidemiological surveillance data. The epidemiological prediction model is used to predict the test data, and the purpose is to verify whether the post-training epidemiological prediction model satisfies the requirements.

Predetermining indicators (eg, accuracy, specificity, timeliness) of the prediction result of the test data may be calculated, and determining whether the post-training epidemic prediction model satisfies a preset condition according to a preset index of the prediction result of the test data . For example, determining whether the accuracy of the prediction result of the test data reaches a preset accuracy, and/or determining whether the specificity of the prediction result of the test data reaches a preset specificity, and/or determining the test data. Whether the timeliness of the forecast results reaches the preset timeliness. If the accuracy of the prediction result of the test data reaches a preset accuracy, and/or the specificity of the prediction result of the test data reaches a preset specificity, and/or the timeliness of the prediction result of the test data reaches Predetermined timeliness, it is judged that the epidemic prediction model satisfies the preset condition, and an optimized epidemiological prediction model is obtained.

Step 104: If the prediction result of the test data does not satisfy the preset condition, fine-tune the epidemic prediction model, and then perform step 105.

If the predicted result of the test data does not satisfy the preset condition, the parameters of the epidemic prediction model are further adjusted.

For example, if the predicted result of the CUSUM prediction model does not satisfy the preset condition, the time window size w, parameters k ₁ and h of the CUSUM prediction model are further adjusted.

For another example, if the prediction result of the EWMA prediction model does not satisfy the preset condition, the weight coefficient λ and the parameter k _{2 of} the EWMA prediction model are further adjusted.

For another example, if the prediction result of the EWMA prediction model does not satisfy the preset condition, the 75th percentile (the 80th percentile is adjusted to the 75th percentile) is taken as the The warning threshold for the moving percentile prediction model.

Step 105: Determine, by using the second training data, a time window size (hereinafter referred to as a grading time window size) for determining an epidemic risk level based on the epidemic prediction model, so that the epidemic prediction model is determined to be a medium risk level and The time point above the risk level is within the real epidemic period, and the time point determined as the low risk level and the medium risk level based on the epidemic prediction model is within the real epidemic non-population period.

The second training data is epidemiological monitoring data. The second training data may be the same as or different from the first training data.

The grading time window size is determined in order to ensure the accuracy of the epidemic risk level based on the epidemiological prediction model.

In the process of determining the size of the grading time window, the grading time window size is adjusted so that the time point determined as the medium risk level and the medium risk level or higher based on the epidemic prediction model is utilized during the real epidemic period. The epidemic prediction model determines that the time points of the low risk level and the medium risk level are within the real epidemic non-population period.

Referring to FIG. 2, determining the hierarchical time window size may specifically include the following steps:

Step 201: Using the epidemic prediction model to predict each time point in the grading time window size time before the preset time point, and dividing the epidemic epidemic in the grading time window size time before the preset time point Period and epidemic period.

The initial value of the hierarchical time window size is a preset value, for example, 3, and is adjusted to a suitable size through steps 201-205, for example, 7.

Step 202: Calculate, according to the second training data, a mean and a standard deviation of epidemiological monitoring data of a non-epidemic epidemic period within the time-scale window size time before the preset time point.

Step 203: Calculate an epidemic risk level division threshold according to the mean and standard deviation of the epidemic monitoring data of the non-epidemic period of the epidemic period before the preset time point size. This step can refer to the description of step 108 below.

Step 204: Determine an epidemic risk level of the preset time point according to the epidemic risk level division threshold corresponding to the second training data. This step can refer to the description of step 109 below.

Step 205: If the epidemic risk level of the preset time point is a medium risk level and a medium risk level, determine whether the preset time point is within a real epidemic period, or if the preset The epidemic risk level at the time point is a low risk and a medium risk level, and it is determined whether the preset time point is within a real epidemic non-popular period.

Step 206: If the epidemic risk level of the preset time point is a medium risk level and a medium risk level, and the preset time point is within a real epidemic epidemic period, or if the preset time point The epidemic risk level is a low risk and a medium risk level, and the preset time point is within a real epidemic non-popular period, and the hierarchical time window size is adjusted.

The hierarchical time window size may be adjusted multiple times in the manner described above using different second training data to adjust the hierarchical time window size to an optimal value.

Step 106: predict, by using the epidemic prediction model, each time point in the grading time window size time before the time point to be measured, and divide the epidemic in the grading time window size time before the time point to be tested Epidemic period and non-epidemic period of epidemic disease.

For example, using the CUSUM prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic period and epidemic time in the grading time window size time before the time point to be measured The disease is not in epidemic. For example, using the CUSUM prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic epidemic period in the grading time window size time before the time point to be tested The epidemic is not in epidemic. For example, the mobile percentile model is used to predict each time point in the time window of the hierarchical time window before the time point is measured, and the epidemic epidemic time in the size time window size before the time point to be measured is divided. Period and epidemic period.

Step 107: Calculate a mean value and a standard deviation of epidemiological monitoring data of a non-epidemic epidemic period within the size time window size period before the time point to be measured.

In step 107, all the epidemic non-population periods in the grading time window size time before the time point to be measured are counted, and all the epidemic diseases in the grading time window size time before the time point to be measured are calculated. The mean and standard deviation of epidemiological surveillance data during the epidemic. For example, the three time epidemic non-population periods are included in the grading time window size time before the time point to be tested, and the mean and standard deviation of the epidemiological monitoring data of the three epidemics are calculated. The mean and standard deviation are a mean and a standard deviation calculated from epidemiological monitoring data of all epidemic non-epidemic periods within the grading time window size time before the time point to be measured.

Step 108: Calculate an epidemic risk level division threshold according to the mean value and the standard deviation of the epidemic monitoring data of the non-epidemic period of the epidemic period in the time period of the classification time window before the time point to be measured.

The epidemic risk level division threshold may include a high school level division threshold, a medium and low level division threshold, and a low/very low level division threshold. The high school level partitioning threshold is used to divide a high risk level and a medium risk level, wherein the low level ranking threshold is used to divide the medium risk level and the low risk level, and the low level ranking threshold is used to divide the low risk level and the pole. Low risk level.

In a specific embodiment, the mean value of the epidemiological monitoring data during the non-prevalence period is μ _w′ , the standard deviation is σ _w′ , and the high school level dividing threshold is μ _w′ +k′ ₁ *σ _w′ , Wherein ₆ ≤ k' ₁ ≤ 9, the middle and low level partitioning threshold is μ _w′ + k ₂ ′ * σ _w′ , wherein 4 ≤ k′ ₂ <6, and the middle and low level partitioning threshold is μ _w′ + k ₃ '*σ _w' , 2≤k' ₃ <4. For example, the high school level dividing threshold is μ _w′ +6*σ _w′ , the middle and low level dividing threshold is μ _w′ +4*σ _w′ , and the middle and low level dividing threshold is μ _w′ +2 *σ _w' .

In other embodiments, the epidemic risk level division threshold may include other quantities and types. For example, the epidemic risk level division threshold may include a high school level division threshold and a medium and low level division threshold. For another example, the epidemic risk level division threshold may include a very high/high level division threshold, a high school level division threshold, a medium and low level division threshold, and a low/very low level division threshold.

Step 109: Determine a prevalence risk level of the time point to be tested according to the epidemic risk level division threshold.

For example, if the epidemic monitoring data at the time point to be tested is greater than or equal to the high school level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is a high risk level. If the epidemic monitoring data at the time point to be tested is smaller than the high school level dividing threshold and greater than or equal to the high school level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is a medium risk level. If the epidemiological monitoring data at the time point to be tested is smaller than the middle and low level dividing threshold and greater than or equal to the low/low level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is a low risk level. If the epidemiological monitoring data at the time point to be tested is less than the low/very low level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is an extremely low risk level.

In an embodiment, the CUSUM prediction model, the EWMA prediction model, and the moving percentile prediction model may be combined for epidemiological grading prediction. Specifically, according to the method of steps 101-109, determining, according to the CUSUM prediction model, the time point to be tested is the first epidemic risk level, and determining the time point to be tested as the second epidemic risk level based on the EWMA prediction model, based on the mobile percentile The predictive model determines that the time point to be tested is a third epidemic risk level, and the final epidemic risk level is obtained according to the first epidemic risk level, the second epidemic risk level, and the third epidemic risk level. Determining whether the first epidemic risk level, the second epidemic risk level, and the third epidemic risk level have at least two epidemic risk levels consistent, if the first epidemic risk level, If at least two epidemiological risk levels are consistent between the second epidemic risk level and the third epidemic risk level, the consistent epidemic risk level is used as the final epidemic risk level.

The epidemic grading prediction method of the first embodiment trains and tests the epidemic prediction model, obtains an optimized epidemiological prediction model, and uses the optimized epidemic prediction model to predict epidemiological monitoring data before the time point is measured, based on The epidemic prediction model determines the grading time window size, and determines the epidemic risk level of the time point to be measured according to the prediction result at each time point before the time point to be measured and the grading time window size. Since the time window size used for the epidemic risk level determination is determined based on the epidemiological prediction model, the first embodiment can improve the accuracy of the epidemiological risk level determination.

Embodiment 2

FIG. 3 is a structural diagram of an epidemic grading prediction apparatus according to Embodiment 2 of the present application. As shown in FIG. 3, the epidemic grading prediction apparatus 10 may include an establishing unit 301, a training unit 302, a testing unit 303, a determining unit 304, and a prediction unit 305.

The establishing unit 301 is configured to establish an epidemic prediction model.

The epidemiological prediction model is used to predict epidemic epidemics and epidemic non-epidemic periods based on epidemiological surveillance data.

The epidemiological monitoring data is time series data. The epidemiological monitoring data may include epidemiological data such as the number of visits to the epidemic, the rate of visits, the number of cases, and the incidence rate. For example, the number of daily visits to an epidemic (eg, flu) can be obtained from a medical institution (eg, a hospital), and the number of daily visits to an epidemic (eg, flu) can be used as epidemiological surveillance data. As another example, the number of daily epidemics of a student's epidemic (eg, flu) can be obtained from the school, and the number of daily epidemics of an epidemic (eg, flu) can be used as epidemiological surveillance data. For example, the number of daily visits to an epidemic (eg, flu) can be obtained from a medical institution (eg, a hospital), and the number of daily visits to an epidemic (eg, flu) can be used as epidemiological surveillance data.

An epidemiological monitoring network composed of a plurality of monitoring points may be established in a preset area (for example, a province, a city, a region), and the epidemiological monitoring data is obtained from the monitoring points. Medical institutions, schools and child care institutions, pharmacies, etc. can be selected as monitoring points to conduct epidemiological surveillance and data collection for the corresponding target population. A place that meets the preset conditions can be selected as the monitoring point. The preset condition may include a number of people, a scale, and the like. For example, select a school with a predetermined number of schools and child care institutions as monitoring points. Another example is to select a pharmacy that has reached the preset size (for example, by daily turnover) as a monitoring point. For another example, select a hospital (for example, the number of people who seek medical treatment in Japan) to reach a preset size as a monitoring point.

Epidemiological data at different time points constitute the epidemiological surveillance data (ie, time series data). For example, epidemiological data collected on a daily basis can be used as epidemiological surveillance data. Alternatively, epidemiological disease data collected on a weekly basis can be used as epidemiological surveillance data.

Medical institutions (mainly including hospitals) are the best place to capture early warning signs of epidemics and are the first choice for epidemiological surveillance. Epidemiological surveillance data can be obtained based on patient visits.

Some prevalent patients go to the pharmacy to buy medicines to relieve early symptoms. Therefore, epidemiological surveillance data can be obtained based on the sales of drugs in pharmacies.

Children and adolescents are at high risk of epidemics and an important link in the spread of epidemics, and monitoring of this population should also be strengthened. Schools and child care institutions are better places to monitor the incidence of epidemics in children and adolescents. Epidemiological surveillance data can be obtained based on the leave of children and adolescents in schools and child care institutions.

Therefore, in this application, medical institutions, schools, child care institutions, and pharmacies are mainly selected to collect epidemiological surveillance data. Of course, the above selection of data sources does not limit the addition or replacement of other focused populations or sites in other embodiments as a source of data for monitoring. For example, hotels can be included in the epidemiological surveillance area to obtain epidemiological surveillance data for hotel residents.

According to the needs, only epidemiological surveillance data collected by any type of monitoring point (such as a medical institution) can be taken. For example, only epidemiological surveillance data collected by the hospital can be taken. Alternatively, epidemiological surveillance data collected from multiple types of monitoring points can be combined. For example, epidemiological surveillance data collected by hospitals can be used as a supplement, supplemented by epidemiological surveillance data from pharmacies.

The epidemic prediction model may include a CUSUM (Cumulative Sum) prediction model, an EWMA (Exponentially Weighted Moving-Average) prediction model, and a mobile percentile prediction model. The three models are introduced separately below.

(1) CUSUM prediction model

The CUSUM prediction model achieves the amplification effect by accumulating small deviations between the actual values (ie epidemiological monitoring data) and the reference values, and improves the sensitivity of small deviations in the prediction process. When the deviations accumulate to a certain extent and exceed the threshold, then It is believed that there has been a turning point, that is, the epidemic has shifted from the non-popular period to the epidemic period.

Let the epidemiological surveillance data X obey the normal distribution, w is the time window size of the CUSUM prediction model, and the initial CUSUM value is C ₀ =0, then the CUSUM value C _t and the threshold H _t of the time point _t are:

C _t =max{0,X _t -(μ _w +k ₁ σ _w )+C _t-1 }

H _t =hσ _t

w is the time window size of the CUSUM prediction model. For example, when the epidemiological surveillance data X is daily epidemiology data, w can take 7 for 7 days (ie, one week). μ _w is the mean of time point tw to t-1 (length w) X, and σ _w is the standard deviation of time point tw to t-1 (length is w) X. K ₁ is a tunable parameter and is generally taken in (0, 3).

If C _{t is} greater than the threshold H _t =hσ _t , it is considered to enter the epidemic epidemic period. h is an adjustable parameter, generally taking 1, 2, and 3. σ _t is the standard deviation of the historical data at time point t.

C _t is the value of the next time point generated based on the value of the previous time point. According to C _t and H _t , the epidemic epidemic period and the beginning of the epidemic non-population period can be quickly determined. If the CUSUM value C _{t is} greater than the threshold H _t , then the epidemic epidemic period is entered. If the CUSUM value C _{t is} less than or equal to the threshold H _t , it enters the epidemic non-population period.

(2) EWMA prediction model

EWMA is an exponentially decreasing weighted moving average. The weighted influence of each value decreases exponentially with time. The more recent the data weighting influence is, the older the data also gives a certain weight value.

Let the epidemiological surveillance data X obey the normal distribution X _t ~ N(μ, σ ² ), and the initial value Z ₀ = X ₀ , then the EWMA value at time point t is:

Z _t =λ×X _t +(1−λ)×Z _t-1

If the EWMA value Z _{t at time t is} greater than UCL, it is considered to enter the epidemic epidemic period.

The constant λ is a weight coefficient and is generally taken within (0, 1).

K ₂ is a tunable parameter and is generally taken in (0, 3).

Z _t is based on the value of the previous time point to generate the value of the next time point. According to Z _t and UCL, the start of the epidemic period and the non-popular period can be quickly determined. If Z _{t is} greater than UCL, it enters the epidemic period. If the CUSUM value Z _{t is} less than or equal to UCL, it enters the epidemic non-population period.

(3) Moving percentile prediction model

The moving percentile prediction model is based on the epidemiological monitoring data of the observation week of the previous year and the pre-set weeks (for example, 2 weeks before and after) as the baseline data, and the specified percentiles are calculated (for example, P5, P10,... , P90, P95, P100) as a candidate early warning threshold, establish an early warning model.

For example, the P80 value of the influenza ILI index for the third week of 2014 represents the 80th percentile of the 10-week morbidity rate for the 1-5th week of 2012-2013, which is taken as the early warning threshold for the third week of 2014. The P75 value for the 20th week of 2014 represents the 75th percentile of the 10-week morbidity rate for the 18th to 22nd week of 2012-2013, which is taken as the warning threshold for the 3rd week of 2014.

Compare epidemiological surveillance data with early warning thresholds and enter epidemic epidemic if epidemiological surveillance data is greater than the pre-warning threshold. If the epidemiological surveillance data is less than or equal to the warning threshold, it enters the epidemic non-population period.

The training unit 302 is configured to train the epidemic prediction model with the first training data.

The first training data is epidemiological surveillance data. The training of the epidemic prediction model is performed by using the first training data, that is, the first training data is predicted by using the epidemic prediction model, and the parameters of the epidemic prediction model are adjusted or selected according to the prediction result of the first training data. .

For example, the first training data is subjected to epidemiological prediction using the CUSUM prediction model, and the time window size w, parameters k ₁ and h of the CUSUM prediction model are adjusted according to the prediction result of the first training data.

For another example, the EWMA prediction model is used to perform epidemiological prediction on the first training data, and the weight coefficient λ and the parameter k _{2 of} the EWMA prediction model are adjusted according to the prediction result of the first training data.

For another example, the first training data is subjected to epidemiological prediction by using the moving percentile prediction model, and the appropriate percentile (for example, the 80th percentile) is selected as the moving percentage according to the prediction result of the first training data. The warning threshold for the bit prediction model.

Specifically, the first training data may be predicted by using the epidemic prediction model, and the prediction result of the first training data is compared with a real epidemic/non-period segmentation result, and the comparison result is adjusted or selected according to the comparison result. The parameters of the epidemic prediction model.

The real epidemic/non-epidemic period is defined by medical methods. The preset indicator of the prediction result of the epidemic prediction model on the first training data may be calculated, and the parameters of the epidemic prediction model are adjusted or selected according to the preset index. For example, three indicators of accuracy, specificity, and timeliness of the prediction result of the epidemic prediction model for the first training data may be calculated, and parameters of the epidemic prediction model are adjusted or selected based on the three indicators.

The calculation methods of accuracy, specificity and timeliness can be as follows:

Accuracy = number of days of effective warning / total number of days of true epidemic epidemic x 100%;

Specificity = number of days without warning / total number of days of true epidemic non-population period x 100%;

Timeliness (ie lag period) = start date of the epidemic period of effective early warning - the start date of the real epidemic epidemic period.

Using the epidemiological prediction model to predict the epidemic epidemic period for the first training data, if it is predicted that the epidemiological epidemic period is a certain day, and it falls within the range of the real epidemic epidemic period, it is recorded as an effective warning. .

The testing unit 303 is configured to use the epidemic prediction model to predict the test data, determine whether the prediction result of the test data meets a preset condition, and if the prediction result of the test data does not meet the preset condition, the test unit The epidemiological prediction model is fine-tuned.

The test data is epidemiological surveillance data. The epidemiological prediction model is used to predict the test data, and the purpose is to verify whether the post-training epidemiological prediction model satisfies the requirements.

Predetermining indicators (eg, accuracy, specificity, timeliness) of the prediction result of the test data may be calculated, and determining whether the post-training epidemic prediction model satisfies a preset condition according to a preset index of the prediction result of the test data . For example, determining whether the accuracy of the prediction result of the test data reaches a preset accuracy, and/or determining whether the specificity of the prediction result of the test data reaches a preset specificity, and/or determining the test data. Whether the timeliness of the forecast results reaches the preset timeliness. If the accuracy of the prediction result of the test data reaches a preset accuracy, and/or the specificity of the prediction result of the test data reaches a preset specificity, and/or the timeliness of the prediction result of the test data reaches Predetermined timeliness, it is judged that the epidemic prediction model satisfies the preset condition, and an optimized epidemiological prediction model is obtained.

If the predicted result of the test data does not satisfy the preset condition, the parameters of the epidemic prediction model are further adjusted.

For example, if the predicted result of the CUSUM prediction model does not satisfy the preset condition, the time window size w, parameters k ₁ and h of the CUSUM prediction model are further adjusted.

For another example, if the prediction result of the EWMA prediction model does not satisfy the preset condition, the weight coefficient λ and the parameter k _{2 of} the EWMA prediction model are further adjusted.

For another example, if the prediction result of the EWMA prediction model does not satisfy the preset condition, the 75th percentile (the 80th percentile is adjusted to the 75th percentile) is taken as the The warning threshold for the moving percentile prediction model.

a determining unit 304, configured to determine, by using the second training data, a time window size (hereinafter referred to as a hierarchical time window size) for determining an epidemic risk level based on the epidemic prediction model, so that the epidemiological prediction model is determined to be middle The time points of the risk level and the medium risk level are within the real epidemic period, and the time points determined as the low risk level and the medium risk level based on the epidemic prediction model are within the real epidemic non-population period.

The second training data is epidemiological monitoring data. The second training data may be the same as or different from the first training data.

The grading time window size is determined in order to ensure the accuracy of the epidemic risk level based on the epidemiological prediction model.

In the process of determining the size of the grading time window, the grading time window size is adjusted so that the time point determined as the medium risk level and the medium risk level or higher based on the epidemic prediction model is utilized during the real epidemic period. The epidemic prediction model determines that the time points of the low risk level and the medium risk level are within the real epidemic non-population period.

The determining unit 304 can determine the hierarchical time window size as follows:

(1) predicting, according to the second training data, each time point in the grading time window size time before the preset time point by using the epidemic prediction model, and dividing the grading time window size before the preset time point Epidemic epidemic period and non-epidemic epidemic period.

The initial value of the hierarchical time window size is a preset value, for example, 3, and the determining unit 304 adjusts to a suitable size, for example, 7.

(2) calculating, according to the second training data, a mean and a standard deviation of the epidemic monitoring data of the epidemic non-population period within the grading time window size time before the preset time point.

(3) Calculating an epidemic risk level division threshold according to the mean and standard deviation of the epidemiological monitoring data of the epidemic non-epidemic period within the size time window size period before the preset time point.

(4) determining an epidemic risk level of the preset time point according to the epidemic risk level dividing threshold.

(5) if the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, determining whether the preset time point is within a real epidemic period, or if the preset The epidemic risk level at the time point is a low risk and a medium risk level, and it is determined whether the preset time point is within a real epidemic non-popular period.

(6) if the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, and the preset time point is within a real epidemic epidemic period, or if the preset time point is The epidemic risk level is a low risk and a medium risk level, and the preset time point is within a real epidemic non-popular period, and the hierarchical time window size is adjusted.

The hierarchical time window size may be adjusted multiple times in the manner described above using different second training data to adjust the hierarchical time window size to an optimal value.

The forecasting unit 305 is configured to use the epidemic prediction model to predict each time point in the grading time window size time before the time point to be measured, and divide the sizing time window size time before the time point to be tested The prevalence of epidemics and the epidemic period of epidemics.

For example, using the CUSUM prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic period and epidemic time in the grading time window size time before the time point to be measured The disease is not in epidemic. For example, using the CUSUM prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic epidemic period in the grading time window size time before the time point to be tested The epidemic is not in epidemic. For example, the mobile percentile model is used to predict each time point in the time window of the hierarchical time window before the time point is measured, and the epidemic epidemic time in the size time window size before the time point to be measured is divided. Period and epidemic period.

The prediction unit 305 is further configured to calculate a mean value and a standard deviation of the epidemic monitoring data of the epidemic non-population period within the grading time window size time before the time point to be measured.

Performing statistics on all epidemic non-population periods in the grading time window size time before the time point to be measured, and calculating the prevalence of all epidemic non-population periods in the grading time window size time before the time point to be measured The mean and standard deviation of the disease surveillance data. For example, the three time epidemic non-population periods are included in the grading time window size time before the time point to be tested, and the mean and standard deviation of the epidemiological monitoring data of the three epidemics are calculated. The mean and standard deviation are a mean and a standard deviation calculated from epidemiological monitoring data of all epidemic non-epidemic periods within the grading time window size time before the time point to be measured.

The prediction unit 305 is further configured to calculate an epidemic risk level division threshold according to the mean value and the standard deviation of the epidemic monitoring data of the non-epidemic period of the epidemic period before the time point of the classification time window.

The epidemic risk level division threshold may include a high school level division threshold, a medium and low level division threshold, and a low/very low level division threshold. The high school level partitioning threshold is used to divide a high risk level and a medium risk level, wherein the low level ranking threshold is used to divide the medium risk level and the low risk level, and the low level ranking threshold is used to divide the low risk level and the pole. Low risk level.

In a specific embodiment, the mean value of the epidemiological monitoring data during the non-prevalence period is μ _w′ , the standard deviation is σ _w′ , and the high school level dividing threshold is μ _w′ +k′ ₁ *σ _w′ , Wherein ₆ ≤ k' ₁ ≤ 9, the middle and low level partitioning threshold is μ _w′ + k ₂ ′ * σ _w′ , wherein 4 ≤ k′ ₂ <6, and the middle and low level partitioning threshold is μ _w′ + k ₃ '*σ _w' , 2≤k' ₃ <4. For example, the high school level dividing threshold is μ _w′ +6*σ _w′ , the middle and low level dividing threshold is μ _w′ +4*σ _w′ , and the middle and low level dividing threshold is μ _w′ +2 *σ _w' .

In other embodiments, the epidemic risk level division threshold may include other quantities and types. For example, the epidemic risk level division threshold may include a high school level division threshold and a medium and low level division threshold. For another example, the epidemic risk level division threshold may include a very high/high level division threshold, a high school level division threshold, a medium and low level division threshold, and a low/very low level division threshold.

The predicting unit 305 is further configured to determine an epidemic risk level of the time point to be tested according to the epidemic risk level dividing threshold.

For example, if the epidemic monitoring data at the time point to be tested is greater than or equal to the high school level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is a high risk level. If the epidemic monitoring data at the time point to be tested is smaller than the high school level dividing threshold and greater than or equal to the high school level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is a medium risk level. If the epidemiological monitoring data at the time point to be tested is smaller than the middle and low level dividing threshold and greater than or equal to the low/low level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is a low risk level. If the epidemiological monitoring data at the time point to be tested is less than the low/very low level dividing threshold, it is determined that the epidemic risk level of the time point to be tested is an extremely low risk level.

In an embodiment, the epidemic grading prediction apparatus 10 may perform epidemiological grading prediction in combination with a CUSUM prediction model, an EWMA prediction model, and a mobile percentile prediction model. Specifically, the epidemic grading prediction apparatus 10 determines that the time point to be tested is the first epidemic risk level based on the CUSUM prediction model, and determines the time point to be tested as the second epidemic risk level based on the EWMA prediction model, based on the mobile percentile The predictive model determines that the time point to be tested is a third epidemic risk level, and the final epidemic risk level is obtained according to the first epidemic risk level, the second epidemic risk level, and the third epidemic risk level. Determining whether the first epidemic risk level, the second epidemic risk level, and the third epidemic risk level have at least two consistency, if the first epidemic risk level, the second epidemic If at least two of the disease risk level and the third epidemic risk level are consistent, the consistent epidemiological risk level is used as the final epidemic risk level.

The epidemic grading prediction device 10 of the second embodiment trains and tests the epidemic prediction model, obtains an optimized epidemic prediction model, and uses the optimized epidemic prediction model to predict epidemiological monitoring data before the time point is measured. The grading time window size is determined based on the epidemic prediction model, and the epidemic risk level of the time point to be measured is determined according to the prediction result at each time point before the time point to be measured and the grading time window size. Since the time window size used for the epidemic risk level determination is determined based on the epidemic prediction model, Embodiment 2 can improve the accuracy of the epidemic risk level determination.

Embodiment 3

4 is a schematic diagram of a computer device according to Embodiment 3 of the present application. The computer device 1 includes a memory 20, a processor 30, and computer readable instructions 40 stored in the memory 20 and executable on the processor 30, such as an epidemic grading prediction program. The processor 30 executes the computer readable instructions 40 to implement the steps in the above-described epidemiological grading prediction method embodiment, such as steps 101-109 shown in FIG. Alternatively, the processor 30, when executing the computer readable instructions 40, implements the functions of the various modules/units of the apparatus embodiments described above, such as units 301-305 of FIG.

Illustratively, the computer readable instructions 40 may be partitioned into one or more modules/units that are stored in the memory 20 and executed by the processor 30, To complete this application. The one or more modules/units may be a series of computer readable instruction segments capable of performing a particular function for describing the execution of the computer readable instructions 40 in the computer device 1. For example, the computer readable instructions 40 may be divided into the establishing unit 301, the training unit 302, the testing unit 303, the determining unit 304, and the predicting unit 305 in FIG.

The computer device 1 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. It will be understood by those skilled in the art that the schematic diagram 4 is merely an example of the computer device 1, and does not constitute a limitation of the computer device 1, and may include more or less components than those illustrated, or may combine some components, or different. The components, such as the computer device 1, may also include input and output devices, network access devices, buses, and the like.

The processor 30 may be a central processing unit (CPU), or may be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor 30 may be any conventional processor or the like, and the processor 30 is a control center of the computer device 1, and connects the entire computer device 1 by using various interfaces and lines. Various parts.

The memory 20 can be used to store the computer readable instructions 40 and/or modules/units by running or executing computer readable instructions and/or modules/units stored in the memory 20, and The various functions of the computer device 1 are realized by calling data stored in the memory 20. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be Data (such as audio data, phone book, etc.) created according to the use of the computer device 1 is stored. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a secure digital (Secure Digital, SD). Card, flash card, at least one disk storage device, flash device, or other volatile solid-state storage device.

The modules/units integrated by the computer device 1 can be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the present application implements all or part of the processes in the foregoing embodiments, and may also be implemented by computer-readable instructions, which may be stored in a non-volatile manner. In reading a storage medium, the computer readable instructions, when executed by a processor, implement the steps of the various method embodiments described above. Wherein, the computer readable instructions comprise computer readable instruction code, which may be in the form of source code, an object code form, an executable file or some intermediate form or the like. The non-transitory readable medium may include any entity or device capable of carrying the computer readable instruction code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read-Only Memory), Random Access Memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media. It should be noted that the contents of the non-volatile readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, Volatile readable media does not include electrical carrier signals and telecommunication signals.

In addition, it is to be understood that the word "comprising" does not exclude other elements or steps. A plurality of units or computer devices recited in the computer device claims can also be implemented by the same unit or computer device in software or hardware. The first, second, etc. words are used to denote names and do not denote any particular order.

It should be noted that the above embodiments are only used to explain the technical solutions of the present application, and are not limited thereto. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be applied. Modifications or equivalents are made without departing from the spirit and scope of the technical solutions of the present application.

Claims (20)

1. An epidemic grading prediction method, the method comprising:

   (1) Establish an epidemiological prediction model;

   (2) training the epidemic prediction model with the first training data;

   (3) using the epidemic prediction model to predict test data, determining whether the prediction result of the test data meets a preset condition, and if the predicted result of the test data satisfies a preset condition, executing (5);

   (4) if the predicted result of the test data does not satisfy the preset condition, fine-tuning the epidemic prediction model, and then executing (5);

   (5) determining, by using the second training data, a grading time window size for determining an epidemic risk level based on the epidemic prediction model, so that the time point based on the epidemic prediction model is determined to be a medium risk level and a medium risk level or higher During the epidemic period of the real epidemic, the time point determined as the low risk level and the medium risk level based on the epidemiological prediction model is within the real epidemic non-population period;

   (6) using the epidemic prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic period within the grading time window size time before the time point to be measured Epidemic period and non-epidemic period of epidemic disease;

   (7) calculating a mean value and a standard deviation of the epidemiological monitoring data of the epidemic non-epidemic period within the size time window size period before the time point to be measured;

   (8) calculating a threshold value of the epidemic risk level according to the mean value and the standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period before the time point of the grading time window;

   (9) determining an epidemic risk level of the time point to be tested according to the threshold value of the epidemic risk level.
2. The method of claim 1 wherein said step (5) comprises:

   Using the epidemic prediction model to predict each time point in the grading time window size time before the preset time point, and classify the epidemic epidemic period and prevalence in the grading time window size time before the preset time point Non-epidemic period of disease;

   Calculating, according to the second training data, a mean value and a standard deviation of epidemiological monitoring data of a non-epidemic epidemic period within the grading time window size time before the preset time point;

   Calculating a threshold value of the epidemic risk level according to the mean and standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period in the grading time window size period before the preset time point;

   Determining an epidemic risk level of the preset time point according to the epidemic risk level dividing threshold;

   If the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, determining whether the preset time point is within a real epidemic period, or if the preset time point is If the epidemic risk level is a low risk or a medium risk level, it is determined whether the preset time point is within a real epidemic non-popular period;

   If the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, and the preset time point is within a real epidemic epidemic period, or if the epidemic period of the preset time point is The risk level is a low risk and a medium risk level, and the preset time point is within a real epidemic non-popular period, and the hierarchical time window size is adjusted.
3. The method of claim 1 wherein said epidemiological prediction model comprises a cumulative and predictive model, an exponentially weighted moving average predictive model, and a mobile percentile predictive model.
4. The method according to any one of claims 1 to 3, wherein the step (2) comprises:

   Using the epidemic prediction model to predict the first training data, comparing the prediction result of the first training data with a real epidemic/non-period segmentation result, and adjusting or selecting according to the comparison result. The parameters of the epidemic prediction model.
5. The method according to claim 4, wherein said comparing the predicted result of said first training data with a real epidemic/non-popular phase dividing result, adjusting or selecting said pop based on the comparison result The parameters of the disease prediction model include:

   Calculating accuracy, specificity, and timeliness of the prediction result of the first training data by the epidemic prediction model, adjusting or selecting parameters of the epidemic prediction model based on the accuracy, specificity, and timeliness.
6. The method according to any one of claims 1 to 3, wherein the epidemiological monitoring data is obtained from the monitoring point by establishing an epidemiological monitoring network composed of a plurality of monitoring points in a preset area. .
7. The method of claim 6 wherein said monitoring points comprise medical institutions, schools and child care institutions, pharmacies that meet a predetermined number or size.
8. An epidemic grading and predicting device, characterized in that the device comprises:

   Establish a unit for establishing an epidemiological prediction model;

   a training unit, configured to train the epidemic prediction model with the first training data;

   a test unit, configured to use the epidemiological prediction model to predict test data, determine whether the prediction result of the test data meets a preset condition, and if the predicted result of the test data does not satisfy a preset condition, Fine-tuning the epidemiological prediction model;

   a determining unit, configured to determine, by using the second training data, a hierarchical time window size based on the epidemic risk prediction model for determining an epidemic risk level, and determining the time based on the epidemic prediction model as a medium risk level and a medium risk level or higher Point in the real epidemic epidemic period, based on the epidemiological prediction model, the time point determined as the low risk level and the medium risk level is within the real epidemic non-population period;

   a prediction unit, configured to use the epidemiological prediction model to predict each time point in the grading time window size time before the time point to be measured, and divide the time in the grading time window size before the time point to be tested The epidemic epidemic period and the epidemic non-population period; calculating the mean and standard deviation of the epidemiological monitoring data of the epidemic non-epidemic period within the grading time window size time before the time point to be measured; according to the time point to be tested Calculating an epidemic risk level dividing threshold by using the mean and standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period in the classification time window size; determining the prevalence of the time point to be tested according to the threshold of the epidemic risk level Disease risk level.
9. A computer apparatus, comprising: a memory for storing at least one computer readable instruction, and a processor for executing the at least one computer readable instruction to implement the following steps :

   (1) Establish an epidemiological prediction model;

   (2) training the epidemic prediction model with the first training data;

   (3) using the epidemic prediction model to predict test data, determining whether the prediction result of the test data meets a preset condition, and if the predicted result of the test data satisfies a preset condition, executing (5);

   (4) if the predicted result of the test data does not satisfy the preset condition, fine-tuning the epidemic prediction model, and then executing (5);

   (5) determining, by using the second training data, a grading time window size for determining an epidemic risk level based on the epidemic prediction model, so that the time point based on the epidemic prediction model is determined to be a medium risk level and a medium risk level or higher During the epidemic period of the real epidemic, the time point determined as the low risk level and the medium risk level based on the epidemiological prediction model is within the real epidemic non-population period;

   (6) using the epidemic prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic period within the grading time window size time before the time point to be measured Epidemic period and non-epidemic period of epidemic disease;

   (7) calculating a mean value and a standard deviation of the epidemiological monitoring data of the epidemic non-epidemic period within the size time window size period before the time point to be measured;

   (8) calculating a threshold value of the epidemic risk level according to the mean value and the standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period before the time point of the grading time window;

   (9) determining an epidemic risk level of the time point to be tested according to the threshold value of the epidemic risk level.
10. The computer apparatus according to claim 9, wherein said step (5) comprises:

    Using the epidemic prediction model to predict each time point in the grading time window size time before the preset time point, and classify the epidemic epidemic period and prevalence in the grading time window size time before the preset time point Non-epidemic period of disease;

    Calculating, according to the second training data, a mean value and a standard deviation of epidemiological monitoring data of a non-epidemic epidemic period within the grading time window size time before the preset time point;

    Calculating a threshold value of the epidemic risk level according to the mean and standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period in the grading time window size period before the preset time point;

    Determining an epidemic risk level of the preset time point according to the epidemic risk level dividing threshold;

    If the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, determining whether the preset time point is within a real epidemic period, or if the preset time point is If the epidemic risk level is a low risk or a medium risk level, it is determined whether the preset time point is within a real epidemic non-popular period;

    If the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, and the preset time point is within a real epidemic epidemic period, or if the epidemic period of the preset time point is The risk level is a low risk and a medium risk level, and the preset time point is within a real epidemic non-popular period, and the hierarchical time window size is adjusted.
11. The computer apparatus according to claim 9, wherein said epidemiological prediction model comprises a cumulative and predictive model, an exponentially weighted moving average predictive model, and a moving percentile predictive model.
12. The computer apparatus according to any one of claims 9 to 11, wherein the step (2) comprises:

    Using the epidemic prediction model to predict the first training data, comparing the prediction result of the first training data with a real epidemic/non-period segmentation result, and adjusting or selecting according to the comparison result. The parameters of the epidemic prediction model.
13. The computer apparatus according to claim 12, wherein said comparing said predicted result of said first training data with a true epidemic/non-popular phase dividing result, adjusting or selecting said said result based on said comparing result The parameters of the epidemic prediction model include:

    Calculating accuracy, specificity, and timeliness of the prediction result of the first training data by the epidemic prediction model, adjusting or selecting parameters of the epidemic prediction model based on the accuracy, specificity, and timeliness.
14. The computer apparatus according to any one of claims 9 to 11, wherein the epidemiological monitoring data is obtained from the monitoring point by establishing an epidemiological monitoring network composed of a plurality of monitoring points in a preset area. get.
15. A non-volatile readable storage medium, characterized in that the non-volatile readable storage medium stores at least one computer readable instruction, the at least one computer readable instruction being executed by a processor to implement the following steps :

    (1) Establish an epidemiological prediction model;

    (2) training the epidemic prediction model with the first training data;

    (3) using the epidemic prediction model to predict test data, determining whether the prediction result of the test data meets a preset condition, and if the predicted result of the test data satisfies a preset condition, executing (5);

    (4) if the predicted result of the test data does not satisfy the preset condition, fine-tuning the epidemic prediction model, and then executing (5);

    (5) determining, by using the second training data, a grading time window size for determining an epidemic risk level based on the epidemic prediction model, so that the time point based on the epidemic prediction model is determined to be a medium risk level and a medium risk level or higher During the epidemic period of the real epidemic, the time point determined as the low risk level and the medium risk level based on the epidemiological prediction model is within the real epidemic non-population period;

    (6) using the epidemic prediction model to predict each time point in the grading time window size time before the time point to be measured, and dividing the epidemic period within the grading time window size time before the time point to be measured Epidemic period and non-epidemic period of epidemic disease;

    (7) calculating a mean value and a standard deviation of the epidemiological monitoring data of the epidemic non-epidemic period within the size time window size period before the time point to be measured;

    (8) calculating a threshold value of the epidemic risk level according to the mean value and the standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period before the time point of the grading time window;

    (9) determining an epidemic risk level of the time point to be tested according to the threshold value of the epidemic risk level.
16. The storage medium of claim 15 wherein said step (5) comprises:

    Using the epidemic prediction model to predict each time point in the grading time window size time before the preset time point, and classify the epidemic epidemic period and prevalence in the grading time window size time before the preset time point Non-epidemic period of disease;

    Calculating, according to the second training data, a mean value and a standard deviation of epidemiological monitoring data of a non-epidemic epidemic period within the grading time window size time before the preset time point;

    Calculating a threshold value of the epidemic risk level according to the mean and standard deviation of the epidemiological monitoring data of the non-epidemic period of the epidemic period in the grading time window size period before the preset time point;

    Determining an epidemic risk level of the preset time point according to the epidemic risk level dividing threshold;

    If the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, determining whether the preset time point is within a real epidemic period, or if the preset time point is If the epidemic risk level is a low risk or a medium risk level, it is determined whether the preset time point is within a real epidemic non-popular period;

    If the epidemic risk level of the preset time point is a medium risk level and a medium risk level or higher, and the preset time point is within a real epidemic epidemic period, or if the epidemic period of the preset time point is The risk level is a low risk and a medium risk level, and the preset time point is within a real epidemic non-popular period, and the hierarchical time window size is adjusted.
17. The storage medium of claim 15, wherein the epidemiological prediction model comprises a cumulative and predictive model, an exponentially weighted moving average predictive model, and a mobile percentile predictive model.
18. The storage medium according to any one of claims 15 to 17, wherein the step (2) comprises:

    Using the epidemic prediction model to predict the first training data, comparing the prediction result of the first training data with a real epidemic/non-period segmentation result, and adjusting or selecting according to the comparison result. The parameters of the epidemic prediction model.
19. The storage medium according to claim 18, wherein said comparing said predicted result of said first training data with a true epidemic/non-popular period dividing result, adjusting or selecting said said result based on said comparing result The parameters of the epidemic prediction model include:

    Calculating accuracy, specificity, and timeliness of the prediction result of the first training data by the epidemic prediction model, adjusting or selecting parameters of the epidemic prediction model based on the accuracy, specificity, and timeliness.
20. The storage medium according to any one of claims 15 to 17, wherein the epidemiological monitoring data is obtained from the monitoring point by establishing an epidemiological monitoring network composed of a plurality of monitoring points in a preset area. get.

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