Epidemiological Processes Research Articles

A between-herd data-driven stochastic model to explore the spatio-temporal spread of hepatitis E virus in the French pig production network.

Hepatitis E virus is a zoonotic pathogen for which pigs are recognized as the major reservoir in industrialised countries. A multiscale model was developed to assess the HEV transmission and persistence pattern in the pig production sector through an integrative approach taking into account within-farm dynamics and animal movements based on actual data. Within-farm dynamics included both demographic and epidemiological processes. Direct contact and environmental transmission routes were considered along with the possible co-infection with immunomodulating viruses (IMVs) known to modify HEV infection dynamics. Movements were limited to 3,017 herds forming the largest community on the swine commercial network in France and data from the national pig movement database were used to build the contact matrix. Between-herd transmission was modelled by coupling within-herd and network dynamics using the SimInf package. Different introduction scenarios were tested as well as a decrease in the prevalence of IMV-infected farms. After introduction of a single infected gilt, the model showed that the transmission pathway as well as the prevalence of HEV-infected pigs at slaughter age were affected by the type of the index farm, the health status of the population and the type of the infected farms. These outcomes could help design HEV control strategies at a territorial scale based on the assessment of the farms’ and network’s risk.

Assessment of Epidemiological Situation on Hemorhhagic Fever with Renal Syndrome around the World and in Russia, Forecast for 2020

Objective of the review is to characterize the nature of epidemiological situation on HFRS around the world and to conduct a comparative analysis of intensity and dynamics of the epidemiological process in the Russian Federation by Federal Districts, as well as make forecast for 2020. The analysis of the epidemiological situation is based on the materials of the official websites of healthcare organizations in the USA and Europe, WHO, the data from operational monitoring carried out by the reference center for HFRS monitoring “Kazan Research Institute of Epidemiology and Microbiology”, materials provided by the Rospotrebnadzor Institutions in the constituent entities of the Russian Federation. The analysis included all administrative territories of the Russian Federation. Statistical processing was carried out by standard methods of variation statistics applying Excel software. The epidemiological situation on hemorrhagic fever with renal syndrome around the world and in Russia remains tense. In the Russian Federation, epidemically active foci are located in the European part of the country, in Western Siberia and in the Far East. Infections over the period of 2010–2019 were registered in 8 federal districts of the Russian Federation, in 58 constituent entities. However, the incidence distribution across the territory of the country was differential. In 97 % of the cases, the incidence was recorded in the European part of Russia. In the Volga Federal District, HFRS incidence amounted to 82.16 % of the total incidence recorded on the whole in Russia. High incidence rates are also recorded in the Central Federal District (CFD). Over the past 10 years (2010–2019), there has been an increase in the incidence of HFRS in the Central and North-West Federal Districts, and a decrease in the incidence in the Ural and Far Eastern Federal Districts. Varying degree of anthropogenic impact on the natural HFRS foci and climate change manifested in increased ambient air temperatures are of great importance for the spread of HFRS over the past decade. At the end of the review the forecast of the development of the epidemiological situation on HFRS in the Russian Federation for 2020 is presented.

OvCWD: An agent‐based modeling framework for informing chronic wasting disease management in white‐tailed deer populations

Abstract1. Wildlife diseases are gathering attention worldwide due to their public health and economic or conservation impacts, and consequently, wildlife agencies are increasingly being tasked with disease surveillance and management responsibilities.2. Wildlife disease surveillance and management is challenging primarily due to complex processes of host population dynamics, some of which are inherently stochastic in nature. Individual heterogeneity in pathogen transmission further complicates our understanding of wildlife disease systems.3. Agent‐based models can incorporate stochasticity as well as individual heterogeneities and facilitate a better understanding of epidemiological processes in wildlife disease systems. Such an understanding is critical for designing and implementing effective disease control strategies.4. We have developed a customizable agent‐based modeling framework (OvCWD) that incorporates nonrandom and heterogeneous aspects of an emerging host–pathogen system (chronic wasting disease [CWD] in white‐tailed deer). Models in this framework link white‐tailed deer demography and behavior with CWD transmission dynamics. Insights gained from model explorations can help us better understand CWD spread in regional deer populations.5. We illustrate OvCWD application by deriving CWD outbreak probabilities for Montcalm County (Michigan, USA) deer population using alternate harvest strategies. The focus is on preemptive harvest strategies that can be implemented before CWD is detected in a population (pre‐establishment phase).6. OvCWD provides a defensible decision‐making context for designing locally relevant CWD control strategies. OvCWD can be readily adapted for simulating CWD in other cervid species as well as for simulating other cervid disease systems.

The epidemiological situation development process mathematical modeling taking into account the covid-19 dissemination features

The paper deals with mathematical models of epidemic development and spread. Such models are based on the Cauchy apparatus for nonlinear ordinary differential equations with a wide class of initial conditions. The methodology for selecting the system coefficients is developed and substantiated, their content and influence on the system parameters are described. The method of setting the initial conditions of the problem is proposed and substantiated. Different variants of epidemic models have been analyzed, at the stage of describing the situation with COVID-19 spread, the expediency of choosing the apparatus of systems of nonlinear ordinary differential equations without taking into account argumentation due to the need to develop an express method of simulating the epidemic propagation process is substantiated. The direction of mathematical modeling of epidemiological situations was developed by taking into account the treatment means, influencing the development of epidemics of the economic situation in the country, the presence of other factors positive (the level of communication of the population, its mobility, treatment methods, education of the population, climatic influences, seasonal effects, etc.) and negative factors. distress, political situation in the country). From the point of view of mathematical modeling, the values ​​of the coefficients and their dynamics are analyzed, the limits of their change are revealed for the efficient description and forecasting of the development of the simulated processes. The numerical implementation of the model uses Runge-Kutta methods, the accuracy of which is selected in the light of the peculiarities of the simulated processes. The calculations confirmed the effectiveness of the proposed model for describing epidemics and pandemics of infections and viruses of various nature, it accurately describes the qualitative behavior of the systems and processes under study, found that all the coefficients of the model accurately describe their impact on the behavior of the model as a whole, reflect trends observed in the detailed study of the spread of COVID-19 in China and Europe Directions for possible further research have been identified.

Etiological and epidemiological features of concomitant acute intestinal infections in children

Objective : to study the frequency, etiological, age, epidemiological features of combined acute intestinal infections in children. Materials and methods. In n the Department of intestinal infections of Pediatric Research and Clinical Center for Infectious Diseases within a year have observed 167 children with combined acute intestinal infections regardless of the severity of the disease. The etiology of the acute intestinal infections was verified by bacteriological, serological methods, PCR reagents “AmpliSens® AII screen-FL. Criteria for inclusion of patients in the study: no signs of nosocomial OKA. Statistical analysis of the results was performed using Excel 2007. Student’s criterion was used to estimate the difference of the studied data. Differences were considered significant at p<0.05. Results. Combined acute intestinal infections were diagnosed in 5,6% of 2968 patients, among whom children under 1 year were 21,6%, from 1 to 3 years – 48,4%, from 4 to 7 years – 18%, from 8 to 14 years – 9%, from 14 to 18 years – 3%. The combination of bacterial pathogens was noted in 12% of patients; viral – in 37,1%; viral and bacterial – in 50,9%. Viral-bacterial AII had rises in the frequency of diagnosis in spring, summer, autumn; viral-viral acute intestinal infections – in spring and autumn, bacterial-bacterial acute intestinal infections – in autumn. Infants in the structure of patients with viral-viral acute intestinal infections were 23,3%, viral-bacterial acute intestinal infections – 29,6%, bacterial-bacterial acute intestinal infections – 5%; young age – 45%; 47,%; 40%; preschool age– 25%; 23,3%; 15,5%; school age – 30%; 8.3%; 7% respectively. Among of associates the bacterial-bacterial acute intestinal infections, diarrheal Escherichia (60%), Salmonella (50%) and Campylobacters (40%) dominated. Among of associates of the viral-viral acute intestinal infections noroviruses (74,2%) and rotaviruses (at 69,4%) dominated. Among of viral associates of viral-bacterial acute intestinal infections more prevalent were rotaviruses (52,9%) and noroviruses (27,1%); among bacterial associates were diarrheal Escherichia (at 51,8%). The results of the study allowed establish etiological, seasonal, age patterns of formation of the epidemiological process of intestinal infections of combined etiology in children.

Nosoi: A stochastic agent-based transmission chain simulation framework in r.

The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavour, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed.We here introduce nosoi, an open‐source r package that offers a complete, tunable and expandable agent‐based framework to simulate transmission chains under a wide range of epidemiological scenarios for single‐host and dual‐host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations.Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user‐specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can—for example—be used to validate a wide range of reconstruction methods, including epidemic modelling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a hands‐on exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.

Long-Term Determinants of Tuberculosis in the Ungulate Host Community of Doñana National Park.

Animal tuberculosis (TB) is endemic in wild boar (Sus scrofa), red deer (Cervus elaphus), fallow deer (Dama dama) and cattle in south and central Spain. In order to clarify the processes that operate in the medium and long-term, we studied TB at the wildlife–livestock interface in Doñana National Park for 14 years (2006–2018) in relation to host density, stochastic factors (rainfall) and environmental features (e.g., aggregation points such as waterholes). Wild boar showed the highest prevalence of TB (76.7%), followed by red deer (42.5%), fallow deer (14.4%) and cattle (10.7%). We found evidence of relevant epidemiological processes which operate over the long-term and interact with host and community ecology. Interestingly, the effect of high wild boar population density on increased TB rates was mediated by sows, which could determine high incidence in young individuals already in maternal groups. Rainfall significantly determined a higher risk of TB in male red deer, probably mediated by sex-related differences in life history traits that determined more susceptibility and/or exposure in comparison to females. The positive association between the prevalence of TB in fallow deer and cattle may indicate significant interspecies transmission (in either direction) and/or similar exposure to risk factors mediated by ecological overlapping of grazing species. The identification of long-term drivers of TB provided evidence that its control in extensive pastoral systems can only be achieved by targeting all relevant hosts and integrating measures related to all the factors involved, such as: population abundance and the aggregation of wild and domestic ungulates, environmental exposure to mycobacteria, cattle testing and culling campaigns and adjustments of appropriate densities.

A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down' in managing the COVID-19 pandemic.

COVID-19 is characterized by an infectious pre-symptomatic period, when newly infected individuals can unwittingly infect others. We are interested in what benefits facemasks could offer as a non-pharmaceutical intervention, especially in the settings where high-technology interventions, such as contact tracing using mobile apps or rapid case detection via molecular tests, are not sustainable. Here, we report the results of two mathematical models and show that facemask use by the public could make a major contribution to reducing the impact of the COVID-19 pandemic. Our intention is to provide a simple modelling framework to examine the dynamics of COVID-19 epidemics when facemasks are worn by the public, with or without imposed ‘lock-down’ periods. Our results are illustrated for a number of plausible values for parameter ranges describing epidemiological processes and mechanistic properties of facemasks, in the absence of current measurements for these values. We show that, when facemasks are used by the public all the time (not just from when symptoms first appear), the effective reproduction number, Re, can be decreased below 1, leading to the mitigation of epidemic spread. Under certain conditions, when lock-down periods are implemented in combination with 100% facemask use, there is vastly less disease spread, secondary and tertiary waves are flattened and the epidemic is brought under control. The effect occurs even when it is assumed that facemasks are only 50% effective at capturing exhaled virus inoculum with an equal or lower efficiency on inhalation. Facemask use by the public has been suggested to be ineffective because wearers may touch their faces more often, thus increasing the probability of contracting COVID-19. For completeness, our models show that facemask adoption provides population-level benefits, even in circumstances where wearers are placed at increased risk. At the time of writing, facemask use by the public has not been recommended in many countries, but a recommendation for wearing face-coverings has just been announced for Scotland. Even if facemask use began after the start of the first lock-down period, our results show that benefits could still accrue by reducing the risk of the occurrence of further COVID-19 waves. We examine the effects of different rates of facemask adoption without lock-down periods and show that, even at lower levels of adoption, benefits accrue to the facemask wearers. These analyses may explain why some countries, where adoption of facemask use by the public is around 100%, have experienced significantly lower rates of COVID-19 spread and associated deaths. We conclude that facemask use by the public, when used in combination with physical distancing or periods of lock-down, may provide an acceptable way of managing the COVID-19 pandemic and re-opening economic activity. These results are relevant to the developed as well as the developing world, where large numbers of people are resource poor, but fabrication of home-made, effective facemasks is possible. A key message from our analyses to aid the widespread adoption of facemasks would be: ‘my mask protects you, your mask protects me’.

Analysis of a Multiscale Model of Ebola Virus Disease

Multiscale models are ones that link the epidemiological processes dealing with the transmission between hosts and the immunological processes dealing with the dynamics within one host. In this study, a multiscale model of Ebola Virus Disease linking epidemiological and immunological processes has been developed and analysed. The model has considered two infectious classes ; the exposed and the infected individuals. Local and global stability analyses of the Disease Free Equilibrium and the Endemic Equilibrium points of the model show that the disease dies out if the basic reproduction number Rc0 < 1 and persists in the population when Rc0 > 1 respectively. Sensitivity analysis shows that the rate of vaccination, v , is the most sensitive parameter. This indicates that effort should be directed towards implementing an effective vaccination strategy to control the spread of the disease. It has also been established that when treatment efficacy is scaled up, the viral load goes down and consequently, the transmission between hosts is also reduced. The impact of treatment on the disease spread has also been established through the coupling function (L∗) . The study indicates that a higher percentage of the exposed and the infected individuals should be treated to control the spread of the disease within the population.

Clinical outcomes of COVID-19 cases and influencing factors in Guangdong province

Objective: To analyze the clinical courses and outcomes of COVID-19 cases and the influencing factors in Guangdong province and provide basis for the formulation or adjustment of medical care and epidemic control strategy for COVID-19. Methods: We collected demographic data, medical histories, clinical courses and outcomes of 1 350 COVID-19 patients reported in Guangdong as of 4 March 2020 via epidemiological investigation and process tracking. Disease severity and clinical course characteristics of the patients and influencing factors of severe illness were analyzed in our study. Results: Among 1 350 cases of COVID-19 cases in Guangdong, 72 (5.3%) and 1 049 (77.7%) were mild and ordinary cases, 164 (12.1%) were severe cases, 58 (4.3%) were critical cases and 7 (0.5%) were fatal. The median duration of illness were 23 days (P(25), P(75): 18, 31 days) and the median length of hospitalization were 20 days (P(25), P(75): 15,27 days). For severe cases, the median time of showing severe manifestations was on the 12(th) day after onset (P(25), P(75): 9(th) to 15(th) days), and the median time of severe manifestation lasted for 8 days (P(25), P(75): 4, 14 days). Among 1 066 discharged/fetal cases, 36.4% (36/99) and 1.0% (1/99) of the mild cases developed to ordinary cases and severe cases respectively after admission; and 5.2% (50/968) and 0.6% (6/968) of the ordinary cases developed to severe cases, and critical cases respectively after admission. In severe cases, 11.4% developed to critical cases (10/88). The influencing factors for severe illness or worse included male (aHR=1.87, 95%CI: 1.43-2.46), older age (aHR=1.67, 95%CI: 1.51-1.85), seeking medical care on day 2-3 after onset (aHR=1.73, 95%CI: 1.20-2.50) pre-existing diabetes (aHR=1.75, 95%CI: 1.12-2.73) and hypertension (aHR=1.49, 95%CI: 1.06-2.09). Conclusions: The course of illness and length of hospitalization of COVID-19 cases were generally long and associated with severity of disease clinical outcomes. The severe cases were mainly occurred in populations at high risk. In the epidemic period, classified management of COVID-19 cases should be promoted according to needs for control and prevention of isolation and treatment for the purpose of rational allocation of medical resources.

An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver.

We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.

Results of the West Nile Fever Agent Monitoring in the Russian Federation in 2019 and the Forecast of Epidemic Situation Development in 2020

The trend towards an increase in the West Nile fever incidence among the population in the Russian Federation, recorded in the season of 2018, continued and led to a significant increase in the incidence in 2019 (the indicator was 2 times higher than the long-term average). The features of manifestations of the epidemiological process of WNF in 2019 were identified: early registration of cases of the disease, activation of natural and natural-anthropourgic foci in the Southern Federal District (90 % of the total incidence in the Russian Federation), an increase in the share of neuro-invasive forms, dominance of patients aged 50 and older in the structure of the incidence, late epidemic season ending. It was established that in the season of 2019, the lineage 2 of WNV circulated in the European part of Russia. In the Volgograd Region, simultaneous presence of the West Nile virus and Sindbis virus in mosquitoes Culex pipiens and Culex modestus was identified. It was shown that the most significant factors for predicting the epidemiological situation on West Nile fever in the Volgograd Region are the average seasonal summer air temperature and monthly average indicators of relative humidity in the spring and summer periods, and the average monthly air temperatures in the spring and summer in the Rostov Region. In the Astrakhan Region, a significant correlation dependence of the influence of the considered factors on the incidence of the population has not been established. The forecast of the development of epidemic situation in 2020 does not exclude a possible increase in the incidence in the territories of the European part of Russia, endemic for West Nile fever, and the occurrence of local outbreaks in individual constituent entities, if the complex of climatic conditions and social factors favorable for West Nile virus coincide.

Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks.

Emerging and endemic animal viral diseases continue to impose substantial impacts on animal and human health. Most current and past molecular surveillance studies of animal diseases investigated spatio-temporal and evolutionary dynamics of the viruses in a disjointed analytical framework, ignoring many uncertainties and made joint conclusions from both analytical approaches. Phylodynamic methods offer a uniquely integrated platform capable of inferring complex epidemiological and evolutionary processes from the phylogeny of viruses in populations using a single Bayesian statistical framework. In this study, we reviewed and outlined basic concepts and aspects of phylodynamic methods and attempted to summarize essential components of the methodology in one analytical pipeline to facilitate the proper use of the methods by animal health researchers. Also, we challenged the robustness of the posterior evolutionary parameters, inferred by the commonly used phylodynamic models, using hemagglutinin (HA) and polymerase basic 2 (PB2) segments of the currently circulating human-like H3 swine influenza (SI) viruses isolated in the United States and multiple priors. Subsequently, we compared similarities and differences between the posterior parameters inferred from sequence data using multiple phylodynamic models. Our suggested phylodynamic approach attempts to reduce the impact of its inherent limitations to offer less biased and biologically plausible inferences about the pathogen evolutionary characteristics to properly guide intervention activities. We also pinpointed requirements and challenges for integrating phylodynamic methods in routine animal disease surveillance activities.

Impact of temporal scales and recurrent mobility patterns on the unfolding of epidemics

Human mobility plays a key role on the transformation of local disease outbreaks into global pandemics. Indeed, the inclusion of population movement into epidemic models has become mandatory for understanding current epidemic episodes as well as designing efficient prevention policies. Following this challenge, here we develop a Markovian framework that enables us to address the impact of recurrent mobility patterns on epidemic onset at different temporal scales. The formalism is validated by comparing its predictions with results from mechanistic simulations. The fair agreement between theory and numerical simulations, enables us to derive an analytical expression for the epidemic threshold, capturing the critical conditions triggering epidemic outbreaks. Finally, by performing an exhaustive analysis of this epidemic threshold, we reveal that the impact of tuning human mobility on the emergence of diseases is strongly affected by the temporal scales associated to both epidemiological and mobility processes.

FEATURES OF THE TRANSMISSION MECHANISM OF VIRAL HEPATITIS C IN UKRAINE

Introduction/objective. The significant part of young people in the structure of hepatitis C virus (HC/HCV infection) incidence, a lot of latent cases of this infection, and the lack of specific prevention may complicate the epidemic situation regarding this infection in Ukraine in the coming years. The authors developed a mathematical model of the HC epidemiological process to determine the most significant factors in this infection transmission in the country. Materials and methods. The study is based on correlation-regression analysis of the relationship between a dependent (or responding) and explanatory (factorial or predictors) variables. In total, the analysis involved 3 dependent variables y1, y2, y3, corresponding to the annual number of acute and chronic HC cases and the number of HC virus seropositive individuals, and 17 predictors x1 – x17, including patients who received etiotropic treatment; patients with mental and behavioral disorders due to narcotics use, including opioids; patients with sexually transmitted infections; the number of visits to dentists; the number of patients who had dentures placed; the number of surgical operations, blood transfusions, endoscopic examinations, laboratory blood tests, hemodialysis, etc. The number of observations (n) of dependent and explanatory variables was equal to 25, which corresponds to the number of administrative-territorial units in Ukraine (24 regions and Kyiv). The quality of regression models was evaluated using multiple correlation coefficients (R), determination coefficients (R2), and regression coefficients (b0, b1, b2). Statistical significance of R2 was determined by F-statistics, regression coefficients – by standard errors (m), t-test, p-value, and the range of 95% confidence intervals (CI). To compare the degree of influence of factor variables over dependent variables in the two-factor regression model, standardized regression coefficients were calculated. The reliability of regression models was evaluated by the statistics of Durbin–Watson (DW), Breusch–Godfrey (BG), and White (W) tests. The relative risk (RR) of HC infection was retrospectively determined in individuals from behavioral and medical risk groups. Results. In mathematical model of the epidemic process of acute HC, statistical significance was demonstrated for only one variable effect – annual number of dentist visits. The obtained regression equation was as follows: y1 = 0.000021 x5 – 11.353, where y1 = annual number of patients with acute HC; х5 = annual number of dentist visits. Statistical characteristics of the model: R = 0.892, R2 = 0.796; F-test: 89.9 for 1 and 23 degrees of freedom, statistical significance for F: 0.0000000021; regression coefficients: b1= 0.000021 (m = ±0.0000023; t = 9.48, tcrit = 1.71; p = 0.0000000021; 95% CІ [0.000017; 0.000026]), b0 = -11.353 (m = ±3.982; t = 2.85, tcrit = 1.71; p = 0.009; 95% CІ [-19.59; -3.116]). When developing a model of the epidemic process of acute HC taking into account the annual number of seropositive individuals, statistical significance was demonstrated only for two variables: annual number of the sexually transmitted infections and annual number of laboratory blood tests. The analytical relationship of variables in this model had the following mathematical expression: y3 = 4.563 x4 + 0.0058 x15 – 36552.721, where y3 = number of HCV-seropositive individuals; x4 = number of sexually transmitted diseases, x15 = number of laboratory blood tests. Statistical characteristics of the model: R = 0.92, R2 = 0.842; F-test: 58.62 for 2 and 22 degrees of freedom, statistical significance for F: 0.00000000153; regression coefficients: b0= -36552.721 (m = ±10649.1; t = 3.43, tcrit = 1.71; p = 0.0024; 95% CІ [-58637.63; -14467.81]), b1 = 0.0058; m = ±0.00082; t = 7.1, tcrit = 1.71; р = 0.0000004; 95% CІ [0.0041; 0.0075]; b2 = 4.563; m = ±1.526; t = 2.99, tcrit = 1.71; р = 0.0067; 95% CІ [1.4; 7.73]. The Durbin–Watson and Breusch–Godfrey tests did not reveal autocorrelation of residues for both regression models: DWU < DWр < 4 – DWU; BG < χ2. White's test shows no heteroscedasticity for both models: W < χ2. The test results indicate the reliability of both regression models. Conclusions. According to our data, at least 84% of HC virus infection cases in Ukraine occur through sexual contact and during laboratory blood sampling, and the role of the latter route of transmission in the HC virus spread was even more significant (standardized regression coefficients are 0.3 and 0.7, respectively). Almost 80% of acute HC cases are associated with dental interventions. Etiotropic treatment of patients with HC at the current level of treatment coverage can reduce the incidence of complications and the risk of death, but it is ineffective as a measure of influence on the first stage of the epidemiological process (source of infection). Drug users have little effect on the intensity of the HC epidemiological process in Ukraine as a whole, despite the fact that the relative risk of HC among this population is quite significant (RR = 6.5; 95% CI [6.39; 6.63]).

Criminological aspects of the spread, containment and prevention of COVID-19

There are many facets of the current pandemic to face and learn about. In addition to medical, political, economic, socio-psychological and other aspects it would be necessary to identify and study criminological aspects too. Relevant approach includes determining the origin of COVID-19, tendencies in the spread of the virus, interaction between epidemiological, economic and criminogenic processes, control systems of pandemic situations and resources, including law enforcement responses to the virus, and socio-economic and criminogenic consequences of COVID-19. The criminological approach in general includes the study of the causes and development of socially unfavourable phenomena, their determining factors, as well as the development of a methodology for improving preventive measures. Particular attention will be paid by the authors to these aspects in their study.

The cholera spread Simulation

One of the frequently used modern epidemiological methods is the Simulation of disease spread. We used AnyLogic simulation. During the model construction, we take into account the specific cholera features. They are: the pathways of infection transmission, the course duration and the duration of the incubation (latent) period, the possibility of vaccination, etc.Two approaches are presented here: system-dynamic model and agentbased one. The system-dynamic model is used for strategic modelling of the epidemiological situation, it reflects the global trends. The agent-based approach allows describing the individual behaviour of each agent person, who independently form events conditioning transitions between states.Used together they make it possible to reflect different aspects of the epidemiological process development. The combination of these two models gives more possibilities for their application in a real situation: give the opportunity to impose tactical nuances on strategic modelling.

Predicting the dynamics of the coronavirus (COVID-19) epidemic based on the case-based reasoning approach

The case-based rate reasoning (CBRR) method is presented for predicting future values of the coronavirus epidemic’s main parameters in Russia, which makes it possible to build shortterm forecasts based on analogues of the percentage growth dynamics in other countries. A new heuristic method for estimating the duration of the transition process of the percentage increase between specified levels is described, taking into account information about the dynamics of epidemiological processes in countries of the spreading chain. The CBRR software module has been developed in the MATLAB environment, which implements the proposed approach and intelligent proprietary algorithms for constructing trajectories of predicted epidemic indicators.

Phyloanatomic characterization of the distinct T cell and monocyte contributions to the peripheral blood HIV population within the host.

Human immunodeficiency virus (HIV) is a rapidly evolving virus, allowing its genetic sequence to act as a fingerprint for epidemiological processes among, as well as within, individual infected hosts. Though primarily infecting the CD4+ T-cell population, HIV can also be found in monocytes, an immune cell population that differs in several aspects from the canonical T-cell viral target. Using single genome viral sequencing and statistical phylogenetic inference, we investigated the viral RNA diversity and relative contribution of each of these immune cell types to the viral population within the peripheral blood. Results provide evidence of an increased prevalence of circulating monocytes harboring virus in individuals with high viral load in the absence of suppressive antiretroviral therapy. Bayesian phyloanatomic analysis of three of these individuals demonstrated a measurable role for these cells, but not the circulating T-cell population, as a source of cell-free virus in the plasma, supporting the hypothesis that these cells can act as an additional conduit of virus spread.

IMPACT OF THE ENVIRONMENT ON DEVELOPMENT OF HELMINTH AND PROTOZOAN INVASIVE ELEMENTS AND CONTEMPORARY METHODS FOR SANITARY- PARASITOLOGICAL DIAGNOSTICS

According to the World Health Organisation, more than 1.5 billion of the world population is affected by parasitic diseases caused by geohelminths. The number of persons suffering from foodborne and waterborne protozoan diseases is similar. In developed countries, including Bulgaria, systematic sanitary and parasitological studies of soil and water are the basis for monitoring and control in protecting public health. Occurrence and prevalence of human parasitic infections is determined by the peculiarities of life cycle of parasites, peculiarities of hosts as reservoir sources, the abiotic and biotic factors of the environment as a complex providing conditions for the development or sterilisation of different parasite stages, as well as socioeconomic factors that play a leading role in the whole epidemiological process.
 Systematic sanitary-parasitological studies require the application of classical and novel reliable, sensitive and practical diagnostic methods that are also easy to perform, economical and efficient enough.