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Here, we introduce a novel framework for modelling the spatiotemporal dynamics of disease spread known as conditional logistic individual-level models (CL-ILM's). This framework alleviates much of the computational burden associated with traditional spatiotemporal individual-level models for epidemics, and facilitates the use of standard software for fitting logistic models when analysing spatiotemporal disease patterns. The models can be fitted in either a frequentist or Bayesian framework. Here, we apply the new spatial CL-ILM to simulated data, semi-real data from the UK 2001 foot-and-mouth disease epidemic, and real data from a greenhouse experiment on the spread of tomato spotted wilt virus. [ABSTRACT FROM AUTHOR]

In this paper, a fractional-order eco-epidemiological model with two populations of prey and predators both vulnerable to infection by predators and harvesting in an imprecise environment is presented. The study proposes two types of functional responses: a non-linear type refuge for infected predators and a linear type refuge for susceptible predators. After developing the model system, the positivity and boundedness of the solutions were analyzed. The local and global stability of the system is studied in order to calculate its equilibrium points. A suitable Lyapunov function is used to study the system’s overall dynamic. To validate the theoretical results and comprehend how changing the system’s characteristics affects its dynamic behavior, a thorough numerical investigation is conducted using MATLAB. [ABSTRACT FROM AUTHOR]

Enforcing strict protocols that prevent transmission of airborne infections in prisons is challenging. We examine a large severe acute respiratory syndrome coronavirus 2 outbreak in a Catalan penitentiary center in February–April 2021, prior to vaccination deployment. The aim was to describe the evolution of the outbreak using classical and genomic epidemiology and the containment strategy applied. The outbreak was initially detected in 1 module but spread to 4, infecting 7 staff members and 140 incarcerated individuals, 6 of whom were hospitalized (4.4%). Genomic analysis confirmed a single origin (B.1.1.7). Contact tracing identified transmission vectors between modules and prevented further viral spread. In future similar scenarios, the control strategy described here may help limit transmission of airborne infections in correctional settings. [ABSTRACT FROM AUTHOR]

In this paper, a disease transmission model coupled virus infection in host with incubation delay and environmental effects is studied. For the virus infection model in host with immune, latent delay and environmental virus invading, the threshold criteria on the global stability of antibody-free and antibody response infection equilibria are established. For the disease transmission model with incubation delay and immune response in host, basic reproduction number R 0 is defined, and the local stability of equilibria are established, i.e., the disease-free equilibrium is locally asymptotically stable if R 0 < 1 , and the endemic equilibrium is locally asymptotically stable if R 0 > 1 . Furthermore, the uniform persistence of positive solutions is studied while there is not the direct transmission of disease by the infected individuals. Finally, the numerical examples are presented to verify the main results. [ABSTRACT FROM AUTHOR]

Introduction: Respiratory syncytial virus (RSV) is associated with significant morbidity worldwide, especially among infants. We evaluated the potential impact of prophylactic nirsevimab, a monoclonal antibody, in infants experiencing their first RSV season, and the number of medically-attended lower respiratory tract infection episodes caused by RSV (RSV-MALRTI) in the USA. Methods: We developed an age-structured, dynamic, deterministic compartmental model reflecting RSV natural history, incorporating USA demographic data and an age-specific contact matrix. We assumed either no effect of nirsevimab on transmission (scenario 1) or a 50% reduction of viral shedding (scenario 2). Model outcomes were RSV-MALRTIs, ICD-9 coded in the Marketscan® database by month. We focused on age groups corresponding to the first 2 years of life, during seven RSV seasons (2008–2015). Results: Scenario 1 illustrated the direct individual benefit when a universal immunization strategy is applied to all infants. In scenario 2, herd protection was observed across age groups, with 15.5% of all avoided cases due to reduced transmission; the greatest impact was in the youngest age group and a benefit was observed in those aged 65+ years. Conclusion: These preliminary data suggest that single-dose nirsevimab will benefit infants experiencing their first RSV season, with a potential increase in effectiveness dependent on nirsevimab's mechanism of action. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus disease (COVID-19) outbreak has become a worldwide problem. Due to globalization and the proliferation of international travel, many countries are now facing local epidemics. The existence of asymptomatic and pre-symptomatic transmissions makes it more difficult to control disease transmission by isolating infectious individuals. To accurately describe and represent the spread of COVID-19, we suggest a susceptible-exposed-infected-hospitalized-removed (SEIHR) model with human migrations, where the “exposed” (asymptomatic) individuals are contagious. From this model, we derive the basic reproduction number of the disease and its relationship with the model parameters. We find that, for highly contagious diseases like COVID-19, when the adjacent region's epidemic is not severe, a large migration rate can reduce the speed of local epidemic spreading at the price of infecting the neighboring regions. In addition, since “infected” (symptomatic) patients are isolated almost immediately, the transmission rate of the epidemic is more sensitive to that of the “exposed” (asymptomatic) individuals. Furthermore, we investigate the impact of various interventions, e.g. isolation and border control, on the speed of disease propagation and the resultant demand on medical facilities, and find that a strict intervention measure can be more effective than closing the borders. Finally, we use some real historical data of COVID-19 caseloads from different regions, including Hong Kong, to validate the modified SEIHR model, and make an accurate prediction for the third wave of the outbreak in Hong Kong.

Since 2019 end, whole of the world is fighting for survival against Covid-19. To overcome the pandemic, global pharmaceutical sector started vaccine research. Early 2021, rose with a hope of vaccine discovery and few companies across the globe have invented and started manufacturing Covid-19 vaccine. As on date vaccination is playing a crucial role in curtaining the spread of this deadly virus caused disease. In this paper, a Compartmental Model is developed to study the spread of Covid-19 taking two different categories of human population into consideration. One is the vaccinated population and other is population without vaccination. Expressions for Reproduction Number are derived for Disease Free Equilibrium (DFE) and Endemic Equilibrium. Stability of the equilibria is also discussed. [ABSTRACT FROM AUTHOR]

Models of influenza transmission have focused on the ability of vaccination, antiviral therapy, and social distancing strategies to mitigate epidemics. Influenza transmission, however, may also be interrupted by hygiene interventions such as frequent hand washing and wearing masks or respirators. We apply a model of influenza disease transmission that incorporates hygiene and social distancing interventions. The model describes population mixing as a Poisson process, and the probability of infection upon contact between an infectious and susceptible person is parameterized by p. While social distancing interventions modify contact rates in the population, hygiene interventions modify p. Public health decision making involves tradeoffs, and we introduce an objective function that considers the direct costs of interventions and new infections to determine the optimum intervention type (social distancing versus hygiene intervention) and population compliance for epidemic mitigation. Significant simplifications have been made in these models. However, we demonstrate that the method is feasible, provides plausible results, and is sensitive to the selection of model parameters. Specifically, we show that the optimum combination of nonpharmaceutical interventions depends upon the probability of infection, intervention compliance, and duration of infectiousness. Means by which realism can be increased in the method are discussed.

Debate persists around the risk–benefit balance of vaccinating adolescents and children against COVID-19. Central to this debate is quantifying the contribution of adolescents and children to the transmission of SARS-CoV-2, and the potential impact of vaccinating these age groups. In this study, we present a novel SEIR mathematical disease transmission model that quantifies the impact of different vaccination strategies on population-level SARS-CoV-2 infections and clinical outcomes. The model employs both age- and time-dependent social mixing patterns to capture the impact of changes in restrictions. The model was used to assess the impact of vaccinating adolescents and children on the natural history of the COVID-19 pandemic across all age groups, using the UK as an example. The base case model demonstrates significant increases in COVID-19 disease burden in the UK following a relaxation of restrictions, if vaccines are limited to those ≥18 years and vulnerable adolescents (≥12 years). Including adolescents and children in the vaccination program could reduce overall COVID-related mortality by 57%, and reduce cases of long COVID by 75%. This study demonstrates that vaccinating adolescents and children has the potential to play a vital role in reducing SARS-CoV-2 infections, and subsequent COVID-19 morbidity and mortality, across all ages. Our results have major global public health implications and provide valuable information to inform a potential pandemic exit strategy.

Highlights • Changes in information content significantly influenced disease model dynamics. • Model outputs were variably sensitive to changes in information content. • The implications of processing decisions should be carefully considered. Abstract Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R 0 , time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions. [ABSTRACT FROM AUTHOR]

Since the discovery of HIV/AIDS there have been numerous mathematical modelsproposed to explain the epidemic of the disease and to evaluate possiblecontrol measures. In particular, several recent studies have looked at thepotential impact of condom usage on the epidemic[1, 2, 3, 4]. We develop a simplemodel for HIV/AIDS, and investigate the effectiveness of condoms as apossible control strategy. We show that condoms can greatly reduce the numberof outbreaks and the size of the epidemic. However, the necessary condom usagelevels are much higher than the current estimates. We conclude that condomsalone will not be sufficient to halt the epidemic in most populationsunless current estimates of the transmission probabilities are high.Our model has only five independentparameters, which allows for a complete analysis. We show that the assumptionsof mass action and standard incidence provide similar results, which impliesthat the results of the simpler mass action model can be used as a good firstapproximation to the peak of the epidemic.

Background: We developed the Apollo Structured Vocabulary (Apollo-SV)--an OWL2 ontology of phenomena in infectious disease epidemiology and population biology--as part of a project whose goal is to increase the use of epidemic simulators in public health practice. Apollo-SV defines a terminology for use in simulator configuration. Apollo-SV is the product of an ontological analysis of the domain of infectious disease epidemiology, with particular attention to the inputs and outputs of nine simulators. Results: Apollo-SV contains 802 classes for representing the inputs and outputs of simulators, of which approximately half are new and half are imported from existing ontologies. The most important Apollo-SV class for users of simulators is infectious disease scenario, which is a representation of an ecosystem at simulator time zero that has at least one infection process (a class) affecting at least one population (also a class). Other important classes represent ecosystem elements (e.g., households), ecosystem processes (e.g., infection acquisition and infectious disease), censuses of ecosystem elements (e.g., censuses of populations), and infectious disease control measures. In the larger project, which created an end-user application that can send the same infectious disease scenario to multiple simulators, Apollo-SV serves as the controlled terminology and strongly influences the design of the message syntax used to represent an infectious disease scenario. As we added simulators for different pathogens (e.g., malaria and dengue), the core classes of Apollo-SV have remained stable, suggesting that our conceptualization of the information required by simulators is sound. Despite adhering to the OBO Foundry principle of orthogonality, we could not reuse Infectious Disease Ontology classes as the basis for infectious disease scenarios. We thus defined new classes in Apollo-SV for host, pathogen, infection, infectious disease, colonization, and infection acquisition. Unlike IDO, our ontological analysis extended to existing mathematical models of key biological phenomena studied by infectious disease epidemiology and population biology. Conclusion: Our ontological analysis as expressed in Apollo-SV was instrumental in developing a simulatorindependent representation of infectious disease scenarios that can be run on multiple epidemic simulators. Our experience suggests the importance of extending ontological analysis of a domain to include existing mathematical models of the phenomena studied by the domain. Apollo-SV is freely available at: http://purl.obolibrary.org/obo/ apollo_sv.owl. [ABSTRACT FROM AUTHOR]

Benthic suspension-feeders can accumulate substantial numbers of microparasitic pathogens by contacting or filtering particles while feeding, thus making them highly vulnerable to infectious diseases. The study of disease dynamics in these marine organisms requires an innovative approach to modeling. To do so, we developed a single-population deterministic compartmental model adapted from the mathematical theory of epidemics. The model is a continuous-time model, unstructured in spatial or age terms, and configured to simulate the dynamics of diverse dose (body burden)-dependent infectious disease transmission processes in suspension feeders caused by susceptible individuals contacting or absorbing (filtering) infectious waterborne pathogens. Different scenarios were simulated to explore the effect of recruitment, filtration rate, particle loss, diffusion-like processes in the water column and non-focal hosts (i.e. non-susceptible in terms of disease) on disease incidence. An increase in recruitment (i.e. new disease free susceptibles) can reduce the prevalence of infection due to the dilution effect of adding more susceptibles, but the disease can spread faster for the same reason. Lower infective particle accumulation rates or increasing particle loss rates in the environment reduce the prevalence of infection. This effect is trivial when the water is saturated with infective particles released by infected and/or dead animals. Diffusion of particles from the local pool available to suspension feeders to the adjacent remote pool, prompted by a large remote volume and high particle exchange, limits epizootic development. Similarly, the likelihood of an epizootic can be constrained in a large susceptible population when competition for pathogens, more ‘active’ in active filter feeders than in passive suspension feeders, reduces the per capita infective particle accumulation rate. In passive suspension feeders, decreasing the area of the feeding surface has the same effect in constraining disease development. The effect of competition for infective particles in essence diluting the infective particle concentration in the water column is magnified when the susceptible population is part of a community with non-focal filter feeders, and is particularly effective in limiting disease development in high infective dose systems. At the same time, this active foraging strategy makes filter feeders more vulnerable to epizootics. The model is a suitable framework for studying the disease dynamics and determinants of disease outbreaks in benthic suspension feeders. [ABSTRACT FROM AUTHOR]

The control of severe acute respiratory syndrome (SARS), a fatalcontagious viral disease that spread to over 32 countries in 2003,was based on quarantine of latently infected individuals and isolation ofindividuals with clinical symptoms of SARS. Owing to the recent ongoing clinical trials ofsome candidate anti-SARS vaccines, this study aims to assess, via mathematical modelling, the potential impact of a SARS vaccine,assumed to be imperfect, in curtailing future outbreaks. Arelatively simple deterministic model is designed for this purpose. It is shown, using Lyapunov function theory and the theory of compound matrices, that the dynamicsof the model are determined by a certain thresholdquantity known as the control reproduction number ($\R_{v}$). If$\R_{v}\le 1$, the disease will be eliminated from the community; whereasan epidemic occurs if $\R_{v}>1$. This study further shows that animperfect SARS vaccine with infection-blocking efficacy is alwaysbeneficial in reducing disease spread within the community, althoughits overall impact increases with increasing efficacy and coverage.In particular, it is shown that thefraction of individuals vaccinated at steady-state and vaccineefficacy play equal roles in reducing disease burden, and thevaccine must have efficacy of at least 75% to lead to effectivecontrol of SARS (assuming $\R=4$). Numerical simulations are used to explore theseverity of outbreaks when $\R_{v}>1$.

An epidemiological model of the dynamics of East Coast Fever (ECF) in East Africa caused by the protozoan parasite Theileria parva and transmitted by the brown-ear tick Rhipicephalus appendiculatus was developed. In the model, ticks are assigned to either on-host or off-host categories both of which differ in their capacity to receive and transmit the disease. Cattle are assigned to categories of susceptible, infected and infectious as well as recovered animals having immunity to the disease. The parameters of the model were estimated from data reported in the literature. A mathematical analysis of the ECF/tick/cattle model with and without disease was conducted. Depending on the ratio between fecundity and mortality rates in cattle and in the absence of disease, different scenarios emerge including extinction of ticks, coexistence of ticks and cattle and total extinction of ticks and cattle. Furthermore, the analysis of the model with the disease yielded threshold conditions for the existence and the persistence of stable coexistence equilibria for the epidemiological system that may lead to enzootic stability. The model was used to identify critical aspects of the dynamics required to develop management strategies: (i) tick control in areas where the disease is absent, (ii) threshold-based tick and disease control, and (iii) conditions permitting the establishment of enzootic stability of the ECF/tick/cattle system. The analysis also identifies critical areas requiring further field investigation, sets the basis for developing a realistic model for field implementation, and provides a tool for project development and evaluation in the context of international research. [Copyright &y& Elsevier]

We extend the age of infection epidemic models to populations divided into an arbitrary number of subgroups and derive a set of final size relations if there are no disease deaths. If there are disease deaths, the final size relations are inequalities, but it is possible to obtain bounds for the epidemic size in terms of the final size for the corresponding model without disease deaths and the disease death rates. If the mixing is proportionate, we obtain an explicit expression for the reproduction number of the model. The heterogeneous mixing age of infection epidemic model is a unified form that includes general compartmental structures and arbitrary distributions of stay in compartments as well as heterogeneity of mixing. [ABSTRACT FROM AUTHOR]

Abstract: Compartmental models for influenza that include control by vaccination and antiviral treatment are formulated. Analytic expressions for the basic reproduction number, control reproduction number and the final size of the epidemic are derived for this general class of disease transmission models. Sensitivity and uncertainty analyses of the dependence of the control reproduction number on the parameters of the model give a comparison of the various intervention strategies. Numerical computations of the deterministic models are compared with those of recent stochastic simulation influenza models. Predictions of the deterministic compartmental models are in general agreement with those of the stochastic simulation models. [Copyright &y& Elsevier]

Abstract: In this paper an HIV/AIDS model is considered which describes the mechanics of sexual transmitted diseases. It will be shown that under some assumptions there can exist two equilibria: an infection-free state and an endemic equilibrium, and with education of the population the endemic equilibrium vanishes and the uninfected one becomes globally asymptotically stable – henceforth the disease will die out. Then in the endemic case the effect of time delay is taken into account in order to achieve a better compatibility with reality. This delay is regarded as the lag due to the evidence that time is needed during which infectious agents infect individuals of the susceptible group. Considering the delay as a bifurcation parameter the possibility of a periodic solution will be studied. [Copyright &y& Elsevier]

Debate persists around the risk–benefit balance of vaccinating adolescents and children against COVID-19. Central to this debate is quantifying the contribution of adolescents and children to the transmission of SARS-CoV-2, and the potential impact of vaccinating these age groups. In this study, we present a novel SEIR mathematical disease transmission model that quantifies the impact of different vaccination strategies on population-level SARS-CoV-2 infections and clinical outcomes. The model employs both age- and time-dependent social mixing patterns to capture the impact of changes in restrictions. The model was used to assess the impact of vaccinating adolescents and children on the natural history of the COVID-19 pandemic across all age groups, using the UK as an example. The base case model demonstrates significant increases in COVID-19 disease burden in the UK following a relaxation of restrictions, if vaccines are limited to those ≥18 years and vulnerable adolescents (≥12 years). Including adolescents and children in the vaccination program could reduce overall COVID-related mortality by 57%, and reduce cases of long COVID by 75%. This study demonstrates that vaccinating adolescents and children has the potential to play a vital role in reducing SARS-CoV-2 infections, and subsequent COVID-19 morbidity and mortality, across all ages. Our results have major global public health implications and provide valuable information to inform a potential pandemic exit strategy. [ABSTRACT FROM AUTHOR]

In mathematical epidemiology, a well-known formula describes the impact of heterogeneity on the basic reproductive number, R 0 , for situations in which transmission is separable and for which there is one source of variation in susceptibility and one source of variation in infectiousness. This formula is written in terms of the magnitudes of the heterogeneities, as quantified by their coefficients of variation, and the correlation between them. A natural question to ask is whether analogous results apply when there are multiple sources of variation in susceptibility and/or infectiousness. In this paper we demonstrate that with three or more coupled heterogeneities, R 0 under separable transmission depends on details of the distribution of the heterogeneities in a way that is not seen in the well-known simpler situation. We provide explicit formulae for the cases of multivariate normal and multivariate log-normal distributions, showing that R 0 can again be expressed in terms of the magnitudes of the heterogeneities and the pairwise correlations between them. The formulae, however, differ between the two multivariate distributions, demonstrating that no formula of this type applies generally when there are three or more coupled heterogeneities. We see that the results of the formulae are approximately equal when heterogeneities are relatively small and show that an earlier result in the literature (Koella, 1991) should be viewed in this light. We provide numerical illustrations of our results and discuss a setting in which coupled heterogeneities are likely to have a major impact on the value of R 0 . We also describe a rather surprising result: in a system with three heterogeneities, R 0 can exhibit non-monotonic behavior with increasing levels of heterogeneity, in marked contrast to the familiar two heterogeneity setting in which R 0 either increases or decreases with increasing heterogeneity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationshipsthat could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to beR= 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28-35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups., (© 2016 The Author(s).)