Epidemic Spread Research Articles

Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China.

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

Identifying influential spreaders in complex network based on the node’s weight and spreading probability

Identifying influential spreaders is a crucial aspect of network science with various applications, including rumor control, viral marketing and epidemic spread limitation. Despite the availability of various methods for identifying these spreaders in complex networks, there remains a fundamental question regarding their accurate and discriminative identification. To address the issues and account for each node’s propagation ability, we propose an algorithm to identify influential spreaders based on the node’s weight and spreading probability (NWSP) for identifying influential spreaders. The effectiveness of the proposed method is evaluated using the Susceptible–Infected–Recovered (SIR) model, Kendall’s Tau ([Formula: see text]) and monotonicity. The proposed method is compared with several well-known metrics, including degree centrality, K-shell decomposition, betweenness centrality, closeness centrality, eigenvector centrality and the centrality method based on node spreading probability (SPC), in ten real networks. Experimental results demonstrate the superiority ability of the proposed algorithm to accurately and discriminatively identify influential spreaders.

A Bayesian latent variable model for the optimal identification of disease incidence rates given information constraints

Abstract We present an original approach for measuring infections as a latent variable and making use of serological and expert surveys to provide ground truth identification during the early pandemic period. Compared to existing approaches, our model relies more on empirical information than strong structural forms, permitting inference with relatively few assumptions of cumulative infections. We also incorporate a range of political, economic, and social covariates to richly parameterize the relationship between epidemic spread and human behaviour. To show the utility of the model, we provide robust estimates of total infections that account for biases in COVID-19 cases and tests counts in the U.S. from March to July of 2020, a period of time when accurate data about the nature of the SARS-CoV-2 virus was of limited availability. In addition, we can show how sociopolitical factors like the Black Lives Matter protests and support for President Donald Trump are associated with the spread of the virus via changes in fear of the virus and cell phone mobility. A reproducible version of this article is available as an Rmarkdown file at https://github.com/CoronaNetDataScience/covid_model.

Infection-induced cascading failures – impact and mitigation

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.

Multicentral Agent-Based Model of Six Epidemic Waves of COVID-19 in the Nizhny Novgorod Region of Russian Federation

Relevance. To investigate the characteristics of the COVID-19 pandemic and introduce timely and effective measures, there is a need for models that can predict the impact of various restrictive actions or characteristics of disease itself on COVID-19 spread dynamics. Employing agent-based models can be attractive because they take into consideration different population characteristics (e.g., age distribution and social activity) and restrictive measures, laboratory testing, etc., as well as random factors that are usually omitted in traditional modifications of the SIR-like dynamic models. Aim. Improvement of the previously proposed agent-based model [23,24] for modeling the spread of COVID-19 in various regions of the Russian Federation. At this stage, six waves of the spread of COVID-19 have been modeled in the Nizhny Novgorod region as a whole region, as well as in its individual cities, taking into account restrictive measures and vaccination of the population. Materials and Methods. In this paper we extend a recently proposed agent-based model for Monte Carlo-based numerical simulation of the spread of COVID-19 with consideration of testing and vaccination strategies. Analysis is performed in MATLAB/ GNU Octave. Results. Developed multicentral model allows for more accurate simulation of the epidemic dynamics within one region, when a patient zero usually arrives at a regional center, after which the distribution chains capture the periphery of the region due to pendulum migration. Furthermore, we demonstrate the application of the developed model to analyze the epidemic spread in the Nizhny Novgorod region of Russian Federation. The simulated dynamics of the daily newly detected cases and COVID-19-related deaths was in good agreement with the official statistical data both for the region as whole and different periphery cities. Conclusions. The results obtained with developed model suggest that the actual number of COVID-19 cases might be 1.5–3.0 times higher than the number of reported cases. The developed model also took into account the effect of vaccination. It is shown that with the same modeling parameters, but without vaccination, the third and fourth waves of the epidemic would be united into one characterized by a huge rise in the morbidity rates and the occurrence of natural individual immunity with the absence of further pandemic waves. Nonetheless, the number of deaths would exceed the real one by about 9–10 times.

Qualitative analysis of solutions for a degenerate partial differential equations model of epidemic spread dynamics

Compartmental models are widely used in mathematical epidemiology to describe the dynamics of infectious diseases or in mathematical models of population genetics. In this study, we study a time-dependent Susceptible-Infectious-Susceptible (SIS) Partial Differential Equation (PDE) model that is based on a diffusion-drift approximation of a probability density from a well-known discrete-time Markov chain model. This SIS-PDE model is conservative due to the degeneracy of the diffusion term at the origin. The main results of this article are the qualitative behavior of weak solutions, the dependence of the local asymptotic property of these solutions on initial data, and the existence of Dirac delta function type solutions. Moreover, we study the long-term behavior of solutions and confirm our analysis with numerical computations.

Contagion dynamics in time-varying metapopulation networks with node's activity and attractiveness.

The metapopulation network model is a mathematical framework used to study the spatial spread of epidemics with individuals' mobility. In this paper, we develop a time-varying network model in which the activity of a population is correlated with its attractiveness in mobility. By studying the spreading dynamics of the SIR (susceptible-infectious-recovered)-type disease in different correlated networks based on the proposed model, we theoretically derive the mobility threshold and numerically observe that increasing the correction between activity and attractiveness results in a reduced mobility threshold but suppresses the fraction of infected subpopulations. It also introduces greater heterogeneity in the spatial distribution of infected individuals. Additionally, we investigate the impact of nonpharmaceutical interventions on the spread of epidemics in different correlation networks. Our results show that the simultaneous implementation of self-isolation and self-protection is more effective in negatively correlated networks than that in positively correlated or non-correlated networks. Both self-isolation and self-protection strategies enhance the mobility threshold and, thus, slow down the spread of the epidemic. However, the effectiveness of each strategy in reducing the fraction of infected subpopulations varies in different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. Our study will provide insights into epidemic prevention and control in large-scale time-varying metapopulation networks.

Impact of information adoption and the resulted self-protective actions on epidemic spreading in awareness-disease coupled multiplex networks

Impact of information adoption and the resulted self-protective actions on epidemic spreading in awareness-disease coupled multiplex networks

The effect of heterogeneous distributions of social norms on the spread of infectious diseases

Abstract The COVID-19 pandemic, caused by the SARS-CoV-2 virus, suddenly erupted at the beginning of 2020 in China and soon spread worldwide. This has caused an outstanding increase on research about the virus itself and, more in general, epidemics in many scientific fields. In this work we focus on the dynamics of the epidemic spreading and how it can be affected by the individual variability in compliance with social norms, i.e., in the adoption of preventive social norms by population's members, which influences the infectivity rate throughout the population and through time. By means of theoretical considerations, we show how such heterogeneities of the infection rate make the population more resistant against the epidemic spreading. Finally, we depict possible empirical tests aimed to confirm our results.

Genome-based surveillance reveals cross-transmission of MRSA ST59 between humans and retail livestock products in Hanzhong, China

Methicillin-resistant Staphylococcus aureus (MRSA) has been recognized in hospitals, community and livestock animals and the epidemiology of MRSA is undergoing a major evolution among humans and animals in the last decade. This study investigated the prevalence of MRSA isolates from ground pork, retail whole chicken, and patient samples in Hanzhong, China. The further characterization was performed by antimicrobial susceptibility testing and in-depth genome-based analysis to identify the resistant determinants and their phylogenetic relationship. A total of 93 MRSA isolates were recovered from patients (n = 67) and retail livestock products (n = 26) in Hanzhong, China. 83.9% (78/93) MRSA isolates showed multiple drug resistant phenotype. Three dominant livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) sequence types were identified: ST59-t437 (n = 47), ST9-t899 (n = 10) and ST398 (n = 7). There was a wide variation among sequence types in the distribution of tetracycline-resistance, scn-negative livestock markers and virulence genes. A previous major human MRSA ST59 became the predominant interspecies MRSA sequence type among humans and retail livestock products. A few LA-MRSA isolates from patients and livestock products showed close genetic similarity. The spreading of MRSA ST59 among livestock products deserving special attention and active surveillance should be enacted for the further epidemic spread of MRSA ST59 in China. Data generated from this study will contribute to formulation of new strategies for combating spread of MRSA.

Stochastic Compartment Model with Mortality and Its Application to Epidemic Spreading in Complex Networks

We study epidemic spreading in complex networks by a multiple random walker approach. Each walker performs an independent simple Markovian random walk on a complex undirected (ergodic) random graph where we focus on the Barabási–Albert (BA), Erdös–Rényi (ER), and Watts–Strogatz (WS) types. Both walkers and nodes can be either susceptible (S) or infected and infectious (I), representing their state of health. Susceptible nodes may be infected by visits of infected walkers, and susceptible walkers may be infected by visiting infected nodes. No direct transmission of the disease among walkers (or among nodes) is possible. This model mimics a large class of diseases such as Dengue and Malaria with the transmission of the disease via vectors (mosquitoes). Infected walkers may die during the time span of their infection, introducing an additional compartment D of dead walkers. Contrary to the walkers, there is no mortality of infected nodes. Infected nodes always recover from their infection after a random finite time span. This assumption is based on the observation that infectious vectors (mosquitoes) are not ill and do not die from the infection. The infectious time spans of nodes and walkers, and the survival times of infected walkers, are represented by independent random variables. We derive stochastic evolution equations for the mean-field compartmental populations with the mortality of walkers and delayed transitions among the compartments. From linear stability analysis, we derive the basic reproduction numbers RM,R0 with and without mortality, respectively, and prove that RM<R0. For RM,R0>1, the healthy state is unstable, whereas for zero mortality, a stable endemic equilibrium exists (independent of the initial conditions), which we obtained explicitly. We observed that the solutions of the random walk simulations in the considered networks agree well with the mean-field solutions for strongly connected graph topologies, whereas less well for weakly connected structures and for diseases with high mortality. Our model has applications beyond epidemic dynamics, for instance in the kinetics of chemical reactions, the propagation of contaminants, wood fires, and others.

Transcriptomic Investigation of the Virus Spectrum Carried by Midges in Border Areas of Yunnan Province

Yunnan province in China shares its borders with three neighboring countries: Myanmar, Vietnam, and Laos. The region is characterized by a diverse climate and is known to be a suitable habitat for various arthropods, including midges which are notorious for transmitting diseases which pose significant health burdens affecting both human and animal health. A total of 431,100 midges were collected from 15 different locations in the border region of Yunnan province from 2015 to 2020. These midges were divided into 37 groups according to the collection year and sampling site. These 37 groups of midges were then homogenized to extract nucleic acid. Metatranscriptomics were used to analyze their viromes. Based on the obtained cytochrome C oxidase I gene (COI) sequences, three genera were identified, including one species of Forcipomyia, one species of Dasyhelea, and twenty-five species of Culicoides. We identified a total of 3199 viruses in five orders and 12 families, including 1305 single-stranded positive-stranded RNA viruses (+ssRNA) in two orders and seven families, 175 single-stranded negative-stranded RNA viruses (−ssRNA) in two orders and one family, and 1719 double-stranded RNA viruses in five families. Six arboviruses of economic importance were identified, namely Banna virus (BAV), Japanese encephalitis virus (JEV), Akabane virus (AKV), Bluetongue virus (BTV), Tibetan circovirus (TIBOV), and Epizootic hemorrhagic disease virus (EHDV), all of which are capable, to varying extents, of causing disease in humans and/or animals. The survey sites in this study basically covered the current distribution area of midges in Yunnan province, which helps to predict the geographic expansion of midge species. The complexity and diversity of the viral spectrum carried by midges identified in the study calls for more in-depth research, which can be utilized to monitor arthropod vectors and to predict the emergence and spread of zoonoses and animal epidemics, which is of great significance for the control of vector-borne diseases.

Stability analysis and numerical simulations of the infection spread of epidemics as a reaction–diffusion model

This paper presents a spatiotemporal reaction–diffusion model for epidemics to predict how the infection spreads in a given space. The model is based on a system of partial differential equations with the Neumann boundary conditions. First, we study the existence and uniqueness of the solution of the model using the semigroup theory and demonstrate the boundedness of solutions. Further, the proposed model's basic reproduction number is calculated using the eigenvalue problem. Moreover, the dynamic behavior of the disease‐free steady states of the model for is investigated. The uniform persistence of the model is also discussed. In addition, the global asymptotic stability of the endemic steady state is examined. Finally, the numerical simulations validate the theoretical results.

Encounter Network and Its Application to Studying Epidemic Spread through Public Transportation Networks

Epidemic diseases have always been a challenging issue in public health. Densely populated urban public transportation systems could be the perfect candidate for transmitting epidemics by direct contact with the infected population and contaminated surfaces. In network analysis, betweenness centrality (BC), as the primary metric, has been used to measure an individual’s communication in the network. This research uses this concept to measure the risk of transmitting disease in public transportation links. For each network node, the set of shortest paths from that node to all other nodes compose a shortest paths sub-graph (tree). Source node total betweenness centrality (TBC) is defined as the sum of BC of all edges of this tree. Similarly, the shortest path sub-graph consisting of the set of the shortest paths from all nodes to a single target node is considered, and the target node TBC is defined as the sum of BC of all edges of this tree. The node encounter matrix is also defined to measure each node pair encounter in the network. The source node TBC of a node depicts the risk of transmitting the disease from that node to other nodes. Conversely, the target node TBC of a node is related to the risk of transmitting the disease from other nodes to that node. The susceptible-infected model is used to evaluate the proposed method by measuring the number of infected nodes.

Dynamics of a Stochastic SVEIR Epidemic Model with Nonlinear Incidence Rate

This paper delves into the analysis of a stochastic epidemic model known as the susceptible–vaccinated–exposed–infectious–recovered (SVEIR) model, where transmission dynamics are governed by a nonlinear function. In the theoretical analysis section, by suitable stochastic Lyapunov functions, we establish that when the threshold value, denoted as R0s, falls below 1, the epidemic is destined for extinction. Conversely, if the reproduction number R0 of the deterministic model surpasses 1, the model manifests an ergodic endemic stationary distribution. In the numerical simulations and data interpretation section, leveraging a graphical analysis with COVID-19 data, we illustrate that random fluctuations possess the capacity to quell disease outbreaks, underscoring the role of vaccines in curtailing the spread of diseases. This study not only contributes to the understanding of epidemic dynamics but also highlights the pivotal role of stochasticity and vaccination strategies in epidemic control and management. The inherent balance and patterns observed in epidemic spread and control strategies, reflect a symmetrical interplay between stochasticity, vaccination, and disease dynamics.

HtpG-A Major Virulence Factor and a Promising Vaccine Antigen against Mycobacterium tuberculosis.

Tuberculosis (TB) is the leading global cause of death f rom an infectious bacterial agent. Therefore, limiting its epidemic spread is a pressing global health priority. The chaperone-like protein HtpG of M. tuberculosis (Mtb) is a large dimeric and multi-domain protein with a key role in Mtb pathogenesis and promising antigenic properties. This dual role, likely associated with the ability of Heat Shock proteins to act both intra- and extra-cellularly, makes HtpG highly exploitable both for drug and vaccine development. This review aims to gather the latest updates in HtpG structure and biological function, with HtpG operating in conjunction with a large number of chaperone molecules of Mtb. Altogether, these molecules help Mtb recovery after exposure to host-like stress by assisting the whole path of protein folding rescue, from the solubilisation of aggregated proteins to their refolding. Also, we highlight the role of structural biology in the development of safer and more effective subunit antigens. The larger availability of structural information on Mtb antigens and a better understanding of the host immune response to TB infection will aid the acceleration of TB vaccine development.

Analyzing the dynamic patterns of COVID-19 through nonstandard finite difference scheme

This paper presents a novel approach to analyzing the dynamics of COVID-19 using nonstandard finite difference (NSFD) schemes. Our model incorporates both asymptomatic and symptomatic infected individuals, allowing for a more comprehensive understanding of the epidemic's spread. We introduce an unconditionally stable NSFD system that eliminates the need for traditional Runge–Kutta methods, ensuring dynamical consistency and numerical accuracy. Through rigorous numerical analysis, we evaluate the performance of different NSFD strategies and validate our analytical findings. Our work demonstrates the benefits of using NSFD schemes for modeling infectious diseases, offering advantages in terms of stability and efficiency. We further illustrate the dynamic behavior of COVID-19 under various conditions using numerical simulations. The results from these simulations demonstrate the effectiveness of the proposed approach in capturing the epidemic's complex dynamics.

Ultrafast Molecular Diagnosis‐Based Solid‐Phase Photonic PCR for Respiratory Pathogen Variant Discrimination

AbstractThe need for an accurate and rapid diagnosis platform for highly contagious viruses, such as pandemic viruses, has rapidly increased. Conventional diagnostic methods in the initial stage of quarantine for the prevention of epidemic spread have encountered limitations such as a long operation time, occupation of a large space, and high cost per single operation. Here, a highly sensitive and selective portable diagnostic platform based on solid‐phase photonic polymerase chain reaction (SP‐PCR) is reported. The thermal cycle is operated by the photothermal energy converted from the photon energy of a light‐emitting diode (LED) through a gold layer to phonons. The formation of an amplicon, which is immobilized through N‐heterocyclic carbene, is confirmed through fluorescence on the surface of the miniaturized SP‐PCR chip. The practical applicability of the newly developed diagnosis platform for the recognition of the presence of a SARS‐CoV‐2‐specific genome sequence is demonstrated utilizing 100 clinical samples, including 72 COVID‐19‐positive clinical samples and 28 negative samples.

A Statistical Review of the Impact of COVID-19 on the World

The COVID-19 epidemic has caused an unparalleled era of disruption, significantly impacting public health, the environment, the economy, and civilizations globally. This data analysis offers a thorough examination of COVID-19's worldwide effects. Through statistical methods, it explores the epidemic's spread, mechanics of transmission, and social repercussions. In addition, it carries out quantitative research to comprehend how the pandemic affects society's behavior, healthcare systems, and the environment. To understand the substantial repercussions of the pandemic, the review combines many datasets and presents data from a reliable source. Through the analysis of both quantitative and qualitative data, this review focuses on the pandemic's effects from several perspectives, such as public health measurements, societal shifts, and environmental changes. The findings demonstrate the horrifying cost of human deaths and describe how society has adapted to new issues and routines. The study additionally examines the data's graphical and tabular format, which comprises the total number of COVID-19 cases and fatalities that have been confirmed in nine different nations.

Vaccination strategies in the disease–behavior evolution model

Previous studies on the co-evolving between vaccination strategies and epidemics mainly assumed that the vaccination strategies were made in the period between two spreading seasons. However, individual cognition during the spreading seasons might also alter the vaccination strategy and inversely influence the epidemic spreading. We propose a coupled disease–behavior model to describe the dynamic evolution of vaccination behavior during the spread of infectious diseases. The model integrates a susceptible–infected–vaccinated (SIV) model with the diffusion of vaccination behavior. We focus on the trade-off between perceptions of infection risk and the vaccination behaviors of neighbors, characterizing individual vaccination opinions. We introduce an opinion-critical value to map vaccination opinions into vaccination behavior. The vaccination coverage of the disease–behavior model is studied in network models and real-world networks. In addition, when societal costs are measured based on the degree of initial vaccinees, the cost of randomly selecting initial vaccinees is lower than selecting individuals with high or low degrees as vaccinees. Evaluating an individual’s ability to transmit vaccination behavior based on the neighbor’s number is inappropriate. We find that the impact of effective spreading rates on group vaccination is not one-sided and that reducing fear and highlighting the dangers of infectious diseases are crucial to increasing vaccination coverage.