Email this article Predicting incidence of Crimean-Congo hemorrhagic fever using satellite monitoring (remote sensing) data in the Stavropol Territory
- Authors: Dubyanskiy V.M.1,2, Prislegina D.A.1,2, Platonov A.E.2
-
Affiliations:
- Stavropol Plague Control Research Institute
- Central Research Institute of Epidemiology
- Issue: Vol 99, No 3 (2022)
- Pages: 322-335
- Section: ORIGINAL RESEARCHES
- Submitted: 28.07.2022
- Accepted: 28.07.2022
- Published: 28.07.2022
- URL: https://microbiol.crie.ru/jour/article/view/1261
- DOI: https://doi.org/10.36233/0372-9311-213
- ID: 1261
Cite item
Abstract
Introduction. With the epidemiological situation for Crimean-Congo hemorrhagic fever (CCHF) remaining tense in many countries worldwide, special attention should be focused on development and improvement of risk-based epidemiological prediction methods.
The aim of the study was to build a prediction model for CCHF incidence dynamics (based on the Stavropol Territory) using satellite monitoring (remote sensing) data.
Materials and methods. We analyzed the climate data obtained from the Space Research Institute of the Russian Academy of Sciences as well as the data of public statistics reports on CCHF incidence from 2005 to 2021. The prediction model incorporated the Bayes theorem and Wald sequential analysis. The information content of the factors was assessed using the Kullback method.
Results. Predictions for each of 26 districts were made stepwise (compared to threshold levels) to predict whether there will be at least one case of CCHF, whether the relative incidence per 100,000 population will exceed the median level (0.9 cases) or the average rate (3.5 cases) or the third quartile rate (4.7 cases). The highest values of information coefficients were obtained for soil temperature and moisture content (at depths of 10 and 40 cm), normalized relative vegetation index, relative humidity, maximum and average air temperature, relative air humidity. During the testing of the model in 2021, false-negative (erroneous) prediction was made for 2 districts.
Discussion. The model proved to be most efficient in prediction of occurrence or absence of cases. More accurate quantitative prediction may be difficult due to subjective factors (including misdiagnosing CCHF cases without hemorrhagic manifestations and administering treatment for other conditions with similar symptoms).
Conclusion. The tests of the model demonstrate its potential. The practical application of the prediction will make healthcare workers more alert when screening and detecting CCHF cases.
Full Text
Introduction
Crimean-Congo hemorrhagic fever (CCHF) is an especially dangerous arboviral infection; the epidemiological situation has been tense for many years in countries of Central Asia and the Middle East as well as in the Russian Federation [1–12].
As there are currently no specific preventive vaccines against this dangerous infection, risk-based prediction of incidence in a population is given special attention to stabilize the epidemiological situation and to serve as a basis for scientifically grounded planning of non-specific preventive measures.
In Turkey, a multi-agent SIR model was offered for making epidemiological predictions [13]. In Russia, the "maximum stability" estimation method and regression analysis were used to build a short-term prediction model to estimate the expected number of CCHF cases (annual rates) in the current year by the incidence of the "key" month [14].
In the meantime, CCHF is a natural focal, transmissible infection; therefore, when selecting a prediction method, the priority should be given to the methods that address the effect of climatic factors on the population of specific vectors of its pathogen — ixodid ticks and, consequently, on the incidence rates, as is demonstrated by many studies in other countries.
In Iran, the researchers, using the time-series analysis and the seasonal auto-regression integrated moving average (SARIMA) model, found a strong correlation between the number of cases and the monthly average air temperature, monthly maximum relative humidity and accumulated precipitation, and using the Poisson regression analysis together with the McFadden pseudo R-squared – the correlation with the maximum temperature values of the previous month [15][16]. In Bulgaria, the one-way analysis of variance (ANOVA) showed that the increase in the average air temperature and the normalized difference vegetation index (NDVI) per unit resulted in a 5.5% increase in the CCHF incidence [17].
Earlier, using the correlation analysis, Bayes theorem and Wald sequential analysis, V.M. Dubyanskiy and D.A. Prislegina developed the method for quantitative risk-based prediction of CCHF incidence in each administrative district of the Stavropol Territory [18–20]. The calculations were based on monthly values of five factors of all seasons (air temperature, relative air humidity, precipitation amounts, snow depth and wind speed), which affected different stages in the life cycle of Hyalomma marginatum ticks, which are the main vectors and reservoirs of the CCHF virus in Russia [10][12][21–25]. The efficiency of the offered method was proved by the fact that the results obtained in 2018 and the actual data had a 87.4% match; the prediction results were used in planning preventive measures.
This study is a continuation of the previous research; its aim is to improve methods used for CCHF incidence prediction and to obtain new data on the relationship between the intensity of epidemic and epizootic processes of this especially dangerous infection and the combined effects of multiple, including previously unaddressed, climatic factors.
Materials and methods
The study incorporated combined methods of epidemiological analysis and mathematical statistics.
The materials included the hydro-meteorological data and NDVI received from the database of the SRI-Monitoring Center for Collective Use of Systems for Archiving, Processing and Analyzing Satellite Data of the RAS Space Research Institute as well as the data of public statistics reports for 2005-2021. To calculate the relative CCHF incidence (per 100,000 population) for administrative districts of the Stavropol Territory, we analyzed the epidemiological survey charts of the infectious disease cluster (Form 357/u), which were provided by the Department of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor) for the Stavropol Territory, as well as the archival data from the Department of the Federal Statistics Service for the North Caucasian Federal District and the Federal Statistics Service for each year of the studied period.
The prediction model of the incidence dynamics was based on the Bayes theorem and Wald sequential analysis [26–28]. The Bayes theorem was intentionally used for building the model, as it helped incorporate divergent effects of multiple factors for each administrative district using data on the frequency of the respective values and occurrence/absence of disease cases as well as made it possible to obtain alternative results (one of the two possible prediction outcomes – absence/occurrence of the case, surpassing/non-surpassing of the selected thresholds by the incidence rates).
The threshold level of positive solution probability was set at 99% (the error probability of 1%). We used numeric values of 13 climatic factors (for each month of the studied period and annual means):
- air temperature — average, maximum and minimum (°C);
- soil temperature at depths of 10 and 40 cm (°C);
- soil moisture content at depths of 10 and 40 cm (%);
- snow depth (m);
- area covered by snow (%);
- pressure (Pa);
- relative air humidity (%);
- precipitation (kg/m2);
- NDVI (relative units).
The information content of 169 parameters was estimated using the Kullback method [26–28]. The factor was deemed as informative, if it demonstrated a larger difference between distributions of two differentiated conditions of the object of study. For example, at the average air temperature of 28°C in June, the probability of at least one case of CCHF within the natural focus of infection is 2 times as high on average as the probability at the temperature of 24°C. This factor can be seen as informative, as the incidence prediction will be true at the ratio of 2:1 at the above temperature. However, the derivative of the information content – the diagnostic coefficient is more convenient to use. It is efficient for estimating the probability of the object being in one of the two studied conditions at certain values of several factors. For example, if the average air temperature in June is 28°C and the average barometric pressure in June is above 103,165.18 Pa, the probability of at least one case of CCHF within the natural focus of infection reaches 0.8.
In addition to using an increased number of factors, the offered model differs fundamentally from the former method of risk-based prediction [18–20] by organization of the data for identification of predictors. Considering a relatively short time series (15 years) and landscape heterogeneity of the Stavropol Territory, the data were organized as follows: The CCHF incidence data broken by administrative districts and years were combined into a cumulative series (a total of 364 values), while the weather and climate variables were distributed according to the incidence.
The incidence was measured in relative numbers of cases per 100,000 population.
Information and prediction coefficients were calculated automatically using the Microsoft Excel 2010-based program developed by the authors.
The step-by-step algorithm for making a prediction using the prediction model:
- Calculation of information and prediction coefficients of factors with the program.
- Making an optimized list of factors (with values of the information coefficient ≥5 in descending order).
- Predicting the occurrence (absence) of at least 1 case of disease per 100,000 population.
- Calculating the incidence per 100,000 population compared to the threshold median level (0.9).
- Calculating the incidence per 100,000 population compared to the average rate (3.5).
- Calculating the incidence per 100,000 population compared to the third quartile rate (4.7).
Calculations for item 2 were made against the selected minimum threshold value (the value of 0.000009, which is lower than the rate of 1 case per 100,000 population). The calculations for the prediction were made by summing up the values of prediction coefficients of the informative factors in accordance with their values for each administrative district until the numeric value of +20 or -20 was received to indicate the occurrence/absence of disease cases with a probability of 99%. Then, when the districts with positive results were identified, the prediction for them was made against other selected threshold levels of incidence.
Results
The prediction model was built for making prediction for CCHF epidemiological situation for each district using the climate data of the previous year. To date, there have been no similar studies in predicting CCHF incidence by using satellite monitoring (remote sensing) data and the identical algorithm.
The model is built for alternative-based predictions: Whether there will be or will not be at least 1 CCHF case in the district, whether the relative incidence (per 100,000 population) will exceed the median level (0.9 cases), the average rate (3.5), the third quartile rate (4.7). The informative predictors for each threshold are presented in Table 1.
Table 1. Climatic and environmental indicators used as predictors of the forecast model
Predictor | Indicator | Will there be at least one sick | Indicator | Above/ below the median | Indicator | Above/ below average | Indicator | Above/ below the third quartile |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
1 | Soil temperature at a depth of 40 cm in June | 1,59 | Soil temperature at a depth of 40 cm in June | 1,89 | NDVI in June | 1,85 | Humidity in June | 1,83 |
2 | Humidity in June | 1,51 | Humidity in June | 1,79 | Humidity in June | 1,84 | NDVI in June | 1,65 |
3 | Soil temperature at a depth of 40 cm in August | 1,31 | Soil temperature at a depth of 40 cm in August | 1,64 | Soil temperature at a depth of 40 cm in June | 1,74 | Soil temperature at a depth of 40 cm in June | 1,62 |
4 | Soil moisture at a depth of 10 cm in June | 1,30 | Soil moisture at a depth 10 cm in June | 1,51 | Maximum air temperature in June | 1,38 | Average annual air humidity | 1,52 |
5 | Average annual air humidity | 1,29 | Average annual air humidity | 1,48 | NDVI in August | 1,35 | Soil moisture at a depth of 10 cm in June | 1,36 |
6 | NDVI in June | 1,18 | NDVI in June | 1,38 | Soil moisture at a depth of 10 cm in June | 1,32 | Maximum air temperature in June | 1,29 |
7 | Soil temperature at a depth of 10 cm in June | 1,15 | Soil temperature at a depth of 10 cm in June | 1,33 | Soil temperature at a depth of 40 cm in August | 1,32 | Soil temperature at a depth of 10 cm in June | 1,24 |
8 | Soil moisture at a depth of 40 cm in June | 1,13 | NDVI in August | 1,33 | Soil temperature at a depth of 10 cm in June | 1,26 | Air humidity in May | 1,23 |
9 | NDVI in August | 1,06 | Humidity in August | 1,24 | Average annual air humidity | 1,26 | Humidity in August | 1,20 |
10 | Humidity in August | 1,06 | Soil moisture at a depth of 40 cm in June | 1,23 | Air temperature in June | 1,24 | Soil temperature at a depth of 40 cm in August | 1,20 |
11 | Soil temperature at a depth of 40 cm in July | 1,04 | Soil temperature at a depth of 40 cm in July | 1,22 | Pressure in May | 1,20 | Air temperature in June | 1,11 |
12 | Maximum air temperature in June | 1,01 | Maximum air temperature in June | 1,18 | Pressure in April | 1,20 | Давление в мае Pressure in May | 1,11 |
13 | Humidity in July | 0,97 | Pressure in February | 1,17 | Pressure in March | 1,18 | Pressure in April | 1,10 |
14 | Pressure in January | 0,97 | Pressure in January | 1,16 | Pressure in June | 1,14 | Pressure in March | 1,07 |
15 | Pressure in October | 0,96 | Pressure in March | 1,15 | Pressure in October | 1,13 | Pressure in June | 1,07 |
16 | NDVI in July | 0,95 | Pressure in June | 1,14 | NDVI in July | 1,13 | NDVI in August | 1,07 |
17 | Pressure in March | 0,95 | Pressure in May | 1,14 | Pressure in August | 1,13 | Soil moisture at a depth of 10 cm in May | 1,05 |
18 | Pressure in August | 0,94 | NDVI in July | 1,13 | Pressure in February | 1,07 | Pressure in February | 1,05 |
19 | Air humidity in May | 0,94 | Pressure in April | 1,13 | Pressure in January | 1,05 | Pressure in October | 1,04 |
Continuation of the Table 1
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
20 | Pressure in February | 0,94 | Humidity in July | 1,12 | Pressure in September | 1,05 | Pressure in August | 1,03 |
21 | Pressure in April | 0,93 | Pressure in October | 1,12 | Average annual pressure | 1,04 | Soil moisture at a depth of 40 cm in June | 1,02 |
22 | Air temperature in June | 0,93 | Pressure in August | 1,11 | Humidity in July | 1,03 | Humidity in July | 0,97 |
23 | Pressure in June | 0,92 | Air temperature in June | 1,11 | Air humidity in May | 1,01 | NDVI in July | 0,96 |
24 | Pressure in May | 0,91 | Pressure in September | 1,09 | NDVI annual average | 1,01 | Pressure in September | 0,96 |
25 | Pressure in September | 0,89 | Average annual pressure | 1,08 | Soil temperature at a depth of 40 cm in July | 0,98 | Average annual pressure | 0,95 |
26 | Average annual pressure | 0,88 | Air humidity in May | 1,07 | Humidity in August | 0,98 | Pressure in January | 0,94 |
27 | Average annual soil temperature at a depth of 40 cm | 0,86 | Average annual soil temperature at a depth of 40 cm | 1,06 | Minimum air temperature in June | 0,97 | Soil temperature at a depth of 40 cm in July | 0,85 |
28 | Soil temperature at a depth of 10 cm in August | 0,84 | Soil temperature at a depth of 10 cm in August | 1,04 | Soil moisture at a depth of 40 cm in June | 0,96 | Minimum air temperature in June | 0,80 |
29 | Soil moisture at a depth of 10 cm in July | 0,78 | Soil moisture at a depth of 10 cm in July | 0,97 | Average annual soil temperature at a depth of 40 cm | 0,86 | Pressure in July | 0,76 |
30 | Minimum air temperature in June | 0,78 | NDVI in September | 0,97 | Soil moisture at a depth of 10 cm in May | 0,86 | Soil temperature at a depth of 10 cm in August | 0,75 |
31 | Soil temperature at a depth of 40 cm in September | 0,77 | Soil temperature at a depth of 40 cm in September | 0,96 | Pressure in July | 0,84 | Soil moisture at a depth of 40 cm in May | 0,73 |
32 | Soil moisture at a depth of 10 cm in May | 0,75 | Pressure in July | 0,95 | Soil temperature at a depth of 10 cm in August | 0,84 | Rainfall in May | 0,73 |
33 | Average annual soil temperature at a depth of 10 cm | 0,73 | Soil moisture at a depth of 10 cm in May | 0,92 | NDVI in September | 0,80 | Average annual precipitation | 0,72 |
34 | Soil temperature at a depth of 10 cm in July | 0,73 | Minimum air temperature in June | 0,91 | Soil moisture at a depth of 10 cm in July | 0,79 | Average annual soil temperature at a depth of 40 cm | 0,72 |
35 | NDVI annual average | 0,72 | NDVI annual average | 0,89 | Air temperature in July | 0,76 | Soil moisture at a depth of 10 cm in July | 0,70 |
36 | Pressure in July | 0,72 | Average annual soil temperature at a depth of 10 cm | 0,88 | Maximum air temperature in July | 0,73 | Rainfall in June | 0,69 |
37 | Minimum air temperature in August | 0,71 | Air temperature in August | 0,88 | Air temperature in August | 0,73 | Air temperature in August | 0,67 |
Continuation of the Table 1
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
38 | Air temperature in August | 0,70 | Soil temperature at a depth of 10 cm in July | 0,84 | Minimum air temperature in August | 0,71 | NDVI average annual | 0,66 |
39 | Minimum average annual air temperature | 0,70 | Minimum average annual air temperature | 0,83 | Soil temperature at a depth of 10 cm in July | 0,68 | Soil moisture at a depth of 10 cm in August | 0,63 |
40 | Average annual air temperature | 0,68 | Minimum air temperature in August | 0,81 | Soil temperature at a depth of 40 cm in September | 0,68 | Average air temperature | 0,61 |
41 | Minimum air temperature in September | 0,66 | Minimum air temperature in September | 0,80 | Average air temperature | 0,67 | NDVI in September | 0,60 |
42 | Soil moisture at a depth of 10 cm in August | 0,65 | Maximum air temperature in July | 0,76 | Rainfall in June | 0,65 | Soil temperature at a depth of 10 cm in July | 0,60 |
43 | Minimum air temperature in May | 0,64 | Air temperature in July | 0,74 | NDVI in May | 0,63 | Air temperature in July | 0,59 |
44 | NDVI in September | 0,63 | Average annual air temperature | 0,69 | Minimum average annual air temperature | 0,63 | Maximum air temperature in July | 0,57 |
45 | Air temperature in July | 0,62 | Rainfall in June | 0,69 | Rainfall in May | 0,60 | Soil moisture at a depth of 40 cm in August | 0,56 |
46 | Soil temperature at a depth of 10 cm in September | 0,59 | Maximum air temperature in August | 0,69 | Minimum air temperature in July | 0,59 | NDVI in May | 0,55 |
47 | Maximum air temperature in August | 0,58 | Rainfall in May | 0,68 | Average annual soil temperature at a depth of 10 cm | 0,58 | Air humidity in September | 0,54 |
48 | Maximum air temperature in July | 0,57 | Soil moisture at a depth of 10 cm in August | 0,68 | Soil moisture at a depth of 40 cm in May | 0,56 | Minimum air temperature in August | 0,53 |
49 | Soil moisture at a depth of 40 cm in August | 0,57 | Soil moisture at a depth of 40 cm in July | 0,68 | Maximum air temperature in August | 0,56 | Humidity in April | 0,53 |
50 | Maximum air temperature average annual | 0,57 | Soil moisture at a depth of 40 cm in August | 0,67 | Soil moisture at a depth of 40 cm in July | 0,55 | Soil temperature at a depth of 40 cm in May | 0,53 |
51 | Soil moisture at a depth of 40 cm in July | 0,55 | Minimum air temperature in May | 0,66 | Average annual precipitation | 0,54 | Air temperature in May | 0,52 |
52 | Maximum air temperature in May | 0,54 | Minimum air temperature in July | 0,66 | Soil temperature at a depth of 40 cm in May | 0,54 | Average annual soil temperature at a depth of 10 cm | 0,52 |
53 | Proportion of snow covered area in January | 0,54 | Soil moisture at a depth of 40 cm in May | 0,66 | Minimum air temperature in September | 0,53 | Soil temperature at a depth of 40 cm in September | 0,51 |
End of the Table 1
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
54 | Minimum air temperature in July | 0,53 | Soil temperature at a depth of 10 cm in September | 0,63 | Minimum air temperature in May | 0,52 | Maximum air temperature in May | 0,51 |
55 | Soil moisture at a depth of 40 cm in May | 0,52 | Maximum air temperature average annual | 0,58 | Maximum air temperature in May | 0,51 | Minimum air temperature average annual | 0,50 |
56 | Rainfall in June | 0,52 | Maximum air temperature in May | 0,58 | Maximum air temperature average annual | 0,51 |
|
|
57 | Rainfall in May | 0,51 | Air temperature in September | 0,56 |
|
|
|
|
58 |
|
| Average annual precipitation | 0,56 |
|
|
|
|
59 |
|
| NDVI in May | 0,55 |
|
|
|
|
60 |
|
| Air humidity in September | 0,52 |
|
|
|
|
61 |
|
| Soil temperature at a depth of 10 cm in May | 0,52 |
|
|
|
|
62 |
|
| Air temperature in May | 0,50 |
|
|
|
|
A total of 57 predictors were used for the threshold "whether there will be or will not be at least one case of CCHF in the district"; 62 predictors were used for the threshold "whether the relative incidence will exceed the median level (0.9 cases per 100,000 population)"; 56 predictors were used for the threshold "whether the relative incidence will exceed the average rate (3.5 cases)"; 55 predictors were used for the threshold "whether the relative incidence will exceed the third quartile rate (4.7 cases)".
The assessment of the information content of factors showed the highest values of information coefficients for soil temperature and moisture content (at depths of 10 cm and 40 cm), NDVI, relative humidity, maximum and average air temperature (in June), relative air humidity and NDVI (in August). During the above months, these climatic factors (according to the literature data) have a strong impact on embryogenesis, survival rate and preimaginal phases and development of H. marginatum, the population of feeders of preimaginal phases, thus having a significant effect on the size of imago tick population in the next year (the year of prediction) [22][24][25].
The prediction model for CCHF incidence dynamics was checked using the historical data for 2018–2020 (Table 2).
Table 2. The results of checking the forecast model of the CCHF morbidity dynamics using retrospective data for 2018-2020
Results | Year | |||||
2018 | 2019 | 2020 | ||||
abs. | % | abs. | % | abs. | % | |
Correct | 16 | 61,5 | 12 | 46,2 | 7 | 26,9 |
False positive | 3 | 11,5 | 7 | 26,9 | 14 | 53,8 |
False negative | 1 | 3,8 | 2 | 7,7 | 1 | 3,8 |
Overestimated | 4 | 15,4 | 5 | 19,2 | 4 | 15,4 |
Underestimated | 2 | 7,7 | 0 | - | 0 | - |
The model-based erroneous predictions were divided into 4 types:
- false-positive — the predicted result is positive; however, no cases have been reported;
- false-negative — the predicted result is negative; however, cases of disease have been detected;
- overestimated — the actual incidence rate is lower than the predicted rate against the threshold level;
- underestimated — the actual incidence rate is higher than the predicted rate against the threshold level.
In 2020, the epidemiological prediction for 2021 was prepared (Table 3).
Table 3. The results of approbation of the forecast model of the CCHF morbidity dynamics for 2021
Administrative region | Forecast result | Factual morbidity (per 100,000 population) |
Alexandrovsky | ≤ 0,000009 | 0 |
Andropovsky | ≤ 0,000009 | 0 |
Apanasenkovsky | > 4,7 | 10,1 |
Arzgirsky | > 4,7 | 8,3 |
Blagodarnensky | > 4,7 | 5,2 |
Budennovsky | > 4,7 | 0 |
Georgievsky | > 0,9 | 0 |
Grachevsky | ≤ 0,000009 | 2,7 |
Izobilnensky | > 4,7 | 0 |
Ipatovsky | > 4,7 | 5,4 |
Kirovsky | ≤ 0,000009 | 0 |
Kochubeevsky | ≤ 0,000009 | 0 |
Krasnogvardeisky | > 4,7 | 5,4 |
Kursky | > 4,7 | 0 |
Levokumsky | > 4,7 | 0 |
Mineralovodsky | ≤ 0,000009 | 0 |
Neftekumsky | > 4,7 | 1,6 |
Novoaleksandrovsky | > 4,7 | 0 |
Novoselytsky | > 4,7 | 0 |
Petrovsky | > 4,7 | 1,4 |
Predgornyy | ≤ 0,000009 | 0 |
Soviet | > 4,7 | 0 |
Stepnovsky | > 4,7 | 0 |
Trunovsky | > 4,7 | 3,4 |
Turkmensky | > 4,7 | 0 |
Shpakovsky | ≤ 0,000009 | 1,3 |
Before we move on to the discussion of the efficiency of prediction, we would like to point out the following aspects. CCHF patients frequently do not develop hemorrhagic syndrome; they can have ARVI-like symptoms [8][9][19]; some patients are diagnosed with other conditions; some cases are not reported [8][9][19]. Therefore, the number of reported cases depends not only on objective environmental factors, but also on subjective factors, including the severity of the disease, the qualification of medical and laboratory personnel at some healthcare facilities [19]. The above subjective factors are not addressed during the prediction. Consequently, the occurrence or absence of disease cases in general can be predicted most efficiently. The more accurate data on incidence rates are more dependent on subjective factors and are less predictable.
The data presented in Table 3 prove the above. Out of 26 administrative districts of the Stavropol Territory, the erroneous prediction was made only for two of them: Grachevsky and Shpakovsky districts; thus, the prediction for the threshold "whether there will be or will not be at least one case of CCHF in the district" was erroneous by 7.7%. The above districts were expected not to have any cases, though, in fact, there were reported cases.
The overestimated number of cases was predicted for 11 districts. Here, it is unlikely that we deal with a serious mistake, as we cannot exclude poor diagnostics (misdiagnosis) or the probability that the case of infection was reported in another district (at the place of the patient’s residence).
Conclusion
The application of the prediction model in 2021 demonstrated its potential. Based on the studied literature, no country has achieved better results in predicting CCHF incidence [24].
In the meantime, the model incorporates primarily weather variables serving as predictors. Although the information used for building the model is completely consistent with the literature data on indirect combined effects of climatic factors on the epidemiological situation of CCHF, the model should include biotic predictors [24][25][30].
The practical application of the prediction will increase the alertness of healthcare workers in the at-risk regions, will improve the detectability of CCHF cases and implementation of non-specific preventive measures.
Focusing on this area, we are planning to test the prediction model in other CCHF-endemic regions (the Rostov Region and the Republic of Kalmykia).
About the authors
V. M. Dubyanskiy
Stavropol Plague Control Research Institute; Central Research Institute of Epidemiology
Email: fake@neicon.ru
ORCID iD: 0000-0003-3817-2513
Vladimir M. Dubyanskiy — D. Sci. (Biol.), Head, Department of epizoo tological monitorning and prognostication, Stavropol Plague Control Research Institute; member of the temporary research team for the implementation of the grant of the Russian Science Foundation, Central Research Institute of Epidemiology.
Stavropol, Moscow
РоссияD. A. Prislegina
Stavropol Plague Control Research Institute; Central Research Institute of Epidemiology
Author for correspondence.
Email: daria775@rambler.ru
ORCID iD: 0000-0002-9522-129X
Daria A. Prislegina — Cand. Sci. (Med.), senior researcher, Laboratory of epidemiology, Stavropol Plague Control Research Institute; member of the temporary research team for the implementation of the grant of the Russian Science Foundation, Central Research Institute of Epidemiology,
Stavropol, Moscow
РоссияA. E. Platonov
Central Research Institute of Epidemiology
Email: fake@neicon.ru
ORCID iD: 0000-0001-7450-0081
Alexander E. Platonov — D. Sci (Biol.), Prof., chief researcher, Laboratory of zoonoses.
Moscow
РоссияReferences
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