CN112397205A - Dengue fever infectious disease prediction method based on meteorological model - Google Patents

Dengue fever infectious disease prediction method based on meteorological model Download PDF

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CN112397205A
CN112397205A CN202011424131.4A CN202011424131A CN112397205A CN 112397205 A CN112397205 A CN 112397205A CN 202011424131 A CN202011424131 A CN 202011424131A CN 112397205 A CN112397205 A CN 112397205A
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weather
dengue fever
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temperature
dengue
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陈静
卜今
胡罡
毛小飞
肖辉
董光辉
乐迁
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Guangzhou South China Biomedical Research Institute Co ltd
CHINA METEOROLOGICAL ADMINISTRATION GUANGZHOU INSTITUTE OF TROPICAL OCEANIC METEOROLOGY INSTITUTE
Institute of Dermatology and Skin Disease Hospital of CAMS
National Sun Yat Sen University
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Guangzhou South China Biomedical Research Institute Co ltd
CHINA METEOROLOGICAL ADMINISTRATION GUANGZHOU INSTITUTE OF TROPICAL OCEANIC METEOROLOGY INSTITUTE
Institute of Dermatology and Skin Disease Hospital of CAMS
National Sun Yat Sen University
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Abstract

The invention provides a dengue fever infectious disease prediction method based on a meteorological model, which comprises a historical data processing process and a prediction process; determining influence weights, seasonal constants and effective accumulated temperatures of various weather phenomena on dengue fever by acquiring historical weather observation data and dengue fever case data in a corresponding period and performing screening processing, determining a prediction model, and completing a historical data processing process; and completing the prediction process of the dengue fever infectious diseases according to the prediction model. The dengue fever infectious disease prediction method provided by the invention realizes the dynamic prediction process of the prediction of dengue fever by constructing a prediction model, can be adjusted in real time along with the change of weather conditions and the change of disease development conditions, and can realize quantitative calculation compared with the qualitative calculation of the old method.

Description

Dengue fever infectious disease prediction method based on meteorological model
Technical Field
The invention relates to the technical field of infectious disease prediction, in particular to a dengue fever infectious disease prediction method based on a meteorological model.
Background
The existing dengue prediction method is based on weather element data such as temperature, humidity, precipitation and the like and dengue case data, and fitting is carried out by using statistical methods such as compare means, nonparametric test, corelate and the like [1], so that a fitting equation is obtained for statistical prediction; or analyzing the mosquito density index of the dengue fever virus carried by Aedes albopictus by adopting SPSS software according to the relationship between meteorological parameters such as air temperature and rainfall and the mosquito density index of the dengue fever virus carried by the Aedes albopictus and establishing a regression equation; and obtaining a fitting forecast equation or a regression forecast equation through a common statistical mode to predict the dengue fever.
Although a relatively good regression equation can be obtained by the method for predicting dengue through a statistical method, the obtained regression equation mainly reflects the statistical relationship among different historical data, and fails to truly reflect the influence of different weather phenomena including different rainfall intensities, high-temperature weather processes, cold air processes, typhoons and the like on mosquito vectors and dengue occurrence and prevalence, so that the prediction equation obtained through statistics is essentially the fitting of the historical process and the reappearance of the new weather process, namely the accuracy is not high in the actual prediction process, and the actual prediction significance is lacked.
Disclosure of Invention
The invention provides a dengue fever infectious disease prediction method based on a meteorological model, aiming at overcoming the technical defects that the influence of different weather phenomena on mosquito vectors and dengue fever occurrence and prevalence cannot be truly reflected, the prediction accuracy is low and the actual prediction significance is lacked in the existing dengue fever prediction method.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a dengue fever infectious disease prediction method based on a meteorological model comprises a historical data processing process and a prediction process; determining influence weights, seasonal constants and effective accumulated temperatures of various weather phenomena on dengue fever by acquiring historical weather observation data and dengue fever case data in a corresponding period and performing screening processing, determining a prediction model, and completing a historical data processing process; and completing the prediction process of the dengue fever infectious diseases according to the prediction model.
In the scheme, the forecasting of the dengue fever realized by the forecasting model constructed by the invention is a dynamic process, can be adjusted in real time along with the change of weather conditions and the change of disease development conditions, and can realize quantitative calculation compared with the qualitative calculation of an old method, wherein the quantitative calculation determines the starting date and the ending date of the annual dengue fever. The method is more accurate than the method of fitting a forecast equation by using historical data, and has practical significance for preventive medicine and emergency treatment.
The historical data processing process specifically comprises the following steps:
a1: acquiring historical weather observation data and dengue fever case data in a corresponding period;
a2: screening historical weather observation data to obtain various weather phenomena and different time periods corresponding to the various weather phenomena;
a3: determining effective accumulated temperature, seasonal constants and the influence degree of various weather phenomena on dengue fever according to dengue fever case data of different time periods corresponding to the various weather phenomena;
a4: quantifying the weather phenomenon into weather elements, and calculating the dengue fever occurrence conditions under different weather elements to obtain probability constants under different weather elements;
a5: and (4) according to the dengue fever occurrence probability calculated by the probability constants under different weather elements, finishing the historical data processing process.
In the step a3, the effective accumulated temperature determining process specifically includes:
defining an effective temperature T', and specifically expressing:
T'＝t-17℃
wherein T represents daily average air temperature, 17 ℃ is public data, when the daily average air temperature is more than 17 ℃, mosquitoes start to move, when the daily average air temperature is less than 17 ℃, the activities of the mosquitoes are reduced, namely when T 'is more than 0, the mosquitoes start to move, and when T' is less than 17 ℃, the activities of the mosquitoes are reduced; calculating the effective accumulated temperature T of different days intervals according to the effective temperature TeThe specific expression is as follows:
Figure BDA0002823992740000021
wherein i represents the ith year, a represents the year of starting the study, b represents the year of finishing the study, and j represents the number of days interval of the effective accumulated temperature; calculating the annual Te90≥0、Te60≥0、Te45≥0、Te35≥0、Te20≥0、Te10Not less than 0 and Te6The date of appearance of the patient is more than or equal to 0, the date of appearance of the first non-input case in each year is obtained according to the dengue fever case data, and the number of days of phase difference N between the two dates is calculatedijCalculating N in the historical data over the yearsijThe mean value and the standard deviation of the temperature of the dengue fever are obtained to obtain the effective accumulated temperature T of the dengue fever onset markeeAnd effective accumulated temperature T of dengue fever end marked。
In step a3, the seasonal constant determination process specifically includes:
defining a seasonal constant C, wherein the specific expression is as follows:
Figure BDA0002823992740000031
wherein M represents a month of 12 months (M e [1,12]), i is the ith year after a year, j is the jth (j e [1,12]) in 12 months, X is the total number of cases in the Mth month in all years, and X is the number of all dengue cases from a year onward to a + i year; when the weather is stable, C is calculated by equation (2), and when the weather is unstable, C is calculated by equation (1); whether the weather is stable or not is determined according to the weather process, and the more the weather process is, the month is unstable; the weather course is derived from a prediction of a future weather course.
Wherein, in the step A4, the weather elements include temperature T, relative humidity T and daily precipitation PtDuration of precipitation PcNo precipitation duration ScAnd high temperature duration days Th(ii) a Let QxThe probability constants corresponding to different weather elements are calculated by the following formula:
Figure BDA0002823992740000032
wherein N isxRepresenting the number of cases under the corresponding weather level of the weather element, determining the corresponding weather level according to the weather bureau for different weather disaster levels, NmaxRepresents the total number of all cases between 2005 and 2016.
Wherein, in the step a5, the calculation formula of the dengue fever occurrence probability Q is specifically as follows:
Figure BDA0002823992740000033
wherein Q isTWhich represents the probability constant for the temperature,Qτwhich represents the probability constant of relative humidity,
Figure BDA0002823992740000034
the amount of precipitation in the day is indicated,
Figure BDA0002823992740000035
the probability constant of the duration of precipitation is represented,
Figure BDA0002823992740000036
representing the probability constant for the duration of no precipitation,
Figure BDA0002823992740000037
representing the high temperature duration day probability constant.
Wherein the prediction process comprises the steps of:
b1: calculating the effective accumulated temperature under the current weather state, and judging whether the effective accumulated temperature is greater than Tee(ii) a If so, executing the step B2, otherwise, outputting the low-incidence season of the dengue fever, and completing the prediction of the dengue fever;
b2: judging whether the current weather state is stable or not, and calculating a seasonal constant;
b3: and (4) calculating the dengue fever occurrence probability Q according to the seasonal constant, and completing the prediction of dengue fever.
Wherein, the method also comprises the prediction of dengue fever high-risk areas, thereby determining the reception areas/concentrated isolation areas, and the specific prediction process is as follows:
Figure BDA0002823992740000038
wherein u represents an input case, o is a non-input case, LxIs the location of the input case, LyAs a non-input case location; and determining a centralized treatment/isolation place by calculating the average distance between the input case and the receiving area/the centralized isolation area, thereby realizing the prediction of the dengue high-risk area.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the dengue fever infectious disease prediction method based on the meteorological model, the dynamic prediction process of the prediction of dengue fever is realized by constructing the prediction model, real-time adjustment can be carried out along with the change of weather conditions and the change of disease development conditions, and compared with the qualitative calculation of an old method, the method can realize quantitative calculation, the starting date and the ending date of the annual dengue fever are determined by the quantitative calculation, the method is more accurate than the method using historical data to fit a prediction equation, and the method has practical significance for preventive medicine and emergency treatment.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flow chart of a historical data processing process.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
In order to avoid the situation that only pure data statistics are carried out as mentioned in the background art, the invention quantitatively extracts weather element data under different weather phenomena and researches the influence of the weather element data on dengue fever. Firstly, screening the weather data of 2005+ 2016, finding out all weather phenomena (precipitation, drought, cooling, high temperature, strong wind and the like) in the time period, and determining the influence degree of various weather phenomena on mosquitoes; since the weather phenomenon cannot be directly used to calculate the effect on mosquitoes or dengue fever, it is necessary to convert with weather elements that can quantify the weather phenomenon. By using the elements commonly used in meteorology: temperature (unit ℃, conforming to T, and being capable of representing high temperature and low temperature, that is, being capable of representing high-temperature weather, cooling process, cold weather, and the like), relative humidity (unit%, symbol τ, being capable of being used under various weather phenomena), daily precipitation (unit mm/24h, being capable of representing precipitation factors in typhoon and rainstorm, and being capable of representing precipitation processes of different levels), in addition, according to specific weather conditions affecting dengue fever, the present research adds three calculable elements, that is, precipitation duration (unit day, symbol Pc), no precipitation duration (unit day, symbol Sc), high-temperature duration days (day, symbol Th), and classifies each element according to weather phenomenon characteristics in 2005-2016, as shown in the following table:
weather phenomena represented by meteorology elements in 12005-2016 years
Figure BDA0002823992740000051
The method comprises the steps of decomposing one weather process into different weather elements for representation, and quantifying the different weather processes; after the weather process is quantified, the influence of the weather process on dengue fever can be calculated through the quantified weather elements.
In the implementation process, as shown in fig. 1, the dengue fever infectious disease prediction method based on the meteorological model comprises a historical data processing process and a prediction process; determining influence weights, seasonal constants and effective accumulated temperatures of various weather phenomena on dengue fever by acquiring historical weather observation data and dengue fever case data in a corresponding period and performing screening processing, determining a prediction model, and completing a historical data processing process; and completing the prediction process of the dengue fever infectious diseases according to the prediction model.
In the specific implementation process, the prediction model constructed by the invention realizes the dengue fever forecast, is a dynamic process, can be adjusted in real time along with the change of weather conditions and the change of disease development conditions, and can realize quantitative calculation compared with the qualitative calculation of an old method, wherein the quantitative calculation determines the starting date and the ending date of the annual dengue fever. The method is more accurate than the method of fitting a forecast equation by using historical data, and has practical significance for preventive medicine and emergency treatment.
More specifically, as shown in fig. 2, the history data processing process specifically includes the following steps:
a1: acquiring historical weather observation data and dengue fever case data in a corresponding period;
a2: screening historical weather observation data to obtain various weather phenomena and different time periods corresponding to the various weather phenomena;
a3: determining effective accumulated temperature, seasonal constants and the influence degree of various weather phenomena on dengue fever according to dengue fever case data of different time periods corresponding to the various weather phenomena;
a4: quantifying the weather phenomenon into weather elements, and calculating the dengue fever occurrence conditions under different weather elements to obtain probability constants under different weather elements;
a5: and (4) according to the dengue fever occurrence probability calculated by the probability constants under different weather elements, finishing the historical data processing process.
In the specific implementation process, the data acquisition mainly comes from meteorological data, and the weather data mainly comprises weather observation data from 1 month and 1 day in 2005 to 6 months and 30 days in 2017. From the provincial Bureau of Guangdong province, including hourly precipitation, daily cumulative precipitation, hourly average humidity, daily average humidity, hourly temperature, daily average temperature, and daily weather phenomena. Dengue case data: data for dengue daily cases from 1/2005 to 2016/12/31. From the disease control center of Guangdong province, the number of daily cases, the time of onset, the detailed address of the case, and whether the case is an input case or not are included.
In the specific implementation process, the acquired data needs to be analyzed, the data analysis comprises three parts, the first part is meteorological data analysis, and the second part is dengue case data analysis; the third part is to analyze the relationship between dengue and weather elements.
Weather data analysis: and (3) utilizing the automatic station data of Guangzhou city, interpolating meteorological elements according to the longitude and latitude of the automatic station and the longitude and latitude of the standing address of the dengue infected case to obtain the weather elements of the residence place of each case at the confirmed date, and then performing corresponding analysis. The method for calculating the weather element data interpolation comprises the following steps:
Figure BDA0002823992740000061
in the above formula: n is the number of samples, Z is the sample point value of the ith point, d is the distance from the ith sample to the interpolation point, and Z is the value to be calculated. When the sample point and the interpolation point coincide, the weight of the sample point is 1, the weights of other points are 0, and the value of the interpolation point is equal to the value of the sample point.
Dengue case data analysis: statistically, the dengue fever in Guangdong province mainly occurs in 7-11 months, and the number of cases is 45870, which accounts for 99.28% of the total number of the dengue fever; the low-incidence stage is 11-6 months, the number of all cases is 335 cases, and the proportion of all cases is only 0.73%; wherein 2 months is the lowest period of annual incidence, and the total cases of 12 years are only 11 people, accounting for 0.02 percent of the total cases. From the outbreak years, 2014 is the most serious year of the outbreak in the last 20 years, 42335 cases are reported all the year round, and account for 91.62% of the total cases; in 2006 and 2013, the reported cases are 1319 and 1311 respectively, which account for 2.85% and 2.84% of the total number of cases. The reported cases in other years are not more than 500 cases.
(1) The ratio of input cases to total cases
In the whole 12 years, the input cases account for 0.74 percent of total morbidity cases, wherein the highest year is 2009, 28 monitoring reported cases are counted all the year round, 14 input cases account for 50 percent; in 2014, 42335 monitoring reported cases in total year, wherein 86 input cases account for 0.2 percent;
(2) temporal profile characterization of input cases
The annual distribution characteristics are as follows: in the annual distribution of input cases, the input cases increase year by year from 2005 to 2014, and the input cases are the most in 2014, reaching 86 people and accounting for 31.16 percent of all input cases; in 2015, the number of input cases is slightly less than 2014, and 70 input cases account for 25.36% of all input cases, which is the second most cases in the past years.
Monthly distribution characteristics: in the input cases, the number of cases is 8-10 months, which is the main input month of the cases, and the total number of cases reaches 163, which accounts for 56.99% of the total number of input cases; wherein the input cases are 10 months at most and 70 persons, and account for 24.48 percent of the total number of all input cases; the second 9 months, 52 input cases, accounting for 18.18% of the total number of all input cases; the minimum is 2 months, the total input cases are 5 persons, and account for 1.75 percent of the total input cases.
Time distribution characteristics of the first input case: in 12 years, the first case of the current year is 2016, 1 and 1 month and the latest case is 2005, 6 and 30 months.
(3) Analysis of the relationship between dengue and meteorological elements
Defining an effective temperature T', and specifically expressing:
T'＝t-17℃
wherein T represents daily average air temperature, 17 ℃ is public data, when the daily average air temperature is more than 17 ℃, mosquitoes start to move, when the daily average air temperature is less than 17 ℃, the activities of the mosquitoes are reduced, namely when T 'is more than 0, the mosquitoes start to move, and when T' is less than 17 ℃, the activities of the mosquitoes are reduced; calculating the effective accumulated temperature T of different days intervals according to the effective temperature TeThe specific expression is as follows:
Figure BDA0002823992740000071
where i denotes the i-th year, i ∈ [2005,2016 ]]J represents the interval of days of effective accumulated temperature; calculating the annual Te90≥0、Te60≥0、Te45≥0、Te35≥0、Te20≥0、Te10Not less than 0 and Te6The date of appearance of the patient is more than or equal to 0, the date of appearance of the first non-input case in each year is obtained according to the dengue fever case data, and the number of days of phase difference N between the two dates is calculatedijCalculating N in the historical data over the yearsijThe mean value and the standard deviation of the temperature of the dengue fever are obtained to obtain the effective accumulated temperature T of the dengue fever onset markeeAnd effective accumulated temperature T of dengue fever end marked。
In the specific implementation, Te45More than or equal to 0 indicates that mosquitoes start to move in a large amount and enter the dengue fever season; t ise6<0 represents mosquitoReduced activity, reduced quantity, entering into the dengue enemy season. The specific calculation process is as follows: calculating the sliding accumulated temperature of 90 days, 60 days, 45 days, 35 days, 20 days, 10 days and 6 days by calculating the meteorological data from 2005 to 2016, and calculating the T value of each year of 12 years in 2005-2016e90≥0、Te60≥0、Te45≥0、Te35≥0、Te20≥0、Te10Not less than 0 and Te6Date of appearance of ≧ 0, and based on the date of appearance of the first non-input case in each year, the number of days of difference Ni between the two dates (i is year i, i e [2005,2016 ]]) Then, the mean and standard deviation of Ni over 12 years were calculated, and the relevant data are shown in the table below. According to the data in the table, Te45And Te6Has very good indication effect on the beginning and the end of dengue fever.
Figure BDA0002823992740000081
In the implementation process, the starting date of dengue fever entering the high-incidence season and the date of dengue fever entering the low-incidence season can be determined through the formulas (2) and (3).
More specifically, in the step a3, the process of determining the seasonal constant is specifically as follows:
defining a seasonal constant C, wherein the specific expression is as follows:
Figure BDA0002823992740000082
wherein M represents a month of 12 months (M e [1,12]), i is year i after 2005, j is the j-th case in 12 months (j e [1,12]), X is the total number of cases in month M in all years, and X is the number of all dengue cases from 2005 onwards in year 2005+ i; when the weather is stable, C is calculated by an equation (a), and when the weather is unstable, C is calculated by an equation (b); whether the weather is stable or not is determined according to the weather process, and the more the weather process is, the month is unstable; the weather process comes from weather forecasts issued by weather stations.
More particularly, toIn the step A4, the weather elements include temperature T, relative humidity τ and daily precipitation PtDuration of precipitation PcNo precipitation duration ScAnd high temperature duration days Th(ii) a Let QxThe probability constants corresponding to different weather elements are calculated by the following formula:
Figure BDA0002823992740000091
wherein N isxRepresenting the number of cases under the corresponding weather level of the weather element, determining the corresponding weather level according to the weather bureau for different weather disaster levels, NmaxRepresents the total number of all cases between 2005 and 2016.
More specifically, in step a5, the calculation formula of the dengue fever occurrence probability Q is specifically as follows:
Figure BDA0002823992740000092
wherein Q isTRepresenting the probability constant of temperature, QτWhich represents the probability constant of relative humidity,
Figure BDA0002823992740000093
the amount of precipitation in the day is indicated,
Figure BDA0002823992740000094
the probability constant of the duration of precipitation is represented,
Figure BDA0002823992740000095
representing the probability constant for the duration of no precipitation,
Figure BDA0002823992740000096
representing the high temperature duration day probability constant.
More specifically, the prediction process comprises the steps of:
b1: calculating the current weather stateEffective accumulated temperature is judged whether the effective accumulated temperature is more than Tee(ii) a If so, executing the step B2, otherwise, outputting the low-incidence season of the dengue fever, and completing the prediction of the dengue fever;
b2: judging whether the current weather state is stable or not, and calculating a seasonal constant;
b3: and (4) calculating the dengue fever occurrence probability Q according to the seasonal constant, and completing the prediction of dengue fever.
Wherein the method further comprises the prediction of dengue fever high-risk areas so as to determine the hospital for the reception, and the specific prediction process is as follows:
Figure BDA0002823992740000097
wherein u represents an input case, o is a non-input case, LxIs the location of the input case, LyAs a non-input case location; and determining the hospital for reception as a centralized treatment place by calculating the average distance between the input case and the hospital for reception, so as to realize the prediction of the high-risk dengue region.
Example 2
More specifically, on the basis of the embodiment 1, in the aspect of dengue fever forecasting, the Guangdong province disease prevention and control center is provided with a set of forecasting and early warning system aiming at dengue fever, namely 'the space-time analysis and early warning system' of dengue fever in Guangdong province, the system can predict the epidemic trend and risk of dengue fever in various places in Guangdong province in 4-8 weeks in the future by using multi-source space-time big data of dengue fever epidemic influence factors, and the data in meteorological aspect is used as the background field of the early warning system to qualitatively predict the development of dengue fever. The system differs from the present invention in that: (1) the method mainly utilizes historical weather data, and adopts a mode of obtaining a qualitative forecast equation and a non-quantitative forecast equation through various fitting modes; (2) the system only takes the weather as a background field, is not combined with a specific weather process in an actual prediction process, and fails to reveal the important influence of the weather process on the dengue fever, so the weather has a single function in the system and does not contribute high to the accuracy of the dengue fever forecast.
More specifically, the failure of the existing dengue fever infectious disease prediction method to combine with the limitations of the weather factor model mainly has the following three aspects:
(1) the barrier between weather forecasting and dengue forecasting techniques: the weather forecasting technology mainly adopts thermodynamic and kinetic equations, focuses on forecasting weather elements and weather phenomena, and the forecasting result is ended to obtain future weather elements and weather phenomena. And the dengue prediction needs to be carried out under historical weather conditions and future weather conditions according to the influence of the weather on the dengue and the feedback of the dengue on the weather. The two technologies are in two completely different discipline fields, and how to determine and quantify the influence of different weather phenomena on dengue fever occurrence and prevalence is the key point of the invention on the premise of breaking the discipline barrier.
(2) The depth of the cross-study of meteorological and dengue fever was not sufficient: at present, the cross-over research on the domestic meteorological phenomena and dengue fever is limited to the subject barriers, the depth is not enough, the correlation research on the meteorological phenomena and dengue fever is mainly limited, and no weather parameter prediction model is reported.
In the implementation process, the weather development process is a continuous process, and the influence on dengue fever is continuous, so that the invention forms a continuous forecasting equation which can be quantitatively calculated by using continuous and quantitative weather elements and quantitative dengue fever infected cases according to the fact that the occurrence and prevalence of dengue fever and the fact that the weather elements are highly correlated are found through research to quantitatively predict dengue fever. The improvement of the invention mainly focuses on the following three aspects:
(1) the qualitative analysis of the traditional weather process is decomposed into quantitative analysis mainly based on weather elements and dengue fever, and the response mechanism of the weather and the dengue fever is determined: by decomposing the weather process into weather elements, the effect of weather on dengue fever is changed from traditional qualitative analysis to quantitative analysis in the present invention. Through quantitative analysis, the research on the influence of weather on dengue fever has a breakthrough, the starting date and the ending date of the dengue fever every year can be quantitatively calculated through meteorological data, and whether the weather change is suitable for the dengue fever epidemics can be quantitatively calculated.
(2) The forecasting mode is changed from single-factor static statistical forecasting to multi-meteorological-element continuous forecasting: traditional dengue prediction is single factor analysis; all conditions cannot be considered, weather conditions which do not appear in history cannot be forecasted, and the application prospect is limited; the invention is a multi-factor analysis, and can carry out future dengue fever occurrence and development forecast through all forecast weather conditions in the future. The traditional forecasting method is static statistics and probability prediction; the invention can be a dynamic multi-element continuous forecasting equation, and the forecasting result is adjusted in real time along with the change of the actually occurring weather conditions and the development condition of dengue fever.
(3) The data precision is greatly improved, and the work prevention guidance significance is as follows: precisely positioning the living address of each case; and secondly, interpolating the meteorological data to match the position of each case by positioning, thereby obtaining the meteorological data highly matched with the case. Ensuring the foundation and feasibility of meteorological element and dengue fever epidemic research.
The improvement of the 3 aspects converts the original relation that only can qualitatively describe the weather and the dengue fever into the relation between the two quantitative calculation, and greatly improves the possibility and the accuracy of the dengue fever forecast under different weather backgrounds.
In the specific implementation process, the technical scheme recorded by the invention can effectively utilize weather observation data and forecast data to carry out quantitative dynamic prediction and forecast on dengue fever; according to the historical dengue fever cases and the historical weather data, the weather elements with high relevance are decomposed to obtain relatively accurate forecasting formulas, the time of dengue fever occurrence and the time of dengue fever ending can be quantitatively forecasted by using the effective forecasting formulas of the weather elements, and the probability of dengue fever occurrence and epidemic under different weather conditions can be accurately forecasted; according to the calculation, the fact that the hospital is a key node for controlling the diffusion of dengue fever is quantitatively obtained, and the key node hospital can be determined through calculation.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (8)

1. A dengue fever infectious disease prediction method based on a meteorological model is characterized by comprising a historical data processing process and a prediction process; determining influence weights, seasonal constants and effective accumulated temperatures of various weather phenomena on dengue fever by acquiring historical weather observation data and dengue fever case data in a corresponding period and performing screening processing, determining a prediction model, and completing a historical data processing process; and completing the prediction process of the dengue fever infectious diseases according to the prediction model.
2. The method for dengue fever infection prediction based on meteorological model according to claim 1, wherein the historical data processing comprises the following steps:
a1: acquiring historical weather observation data and dengue fever case data in a corresponding period;
a2: screening historical weather observation data to obtain various weather phenomena and different time periods corresponding to the various weather phenomena;
a3: determining effective accumulated temperature, seasonal constants and the influence degree of various weather phenomena on dengue fever according to dengue fever case data of different time periods corresponding to the various weather phenomena;
a4: quantifying the weather phenomenon into weather elements, and calculating the dengue fever occurrence conditions under different weather elements to obtain probability constants under different weather elements;
a5: and (4) according to the dengue fever occurrence probability calculated by the probability constants under different weather elements, finishing the historical data processing process.
3. The method for dengue fever infection prediction based on meteorological model as claimed in claim 2, wherein in the step A3, the determination of the effective accumulated temperature is specifically as follows:
defining an effective temperature T', and specifically expressing:
T'＝t-17℃
wherein T represents daily average air temperature, 17 ℃ is public data, when the daily average air temperature is more than 17 ℃, mosquitoes start to move, when the daily average air temperature is less than 17 ℃, the activities of the mosquitoes are reduced, namely when T 'is more than 0, the mosquitoes start to move, and when T' is less than 17 ℃, the activities of the mosquitoes are reduced; calculating the effective accumulated temperature T of different days intervals according to the effective temperature TeThe specific expression is as follows:
Figure FDA0002823992730000011
wherein i represents the ith year, i ∈ [ a, b ]]Wherein, a represents the year of starting the research, b represents the year of finishing the research, and j represents the days interval of the effective accumulated temperature; calculating the annual Te90≥0、Te60≥0、Te45≥0、Te35≥0、Te20≥0、Te10Not less than 0 and Te6The date of appearance of the patient is more than or equal to 0, the date of appearance of the first non-input case in each year is obtained according to the dengue fever case data, and the number of days of phase difference N between the two dates is calculatedijCalculating N in the historical data over the yearsijThe mean value and the standard deviation of the temperature of the dengue fever are obtained to obtain the effective accumulated temperature T of the dengue fever onset markeeAnd effective accumulated temperature T of dengue fever end marked。
4. The method for dengue fever infection prediction based on meteorological model as claimed in claim 3, wherein in the step A3, the determination process of the seasonal constant is specifically as follows:
defining a seasonal constant C, wherein the specific expression is as follows:
Figure FDA0002823992730000021
wherein M represents a month of 12 months (M e [1,12]), i is the ith year after a year, j is the jth (j e [1,12]) in 12 months, X is the total number of cases in the Mth month in all years, and X is the number of all dengue cases from a year onward to a + i year; when the weather is stable, C is calculated by equation (2), and when the weather is unstable, C is calculated by equation (1); whether the weather is stable or not is determined according to the weather process, and the more the weather process is, the month is unstable; the weather course is derived from a prediction of a future weather course.
5. The method for dengue fever infection prediction based on meteorological model as claimed in claim 4, wherein in the step A4, the weather elements comprise temperature T, relative humidity τ and daily precipitation PtDuration of precipitation PcNo precipitation duration ScAnd high temperature duration days Th(ii) a Let QxThe probability constants corresponding to different weather elements are calculated by the following formula:
Figure FDA0002823992730000022
wherein N isxRepresenting the number of cases under the corresponding weather level of the weather element, determining the corresponding weather level according to the weather bureau for different weather disaster levels, NmaxRepresenting the total number of all cases from year a to year b.
6. The method for dengue fever infection prediction based on meteorological model as claimed in claim 5, wherein in the step A5, the calculation formula of the dengue fever occurrence probability Q is specifically as follows:
Figure FDA0002823992730000023
wherein Q isTRepresenting the probability constant of temperature, QτWhich represents the probability constant of relative humidity,
Figure FDA0002823992730000024
the amount of precipitation in the day is indicated,
Figure FDA0002823992730000025
the probability constant of the duration of precipitation is represented,
Figure FDA0002823992730000026
representing the probability constant for the duration of no precipitation,
Figure FDA0002823992730000027
representing the high temperature duration day probability constant.
7. The method of claim 6, wherein the prediction process comprises the steps of:
b1: calculating the effective accumulated temperature under the current weather state, and judging whether the effective accumulated temperature is greater than Tee(ii) a If so, executing the step B2, otherwise, outputting the low-incidence season of the dengue fever, and completing the prediction of the dengue fever;
b2: judging whether the current weather state is stable or not, and calculating a seasonal constant;
b3: and (4) calculating the dengue fever occurrence probability Q according to the seasonal constant, and completing the prediction of dengue fever.
8. The method for dengue fever infection prediction based on meteorological model according to claim 7, further comprising prediction of dengue fever high risk areas, thereby determining the areas for reception/central isolation, the specific prediction process is:
Figure FDA0002823992730000031
wherein u represents an input case, o is a non-input case, LxIs the location of the input case, LyAs a non-input case location; and determining a centralized treatment/isolation place by calculating the average distance between the input case and the receiving area/the centralized isolation area, thereby realizing the prediction of the dengue high-risk area.
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