CN116913547B - Influenza monitoring method, device, medium and equipment based on punishment Morgan index - Google Patents
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Abstract
The invention relates to the technical field of risk monitoring, in particular to an influenza monitoring method, device, medium and equipment based on a punishment Morgan index, which comprises the steps of collecting historical disease data in preset time and calculating the population influenza infection rate in unit time; calculating average population influenza infection rate in a fixed-point time segment according to the population influenza infection rate in the unit time, and generating punishment parameters through the average population influenza infection rate; and (3) carrying the punishment parameters into a Morlan index model, generating a punishment Morlan index model for influenza monitoring, and calculating to obtain the punishment Morlan index. According to the influenza monitoring method, device, medium and equipment based on the punishment ring Morganan index, provided by the invention, the punishment ring parameter is introduced to calculate the punishment ring Morganan index of the current day based on the current day actual influenza data and the historical influenza data of each lower level unit in the region, and whether the predicted influenza has a spreading risk or an outbreak trend is judged according to the calculated punishment ring Morganan index.
Description
Technical Field
The invention relates to the technical field of risk monitoring, in particular to an influenza monitoring method, device, medium and equipment based on a punishment Morgan index.
Background
Influenza is transmitted through contact, the transmission path comprises and is not limited to physical infection, air transmission and body fluid transmission, the degree of harm depends on the nature of influenza, the infectivity and the harm are one of the important properties, and if one influenza has a huge infection base, the mutation of the influenza needs to be concerned, so that the occurrence of pathogenicity and lethality of the influenza are avoided to be greatly improved. Under various forms of globalization, highly infectious, lethal influenza is one of the factors threatening human safety, and there is a need to control the number of influenza infected people. Because of the monitorability and predictability of influenza, the trend of monitoring the spread of influenza and timely intervening in increasing numbers is of paramount importance in areas with smooth development. The traditional epidemiological monitoring method mainly performs statistics and quantitative analysis according to values, fails to consider globally, lacks dynamic real-time performance and is easy to misjudge. Therefore, a proper data analysis model needs to be established, so that the intervention can be timely performed in the early stage of influenza outbreak, and the influence caused by the influenza outbreak is reduced.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an influenza monitoring method based on a punishment withdrawal molan index, which comprises the following steps:
s100, collecting historical disease data in a preset time, and calculating the population influenza infection rate in unit time;
s200, calculating average population influenza infection rate in a fixed-point time segment according to the population influenza infection rate in the unit time, and generating punishment parameters through the average population influenza infection rate;
s300, carrying punishment parameters into a Morlan index model, generating a punishment Morlan index model for influenza monitoring, and calculating to obtain a punishment Morlan index so as to monitor the influenza condition of a prediction place;
the calculation formula of the punishment parameters in S200 is as follows:
wherein T is i Favg is the average influenza infection rate of the population, F i Influenza infection rate for the ith block population;
the formula of the punishment Morgan index model in S300 is as follows:
wherein T is i Is punishment and withdrawal parameter, n is total number of blocks, w ij As the space weight value, F i Influenza infection rate for the ith block population, F j Indicating the influenza infection rate of the jth block,。
further, the calculation formula of the population influenza infection rate of the probability per unit time in step S100 is:
where f is the number of influenza infection rates of the population on the same day and N is the total population on the same day.
Further, the fixed point time segment is a time segment formed by s days before and s days after the same period of the fixed point time of nearly y years and the fixed point time.
Further, the formula for calculating the average population influenza infection rate in S200 is:
wherein y is the number of years of nearly y years, and the value range of x is [ -s, s],For the influenza infection rate of the population on day x of k, x=0 when the same as the setpoint time.
The invention also provides an influenza monitoring device based on the punishment Morgan index, which comprises:
the acquisition module is used for acquiring historical disease data in a preset time and calculating the population influenza infection rate in unit time;
the processing module is used for calculating average population influenza infection rate in a fixed-point time segment according to the population influenza infection rate in the unit time, and generating punishment parameters through the average population influenza infection rate;
the analysis module is used for bringing punishment parameters into the Morlan index model, generating a punishment Morlan index model for influenza monitoring, and calculating to obtain punishment Morlan index so as to monitor the influenza condition of a prediction place;
the calculation formula of the punishment parameters in the processing module is as follows:
wherein T is i Favg is the average influenza infection rate of the population, F i Influenza infection rate for the ith block population;
the formula of the punishment Morgan index model in the analysis module is as follows:
wherein T is i Is punishment and withdrawal parameter, n is total number of blocks, w ij As the space weight value, F i Influenza infection rate for the ith block population, F j Indicating the influenza infection rate of the jth block,。
the present invention also provides a computer readable storage medium storing computer instructions that when executed by a processor implement an influenza monitoring method based on a punishment morlan index as described in any of the above embodiments.
The invention also provides an electronic device comprising at least one processor and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor to cause the processor to perform the method of influenza monitoring based on punishment Morand index as described in any of the embodiments above.
Based on the above, compared with the prior art, the influenza monitoring method, device, medium and equipment based on the punishment-abstinence-morgan index provided by the invention are based on current day actual influenza data and historical influenza data of each lower unit in the region, and punishment-abstinence parameters are introduced to calculate the current day influenza rate punishment-abstinence-morgan index, and whether the predicted influenza has a spreading risk or an outbreak trend is judged according to the calculated punishment-abstinence-morgan index so as to facilitate timely intervention in the early stage of influenza outbreak.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
For a clearer description of embodiments of the invention or of the solutions of the prior art, the drawings that are needed in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the invention, and that other drawings can be obtained from them without inventive effort for a person skilled in the art; the positional relationships described in the drawings in the following description are based on the orientation of the elements shown in the drawings unless otherwise specified.
FIG. 1 is a flow chart of an influenza monitoring method based on a punishment Mortiered index according to an embodiment of the present invention;
FIG. 2 is a functional image of punishment parameters provided by a second embodiment of the present invention;
FIG. 3 is a geographic image of a real-time ground provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an influenza monitor based on punishment morlan index according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention; the technical features designed in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that all terms used in the present invention (including technical terms and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs and are not to be construed as limiting the present invention; it will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Example 1
A monitoring model building method based on a punishment Morgan index comprises the following steps:
s100, collecting historical data in preset time, and calculating monitoring probability of unit time;
s200, calculating average monitoring probability in a fixed point time segment according to the monitoring probability of the unit time, and generating punishment parameters through the average monitoring probability;
s300, introducing punishment parameters into the Mortiered index model to generate the punishment Mortiered index model.
The calculation formula of the monitoring probability of the unit time in the step S100 is as follows;
where f is the monitoring history data of the day, and N is the population total of the day.
According to the embodiment, the existing Morgan index calculation mode is improved, the data of average monitoring probability larger than the average value are amplified to a certain extent by using punishment parameters, no additional processing is performed on the data lower than the average monitoring probability, the trend of data change is amplified in the calculation process due to the introduction of punishment parameters, in the process, early warning signals are advanced, the amplified signals enable the trend to have advancement, and compared with the original Morgan index calculation mode, the calculation mode provided by the embodiment is more prospective in the monitoring field.
Example two
In the second embodiment, the construction of the monitoring model is refined based on the first embodiment, specifically: the fixed point time segment is a time segment formed by s days before and s days after the same period of the fixed point time of nearly y years.
The formula for calculating the average monitoring probability in S200 is:
wherein y is the number of years of nearly y years, and the value range of x is [ -s, s],For the influenza infection rate of the population on day x of k, x=0 when the same as the setpoint time.
The calculation formula of the punishment parameters in S200 is as follows:
wherein T is i Favg is the average monitoring probability, fi is the attribute value of the ith block, and FIG. 2 below is Fi>Favg, and favg=0.01.
The formula of the punishment Morgan index model in S300 is as follows:
wherein T is i Is punishment and withdrawal parameter, n is total number of blocks, w ij As the space weight value, F i For the attribute value of the ith block, F j The attribute value representing the j-th block,。
finally, the value range of the index value I of the punishment Morganella is calculated to be (-1, 1).
When the punishment is performed, the closer the value I of the morgan index is to 0, the weaker the relation between the monitoring data and the space is.
When the punishment is stopped, the index value I >0 of the Morganella is positive, the monitoring data and the spatial distribution are positively correlated, and the more the calculated value I approaches to 1, the stronger the association relation between the monitoring data and the spatial distribution is.
On the contrary, when the punishment is stopped and the calculated value I approaches to-1, the monitoring data and the spatial distribution show a stronger negative correlation.
The embodiment determines the fixed point time according to the monitoring date, is suitable for all monitoring fields with high correlation with time in reality, performs statistical calculation according to data of about s days of the same day of y years before the fixed point time, effectively includes seasonal, humiture and population migration in calculation factors, improves accuracy of the correlation between the monitoring fields and space, and avoids influence on judgment caused by factors such as seasons, temperatures, population migration (like spring festival) and the like.
Example III
The influenza monitoring method based on the punishment Morganan index, as shown in figure 1, comprises the following steps:
s100, collecting historical disease data in a preset time, and calculating the population influenza infection rate in unit time;
s200, calculating average population influenza infection rate in a fixed-point time segment according to the population influenza infection rate in the unit time, and generating punishment parameters through the average population influenza infection rate;
s300, carrying the punishment parameters into a Morlan index model, generating a punishment Morlan index model for influenza monitoring, and calculating to obtain a punishment Morlan index so as to monitor the influenza condition of a prediction place.
And under the time point of not outbreak of influenza, constructing a daily influenza infection proportion according to the current day influenza infection rate data and population data total quantity N reported by a lower unit in the region, and calculating a global influenza rate punishment Morand index by combining geographic position information and historical influenza data.
According to the calculated value of the model of the punishment Morgan index, public health departments increase or decrease the attention to the local influenza condition according to the requirement. Under the early warning condition that influenza outbreaks possibly exist, the influenza conditions of a plurality of subordinate units with highest influenza rate and peripheral units thereof are focused, the influenza outbreaks are dealt with at any time, and manual intervention preparation is made.
Before calculation, the method is required to be divided into a plurality of blocks predictively, meanwhile, the space weight value between the blocks is judged according to the adjacency of the blocks, if the geographic positions of the two blocks are in contact, the space weight value is 1, and if the geographic positions of the two blocks are not in contact, the space weight value is 0.
The population infection rate is calculated according to the population infection number and population total number on the same day, the population infection rate of the whole prediction area can be calculated, and the calculation can be performed by each block, wherein the specific formula is as follows:
wherein F is the number of influenza infections of the population on the same day, N is the total number of the population on the same day, and F is the human influenza infection rate obtained by calculation.
Calculating the average population influenza infection rate based on the fixed point time periodThe fixed point time segment is a time segment formed by s days before and s days after the same period of the fixed point time of nearly y years and the fixed point time.
Then, the formula for calculating the average population influenza infection rate in S200 is:
wherein y is the number of years of nearly y years, and the value range of x is [ -s, s],For the influenza infection rate of the population on day x of the k, x=0 when the same as the setpoint time;
in a preferred embodiment, s is 3 and the average human average influenza infection rate Favg of the site to be tested is calculated from historical influenza data for 7 days including 3 days before and 3 days after the last y years:
it should be noted that, under the condition of calculation permission, the value of y is that as many years as possible can be acquired, the more accurate the acquired years are, but the value of y is [1,30] in consideration of calculation and data collection convenience, preferably, the value of y is 5 or 10. Meanwhile, the transmission of epidemic diseases is greatly influenced by seasons and weather, so that the value range of s is [1,15], preferably, the value of s is 3, and the acquired fixed-point time slice is just one week.
Selecting any block, and calculating punishment parameters of each block one by one, wherein a calculation formula of the punishment parameters in S200 is as follows:
wherein T is i F is punishment and withdrawal parameter avg To average influenza infection rate of human mouth, F i Influenza infection rate was demographics for the i-th block.
The formula of the punishment Morgan index model in S300 is as follows:
wherein T is i Is punishment and withdrawal parameter, n is total number of blocks, w ij The spatial weight value is 1 if the i block is adjacent to the j block, and is 0 if the i block is not adjacent to the j block; f (F) i For the attribute value of the ith block, F j The attribute value representing the j-th block,。
finally, the value range of the index value I of the punishment Morganella is calculated to be (-1, 1).
When the index value I of the disciplinary Morganella is closer to 0, the disease occurrence relationship and the spatial relationship are weaker, and the disciplinary is random.
When the index value I >0 of the disciplinary model is over, the occurrence relationship of the disease and the spatial distribution are positively correlated, and the more the calculated value I approaches to 1, the stronger the association relationship between the monitoring data and the spatial distribution is, namely the occurrence relationship of the disease and the spatial relationship are stronger, and the aggregation phenomenon exists.
On the contrary, when the punishment and abstinence molesta index value I is less than 0 and approaches to-1, the occurrence relationship of the disease and the spatial distribution are more negative, namely the occurrence relationship of the disease and the spatial relationship are more strong, and the dispersion phenomenon exists.
Based on the spatial spread characteristics of influenza, spatial distribution must exhibit a positive correlation with the spread of influenza. The early warning signal of the disciplinary Morgan index in the invention moves forward, and the calculated value can deviate to be near 0 value when the early warning signal is not generated.
In one embodiment, as exemplified by S city, S city has 6 lower unit a-F regions, n=6. As shown in fig. 3, when the spatial weight value of the geographic position contact is set to 1 and the spatial weight value of the non-geographic position contact is set to 0, the spatial weight value w between the regions ij The following table 1 is formed:
TABLE 1
Assume that the average influenza infection rate in the region is about five years0.00716664666666667, and the daily population infection rate of each block is shown in table 2 below:
TABLE 2
From which it can be calculated from the history
=4.833333/>10 -6
The punishment parameters for each zone are shown in Table 3 below:
TABLE 3 Table 3
Spatial weight value after merging punishment parametersAs shown in table 4 below:
TABLE 4 Table 4
The final result of the punishment Morgan index model is:
= 16.1713927
= 3.0913/>10 -06
0.237133464
due to=0.237133464>0 shows that the influenza transmission and the spatial distribution are positively correlated, and the numerical value reaches approximately 0.25, so that a transmission trend with a certain scale is formed. The region should decide whether to formulate and issue influenza intervention measures according to the current policy requirements, economic development, personnel flow and other conditions.
The first and second established monitoring models are applied to the field of epidemic disease monitoring, season and weather factors of epidemic disease onset and infection are considered, whether outbreak and spread influenza exists locally can be monitored to a certain extent, so that prevention and control personnel can know the influenza spreading state in time, and relevant measures for reducing or even blocking the spreading trend of the influenza are issued.
Based on current day actual influenza data of each lower level unit in the region, the method compares the current day influenza rate punishment and withdrawal morbid index with the punishment and withdrawal morbid index of the lower level unit, and verifies whether the current day actual influenza quantity has an outbreak trend or not so as to support public health departments to make decisions and issue intervention measures. And the lower level units of the region calculate the local current population influenza infection rate through the local current population and upload the data to the region. And (3) calculating the global influenza rate punishment index of the current day by combining the current day influenza rate reported by the lower level units with the historical average population influenza infection rate and the geographic position information. And if the calculated value meets a certain condition, the public health department is required to send an early warning notice to the lower level departments, and a plurality of lower level units with the largest influenza rate and peripheral units thereof are subjected to influenza monitoring and are ready for intervention operation at any time, so that the influence caused by influenza infection is reduced.
Example IV
Influenza monitoring device based on punishment Morgan index, as shown in FIG. 4, includes:
the acquisition module is used for acquiring historical disease data in a preset time and calculating the population influenza infection rate in unit time;
the processing module is used for calculating average population influenza infection rate in a fixed-point time segment according to the population influenza infection rate in the unit time, and generating punishment parameters through the average population influenza infection rate;
the analysis module is used for bringing punishment parameters into the Morlan index model, generating a punishment Morlan index model for influenza monitoring, and calculating to obtain punishment Morlan index so as to monitor the influenza condition of a prediction place;
the calculation formula of the punishment parameters in the processing module is as follows:
wherein T is i Favg is the average influenza infection rate of the population, F i Influenza infection rate for the ith block population;
the formula of the punishment Morgan index model in the analysis module is as follows:
wherein T is i Is punishment and withdrawal parameter, n is total number of blocks, w ij As the space weight value, F i Influenza infection rate for the ith block population, F j Indicating the influenza infection rate of the jth block,。
example five
A computer readable storage medium storing computer instructions that when executed by a processor implement an influenza monitoring method based on a punishment molan index as described in any of the embodiments above.
In specific implementation, the computer readable storage medium is a magnetic Disk, an optical Disk, a Read-only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD) or a Solid State Drive (SSD); the computer readable storage medium may also include a combination of the above types of memory.
Example six
An electronic device, as shown in fig. 5, comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor to cause the processor to perform the punishment Morgan index-based influenza monitoring method as described in any of the embodiments above.
In particular, the number of processors may be one or more, and the processors may be central processing units (Central Processing Unit, CPU). The processor may also be any other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be communicatively coupled to the processors via a bus or other means, the memory storing instructions executable by the at least one processor to cause the processor to perform a punishment Morgan index based influenza monitoring method as described in the method embodiments above.
In addition, it should be understood by those skilled in the art that although many problems exist in the prior art, each embodiment or technical solution of the present invention may be modified in only one or several respects, without having to solve all technical problems listed in the prior art or the background art at the same time. Those skilled in the art will understand that nothing in one claim should be taken as a limitation on that claim.
Although terms such as historical data, monitoring probabilities, average monitoring probabilities, punishment parameters, a molan index model, a punishment molan index model, etc. are more used herein, the possibility of using other terms is not excluded. These terms are used merely for convenience in describing and explaining the nature of the invention; they are to be interpreted as any additional limitation that is not inconsistent with the spirit of the present invention; the terms first, second, and the like in the description and in the claims of embodiments of the invention and in the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (5)
1. The influenza monitoring method based on the punishment Morganan index is characterized by comprising the following steps of:
s100, collecting historical disease data in a preset time, and calculating the population influenza infection rate in unit time;
s200, calculating average population influenza infection rate in a fixed-point time segment according to the population influenza infection rate in the unit time, and generating punishment parameters through the average population influenza infection rate;
s300, carrying punishment parameters into a Morlan index model, generating a punishment Morlan index model for influenza monitoring, and calculating to obtain a punishment Morlan index so as to monitor the influenza condition of a prediction place;
the calculation formula of the punishment parameters in S200 is as follows:
wherein T is i Favg is the average influenza infection rate of the population, F i Influenza infection rate for the ith block population;
the formula of the punishment Morgan index model in S300 is as follows:
wherein I is the index value of punishment and abstinence, T i Is punishment and withdrawal parameter, n is total number of blocks, w ij As the space weight value, F i Influenza infection rate for the ith block population, F j Indicating the influenza infection rate of the jth block,;
the fixed point time segment is a time segment formed by s days before and s days after the same period of the fixed point time of nearly y years;
the formula for calculating average population influenza infection rate in S200 is:
wherein y is the number of years of nearly y years, and the value range of x is [ -s, s],For the influenza infection rate of the population on day x of k, x=0 when the same as the setpoint time.
2. The method for influenza monitoring based on the punishment Morganan index according to claim 1, wherein the calculation formula of the influenza infection rate of the population per unit time in the step S100 is as follows:
where f is the number of influenza infections in the population on the same day and N is the total number of population on the same day.
3. Influenza monitoring device based on punishment Morgan index, its characterized in that includes:
the acquisition module is used for acquiring historical disease data in a preset time and calculating the population influenza infection rate in unit time;
the processing module is used for calculating average population influenza infection rate in a fixed-point time segment according to the population influenza infection rate in the unit time, and generating punishment parameters through the average population influenza infection rate;
the analysis module is used for bringing punishment parameters into the Morlan index model, generating a punishment Morlan index model for influenza monitoring, and calculating to obtain punishment Morlan index so as to monitor the influenza condition of a prediction place;
the calculation formula of the punishment parameters in the processing module is as follows:
wherein T is i Favg is the average influenza infection rate of the population, F i Influenza infection rate for the ith block population;
the formula of the punishment Morgan index model in the analysis module is as follows:
wherein I is the index value of punishment and abstinence, T i Is punishment and withdrawal parameter, n is total number of blocks, w ij As the space weight value, F i Influenza infection rate for the ith block population, F j Indicating the influenza infection rate of the jth block,;
the fixed point time segment is a time segment formed by s days before and s days after the same period of the fixed point time of nearly y years;
the formula for calculating average population influenza infection rate in S200 is:
wherein y is the number of years of nearly y years, and the value range of x is [ -s, s],For the influenza infection rate of the population on day x of k, x=0 when the same as the setpoint time.
4. A computer-readable storage medium, characterized by: the computer readable storage medium stores computer instructions that when executed by a processor implement the method of influenza monitoring based on punishment molan index as claimed in any one of claims 1-2.
5. An electronic device, characterized in that: comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor to cause the processor to perform the punishment molan index based influenza monitoring method as claimed in any one of claims 1-2.
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| CN108701114A (en) * | 2015-10-19 | 2018-10-23 | 医疗咨询有限公司 | Use the system and method for medical care precess system evaluation and selection health care professionals |
| KR102357236B1 (en) * | 2021-06-25 | 2022-02-08 | 주식회사 빅스터 | Availability index prediction system according to the floating population by region and the confirmed population of infectious diseases |
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