CN111540476B - Respiratory infectious disease infectious tree reconstruction method based on mobile phone signaling data - Google Patents
Respiratory infectious disease infectious tree reconstruction method based on mobile phone signaling data Download PDFInfo
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Abstract
The invention discloses a respiratory infectious disease infectious tree reconstruction method based on mobile phone signaling data, which comprises the following steps: s1: judging potential infection relations among cases; s2: analyzing spatio-temporal co-occurrence relation among cases; s3: co-occurrence network construction and community division; s4: judging the infection relation of disease cases in the community; s5: determination and visualization of the site of infection. The invention can realize the quick judgment of the infection relation of the cases and the determination and visualization of the infection positions during the outbreak of the respiratory infectious diseases, breaks through the limitations of time and labor consumption and unclear and visual results of the traditional epidemiological investigation, can quickly reconstruct the infection trees among the cases, determine the space positions of the infection and display the infection trees on a map in a visual mode, is beneficial to mastering the disease transmission process in time and improves the virus tracing and transmission blocking efficiency.
Description
Technical Field
The invention relates to an infection tree reconstruction method, in particular to a respiratory infectious disease infection tree reconstruction method based on mobile phone signaling data.
Background
Respiratory infectious diseases, one of the most common epidemics, are easily transmitted from person to person, resulting in major outbreaks. The understanding of the infection relationship of respiratory infectious diseases among cases and the determination of the spatial position of infection occurrence are of great significance for mastering the epidemic situation development, blocking the virus transmission and the like. Currently, the determination of the infection relation and the infection occurrence location of a case mainly depends on epidemiological investigation, i.e. by inquiring the case, the recent activity track and the close contact person are known, and the potential infectors and the infected persons are further determined. This method is time-consuming and labor-consuming, and it is difficult to visually display the infection relationship and the location of infection among cases. Meanwhile, the description of the surveyed object may have subjective judgment and memory omission, and the survey result is usually not accurate and comprehensive enough.
Actually, based on the signaling data generated by the mobile phone carried by the case, the historical movement track information can be completely acquired. Based on this, the prior patent document CN105740615B proposes to infer the infection time and location according to the moving track of a single infected person and the environmental conditions of the passing area, and then track the infection source by analyzing the similar points of the tracks of different infected persons; patent document CN109360660A proposes to compare the infected person movement track with an epidemic situation risk map to obtain infection high probability occurrence points, and to perform association rule or character string pattern mining on different infected person tracks to analyze possible propagation trends. Although the mobile phone signaling tracks are applied to infectious disease prevention and control, when the case tracks are analyzed, on one hand, only the crowd infection possibly caused by environmental factors is considered, the spread of the disease among people is ignored, and the infection process of the disease among cases cannot be reflected; on the other hand, tracking of infection sources and infection high-probability occurrence points is based only on similar points of the trajectories of infected persons, and epidemic infection mechanisms are not considered, so that the results may have deviation.
In view of the above problems, it is urgently needed to provide a respiratory infectious disease infectious tree reconstruction method based on mobile phone signaling data, which can solve the problems of time and labor consumption, unclear and visual results and the like of the traditional epidemiological investigation.
Disclosure of Invention
In order to solve the defects of the technology, the invention provides a respiratory infectious disease infectious tree reconstruction method based on mobile phone signaling data.
In order to solve the technical problems, the invention adopts the technical scheme that: a respiratory infectious disease infectious tree reconstruction method based on mobile phone signaling data comprises the following steps:
s1: judging potential infection relations among cases;
s2: analyzing spatio-temporal co-occurrence relation among cases;
s3: co-occurrence network construction and community division;
s4: judging the infection relation of disease cases in the community;
s5: determination and visualization of the site of infection.
Further, the specific method for judging the potential infection relationship among cases in S1 is as follows: the infection period (the time period possibly infected by other cases) and the infection period (the time period with infectivity) of each case are calculated according to the disease onset time, the diagnosis time and the latent period, and then whether the infection period and the infection period between the cases are overlapped or not is judged, if so, the infection relationship between the two cases is possible, and if not, the infection relationship is not present.
Further, the specific method for analyzing the spatio-temporal co-occurrence relationship among cases in S2 is as follows:
for the case pairs which possibly have infection relations, respectively extracting the mobile phone signaling data corresponding to the cases in the infection period and the infected period to form a movement track, and expressing the movement track by using a space-time cube model; then combining with epidemic infection mechanism, taking the topological intersection calculation of the track space-time cube as the core, analyzing the co-occurrence relationship among cases, specifically comprising space-time co-occurrence (the same time appears in the same place, and direct contact infection is possible) and space co-occurrence (the same time appears in the same place, and indirect contact infection is possible); if the co-occurrence relationship exists, the volume of the intersection of the space-time cubes of the two tracks is further calculated to be used as the co-occurrence strength.
Further, the specific method for co-occurrence network construction and community division in S3 includes:
constructing a case co-occurrence network based on the potential infection relations judged in S1 and the inter-case co-occurrence strengths calculated in S2, wherein the nodes represent each case, the edges represent possible infection relations among cases, and the inter-case co-occurrence strengths are used as weights of the edges; then, the network is divided into communities, so that the co-occurrence strength of cases in the same community is high, the possibility of infection is high, and the co-occurrence strength of cases in different communities is low, and the possibility of infection is low.
Further, the specific method for inferring the infection relation of the disease cases in the community in the S4 comprises the following steps:
for cases classified in the same community, firstly judging whether the cases come from an epidemic focus or go to an epidemic situation gathering outbreak place; if yes, marking the input cases as infection sources, then backtracking each non-input case according to the attack time until determining the corresponding infection source and connecting to form an infection branch, and finally connecting all cases to form a complete infection tree.
Further, the specific method for determining and visualizing the infection origin in S5 includes:
and (4) judging the spatial position of infection among cases based on the infection tree constructed in the S4 and combining the result of the space-time co-occurrence analysis in the S2, and visually displaying the spatial position on a map.
The invention discloses a respiratory infectious disease tree reconstruction method based on mobile phone signaling data, which judges the infection relation among cases by analyzing the space-time co-occurrence relation among the cases and combining with the epidemic disease infection mechanism, determines the space position of infection occurrence on the basis, and then carries out visual display on a map; the method can realize quick judgment of case infection relation and determination and visualization of infection positions during respiratory infectious disease outbreak, breaks through the limitations that the traditional epidemiological investigation consumes time and labor and results are not clear and intuitive enough, is beneficial to mastering the disease transmission process in time, and improves the virus tracing and transmission blocking efficiency.
Drawings
FIG. 1 is a general flow diagram of the process of the present invention.
FIG. 2 is a schematic diagram of spatio-temporal co-occurrence analysis.
Fig. 3 is a schematic diagram illustrating community division and infection relationship determination in a co-occurrence network.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The invention discloses a respiratory infectious disease tree reconstruction method based on mobile phone signaling data, the general flow of which is shown in figure 1, and the method mainly comprises the following steps:
s1: judging potential infection relations among cases;
s2: analyzing spatio-temporal co-occurrence relation among cases;
s3: co-occurrence network construction and community division;
s4: judging the infection relation of disease cases in the community;
s5: determination and visualization of the site of infection.
The method of the present invention will be described in detail with reference to specific examples;
s1: judging potential infection relation among cases:
the purpose of the step is to firstly judge whether the two cases possibly have infection relation from the time perspective, and further analyze the case pairs possibly having infection relation, thereby reducing the data analysis amount;
for each case, two important time periods are defined, one being the infectious phase and the other being the infectious phase;
the infectious stage refers to the period of time that a case is likely to be infected by other cases, provided that the longest latent period of a respiratory infectious disease is T0Then it can be speculated that the case may have been infected at some time between the time of onset minus the maximum latency to the time of onset, this time period being referred to as the infected period, i.e.:
t (infectious period) ═ time to onset-maximum incubation period, time to onset)
The infection phase refers to the period of time that a case is contagious, i.e., the period of time from the beginning of the case to the time it is isolated after its diagnosis, and for diseases that are not contagious in the latent phase, the case infection phase is:
t (infectious phase) ═ time of onset, time of confirmed diagnosis)
For diseases that are infectious in the latent phase, the case infectious phase can be expressed as:
t (infection period) ═ time of onset-maximum incubation time, time of confirmed diagnosis)
Then, it is judged whether or not the infection period and the infected period overlap between any two cases, and if so, the two cases may have an infection relationship, and if not, the two cases may not have an infection relationship.
S2: analysis of spatio-temporal co-occurrence relationship among cases:
the purpose of the step is to extract the movement track of a case pair possibly having an infection relation based on the mobile phone signaling data, and then carry out the analysis of the space-time co-occurrence relation by combining with the epidemic disease infection mechanism, thereby evaluating the possibility of infection of the case pair and the epidemic disease infection mechanism;
for case pairs (A and B) with possible infection relations, if the infection period of A and the infection period of B overlap, extracting the movement track of A in the infection period and the movement track of B in the infection period; if the infected period of A and the infection period of B overlap, extracting the moving track of A in the infected period and the moving track of B in the infection period; if both overlap, the movement trajectory in the infection period is extracted for the case with earlier onset time, and the movement trajectory in the infection period is extracted for the other case.
The specific form of the movement track can be expressed as formula (i):
Tra_move＝{(x1，y1，t1)，(x2，y2，t2)，……，(xi，yi，ti) Formula (I)
Wherein x isiAnd yiIndicates that the case is at tiThe position coordinates of the time of day.
In order to facilitate the space-time co-occurrence analysis, a space-time cube model is constructed to express the movement track. Firstly, dividing a research area into 500 m-500 m grids, then mapping track points into corresponding grids, regarding each track point, taking the corresponding grid as a region of case activity, and taking a time period from a time stamp of the track point to a time stamp of the next track point as the activity time of the case in the region, so that in a three-dimensional space-time cube, a case track is composed of a series of small cubes, and the co-occurrence relation between two cases can be obtained by performing intersection calculation on the track space-time cubes of the two cases.
According to epidemic infection mechanisms, respiratory infectious diseases mainly have two possible transmission ways among people, one is direct contact transmission, namely close contact occurs in life, work, study, same-row and the like with infected people; the other is indirect contact transmission, that is, after the infected person stays at a certain place, the virus is suspended in the air or attached to an object and inhaled or contacted by the later person. From the perspective of spatiotemporal analysis, the direct contact propagation occurs at the same place corresponding to the same time of two cases, namely, spatiotemporal co-occurrence occurs; indirect contact transmission occurs at the same location corresponding to two cases at different times, i.e. spatial co-occurrence occurs, and the time interval between the departure of the former and the arrival of the latter is shorter than the time that the virus can survive in vitro.
Taking the spatio-temporal co-occurrence analysis diagram shown in FIG. 2 as an example, case 1 and case 2 are shown at t2Time of daySplit after encounter (chance), at t5Meet again at a moment and go in parallel to t6(Co-current) whereas cases 1 and 3 are at t3-t4Stay in the same place (co-located) for a period of time. Therefore, the spatio-temporal co-occurrence of case 1 and cases 2 and 3 may occur with direct contact infection; case 2 and case 3 did not come into direct contact, but case 3 appeared in a place where case 2 had stayed, i.e., spatial co-occurrence occurred, and the arrival time (t) of case 3 was determined1) And departure time (t) of case 20) The interval between cases is shorter than the time that the virus survived in vitro, and the time of onset of case 2 is earlier than that of case 3, indirect contact infection between case 2 and case 3 may occur.
Therefore, for the spatiotemporal co-occurrence, the case track cubes are directly used for intersection, if the intersection exists, the spatiotemporal co-occurrence occurs between the two cases, and the intersected volume reflects the spatiotemporal co-occurrence strength I between the cases1The greater the intensity, the greater the likelihood of infection; for space co-occurrence, a buffer cube with the length of t is made in the time direction of a case track cube by using the time length (t) of virus survival in vitro, then intersection is carried out on the buffer cubes of two case tracks, if intersection exists, the two case tracks are subjected to space co-occurrence in a certain time interval, and the intersected volume reflects the space co-occurrence strength I between the case tracks2The greater the intensity, the greater the likelihood of infection.
For further comparison, the total co-occurrence intensity I between cases is defined as shown in formula (c):
I＝α·I1+β·I2formula (c)
Wherein alpha and beta are respectively the weight of space-time co-occurrence and space co-occurrence, and respectively reflect the possibility of direct contact infection and indirect contact infection. According to the respiratory infectious disease mechanism, indirect contact infection usually needs to satisfy three conditions of a closed space, a long time and high concentration of virus at the same time to be possible, and the probability is low, so the corresponding weight is low. Taking SARS virus and the new coronavirus COVID-19 as an example, direct contact transmission is the main mode of transmission, indirect contact transmission exists only, but the possibility is extremely low, so that the value of alpha is set to be far larger than that of beta, such as alpha is 0.99, and beta is 0.01.
S3: co-occurrence network construction and community division:
the method comprises the steps of constructing a co-occurrence network among cases on the basis of space-time co-occurrence analysis, and then judging which cases have relatively high co-occurrence strength and relatively high possibility of having an infection relation through a network analysis method, and which cases have relatively low co-occurrence strength and relatively low possibility of having the infection relation;
as shown in FIG. 3, given a series of possible infection-related case pairs and the co-occurrence strength I between them, a weighted co-occurrence network is constructed, wherein the nodes represent each case, the edges represent possible infection-related cases, and the co-occurrence strength I of the two is used as the weight of the edges. Generally, the co-occurrence intensity is greater between cases (e.g., relatives, colleagues, close contacts, etc.) on the same infectious tree, while the co-occurrence intensity is less or no co-occurrence between cases on different infectious trees. The feature enables the inter-case co-occurrence network to form a community structure, namely, the co-occurrence intensity between cases in a community is large, and the co-occurrence intensity between different community cases is small. Therefore, the community division algorithm in the network analysis is used for dividing the co-occurrence network among cases.
The community division selection is based on the algorithm of modularity, and for a weighting network, the modularity Q is defined as the formula (IV):
where m is the number of edges in the network, AijIs the weight of the edge between node i and node j, kiAnd kjRespectively the sum of the weights of the edges connected to the two nodes, ciAnd cjRespectively indicate communities of nodes i and j, if ci＝cjThen delta (c)i,cj) 1, otherwise Δ (c)i,cj) 0; the larger the modularity value is, the more the network society isThe better the zone partition effect, so by searching the possible space of community partition, the community partition that maximizes the Q value can be obtained.
S4: and (3) deducing the infection relation of disease cases in the community:
the purpose of this step is to further judge the infection relation of the cases in the same community according to the information of the onset time, whether the cases come from the epidemic focus area, whether the cases go to the epidemic situation gathering outbreak area, etc., and form a complete infectious tree.
After the community division is performed on the case co-occurrence network in step S3, the possibility of infection among cases in the same community is higher than the possibility of infection among cases in different communities. Therefore, for the cases in the same community, firstly, whether the cases go to the epidemic area or the area with the aggregated infection outbreak in the infected period is judged according to the moving track, if so, the cases are likely to be infected in the epidemic area or the aggregated infection outbreak area, then carry viruses and infect other cases in the community, and the cases are marked as input cases (such as the cases with the dotted line boxes in fig. 3 (b)) as the infection sources.
Next, for each non-input case, backtracking is performed according to the time of onset until its corresponding source of infection is found. Specifically, the method comprises the following steps: if a certain case only corresponds to a case and is in the same community with the case, and the co-occurrence relationship exists, and the onset time is earlier, the case is taken as the infection source of the case; if a certain case corresponds to a plurality of cases and is in the same community and has a co-occurrence relationship with the cases, and the co-occurrence time is earlier, whether the cases co-occur in the same time period is further judged according to the co-occurrence time, if so, aggregated infection possibly occurs among the cases, such as co-life, dinner gathering, working together and the like, and for the condition, whether input cases exist in the cases is further judged, if so, the input cases are the infection sources, and if not, the case with the earliest occurrence is selected as the infection source; if the co-occurrence does not occur within the same time period, the cases are all likely to be the infection source of the case, and for this case, it is also determined whether there is an input case among the cases, and if so, the input case is the infection source, and if not, the case with the greatest co-occurrence intensity is selected as the infection source of the case.
For each non-incoming case in the community, after its source of infection is determined, the connections form an infectious branch. If some case does not go through the epidemic source area and the gathering infection outbreak area and has no corresponding infection source in the community, the case possibly infects cases of other communities, so that cases of other communities with the co-occurrence relation with the case are found, then the infection source of the case is determined by the same method, and finally a complete infectious tree is formed.
S5: determination and visualization of the site of infection:
the purpose of this step is to determine the infection occurrence location based on the infection relationship between cases determined in the previous step, and to combine the results of the spatio-temporal co-occurrence analysis in step S2, and to visually display the location on the map.
After the infection relation is determined, for the case pair having the infection relation, the place where the infection occurs can be determined based on the co-occurrence of the two cases in time and space. If the two cases occur in one place, the place is the infection place; if two cases occur at two or more sites, the site with the greatest intensity of co-occurrence is the site where the infection occurs. Then, on the basis, a map is utilized for visual display, and the method specifically comprises the following two aspects:
on one hand, the spatiotemporal evolution process of each infectious tree is displayed in a dynamic form by combining the infection relation and the infection occurrence position, which is beneficial to visually knowing the virus propagation characteristics and development situation;
on the other hand, the number of infection at different positions is counted, and then symbols with different sizes are displayed on a map, so that the method is beneficial to finding the areas with high risk level of the aggregated infection hair and the epidemic situation.
Compared with the prior art, the respiratory infectious disease tree reconstruction method based on the mobile phone signaling data has the following advantages:
(1) the rapid reconstruction of the case infection relation is carried out based on the mobile phone signaling data, so that time and labor are saved compared with the traditional epidemiology investigation method, and the acquired information is more comprehensive and accurate;
(2) when the case track is analyzed, the epidemic disease infection mechanism is comprehensively considered, the possible infection relation is reflected by the spatio-temporal relation among cases, and the judgment on the infection relation is more accurate.
(3) The infection relation is constructed, and meanwhile, the place where the infection occurs is determined, so that the infection tree is visually displayed on a map, the time-space diffusion process of the virus can be intuitively known, and epidemic risks of different places can be evaluated.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.
Claims (1)
1. A respiratory infectious disease infectious tree reconstruction method based on mobile phone signaling data is characterized in that: the method comprises the following steps:
s1: judging potential infection relations among cases;
s2: analyzing spatio-temporal co-occurrence relation among cases;
s3: co-occurrence network construction and community division;
s4: judging the infection relation of disease cases in the community;
s5: determination and visualization of the site of infection;
the specific method for judging the potential infection relation among cases in the S1 comprises the following steps:
calculating the infected period and the infected period of each case according to the morbidity time, the confirmed diagnosis time and the incubation period, and then judging whether the infected period and the infected period between the cases are overlapped, if so, an infection relation possibly exists between the two cases, and if not, the infection relation does not exist;
the concrete method for analyzing the spatio-temporal co-occurrence relationship among cases in the S2 comprises the following steps:
for the case pairs which possibly have infection relations, respectively extracting the mobile phone signaling data corresponding to the cases in the infection period and the infected period to form a movement track, and expressing the movement track by using a space-time cube model; then analyzing the co-occurrence relationship among cases by combining an epidemic disease infection mechanism and taking the topological intersection calculation of a track space-time cube as a core, wherein the co-occurrence relationship specifically comprises space-time co-occurrence and space co-occurrence; if the co-occurrence relationship exists, further calculating the volume of the intersection of the space-time cubes of the two tracks as the co-occurrence strength;
the specific method for co-occurrence network construction and community division in the S3 includes:
constructing a case co-occurrence network based on the potential infection relations judged in S1 and the inter-case co-occurrence strengths calculated in S2, wherein the nodes represent each case, the edges represent possible infection relations among cases, and the inter-case co-occurrence strengths are used as weights of the edges; then, carrying out community division on the network, so that the co-occurrence strength of cases in the same community is higher, the possibility of infection relation is higher, and the co-occurrence strength of cases in different communities is weaker, and the possibility of infection relation is lower;
the specific method for deducing the infection relation of the disease cases in the community in the S4 comprises the following steps:
for cases classified in the same community, firstly judging whether the cases come from an epidemic focus or go to an epidemic situation gathering outbreak place; if yes, marking the input cases as infection sources, then backtracking each non-input case according to the attack time until determining the corresponding infection source and connecting to form an infection branch, and finally connecting all cases to form a complete infection tree;
the specific method for determining and visualizing the infection site in S5 comprises:
and (4) judging the spatial position of infection among cases based on the infection tree constructed in the S4 and combining the result of the space-time co-occurrence analysis in the S2, and visually displaying the spatial position on a map.
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