CN115036040A - Epidemic situation space-time early warning method fusing population of fever and population background data - Google Patents
Epidemic situation space-time early warning method fusing population of fever and population background data Download PDFInfo
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Abstract
The invention provides an epidemic situation space-time early warning method fusing the number of febrile people and population background data, which comprises the following steps: step S1, collecting and preprocessing fever symptom data; step S2, extracting fever symptom time and space information; step S3, constructing a fever case density index and a space-time scanning window; step S4, prospective space-time rearrangement scanning and significance calculation; and step S5, recognizing the early warning result. The method utilizes early typical symptom data of the epidemic, considers the relation between time and space, and brings population factors into consideration, thereby effectively improving the timeliness and accuracy of epidemic early warning.
Description
Technical Field
The invention relates to the technical field of spatiotemporal information, in particular to an epidemic situation spatiotemporal early warning method fusing the number of febrile people and population background data.
Background
Epidemic diseases are important factors threatening the physical health of human beings, and the development of the global economic society is seriously hindered. Early warning is carried out on the development of epidemic events, and scientific and timely guidance can be provided, so that the loss of lives and property is reduced. Therefore, it is of great significance to research how to early warn the outbreak of epidemic disease.
Epidemic early warning means that on the premise of lacking of a determined reaction relation, epidemic data are collected, sorted and analyzed, disease condition information is comprehensively researched and judged, action plans are adjusted after full demonstration, an alarm is given before or in the early stage of the next outbreak of epidemic, relevant departments are prompted to react in time, and therefore harm to high-risk area crowds is reduced to the maximum extent [1] . The method is mainly realized by utilizing time and space characteristics to construct rules based on confirmed case data, and the related methods are generally divided into two types: a time-based aggregative approach and a spatiotemporal aggregative approach. The time aggregation method is based on the principle that early warning analysis is carried out on the number of patients, the illness time and the total population according to a distribution model and on the basis of the history of a circular or elliptical scanning window, and the representative method is a forward-looking time scanning method based on Poisson distribution. The principle based on the space-time aggregation method is that the number of patients, the sick time and the geographic position are determined according to the history in a cylindrical space-time scanning window, the influence of population number factors in a research area is not considered, early warning analysis is carried out according to a certain distribution model, and the representative method is a prospective space-time rearrangement scanning method [2] . The latter method has higher control sensitivity and is widely used in early warning research of various epidemics.
Reference documents:
[1] yan Xue, Zhang Xiaorui, Zhuminghao. application of prospective spatiotemporal rearrangement scanning statistics in New crown pneumonia epidemic situation early warning [ J ]. Beijing university of construction, proceedings 2021,37(04):69-74.
[2]KulldorffM,Heffernan R,Hartman J,et al.A space-time permutation scan statistic for disease outbreak detection[J].PLoS,2005,2(3):e59.
The disadvantages of the prior art are mainly reflected in the aspects of method and data. (1) The early warning method based on diagnosed cases has obvious lag, the existing epidemic early warning research is usually based on the data of the diagnosed cases of the epidemic, and the early warning research of the epidemic is carried out by considering less data based on the early typical symptoms of the epidemic. Considering that a case takes a certain time from the occurrence of early symptoms to the confirmation of diagnosis, the early warning can generate obvious lag effect. (2) The traditional algorithm based on spatiotemporal rearrangement is mainly constructed based on confirmed cases, and because the theoretical hypothesis can not consider population background data, false early warning and missed early warning are often generated when the method is directly used for analyzing early symptoms of epidemic diseases such as fever. For example, in a region with a large population, the number of people who generate heat is correspondingly large, and the early warning signal detected based on the existing method is possibly a false early warning signal; similarly, although the number of people generating heat is small in some areas, the proportion of heat generation is high, and an early warning signal should be generated.
Disclosure of Invention
In view of the above, the present invention provides a space-time epidemic situation early warning method combining the number of febrile people and population background data, which utilizes the early typical symptom data of the epidemic, considers the relationship between time and space, and incorporates population factors, thereby effectively improving the timeliness and accuracy of the early warning of the epidemic.
In order to achieve the purpose, the invention adopts the following technical scheme: the epidemic situation space-time early warning method fusing the number of fever people and population background data comprises the following steps:
step S1, collecting and preprocessing fever symptom data;
step S2, extracting fever symptom time and space information;
step S3, constructing a fever case density index and a space-time scanning window;
step S4, prospective space-time rearrangement scanning and significance calculation;
and step S5, recognizing the early warning result.
In a preferred embodiment, the step S1 includes the following steps:
step S11, removing abnormal values; removing data with key numerical values such as addresses, time and the like being null and time periods being out of a research time range;
step S12, converting the address description information in the fever symptom data into space coordinate position data, typically expressed by longitude and latitude, and coding the geographic analysis unit;
and step S13, eliminating data outside the range of the research area.
In a preferred embodiment, the step S2 includes the following steps:
step S21, extracting fever symptom time information; extracting the number of fever symptom cases of each geographical analysis unit in each time analysis unit in the research area and the research period;
step S12, extracting fever symptom space information; extracting longitude and latitude coordinates of each geographic analysis unit, wherein the longitude and latitude coordinates are the circle centers of the bottom surfaces of the scanning windows during scanning; the geographical analysis unit and the time analysis unit are determined according to actual conditions, the common geographical analysis unit comprises administrative regions such as counties and districts, towns and towns, and the common time analysis unit comprises hours, days and months.
In a preferred embodiment, the step S3 includes the following steps:
step S31, constructing calculation indexes of the fusion population number of the research area, including the total fever case density of the research area, the actual fever case density in the cylindrical window, the expected fever case density and the GLR value of each scanning window;
step S32, constructing a space-time cylinder scanning window; the heating cases are scanned in a traversing way in a space-time range through a series of space-time cylinder scanning windows with different positions and sizes, wherein the bottom surface of the cylinder corresponds to the space range, and the height of the cylinder represents the time range.
In a preferred embodiment, a three-dimensional space coordinate system of X, Y and Z is established, a plane area enclosed by the X and the Y is a research area, and Z is a time axis; constructing a cylinder space-time scanning window, wherein the height of a cylinder represents a research time interval, namely the difference between scanning ending time and scanning starting time, and the scanning ending time of each cylinder is consistent for prospective space-time rearrangement scanning;
setting a solid circle point to represent an occurred event, marking a point mapped by the position coordinates (x, y) of the space point in the research area by a value k, and setting v observation points in the research area in total, wherein k belongs to { k ∈ { k 1 ，k 2 ，...，k v }，v≥ 1，v∈Z，k 1 The value, k, representing the 1 st observation point 2 Value, k, representing the 2 nd observation point v A value representing the v-th observation point; then a space-time matrix N is obtained in a space-time domain where i belongs to {1,2,. F,. F }, F is more than or equal to 1 and less than or equal to F, F belongs to R, j belongs to {1,2,. T,. T }, T is more than or equal to 1 and less than or equal to B, and T belongs to R; wherein, the set {1,2,. C) represents all spatial analysis units with a certain observation point as a starting scanning point, and F concentric circles are total; the set {1,2,. T } represents all corresponding time analysis units within the research time threshold, and T represents the research time upper limit; then a certain observation point k m The space-time matrix N is expressed by formula 1, m is more than or equal to 1 and less than or equal to v, and m belongs to Z:
wherein n is 11 ，n 12 ，...，n FT Representing all cylinder scanning windows in the scanning process with the observation point as a starting point; let C be sd The total number E of fever cases in the time range of d days in the area s covered by a certain scanning window is expressed by formula 2:
is provided with C s Number of cases in d days for a certain observation point over the whole study area s, C d The number of cases in the whole study time range in the study area s for a certain observation point; expressed by equations (3) and (4), respectively:
obtaining the expected value of the disease cases of each observation point per day according to the observation value, and calculating by using a formula (5):
constructing a fever case density index Q, and setting a total research area M to be composed of G sub-areas, namely M ═ M g G is more than or equal to 1 and less than or equal to G, and G belongs to R, for example, if M is provincial level, G can be city, county or district; let the total population number of the study area M be P, the population number of each subarea is P 1 、P 2 、...、P G And satisfy
Then the fever case density for region g is:
wherein E is g Number of observed cases for subregion g; then the expected fever case density within window a, a is scanned for any cylinder during the scan:
let C A The actual fever case density in the cylindrical window A, C A Obey mean of mu A In a super-geometric distribution of s Q sd Sum Σ d Q sd Relative to Σ s ∑ d Q sd Very small, C A Approximate obedient mean value of mu A Poisson distribution of (a); based on the method, whether the cylinder window A is gathered or not is judged by adopting a Poisson generalized likelihood function, and the expression of GLR is as follows:
in a preferred embodiment, the step S4 includes the following steps:
step S41, performing prospective space-time rearrangement scanning on the whole research area and the research time period by using a space-time cylinder window, wherein the circle center of the cylinder is specified in the second step, and the cylinder scanning window is continuously expanded according to the set time step length and the set scanning radius step length until all the cylinder windows cover the whole research area and the whole time period; performing index calculation on all the cylindrical windows, wherein the index calculation comprises the total fever case density in the research area, the actual fever case density in the cylindrical windows, the expected fever case density and the GLR value of each scanning window;
and step S42, generating a simulation data set by using a Monte Carlo method according to the fever cases according to a Poisson distribution rule, calculating a corresponding GLR value, and verifying whether the cylinder window has statistical significance by using the simulation GLR of the simulation data set.
In a preferred embodiment, a prospective spatiotemporal rescheduling scan is performed, the key parameters of the scan are set, a GLR value is calculated for each cylinder window formed during the scan, the GLR value reflects the probability of cylinder window clustering, and when the GLR value is large, the clustering is significant;
performing confidence analysis on the scanning window, wherein the invalid assumption of the scanning window is as follows: the probability of the occurrence of the heating event in time and space is completely random, and the case distribution inside and outside a scanning window is not different; because the probability distribution of the scanning statistic of the window is difficult to obtain, a P value is calculated by using a Monte Carlo hypothesis test method, and random detection is carried out on a gathering area which is possibly abnormal;
the specific simulation process is as follows: firstly, calculating a Poisson function expected value according to a research time range and time analysis unit, a geographical analysis unit and an accumulated fever case in original data, and determining a specific form of the Poisson function; secondly, according to the determined Poisson function, on the premise that the total number of fever cases is not changed, the number of fever cases in each time analysis unit of each geographic analysis unit is randomly calculated; then, independently executing the previous step for multiple times to form a corresponding random scheme; finally, calculating GLR values in corresponding space-time scanning windows based on a randomly generated scheme, and using the GLR values as a reference for checking whether the current distribution is abnormal;
the more the simulation data sets are, the higher the significance detection reliability is; generally, the number of simulation data sets in scanning is not less than 999, namely 999 simulated GLRs are obtained, the 999 simulated GLRs and the actual GLRs are sorted from large to small, and the window P value with the highest aggregation degree corresponding to the GLR is calculated according to the ranking order of the GLRs of the real data sets, for example, the real GLR is sorted to the 50 th position, the P value is 50/(999+1) 0.05, and the confidence coefficient is 95%; if the order is 10 th bit, the confidence of the P value is 10/(999+1) ═ 0.01 is 99%, and so on.
In a preferred embodiment, the step S5 specifically includes: identifying Monte Carlo simulation results, and screening out early warning results with statistical significance, including early warning ranges and early warning time periods; for the space-time early warning events with significant results, if a space-time inclusion relationship exists, namely the time range and the space range of a certain space-time early warning event are completely included by another space-time early warning time, the included events are merged into the inclusion early warning events.
Compared with the prior art, the invention has the following beneficial effects:
(1) the early warning method constructed based on the early typical symptoms of the epidemic diseases such as fever is higher in timeliness.
(2) The space-time rearrangement algorithm for fusing population background data, which is provided for the characteristics of the population that generates heat, can effectively avoid false early warning caused by the population background data and improve the accuracy of the early warning.
(3) The early warning research of the epidemic disease is carried out based on the data of the early typical symptoms of the epidemic disease, and the early warning research can be carried out by using the early typical symptom data of the epidemic disease under the condition that the confirmed case data of the epidemic disease cannot be obtained or is lost in time.
Drawings
FIG. 1 is a flow chart of an epidemic situation spatiotemporal early warning method fusing population of fever and population background data according to a preferred embodiment of the present invention;
FIG. 2 is a schematic view of a spatio-temporal scanning window in accordance with a preferred embodiment of the present invention;
fig. 3 is a diagram of the duration of the heat surge in 3 months in each city according to the preferred embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application; as used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
An epidemic situation space-time early warning method fusing the number of febrile people and population background data, referring to fig. 1 to 3, comprises the following steps:
step S1, collecting and preprocessing fever symptom data;
step S2, extracting fever symptom time and space information;
step S3, constructing a fever case density index and a space-time scanning window;
step S4, prospective space-time rearrangement scanning and significance calculation;
and step S5, recognizing the early warning result.
The step S1 includes the steps of:
step S11, removing abnormal values; removing data with key numerical values such as addresses, time and the like being null and time periods being out of a research time range;
step S12, converting the address description information in the fever symptom data into space coordinate position data, typically expressed by longitude and latitude, and coding the geographic analysis unit;
and step S13, removing data outside the range of the research area.
The step S2 includes the steps of:
step S21, extracting fever symptom time information; extracting the number of fever symptom cases of each geographical analysis unit in each time analysis unit in the research area and the research period;
step S12, extracting fever symptom space information; extracting longitude and latitude coordinates of each geographic analysis unit, wherein the longitude and latitude coordinates are the circle centers of the bottom surfaces of the scanning windows during scanning; the geographical analysis unit and the time analysis unit are determined according to actual conditions, the frequently-used geographical analysis unit comprises administrative regions such as counties and districts, towns and the like, and the frequently-used time analysis unit comprises hours, days and months.
The step S3 includes the steps of:
step S31, constructing calculation indexes of the fusion population number of the research area, including the total fever case density of the research area, the actual fever case density in the cylindrical window, the expected fever case density and the GLR value of each scanning window;
step S32, constructing a space-time cylinder scanning window; the heating cases are scanned in a traversing way in a space-time range through a series of space-time cylinder scanning windows with different positions and sizes, wherein the bottom surface of the cylinder corresponds to the space range, and the height of the cylinder represents the time range.
Establishing a three-dimensional space coordinate system of X, Y and Z, wherein a plane area enclosed by the X and the Y is a research area, and Z is a time axis; constructing a cylinder space-time scanning window, wherein the height of a cylinder represents a research time interval, namely the difference between scanning ending time and scanning starting time, and the scanning ending time of each cylinder is consistent for prospective space-time rearrangement scanning;
setting a solid circle point to represent an occurred event, marking a point mapped by a space point position coordinate (x, y) in a research area by a numerical value k, and setting a total of v observation points in the research area, wherein k belongs to { k ∈ [ ({ k }) 1 ，k 2 ，...，k v }，v≥ 1，v∈Z，k 1 The value, k, representing the 1 st observation point 2 The value, k, representing the 2 nd observation point v A value representing the v-th observation point; then a space-time matrix N is obtained in a space-time domain where i belongs to {1,2,. F,. F }, F is more than or equal to 1 and less than or equal to F, F belongs to R, j belongs to {1,2,. T,. T }, T is more than or equal to 1 and less than or equal to B, and T belongs to R; wherein, the set {1,2,. C) represents all spatial analysis units with a certain observation point as a starting scanning point, and F concentric circles are total; the set {1,2,. T } represents all corresponding time analysis units within the research time threshold, and T represents the research time upper limit; then a certain observation point k m The space-time matrix N is expressed by a formula 1, m is more than or equal to 1 and less than or equal to v, and m belongs to Z:
wherein n is 11 ，n 12 ，...，n FT Representing all cylinder scanning windows in the scanning process with the observation point as a starting point; let C be sd The total number E of fever cases in the time range of d days in the area s covered by a certain scanning window is expressed by formula 2:
is provided with C s Number of cases in d days for a certain observation point over the whole study area s, C d The number of cases in the whole study time range in the study area s for a certain observation point;expressed by equations (3) and (4), respectively:
and obtaining the expected value of the disease cases of each observation point per day according to the observation values, and calculating by using a formula (5):
constructing a fever case density index Q, and setting a total research area M to be composed of G sub-areas, namely M ═ M g G is less than or equal to 1 and less than or equal to G, and G belongs to R }, for example, if M is provincial level, G can be city, county or district; let the total population number of the study area M be P, the population number of each subarea is P 1 、P 2 、...、P G And satisfy
Then the fever case density for region g is:
wherein E is g Number of observed cases for subregion g; then the expected fever case density within window a, a is scanned for any cylinder during the scan:
let C A The actual fever case density in the cylindrical window A, C A Obey mean of mu A In a super-geometric distribution of s Q sd Sum Σ d Q sd Relative to Σ s ∑ d Q sd Very small, C A Approximate obedience mean value of mu A Poisson distribution of (a); based on the method, whether the cylinder window A is gathered or not is judged by adopting a Poisson generalized likelihood function, and the expression of GLR is as follows:
the step S4 includes the steps of:
step S41, performing prospective space-time rearrangement scanning on the whole research area and the research time period by using a space-time cylinder window, wherein the circle center of the cylinder is specified in the second step, and the cylinder scanning window is continuously expanded according to the set time step length and the set scanning radius step length until all the cylinder windows cover the whole research area and the whole time period; performing index calculation on all the cylindrical windows, wherein the index calculation comprises the total fever case density in the research area, the actual fever case density in the cylindrical windows, the expected fever case density and the GLR value of each scanning window;
and step S42, generating a simulation data set by using a Monte Carlo method according to the fever cases according to a Poisson distribution rule, calculating a corresponding GLR value, and verifying whether the cylinder window has statistical significance by using the simulation GLR of the simulation data set.
Performing prospective space-time rearrangement scanning, setting key parameters of the scanning, calculating a GLR value in each cylinder window formed in the scanning process, wherein the GLR value reflects the possibility of aggregation of the cylinder windows, and when the GLR value is large, the aggregation is obvious;
performing confidence analysis on the scanning window, wherein the invalid assumption of the scanning window is as follows: the probability of the occurrence of the fever event in time and space is completely random, and the distribution of the cases inside and outside the scanning window is not different; because the probability distribution of the scanning statistic of the window is difficult to obtain, a P value is calculated by using a Monte Carlo hypothesis test method, and random detection is carried out on a gathering area which is possibly abnormal;
the specific simulation process is as follows: firstly, calculating a Poisson function expectation value according to a research time range and time analysis unit, a geographical analysis unit and accumulated fever cases in original data, and determining a specific form of a Poisson function; secondly, according to the determined Poisson function, on the premise that the total number of fever cases is not changed, the number of fever cases in each time analysis unit of each geographic analysis unit is randomly calculated; then, independently executing the previous step for multiple times to form a corresponding random scheme; finally, calculating GLR values in corresponding space-time scanning windows based on a randomly generated scheme, and using the GLR values as a reference for checking whether the current distribution is abnormal;
the more the simulation data sets are, the higher the significance detection reliability is; generally, the number of simulation data sets in scanning is not less than 999, namely 999 simulated GLRs are obtained, the 999 simulated GLRs and the actual GLRs are sorted from large to small, and the window P value with the highest aggregation degree corresponding to the GLR is calculated according to the ranking order of the GLRs of the real data sets, for example, the real GLR is sorted to the 50 th position, the P value is 50/(999+1) 0.05, and the confidence coefficient is 95%; if the sequence is 10 th bit, the confidence of 0.01 to 10/(999+1) is 99%, and so on.
The step S5 specifically includes: identifying Monte Carlo simulation results, and screening out early warning results with statistical significance, including early warning ranges and early warning time periods; for the space-time early warning events with significant results, if a space-time inclusion relationship exists, namely the time range and the space range of a certain space-time early warning event are completely included by another space-time early warning time, the included events are merged into the inclusion early warning events.
The invention sets two simulation data sets to simulate and verify the feasibility of the method:
simulation dataset 1: as shown in fig. 3, the target time period is 3 months and 1 day in 2021 years and 3 months and 30 days in 2021 years, the research area comprises 4 prefectural cities (A, B, C, D cities), 5000 fever cases are taken in 3 months and 5 days in A city and 3 months and 15 days in A city, and the population number is 500 million persons; 4000 fever cases in the B city every 3 months, 10 days to 3 months, 20 days, and the population is 50 ten thousand; 3000 fever cases per day in 3 months and 15 days to 3 months and 25 days in C city, the population is 40 ten thousand, 500 fever cases per day in 3 months and 20 days to 3 months and 30 days in D city, the population is 5 ten thousand. The number of fever cases on the remaining days was 0.
Simulation data set 2: the target time period was between 23 and 7 days 2/2021 and 3/2021, and the study area was city E. Let E market suddenly increase fever cases in 1 day of 3 months, and confirmed cases appear in 7 days of 3 months. 500 fever cases per day in 23 days-2 months and 28 days in E city, 3000 fever cases per day in 1 day-3 months and 6 days in 3 months, 300 confirmed cases are added every day from 7 days in 3 months to 12 days in 3 months, and the population number in E city is 30 ten thousand.
As can be seen from the simulation data set 1, although the fever cases in the A city are more, the population base number is larger, so the fever rate is not high, and no early warning should be generated; in the case of city D, although the number of fever cases is small, the fever rate is abnormally high, and therefore, a corresponding warning should be generated. And the number of fever cases and the fever ratio of the B city and the C city are integrally higher, and corresponding early warning should be generated. As can be seen from the simulation data set 2, the number of confirmed cases varied between late and febrile cases. Next, the present invention separately tests the above-mentioned simulation data sets using the classical method and the method proposed in the present invention, respectively. See attached tables 1 and 2 for results.
When the classical spatiotemporal rearrangement detection method is adopted (without considering population information), the indexes depended on are fever cases, so early warnings are respectively generated in A, B, C in corresponding time periods, however, the region covered by the early warning result does not contain D (attached table 1) because the number of the fever cases per day in the D market is less than that in other three regions. Therefore, the result of the traditional method is that the area A has 'false early warning' and the area D has 'leakage early warning'.
When the method is adopted, after the population number is taken into consideration, because the population base number of the D city is less, the density of fever cases is higher, and early warning can be generated in the corresponding time period of the D area; on the other hand, in the city a, although the number of fever patients was large, the number of population was large, and no warning was generated in the analysis from the viewpoint of the rate of fever (see table 1). Therefore, the prediction result of the method is more scientific and reasonable.
Prospective spatiotemporal rearrangement scanning and significance calculation are carried out on the simulation data set 2 under the condition of considering the population number, and an aggregative early warning result is obtained (attached table 2): the early warning time generated by the method provided by the invention is obviously superior to the early warning event based on the classical time-space rearrangement detection method.
Attached table 1 simulation dataset 1 early warning results
Attached table 2 simulation dataset 2 early warning results
The results are integrated to show that the method can effectively avoid false early warning caused by population background data, can utilize the early typical symptom data of the epidemic disease to research, realizes early warning, and improves the accuracy and timeliness of early warning.
Claims (8)
1. The epidemic situation space-time early warning method fusing the number of the febrile people and the population background data is characterized by comprising the following steps of:
step S1, collecting and preprocessing fever symptom data;
step S2, extracting fever symptom time and space information;
step S3, constructing a fever case density index and a space-time scanning window;
step S4, prospective space-time rearrangement scanning and significance calculation;
and step S5, recognizing the early warning result.
2. The epidemic situation space-time early warning method combining the number of febrile people and the population background data according to claim 1, wherein the step S1 includes the following steps:
step S11, removing abnormal values; removing data with key numerical values such as addresses, time and the like being null and time periods being out of a research time range;
step S12, converting the address description information in the fever symptom data into space coordinate position data, typically expressed by longitude and latitude, and encoding the geographic analysis unit;
and step S13, removing data outside the range of the research area.
3. The epidemic situation space-time early warning method combining the number of febrile people and the population background data according to claim 1, wherein the step S2 includes the following steps:
step S21, extracting fever symptom time information; extracting the number of fever symptom cases of each geographical analysis unit in each time analysis unit in the research area and the research period;
step S12, extracting fever symptom space information; extracting longitude and latitude coordinates of each geographic analysis unit, wherein the longitude and latitude coordinates are the circle centers of the bottom surfaces of the scanning windows during scanning; the geographical analysis unit and the time analysis unit are determined according to actual conditions, the frequently-used geographical analysis unit comprises administrative regions such as counties and districts, towns and the like, and the frequently-used time analysis unit comprises hours, days and months.
4. The epidemic situation space-time early warning method combining the number of febrile people and the population background data according to claim 1, wherein the step S3 includes the following steps:
step S31, constructing calculation indexes of the fusion population number of the research area, including the total fever case density of the research area, the actual fever case density in the cylindrical window, the expected fever case density and the GLR value of each scanning window;
step S32, constructing a space-time cylinder scanning window; the heating cases are scanned in a traversing way in a space-time range through a series of space-time cylinder scanning windows with different positions and sizes, wherein the bottom surface of the cylinder corresponds to the space range, and the height of the cylinder represents the time range.
5. The epidemic situation space-time early warning method fusing the number of the fever people and the population background data according to claim 4, characterized in that a three-dimensional space coordinate system of X, Y and Z is established, a plane area enclosed by X and Y is a research area, and Z is a time axis; constructing a cylinder space-time scanning window, wherein the height of a cylinder represents a research time interval, namely the difference between scanning ending time and scanning starting time, and the scanning ending time of each cylinder is consistent for prospective space-time rearrangement scanning;
setting a solid circle point to represent an occurred event, marking a point mapped by the position coordinates (x, y) of the space point in the research area by a value k, and setting v observation points in the research area in total, wherein k belongs to { k ∈ { k 1 ,k 2 ,…,k v },v≥1,v∈Z，k 1 The value, k, representing the 1 st observation point 2 The value, k, representing the 2 nd observation point v A value representing the v-th observation point; then a space-time matrix N is obtained in a space-time domain where i belongs to {1,2,. F,. F }, F is more than or equal to 1 and less than or equal to F, F belongs to R, j belongs to {1,2,. T,. T }, T is more than or equal to 1 and less than or equal to B, and T belongs to R; wherein, the set {1,2,. C } represents all spatial analysis units taking a certain observation point as a starting scanning point, and F concentric circles are total; the set 1,2,. T represents all corresponding time analysis units within the research time threshold, and T represents the upper limit of the research time; then a certain observation point k m The space-time matrix N is expressed by a formula 1, m is more than or equal to 1 and less than or equal to v, and m belongs to Z:
wherein n is 11 ,n 12 ,…,n FT Representing all cylinder scanning windows in the scanning process by taking the observation point as a starting point; suppose C sd The total number E of fever cases in the time range of d days in the area s covered by a certain scanning window is expressed by formula 2:
let C s Number of cases in d days for a certain observation point over the whole study area s, C d The number of cases in the whole study time range in the study area s for a certain observation point; expressed by equations (3) and (4), respectively:
and obtaining the expected value of the disease cases of each observation point per day according to the observation values, and calculating by using a formula (5):
constructing a fever case density index Q, and setting a total research area M to be composed of G sub-areas, namely M is M g G is more than or equal to 1 and less than or equal to G, and G belongs to R, for example, if M is provincial level, G is city, county or district; let the total population number of the study area M be P, the population number of each subarea is P 1 、P 2 、…、P G And satisfy
Then the fever case density for region g is:
wherein E is g Number of observed cases for subregion g; then the expected fever case density within window a, a is scanned for any cylinder during the scan:
let C A The actual fever case density in the cylindrical window A, C A Obey mean is mu A In a super-geometrical distribution of s Q sd Sum Σ d Q sd Relative to Σ s ∑ d Q sd Very small, C A Approximate obedient mean value of mu A Poisson distribution of (a); based on this, whether the cylinder window A is gathered or not is judged by adopting a Poisson generalized likelihood function, and the expression of GLR is as follows:
6. the epidemic situation space-time early warning method combining the number of febrile people and the population background data according to claim 1, wherein the step S4 includes the following steps:
step S41, performing prospective space-time rearrangement scanning on the whole research area and the research time period by using a space-time cylinder window, wherein the circle center of the cylinder is specified in the second step, and the cylinder scanning window is continuously expanded according to the set time step length and the set scanning radius step length until all the cylinder windows cover the whole research area and the whole time period; performing index calculation on all the cylindrical windows, wherein the index calculation comprises the total fever case density in the research area, the actual fever case density in the cylindrical windows, the expected fever case density and the GLR value of each scanning window;
and step S42, generating a simulation data set by using a Monte Carlo method according to the fever cases according to a Poisson distribution rule, calculating a corresponding GLR value, and verifying whether the cylinder window has statistical significance by using the simulation GLR of the simulation data set.
7. The epidemic situation space-time early warning method fusing the number of febrile people and the population background data according to claim 6, characterized in that prospective space-time rearrangement scanning is performed, key parameters of the scanning are set, a GLR value is calculated for each cylinder window formed in the scanning process, the GLR value reflects the possibility of aggregation of the cylinder windows, and when the GLR value is larger, the aggregation is significant;
performing confidence analysis on the scanning window, wherein the invalid assumption of the scanning window is as follows: the probability of the occurrence of the fever event in time and space is completely random, and the distribution of the cases inside and outside the scanning window is not different; because the probability distribution of the scanning statistic of the window is difficult to obtain, a P value is calculated by using a Monte Carlo hypothesis test method, and random detection is carried out on a gathering area which is possibly abnormal;
the specific simulation process is as follows: firstly, calculating a Poisson function expected value according to a research time range and time analysis unit, a geographical analysis unit and an accumulated fever case in original data, and determining a specific form of the Poisson function; secondly, according to the determined Poisson function, on the premise that the total number of fever cases is not changed, the number of fever cases in each time analysis unit of each geographic analysis unit is randomly calculated; then, independently executing the previous step for multiple times to form a corresponding random scheme; finally, calculating GLR values in corresponding space-time scanning windows based on a randomly generated scheme, and using the GLR values as a reference for checking whether the current distribution is abnormal;
the more the simulation data sets are, the higher the significance detection reliability is; generally, the simulated data sets in the scanning are not less than 999, namely 999 simulated GLRs are obtained, the 999 simulated GLRs and the actual GLRs are combined together to be sorted from large to small, and the window P value with the highest aggregation degree corresponding to the GLR is calculated according to the sorting order of the actual data sets GLR, for example, the actual GLR is sorted into the 50 th order, the P value of which is 50/(999+1) which is 0.05, and the confidence coefficient of which is 95%; if the order is 10 th bit, the confidence of the P value is 10/(999+1) ═ 0.01 is 99%, and so on.
8. The epidemic situation space-time early warning method combining the number of febrile people and the population background data according to claim 1, wherein the step S5 specifically comprises: identifying Monte Carlo simulation results, and screening out early warning results with statistical significance, including early warning ranges and early warning time periods; for spatiotemporal early warning events with significant results, if a spatiotemporal containment relationship exists, i.e. the time range and the space range of a certain spatiotemporal early warning event are completely included by another spatiotemporal early warning time, the contained events are merged into a contained early warning event.
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