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Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the wisdom of crowds public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.

Summary: Securing an e‐commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is the complexity of the approach. The epidemic‐based recurrent long short‐term memory (E‐RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E‐RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e‐commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL‐KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber‐attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems. [ABSTRACT FROM AUTHOR]

This paper presents an existence result for a novel nonlinear Caputo q-fractional p-Laplacian epidemiological model on time scales. The proof of the main result leverages Arzelà–Ascoli and Schauder’s fixed point theorems. Additionally, a set of open questions is addressed to further enrich this field of valuable scientific research [ABSTRACT FROM AUTHOR]

In this paper, we introduce and study an age‐structured epidemiological compartment model and its respective problem, applied but not limited to the COVID‐19 pandemic, in order to investigate the role of the age of the individuals in the evolution of epidemiological phenomena. We investigate the well‐posedness of the model, as well as the global dynamics of it in the sense of basic reproduction number, via constructing Lyapunov functions. [ABSTRACT FROM AUTHOR]

In this study, we conduct a global stability analysis of an extended Susceptible-Unidentified infected-Confirmed (SUC) epidemic mathematical model. In the original SUC model, the entire population consists of individuals who are susceptible, those with unidentified infections, and those with confirmed infections, without accounting for births and deaths. In the proposed extended SUC model, we incorporate the dynamics of births and deaths into the original SUC model. We analyze the global stability of this extended SUC epidemic mathematical model and perform several computational experiments to validate the global stability analysis. Through this realistic extended SUC model, we aim to advance the current understanding of epidemiological modeling and provide valuable insights for guiding public health interventions and policies. [ABSTRACT FROM AUTHOR]

The Sommerfeld–Page equation describes the non-relativistic dynamics of a classical electron modeled by a sphere of finite size with a uniform surface charge density. It is a delay differential equation, and almost no exact solution of this equation was known until recently. However, progress has been made, and an analytical solution was recently found for an almost identical delay differential equation, which arose in the context of the mathematical modeling of the COVID-19 epidemics. Inspired by this research, we offer a pedagogical exposition of how one can find an analytical solution of the Sommerfeld–Page equation. Editor's Note: The Sommerfeld–Page equation governs the classical dynamics of a uniformly charged sphere, such as an electron, when considered to be a classical particle of finite size. It was introduced in relation to the radiation reaction problem in classical electron theory: If the electron has a finite size and since electromagnetic interactions can only propagate at the speed of light, does part of the electron exert a back-action on the other parts? This paper proposes an analytical solution to this complex problem, obtained by solving delay differential equations, which is appropriate for advanced electromagnetism or mathematical physics classes. [ABSTRACT FROM AUTHOR]

Introduction: The SIR (Susceptible-Infected-Recovered) model is one of the simplest and most widely used frameworks for understanding epidemic outbreaks. Methods: A second-order dynamical system for the R variable is formulated using an infinite exponential series expansion, and a recursion relation is established between the series coefficients. A numerical approximation scheme for the R variable is also developed. Results: The proposed numerical method is compared to a double exponential (DE) nonlinear approximate analytic solution, which reveals two coupled timescales: a relaxation timescale, determined by the ratio of the model's time constants, and an excitation timescale, dictated by the population size. The DE solution is applied to estimate model parameters for a well-known epidemiological dataset—the boarding school flu outbreak. Discussion: From a theoretical standpoint, the primary contribution of this work is the derivation of an infinite exponential, Dirichlet, series for the model variables. Truncating the series yields a finite approximation, known as a Prony series, which can be interpreted as a sequence of coupled exponential relaxation processes, each with a distinct timescale. This apparent complexity can be approximated well by the DE solution, which appears to be of main practical interest. [ABSTRACT FROM AUTHOR]

Seasonal influenza causes significant annual morbidity and mortality worldwide. In France, it is estimated that, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Traditionally, mathematical models used for epidemic forecasting can either include parameters capturing the infection process (mechanistic or compartmental models) or rely on time series analysis approaches that do not make mechanistic assumptions (statistical or phenomenological models). While the latter make extensive use of past epidemic data, mechanistic models are usually independently initialized in each season. As a result, forecasts from such models can contain trajectories that are vastly different from past epidemics. We developed a mechanistic model that takes into account epidemic data from training seasons when producing forecasts. The parameters of the model are estimated via a first particle filter running on the observed data. A second particle filter is then used to produce forecasts compatible with epidemic trajectories from the training set. The model was calibrated and tested on 35 years' worth of surveillance data from the French Sentinelles Network, representing the weekly number of patients consulting for ILI over the period 1985–2019. Our results show that the new method improves upon standard mechanistic approaches. In particular, when retrospectively tested on the available data, our model provides increased accuracy for short-term forecasts (from one to four weeks into the future) and peak timing and intensity. Our new approach for epidemic forecasting allows the integration of key strengths of the statistical approach into the mechanistic modelling framework and represents an attempt to provide accurate forecasts by making full use of the rich surveillance dataset collected in France since 1985. Author summary: Seasonal influenza causes significant morbidity and mortality worldwide. In France, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Forecasting the future trajectory of an epidemic in real-time can inform public health responses. Traditionally, two types of mathematical models are used to forecast infectious diseases outbreaks: (1) mechanistic models that explicitly capture transmission mechanisms and (2) statistical models that rely on the similarity between epidemic dynamics over the years. In contrast to statistical models, mechanistic models usually do not use information on past seasons to inform forecasts. This may result in poor performance when forecasted trajectories are very different from past epidemics. Here, we propose a framework that combines these two approaches by allowing mechanistic models to learn from trends observed in past epidemics. We evaluate this approach in the context of seasonal influenza in France. Our results show that the new method improves upon standard mechanistic approaches. In particular, our model provides increased accuracy for predicting the epidemic trajectory one to four weeks into the future, as well as the timing and size of the peak. Our new approach for epidemic forecasting allows combining statistical and mechanistic models to improve forecasts performance. [ABSTRACT FROM AUTHOR]

This paper is concerned with the qualitative analysis of a diffusion SIR epidemic model with networked delay and nonlinear incidence rate. First, we prove the existence and uniqueness of the model by using the method of upper and lower solutions. Then, we prove that the trivial equilibrium (0, 0) is unstable; the disease‐free equilibrium (N, 0) is locally asymptotically stable if the reproduction number R0≤1 and unstable if R0>1; the endemic equilibrium (S∗, I∗) is locally asymptotically stable if R0≤R1 or if R1τ10∗. Moreover, when R1

Mathematical modelling of (re)emerging infectious respiratory diseases among humans poses multiple challenges for modellers, which can arise as a result of limited data and surveillance, uncertainty in the natural history of the disease, as well as public health and individual responses to outbreaks. Here, we propose a COVID-19-inspired health state diagram (HSD) to serve as a foundational framework for conceptualising the modelling process for (re)emerging respiratory diseases, and public health responses, in the early stages of their emergence. The HSD aims to serve as a starting point for reflection on the structure and parameterisation of a transmission model to assess the impact of the (re)emerging disease and the capacity of public health interventions to control transmission. We also explore the adaptability of the HSD to different (re)emerging diseases using the characteristics of three respiratory diseases of historical public health importance. We outline key questions to contemplate when applying and adapting this HSD to (re)emerging infectious diseases and provide reflections on adapting the framework for public health-related interventions. [ABSTRACT FROM AUTHOR]

The mathematical modeling of infectious diseases plays a vital role in understanding and predicting disease transmission, as underscored by recent global outbreaks; to delve deep into the dynamic of infectious disease considering latent period presciently is inevitable as it bridges the gap between realistic nature and mathematical modeling. This study extended the classical Susceptible–Infected–Recovered (SIR) model by incorporating vaccination strategies during incubation. We introduced multiple time delays to an account incubation period to capture realistic disease dynamics better. The model is formulated as a system of delay differential equations that describe the transmission dynamics of diseases such as polio or COVID-19, or diseases for which vaccination exists. Critical aspects of the study include proving the positivity of the model's solutions, calculating the basic reproduction number ( R 0 ) using next-generation matrix theory, and identifying disease-free and endemic equilibrium points. The local stability of these equilibria is then analyzed using the Routh–Hurwitz criterion. Due to the complexity introduced by the delay components, we examine the stability by studying the roots of a fourth-degree exponential polynomial. The effects of educational campaigns and vaccination efficacy are also investigated as control measures. Furthermore, an optimization problem is formulated, based on Pontryagin's maximum principle, to minimize the number of infections and associated intervention costs. Numerical simulations of the delay differential equations are conducted, and a modified Runge–Kutta method with delays is used to solve the optimal control problem. Finally, we present a few simulation results to illustrate the analytical findings. [ABSTRACT FROM AUTHOR]

Four fundamental models that describe the spread of Monkeypox disease are analyzed: the SIR-SIR, SEIR-SIR, SIR-SEIR, and SEIR-SEIR models. They form the basis of most Monkeypox diseases models that are currently discussed in the literature. It is shown that the way the model subpopulations are organized in disease outbreaks and evolve relative to each other is determined by the relevant unstable system eigenvectors, also called order parameters. For all models, analytical expressions of the order parameters are derived. Under appropriate conditions these order parameters describe the initial outbreak phases of exponential increase in good approximation. It is shown that all four models exhibit maximally two order parameters and maximally one human–animal order parameter. The human–animal order parameter firmly connects the outbreak dynamics in the animal system with the dynamics in the human system. For the special case of the SIR-SIR model, it is found that the two possible order parameters completely describe the dynamics of infected humans and animals during entire infection waves. Finally, a simulation of a Monkeypox infection wave illustrates that in line with the aforementioned analytical results the leading order parameter explains most of the variance in the infection dynamics. [ABSTRACT FROM AUTHOR]

In this study, a nonlinear dynamic SEVIQR measles epidemic model is constructed and analyzed using the novel Caputo fractional‐order derivative operator. The model's existence and uniqueness are established. In addition, the model equilibria are determined, and the novel Jacobian determinant method recently constructed in the literature of epidemiological modeling of infectious diseases is applied to determine the threshold quantity, R0. Furthermore, we construct appropriate Lyapunov functions to establish the global asymptotic stability of the disease‐free and endemic equilibrium points. Finally, the numerical solution of the model is executed employing the efficient and widely known Adams‐type predictor‐corrector iterative scheme, and simulation is conducted to investigate the impact of memory index and diverse preventive measures on the occurrence of the disease. Numerical simulation of the model indicates that quarantine, vaccination, and treatment can reduce the numbers of infectious and exposed populations, thereby controlling the disease. Therefore, it is recommended that the government provide financial assistance for vaccine distribution. [ABSTRACT FROM AUTHOR]

ABSTRACT Flavobacterium psychrophilum, the causative agent of bacterial cold water disease (BCWD), is one of the leading pathogens in rainbow trout (Oncorhynchus mykiss) aquaculture. To date, there is little knowledge of the transmission kinetics of F. psychrophilum over the course of infection. In particular, how transmission is affected by host genotype and pathogen exposure dosage are not well studied. In order to fill in these knowledge gaps, we exposed two divergently selected lines of rainbow trout (ARS‐Fp‐R and ARS‐Fp‐S) to a range of dosages of F. psychrophilum (strain CSF117‐10). We then measured mortality and bacterial shedding to estimate transmission risk at multiple time points since initial infection. As dosage increased, the number of fish shedding and the amount of bacteria shed increased ranging from 0% to 100% and 103 to 108 cells fish−1 h−1, respectively. In addition, we found that disease resistance (survival) was not correlated with transmission risk blocking, in that 67% of fish which shed bacteria experienced no clinical disease. In general, fish mortality began on Day 3, peaked between Days 5–7 and was higher in the ARS‐Fp‐R line. Results from this study could be used to develop epidemiological models and improve disease management, particularly in the context of aquaculture and selective breeding. [ABSTRACT FROM AUTHOR]

While it is recognised that most, if not all, multicellular organisms harbour neoplastic processes within their bodies, the timing of when these undesirable cell proliferations are most likely to occur and progress throughout the organism's lifetime remains only partially documented. Due to the different mechanisms implicated in tumourigenesis, it is highly unlikely that this probability remains constant at all times and stages of life. In this article, we summarise what is known about this variation, considering the roles of age, season and circadian rhythm. While most studies requiring that level of detail be done on humans, we also review available evidence in other animal species. For each of these timescales, we identify mechanisms or biological functions shaping the variation. When possible, we show that evolutionary processes likely played a role, either directly to regulate the cancer risk or indirectly through trade‐offs. We find that neoplastic risk varies with age in a more complex way than predicted by early epidemiological models: rather than resulting from mutations alone, tumour development is dictated by tissue‐ and age‐specific processes. Similarly, the seasonal cycle can be associated with risk variation in some species with life‐history events such as sexual competition or mating being timed according to the season. Lastly, we show that the circadian cycle influences tumourigenesis in physiological, pathological and therapeutic contexts. We also highlight two biological functions at the core of these variations across our three timescales: immunity and metabolism. Finally, we show that our understanding of the entanglement between tumourigenic processes and biological cycles is constrained by the limited number of species for which we have extensive data. Improving our knowledge of the periods of vulnerability to the onset and/or progression of (malignant) tumours is a key issue that deserves further investigation, as it is key to successful cancer prevention strategies. [ABSTRACT FROM AUTHOR]

This study investigates a symmetric fractional-order epidemic model with time delays and non-monotonic incidence rates, considering two viral strains. By confirming the existence, uniqueness, and boundedness of the system's solutions, the research ensures the model's well-posedness, guaranteeing its mathematical soundness and practical relevance. The study calculates and evaluates the equilibrium points and the basic reproduction numbers R 0 1 and R 0 2 to understand the dynamic behavior of the model under different parameter settings. Through the application of the Lyapunov method, the research examines the asymptotic global stability of the system, determining whether it will converge to a particular equilibrium state over time. Furthermore, Hopf bifurcation theory is employed to investigate potential periodic solutions and bifurcation scenarios, highlighting how the system might shift from stability to periodic oscillations under certain conditions. By utilizing the Adams-Bashforth-Moulton numerical simulation method, the theoretical results are validated, reinforcing the conclusions and demonstrating the model's applicability in real-world contexts. It emphasizes the importance of fractional-order models in addressing epidemiological issues related to time delays (τ), individual heterogeneity (m, k), and memory effects (θ), offering greater accuracy compared with traditional integer-order models. In summary, this research provides a theoretical foundation and practical insights, enhancing the understanding and management of epidemic dynamics through fractional-order epidemic models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel normalized time-fractional susceptible–infected–removed (SIR) model that incorporates memory effects into epidemiological dynamics. The proposed model is based on a newly developed normalized time-fractional derivative, which is similar to the well-known Caputo fractional derivative but is characterized by the property that the sum of its weight function equals one. This unity property is crucial because it helps with evaluating how the fractional order influences the behavior of time-fractional differential equations over time. The normalized time-fractional derivative, with its unity property, provides an intuitive understanding of how fractional orders influence the SIR model's dynamics and enables systematic exploration of how changes in the fractional order affect the model's behavior. We numerically investigate how these variations impact the epidemiological dynamics of our normalized time-fractional SIR model and highlight the role of fractional order in improving the accuracy of infectious disease predictions. The appendix provides the program code for the model. [ABSTRACT FROM AUTHOR]

Background: COVID-19 will not be the last pandemic of the twenty-first century. To better prepare for the next one, it is essential that we make honest appraisals of the utility of different responses to COVID. In this paper, we focus specifically on epidemiologic forecasting. Characterizing forecast efficacy over the history of the pandemic is challenging, especially given its significant spatial, temporal, and contextual variability. In this light, we introduce the Weighted Contextual Interval Score (WCIS), a new method for retrospective interval forecast evaluation. Methods: The central tenet of the WCIS is a direct incorporation of contextual utility into the evaluation. This necessitates a specific characterization of forecast efficacy depending on the use case for predictions, accomplished via defining a utility threshold parameter. This idea is generalized to probabilistic interval-form forecasts, which are the preferred prediction format for epidemiological modeling, as an extension of the existing Weighted Interval Score (WIS). Results: We apply the WCIS to two forecasting scenarios: facility-level hospitalizations for a single state, and state-level hospitalizations for the whole of the United States. We observe that an appropriately parameterized application of the WCIS captures both the relative quality and the overall frequency of useful forecasts. Since the WCIS represents the utility of predictions using contextual normalization, it is easily comparable across highly variable pandemic scenarios while remaining intuitively representative of the in-situ quality of individual forecasts. Conclusions: The WCIS provides a pragmatic utility-based characterization of probabilistic predictions. This method is expressly intended to enable practitioners and policymakers who may not have expertise in forecasting but are nevertheless essential partners in epidemic response to use and provide insightful analysis of predictions. We note that the WCIS is intended specifically for retrospective forecast evaluation and should not be used as a minimized penalty in a competitive context as it lacks statistical propriety. Code and data used for our analysis are available at https://github.com/maximilian-marshall/wcis. [ABSTRACT FROM AUTHOR]

Plant viruses threaten food security and are often transmitted by insect vectors. Non-persistently transmitted (NPT) plant viruses are transmitted almost exclusively by aphids. Because virions attach to the aphid's stylet (mouthparts) and are acquired and inoculated via brief epidermal probes, the aphid–virus interaction is highly transient, with a very short aphid virus retention time. Many NPT viruses manipulate their host plant's phenotype to change aphid behaviour to optimise virus transmission. Epidemiological models of this have overlooked a key feature of aphid NPT virus retention: probing or feeding on a plant causes aphids to lose the virus. Furthermore, experimental studies suggest aphids could possibly inoculate multiple healthy plants within one infective period if they do not feed. Consequences of this for virus manipulation of host plant phenotype have not been explored. Our new compartmental epidemiological model includes both behaviour-based aphid dispersal and infectivity loss rates, and the ability of infective aphids to probe multiple plants before virus loss. We use our model to explore how NPT virus-induced host phenotypes affect epidemic outcomes, comparing these results to representative previous models. We find that previous models behave fundamentally differently and underestimate the benefit of an 'attract-and-deter' phenotype, where the virus induces increased aphid attraction to infected plants but deters them from prolonged feeding. Our results also highlight the importance of characterising NPT virus retention upon the aphid during probing. Allowing for multiple infective probes increases disease incidence and the effectiveness of virus manipulation, with implications for epidemic prediction and control. Author summary: Plant viruses can cause devastating disease epidemics. Non-persistently transmitted viruses are almost always vectored (transmitted between plants) by aphids. Experiments show virus infection can affect whether aphids are attracted to plants (by altering how infected plants 'smell'), as well as whether aphids settle for an extended feed after a brief initial probe (by altering how infected plants 'taste'). Since virus transmission requires an individual aphid to briefly probe an infected plant followed by one or more healthy plant(s), this strongly affects disease transmission. However, most studies exploring virus epidemics do not account for how aphid feeding behaviour affects how long an aphid holds the virus for, or that an aphid could infect multiple healthy plants before losing the virus. We use mathematical modelling to dissect how these aspects of aphid feeding behaviour affect virus transmission, particularly when viruses manipulate the 'smell' and 'taste' of plants. We show how previous studies, by omitting crucial aspects of aphid feeding behaviour, underestimate how viruses can promote their own transmission. We also highlight that there are very few experimental studies exploring the number of plants an aphid with the virus can consecutively infect, which is a key parameter affecting the severity of epidemics. [ABSTRACT FROM AUTHOR]

This paper studies a nonlinear epidemiological model called susceptible, exposed, asymptomatic infected, infected, and recovered (SEAIR) with a control input (vaccination), which helps to design a sliding mode vaccination strategy by tracking the prevalence of infectious diseases. First, the effective reproduction number is adjusted to the expected value to control infectious disease outbreaks, even with model parameter changes and systematic perturbations. Meanwhile, based on the sliding mode control theory, two control strategies, namely, first‐order sliding mode control and super‐twisting control, are proposed to control the spread of COVID‐19. Besides, an integral sliding surface associated with the effective reproduction number R0(t)$$ {R}_0(t) $$ is constructed to eliminate the chattering problem of the system. Moreover, the sliding mode controller is robust to uncertainties and external perturbations. The stability and finite‐time convergence of the closed‐loop control system are verified by Lyapunov's second method. Also, the effect of the two control strategies on the system performance is evaluated by numerical simulations. Furthermore, this paper presents a practical case simulation based on data from Wuhan. The results indicate that universal vaccination is crucial in the early stage of the COVID‐19 pandemic. As the epidemic progresses, vaccination is still an essential approach for epidemic prevention and control. [ABSTRACT FROM AUTHOR]

In this work, we derive a system of Boltzmann-type equations to describe the spread of contact-based infections, such as SARS-CoV-2 virus, at the microscopic scale, that is, by modeling the human-to-human mechanisms of transmission. To this end, we consider two populations, characterized by specific distribution functions, made up of individuals without symptoms (population 1) and infected people with symptoms (population 2). The Boltzmann operators model the interactions between individuals within the same population and among different populations with a probability of transition from one to the other due to contagion or, vice versa, to recovery. In addition, the influence of innate and adaptive immune systems is taken into account. Then, starting from the Boltzmann microscopic description we derive a set of evolution equations for the size and mean state of each population considered. Mathematical properties of such macroscopic equations, as equilibria and their stability, are investigated, and some numerical simulations are performed in order to analyze the ability of our model to reproduce the characteristic features of Covid-19 type pandemics. [ABSTRACT FROM AUTHOR]

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In this work, we investigate the numerical performance of a continuous data assimilation algorithm based on ideas from the feedback control theory of dynamical systems, in the context of the SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental mathematical model of epidemics. The motivation behind this work is to propose a new approach that estimates the infection rate parameter in the SEIR model. However, this model may have limited predictive value due to the idealized assumptions underlying it and measurement errors in the experimental data and parameters. We first implement this model as a paradigm because of its application to more realistic epidemic models, then we present our theoretical results. In addition, we provide numerous numerical simulations and significant examples to illustrate the first part of our work. [ABSTRACT FROM AUTHOR]

During various stages of the COVID‐19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio‐economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID‐19 era, aimed at enhancing vaccine distribution and establishing a standardized framework for future pandemic preparedness. These models address critical issues such as site selection, inventory management, allocation strategies, distribution logistics, and route optimization encountered during the COVID‐19 crisis. A unified framework is employed to describe the models, emphasizing their integration with epidemiological models to facilitate a holistic understanding. This article also summarizes evolving nature of literature, relevant research gaps, and authors' perspectives for model selection. Finally, future research scopes are detailed both in the context of modeling and solutions approaches. [ABSTRACT FROM AUTHOR]

Using an artificial neural network and the Bayesian Regularization Technique (NNs-BRT), the stochastic method's strength is used to analyze the differential system, illustrating a nonlinear smoke epidemic differential model (NSED). This allows for a more precise, dependable, cost-effective, dynamic calculating approach. In addition to experiments utilizing nonlinear mathematical structures through five distinct classes of differential operators-smokers contemplating quitting, infrequent smokers, regular smokers, those who have temporarily abstained from smoking, and those who have permanently quit smoking the NSED framework has been established. By allocating 25% of the data for testing and validation and 75% for training, the proposed NNs-BRT can determine the estimated solutions of five different examples based on the numerical computation of the NSED system employing Adams techniques. To prove the accuracy of the given NNs-BRTs, a comparison of the results from the dataset obtained using the Adam method for different scenarios reflecting variance in recruitment rate, effective contact rate between C and A, natural death rate, how quickly occasional smokers become regular smokers, contact rate between smokers, and temporary quitters, number of smokers quitting, and percentage of smokers who are still leaving for good is made. The use of error histograms, mean square error, regression, correlation, and state transitions is also examined in numerical replications of the NNs-BRTs to verify their competence, dependability, accuracy, consistency, and proficiency. [ABSTRACT FROM AUTHOR]

The generalized tempered stable (GTS) distribution is an optimal choice for modeling disease propagation, as it effectively captures the heavy-tailed nature of such events. This attribute is crucial for evaluating the impact of large-scale outbreaks and formulating effective public health interventions. In our study, we introduce a comprehensive stochastic epidemic model that incorporates various intervention strategies and utilizes Lévy jumps characterized by the GTS distribution. Notably, our proposed stochastic system does not exhibit endemic or disease-free states, challenging the conventional approach of assessing disease persistence or extinction based on asymptotic behavior. To address this, we employed a novel stochastic analysis approach to demonstrate the potential for disease eradication or continuation. We provide numerical examples to highlight the importance of incorporating the GTS distribution in epidemiological modeling. These examples validate the accuracy of our results and compare our model's outcomes with those of a standard system using basic Lévy jumps. The purposeful use of the GTS distribution accounts for the heavy-tailed nature of disease incidence or vector abundance, enhancing the precision of models and predictions in epidemiology. [ABSTRACT FROM AUTHOR]

Background: To make the best use of health resources, it is crucial to understand the healthcare needs of a population—including how needs will evolve and respond to changing epidemiological context and patient behaviour—and how this compares to the capabilities to deliver healthcare with the existing workforce. Existing approaches to planning either rely on using observed healthcare demand from a fixed historical period or using models to estimate healthcare needs within a narrow domain (e.g., a specific disease area or health programme). A new data-grounded modelling method is proposed by which healthcare needs and the capabilities of the healthcare workforce can be compared and analysed under a range of scenarios: in particular, when there is much greater propensity for healthcare seeking. Methods: A model representation of the healthcare workforce, one that formalises how the time of the different cadres is drawn into the provision of units of healthcare, was integrated with an individual-based epidemiological model—the Thanzi La Onse model—that represents mechanistically the development of disease and ill-health and patients' healthcare seeking behaviour. The model was applied in Malawi using routinely available data and the estimates of the volume of health service delivered were tested against officially recorded data. Model estimates of the "time needed" and "time available" for each cadre were compared under different assumptions for whether vacant (or established) posts are filled and healthcare seeking behaviour. Results: The model estimates of volume of each type of service delivered were in good agreement with the available data. The "time needed" for the healthcare workforce greatly exceeded the "time available" (overall by 1.82-fold), especially for pharmacists (6.37-fold) and clinicians (2.83-fold). This discrepancy would be largely mitigated if all vacant posts were filled, but the large discrepancy would remain for pharmacists (2.49-fold). However, if all of those becoming ill did seek care immediately, the "time needed" would increase dramatically and exceed "time supply" (2.11-fold for nurses and midwives, 5.60-fold for clinicians, 9.98-fold for pharmacists) even when there were no vacant positions. Conclusions: The results suggest that services are being delivered in less time on average than they should be, or that healthcare workers are working more time than contracted, or a combination of the two. Moreover, the analysis shows that the healthcare system could become overwhelmed if patients were more likely to seek care. It is not yet known what the health consequences of such changes would be but this new model provides—for the first time—a means to examine such questions. [ABSTRACT FROM AUTHOR]

Infection duration affects individual host fitness and between-host transmission. Whether an infection is cleared or becomes chronic depends on the complex interaction between host immune responses and parasite growth. Empirical and theoretical studies have suggested that there are critical thresholds of parasite dose that can determine clearance versus chronicity, driven by the ability of the parasite to manipulate host immunity. However, the mammalian immune response is characterized by strong positive and negative feedback loops that could generate duration thresholds even in the absence of direct immunomodulation. Here, we derive and analyse a simple model for the interaction between T-cell subpopulations and parasite growth. We show that whether an infection is cleared or not is very sensitive to the initial immune state, parasite dose and strength of immunological feedbacks. In particular, chronic infections are possible even when parasites provoke a strong and effective immune response and lack any ability to immunomodulate. Our findings indicate that the initial immune state, which often goes unmeasured in empirical studies, is a critical determinant of infection duration. This work also has implications for epidemiological models, as it implies that infection duration will be highly variable among individuals, and dependent on each individual's infection history. [ABSTRACT FROM AUTHOR]

Dengue is a viral infection caused by an arbovirus and spread through the bites of mosquitoes of the genus Aedes which causes significant human and economic damage every year. Preventive measures have been taken to reduce the proliferation of the transmitting mosquito and efforts are being made to obtain an efficient vaccine for the disease. Parallel to these measures, several studies present a model for the spread of the disease in order to determine relevant factors in the process and thus guide preventive measures and future vaccination initiatives.Within this context, this work brings a modeling for the spread of the disease through a system of differential equations where the geographic region studied (a Brazilian city) is divided into sub-regions with human circulation between them, thus making humans the spreader of the disease.Using the model, it was possible to characterize the time series of people infected with dengue in the city of Goiânia, Brazil, between 2000 and 2003 using two and three subdivisions of the geographic region studied. Seasonal cycles of the disease have been described without exhausting the number of susceptible humans. [ABSTRACT FROM AUTHOR]

Pathogens such as bacteria, fungi and viruses are important components of soil and aquatic communities, where they can benefit from decaying and living organic matter, and may opportunistically infect human and animal hosts. One-third of human infectious diseases is constituted by sapronotic disease agents that are natural inhabitants of soil or aquatic ecosystems. They are capable of existing and reproducing in the environment outside of the host for extended periods of time. However, as ecological research on sapronosis is infrequent and epidemiological models are even rarer, very little information is currently available. Their importance is overlooked in medical and veterinary research, as well as the relationships between free environmental forms and those that are pathogenic. Here, using dynamical models in realistic aquatic metacommunity systems, we analyze sapronosis transmission, using the human pathogen Mycobacterium ulcerans that is responsible for Buruli ulcer. We show that the persistence of bacilli in aquatic ecosystems is driven by a seasonal upstream supply, and that the attachment and development of cells to aquatic living forms is essential for such pathogen persistence and population dynamics. Our work constitutes the first set of metacommunity models of sapronotic disease transmission, and is highly flexible for adaptation to other types of sapronosis. The importance of sapronotic agents on animal and human disease burden needs better understanding and new models of sapronosis disease ecology to guide the management and prevention of this important group of pathogens. Author summary: Bacterial pathogens responsible for sapronotic diseases represent a threat to public health, accounting for one-third of human infections. However, little is known about variations in infection risk in time and space, hence limiting control strategies development, due to a lack of research on these pathogens. In this study, we developed a first realistic mathematical model to describe the spatial and temporal dynamics of sapronotic bacteria and their interactions with wildlife in aquatic ecosystems. Our model allows identifying the characteristics of wildlife-sapronotic bacteria interactions that play the most important role in determining disease risks in animals and humans. [ABSTRACT FROM AUTHOR]

The dynamics of the propagation and outspread of infectious diseases are eminently intricate, mainly due to the heterogeneity of the host individuals. In this paper, an age‐stratified SEIR (susceptible‐exposed‐infected‐recovered) epidemiological model incorporating saturated treatment function and heterogeneous contact rates is developed to study infectious disease transmission dynamics among various age groups. The expression for the basic reproduction number R0$$ {R}_0 $$ and conditions for the global stability of the system have been derived by a recently developed graph‐theoretic (GT) approach. Digraph reduction creates a GT version of the Gauss elimination method for computing the R0$$ {R}_0 $$. The global dynamics results have been formed by constructing the Lyapunov function using a GT approach. The endemic equilibrium exists uniquely if R0>1$$ {R}_0>1 $$, whereas the disease‐free equilibrium is observed to be globally stable if R0≤1$$ {R}_0\le 1 $$. The numerical simulations are demonstrated by extracting the daily reported COVID‐19 cases for the second wave in Italy. The age‐dependent contact matrix for the Republic of Italy (data sourced from the POLYMOD study) is computed via paper–diary methodology (PDM) grounded on a population‐prospective survey in European countries. Our numerical findings imply that (i) for the age group (20–49) years and (50–69) years, the number of infected persons is quite double as compared with the exposed cases; (ii) approximately 50% of positive cases lies in (20–69) years age group; (iii) for the (00–19) years age group, only half of the exposed individuals got infected; and (iv) a consistent graph is detected for the age group of (70–99) years in both cases; it shows that almost all the exposed got infected. [ABSTRACT FROM AUTHOR]

Reverse epidemiology is a mathematical modelling tool used to ascertain information about the source of a pathogen, given the spatial and temporal distribution of cases, hospitalisations and deaths. In the context of a deliberately released pathogen, such as Bacillus anthracis (the disease-causing organism of anthrax), this can allow responders to quickly identify the location and timing of the release, as well as other factors such as the strength of the release, and the realized wind speed and direction at release. These estimates can then be used to parameterise a predictive mechanistic model, allowing for estimation of the potential scale of the release, and to optimise the distribution of prophylaxis. In this paper we present two novel approaches to reverse epidemiology, and demonstrate their utility in responding to a simulated deliberate release of B. anthracis in ten locations in the UK and compare these to the standard grid-search approach. The two methods—a modified MCMC and a Recurrent Convolutional Neural Network—are able to identify the source location and timing of the release with significantly better accuracy compared to the grid-search approach. Further, the neural network method is able to do inference on new data significantly quicker than either the grid-search or novel MCMC methods, allowing for rapid deployment in time-sensitive outbreaks. Author summary: In this paper we demonstrate three methods for estimating the source location and timing of a deliberate release of Bacillus anthracis based on the temporal and spatial distribution of cases. Two of our proposed methods, a modified MCMC approach and a neural network based approach, provide significant improvements over previous methods by directly addressing the problematic parameter-likelihood surface, and, in the case of the neural network approach, addressing the slow deployment speeds of existing methods. Our results represent a major step forward in the accuracy and speed of epidemiological back-calculation. [ABSTRACT FROM AUTHOR]

Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forecasts or transmission scenarios, and to assess the impact of vaccination campaigns. However, their calibration is not straightforward, since many factors contribute to the rapid change of the transmission dynamics. For example, there might be changes in the individual awareness, the imposition of non-pharmacological interventions and the emergence of new variants. As a consequence, model parameters such as the transmission rate are doomed to vary in time, making their assessment more challenging. Here, we propose to use Physics-Informed Neural Networks (PINNs) to track the temporal changes in the model parameters and the state variables. PINNs recently gained attention in many engineering applications thanks to their ability to consider both the information from data (typically uncertain) and the governing equations of the system. The ability of PINNs to identify unknown model parameters makes them particularly suitable to solve ill-posed inverse problems, such as those arising in the application of epidemiological models. Here, we develop a reduced-split approach for the implementation of PINNs to estimate the temporal changes in the state variables and transmission rate of an epidemic based on the SIR model equation and infectious data. The main idea is to split the training first on the epidemiological data, and then on the residual of the system equations. The proposed method is applied to five synthetic test cases and two real scenarios reproducing the first months of the Italian COVID-19 pandemic. Our results show that the split implementation of PINNs outperforms the joint approach in terms of accuracy (up to one order of magnitude) and computational times (speed up of 20%). Finally, we illustrate that the proposed PINN-method can also be adopted to produced short-term forecasts of the dynamics of an epidemic. Author summary: During the recent COVID-19 pandemic, we all became familiar with the reproduction number, a crucial quantity to determine if the number of infections is going to increase or decrease. Understanding the past changes of this quantity is fundamental to produce realistic forecasts of the epidemic and to plan possible containment strategies. There are several methods to infer the values of the reproduction number and, thus, the number of new infections. Statistical methods are based on the analysis of the collected epidemiological data. Instead, modeling approaches (such as the popular SIR model) attempt constructing a set of mathematical equations whose solution aims at approximating the dynamics underlying the data. In this paper, we explore the use of a recently developed technique called Physics-Informed Neural Network, which tries to combine the two approaches and to simultaneously fit the data, infer the dynamics of the unknown parameters, and solve the model equations. The proposed PINN implementations are tested in different scenarios using both synthetic and real-world data referred to the COVID-19 pandemic outbreak in Italy. The promising results can pave the way for a wider use of PINNs in epidemiological applications. [ABSTRACT FROM AUTHOR]

The incidence of dengue virus disease has increased globally across the past half-century, with highest number of cases ever reported in 2019 and again in 2023. We analyzed climatological, epidemiological, and phylogenomic data to investigate drivers of two decades of dengue in Cambodia, an understudied endemic setting. Using epidemiological models fit to a 19-y dataset, we first demonstrate that climate-driven transmission alone is insufficient to explain three epidemics across the time series. We then use wavelet decomposition to highlight enhanced annual and multiannual synchronicity in dengue cycles between provinces in epidemic years, suggesting a role for climate in homogenizing dynamics across space and time. Assuming reported cases correspond to symptomatic secondary infections, we next use an age-structured catalytic model to estimate a declining force of infection for dengue through time, which elevates the mean age of reported cases in Cambodia. Reported cases in >70-y-old individuals in the 2019 epidemic are best explained when also allowing for waning multitypic immunity and repeat symptomatic infections in older patients. We support this work with phylogenetic analysis of 192 dengue virus (DENV) genomes that we sequenced between 2019 and 2022, which document emergence of DENV-2 Cosmopolitan Genotype-II into Cambodia. This lineage demonstrates phylogenetic homogeneity across wide geographic areas, consistent with invasion behavior and in contrast to high phylogenetic diversity exhibited by endemic DENV-1. Finally, we simulate an age-structured, mechanistic model of dengue dynamics to demonstrate how expansion of an antigenically distinct lineage that evades preexisting multitypic immunity effectively reproduces the older-age infections witnessed in our data. [ABSTRACT FROM AUTHOR]

Pathogen epidemics are key threats to human and wildlife health. Across systems, host protection from pathogens following initial exposure is often incomplete, resulting in recurrent epidemics through partially-immune hosts. Variation in population-level protection has important consequences for epidemic dynamics, but how acquired protection influences inter-individual heterogeneity in susceptibility and its epidemiological consequences remains understudied. We experimentally investigated whether prior exposure (none, low-dose, or high-dose) to a bacterial pathogen alters host heterogeneity in susceptibility among songbirds. Hosts with no prior pathogen exposure had little variation in protection, but heterogeneity in susceptibility was significantly augmented by prior pathogen exposure, with the highest variability detected in hosts given high-dose prior exposure. An epidemiological model parameterized with experimental data found that heterogeneity in susceptibility from prior exposure more than halved epidemic sizes compared with a homogeneous population with identical mean protection. However, because infection-induced mortality was also greatly reduced in hosts with prior pathogen exposure, reductions in epidemic size were smaller than expected in hosts with prior exposure. These results highlight the importance of variable protection from prior exposure and/or vaccination in driving population-level heterogeneity and epidemiological dynamics. Author summary: Individuals in a population are often highly variable in whether or not they get sick during an outbreak. This variability between individuals has important consequences for how widely a disease can spread. Therefore, it is useful to understand what drives the amount of variability in susceptibility present in populations as a whole. Because individuals in a population can have quite variable immune responses to the same type and dose of pathogen infection, one possibility is that populations of individuals with acquired immune protection from prior pathogen exposure are actually more variable in their susceptibility than populations of individuals with no previous pathogen infection. We tested this possibility using a songbird disease system as a model. We found that populations of birds for which we had generated immune protection by exposing them to a bacterial pathogen were far more variable in their susceptibility than birds that had never been infected before. We also show that this population-level variability in itself can strongly reduce the size of disease outbreaks. [ABSTRACT FROM AUTHOR]

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi‐host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment.Our objective was to assess whether a more comprehensive approach, using a movement‐based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches.The model was built from GPS‐collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free‐range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model.Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes.Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi‐host systems in Mediterranean environments. [ABSTRACT FROM AUTHOR]

Innovations in aerobiological and epidemiological modeling are enabling the development of powerful techniques to infer connectivity networks for transboundary pathogens in ways that were not previously possible. The innovations are supported by improved access to historical and near real-time highly resolved weather data, multi-country disease surveillance data, and enhanced computing power. Using wheat rusts as an exemplar, we introduce a flexible modeling framework to identify characteristic pathways for long-distance spore dispersal within countries and beyond national borders. We show how the models are used for near real-time early warning systems to support smallholder farmers in East Africa and South Asia. Wheat rust pathogens are ideal exemplars because they continue to pose threats to food security, especially in regions of the world where resources for control are limited. The risks are exacerbated by the rapid appearance and spread of new pathogenic strains, prodigious spore production, and long-distance dispersal for transboundary and pandemic spread. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, over one thousand papers were published on "Susceptible- Exposed-Infectious-Removed" (SEIR) epidemic computational models. The English word "exposed" in its vernacular and public health usage means a state of having been in contact with an infectious individual, but not necessarily infected. In contrast, the term "exposed" in SEIR modeling usage typically stands for a state of already being infected but not yet being infectious to others, a state more properly termed "latently infected." In public health language, "exposed" means possibly infected, yet in SEIR modeling language, "exposed" means already infected. This paper retraces the conceptual and mathematical origins of this terminological disconnect and concludes that epidemic modelers should consider using the "SLIR" notational short-hand (L for Latent) instead of SEIR. [ABSTRACT FROM AUTHOR]

In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations. [ABSTRACT FROM AUTHOR]

The basic reproduction number, R0, is a well-known quantifier of epidemic spread. However, a class of existing methods for estimating R0 from incidence data early in the epidemic can lead to an over-estimation of this quantity. In particular, when fitting deterministic models to estimate the rate of spread, we do not account for the stochastic nature of epidemics and that, given the same system, some outbreaks may lead to epidemics and some may not. Typically, an observed epidemic that we wish to control is a major outbreak. This amounts to implicit selection for major outbreaks which leads to the over-estimation problem. We formally characterised the split between major and minor outbreaks by using Otsu's method which provides us with a working definition. We show that by conditioning a 'deterministic' model on major outbreaks, we can more reliably estimate the basic reproduction number from an observed epidemic trajectory. [ABSTRACT FROM AUTHOR]

Background: Menopausal hormone therapy (MHT) can alleviate menopausal symptoms but has been associated with an increased risk of breast cancer. MHT prescription should be preceded by individualised risk/benefit evaluation; however, data outlining the impact of family history alongside different MHT therapeutic approaches are lacking. Aim: To quantify the risks associated with MHT use in women with varying breast cancer family histories of developing and dying from breast cancer. Design and setting: An epidemiological modelling study for women in England using the BOADICEA breast cancer prediction model and data relating to MHT use and breast cancer risk taken from research by the Collaborative Group on Hormonal Factors in Breast Cancer. Method: The risk of developing and dying from breast cancer between the ages of 50 and 80 years was modelled in women with four different breast cancer family history profiles: 'average', 'modest', 'intermediate', and 'strong' by using 1) background risks of breast cancer by age and family history, 2) relative risks for breast cancer associated with MHT use, and 3) 10-year breast cancer-specific net mortality rates. This study modelled use of combined oestrogen-progestogen MHT (cyclical or continuous) and oestrogen-only MHT. Results: For a woman of 'average' family history taking no MHT, the cumulative breast cancer risk (age 50–80 years) is 9.8%, and the risk of dying from the breast cancer is 1.7%. In this model, 5 years' exposure to combined-cyclical MHT (age 50–55 years) was calculated to increase these risks to 11.0% and 1.8%, respectively. For a woman with a 'strong' family history taking no MHT, the cumulative breast cancer risk is 19.6% (age 50–80 years), and the risk of dying from the breast cancer is 3.2%. With 5 years' exposure to MHT (age 50–55 years), this model showed that these risks increase to 22.4% and 3.5%, respectively. Conclusion: In this model, both family history and MHT are associated with increased risk of breast cancer. Estimates of the risks of breast cancer associated with MHT for women with different family histories can be used to support decision making around MHT prescription for women experiencing menopausal symptoms. [ABSTRACT FROM AUTHOR]

This research delineates the advancement of a refined predictive algorithm centered on the Generalized Least Deviation Method (GLDM) specifically configured for analyzing COVID-19 infection trends in Dagestan using univariate time series data. Our methodology is characterized by its enhancement of forecast precision through diligent minimization of a bespoke loss function. The algorithm's innovation lies in its formulation, incorporating second-order relationships within the time series data: ...; where nj are the computed weights ascribed to historical data, and 3t denotes the error component. Our empirical analysis substantiates that by strategically accentuating pertinent coefficients and optimizing the loss function, there is a significant elevation in the model's forecasting accuracy. Consequently, the refined second-order GLDM model emerges as an advanced and applicable instrument for the prognostication of COVID-19 infection cases in Dagestan. [ABSTRACT FROM AUTHOR]

In this paper I apply the epidemiological model of the spread of beliefs and how they become cultural representations to the field of fallacies. The model suggests that beliefs tend to replicate as a virus does in a potential epidemic, and those strains that are dominant in a given socio-cultural sphere become cultural representations. My ultimate aim is to denounce the fact that some presumptive argumentation schemes are widely applied as definitive arguments, but turn out to be instances of common and traditional fallacies. Moreover, some such fallacies have managed to colonise the human mind and become cultural representations in society today. Adopting the approach I advocate here, we could say that the fallacy has become a belief, which has then managed to replicate like a virus, and finally the fallacy has become a cultural representation. One of the great harms that results from this process is that it is very difficult to open up effective lines of argument that expose the fallacious nature of these new and perverse cultural representations. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, the Chinese government implemented nationwide public health interventions to control its spread. However, the impact of these measures on other infectious diseases remains unclear. Methods: The incidence of three types of notifiable infectious diseases in China were analyzed between 2013 and 2021. The seasonal Mann-Kendall test and Mann-Kendall mutation test were employed to examine trends and mutations in the time series. Based on the counterfactual inference, historical incidence rates were employed to construct SARIMA models and predict incidence between January 2020 and December 2021. Differences between reported and predicted incidences during the pandemic were compared using the Mann-Whitney U test. Results: Between 2013 and 2019, the incidence rate of three types of notifiable infectious diseases fluctuated between 494.05/100,000 and 550.62/100,000. No discernible trend was observed for types A and B infectious diseases (Z = −1.344, P = 0.18). A significant upward trend was observed for type C infectious diseases (Z = 2.56, P = 0.01). In 2020, the overall incidence rate of three types of notifiable infectious diseases decreased to 367.08/100,000. Compared to predicted values, the reported incidence of three types of infectious diseases was, on average, 30.05% lower in 2020 and 16.58% lower in 2021. Conclusion: The public health interventions implemented during the pandemic had a positive consequence on the prevention and control of other infectious diseases, with a particularly notable effect on type C infectious diseases. Among the diseases with different transmission routes, respiratory diseases and gastrointestinal or enteroviral diseases decreased significantly. [ABSTRACT FROM AUTHOR]

The benefits of social living are well established, but sociality also comes with costs, including infectious disease risk. This cost–benefit ratio of sociality is expected to change across individuals' lifespans, which may drive changes in social behaviour with age. To explore this idea, we combine data from a group-living primate for which social ageing has been described with epidemiological models to show that having lower social connectedness when older can protect against the costs of a hypothetical, directly transmitted endemic pathogen. Assuming no age differences in epidemiological characteristics (susceptibility to, severity and duration of infection), older individuals suffered lower infection costs, which was explained largely because they were less connected in their social networks than younger individuals. This benefit of 'social ageing' depended on epidemiological characteristics and was greatest when infection severity increased with age. When infection duration increased with age, social ageing was beneficial only when pathogen transmissibility was low. Older individuals benefited most from having a lower frequency of interactions (strength) and network embeddedness (closeness) and benefited less from having fewer social partners (degree). Our study provides a first examination of the epidemiology of social ageing, demonstrating the potential for pathogens to influence the evolutionary dynamics of social ageing in natural populations. This article is part of the discussion meeting issue 'Understanding age and society using natural populations'. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 causes a serious threat to human health and life around the world and puts enormous pressure on the healthcare system. The lockdown policy has effectively reduced the number of cases and suppressed the spread of the COVID-19 epidemic, but it also requires high social and economic costs. In this paper, we aim to use feedback control to help decision-makers establish lockdown policies, which can effectively constrain the spread of the COVID-19 pandemic with minimalfinancial loss. The time-dependent susceptible-infected-removed (SIR) model is used for the dynamics of the COVID-19 pandemic. The feedback control is based on a modified nonlinear active disturbance rejection controller (ADRC) that includes modified nonlinear extended state and nonlinear state error feedback with parameters tuned by particle swarm optimization, and the performances are compared with a well-known proportional- –integral–-derivative (PID) controller. The final simulation results show that the modified nonlinear ADRC has better performances and is more robust against uncertainties in the parameters of the epidemiological model. [ABSTRACT FROM AUTHOR]

Symptomatic and asymptomatic individuals play a significant role in the transmission dynamics of novel Coronaviruses. By considering the dynamical behaviour of symptomatic and asymptomatic individuals, this study examines the temporal dynamics and optimal control of Coronavirus disease propagation using an epidemiological model. Biologically and mathematically, the well-posed epidemic problem is examined, as well as the threshold quantity with parameter sensitivity. Model parameters are quantified and their relative impact on the disease is evaluated. Additionally, the steady states are investigated to determine the model's stability and bifurcation. Using the dynamics and parameters sensitivity, we then introduce optimal control strategies for the elimination of the disease. Using real disease data, numerical simulations and model validation are performed to support theoretical findings and show the effects of control strategies. [ABSTRACT FROM AUTHOR]

An increasing number of disruptions in ports, plants and warehouses have generated ripple effects over supply networks impacting economic activity. We demonstrate how the spread of the pandemic geographically expands the ripple effect by reducing the workers’ participation in production, so undermining the ability of firms and, as a result, the entire cross-border sup- ply chain network to satisfy customers’ demands. Our model of the spatio-temporal dynamics of the propagation of Covid-19 infection for supply networks contributes toward ripple effect visualisation and quantification by combining the flow of goods and materials through a typical global supply chain with an epidemiological model. The model enables prospective analyses to be performed in what-if scenarios to simulate the impact on the workforce in each node. The outcome should be helpful tools for managers and scholars. Results from this research will help mitigate the impact and spread of a pandemic in a particular region and the ability of a supply network to overcome the ripple effect. A stylised case study of a cross-border supply chain illustrates the ripple effect by showing how waves with crests at varying dates impact the ability to serve demand showing how a supply chain manager can obtain a forward-looking picture. [ABSTRACT FROM AUTHOR]

Epidemiological compartmental models, such as SEIR (Susceptible, Exposed, Infectious, and Recovered) models, have been generally used in analyzing epidemiological data and forecasting the trajectory of transmission of infectious diseases such as COVID-19. Experience shows that accurately forecasting the trajectory of COVID-19 transmission curve is a big challenge for researchers in the field of epidemiological modeling because multiple unquantified factors can affect the trajectory of COVID-19 transmission. In the past years, we used a new compartmental model, l-i SEIR model, to analyze the COVID-19 transmission trend in the United States. Unlike the conventional SEIR model and the delayed SEIR model that use or partially use the approximation of temporal homogeneity, the l-i SEIR model takes into account chronological order of infected individuals in both latent (l) period and infectious (i) period, and thus improves the accuracy in forecasting the trajectory of transmission of infectious diseases, especially during periods of rapid rise or fall in the number of infections. This paper describes (1) how to use the new SEIR model (a mechanistic model) combined with fitting methods to simulate or predict trajectory of COVID-19 transmission, (2) how social interventions and new variants of COVID-19 significantly change COVID-19 transmission trends by changing transmission rate coefficient βn, the fraction of susceptible people (Sn/N), and the reinfection rate, (3) why accurately forecasting COVID-19 transmission trends is difficult, (4) what are the strategies that we have used to improve the forecast outcome and (5) what are some successful examples that we have obtained. [ABSTRACT FROM AUTHOR]