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A challenging SIR epidemiological dynamic model with harmonic rate of incidence and nonlinear rate of recovery is developed to examine the effects of available beds in hospitals and intervention decrease upon the propagation of viral disease. The incorporation of the harmonic mean as an incidence rate is the novelty of the present manuscript. The assumed incidence rate is less sensitive to the larger values of the variables and proves more advantageous for highly skewed data compared to the bi-linear and monod types of incidence rates. For model's stability, precise mathematical conclusions have been produced. The model has two states of equilibrium: an infection-free equilibrium (E 0) , whenever the threshold number assumes values less than one, and a disease-present state (E 1) , whenever R 0 > 1. We employ the principle of LaSalle invariance and Lyapunov's direct technique to demonstrate that the basic threshold quantity R 0 < 1 indicates the global asymptotic stability of E 0. Also whenever 1 < R 0 then the equilibrium point E 1 is stable, under specific parametric conditions. The optimal control strategies for the described model are chosen with the aid of maximum principle of Pontryagin. To support the analytical conclusions, some numerical results are presented. The study's findings show that the model can accurately reflect the complex dynamics of numerous epidemic diseases, resulting in a considerable decrease in infection transmission. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic placed significant strain on many health systems and economies. Mitigation policies decreased health impacts but had major macroeconomic impact. This article reviews models combining epidemiological and macroeconomic projections to enable policy makers to consider both macroeconomic and health objectives. A scoping review of epidemiological-macroeconomic models of COVID-19 was conducted, covering preprints, working articles, and journal publications. We assessed model methodologies, scope, and application to empirical data. We found 80 articles modeling both the epidemiological and macroeconomic outcomes of COVID-19. Model scope is often limited to the impact of lockdown on health and total gross domestic product or aggregate consumption and to high-income countries. Just 14% of models assess disparities or poverty. Most models fall under 4 categories: compartmental-utility-maximization models, epidemiological models with stylized macroeconomic projections, epidemiological models linked to computable general equilibrium or input-output models, and epidemiological-economic agent-based models. We propose a taxonomy comparing these approaches to guide future model development. The epidemiological-macroeconomic models of COVID-19 identified have varying complexity and meet different modeling needs. Priorities for future modeling include increasing developing country applications, assessing disparities and poverty, and estimating of long-run impacts. This may require better integration between epidemiologists and economists. • Formal processes in many countries split epidemiological and macroeconomic modeling informing policy responses to shocks such as COVID, potentially missing feedback loops. • Our scoping review of epidemiological-macroeconomic modeling techniques identified 4 main approaches: compartmental-utility-maximization models, which provide a theoretical representation of voluntary behavior change; epidemiological models with stylized macroeconomic projections producing simple estimates of overall gross domestic product impacts; epidemiological models linked to computable general equilibrium or input-output models, often used to simulate sector-specific impacts; and epidemiologic-economic individual-based models that have been used to model, among others, labor market friction and the implementation of fiscal packages. • There are important remaining gaps in terms of equity and poverty simulations, consideration of long-run health and economic trends, and modeling for low- and middle-income country contexts. [ABSTRACT FROM AUTHOR]

Covid-19 has triggered a massive global challenge with 1,576,069 overall confirmed cases, 1,545,429 recovered cases, and 30,640 deaths across Pakistan. Epidemiological models have always played an important role in understanding the dynamics of such infectious diseases. The severity of Covid-19 infection depends on several factors such as immune system and virus variants. Although detailed epidemiological models which consider these factors are generally more accurate, their complexity limits their application due to the unavailability of comprehensive historical data. Therefore, in this paper, we proposed a Susceptible-Non-severely Infected-Severely Infected-Recovered-Deceased model, for the prediction of Covid-19 infections. The proposed model broadly classifies Covid-19 infections into non-severe and severe infections. This classification is based on the overall effect of virus variants and individual immune systems. The important epidemiological proprieties of the model such as equilibrium points, stabilities and bifurcation analysis are discussed. The model is fitted to the Covid-19 data of Pakistan to predict the non-severe and severe infections over the next four months. The comparison of predicted and actual infections shows the forecasting efficiency of the model. Moreover, the prediction of a severely-infected population in the upcoming months can be of significant help to policy-makers in the efficient management of hospitalization services. [ABSTRACT FROM AUTHOR]

• An epidemic model with provaccination and antivaccination susceptible groups along with four behavior model equations is considered. • At the Nash equilibrium, both groups behave alike. • The social efficiency deficit determines the existence of a Social Dilemma. In this study, an epidemiological model with the provaccination and antivaccination susceptible groups is proposed, and the social dilemma of the model is analyzed. During a pandemic, such as the current COVID-19, many individuals get confused to choose the option of adopting a provaccination or antivaccination strategy based on the number of infected people and the payoff of being infected. In the proposed model, people can obtain immunity either through vaccination or by getting infected with the disease which is known as natural immunity. In addition, increasing the waning immunity influences the choice of adopting the provaccination or antivaccination strategy. We used the behavior model to analyze the choice of the two strategies, where any individual can choose a strategy based on the number of infected individuals from each group. Moreover, individuals who are already infected can choose their strategy based on the payoff of their disease cost or vaccination cost. Our results show that, at Nash equilibrium, individuals in both groups behave the same. Further, from our numerical results, increasing the number of vaccinations can reduce the social dilemma whereas an increase in the waning immunity rate increases the social dilemma. [ABSTRACT FROM AUTHOR]

• A new fractional order age-specific smoke epidemic model is proposed. • The Banach fixed point theorem and the Krasnoselskiis type are used to analyze the model's existence and uniqueness. • The Lagrange interpolation is used to obtain the desired solution analytically. This paper presents a nonlinear fractional mathematical model for the smoke epidemic that includes two age groups. To solve the smoke epidemic, the Atangana-Baleanu-Caputo fractional derivative is used. The Banach and Krasnoselskii type fixed point theorem is used to determine existence and uniqueness. We explored model stability using the Hyers-Ulam form of stability. Using Lagrange interpolation, the behaviour of the smoke epidemic of the 2-age group model is generated. The numerical simulation shows that the model has potential for both groups, and that age-specific interventions can be used to reduce smoking rates in the general population. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 and its variants have been investigated using a variety of mathematical models. In contrast to multi-strain models, SARS-CoV-2 models exhibit a memory effect that is often overlooked and more realistic. Atangana-Baleanu's fractional-order operator is discussed in this manuscript for the analysis of the transmission dynamics of SARS-CoV-2. We investigated the transmission mechanism of the SARS-CoV-2 virus using the non-local Atangana-Baleanu fractional-order approach taking into account the different phases of infection and transmission routes. Using conventional ordinary derivative operators, our first step will be to develop a model for the proposed study. As part of the extension, we will incorporate fractional order derivatives into the model where the used operator is the fractional order operator of order Ψ 1 . Additionally, some basic aspects of the proposed model are examined in addition to calculating the reproduction number and determining the possible equilibrium. Stability analysis of the model is conducted to determine the necessary equilibrium conditions as they are also required in developing a numerical setup. Utilizing the theory of nonlinear functional analysis, for the model, Ulam-Hyers' stability is established. We present a numerical scheme based on Newton's polynomial in order to set up an iterative algorithm for the proposed ABC system. The application of this scheme to a variety of values of Φ 1 indicates that there is a relationship between infection dynamics and the derivative's order. We present further simulations which demonstrate the importance and cruciality of different parameters, as well as their effect on the dynamics and administer the disease. Furthermore, this study will provide a better understanding of the mechanisms underlying contagious diseases, thus supporting the development of policies to control them. [ABSTRACT FROM AUTHOR]

• I study the co-evolution between party platform and voter opinion. • Natural experiment: a Covid-19 policy U-turn by a major austrian right-wing party (FPÖ). • Study based on individual-level and aggregate-level panel data, supported by simulations. • Evidence for a bi-directional feedback between voter beliefs and party policy. I study the co-evolution between public opinion and party policy in situations of crises by investigating a policy U-turn of a major Austrian right-wing party (FPÖ) during the Covid-19 pandemic. My analysis suggests the existence of both i) a "Downsian" effect, which causes voters to adapt their party preferences based on policy congruence and ii) a "party identification" effect, which causes partisans to realign their policy preferences based on "their" party's platform. Specifically, I use individual-level panel data to show that i) "corona skeptical" voters who did not vote for the FPÖ in the pre-Covid-19 elections of 2019 were more likely to vote for the party after it embraced "corona populism", and ii) beliefs of respondents who declared that they voted for the FPÖ in 2019 diverged from the rest of the population in three out of four health-related dimensions only after the turn, causing them to underestimate the threat posed by Covid-19 compared to the rest of the population. Using aggregate-level panel data, I study whether the turn has produced significant behavioral differences which could be observed in terms of reported cases and deaths per capita. Paradoxically, after the turn the FPÖ vote share is significantly positively correlated with deaths per capita, but not with the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing, which causes a correlation between the number of "corona skeptics" and the share of unreported cases after the turn. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study that involves information about participants' true vs. reported infection status. I finally study a simple heterogeneous mixing epidemiological model and show that a testing bias can indeed explain the apparent paradox of an increase in deaths without an increase in reported cases. My results can, among others, be used to enrich formal analyses regarding the co-evolution between voter and party behavior. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nematode control in cattle is complicated by drug resistance and climate change. • A model was developed to predict gastrointestinal nematode (GIN) epidemiology under varying conditions. • The model incorporates cattle growth, infection and immunity, grass availability, weather. • Predictions were validated against empirical studies of GIN in northern Europe. • The model applies to Ostertagia ostertagi , and possibly to co-infecting Cooperia. Gastrointestinal nematode (GIN) infections are ubiquitous and often cause morbidity and reduced performance in livestock. Emerging anthelmintic resistance and increasing change in climate patterns require evaluation of alternatives to traditional treatment and management practices. Mathematical models of parasite transmission between hosts and the environment have contributed towards the design of appropriate control strategies in ruminants, but have yet to account for relationships between climate, infection pressure, immunity, resources, and growth. Here, we develop a new epidemiological model of GIN transmission in a herd of grazing cattle, including host tolerance (body weight and feed intake), parasite burden and acquisition of immunity, together with weather-dependent development of parasite free-living stages, and the influence of grass availability on parasite transmission. Dynamic host, parasite and environmental factors drive a variable rate of transmission. Using literature sources, the model was parametrised for Ostertagia ostertagi , the prevailing pathogenic GIN in grazing cattle populations in temperate climates. Model outputs were validated on published empirical studies from first season grazing cattle in northern Europe. These results show satisfactory qualitative and quantitative performance of the model; they also indicate the model may approximate the dynamics of grazing systems under co-infection by O. ostertagi and Cooperia oncophora , a second GIN species common in cattle. In addition, model behaviour was explored under illustrative anthelmintic treatment strategies, considering impacts on parasitological and performance variables. The model has potential for extension to explore altered infection dynamics as a result of management and climate change, and to optimise treatment strategies accordingly. As the first known mechanistic model to combine parasitic and free-living stages of GIN with host feed-intake and growth, it is well suited to predict complex system responses under non-stationary conditions. We discuss the implications, limitations and extensions of the model, and its potential to assist in the development of sustainable parasite control strategies. [ABSTRACT FROM AUTHOR]

Before the pandemic, distance learning was not a widely adopted option for science and engineering programs where in some courses, such as chemistry, electromagnetism, or fluid mechanics, etc., attending to laboratories and workshops was in most cases mandatory. The lockdown forced us to innovate, searching alternative ways to teach experimental phenomena, suddenly replaced with simulation science and technology, subjects that although rely on computers, also suffered changes from the transition. In this contribution, we propose an undergraduate course on simulation for chemical engineering, departing from the fact that modeling, and simulation are multipurpose and multidisciplinary tools. The course aims to reinforce the concepts of dynamical systems by using analogies between process engineering examples and other disciplines, particularly, epidemiology. For this purpose, a final project on modeling the dynamics of the COVID 19 pandemic in Mexico was designed and validated with a public database from the Mexican Secretariat of Health. By doing this, the students got in touch with the evolution of the dynamics outside of school hours, since it was common to see weekly updates and extrapolation trends of the pandemic, thus applying their skills to the final project. It was found that success factors were the use of official data, the use of Graphical User Interfaces to explore diverse simulation scenarios and the final project. The transition to the Distance Learning faced several challenges that were partially coped with the redesign of the course. • Chemical engineers' skills can be useful in epidemiological modeling. • Analogies between dynamical models may find solutions in different STEM fields. • Future chemical engineers can collaborate in multidisciplinary teams. • Distance learning allows to innovate analyzing current events such as COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Aging is often attributed to the detrimental side effects of beneficial traits but not a programmed adaptive process. Alternatively, the pathogen control hypothesis posits that defense against infectious diseases may provide a strong selection force for restriction of lifespan. Aging might have evolved to remove older individuals who carry chronic diseases that may transmit to their younger kin. Thus, selection for shorter lifespans may benefit kin's fitness. The pathogen control hypothesis addresses arguments typically raised against adaptive aging concepts: it explains the benefit of shorter lifespan and the absence of mutant variants that do not age. We discuss the consistency and explanatory power of this hypothesis and compare it with classic hypotheses of aging. The evolutionary origins and the mechanisms of aging are unclear. Most researchers believe aging is an entropic process or a detrimental side effect of some beneficial traits but not an adaptive program. A limited lifespan might provide benefits by preventing epidemics of infectious diseases. We propose that pathogen–host interactions are the primary reason for the evolution of adaptive programmed aging. Knowledge of the evolutionary origins of aging may instruct strategies to decipher its mechanisms and develop anti-aging interventions. [ABSTRACT FROM AUTHOR]

• Incorporate uncertain quantities through interval analysis. • Compute guaranteed enclosures for a dengue transmission model. • Choose uncertain parameters through the sensitivity analysis. • Consider uncertainty in non-identifiable parameters and initial conditions. Epidemiological models have become powerful tools for studying and understanding the characteristics and impact of transmitted diseases in a population. However, these models usually require specifying several values of input parameters obtained from experimental data, characterized by high uncertainty levels due to biological variation. This situation is evident for models that simulate the transmission of vector-borne diseases such as dengue, our case study. Therefore, treating and modeling this uncertainty is essential to ensure the robustness of designed models. For this, we propose to model the uncertainty through interval analysis by representing the input parameters and initial conditions by real closed intervals in the forward problem. This approach has the advantage of making a minimal number of assumptions concerning uncertainties, unlike the traditional methods (probabilistic and fuzzy). To illustrate the performance of this methodology, we consider a coupled ODE system of seven state variables and nine parameters, representing the transmission of Dengue between host-vector populations. Additionally, to enhance the use of the numerical method utilized for solving the system, the uncertain quantities (parameters and initial conditions) are determined based on the results of (i) the sensitivity analysis of R 0 , (ii) the structural identifiability analysis of the model, (iii) the characteristics of the available information about mosquito population, and (iv) dengue incidence data in two municipalities in Colombia, Itagüí and Neiva, during the outbreaks in 2016. We believe that the methodology proposed here to select and incorporate uncertainty in epidemiological models through interval analysis is widely applicable to other phenomena and models in science and engineering. [ABSTRACT FROM AUTHOR]

Risk communication tools based on epidemiological models can help inform decision-making, but must be responsive to health literacy needs to be effective. To facilitate informed choice about risks and benefits of COVID-19 vaccination, an epidemiological model called the COVID-19 Risk Calculator (CoRiCal) tool was developed by a multi-disciplinary team. This paper demonstrates how to use health literacy principles to improve consumer understanding of COVID-19 and vaccine effects, using a range of methods that could be applied to any health emergency. Stage 1: Health literacy optimisation and user testing to reduce improve understandability (n = 19). Stage 2: Experiments to explore the effect of risk communication formats on perceived understanding including probability, graphs, evaluative labels and comparison risks (n = 207). Stage 3: Randomised controlled trial (n = 2005) with 4 arms: 1) standard government information; 2) standard CoRiCal output based on bar graphs; 3) animation explaining bar graphs in "x per million" format; 4) animation explaining bar graphs in "1 in x chance" format. The primary outcome was knowledge about COVID-19 risk. Stage 1 reduced the complexity of the text and graphs. Stage 2 showed that different risk communication formats change perceived understanding, with a preference for evaluative labels across 2 experiments and some indication people with lower health literacy had a greater preference for bar graphs. Stage 3 showed both animations increased knowledge compared to standard government information. There was no difference between the probability formats, or by health literacy level. The results showed that simple explanations of complex epidemiological models improve knowledge about COVID-19 and vaccination. This demonstrates how health literacy design principles and short animations can be used to support informed decision making about health emergencies. • People with low health literacy have worse knowledge and health outcomes. • Risk communication tools can simplify complex vaccination evidence. • Health literacy design principles can be used to address consumer needs. • Short animations using multiple risk formats can improve vaccine knowledge. [ABSTRACT FROM AUTHOR]

• Available vector control methods have been valuable but are not sustainable. • Mosquitoes' behavioural traits are the key determinants of disease transmission. • This review discusses recent developments in behaviour-monitoring tools. • The review covers tools to monitor fitness and blood-feeding related behaviours. • Behaviour-assaying techniques can support or replace existing monitoring methods. Mosquito-borne diseases such as malaria, dengue, Zika, and chikungunya cause significant morbidity and mortality globally, resulting in over 600,000 deaths from malaria and around 36,000 deaths from dengue each year, with millions of people infected annually, leading to substantial economic losses. The existing mosquito control measures, such as long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS), helped to reduce the infections. However, mosquito-borne diseases are still among the deadliest diseases, forcing us to improve the existing control methods and look for alternative methods simultaneously. Advanced monitoring techniques, including remote sensing, and geographic information systems (GIS) have significantly enhanced the efficiency and effectiveness of mosquito control measures. Mosquitoes' behavioural traits, such as locomotion, blood-feeding, and fertility are the key determinants of disease transmission and epidemiology. Technological advancements, such as high-resolution cameras, infrared imaging, and artificial intelligence (AI) driven object detection models, including groundbreaking convolutional neural networks, have provided efficient and precise options to monitor various mosquito behaviours, including locomotion, oviposition, fertility, and host-seeking. However, they are not commonly employed in mosquito-based research. This review highlights the novel and significant advancements in behaviour-monitoring tools, mostly from the last decade, due to cutting-edge video monitoring technology and artificial intelligence. These advancements can offer enhanced accuracy, efficiency, and the ability to quickly process large volumes of data, enabling detailed behavioural analysis over extended periods and large sample sizes, unlike traditional manual methods prone to human error and labour-intensive. The use of behaviour-assaying techniques can support or replace existing monitoring techniques and directly contribute to improving control measures by providing more accurate and real-time data on mosquito activity patterns and responses to interventions. This enhanced understanding can help establish the role of behavioural changes in improving epidemiological models, making them more precise and dynamic. As a result, mosquito management strategies can become more adaptive and responsive, leading to more effective and targeted interventions. Ultimately, this will reduce disease transmission and significantly improve public health outcomes. [Display omitted] In this study, we reviewed potential technologies available for monitoring mosquito behaviours related to fitness and blood feeding. Fitness related behaviours include flight tracking, oviposition, and fertility assessments, while blood feeding related behaviours include host seeking, probing, and engorgement dynamics. [ABSTRACT FROM AUTHOR]

This study investigates the utilization of various mathematical models for comprehending and managing outbreaks of infectious diseases, with a specific focus on how different distributions of incubation times influence predictions regarding epidemics. Two methodologies are examined: a compartmental SEnIR ODE model, which represents an enhanced version of the mean-field SEIR model, and a stochastic agent-based complex network model. Our findings demonstrate that the selection of diverse incubation time distributions can result in noteworthy discrepancies in critical epidemic forecasts, highlighting the crucial role of precise modeling in shaping effective public health interventions. The research underscores the necessity of integrating authentic distribution patterns into epidemic modeling to increase its reliability and applicability. • We show how key epidemiological predictions depend on the incubation time distribution. • We utilized two kinds of models: mean-field SEIR models, and agent-based models. • We emphasize that integrating authentic distributions increases the models' reliability and applicability. [ABSTRACT FROM AUTHOR]

Prolonged exposure to free silica leads to the development of silicosis, wherein activated fibroblasts play a pivotal role in its pathogenesis and progression. Fibroblast Activation Protein (FAP), as a biomarker for activated fibroblasts, its expression pattern and role in key aspects of silicosis pathogenesis remain unclear. This study elucidated the expression pattern and function of FAP through population-based epidemiological investigations, establishment of mouse models of silicosis, and in vitro cellular models. Results indicated a significant elevation of FAP in plasma from silicosis patients and lung tissues from mouse models of silicosis. In the cellular model, we observed a sharp increase in FAP expression early in the differentiation process, which remained high expression. Inhibition of FAP suppressed fibroblast differentiation, while overexpression of FAP produced the opposite effect. Moreover, fibroblast-derived FAP can alter the phenotype and function of neighboring macrophages. In summary, we revealed a high expression pattern of FAP in silicosis and its potential mechanistic role in fibrosis, suggesting FAP as a potential therapeutic target for silicosis. [Display omitted] • The expression of FAP is increased in silicosis. • FAP increases earlier than transdifferentiation markers, regulating the process. • Fibroblast-derived FAP can alter the phenotype and function of macrophages. [ABSTRACT FROM AUTHOR]

The real-time prediction and estimation of the spread of diseases, such as COVID-19 is of paramount importance as evidenced by the recent pandemic. This work is concerned with the distributed parameter estimation of the time–space propagation of such diseases using a diffusion–reaction epidemiological model of the susceptible–exposed–infected–recovered (SEIR) type. State estimation is based on continuous measurements of the number of infections and deaths per unit of time and of the host spatial domain. The observer design method is based on positive definite matrices to parameterize a class of Lyapunov functionals, in order to stabilize the estimation error dynamics. Thus, the stability conditions can be expressed as a set of matrix inequality constraints which can be solved numerically using sum of squares (SOS) and standard semi-definite programming (SDP) tools. The observer performance is analyzed based on a simplified case study corresponding to the situation in France in March 2020 and shows promising results. • A non-linear observer is proposed for the SEIR model of COVID-19 spread. • Global measurements are considered on the whole the spatial domain. • Lure description of the estimation error system. • The observer design is based on a Lyapunov functional and linear matrix inequalities. • A case study illustrates the good performance. [ABSTRACT FROM AUTHOR]

Crimean-Congo haemorrhagic fever (CCHF) is a priority emerging pathogen for which a licensed vaccine is not yet available. We aim to assess the feasibility of conducting phase III vaccine efficacy trials and the role of varying transmission dynamics. We calibrate models of CCHF virus (CCHFV) transmission among livestock and spillover to humans in endemic areas in Afghanistan, Turkey and South Africa. We propose an individual randomised controlled trial targeted to high-risk population, and use the calibrated models to simulate trial cohorts to estimate the minimum necessary number of cases (trial endpoints) to analyse a vaccine with a minimum efficacy of 60%, under different conditions of sample size and follow-up time in the three selected settings. A mean follow-up of 160,000 person-month (75,000–550,000) would be necessary to accrue the required 150 trial endpoints for a target vaccine efficacy of 60 % and clinically defined endpoint, in a setting like Herat, Afghanistan. For Turkey, the same would be achieved with a mean follow-up of 175,000 person-month (50,000–350,000). The results suggest that for South Africa the low endemic transmission levels will not permit achieving the necessary conditions for conducting this trial within a realistic follow-up time. In the scenario of CCHFV vaccine trial designed to capture infection as opposed to clinical case as a trial endpoint, the required person-months is reduced by 70 % to 80 % in Afghanistan and Turkey, and in South Africa, a trial becomes feasible for a large number of person-months of follow-up (>600,000). Increased expected vaccine efficacy > 60 % will reduce the required number of trial endpoints and thus the sample size and follow-time in phase III trials. Underlying endemic transmission levels will play a central role in defining the feasibility of phase III vaccine efficacy trials. Endemic settings in Afghanistan and Turkey offer conditions under which such studies could feasibly be conducted. [ABSTRACT FROM AUTHOR]

Immunity to SARS-CoV-2 following vaccination wanes over time in a non-linear fashion, making modelling of likely population impacts of COVID-19 policy options challenging. We observed that it was possible to mathematize non-linear waning of vaccine effectiveness (VE) on the percentage scale as linear waning on the log-odds scale, and developed a random effects logistic regression equation based on UK Health Security Agency data to model VE against Omicron following two and three doses of a COVID-19 vaccine. VE on the odds scale reduced by 47% per month for symptomatic infection after two vaccine doses, lessening to 35% per month for hospitalisation. Waning on the odds scale after triple dose vaccines was 35% per month for symptomatic disease and 19% for hospitalisation. This log-odds system for estimating waning and boosting of COVID-19 VE provides a simple solution that may be used to parametrize SARS-CoV-2 immunity over time parsimoniously in epidemiological models. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus disease (COVID-19) has spread rapidly worldwide, and the outbreak of the disease was designated a global pandemic by the World Health Organization. Such outbreaks would certainly be catastrophic for some of the best-ranked health systems and would be more catastrophic in countries with more fragile health systems. Accordingly, the World Health Organization and other organizations have been appealing to donor countries to support a rapid response plan. The primary objectives of this response plan are to appeal for funds from donors and to distribute these funds to the most affected countries based on the requirements. In this study, we developed a mathematical model to provide initial insights into the efficient and equitable distribution of urgent funds to high-priority countries. Three phases were proposed for the construction of this mathematical model. In the first phase, the final epidemic sizes in all the target countries were predicted by using three epidemiological models. In the second phase, the urgent requirements for each country were estimated in parallel with the estimates issued by the humanitarian response plan, based on the size of the epidemic and several other factors. In the third and final phase, a multi-objective optimization model was proposed. The first objective was to maximize the funds from donors to cover all the requirements. The second objective was to minimize the unmet demands by ensuring a fair distribution of the urgent funds based on the requirements of the target countries. Predictions of the basic reproduction numbers and the final epidemic sizes were calculated for all target countries. The urgent requirements were estimated, and the requirements issued by the humanitarian response plan for all target countries were also considered. Moreover, a proposed response plan for the distribution network was demonstrated. Donors must provide urgent funds exceeding US$ 2,608,084,209 to cover at least 40 % of each target country's requirements. Overall, results demonstrate the importance of an urgent and fair distribution of funds to the target countries to overcome the outbreak of COVID-19. Rapid responses by donor countries to humanitarian appeals will facilitate the immediate and fair distribution of relief supplies to the poorest countries. This distribution may help to support health systems, restrain the spread of COVID-19, and prevent an unlimited catastrophe. [ABSTRACT FROM AUTHOR]

Relative to the rapid increase in available health information, little has been published on the differential impact misinformation has on the health of communities. Observations during the height of the COVID-19 pandemic indicated there were communities that made decisions that negatively impacted health outcomes beyond expectations; we propose that health misinformation was a contributor to poor health outcomes. Health misinformation exposure varies across communities and preliminary research suggests that some communities are more vulnerable to the impact of health misinformation than others. However, few studies have evaluated the connection between health misinformation and healthcare disparities. In this paper, we (a) review the current literature on misinformation and its impact on health disparities, (b) expand on prior epidemiological models to explain the communal spread of misinformation and the link to disparate health outcomes, (c) identify gaps in knowledge about communal misinformation spread (d) review promising interventions to halt the adverse impact of misinformation. • Health misinformation gains credence in vulnerable communities as a result of collective beliefs and lived experiences. • Health misinformation can alter health decisions or a population and lead to health disparities. • Gaps in research related to misinformation and misinformation-related harm. • Actionable tools to dampen the impact of health misinformation. [ABSTRACT FROM AUTHOR]

Bluetongue virus (BT) is a vector-borne virus that causes a disease, called bluetongue, which results in significant economic loss and morbidity in sheep, cattle, goats and wild ungulates across all continents of the world except Antarctica. Despite the geographical breadth of its impact, most BT epidemiological models are informed by parameters derived from the 2006–2009 BTV-8 European outbreak. The aim of this study was to develop a highly adaptable model for BT which could be used elsewhere in the world, as well as to identify the parameters which most influence outbreak dynamics, so that policy makers can be properly informed with the most current information to aid in disease planning. To provide a framework for future outbreak modelling and an updated parameterisation that reflects natural variation in infections, a newly developed and parameterised two-host, two-vector species ordinary differential equation model was formulated and analysed. The model was designed to be adaptable to be implemented in any region of the world and able to model both epidemic and endemic scenarios. It was parameterised using a systematic literature review of host-to-vector and vector-to-host transmission rates, host latent periods, host infectious periods, and vaccine protection factors. The model was demonstrated using the updated parameters, with South Africa as a setting based on the Western Cape's known cattle and sheep populations, local environmental parameters, and Culicoides spp. presence data. The sensitivity analysis identified that the duration of the infectious period for sheep and cows had the greatest impact on the outbreak length and number of animals infected at the peak of the outbreak. Transmission rates from cows and sheep to C. imicola midges greatly influenced the day on which the peak of the outbreak occurred, along with the duration of incubation period, and infectious period for cows. Finally, the protection factor of the vaccine had the greatest influence on the total number of animals infected. This knowledge could aid in the development of control measures. Due to gradual climate and anthropological change resulting in alterations in vector habitat suitability, BT outbreaks are likely to continue to increase in range and frequency. Therefore, this research provides an updated BT modelling framework for future outbreaks around the world to explore transmission, outbreak dynamics and control measures. [ABSTRACT FROM AUTHOR]

• Traditional epidemiological models may struggle to accurately capture the chaotic nature of a pandemic/epidemic. • Chaos theory applied to epidemiology can capture and help to invistigate the non-linear dynamics underlying the progression of COVID-19 and other pandemics/epidemics. • Chaos theory can assist in analyzing complex interactions that can either exacerbate or mitigate the spread of a pathogen circulating in the human population. • Chaos theory might give information on the chances for an emerging virus to establish a long-lasting relationship with a new host. • Insights from chaos theory underscore the importance of flexibility and adaptability in response strategies. The chaos theory, a field of study in mathematics and physics, offers a unique lens through which to understand the dynamics of the COVID-19 pandemic. This theory, which deals with complex systems whose behavior is highly sensitive to initial conditions, can provide insights into the unpredictable and seemingly random nature of the pandemic's spread. In this review, we will discuss some literature data with the aim of showing how chaos theory could provide valuable perspectives in understanding the complex and dynamic nature of the COVID-19 pandemic. In particular, we will emphasize how the chaos theory can help in dissecting the unpredictable, non– linear progression of the disease, the importance of initial conditions, and the complex interactions between various factors influencing its spread. These insights are crucial for developing effective strategies to manage and mitigate the impact of the pandemic. [ABSTRACT FROM AUTHOR]

[Display omitted] • Constructed spatial linkages between areas using pine wood transportation data. • Modification of the dynamical model for predicting COVID-19 to predict PWD propagation. • The model applies on 5.68*106km2 which is more than 100 times larger than previous studies, and the AUC reaches 0.89. • Ability to make quantitative predictions about the risk of infection in potential areas over the next year. In recent decades, the tree killing pine wilt disease (PWD) has been spreading globally and has caused substantial damage, particularly in China, resulting in annual losses of nearly $15 billion. This disease is caused by the pine wood nematode (Bursaphelenchus xylophilus) and is transmitted by its vector insect (Monochamus spp.). However, there remains limited understanding of the factors influencing PWD transmission, especially anthropogenic factors. For the first time, we employed a dataset of propagation factors that based on the transportation of pine wood transmission and used an enhanced infectious disease dynamics method to quantitatively predict the likelihood of future PWD infections across the entire distribution range of pine trees in China. The validation results show that the risk of infection in potential areas of PWD will further increase in China, with the average risk surpassing 30% in 2023 and reaching 50% by 2025 in the study areas. Meanwhile, we observe that human factors, such as transportation of timber, are becoming the main cause of the rapid spread of PWD and require extra vigilance. By incorporating spatial connections between samples as features for PWD prediction, the Area Under the Curve (AUC) score of the model reached 0.90. This signifies a highly accurate prediction of the pivotal areas for PWD dissemination, providing valuable guidance for the precise development of prevention and control measures, and presenting innovative approaches for analogous studies. [ABSTRACT FROM AUTHOR]

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

Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (BUGS) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of BUGS to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (JAGS) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling. [ABSTRACT FROM AUTHOR]

Several vaccines for SARS-CoV-2 are expected to be available in Australia in 2021. Initial supply is limited and will require a judicious vaccination strategy until supply is unrestricted. If vaccines have efficacy as post-exposure prophylaxis (PEP) in contacts, this provides more policy options. We used a deterministic mathematical model of epidemic response with limited supply (age-targeted or ring vaccination) and mass vaccination for the State of New South Wales (NSW) in Australia. For targeted vaccination, the effectiveness of vaccinating health workers, young people and older adults was compared. For mass vaccination, we tested varying vaccine efficacy (VE) and distribution capacities. With a limited vaccine stockpile enough for 1 million people in NSW, if there is efficacy as PEP, the most efficient way to control COVID-19 will be ring vaccination, however at least 90% of contacts per case needs to be traced and vaccinated. Health worker vaccination is required for health system resilience. Age based strategies with restricted doses make minimal impact on the epidemic, but vaccinating older people prevents more deaths. Herd immunity can only be achieved with mass vaccination. With 90% VE against all infection, herd immunity can be achieved by vaccinating 66% of the population. A vaccine with less than 70% VE cannot achieve herd immunity and will result in ongoing risk of outbreaks. For mass vaccination, distributing at least 60,000 doses per day is required to achieve control. Slower rates of vaccination will result in the population living with COVID-19 longer, and higher cases and deaths. [ABSTRACT FROM AUTHOR]

This study combines an epidemiological model (Susceptible-Infected-Recovered, S-I-R) with the Shorter-term Decisions Simulator (SDS), an activity-based microsimulation model developed for Halifax, Canada. The decision support tool assesses infected individuals' travel behavior during the COVID-19 pandemic. The results of the S-I-R model reveal the rate of infection, p=0.39 , the rate of recovery, q=0.34 , and the reproduction number, R 0 =1.15 in Halifax region. In terms of activity participation, most infected individuals are found to participate in two activities (53%) followed by three activities (25%). Infected individuals share a similar type of proportion for work, shopping, and recreational type of activity (23%, 20% and 19%, respectively). In response to travel distance and travel duration for the infected individuals, the age group of 30 to 40 shows a higher density than others. This paper's findings will help policymakers to develop policy interventions to deal with ongoing, emerging COVID-19 public health crisis. [ABSTRACT FROM AUTHOR]

There are many harmful effects of smoking. It not only engulfs the health and life of smokers, but also pollutes the air, endangers the life and health of others, and brings a heavy burden to public health. To this end, a delayed smoking model including potential smokers, occasional smokers, smokers, temporary quitters, permanent quitters and smokers with some illness, is investigated in the present paper. Firstly, local stability and existence of Hopf bifurcation of the model is conducted. Secondly, global exponential stability is explored. Lastly, we numerically simulate the correctness of the obtained theoretical results in the paper. [ABSTRACT FROM AUTHOR]

The susceptible-infected-recovered (SIR) model is perhaps the most basic epidemiological model for the evolution of disease spread within a population. Because of its direct representation of fundamental physical quantities, a true solution to an SIR model possesses a number of qualitative properties, such as conservation of the total population or positivity or monotonicity of its constituent populations, that may only be guaranteed to hold numerically under step-size restrictions on the solver. Operator-splitting methods with order greater than two require backward sub-steps in each operator, and the effects of these backward sub-steps on the step-size restrictions for guarantees of qualitative correctness of numerical solutions are not well studied. In this study, we analyze the impact of backward steps on step-size restrictions for guaranteed qualitative properties by applying third- and fourth-order operator-splitting methods to the SIR epidemic model. We find that it is possible to provide step-size restrictions that guarantee qualitative property preservation of the numerical solution despite the negative sub-steps, but care must be taken in the choice of the method. Results such as this open the door for the design and application of high-order operator-splitting methods to other mathematical models in general for which qualitative property preservation is important. [ABSTRACT FROM AUTHOR]

• The application of reduced-order models to an epidemiological SEIRS model. • We introduce two digital twins of a SEIRS model applied to an idealised town. • The application of highly novel BDLSTM- and GAN-based reduced-order model approaches. • These two frameworks are accurate when compared to the original SEIRS model data. • These frameworks are data-agnostic and could be generalised to other domains. The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus, there is an urgent need to study the dynamics of epidemiological models to gain a better understanding of how such diseases spread. While epidemiological models can be computationally expensive, recent advances in machine learning techniques have given rise to neural networks with the ability to learn and predict complex dynamics at reduced computational costs. Here we introduce two digital twins of a SEIRS model applied to an idealised town. The SEIRS model has been modified to take account of spatial variation and, where possible, the model parameters are based on official virus spreading data from the UK. We compare predictions from one digital twin based on a data-corrected Bidirectional Long Short-Term Memory network with predictions from another digital twin based on a predictive Generative Adversarial Network. The predictions given by these two frameworks are accurate when compared to the original SEIRS model data. Additionally, these frameworks are data-agnostic and could be applied to towns, idealised or real, in the UK or in other countries. Also, more compartments could be included in the SEIRS model, in order to study more realistic epidemiological behaviour. [ABSTRACT FROM AUTHOR]

• Delaying the vaccination deployment strongly impacts hospitalizations and deaths. • The delay impact depends on the vaccination strategy and on the incidence curve. • Under uncontrolled disease, vaccination delay leads to significantly more deaths. • Massive COVID-19 test is necessary to track the disease spread and vaccination. • A novel data-driven approach using SEIR-type models with remarkable data adherence. By the beginning of December 2020, some vaccines against COVID-19 already presented efficacy and security, which qualify them to be used in mass vaccination campaigns. Thus, setting up strategies of vaccination became crucial to control the COVID-19 pandemic. We use daily COVID-19 reports from Chicago and New York City (NYC) from 01-Mar2020 to 28-Nov-2020 to estimate the parameters of an SEIR-like epidemiological model that accounts for different severity levels. To achieve data adherent predictions, we let the model parameters to be time-dependent. The model is used to forecast different vaccination scenarios, where the campaign starts at different dates, from 01-Oct-2020 to 01-Apr-2021. To generate realistic scenarios, disease control strategies are implemented whenever the number of predicted daily hospitalizations reaches a preset threshold. The model reproduces the empirical data with remarkable accuracy. Delaying the vaccination severely affects the mortality, hospitalization, and recovery projections. In Chicago, the disease spread was under control, reducing the mortality increment as the start of the vaccination was postponed. In NYC, the number of cases was increasing, thus, the estimated model predicted a much larger impact, despite the implementation of contention measures. The earlier the vaccination campaign begins, the larger is its potential impact in reducing the COVID-19 cases, as well as in the hospitalizations and deaths. Moreover, the rate at which cases, hospitalizations and deaths increase with the delay in the vaccination beginning strongly depends on the shape of the incidence of infection in each city. [ABSTRACT FROM AUTHOR]

In this work the concept of fractional derivative is used to improve a mathematical model for the spread of the novel strain of corona virus disease COVID-19 in Egypt. We establish the dynamics model to predict the transmission of COVID-19. The results predicted by the model show a good agreement with the actual reported data. The effect of precautionary measures on the behavior of the model was studied and it was confirmed that the quarantine period should be long enough to achieve the desired result. [ABSTRACT FROM AUTHOR]

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future. [ABSTRACT FROM AUTHOR]

Various mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models. Articles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail. Among 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions. There was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type. [ABSTRACT FROM AUTHOR]

• A first integral of the SIR epidemiological model was solved in terms of the Lambert W function. • The SIR model was solved parametrically by quadratures. • The parametric solution was computed numerically and compared to Runge–Kutta numerical integration. In their article, N. A. Kudryashov, M. A. Chmykhov, M. Vigdorowitsch, "Analytical features of the SIR model and their applications to COVID-19", Applied Mathematical Modelling 90 (2021) 466–473, aimed to establish the relationships among S, I and R populations, as well as to suggest a form for the exact solution of the SIR model. One of the equations given there is not correct, which leads to an error in the solution. The objective of the present letter is to present the correct parametric solution and to discuss its numerical approximation by direct quadrature. [ABSTRACT FROM AUTHOR]

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics. [ABSTRACT FROM AUTHOR]

• The adsorption kinetics is viewed as the "infectious of adsorbates by adsorbents". • The MPQR model for batch/continuous adsorption is proposed based on epidemiology. • A convenient Excel software sheet provides methods for solving the MPQR model. • The model excels in precisely describing adsorption kinetics across diverse fields. Adsorption is a unit operation process with broad applications in environmental, pharmaceutical, and chemical fields, with its most significance in environmental fields for water and wastewater treatment. Adsorption involves continuous/batch modes with fixed/dispersed adsorbents, leading to diverse systems. The adsorption kinetic models provide essential insights for effectively designing these systems. However, many adsorption models are semi-empirical/empirical, making it challenging to identify the adsorption mechanisms. Additionally, a consistent method for modelling the adsorption kinetics of different processes would be helpful for the comparison and analysis of various adsorption systems, but no such unified model is available. In epidemiological modeling, populations are often categorized into susceptible, infected, and removed individuals, simplifying disease transmission dynamics without considering individual-level movement intricacies. Likewise, we have employed a similar approach within adsorption systems, classifying adsorbates into absorbable, adsorbed, and removed (to the effluent) segments, thus developing the Monolayer-Absorbable-Adsorbed-Removed (MPQR) kinetics model. This model is applicable to continuous/batch adsorption systems, regardless of whether fixed or dispersed adsorbents are employed. The model was validated using experimental data across water/wastewater treatment, drug separation/purification, metal recovery, and desalination. The results showed that our model successfully fitted the kinetic data from various adsorption systems. It outperformed commonly used models for continuous/batch adsorption. The model allowed us to directly compare the parameters among various adsorption processes. The solving method based on Excel was provided and can be used by the researchers. Our model offers a versatile and unified approach to model adsorption kinetics, enabling the analysis and design of various adsorption systems. [Display omitted] [ABSTRACT FROM AUTHOR]

This study explores the impact of a simulated radiological dispersal device (RDD) event in an urban area on young adults around 20 years old. The RDD releases radioactive Cs-137 (7.0E+3 Ci), a common industrial sterilization source. The study aims to demonstrate that combining computational codes and epidemiological models can produce valuable data to guide initial actions when confronting a hostile radioactive environment. The HotSpot Health Physics and RESRAD-RDD codes were used in the simulation to evaluate the event's initial phase. The codes were executed together, and the HotSpot output data was input into RESRAD-RDD. Based on simulated radiation dose levels, estimated doses were incorporated into radioepidemiological models proposed by the Committee on Biological Effects of Ionizing Radiation (BEIR V or VII report). Despite limitations, data transfer between the models revealed no discontinuities or antagonisms. Radiation doses were simulated under three exposure conditions and two atmospheric release modes (day or night), suggesting that atmospheric conditions, sex, and exposure routine can strongly influence the perception of radiation impacts. This combination of methods can increase situational awareness and help with decision-making and developing coping strategies. • The simulation benefits from applying independent codes for generating event data. • The findings offer guidelines for improving situational awareness. • Convergence can improve risk reduction and resource allocation strategies. [ABSTRACT FROM AUTHOR]

Tree populations worldwide are facing an unprecedented threat from a variety of tree diseases and invasive pests. Their spread, exacerbated by increasing globalisation and climate change, has an enormous environmental, economic and social impact. Computational individual-based models are a popular tool for describing and forecasting the spread of tree diseases due to their flexibility and ability to reveal collective behaviours. In this paper we present a versatile individual-based model with a Gaussian infectivity kernel to describe the spread of a generic tree disease through a synthetic treescape. We then explore several methods of calculating the basic reproduction number R 0 , a characteristic measurement of disease infectivity, defining the expected number of new infections resulting from one newly infected individual throughout their infectious period. It is a useful comparative summary parameter of a disease and can be used to explore the threshold dynamics of epidemics through mathematical models. We demonstrate several methods of estimating R 0 through the individual-based model, including contact tracing, inferring the Kermack–McKendrick SIR model parameters using the linear noise approximation, and an analytical approximation. As an illustrative example, we then use the model and each of the methods to calculate estimates of R 0 for the ash dieback epidemic in the UK. • Computational individual-based models can simulate the spread of plant disease. • Disease progression can be summarised by the epidemiological parameter R 0. • R 0 can be calculated from an individual-based model in several ways. • Methods include: analytic expressions, parameter inference, and contact-tracing. • Each has (dis)advantages — the best choice will vary depending on requirements. [ABSTRACT FROM AUTHOR]

Epidemiological models of diseases with periodically varying infection rates are known to possess a multiplicity of subharmonic resonant modes. With increasing period, these modes are known to be characterized by outbreaks of dramatically increased severity. Primarily through direct numerical simulation, prior studies have provided insight into their detailed structure and parametric dependence. The hallmark of these periodic modes are relatively short, rapidly varying outbreaks, interspersed by longer, slowly varying inter-epidemic periods. By leveraging this dualistic structure, we develop a hierarchical framework to construct approximate solutions to the seasonal-SIR model. Comparisons with numerical simulation of the full model show a strong level of agreement, not only in the limit that the period is long but also for moderate to shorter periods as well. The underlying and explicit analytic matching conditions provide direct insight into the existence of individual harmonic modes within a given parameter regime and detailed connection to the model parameters. [Display omitted] • Asymptotic construction of subharmonic resonant solutions via matched layer approach. • Refinements illustrate key structural features. • Resulting approximations bear increasingly close agreement with results of DNS. • Associated analytic matching conditions provide direct insight into model features. • Existence and location of specific solutions within the broader parameter space. [ABSTRACT FROM AUTHOR]

• We have solved the SIR transmission-dynamics equations analytically with (ESIR model) and without (D model) recovery assumptions. • Our models can characterize the evolution of a pandemic at its different stages (exponential and normal phases, etc). • Our models can be extended to describe additional spatial-time effects such as the release of lockdown measures. • Similar results from ESIR and D models suggest that most susceptibles become infected, asymptomatic and eventually recover. • Similar trends suggest a common pandemic evolution with universal parameters. Several analytical models have been developed in this work to describe the evolution of fatalities arising from coronavirus COVID-19 worldwide. The Death or 'D' model is a simplified version of the well-known SIR (susceptible-infected-recovered) compartment model, which allows for the transmission-dynamics equations to be solved analytically by assuming no recovery during the pandemic. By fitting to available data, the D-model provides a precise way to characterize the exponential and normal phases of the pandemic evolution, and it can be extended to describe additional spatial-time effects such as the release of lockdown measures. More accurate calculations using the extended SIR or ESIR model, which includes recovery, and more sophisticated Monte Carlo grid simulations – also developed in this work – predict similar trends and suggest a common pandemic evolution with universal parameters. The evolution of the COVID-19 pandemic in several countries shows the typical behavior in concord with our model trends, characterized by a rapid increase of death cases followed by a slow decline, typically asymmetric with respect to the pandemic peak. The fact that the D and ESIR models predict similar results – without and with recovery, respectively – indicates that COVID-19 is a highly contagious virus, but that most people become asymptomatic (D model) and eventually recover (ESIR model). [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the historical roots of the sufficient component cause model, which is very well known in epidemiology. I have analyzed Max Verworn's writings on the description of the sufficient component cause model. Verworn introduced a precursor of the sufficient component cause model as early as 1912, possibly inspired by Ernst Mach. He argued for the abandonment of the concept of "cause" (singular). Instead, he preferred the term "conditions." Unlike Karl Pearson, Verworn was not opposed to causal considerations. However, according to Verworn, every process or state is determined by numerous conditions and not by a single factor or "cause." Instead of speaking of causalism, Verworn preferred to speak of conditionalism. The earliest description of the concept of the sufficient component cause model known in the epidemiological literature since 1976 dates back at least to 1912. [ABSTRACT FROM AUTHOR]

In this paper, a new version of the well-known epidemic mathematical SEIR model is used to analyze the pandemic course of COVID-19 in eight different countries. One of the proposed model's improvements is to reflect the societal feedback on the disease and confinement features. The SEIR model parameters are allowed to be time-varying, and the ranges of their values are identified by using publicly available data for France, Italy, Spain, Germany, Brazil, Russia, New York State (US), and China. The identified model is then applied to predict the SARS-CoV-2 virus propagation under various conditions of confinement. For this purpose, an interval predictor is designed, allowing variations and uncertainties in the model parameters to be taken into account. The code and the utilized data are available on Github. [ABSTRACT FROM AUTHOR]