Complementary infectious disease monitoring method based on linkage between sample monitoring system and citizen-participatory monitoring system

Abstract

Provided is a complementary infectious disease monitoring method based on the linkage between a sample monitoring system and a citizen-participatory monitoring system. The infectious disease monitoring method according to an embodiment of the present invention: calculates an infectious disease trend by using infectious disease diagnosis data according to a sample monitoring system; predicts an infectious disease trend by using infectious disease diagnosis data according to a citizen-participatory monitoring system; and calculates a final infectious disease trend by integrating the two calculated / predicted infectious disease trends. Accordingly, by linking the sample monitoring system and the citizen-participating monitoring system to identify the infectious disease trend through mutual complementation, it is possible to quickly and reliably identify the infectious disease trend by complementing each problem of the two monitoring systems.

Description

Complementary infectious disease surveillance method based on linkage between sample surveillance systems and citizen participation surveillance systems

[0001] The present invention relates to an infectious disease surveillance method, and more specifically, to an infectious disease surveillance method that improves upon the respective disadvantages inherent in a sample surveillance system and a citizen participation surveillance system.

[0002] In the response to infectious diseases, sample surveillance systems and citizen-participatory surveillance systems play important roles in providing disease trends in different ways at their respective positions, but both have the following limitations.

[0003] Since the sample surveillance system is based on collecting infectious disease occurrence data from medical institutions, the reliability of the data is high, but there is a problem in that the sample data is small and infectious disease trends appear relatively late.

[0004] Since the citizen-participatory surveillance system involves ordinary citizens directly participating in infectious disease surveillance activities through smartphone applications, it provides a large amount of sample data and allows for the rapid emergence of disease trends, but it has the problem of low data reliability.

[0005] Accordingly, measures are needed to complement both surveillance systems to accurately and rapidly identify infectious disease trends.

[0006] The present invention has been devised to solve the aforementioned problems, and the objective of the present invention is to provide a complementary infectious disease surveillance method based on the linkage of the two surveillance systems as a means to mutually complement the problems of the sample surveillance system and the citizen participation surveillance system.

[0007] A method for monitoring infectious diseases according to an embodiment of the present invention for achieving the above objective comprises: a step of collecting first infectious disease diagnostic data according to a first type of infectious disease monitoring system; a step of calculating a first infectious disease trend using the first infectious disease diagnostic data; a step of collecting second infectious disease diagnostic data according to a second type of infectious disease monitoring system; a step of predicting a second infectious disease trend using the second infectious disease diagnostic data; and a step of integrating the first infectious disease trend and the second infectious disease trend from the prediction step to calculate a final infectious disease trend.

[0008] The first type of infectious disease surveillance system is a sample surveillance system based on testing by medical institutions, and the second type of infectious disease surveillance system may be a citizen participation-based surveillance system based on symptom input by the general public through an application installed on a mobile terminal.

[0009] Infectious disease trends may include the incidence rate, growth rate, and transmission speed of the disease.

[0010] The calculation step may involve applying a first weight to the first infectious disease trend, applying a second weight to the second infectious disease trend, and then summing them to calculate the final infectious disease trend.

[0011] The calculation step may involve reducing the first weight by a specific amount and increasing the second weight by a specific amount if the number of second infectious disease diagnosis data is greater than or equal to a specific ratio of 'the total number of infectious disease diagnosis data obtained by adding the number of first infectious disease diagnosis data and the number of second infectious disease diagnosis data'.

[0012] The calculation step may be to increase the first weight by a specific amount and decrease the second weight by a specific amount if the incidence rate of the infectious disease according to the second infectious disease trend is greater than or equal to a specific ratio to the incidence rate of the infectious disease according to the first infectious disease trend.

[0013] The calculation step may involve reducing the first weight by a specific amount and increasing the second weight by a specific amount if the increase rate of the infectious disease according to the second infectious disease trend is greater than or equal to a specific ratio to the increase rate of the infectious disease according to the first infectious disease trend.

[0014] The calculation step may involve reducing the first weight by a specific amount and increasing the second weight by a specific amount if the infection rate of the infectious disease according to the second infectious disease trend is greater than or equal to a specific ratio to the infection rate of the infectious disease according to the first infectious disease trend.

[0015] The calculation step may involve increasing the first weight by a specific amount and decreasing the second weight by a specific amount if there are multiple infectious diseases currently prevalent with the same major symptoms.

[0016] According to another aspect of the present invention, an infectious disease surveillance server is provided, characterized by comprising: a communication unit that collects first infectious disease diagnostic data according to a first type of infectious disease surveillance system and collects second infectious disease diagnostic data according to a second type of infectious disease surveillance system; and a processor that calculates a first infectious disease trend using the first infectious disease diagnostic data, predicts a second infectious disease trend using the second infectious disease diagnostic data, and calculates a final infectious disease trend by integrating the first infectious disease trend and the second infectious disease trend in the prediction step.

[0017] According to another aspect of the present invention, an infectious disease surveillance method is provided, comprising: a step of calculating a first infectious disease trend using first infectious disease diagnostic data according to a first infectious disease surveillance system; a step of predicting a second infectious disease trend using second infectious disease diagnostic data according to a second infectious disease surveillance system; and a step of integrating the first infectious disease trend and the second infectious disease trend from the prediction step to calculate a final infectious disease trend.

[0018] According to another aspect of the present invention, an infectious disease surveillance server is provided, characterized by comprising: a processor that calculates a first infectious disease trend using first infectious disease diagnostic data according to a first infectious disease surveillance system, predicts a second infectious disease trend using second infectious disease diagnostic data according to a second infectious disease surveillance system, and calculates a final infectious disease trend by integrating the first infectious disease trend and the second infectious disease trend in the prediction stage; and a storage unit that provides storage space required for the processor.

[0019] As explained above, according to the embodiments of the present invention, by linking a sample surveillance system and a citizen participation surveillance system to identify infectious disease trends through mutual complementarity, the respective problems of the two surveillance systems are compensated for, thereby enabling rapid and reliable identification of infectious disease trends.

[0020] FIG. 1 shows the configuration of an infectious disease surveillance system according to one embodiment of the present invention,

[0021] FIG. 2 shows the flow of an infectious disease surveillance method according to another embodiment of the present invention,

[0022] FIG. 3 is a detailed flowchart of step S550 of FIG. 2.

[0023] FIG. 4 is a configuration of an infectious disease surveillance server according to another embodiment of the present invention.

[0024] The present invention will be described in more detail below with reference to the drawings.

[0025] An embodiment of the present invention presents a complementary infectious disease surveillance method based on the linkage of a sample surveillance system and a citizen participation surveillance system. This technology aims to compensate for the respective shortcomings of the two surveillance systems by identifying infectious disease trends through mutual complementarity by linking the sample surveillance system and the citizen participation surveillance system.

[0026] FIG. 1 is a diagram illustrating the configuration of an infectious disease surveillance system according to an embodiment of the present invention. As illustrated, the infectious disease surveillance system according to an embodiment of the present invention comprises medical institution servers (100), user smartphones (200), an infectious disease surveillance server (300), and a public health institution server (400).

[0027] The medical institution servers (100) are servers established in medical institutions, and transmit infectious disease diagnosis data obtained from medical staff examinations according to the sample surveillance system to the infectious disease surveillance server (300).

[0028] User smartphones (200) are terminals carried by the general public for a citizen participation surveillance system. User smartphones (200) receive symptoms such as body temperature, cough, and chills through a self-diagnosis application and transmit the generated infectious disease diagnosis data to the infectious disease surveillance server (300).

[0029] The infectious disease surveillance server (300) identifies infectious disease trends by using infectious disease diagnosis data based on a sample surveillance system collected from medical institution servers (100) and infectious disease diagnosis data based on a citizen participation surveillance system collected from user smartphones (200).

[0030] The infectious disease trends identified by the infectious disease surveillance server (300) are provided to the public health institution server (400) and can also be provided to medical institution servers (100) and user smartphones (200).

[0031] The process of the infectious disease surveillance server (300) identifying the trend of infectious diseases will be explained in detail below with reference to FIG. 2. FIG. 2 is a diagram illustrating the flow of an infectious disease surveillance method according to another embodiment of the present invention.

[0032] As described above, first, the infectious disease surveillance server (300) collects infectious disease diagnosis data according to the sample surveillance system from medical institution servers (100) (S510). Then, the infectious disease surveillance server (300) calculates an infectious disease trend from the infectious disease diagnosis data collected in step S510 (S520). The infectious disease trend calculated in step S520 includes the incidence rate, growth rate, and transmission speed of the infectious disease.

[0033] Next, the infectious disease surveillance server (300) collects infectious disease diagnosis data according to the citizen participation surveillance system from user smartphones (200) (S530). Then, the infectious disease surveillance server (300) predicts the trend of infectious diseases from the infectious disease diagnosis data collected in step S530 (S540).

[0034] Due to the low reliability of the citizen-participatory surveillance system, the S540 stage was described as predicting infectious disease trends. Similar to the infectious disease trends calculated in the S520 stage, the infectious disease trends predicted in the S540 stage include the incidence rate, growth rate, and transmission speed.

[0035] Afterwards, the infectious disease surveillance server (300) calculates the final infectious disease trend by integrating the infectious disease trend based on the sample surveillance system calculated in step S520 and the infectious disease trend based on the citizen participation surveillance system predicted in step S540 (S550).

[0036] The infectious disease surveillance server (300) can provide the final infectious disease trend calculated in step S550 to the public institution server (400) and also to medical institution servers (100) and user smartphones (200) (S560).

[0037] In the S550 stage, the final infectious disease trend is calculated by applying weights to the infectious disease trend based on the sample surveillance system and the infectious disease trend based on the citizen participation surveillance system, respectively, according to the following mathematical formula, and then summing them up.

[0038] IDF = αIDF_S + βIDF_C (α+β=1)

[0039] Here, IDF (Infectious Disease Trends) is the final infectious disease trend calculated at step S550, IDF_S is the infectious disease trend based on the sentinel surveillance system (incidence rate, growth rate, and transmission speed of infectious diseases) calculated at step S520, IDF_C is the infectious disease trend based on the community-based surveillance system (incidence rate, growth rate, and transmission speed of infectious diseases) predicted at step S540, α is the weight of IDF_S, and β is the weight of IDF_C.

[0040] A more specific method for calculating the final infectious disease trend is described below with reference to FIG. 3. FIG. 3 is a detailed flowchart of step S550 of FIG. 2. To calculate the final infectious disease trend, the weights (α) of IDF_S and the weights (β) of IDF_C must first be determined, and these vary according to the calculated / predicted infectious disease trend.

[0041] First, as shown in FIG. 3, the initial values ​​of the weight (α) of IDF_S and the weight (β) of IDF_C are set, and the weight increment / decrement value is set (S551). The initial values ​​of the weight (α) of IDF_S and the weight (β) of IDF_C can be set to 0.5 each, and the weight increment / decrement value can be set to 0.1. However, the specific initial values ​​can be changed.

[0042] If the 'number of infectious disease diagnosis data based on the citizen participation surveillance system' is greater than a specific percentage (e.g., 80%) of the 'total number of infectious disease diagnosis data', the weight (α) of IDF_S is reduced by the weight increase / decrease amount (e.g., α→α-0.1), and the weight (β) of IDF_C is increased by the weight increase / decrease amount (e.g., β→β+0.1, S552).

[0043] Here, 'total number of infectious disease diagnostic data' refers to the sum of 'infectious disease diagnostic data based on the sample surveillance system' and 'infectious disease diagnostic data based on the citizen participation surveillance system'.

[0044] This situation occurs when the volume of infectious disease diagnostic data based on a citizen-participatory surveillance system is significantly high, and it primarily takes place during the early stages of an infectious disease spread. In situations where diagnostic data based on a sample surveillance system is significantly low, the weights are adjusted as described above to identify infectious disease trends more quickly.

[0045] Meanwhile, if the incidence rate of infectious diseases based on the infectious disease trend (IDF_C) based on the citizen participation surveillance system is greater than the incidence rate of infectious diseases based on the infectious disease trend (IDF_S) based on the sample surveillance system, for example, if the former is 150% or more of the latter, the weight (α) of IDF_S is increased by the weight increase / decrease amount (e.g., α→α+0.1), and the weight (β) of IDF_C is decreased by the weight increase / decrease amount (e.g., β→β-0.1, S553).

[0046] This situation involves an exceptionally high rate of infectious disease diagnoses in the citizen-participatory surveillance system. Since this is highly likely to be caused by erroneous self-diagnoses by non-expert citizens, the weights are adjusted as described above to correct for this.

[0047] On the other hand, if the increase rate of infectious diseases based on the infectious disease trend based on the citizen participation surveillance system (IDF_C) is greater than the increase rate of infectious diseases based on the infectious disease trend based on the sample surveillance system (IDF_S) by a certain ratio, for example, if the former is 200% or more of the latter, the weight (α) of IDF_S is reduced by the weight increase / decrease amount (e.g., α→α-0.1), and the weight (β) of IDF_C is increased by the weight increase / decrease amount (e.g., β→β+0.1, S554).

[0048] In a citizen-participatory surveillance system, a high growth rate in infectious disease diagnoses may indicate an explosive surge in the disease; therefore, the weights are adjusted as above to identify infectious disease trends more quickly.

[0049] On the other hand, if the infection rate of an infectious disease based on the infectious disease trend (IDF_C) based on the citizen participation surveillance system is greater than the infection rate of an infectious disease based on the infectious disease trend (IDF_S) based on the sample surveillance system, for example, if the former is 200% or more of the latter, the weight (α) of IDF_S is reduced by the weight increase / decrease amount (e.g., α→α-0.1), and the weight (β) of IDF_C is increased by the weight increase / decrease amount (e.g., β→β+0.1, S555).

[0050] Meanwhile, if there are multiple infectious diseases currently prevalent with the same major symptoms, for example, if COVID-19 and influenza are currently prevalent together, the weight (α) of IDF_S is increased by the weight increment value (e.g., α→α+0.1), and the weight (β) of IDF_C is decreased by the weight increment value (e.g., β→β-0.1, S556).

[0051] The weights are adjusted as above to reflect the high possibility that infectious disease diagnoses based on the citizen-participatory surveillance system may misdiagnose other infectious diseases.

[0052] Using the following adjusted final weights (α, β), the infectious disease trend based on the sample surveillance system (incidence rate, growth rate, and transmission speed of the infectious disease) and the infectious disease trend based on the citizen participation surveillance system (incidence rate, growth rate, and transmission speed of the infectious disease) are weighted to calculate the final infectious disease trend (S557).

[0053] Hereinafter, the configuration of the infectious disease surveillance server (300) illustrated in FIG. 1 will be described in detail with reference to FIG. 4. FIG. 4 is a diagram illustrating the configuration of an infectious disease surveillance server (300) according to another embodiment of the present invention.

[0054] An infectious disease surveillance server (300) according to an embodiment of the present invention can be implemented as a computing system comprising a communication unit (310), a processor (320), and a storage unit (330) as illustrated.

[0055] The communication unit (310) is a communication interface for connecting to an external network or external device, and communicates with medical institution servers (100), user smartphones (200), and health-related public institution servers (400).

[0056] The processor (320) integrates the infectious disease trend based on the sample surveillance system and the infectious disease trend based on the citizen participation surveillance system according to the procedure described in FIG. 2 above, and identifies the final infectious disease trend.

[0057] The storage unit (330) provides storage space necessary for the processor (320) to function and operate.

[0058] Up to now, a complementary infectious disease surveillance method based on the linkage of a sample surveillance system and a citizen participation surveillance system has been explained in detail with preferred embodiments.

[0059] In the above embodiment, by linking the sample surveillance system and the citizen participation surveillance system to identify infectious disease trends through mutual complementarity, the respective problems of the two surveillance systems were compensated for, thereby enabling rapid and reliable identification of infectious disease trends.

[0060] Meanwhile, it goes without saying that the technical concept of the present invention may also be applied to a computer-readable recording medium containing a computer program that enables the device and method according to the present embodiment to perform their functions. Furthermore, the technical concept according to various embodiments of the present invention may be implemented in the form of computer-readable code recorded on a computer-readable recording medium. A computer-readable recording medium may be any data storage device that can be read by a computer and store data. For example, a computer-readable recording medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical disk, hard disk drive, etc. Additionally, computer-readable code or a program stored on a computer-readable recording medium may be transmitted through a network connected between computers.

[0061] Furthermore, although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. Various modifications are possible by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present invention.

Claims

1. A step of collecting first infectious disease diagnostic data according to the first type of infectious disease surveillance system; A step of calculating a trend of the first infectious disease using the first infectious disease diagnosis data; A step of collecting second infectious disease diagnostic data according to the second type of infectious disease surveillance system; A step of predicting the trend of the second infectious disease using second infectious disease diagnostic data; A method for monitoring infectious diseases characterized by including a step of integrating a first infectious disease trend and a second infectious disease trend in a prediction step to calculate a final infectious disease trend.

2. In Claim 1, The first type of infectious disease surveillance system is, It is a sample surveillance system based on examinations by medical institutions, and The second type of infectious disease surveillance system is, An infectious disease surveillance method characterized by a citizen participation-based surveillance system based on symptom input by the general public through an application installed on a mobile terminal.

3. In Claim 1, The trend of infectious diseases is, A method for monitoring infectious diseases characterized by including the incidence rate, growth rate, and transmission speed of the infectious disease.

4. In Claim 3, The calculation step is, An infectious disease surveillance method characterized by applying a first weight to a first infectious disease trend, applying a second weight to a second infectious disease trend, and then summing them to calculate a final infectious disease trend.

5. In Claim 4, The calculation step is, An infectious disease surveillance method characterized by reducing a first weight by a specific amount and increasing a second weight by a specific amount if the number of second infectious disease diagnostic data is greater than or equal to a specific ratio of 'the total number of infectious disease diagnostic data obtained by adding the number of first infectious disease diagnostic data and the number of second infectious disease diagnostic data'.

6. In Claim 4, The calculation step is, An infectious disease surveillance method characterized by increasing a first weight by a specific amount and decreasing a second weight by a specific amount if the incidence rate of an infectious disease according to a second infectious disease trend is greater than or equal to a specific ratio to the incidence rate of an infectious disease according to a first infectious disease trend.

7. In Claim 4, The calculation step is, An infectious disease surveillance method characterized by reducing a first weight by a specific amount and increasing a second weight by a specific amount if the increase rate of an infectious disease according to a second infectious disease trend is greater than or equal to a specific ratio to the increase rate of an infectious disease according to a first infectious disease trend.

8. In Claim 4, The calculation step is, An infectious disease surveillance method characterized by reducing a first weight by a specific amount and increasing a second weight by a specific amount if the infection rate of an infectious disease according to a second infectious disease trend is greater than or equal to a specific ratio to the infection rate of an infectious disease according to a first infectious disease trend.

9. In Claim 4, The calculation step is, An infectious disease surveillance method characterized by increasing a first weight by a specific amount and decreasing a second weight by a specific amount when there are multiple infectious diseases currently prevalent with the same major symptoms.

10. A communication unit that collects first infectious disease diagnostic data according to a first type of infectious disease surveillance system and collects second infectious disease diagnostic data according to a second type of infectious disease surveillance system; An infectious disease surveillance server characterized by including a processor that calculates a first infectious disease trend using first infectious disease diagnostic data, predicts a second infectious disease trend using second infectious disease diagnostic data, and calculates a final infectious disease trend by integrating the first infectious disease trend and the second infectious disease trend in the prediction stage.

11. A step of calculating a first infectious disease trend using first infectious disease diagnostic data according to the first type of infectious disease surveillance system; A step of predicting the trend of a second infectious disease using second infectious disease diagnostic data according to a second type of infectious disease surveillance system; A method for monitoring infectious diseases characterized by including a step of integrating a first infectious disease trend and a second infectious disease trend in a prediction step to calculate a final infectious disease trend.

12. A processor that calculates a first infectious disease trend using first infectious disease diagnostic data according to a first infectious disease surveillance system, predicts a second infectious disease trend using second infectious disease diagnostic data according to a second infectious disease surveillance system, and calculates a final infectious disease trend by integrating the first infectious disease trend and the second infectious disease trend in the prediction stage; and An infectious disease surveillance server characterized by including a storage unit that provides storage space required for a processor.

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