Information technology for forecasting epidemiological threats based on the telegraphic equation

The dynamics of viral infection spread, whether in laboratory cultures or in naturally infected hosts, reflects a coupling of biological and physical processes that remain to be fully elucidated. Biological processes include the kinetics of virus growth in infected cells while physical processes include transport of virus progeny from infected cells, where they are produced, to susceptible cells, where they initiate new infections. Mechanistic models of infection spread have been widely developed for systems where virus growth is coupled with transport of virus particles by diffusion, but they have yet to be developed for systems where viruses move under the influence of fluid flows. Recent experimental observations of flow-enhanced infection spread in laboratory cultures motivate here the development of initial continuum and discrete virus-particle models of infection spread. The magnitude of a dimensionless group, the Damköhler number, shows how parameters that characterize particle adsorption to cells, strain rates that reflect flow profiles, and diffusivities of virus particles combine to influence the spatial pattern of infection spread.

A flow-regulated infection rate is defined for a Susceptible-Infected-Susceptible (SIS) model revealing the network structural properties that influence the spread of infections. The infection rate is linked to the flow between compartments in the associated positive compartmental system, providing a self-regulatory effect on the spreading dynamics. This translates to infection carriers preferring to visit healthier sites over more infected ones. A flow-independent epidemic threshold is defined that sets the conditions on the graph’s structure and the aggregate infection rate for a disease to either spread or die out. An individual-based mean-field approach returns results comparable to models with constant infection rates. This approach lends itself well to model the spread of infectious diseases as well as threats in IT networks, pharmacokinetics and the spread of disruptions on infrastructure networks.

This work is contextualized in research in modeling and simulation of infection spread within a community or population, with the objective to provide a public health and policy tool in assessing the dynamics of infection spread and the qualitative impacts of public health interventions. This work uses the integration of real data sources into an Agent Based Model (ABM) to simulate respiratory infection spread within a small municipality. Novelty is derived in that the data sources are not necessarily obvious within ABM infection spread models. The ABM is a spatial-temporal model inclusive of behavioral and interaction patterns between individual agents on a real topography. The agent behaviours (movements and interactions) are fed by census / demographic data, integrated with real data from a telecommunication service provider (cellular records) and person-person contact data obtained via a custom 3G Smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion that can be used by policy and decision makers. The data become real-world inputs into individual SIR disease spread models and variants, thereby building credible and non-intrusive models to qualitatively simulate and assess public health interventions at the population level.

The world socio‐economic situation contributes to increasing poverty, disparities and poor accces to health. Rising poverty levels and disparities continue to slow, and in some cases negate, the hard won achievement in global health. Natural disasters are also on the rise, killing people, destroying services, communities and medical infrastructure, and further exacerbating the plight of the poor and already medically underserved populations. A more efficient use, collaboration and integration of national and international medical and health resources and services will help improve health in vulnerable populations.

Funding: MRC injects funds into medical informatics

In the field of complex networks, understanding the spreading dynamics in multi-layer networks is crucial for real-world applications such as epidemic control and the analysis of social phenomenon. In this study, a two-layer interconnected network is established, with each layer modeled as a small-world network. To investigate the spreading dynamics on this two-layer network, the classic susceptible-infected-susceptible (SIS) model is applied to it. The governing equations for the Infection proportions on each layer are developed first, followed by the proof of the existence of steady solutions (the proportion of finally infected nodes in the network) and their analytical derivation. Then, the computational model is developed accurately track the time evolution of these solutions using the 4th-order Runge–Kutta method. Finally, a numerical investigation on the influence of parameters is carried out. Three categories of parameters are considered, including inter-layer parameters, intra-layer parameters, and recovery-related parameters. Their effects on the steady solution and the infection velocity of the SIS model on the two-layer networks are summarized. It is found that both inter-layer and intra-layer parameters significantly impact the infection dynamics, an increase in these parameters leads to an increase in the final Infection proportions and a decrease in the time to reach this state. For recovery-related parameters, there exists a maximum value due to the balance of contributions from different aspects. Besides, when the scales of the two layers are unequal, the influence of intra-layer parameters is more obvious for the layer with a smaller scale. The study may contribute to the understanding of spreading dynamics on multi-layer complex networks. It provides practical guidance for managing and controlling the spread of infections in real-world scenarios. The insights gained are valuable for related research and applications in areas like epidemiology and social network analysis.

ImportanceAfrican swine fever (ASF) is a significant transboundary disease with profound global economic impacts. ASF, caused by the Asfarviridae virus, is highly resilient and has caused substantial economic losses in affected regions, with no available vaccination or treatment. We conducted a comprehensive analysis of the spatial clusters and potential factors contributing to the ASF epidemic in China from August 2018 to April 2021.ObjectiveThe study aimed to trace ASF transmission, map the spatial distribution and spread dynamics, and model the spread to evaluate the potential control strategies.MethodsStandardized deviational ellipse analysis was used to identify the directionality and distribution of ASF outbreaks. Ordinary least squares regression and geographically weighted regression were applied to account for spatial heterogeneity and explore the localized relationships within the data set.ResultsThe ASF transmission in China was delineated across three phases, characterized by an initial rapid spread, a subsequent westward expansion, and a final plateau phase with a southwestward trend. The key factors influencing ASF transmission included the pig population density, wind speeds, altitude, and the presence of high-risk roads, with seasonality also playing a significant role in the disease incidence.Conclusions and RelevanceThis research highlights the significant impact of small-scale farming practices and the live pig transportation network on ASF spread. In addition, it identifies key epidemiological factors, including the pig density and seasonal variations, which are crucial for developing effective disease control strategies.

The role of fire in expanding the global distribution of savanna is well recognized. Empirical observations and modeling suggest that fire spread has a threshold response to fuel-layer continuity, which sets up a positive feedback that maintains savanna-forest bistability. However, modeling has so far failed to examine fire spread as a spatial process that interacts with vegetation. Here, we use simple, well-supported assumptions about fire spread as an infection process and its effects on trees to ask whether spatial dynamics qualitatively change the potential for savanna-forest bistability. We show that the spatial effects of fire spread are the fundamental reason that bistability is possible: because fire spread is an infection process, it exhibits a threshold response to fuel continuity followed by a rapid increase in fire size. Other ecological processes affecting fire spread may also contribute including temporal variability in demography or fire spread. Finally, including the potential for spatial aggregation increases the potential both for savanna-forest bistability and for savanna and forest to coexist in a landscape mosaic.

An algorithm for countering the threat of an aviation accident is proposed. It is realized in a decision-making support device, which is the main element of the aircraft flight safety control system and appears to be a dynamic expert system. A feature of the proposed device is generation of recommendations to a pilot to get out of an aviation accident upon identifying significant variation in time of the input variables affecting the aircraft flight safety based on information about psychophysical state of the pilot, technical conditions of the control object, external influencing factors, as well as forecast of alterations in the flight conditions. A corresponding block diagram is provided to describe the aircraft flight safety control system. An algorithm to support decision making by the crew is constructed making it possible to generate recommendations for the pilot and signals to the control system to increase the aircraft safety level. The set of decision-making support rules is evaluated for completeness and absence of data inconsistency. Numerical simulation of the algorithm operation accompanied by evaluation of the set of decision-making support rules confirmed its operating capability. Results obtained could be introduced in development of the aircraft flight safety control systems

Group testing is an efficient algorithmic approach to the infection identification problem, based on mixing the test samples and testing the mixed samples instead of individually testing each sample. In this paper, we consider the dynamic infection spread model that is based on the discrete SIR model, which assumes the disease to be spread over time via infected and non-isolated individuals. In our system, the main objective is not to minimize the number of required tests to identify every infection, but instead, to utilize the available, given testing capacity T at each time instance to efficiently control the infection spread. We introduce and study a novel performance metric, which we coin as ϵ-disease control time. This metric can be used to measure how fast a given algorithm can control the spread of a disease. We characterize the performance of the dynamic individual testing algorithm and introduce a novel dynamic SAFFRON-based group testing algorithm. We present theoretical results and implement the proposed algorithms to compare their performances.

Subject: The economic and social development of the state is largely determined by the current system of innovation and investment development. In this paper, we have studied these systems in Russia and abroad, determined their development in modern conditions, and identified external and internal factors of influence. Goals: Analyze the state of the world and Russian investment potential and prospects for its development. Methodology: The research is based on a dialectical approach, which uses system and institutional approaches, statistical method, comparative analysis, methods of tabular, and graphical data visualization. Results: The processes of development of the world and Russian investment system in modern conditions were studied. The prospects for innovative and investment development are defined. Conclusions: As a result of the study, it was determined that the Russian Federation needs a successfully developing system of innovative investment development that ensures financial independence from international economic influence. However, there are a number of problems that need to be solved at the level of Federal and regional legislative and Executive structures in order to create investment attractiveness and legal security for both Russian and international investors. The prospect of innovative development in Russia is possible in terms of economic structure based on raw material exports, due to the development of the industrial sector of the national economy, high technology, industry, aerospace, energy and information technology, and the development of agricultural production.

Veterinary medicine's increasing role in global health.

This research presents quantitative assessment of the biological risk level associated with spread of infectious and parasitic potato plant (Solanum tuberosum) diseases, based on the parameters of transmission and contagion mechanisms and pathways. The authors conducted a point-rating analysis of threats of potato infection or infestation with pathogens of different etiologies: bacterial, viral, helminthic and mycotic. The developed system of analysis is flexible and implies regular monitoring and data update in case of the globalization and world trade bring new strains of infections and varieties of parasites, which will be able to adapt to the climatic conditions of a particular region and the means of chemical plant protection. As a result, the combination of modern approaches to the field of potato plant disease detection and the proposed methodology will allow the development of a comprehensive system for monitoring and forecasting the spread of infections, which will make it possible to take timely and effective measures to protect the crop. The use of optical methods in combination with the analysis of data on pathogens will increase the accuracy of diagnostics and the effectiveness of prevention, contributing to the sustainable development of agriculture.

Introduction. The main catalyst for effective transformations in the industrial sector of the economy is innovation and investment policy, which aims to increase the potential, namely the capacity of the manufacturing sector of the Ukrainian economy. The innovation and investment potential closely reflect the objectively existing possibility of socio-economic development of the human system. The purpose of the article is to clarify the basic principles and approaches that reflect the innovation and investment potential and the essence of innovation and investment development. Results. The state ideology in the field of innovation and investment policy is considered. A generalization of the concept of “development” of the philosophical point of view is presented. The decisive role of development in understanding the essence of everything is proved. The vector of development in terms of its regressive or progressive manifestation is considered. It is determined that the stages of development are the stages of system change, which include: appearance, upward stage of development, maximal development or maturity, regressive changes or downward stage, disintegration and death. The tendency of differentiation in socio-economic systems is determined. The essence of “becoming” as a stage in the system development is considered. The essence of the system's maturity is revealed. External and internal sources of system development are considered. On the basis of the theoretical generalization, the key properties of the concept of “development” are given: it acts as a form of constant movement forward from simple to complex form; the degree of development is determined by the influence of external and internal factors; the way of development of the system is determined by its basic motives, the degree of recognition by the system of objectively existing reality; development is possible if it’s provided by the quantitative parameters of the end goal; the form of its implementation is cyclical or spiral; analysis of development must be carried out in two directions of research: in the material and spiritual worlds. The definition of “innovation” is given. The essence of scientific and technological progress as a basis of economic development is considered and the vision of leading scientists-economists on this phenomenon is presented. Quantitative estimates of the impact of innovative GDP growth are provided. The quantitative contribution of various factors of economic growth of a market economy is given. Key words: management, enterprise, potential, innovations, investments, innovation and investment develop-ment.

Understanding the spatiotemporal dynamics of infectious disease spread is critical for anticipating epidemic trajectories and guiding public health responses. Accurate forecasts of where and when outbreaks are likely to emerge can support efficient resource allocation, particularly during the early stages of epidemics when surveillance data are limited. In this study, we used empirical human mobility data derived from county-level commuting and air traffic flows, and a theoretical mobility model (the radiation model) to study the relative order of epidemic onset across spatial scales. These mobility models were incorporated into a metapopulation framework to predict the spread of three major respiratory pathogens: COVID-19, seasonal influenza, and respiratory syncytial virus (RSV). We applied this framework to county-level transmission within South Carolina and state-level introductions across the United States. In both empirical and theoretical mobility scenarios, we found that effective distance, a network-based measure of mobility-informed proximity, reliably predicts the relative timing of epidemic onset. These results demonstrate that mobility-informed metapopulation models can capture consistent spatiotemporal patterns across disease systems and spatial scales, even in the absence of detailed epidemiological parameters. This highlights their potential as scalable, data-efficient tools for outbreak forecasting and early public health planning.