Epidemiological Processes Research Articles

Influence of Transfer Epidemiological Processes on the Formation of Endemic Equilibria in the Extended SEIS Model

In the present paper, a modification of the standard mean-field model is considered, allowing for the description of the formation of a dynamic equilibrium between infected and recovered persons in a population of constant size. The key point of this model is that it highlights two-infection transfer mechanisms depending on the physical nature of the contact between people. We separate the transfer mechanism related directly to the movement of people (the so-called transport processes) from the one occurring at zero relative speed of persons (the so-called social contacts). Under the framework of a physical chemical analogy, the dependencies for the infection transfer rate constants are proposed for both purely transport and social mechanisms of spread. These dependencies are used in discussing the formation of quasi-stationary states in the model, which can be interpreted as endemic equilibrium states. The stability of such endemic equilibria is studied by the method of Lyapunov function.

Evolutionary dynamics of voluntary vaccination for imperfect multi-efficacy vaccines

Voluntary vaccination is crucial for public health. The likelihood that the vaccinated get fully immune is typically referred to as the vaccine efficacy. In addition to this, the efficacy of vaccines also depends on inhibiting the transmission rates and protecting patients from critical symptoms. It has yet to be answered how the multi-efficacy of vaccines affects the vaccination behaviour. Here, we propose a game theoretical model of vaccination behaviour with a heterogeneous-transmission epidemiological process. Intuitively, individuals are likely to take vaccination for either high-efficient vaccines or epidemics with a large basic reproductive number. We find, however, a low economic discount rate can promote the uptake level for low-effectiveness vaccines and epidemics with small basic reproductive numbers. This result suggests that the perceived cost is more important than the risk of infection and the effectiveness of vaccines. In addition, we develop an analytical approximation to address the challenge arising from the coupled dynamics of vaccination and disease spread. This study shows how the interplay among three types of vaccine efficacies (transmission blocking, symptom mitigating and full immunity protecting) affects individual vaccination decisions and epidemic outcomes, and the model can be insightful for epidemic control.

Epidemiological investigation support application and user evaluation based on infectious disease self-management model in the endemic era.

Objectives: Rapid epidemiological investigations are fundamental to prevent the spread of infectious diseases such as coronavirus disease 2019. An epidemiological investigation presents significant challenges for both epidemiologists and infected individuals. It requires creating an environment that enables people to independently manage infectious diseases and voluntarily participate in epidemiological investigations. Methods: We developed the KODARI application, an epidemiological investigation support system that users can voluntarily use. We developed the questionnaires based on literature reviews. We evaluated the application through an online survey from December 2 to 14, 2022. Results: The application automatically or manually collect epidemiological investigation information. The application improved data accuracy through accurate information collection. It voluntarily can transmit self-management information to epidemiologist terminals or users in real time. We collected 248 users from an online survey. Most users had high ratings and willingness to use. They have willingness to manage infectious patients was substantial. The application was evaluated as helpful for epidemiological investigations and could shorten the time required for epidemiological investigations by more than 30min. Conclusion: The application proposes a model based on people's voluntary participation. We demonstrated that the application could enhance epidemiological investigations and diminish the duration of existing epidemiological investigation processes.

Identifying Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data.

Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at github.com/MolEvolEpid/SEEPS.

Tensor product algorithms for inference of contact network from epidemiological data

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number of network nodes, which makes this optimisation computationally challenging. For each network, its likelihood is the probability for the observed data to appear during the evolution of the epidemiological process on this network. This probability can be very small, particularly if the network is significantly different from the ground truth network, from which the observed data actually appear. A commonly used stochastic simulation algorithm struggles to recover rare events and hence to estimate small probabilities and likelihoods. In this paper we replace the stochastic simulation with solving the chemical master equation for the probabilities of all network states. Since this equation also suffers from the curse of dimensionality, we apply tensor train approximations to overcome it and enable fast and accurate computations. Numerical simulations demonstrate efficient black-box Bayesian inference of the network.

The Specificity of Epizootic and Epidemiological Processes in Natural Foci of Hemorrhagic Fever with Renal Syndrome and Tick-Borne Encephalitis in Russia, as the Basis for the Prospects of Creating a Combined Vaccine for the Prevention of These Infections.

Hemorrhagic fever with renal syndrome (HFRS) and tick-borne encephalitis (TBE) are the most common viral diseases in Russia. HFRS is caused by six different types of hantaviruses: Hantaan, Amur, Seoul, Puumala, Kurkino, and Sochi, which are transmitted to humans through small mammals of the Muridae and Cricetidae families. TBE is caused by viruses belonging to five different phylogenetic subtypes. The similarities in the ecology of HFRS and TBE pathogens is presented here. Hantavirus-infected small mammals can transmit the virus to uninfected animals, and ticks can also transmit hantavirus to other ticks and mammals. Hantavirus transmission from ticks to humans is possible only hypothetically based on indirect data. Over the past 23 years, 164,582 cases of HFRS (4.9 per 105 people) and 71,579 cases of TBE (2.5 per 105 people) were registered in Russia. The mortality rate was 0.4% (668 cases) in HFRS and 1.6% deaths (1136 cases) in TBE. There were 4030 HFRS (2.5%) and 9414 TBE (13%) cases in children under 14 years old. HFRS and TBE cases were registered in 42 out of 85 Russian regions; in 18-only HFRS, in 13-only TBE, and 12 had no reported cases. The prospects of applying a combined vaccine for HFRS and TBE prevention are shown in this paper.

A useful parametric specification to model epidemiological data: Revival of the Richards' curve.

A useful parametric specification for the expected value of an epidemiological process is revived, and its statistical and empirical efficacy are explored. The Richards' curve is flexible enough to adapt to several growth phenomena, including recent epidemics and outbreaks. Here, two different estimation methods are described. The first, based on likelihood maximisation, is particularly useful when the outbreak is still ongoing and the main goal is to obtain sufficiently accurate estimates in negligible computational run-time. The second is fully Bayesian and allows for more ambitious modelling attempts such as the inclusion of spatial and temporal dependence, but it requires more data and computational resources. Regardless of the estimation approach, the Richards' specification properly characterises the main features of any growth process (e.g. growth rate, peak phase etc.), leading to a reasonable fit and providing good short- to medium-term predictions. To demonstrate such flexibility, we show different applications using publicly available data on recent epidemics where the data collection processes and transmission patterns are extremely heterogeneous, as well as benchmark datasets widely used in the literature as illustrative.

A method for characterizing disease emergence curves from paired pathogen detection and serology data

Abstract Wildlife disease surveillance programs and research studies track infection and identify risk factors for wild populations, humans and agriculture. Often, several types of samples are collected from individuals to provide more complete information about an animal's infection history. Methods that jointly analyse multiple data streams to study disease emergence and drivers of infection via epidemiological process models remain underdeveloped. Joint‐analysis methods can more thoroughly analyse all available data, more precisely quantifying epidemic processes, outbreak status, and risks. We contribute a paired data modelling approach that analyses multiple samples from individuals. We use ‘characterization maps’ to link paired data to epidemiological processes through a hierarchical statistical observation model. Our approach can provide both Bayesian and frequentist estimates of epidemiological parameters and state. Our approach can also incorporate test sensitivity and specificity, and we propose model fit diagnostics. We motivate our approach through the need to use paired pathogen and antibody detection tests to estimate parameters and infection trajectories for the widely applicable susceptible, infectious, recovered (SIR) model. We contribute general formulas to link characterization maps to arbitrary process models and datasets and an extended SIR model that better accommodates paired data. We find via simulation that paired data can more efficiently estimate SIR parameters than unpaired data, requiring samples from 5 to 10 times fewer individuals. We use our method to study SARS‐CoV‐2 in wild white‐tailed deer (Odocoileus virginianus) from three counties in the United States. Estimates for average infectious times corroborate captive animal studies. The estimated average cumulative proportion of infected deer across the three counties is 73%, and the basic reproductive number (R0) is 1.88. Wildlife disease surveillance programs and research studies can use our methods to jointly analyse paired data to estimate epidemiological process parameters and track outbreaks. Paired data analyses can improve precision and accuracy when sampling is limited. Our methods use general statistical theory to let applications extend beyond the SIR model we consider and to more complicated examples of paired data. The methods can also be embedded in larger hierarchical models to provide landscape‐scale risk assessment and identify drivers of infection.

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINNs) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatio-temporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

E3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Application of geospatial multi-agent system for simulation of different aspects of tuberculosis transmission

BACKGROUND. The paper presents epidemiological process modeling, with a focus on tuberculosis utilizing multi-agent system. MATERIALS AND METHODS. This study involves the development of an algorithm that uses the potential of artificial intelligence to create a geospatial model that highlights the different routes of tuberculosis transmission. The simulation process itself is characterized by a number of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, spread of infection, evolution of disease trajectories, rigorous statistical calculations and transition to the next day. A comprehensive description of the course of active tuberculosis according to official data of the World Health Organization is given. Each agent is provided with an appropriate and consistent model that includes relevant health attributes and necessary rules for their dynamic evolution. CONCLUSIONS. The model’s results exhibit stability and lack of significant fluctuations. The statistical values obtained for infected, latent, and recovered individuals align well with known medical data, confirming the model’s adequacy. The proposed model allows for tracking and analyzing the life and behavior of each individual agent, enabling a thorough assessment of tuberculosis infection spread and the development of prevention strategies.

Features of the Epidemiological Process of Measles Incidence in the Rural Population in the Territory of FGP no. 7 in the City Family Doctors Center Jalal-Abad of the Kyrgyz Republic for 2023

The article discusses the features of the epidemiological process of the incidence of measles infection in the rural population in the territory of the group of family doctors (FGP) No. 7 in the Family Medicine Center (FMC) in the city of Jalal-Abad. On the territory of FGP No. 7, registration of measles infection began from November to December 2023. A high prevalence rate was recorded among young children: in first place were children under one year of age (13.3 per 1000 children); in the second — children 1-2 years old (3.9 per 1000 children); in third place are children 3-5 years old (2.4 per 1000 children) and 6-9 years old (0.9 per 1000 children). No cases of measles were found among adults. By category of cases, a large proportion were unvaccinated children (87.5%), including 42.9% by age and 57.1% among refuseniks. In terms of the number of cases, a large share was made up of unorganized children (87.5%) and children attending kindergartens (12.5%). On the territory of the FGP, in 8 measles foci, a total of 112 contact persons were found, among them there were no cases of measles, however, focality was noted in a family with one case and in two families with two cases each. All contact persons in the outbreaks were vaccinated with MMR and MCV.

Network Spreading from Network Dimension.

Continuous-state network spreading models provide critical numerical and analytic insights into transmission processes in epidemiology, rumor propagation, knowledge dissemination, and many other areas. Most of these models reflect only local features such as adjacency, degree, and transitivity, so can exhibit substantial error in the presence of global correlations typical of empirical networks. Here, we propose mitigating this limitation via a network property ideally suited to capturing spreading. This is the network correlation dimension, which characterizes how the number of nodes within range of a source typically scales with distance. Applying the approach to susceptible-infected-recovered processes leads to a spreading model which, for a wide range of networks and epidemic parameters, can provide more accurate predictions of the early stages of a spreading process than important established models of substantially higher complexity. In addition, the proposed model leads to a basic reproduction number that provides information about the final state not available from popular established models.

Особенности организации центрального стерилизационного отделения для лечения пациентов гнойно-хирургического профиля

The article was prepared on the basis of a report made at the scientific and practical conference «Organization of deratization measures in modern conditions, topical issues of disinfection activities in the conditions of medical organizations of various profiles,» held May 15-16, 2024 in St. Petersburg. Purulent-septic infections cause damage to the health and life of patients, as well as cause social and economic damage. Disinfection and sterilization measures are one of the most important measures in the system of anti-epidemic measures aimed at breaking the mechanism of transmission of infectious agents, which leads to the cessation of the development of the epidemiological process and the spread of ISMP. Keywords: hospital, purulent-septic infections, central sterilization department, diabetes mellitus, preventive measures, anti-epidemic measures, infections associated with the provision of medical care, nosocomial infections.

Вопросы эпидемиологии дирофиляриоза в России и Европе в XXI веке

The participation of mosquitoes in the epidemiological process of invasion caused by Dirofilaria spp. – parasites of carnivorous animals and humans is considered. Mosquitoes of the genera Aedes (Stegomyia), Culex, Anopheles, Coquillettidia (Mansonia) participate in the transmission of microfilariae – larvae of helminths. The problem of dirofilariasis is especially acute in cities in the presence of infected dogs, primarily service and stray dogs, and mosquitoes, which contributes to the year-round transmission of the invasion by vector. According to Rospotrebnadzor, since 2016 There is a tendency to increase the incidence of dirofilariasis among the Russian population. The methods of treatment of dirofilariasis are surgical. No dirofilariasis has been detected in mosquitoes infected with the Wolbachia bacterium. Keywords: morbidity, infection, spread, dirofilariae, mosquitoes, wolbachia, prevention (insecticides, larvicides).

Evolution and genetic characterization of Seoul virus in wild rats Rattus norvegicus from an urban park in Lyon, France 2020-2022.

Seoul virus (SEOV) is an orthohantavirus primarily carried by rats. In humans, it may cause hemorrhagic fever with renal syndrome (HFRS). Its incidence is likely underestimated and given the expansion of urban areas, a better knowledge of SEOV circulation in rat populations is called for. Beyond the need to improve human case detection, we need to deepen our comprehension of the ecological, epidemiological, and evolutionary processes involved in the transmission of SEOV. We performed a comprehensive serological and molecular characterization of SEOV in Rattus norvegicus in a popular urban park within a large city (Lyon, France) to provide essential information to design surveillance strategies regarding SEOV. We sampled rats within the urban park of 'La Tête d'Or' in Lyon city from 2020 to 2022. We combined rat population genetics, immunofluorescence assays, SEOV high-throughput sequencing (S, M, and L segments), and phylogenetic analyses. We found low structuring of wild rat populations within Lyon city. Only one sampling site within the park (building created in 2021) showed high genetic differentiation and deserves further attention. We confirmed the circulation of SEOV in rats from the park with high seroprevalence (17.2%) and high genetic similarity with the strain previously described in 2011 in Lyon city. This study confirms the continuous circulation of SEOV in a popular urban park where the risk for SEOV transmission to humans is present. Implementing a surveillance of this virus could provide an efficient early warning system and help prepare risk-based interventions. As we reveal high gene flow between rat populations from the park and the rest of the city, we advocate for SEOV surveillance to be conducted at the scale of the entire city.

Applying geospatial multi-agent system to model various aspects of tuberculosis transmission

IntroductionThe paper presents epidemiological process modeling, with a focus on tuberculosis utilizing multi-agent system. Material and methodsThis study involves the development of an algorithm that harnesses the potential of artificial intelligence to create a geospatial model that highlights the different pathways of TB transmission. The modeling process itself is characterized by a series of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, propagation of the spread of infection, the evolution of disease trajectories, rigorous statistical calculations and transition to the following day. A comprehensive description of the course of active tuberculosis is presented, following the official hypothesis recommended by the World Health Organization. A comprehensive simulation, illustrating the propagation of tuberculosis in an entirely healthy environment devoid of any preventive or therapeutic measures, is presented. To ascertain the adequacy of the model and its sensitivity to the principal parameters governing the course of tuberculosis, a series of experiments were meticulously conducted, employing three distinct approximations, namely: the basic model, the model incorporating mortality factors, and the comprehensive model, encompassing all relevant aspects. ConclusionsThe model's results exhibit stability and lack of significant fluctuations. The statistical values obtained for infected, latent, and recovered individuals align well with known medical data, confirming the model's adequacy.

Solving a Predictive Model of Infectious Disease in Epidemiology and Epizootiology by Assigning Scores to Individuals

Every mathematical solution to a problem corresponds to a model that more or less corresponds to reality. One of the many possibilities is the solution to epidemiological problems (processes) using the susceptible-infected-removed (SIR) model. Like any model, this model also has its advantages and benefits that allow us to close such a complicated reality as the origin and course of the epidemic into simple differential equations (DE) and through the model’s parameters modify it and compare it with reality. This is possible only in the case of a comprehensive understanding of reality in all its breadth and depth. This study focuses on another solution to the epidemiological or epizootiological processes based on the newly designed simulation, which assigns individuals a certain score in time intervals. In this case, the score from the simulation has a normal probability distribution with certain parameters N (µ, σ) in given time intervals. This score level also divides the given individuals into certain groups based on the given (selected) critical level. This determines whether individuals are with manifestations of the disease or without signs of the disease. In the study, the epidemic curve (EC) simulation results could be used to solve the current pandemic caused by COVID-19. It can be stated that the newly proposed model could be more suitable because it permanently confirms the agreement of experimental and expected frequencies.

A multidisciplinary obesity registry for Iranian adults: a protocol.

Obesity is a global health challenge due to its high prevalence and increased related morbidity and mortality. Accordingly, creating a platform to enter data on patients with obesity, treatment process and outcome is mandatory. The objective of this registry is to provide such a database regarding Iranian adults with obesity in order to facilitate future research designs and effective decision making to control this condition. After obtaining informed consent from eligible individuals, a practical questionnaire will be used to gather information about basic characteristics, family history, past medical history, regular medications, dietary intakes and other relevant information of adults with obesity. This data along with the results of laboratory tests, physical examination, and anthropometric measurements will be registered into the registry system by trained members of the team during an in-person visit. Patients will undergo a specific treatment plan based on the multidisciplinary medical team's decision. Then, changeable variables and the outcomes of their treatment process will be registered later in the follow-up sessions. This registry aims to provide a comprehensive dataset on the epidemiology, outcomes and management process of obesity to serve as a foundation for later research projects and improve medical approaches toward this condition.

Selfish Grower Behavior Can Group-optimally Eradicate Plant Diseases Caused by Coinfection

Game-theoretic behavioral and epidemiological models suggest that it is impossible to eradicate a disease through voluntary control actions when individuals behave according to their own interests. The simple reason is that free-riding behavior, which is best for self-interest, leads to a control coverage on the group level that is insufficient to eradicate the disease. Here we show that, for diseases that are caused by coinfection, self-interest can actually result in the socially optimal outcome of disease eradication. Our result challenges the conventional wisdom that selfish behavior undermines the group interest; it resolves a social dilemma in the absence of any cooperation, institutional arrangements, or social norms. Our model is motivated by coinfecting plant viruses, common among agricultural crops and natural plants, and the behavioral dynamics of growers to adopt protective action (biological or chemical control). The epidemiological scenario, in which the disease is eradicated by self-interest, is characterized by a positive feedback process in which coinfection enhances infectivity. Similar feedback structures exist for a range of typical epidemiological processes that facilitate disease persistence if prevalence is sufficiently large. The underlying mechanism may therefore be applicable to other diseases.