Search

Copyright of Revista Española de Quimioterapia is the property of Sociedad Espanola de Quimioterapia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim: This study aimed to investigate the impact of communicable diseases with epidemic potential in complex emergency (CE) situations, focusing on the epidemiological profile of incidence and mortality and exploring underlying factors contributing to increased epidemic risks., Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines, we conducted a scoping review of articles published between 1990 and 2022. The search included terms related to complex emergencies, communicable diseases, outbreaks, and epidemics. We identified 92 epidemics related to CE occurring in 32 different countries., Results: Communicable diseases like Shigellosis, Cholera, Measles, Meningococcal meningitis, Yellow Fever, and Malaria caused significant morbidity and mortality. Diarrhoeal diseases, particularly Cholera and Shigellosis, had the highest incidence rates. Shigella specifically had an incidence of 241.0 per 1000 (people at risk), with a mortality rate of 11.7 per 1000, while Cholera's incidence was 13.0 per 1000, with a mortality rate of 0.22 per 1000. Measles followed, with an incidence of 25.0 per 1000 and a mortality rate of 0.76 per 1000. Meningococcal Meningitis had an incidence rate of 1.3 per 1000 and a mortality rate of 0.13 per 1000. Despite their lower incidences, yellow fever at 0.8 per 1000 and malaria at 0.4 per 1000, their high case fatality rates of 20.1% and 0.4% remained concerning in CE. The qualitative synthesis reveals that factors such as water, sanitation, and hygiene, shelter and settlements, food and nutrition, and public health and healthcare in complex emergencies affect the risk of epidemics., Conclusion: Epidemics during complex emergencies could potentially lead to a public health crisis. Between 1990 and 2022, there have been no statistically significant changes in the trend of incidence, mortality, or fatality rates of epidemic diseases in CE. It is crucial to understand that all epidemics identified in CE are fundamentally preventable., (© 2024 The Authors Tropical Medicine & International Health Published by John Wiley & Sons Ltd.)

Background: Low- and middle-income countries (LMICs), including South Africa, are currently experiencing multiple epidemics: HIV and the rising burden of non-communicable diseases (NCDs), leading to different patterns of multimorbidity (the occurrence of two or more chronic conditions) than experienced in high income settings. These adversely affect health outcomes, increase patients' perceived burden of treatment, and impact the workload of self-management. This paper outlines the methods used in a qualitative study exploring burden of treatment among people living with HIV/NCD multimorbidity in South Africa. Methods: We undertook a comparative qualitative study to examine the interaction between individuals' treatment burden (self-management workload) and their capacity to take on this workload, using the dual lenses of Burden of Treatment Theory (BoTT) and Cumulative Complexity Model (CuCoM) to aid conceptualisation of the data. We interviewed 30 people with multimorbidity and 16 carers in rural Eastern Cape and urban Cape Town between February-April 2021. Data was analysed through framework analysis. Findings: This paper discusses the methodological procedures considered when conducting qualitative research among people with multimorbidity in low-income settings in South Africa. We highlight the decisions made when developing the research design, recruiting participants, and selecting field-sites. We also explore data analysis processes and reflect on the positionality of the research project and researchers. Conclusion: This paper illustrates the decision-making processes conducting this qualitative research and may be helpful in informing future research aiming to qualitatively investigate treatment burden among patients in LMICs. [ABSTRACT FROM AUTHOR]

We present an analysis of an epidemic spreading process on an Apollonian network that can describe an epidemic spreading in a non-sedentary population. We studied the modified diffusive epidemic process using the Monte Carlo method by computational analysis. Our model may be helpful for modeling systems closer to reality consisting of two classes of individuals: susceptible (A) and infected (B). The individuals can diffuse in a network according to constant diffusion rates [Formula: see text] and [Formula: see text], for the classes A and B, respectively, and obeying three diffusive regimes, i.e., [Formula: see text], [Formula: see text], and [Formula: see text]. Into the same site i, the reaction occurs according to the dynamical rule based on Gillespie's algorithm. Finite-size scaling analysis has shown that our model exhibits continuous phase transition to an absorbing state with a set of critical exponents given by [Formula: see text], [Formula: see text], and [Formula: see text] familiar to every investigated regime. In summary, the continuous phase transition, characterized by this set of critical exponents, does not have the same exponents of the mean-field universality class in both regular lattices and complex networks., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots., (© 2023. The Author(s).)

Introduction: The use of digital technology in HIV-related interventions and implementation strategies is increasing. Whether the use of technology is to directly improve patient outcomes (ie, part of the intervention) or as part of the strategy to implement interventions has important implications. In this article, we present 5 case studies of projects that feature the use of technology in HIV-related implementation research to identify and describe challenges specific to technology-based implementation research about study design, outcome measurement, implementing in an evolving technology landscape, and equity., Methods: For each case study, we identified the technological components, classified the components as intervention or implementation strategy, and identified implications for measuring performance and ensuring equity. The Exploration, Preparation, Implementation, and Sustainment framework was used to identify the research stage of each project., Results: Technology is being leveraged across a diverse array of implementation strategies to promote Ending the HIV Epidemic in the United States. The case studies were primarily in the exploration and preparation phases of implementation, yet technology played a different role in each project- developing educational materials, mass media to recruit participants or distribute evidence-based campaigns, providing training, guiding tailoring, and implementing novel methods to democratize intervention development., Discussion: Technology can play multiple roles in HIV-related implementation research projects, including serving as the intervention, being leveraged within implementation strategies, or both. We identified multiple considerations across projects that should be taken into account when measuring success and planning for equitable and sustained impact., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Upon the development of a therapeutic, a successful response to a global pandemic relies on efficient worldwide distribution, a process constrained by our global shipping network. Most existing strategies seek to maximize the outflow of the therapeutics, hence optimizing for rapid dissemination. Here we find that this intuitive approach is, in fact, counterproductive. The reason is that by focusing strictly on the quantity of disseminated therapeutics, these strategies disregard the way in which this quantity distributes across destinations. Most crucially-they overlook the interplay of the therapeutic spreading patterns with those of the pathogens. This results in a discrepancy between supply and demand, that prohibits efficient mitigation even under optimal conditions of superfluous flow. To solve this, we design a dissemination strategy that naturally follows the predicted spreading patterns of the pathogens, optimizing not just for supply volume, but also for its congruency with the anticipated demand. Specifically, we show that epidemics spread relatively uniformly across all destinations, prompting us to introduce an equality constraint into our dissemination that prioritizes supply homogeneity. This strategy may, at times, slow down the supply rate in certain locations, however, thanks to its egalitarian nature, which mimics the flow of the pathogens, it provides a dramatic leap in overall mitigation efficiency, potentially saving more lives with orders of magnitude less resources., (© 2022. The Author(s).)

Mathematical models play a crucial role in evaluating real-life processes qualitatively and quantitatively. They have been extensively employed to study the spread of diseases such as hepatitis B, COVID-19, influenza, and other epidemics. Many researchers have discussed various types of epidemiological models, including deterministic, stochastic, and fractional order models, for this purpose. This article presents a comprehensive review and comparative study of the transmission dynamics of fractional order in epidemiological modeling. A significant portion of the paper is dedicated to the graphical simulation of these models, providing a visual representation of their behavior and characteristics. The article further embarks on a comparative analysis of fractional-order models with their integer-order counterparts. This comparison sheds light on the nuances and subtleties that differentiate these models, thereby offering valuable insights into their respective strengths and limitations. The paper also explores time delay models, non-linear incidence rate models, and stochastic models, explaining their use and significance in epidemiology. It includes studies and models that focus on the transmission dynamics of diseases using fractional order models, as well as comparisons with integer-order models. The findings from this study contribute to the broader understanding of epidemiological modeling, paving the way for more accurate and effective strategies in disease control and prevention. [ABSTRACT FROM AUTHOR]

The focus of this paper is on COVID-19, the December 2019 dated respiratory epidemic or infection that has ravaged most, if not all, of the world's 253 "countries" (consisting of 194 independent nation-states, 55 dependent-states, Antarctica, and 3 other territories). Readers of this paper should be aware of at least three facts. First, as of January 2022, the submission date of this paper, COVID-19 continues to impact the world, although to a much lesser extent, given the protection afforded by the mRNA vaccines, the boosters, and the antiviral medications (e.g., oseltamivir, penciclovir, acyclovir, Paxlovid, etc.). Second, the paper should be regarded as being inconclusive in both its outlook and its list of references; it provides, at best, an intermediary account of the continuing COVID-19 pandemic. Third, like the Spanish Flu of 1918, it is speculated that the COVID-19 pandemic will not endure beyond three years and will conclude as an endemic problem by, hopefully, early 2023. Therefore, the paper is partitioned into three sections which, respectively, address the infection's three phases: epidemic (initial infection), pandemic (worldwide infection) and endemic (pervasive but nonlife-threatening infection) phases. The lessons learned from the range of decisions made throughout the COVID-19 phases should help to inform and better prepare the world for future pathogens and deadly diseases. [ABSTRACT FROM AUTHOR]

With the advancement of technology, social media has become an integral part of people's daily lives. This has resulted in the emergence of a new group of individuals known as "professional operation people". These individuals actively engage with social media platforms, taking on roles as content creators, influencers, or professionals utilizing social media for marketing and networking purposes. Therefore, in this article, we designed a six-dimensional fractional-order social media addiction model (FOSMA) in the sense of Caputo, which took into account the professional operations population. Initially, we established the positivity and boundedness of the FOSMA model. After that, the basic regeneration number and the equilibrium points (no addiction equilibrium point and addiction equilibrium point) were computed. Then, the local asymptotic stability of the equilibrium points were proved. In order to investigate the bifurcation behavior of the model when R 0 = 1 , we extended the Sotomayor theorem from integer-order to fractional-order systems. Next, by the frequency analysis method, we converted the fractional order model into an equivalent partial differential system. The tanh function was introduced into the scheme of sliding mode surface. The elimination of addiction was achieved by the action of the fractional order sliding mode control law. Finally, simulation results showed that fractional order values, nonlinear transmission rates, and specialized operating populations had a significant impact on predicting and controlling addiction. The fractional-order sliding mode control we designed played an important role in eliminating chatter, controlling addiction, and ensuring long-term effectiveness. The results of this paper have far-reaching implications for future work on modeling and control of fractional-order systems in different scenarios, such as epidemic spread, ecosystem stabilization, and game addiction. With the advancement of technology, social media has become an integral part of people's daily lives. This has resulted in the emergence of a new group of individuals known as "professional operation people". These individuals actively engage with social media platforms, taking on roles as content creators, influencers, or professionals utilizing social media for marketing and networking purposes. Therefore, in this article, we designed a six-dimensional fractional-order social media addiction model (FOSMA) in the sense of Caputo, which took into account the professional operations population. Initially, we established the positivity and boundedness of the FOSMA model. After that, the basic regeneration number and the equilibrium points (no addiction equilibrium point and addiction equilibrium point) were computed. Then, the local asymptotic stability of the equilibrium points were proved. In order to investigate the bifurcation behavior of the model when we extended the Sotomayor theorem from integer-order to fractional-order systems. Next, by the frequency analysis method, we converted the fractional order model into an equivalent partial differential system. The tanh function was introduced into the scheme of sliding mode surface. The elimination of addiction was achieved by the action of the fractional order sliding mode control law. Finally, simulation results showed that fractional order values, nonlinear transmission rates, and specialized operating populations had a significant impact on predicting and controlling addiction. The fractional-order sliding mode control we designed played an important role in eliminating chatter, controlling addiction, and ensuring long-term effectiveness. The results of this paper have far-reaching implications for future work on modeling and control of fractional-order systems in different scenarios, such as epidemic spread, ecosystem stabilization, and game addiction. [ABSTRACT FROM AUTHOR]

This paper examines the writings of European travelers, chaplains, and resident doctors on pandemics in the Mediterranean regions from the 16th to the 19th centuries. Using French comparative literary theory, the article highlights how Muslim communities in Egypt, Turkey, Aleppo, and Mecca were stereotyped based on their belief in predestination, their failure to avoid contamination, and their lack of social distancing during plague outbreaks. This paper argues that travelers were influenced by Renaissance humanism, Ars Apodemia, religious discourses, and texts, such as plague tracts, model town concepts, the book of orders, and tales, and that they essentialized Mediterranean Islamicate societies by depicting contamination motifs supposedly shaped by the absence of contagion theory in prophetic medicines. Regarding plague science, this paper concludes that Christian and Muslim intellectuals had similar approaches until the Black Death and that Arabs were eclectic since the Abbasid period. This paper further maintains that the travelers' approaches fostered chauvinism and the cultural hegemony of the West over the Orient since the Renaissance and Enlightenment periods, driven by eschatology, conversion, and power structure narratives. [ABSTRACT FROM AUTHOR]

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds. [ABSTRACT FROM AUTHOR]

The basic reproduction, or reproductive number, is a useful index that indicates whether or not there will be an epidemic. However, it is also very important to determine whether an epidemic will eventually decrease and disappear or persist as an endemic. Different infectious diseases have different behaviors and mathematical models used to simulated them should capture the most important processes; however, the models also involve simplifications. Influenza epidemics are usually short-lived and can be modeled with ordinary differential equations without considering demographics. Delays such as the infection time can change the behavior of the solutions. The same is true if there is permanent or temporary immunity, or complete or partial immunity. Vaccination, isolation and the use of antivirals can also change the outcome. In this paper, we introduce several new models and use them to find the effects of all the above factors paying special attention to whether the model can represent an infectious process that eventually disappears. We determine the equilibrium solutions and establish the stability of the disease-free equilibrium using various methods. We also show that many models of influenza or other epidemics with a short duration do not have solutions with a disappearing epidemic. The main objective of the paper is to introduce different ways of modeling immunity in epidemic models. Several scenarios with different immunities are studied since a person may not be re-infected because he/she has total or partial immunity or because there were no close contacts. We show that some relatively small changes, such as in the vaccination rate, can significantly change the dynamics; for example, the existence and number of the disease-free equilibria. We also illustrate that while introducing delays makes the models more realistic, the dynamics have the same qualitative behavior. [ABSTRACT FROM AUTHOR]

Since 2019n-CoV has swept the whole world, people's daily life is affected seriously and the public space system is facing to major challenges. Community public space should be re-evaluated and optimized as people change the way they use it. This research conducted a nationwide questionnaire survey in China, which collected the opinions of residents on community public space from the perspective of epidemic prevention and social needs to select research indicators. The questionnaire data were processed through the frequency analysis method, precedence chart and TOPSIS. We found: (1) under the background of the epidemic, people pay more attention to the satisfaction of leisure activities in community public space and the control of social spacing during activities; (2) the current assessment result of community public space risk of 2019-nCoV exposure is 0.386, which is relatively high; and (3) the exposure risk of the community can be effectively reduced by controlling for the two indicators of social distance and social facility sterilization. When the social distance is 1.8–3 m and the facility sterilization is once a day, the community public space minimizes exposure risk. In view of the above results, this paper selects the Wuhan start-up area of Wuhan, China, as an example for carrying out the optimization design of community public space based on the epidemic and the design the space optimization model for the two indicators of social distance and facility disinfection from both software and hardware aspects. [ABSTRACT FROM AUTHOR]

This paper focuses on a SIRS infectious model of nonlocal dispersal adopted with age structure. We primarily investigate the existence and nonexistence of traveling wave solutions connecting the disease-free equilibrium state and the endemic equilibrium state. To be more precise, we obtain the existence of traveling wave solutions by constructing suitable upper and lower solutions and then applying Schauder's fixed point theorem when R0 > 1 and c > c*. In addition, we prove the nonexistence of traveling wave solutions by applying the Laplace transform for R0 > 1 and 0 < c < c*. [ABSTRACT FROM AUTHOR]

Objective: Epidemics are sudden and rapidly spreading. Hospitals in underdeveloped areas are particularly vulnerable in case of an outbreak. This paper aims to assess the epidemic risk state and its change trend of hospitals in different epidemic stages, identify the key factors affecting hospital epidemic risk change, provide priority reference for hospital epidemic risk control, and enhance the hospital's ability to respond to sudden epidemics., Methods: Based on Grounded theory, the epidemic risk indicators that affect hospital safety are summarized. The concept of epidemic risk state and its random state space is proposed according to Markov chain theory. The impact of each indicator on the random risk state and its change is comprehensively assessed from two aspects: risk occurrence probability and risk loss. Finally, the assessment of the hospital epidemic risk state and its change at different stages is achieved., Results: The stable risk states of public hospitals in underdeveloped areas in non-epidemic stage t 0, early epidemic stage t 1, and outbreak stage t 2 are P ^ t 0 ( S n ) = { 0 . 142 , 0 . 546 , 0 . 220 , 0 . 093 } , P ^ t 1 ( S n ) = { 0 . 025 , 0 . 364 , 0 . 254 , 0 . 357 } , and P ^ t 2 ( S n ) = { 0 . 020 , 0 . 241 , 0 . 191 , 0 . 548 } , respectively. In non-epidemic stage, the key factor in improving the hospital epidemic risk state is emergency funding. In early epidemic stage, the key factors in improving the hospital epidemic risk state are the training of medical staff in epidemic prevention skills and the management of public health. In outbreak state, the key factor in improving the hospital epidemic risk state is the training of medical staff in epidemic prevention skills and psychological awareness., Conclusion: This paper proposes the concept of epidemic risk state, providing an effective assessment method for the epidemic risk state and its change trend in public hospitals. According to the assessment, public hospitals in underdeveloped areas in different epidemic stages should adopt different risk control strategies to improve their current risk state. Blind risk control is inefficient and may even cause the epidemic risk to transition toward a more dangerous state., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang, Miao, Li, Jiang and Jiang.)

Malaria is a fever condition that results from Plasmodium parasites, which are transferred to humans by the attacks of infected female Anopheles mosquitos. The deterministic compartmental model was examined using stability theory of differential equations. The reproduction number was obtained to be asymptotically stable conditions for the disease-free, and the endemic equilibria were determined. More so, the qualitatively evaluated model incorporates time-dependent variable controls which was aimed at reducing the proliferation of malaria disease. The reproduction number R o was determined to be an asymptotically stable condition for disease free and endemic equilibria. In this paper, we used various schemes such as Runge-Kutta order 4 (RK-4) and non-standard finite difference (NSFD). All of the schemes produce different results, but the most appropriate scheme is NSFD. This is true for all step sizes. Various criteria are used in the NSFD scheme to assess the local and global stability of disease-free and endemic equilibrium points. The Routh-Hurwitz condition is used to validate the local stability and Lyapunov stability theorem is used to prove the global asymptotic stability. Global asymptotic stability is proven for the disease-free equilibrium when R 0 ≤ 1 . The endemic equilibrium is investigated for stability when R 0 ≥ 1 . All of the aforementioned schemes and their effects are also numerically demonstrated. The comparative analysis demonstrates that NSFD is superior in every way for the analysis of deterministic epidemic models. The theoretical effects and numerical simulations provided in this text may be used to predict the spread of infectious diseases., (© 2024. The Author(s).)

The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data. [ABSTRACT FROM AUTHOR]

This work focused on studying the effect of vaccination rate κ on reducing the outbreak of infectious diseases, especially if the infected individuals do not have any symptoms. We employed the fractional order derivative in this study since it has a high degree of accuracy. Recently, a lot of scientists have been interested in fractional-order models. It is considered a modern direction in the mathematical modeling of epidemiology systems. Therefore, a fractional order of the SEIR epidemic model with two types of infected groups and vaccination strategy was formulated and investigated in this paper. The proposed model includes the following classes: susceptible S (t) , exposed E (t) , asymptomatic infected I c (t) , symptomatic infected I η (t) , vaccinated V (t) , and recovered R (t) . We began our study by creating the existence, non-negativity, and boundedness of the solutions of the proposed model. Moreover, we established the basic reproduction number R 0 , that was used to examine the existence and stability of the equilibrium points for the presented model. By creating appropriate Lyapunov functions, we proved the global stability of the free-disease equilibrium point and endemic equilibrium point. We concluded that the free-disease equilibrium point is globally asymptotically stable (GAS) when R 0 ≤ 1 , while the endemic equilibrium point is GAS if R 0 > 1 . Therefore, we indicated the increasing vaccination rate κ leads to reducing R 0 . These findings confirm the important role of vaccination rate κ in fighting the spread of infectious diseases. Moreover, the numerical simulations were introduced to validate theoretical results that are given in this work by applying the predictor-corrector PECE method of Adams-Bashforth-Moulton. Further more, the impact of the vaccination rate κ was explored numerically and we found that, as κ increases, the R 0 is decreased. This means the vaccine can be useful in reducing the spread of infectious diseases. This work focused on studying the effect of vaccination rate on reducing the outbreak of infectious diseases, especially if the infected individuals do not have any symptoms. We employed the fractional order derivative in this study since it has a high degree of accuracy. Recently, a lot of scientists have been interested in fractional-order models. It is considered a modern direction in the mathematical modeling of epidemiology systems. Therefore, a fractional order of the SEIR epidemic model with two types of infected groups and vaccination strategy was formulated and investigated in this paper. The proposed model includes the following classes: susceptible , exposed , asymptomatic infected , symptomatic infected , vaccinated , and recovered . We began our study by creating the existence, non-negativity, and boundedness of the solutions of the proposed model. Moreover, we established the basic reproduction number , that was used to examine the existence and stability of the equilibrium points for the presented model. By creating appropriate Lyapunov functions, we proved the global stability of the free-disease equilibrium point and endemic equilibrium point. We concluded that the free-disease equilibrium point is globally asymptotically stable (GAS) when , while the endemic equilibrium point is GAS if . Therefore, we indicated the increasing vaccination rate leads to reducing . These findings confirm the important role of vaccination rate in fighting the spread of infectious diseases. Moreover, the numerical simulations were introduced to validate theoretical results that are given in this work by applying the predictor-corrector PECE method of Adams-Bashforth-Moulton. Further more, the impact of the vaccination rate was explored numerically and we found that, as increases, the is decreased. This means the vaccine can be useful in reducing the spread of infectious diseases. [ABSTRACT FROM AUTHOR]

This paper aims to enhance our knowledge about late-medieval epidemic outbreaks in specific parts of Eastern Central Europe. The first part on modern-day Eastern Germany discusses narrative evidence and its use in the current research on plague history, before bringing in municipal records on testaments and conveyances from Görlitz and Stralsund for the reconstruction of seasonality and mortality rates, as well as funeral inscriptions and pictorial evidence from Erfurt as indirect indicators of plague waves. After a brief discussion of the scarce narrative sources, the second part of the paper concerning Bohemia works with the evidence of the Libri Confirmationum, a source originating from the chancellery of the archbishops of Prague. Every new appointment to a benefice was supposed to be approved by one of the vicars general of the archbishop, and this confirmation usually gives the reason for the vacancy. Expanding on Eduard Maur's research, death statistics and their frequency are analyzed statistically. The paper provides insight into new evidence for the reconstruction of plague waves, mortality rates and seasonality, and thereby highlights the characteristics of the plague in Eastern Central Europe. [ABSTRACT FROM AUTHOR]

In the first part of this paper, we review old and new results about the influence of host population heterogeneity on (various characteristics of) epidemic outbreaks. In the second part we highlight a modelling issue that so far has received little attention: how do contact patterns, and hence transmission opportunities, depend on the size and the composition of the host population? Without any claim on completeness, we offer a range of potential (quasi-mechanistic) submodels. The overall aim of the paper is to describe the state-of-the-art and to catalyse new work. [ABSTRACT FROM AUTHOR]

Copyright of TEME: Casopis za Društvene Nauke is the property of TEME: Casopis za Drustvene Nauke and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim: This study aims to examine the features of online pediatric pharmacy consultations in Southwest China, an area with limited medical resources during the post-epidemic era and to explore the factors affecting the dialogue volume in online consultations., Methods: A cross-sectional study was conducted in southwest China from April 2022 to March 2023. The study encompassed a cohort of 2,526 children, ranging from 0 to 18 years old. The collected data encompassed patient gender, age, weight, department, drugs involved in consultation services, types of questions consulted, consultation start time, and the dialogue volume in online consultations. Descriptive statistics and an ordered multicategorical logistic regression analysis were performed. SPSS 26.0 software was used for data analysis., Results: Among the total Internet pharmacy consultation services offered, the infant group constituted the largest portion (n = 1021). Children with internal medicine concerns have greater online consultation needs (n = 455). The peak usage of Internet pharmacy consultation occurred in the fall at 11:00 a.m. (n = 73). Factors influencing the dialogue volume in online consultations include the non-availability of Chinese medications [P = 0.033, OR = 0.81, 95 % CI (0.67-0.98)], consultation initiation during the spring [P = 0.002, OR = 1.52, 95 % CI(1.17-1.97)] or afternoon [P = 0.012, OR = 1.36, 95 % CI (1.07-1.72)] and weight＜15 kg group [P = 0.038, OR = 2.19, 95 % CI (1.04-4.58)]., Conclusion: Our findings provide important information for peers to carry out their work. In the post-epidemic era, emphasis should be placed on addressing concerns related to children's internal medicine and otolaryngology. Optimizing healthcare resource allocation should take into account the time-seasonal variability of children's consultation behaviors. Furthermore, it is recommended to focus on weight＜15 kg children in inquiries, absorb sufficient knowledge about Chinese medicines, and schedule more pharmacists to participate in consultation services during the spring or in every afternoon. These findings help the authorities of online platforms and the National Health Commission to rationalize the allocation of healthcare resources, optimize the quality of service delivery, and develop new policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh-Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if R 0 < 1 and endemic equilibrium is locally asymptotically stable if R 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable if R 0 < 1 and endemic equilibrium is globally asymptotically stable if R 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks., (© 2024. The Author(s).)

This paper examines the supply and utilization of traditional Chinese medicine (TCM) in Hong Kong during the influenza epidemics of the 1950s and 1960s. Existing narratives of TCM in Hong Kong have predominantly framed with within the dichotomy of Western medicine "Xiyi" and Chinese medicine "Zhongyi," portraying TCM as marginalized and nearly wiped out by colonial power. Departing from this binary opposition, this study views TCM as an autonomous space that had never been subjugated by the colonial power which opted for minimal interventionist approach toward TCM. By adopting diachronic and synchronic perspectives on Hong Kong's unique environment shaped by its colonial history and the geopolitics of the Cold War in East Asia, particularly its relationships with "China," this research seeks to reassess the role and status of TCM in post-World War II Hong Kong. In Hong Kong, along with other countries in East Asia, traditional medicine has ceded its position as mainstream medicine to Western medicine. Faced with the crisis of "extinction," Chinese medical professionals, including medical practitioners and merchant groups, persistently sought solidarity and "self-renewal." In the 1950s and 1960s, the colonial authorities heavily relied on private entities, including charity hospitals and clinics; furthermore, there was a lack of provision of public healthcare and official prevention measures against the epidemic influenza. As such, it is not surprising that the Chinese utilized TCM, along with Western medicine, to contain the epidemics which brought about an explosive surge in the number of patients from novel influenza viruses. TCM was significantly consumed during these explosive outbreaks of influenza in 1957 and 1968. In making this argument, this paper firstly provides an overview of the associations of Chinese medical practitioners and merchants who were crucial to the development of TCM in Hong Kong. Secondly, it analyzes one level of active provision and consumption of Chinese medicine during the two flu epidemics, focusing on the medical practices of TCM practitioners in the 1957 epidemic. While recognizing the etiologic agent or agents of the disease as influenza viruses, the group of Chinese medical practitioners of the Chinese Medical Society in Hong Kong adopted the basic principles of traditional medicine regarding influenza, such as Shanghanlun and Wenbingxue, to distinguish the disease status among patients and prescribe medicine according to correct diagnoses, which were effective. Thirdly, this paper examines the level of folk culture among the people, who utilized famous prescriptions of Chinese herbal medicine and alimentotherapy, in addition to Chinese patent medicines imported from mainland China. In the context of regional commercial network, this section also demonstrates how Hong Kong served as a sole exporting port of medicinal materials (e.g., Chinese herbs) and Chinese patent medicines from the People's Republic of China to capitalist markets, including Hong Kong, under the socialist planned or controlled economy in the 1950s and 1960s. It was not only the efficacy of TCM in restoring immunity and alleviating symptoms of the human body, but also the voluntary efforts of these Chinese medical practitioners who sought to defend national medicine "Guoyi," positioning it as complementary and alternative medicine to scientific medicine. Additionally, merchants who imported and distributed Chinese medicinal materials and national "Guochan" Chinese patent medicine played a crucial role, as did the people who utilized Chinese medicine, all of which contributed to making TCM thrive in colonial Hong Kong.

This paper is concerned with a reaction-diffusion SIS (susceptible-infected-susceptible) epidemic model governed by a saturated incidence function and linear birth-death growth in advective environments. The related model without birth and death case has been studied in our previous work [8]. In this paper, our main purpose is to investigate the combined effects of varying total population, saturation infection mechanism and spatial heterogeneity on the transmission dynamics and spatial distribution of disease. The extinction and persistence of the infectious disease in terms of the basic reproduction number are established. We discuss the global attractivity of the equilibria in two special cases and explore the asymptotic profiles of the endemic equilibrium with respect to the dispersal and advection rates. Compared with the results for the model without birth and death in [8] , our findings indicate that the linear birth-death growth can enhance the persistence of an infectious disease and may provide some prospective applications in disease prevention and control. [ABSTRACT FROM AUTHOR]

The global outbreak of the novel coronavirus has had a great impact on commercial areas, particularly in Wuhan, the first city in China affected by the epidemic. Exploring the changing pattern of these areas during the epidemic period is crucial for effectively restoring the urban economic level and promoting economic development on the basis of epidemic prevention and control in the post-epidemic era. The paper proposes a resilience analysis of urban commercial areas during the COVID-19 epidemic using time-series night-time light remote sensing data. Based on the constructed time-series night-time light remote sensing dataset and considering the urban impervious surface, the urban commercial area was extracted under multi-scale segmentation. Based on night-time light data before and after the epidemic, the resilience of representative commercial areas in Wuhan City, Hubei Province during the COVID-19 epidemic was analysed using a modified improved resilience assessment framework. The results showed that the lighting values of commercial areas and impervious areas decreased by up to 30% following the implementation of city lockdown measures. The change of light brightness in the commercial areas was more prominent, and the range of change is larger than that in the non-commercial areas. The lighting brightness of the commercial areas exhibited a significant downward trend throughout the entire lockdown period, and the downward trend will still remain for a period of time after unlocking. Among all the commercial areas, Wuguang commercial area demonstrated the highest recovery rate during the lockdown period and instantaneous recovery rate after the lockdown, with relatively low closure loss and the highest level of resilience. [ABSTRACT FROM AUTHOR]

Corona virus disease 2019 (COVID-19) pandemic, sparked by the emergence of a novel coronavirus in early 2020, has prompted a surge in published articles. This study aims to systematically analyse the characteristics and trends of impactful research in the field. The 100 most-cited publications associated with COVID-19 were identified by two independent reviewers using the 'Web of Science' database across all available journals up to the year 2023. Data collected include country, citation count, subject, level of evidence (using Oxford Centre for Evidence-Based Medicine System 2011), impact factor, funding, and study design. We identified 394 038 publications, and the 100 most-cited publications were ranked. These were cited by a total of 283 034 articles (median citation = 767), median impact factor of 66.9 and 72 articles with fundings. China (n  = 44), USA (n  = 19), and UK (n  = 13) were the three highest contributors (n  = 220 505). Most articles were level 5 evidence (n  = 48), followed by level 3 (n  = 28), 4 (n  = 14), 2 (n  = 7), and 1 (n  = 3). The main subjects were mechanism of action and structures of SARS-CoV-2 virus (n  = 18) and impact of COVID-19 on public health (n  = 18). Publications in 2022 and 2023 predominantly focused on the impact of COVID-19. Majority of the highly cited studies were of low-to-moderate quality, with only 10 consisting of randomized controlled trials or systematic reviews with or without meta-analysis. These findings reflect a growing interest in understanding the impact of COVID-19 pandemic on public and mental health. This analysis found the potential for future double-blinded randomized controlled trials to validate existing findings. [ABSTRACT FROM AUTHOR]

There is uncertainty in the results of any mathematical model due to different reasons. It is important to estimate this uncertainty. Sensitivity analysis is commonly used to estimate how the changes in the input parameters affect the solutions of the model. In this paper, we discuss different ways of performing local and global sensitivity analyses and apply them to two models: an epidemic model and a new myocardial infarction model, both based on ordinary differential equations. The first model is a simple model used to explain the ideas, while the second one shows how to apply them to a model with more state variables and parameters. We find that if the parameters are not accurately known, local sensitivity analysis can be misleading and that global sensitivity methods that sample the whole parameter space, varying all the values of the parameters at the same time, are the most reliable. We also show how the sensitivity analysis results can be used to determine the uncertainty in the results of the model. We present numerical simulations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model's application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases. [ABSTRACT FROM AUTHOR]

In epidemic modeling, interpretation of compartment quantities, such as s, i, and r in relevant equations, is not always straightforward. Ambiguities regarding whether these quantities represent numbers or fractions of individuals in each compartment rise questions about significance of the involved parameters. In this paper, we address these challenges by considering a density-dependent epidemic modelling by a birth-death process approach inspired by Kurtz from 1970s'. In contrast to existing literature, which employs population size scaling under constant population condition, we scale with respect to the area. Namely, under the assumption of spatial homogeneity of the population, we consider the quantities of susceptible, infective and recovered per unit area. This spatial scaling allows diffusion approximation for birth-death type epidemic models with varying population size. By adopting this approach, we anticipate to contribute to a clear and transparent description of compartment quantities and parameters in epidemic modeling. [ABSTRACT FROM AUTHOR]

In this paper, we discuss an epidemic switched system. A susceptible–infected–treated model is considered. The course of an epidemic is profoundly influenced by the allocation of resources. If these resources are limited, then we need to devise an optimal distribution strategy. One significant case to study is when the drug supply is insufficient. We study a control problem that minimizes the total outbreak size of the epidemic and optimizes the rate of vaccination/isolation control by minimizing the suitable functional subject to resource constraints. In the end, simulations are performed for illustrations. [ABSTRACT FROM AUTHOR]

The whole world is experiencing a novel infection called Coronavirus brought about by a Covid since 2019. The main concern about this disease is the absence of proficient authentic medicine The World Health Organization (WHO) proposed a few precautionary measures to manage the spread of illness and to lessen the defilement in this manner decreasing cases. In this paper, we analyzed the Coronavirus dataset accessible in Kaggle. The past contributions from a few researchers of comparative work covered a limited number of days. Our paper used the covid19 data till May 2021. The number of confirmed cases, recovered cases, and death cases are considered for analysis. The corona cases are analyzed in a daily, weekly manner to get insight into the dataset. After extensive analysis, we proposed machine learning regressors for covid 19 predictions. We applied linear regression, polynomial regression, Decision Tree Regressor, Random Forest Regressor. Decision Tree and Random Forest given an r-square value of 0.99. We also predicted future cases with these four algorithms. We can able to predict future cases better with the polynomial regression technique. This prediction can help to take preventive measures to control covid19 in near future. All the experiments are conducted with python language. [ABSTRACT FROM AUTHOR]

The modeling and control of epidemics, such as the novel Coronavirus, have become crucial on a global scale, for effective management of epidemic situations. This paper is focused on using the mean field approach for modeling and control of epidemics. The mean field approach is an effective alternative to the classical approach of using continuous time Markov decision process (CTMDP) models, which suffer from the curse of dimensionality and entail knowledge of global state information. The mean field approach captures the collective behavior of a dynamic system consisting of numerous interacting nodes representing individuals in the population. The objectives of this paper are twofold: (a) to provide an overview of the mean field approach to epidemic modeling and control, and (b) to present recent advances in this area. Emphasizing the importance of containing and suppressing epidemic spread through non-pharmaceutical interventions, the paper highlights the need to minimize loss of lives, reduce suffering, and alleviate the burden on the public healthcare system. A potential challenge here is the presence of a section of the population who act on their free will and deviate from recommended best practices, which could lead to a potential public health crisis. Motivated by this, the paper explores two specific threads to modeling and control. The first thread assumes that individual nodes comply with a socially optimal control policy mandated by a regulatory authority. The second thread allows for independent and strategic behavior by the individual nodes, modeled as a mean field game, where the strategies of rational agents are based on mean field Nash equilibria. The paper begins by discussing the modeling of epidemics using an extended SIVR (Susceptible-Infected-Vaccinated-Recovered) compartmental model, accompanied by an illustrative example. Next, a literature review is provided, focusing on the mean field approach for socially optimal control of epidemics and how a regulatory authority can effectively contain the spread. Subsequently, the paper presents an update on the use of mean field game-based approaches in studying epidemic spread and control. Finally, future research directions in this important area are discussed. [ABSTRACT FROM AUTHOR]

Background: Involving consumers in systematic reviews can make them more valuable and help achieve goals around transparency. Systematic reviews are technically complex and training can be needed to enable consumers to engage with them fully. The Cochrane Common Mental Disorders group sought to engage people with lived experience of mental health problems in the Voice of Experience College, three workshops introducing them to systematic review methods and to opportunities to contribute as Cochrane consumers. We aimed to collectively evaluate the College from the perspective of both facilitators and consumers, to critically reflect on the experience, and to identify how the College could be sustained and spread to other review groups. Methods: This study was a longitudinal qualitative and collaborative evaluation, structured around normalisation process theory. Both facilitators and consumers were involved in not only providing their perspectives but also reflecting on these together to identify key learning points. Results: The workshops were positively evaluated as being engaging and supportive, largely due to the relational skills of the facilitators, and their willingness to engage in joint or two‐way learning. The College suffered from a lack of clarity over the role of consumers after the College itself, with a need for greater communication to check assumptions and clarify expectations. This was not achieved due to pandemic disruptions, which nevertheless demonstrated that resources for involvement were not prioritised as core business during this period. Conclusions: Soft skills around communication and support are crucial to effective consumer engagement. Sustaining involvement requires sustained communication and opportunities to reflect together on opportunities and challenges. This requires committed resources to ensure involvement activity is prioritised. This is critical as negative experiences later in the involvement journey can undermine originally positive experiences if contributors are unclear as to what their involvement can lead to. Open discussions about this are necessary to avoid conflicting assumptions. The spread of the approach to other review groups could be achieved by flexibly adapting to group‐specific resources and settings, but maintaining a core focus on collaborative relationships as the key mechanism of engagement. Patient and Public Contribution: Public contributors were collaborators throughout the evaluation process and have co‐authored the paper. [ABSTRACT FROM AUTHOR]

In this paper, we investigated a new heroin–cocaine epidemic model which incorporates spatial heterogeneity and nonlinear incidence rate. The main project of this paper is to explore the threshold dynamics in terms of the basic reproduction number $ \mathcal {R}_0 $ R 0 , which was defined by applying the next-generation operator. The threshold type results shown that if $ \mathcal {R}_0 \lt 1 $ R 0 < 1 , then the drug-free steady state is globally asymptotically stable. If $ \mathcal {R}_0 \gt 1 $ R 0 > 1 , then heroin–cocaine spread is uniformly persistent. Furthermore, the globally asymptotic stability of the drug-free steady state has been established for the critical case of $ \mathcal {R}_0=1 $ R 0 = 1 by analysing the local asymptotic stability and global attractivity. [ABSTRACT FROM AUTHOR]

Epidemic spatial–temporal risk analysis, e.g., infectious number forecasting, is a mainstream task in the multivariate time series research field, which plays a crucial role in the public health management process. With the rise of deep learning methods, many studies have focused on the epidemic prediction problem. However, recent primary prediction techniques face two challenges: the overcomplicated model and unsatisfactory interpretability. Therefore, this paper proposes an Interpretable Spatial IDentity (ISID) neural network to predict infectious numbers at the regional weekly level, which employs a light model structure and provides post-hoc explanations. First, this paper streamlines the classical spatio-temporal identity model (STID) and retains the optional spatial identity matrix for learning the contagion relationship between regions. Second, the well-known SHapley Additive explanations (SHAP) method was adopted to interpret how the ISID model predicts with multivariate sliding-window time series input data. The prediction accuracy of ISID is compared with several models in the experimental study, and the results show that the proposed ISID model achieves satisfactory epidemic prediction performance. Furthermore, the SHAP result demonstrates that the ISID pays particular attention to the most proximate and remote data in the input sequence (typically 20 steps long) while paying little attention to the intermediate steps. This study contributes to reliable and interpretable epidemic prediction through a more coherent approach for public health experts. [ABSTRACT FROM AUTHOR]

Introduction. Candida auris is a relatively novel pathogen first described in 2009 in Japan. It has increased its presence worldwide, becoming a public health concern due to its innate resistance to antifungals and outbreak potential. Methods. We performed a query using the word "Candida auris" from the Scopus database, further performing a bibliometric analysis with the open-source R package Bibliometrix. Results. 907 original articles were retrieved, allowing us to map the principal authors, papers, journals, and countries involved in this yeast research, as well as analyze current and future trends and the number of published articles. Conclusion. C. auris will continue to be a pivotal point in fungal resistance research, either for a better understanding of its resistance and pathogenic mechanisms or for developing novel drugs. [ABSTRACT FROM AUTHOR]

This paper considers the dynamical properties of a space and time discrete fractional reaction–diffusion epidemic model, introducing a novel generalized incidence rate. The linear stability of the equilibrium solutions of the considered discrete fractional reaction–diffusion model has been carried out, and a global asymptotic stability analysis has been undertaken. We conducted a global stability analysis using a specialized Lyapunov function that captures the system's historical data, distinguishing it from the integer-order version. This approach significantly advanced our comprehension of the complex stability properties within discrete fractional reaction–diffusion epidemic models. To substantiate the theoretical underpinnings, this paper is accompanied by numerical examples. These examples serve a dual purpose: not only do they validate the theoretical findings, but they also provide illustrations of the practical implications of the proposed discrete fractional system. [ABSTRACT FROM AUTHOR]

Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers. [ABSTRACT FROM AUTHOR]

As the media have continued to pay increasing attention to pig epidemic events, some local pig epidemic events may have a large degree of negative impact on the pork market and the whole pig industry chain, leading to pork price fluctuations. Strengthening pig epidemic control, monitoring media reporting sentiment, and stabilizing pork price fluctuations are important measures to improve the economy and people's livelihood. This paper sets out to identify the relationship between the negative media sentiment about the pig epidemic and the market risk of pork prices within a setting with pig epidemic risk. Based on the provincial panel data of China from January 2011 to December 2022, this paper uses the spatial panel Durbin model to investigate the impact of negative media sentiment about the pig epidemic on pork price fluctuations from the perspective of local and spillover effects, and further discusses the mechanism of consumer sentiment. The empirical results show that: (1) The negative media sentiment about the pig epidemic significantly exacerbates pork price fluctuations, and there is a single threshold effect, which is weakened after crossing the threshold value. (2) The negative media sentiment about the pig epidemic has a significant positive spillover effect on pork price fluctuations, showing the characteristics of "being a neighbor". The spatial spillover effect shows a significant spatial attenuation feature and an inverted U-shaped change with the inflection point at 1400 km. (3) The effect is related to the heterogeneity of media reputation. The local aggravation effect of local media's negative sentiment on pork price fluctuations is greater than that of central media and information network platforms. In terms of the spatial spillover effect, the negative sentiment of the information network platforms has the strongest effect on the aggravation of pork price fluctuations in neighboring regions. (4) The mechanism study finds that the negative media sentiment about the pig epidemic positively affects pork price fluctuations through the path of "consumer sentiment". Therefore, this research recommends that the government department should strengthen the supervision of media sentiment about the pig epidemic and reasonably guide consumer sentiment to stabilize the pork market. [ABSTRACT FROM AUTHOR]

The end of a pandemic is as much a political act as biological reality. It is over not simply when case counts or deaths are reduced to some objectively determined acceptable level but also when, and if, the public accepts the stories that politicians and health officials tell about it. This paper has three aims. First, to develop the concept of a pandemic illness narrative – a public narrative that makes the experience of an outbreak meaningful to a community of people and explains when it will be finished. Using the case of the United States, the paper then examines how American state organisations and public health officials tried to disseminate a version of the 'restitution illness narrative' to make sense out of the COVID-19 pandemic and explain how it would ultimately end. Lastly, the paper describes the factors that made this narrative ultimately implausible to the American public. As most Americans are now seemingly indifferent about the pandemic, it has ended in the United States without ever actually being narratively concluded. [ABSTRACT FROM AUTHOR]