CN112349426A - Method for predicting development trend of unknown novel viruses - Google Patents
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Abstract
The invention discloses a method for predicting the development trend of an unknown novel virus, belonging to the technical field of epidemic propagation and control. Which comprises the following steps: sorting data, dividing data, modeling data, predicting a model and outputting a result; the invention has the beneficial effects that: according to the prediction method of the development trend of the unknown novel virus, modeling regression analysis is carried out according to different development stages of the epidemic situation, and the problem that the model lacks pertinence is solved; model iteration is carried out along with the data updated every day, comparison and correction with the actual situation are achieved, and accuracy of prediction and estimation of the development trend of the infectious diseases is guaranteed.
Description
Technical Field
The invention relates to a method for predicting the development trend of an unknown novel virus, belonging to the technical field of epidemic propagation and control.
Background
The influence of infectious diseases on human beings is concerned consistently, and the consequences of the novel infectious diseases appearing in recent years, such as SARS (severe acute respiratory syndrome) in 2003, avian influenza in 2005, hand-foot-and-mouth disease in 2008, H1N1 influenza A in 2009, are very serious, except direct casualties and huge medical expenses, indirect influence on economy and harm to the psychological and social stability of people.
The starting, outbreak and control processes of the epidemic situation of the infectious disease all follow corresponding objective rules, and the evolution process of the infectious disease is scientifically predicted, so that the method is an important link for a decision-making department to correctly judge the situation and make an appropriate response.
At present, the prediction and estimation of the development trend of infectious diseases are mostly based on lag or unilateral information, a model basis of reliability pertinence is lacked, and some data processing methods do not compare with actual conditions, analyze and correct, so that various prediction conclusions are greatly different and even contradict with one another.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for predicting the development trend of the unknown novel virus is provided, and the problem that the prediction conclusion is inaccurate due to the fact that the prediction and estimation of the development trend of the infectious diseases in the prior art are based on or lag or unilateral information and lack of a reliable and targeted model foundation is solved.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a method for predicting the development trend of unknown novel viruses comprises the following steps:
s1: data are sorted; according to actual transmission data of the virus from day 1 to day n, sorting the data according to the time, the actual suspected number of people and the actual confirmed number of people;
s2: dividing data; plotting a scatter plot/smooth curve from the actual data in S1 with the time on the horizontal axis and the actual suspected number of people and the actual confirmed number of people on the vertical axis, respectively, as time progresses;
s3: modeling data; according to the scatter/smooth curve drawn in the S2, determining corresponding time stages according to the current epidemic situation development, and respectively establishing an exponential increasing regression model, a polynomial regression model and an exponential decreasing regression model according to the data distribution characteristics corresponding to each stage; with R2Evaluating the fitting capacity of the model, and performing iteration of the model according to the newly added data every day;
s4: model prediction; predicting the number of confirmed persons and the number of suspected persons in the three days in the future by using the data model established in the S3;
s5: outputting a result; and outputting the predicted data to a display end in a visualized mode.
The invention has the beneficial effects that: according to the prediction method for the development trend of the unknown novel virus, modeling regression analysis is carried out according to actual propagation data of the virus from day 1 to day n and different development stages of epidemic situations, so that the problem that a model is lack of pertinence is solved; model iteration is carried out along with the data updated every day, comparison and correction with the actual situation are achieved, and accuracy of prediction and estimation of the development trend of the infectious diseases is guaranteed.
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FIG. 1 is a schematic flow chart of the steps of the present invention;
FIG. 2 is a graph of the number of diagnosed people present nationwide at various time periods;
FIG. 3 is a graph of the number of suspected national infections at various time periods;
FIG. 4 is a graph of the comparative trend of the predicted data and the actual data of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Example (b):
fig. 1 is a schematic flow chart of steps of the present invention, and as shown in fig. 1, the present invention provides a method for predicting an unknown new virus development trend, which includes the following steps and specific implementation processes:
the number of people diagnosed with the national new coronary pneumonia is shown in fig. 2 between 1 and 10 days of 2020 and 3 and 10 months of 2020, and the number of people suspected of the national new coronary pneumonia is shown in fig. 3 between 1 and 10 days of 2020 and 3 and 10 months of 2020.
S1: data are sorted; according to the actual propagation data of the novel coronavirus from 1/10/2020 to 3/10/2020, data sorting is carried out according to the time, the actual suspected number and the actual confirmed number;
s2: dividing data; plotting a scatter plot/smooth curve from the actual data in S1 with the time on the horizontal axis and the actual suspected number of people and the actual confirmed number of people on the vertical axis, respectively, as time progresses;
s3: modeling data; determining a corresponding time phase according to the scatter point/smooth curve drawn in the S2 and the current epidemic situation development, and dividing data according to the time phase; according to the infectious disease transmission mechanism, the following development trends can be predicted to appear in epidemic situations:
(1) in the initial stage of epidemic outbreak, due to the fact that spring festival is positive, people mobility is strong, meanwhile, effective prevention and control policies are not provided in all places, and existing confirmed cases and existing suspected cases grow exponentially in the stage;
(2) with the increase of the stage and the control force of the prevention and control policies of various places, the existing confirmed cases and the existing suspected cases in the stage are slowly reduced from growth to slow development, and the polynomial distribution is obeyed;
(3) due to the intervention of effective treatment modalities and the persistence of strict preventive and control policies, the reduction rate of existing confirmed cases and existing suspected cases at this stage is increased and exponentially reduced.
Respectively establishing an exponential increasing regression model, a polynomial regression model and an exponential decreasing regression model according to the corresponding data distribution characteristics of each stage (polynomial regression refers to a regression analysis method for researching a polynomial between a dependent variable and one or more independent variables; the polynomial regression has the greatest advantage that a measured point can be approximated by increasing high-order terms of x until satisfaction is reached, wherein the exponential increasing regression model and the exponential decreasing regression model both belong to exponential autoregressive models, and the exponential autoregressive model is a nonlinear model); with R2Evaluating the fitting capacity of the model, and performing iteration of the model according to the newly added data every day; (goodness of fit refers to the degree of fit of the regression line to the observed value. the statistic for measuring goodness of fit is the coefficient of likelihood (also known as the deterministic coefficient) R2。 R2The maximum value is 1. R2The closer the value of (1) is, the better the fitting degree of the regression straight line to the observed value is; otherwise, R2The more value ofSmall, indicates that the regression line fits the observed values less well. ) The following were used:
the number of currently diagnosed people in the country:
2020.01.10-2020.02.04, epidemic development follows exponential growth. Modeling analysis and prediction are carried out based on the data of the stage; 2020.02.04-2020.02.26, epidemic development follows polynomial distribution. Modeling analysis and prediction are carried out based on the data of the stage; meanwhile, from 2 months and 18 days, the latest data released by the national health commission is added to the data set for training every day, and the model is updated;
2020.02.26-2020.03.09, the epidemic development obeys an exponential decrease. Modeling analysis and prediction are carried out based on the data of the stage; meanwhile, from 2 months and 18 days, the latest data released by the national health commission is added to the data set for training every day, and the model is updated;
the number of suspected people existing nationwide:
2020.01.10-2020.01.30, epidemic development follows exponential growth. Modeling analysis and prediction are carried out based on the data of the stage;
2020.01.30-2020.02.08, epidemic development follows polynomial distribution. Modeling analysis and prediction are carried out based on the data of the stage; meanwhile, from 2 months and 18 days, the latest data released by the national health commission is added to the data set for training every day, and the model is updated;
2020.02.08-2020.03.09, the epidemic development obeys an exponential decrease. Modeling analysis and prediction are carried out based on the data of the stage; meanwhile, from 2 months and 18 days, the latest data released by the national health commission is added to the data set for training every day, and the model is updated;
s4: model prediction; predicting the number of confirmed persons and the number of suspected persons in the three days in the future by using the data model established in the S3;
s5: outputting a result; the predicted data is visually output to a display end, and as shown in fig. 4, a prediction trend graph of the national new coronary pneumonia predicted by the method of the present invention is shown.
According to the prediction method of the development trend of the unknown novel virus, modeling regression analysis is carried out according to different development stages of the epidemic situation, and the problem that the model lacks pertinence is solved; model iteration is carried out along with the data updated every day, comparison and correction with the actual situation are achieved, and accuracy of prediction and estimation of the development trend of the infectious diseases is guaranteed.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (1)
1. A method for predicting the development trend of unknown novel viruses is characterized by comprising the following steps:
s1: data are sorted; according to actual transmission data of the virus from day 1 to day n, sorting the data according to the time, the actual suspected number of people and the actual confirmed number of people;
s2: dividing data; plotting a scatter plot/smooth curve from the actual data in S1 with the time on the horizontal axis and the actual suspected number of people and the actual confirmed number of people on the vertical axis, respectively, as time progresses;
s3: modeling data; according to the scatter/smooth curve drawn in the S2, determining corresponding time stages according to the current epidemic situation development, and respectively establishing an exponential increasing regression model, a polynomial regression model and an exponential decreasing regression model according to the data distribution characteristics corresponding to each stage; with R2Evaluating the fitting capacity of the model, and performing iteration of the model according to the newly added data every day;
s4: model prediction; predicting the number of confirmed persons and the number of suspected persons in the three days in the future by using the data model established in the S3;
s5: outputting a result; and outputting the predicted data to a display end in a visualized mode.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113345599A (en) * | 2021-08-04 | 2021-09-03 | 医渡云（北京）技术有限公司 | Epidemic situation prediction method, epidemic situation prediction device, storage medium and electronic equipment |
| CN117877753A (en) * | 2024-03-12 | 2024-04-12 | 江南大学附属医院 | Pandemic monitoring method, system, equipment and medium based on multivariate data |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113345599A (en) * | 2021-08-04 | 2021-09-03 | 医渡云（北京）技术有限公司 | Epidemic situation prediction method, epidemic situation prediction device, storage medium and electronic equipment |
| CN117877753A (en) * | 2024-03-12 | 2024-04-12 | 江南大学附属医院 | Pandemic monitoring method, system, equipment and medium based on multivariate data |
| CN117877753B (en) * | 2024-03-12 | 2024-05-17 | 江南大学附属医院 | Pandemic monitoring method, system, equipment and medium based on multivariate data |
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