**Acknowledgements**

Our first model takes the following form of the regression equation:

20 Meningoencephalitis - Disease Which Requires Optimal Approach in Emergency Manner

ceptible to the IMD among children aged 0–14, or APPS (%); 8.26 (or "b<sup>1</sup>

Our second model takes the following form of the regression equation:

sons who had contact with IMD patients (or among total population), %; Х<sup>2</sup>

our models do not take into account the potential heterogeneity of the pathogen.

tantly, allow to develop tools and strategies for control and prevention.

+ 227.63 X<sup>2</sup>

if Y = 0; Х<sup>1</sup>

is changed to 1%; 227.63 (or "b<sup>2</sup>

= 14.56 with p = 2.13 × 10−11; b<sup>2</sup>

and b2

= approximate proportion of the population sus-

and Х<sup>2</sup>

is changed to 1%.

= 0.9404, i.e. 94.04%) that statistically significance explains IMD incidence and

+ 469.13 X<sup>2</sup>

where Y1 = IMD incidence per 100,000 children 0–14 years; −7.43 (or "a") = constant, which

In the model, the coefficient of multiple correlation R = 0.9697 and its standard error is equal

shows the high descriptive properties of the model. Ultimately, this model appears highly significant (Fisher's exact test = 142.04 p < 0.05 at 95% confidence) describing the totality of the properties of the epidemic process of MD among children aged 0–14. Analysis of the residuals values of the model did not find any autocorrelation. Overall, the model encompasses all

proportion of the population susceptible to the IMD among total population, APPSIMD, %. The model has excellent descriptive properties and statistically significant. The coefficient of multiple correlation r = 0.9937 and its standard error is equal to 0.0645, accordingly with r<sup>2</sup>

0.9875. Residuals analysis of the model did not find any autocorrelation (i.e., almost normal

Model limitation: Our models use aggregated data form a survey, and therefore, our model does not allow for an adequate formal residual analysis. In order to perform such type of analysis, it requires to build at least 50 times of such models from necessary data sets. Also,

Altogether the present and past surveillance of bacterial meningitis in Ukraine provide a unique source for a comprehensive understanding of the disease dynamics and, most impor-

, (3)

) and constant "a" at incidence

, (4)

= approximate

=

= prevalence of carriage among per-

= prevalence of carriage

") = regression coef-

") = regression

= 15.39 with

Y1 = − 7.43 + 8.26 X<sup>1</sup>

corresponds to the mathematical expectation Х<sup>1</sup>

among healthy children aged 0–14 (%); Х<sup>2</sup>

ficient showing the change of level Y, if Х<sup>1</sup>

p = 8.38 × 10−12; a = 7.54 with p = 5.59 × 10−7).

properties and is statistically significant.

Y2 = − 1.59 + 0.89 X<sup>1</sup>

where Y2 = IMD incidence per 100,000 population; Х<sup>1</sup>

to 0.5069 (R<sup>2</sup>

distribution.)

**6. Conclusion**

coefficient showing the change of level Y, if Х<sup>2</sup>

Y is statistically significant (Student exact test: b<sup>1</sup>

Note that the influence of the regression coefficients (b<sup>1</sup>

The authors would like to acknowledge the United States Department of Defense, Defense, Threat Reduction Agency (DTRA), Cooperative Biological Engagement Program (CBEP) for their support to develop this manuscript. While DTRA/CBEP did not support the research described in this publication, the Program supported the presentation of this research in an international forum and supported grantsmanship training related to the development of this manuscript. The contents of this publication are the responsibility of the authors and do not necessarily reflect the views of DTRA or the United States Government.
