Fast-track interventions for HIV and AIDS epidemic control among key populations: A rapid review.

BACKGROUND: Targeted interventions for key populations remain critical for realisation of epidemic control for human immunodeficiency virus (HIV) infection because of the causal relationship between HIV infection in the general population and among key population groups. AIM: To consolidate evidence on the fast-track interventions towards achieving HIV epidemic control among key populations. METHODS: A rapid scoping review was conducted using the methodological framework by Arksey and O' Malley. The Population, Intervention, Context and Outcome (PICO) framework was used to identify relevant studies using key words with Boolean operators in electronic data bases, namely CINHAL, Web of Science, Psych Info and Sabinet. Studies were extracted using a modified data extraction tool, and results were presented narratively. RESULTS: A total of 19 articles were included in this review. Most articles were primary studies (n = 17), while another involved the review of existing literature and policies (n = 2) and routinely collected data (n = 1). Most studies were conducted in the United States of America (n = 6), while another were conducted in China, Kenya, Botswana, South Africa and Mozambique. All studies revealed findings on tested interventions to achieve HIV epidemic control among key populations. CONCLUSION: Effective interventions for HIV epidemic control were stand-alone behavioural preventive interventions, stand-alone biomedical preventive strategies and combination prevention approaches. Furthermore, the findings suggest that effective activities to achieve HIV epidemic control among key populations should be centred around prevention.Contribution: The findings of this study have policy and practice implications for high HIV burden settings such as South Africa in terms of interventions to facilitate realisation of the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets, thereby contributing to HIV epidemic control.

Assessment of health care workers preparedness to epidemics: A case of Ebola virus disease preparedness in private hospitals in Kampala, Uganda.

INTRODUCTION: Unrecognized Ebola Virus Disease (EVD) can lead to multiple chains of transmissions if the first caretakers are not trained and prepared. This study aimed to assess healthcare workers (HCWs) preparedness in private hospitals located in Kampala, to detect, respond and prevent EVD. METHODOLOGY: A descriptive cross-sectional study was carried out among HCWs in direct clinical care provision in four private hospitals, and in one Ebola Treatment Unit (ETU) using a self-administered questionnaire from March to June 2020. RESULTS: 222 HCWs agreed to participate aged from 19 to 64 years and with 6 months to 38 years of practice where most were nurses (44%). 3/5 hospitals did not have written protocols on EVD case management, and only one (ETU) had an exclusive emergency team. 59% were not sure whether contact tracing was taking place. Private hospitals were not included in EVD trainings organized by the Ministry of Health (MoH). In addition, HCWs in private hospitals were not empowered by the MoH to take part in EVD case management. Despite these shortcomings, only 66% of HCWs showed an interest to be immunized. Knowledge about potential Ebola vaccines was generally poor. CONCLUSIONS: In Kampala, Uganda, establishment of a more comprehensive preparedness and response strategy for EVD outbreaks is imperative for HCWs in private facilities, including a wide vaccination educational program on Ebola vaccination. The findings from this study if addressed will likely improve the preparedness and management of future Ebola outbreaks in Uganda.

Impact of simplified HCV diagnostic strategies on the HCV epidemic among men who have sex with men in the era of HIV oral pre-exposure prophylaxis in Taiwan: a modelling study.

INTRODUCTION: Simplified hepatitis C virus (HCV) diagnostic strategies have the potential to improve HCV diagnoses and treatment. We aimed to investigate the impact of simplified HCV diagnostic strategies on HCV incidence and its effect on HCV diagnosis and treatment among men who have sex with men (MSM) regardless of HIV status and use of HIV pre-exposure prophylaxis (PrEP) in Taiwan. METHODS: A compartmental deterministic model was developed to describe the natural history of HCV disease progression, the HCV care cascade and the HIV status and PrEP using among MSM. The model was calibrated to available data for HCV and HIV epidemiology and population demographics in Taiwan. We simulated the epidemic from 2004 and projected the impact of simplified testing strategies on the HCV epidemic among MSM over 2022-2030. RESULTS: Under the current testing approach in Taiwan, total HCV incidence would increase to 12.6 per 1000 person-years among MSM by 2030. Single-visit point-of-care RNA testing had the largest impact on reducing the number of new HCV infections over 2022-2030, with a 31.1% reduction (interquartile range: 24.9%-32.8%). By 2030, single-visit point-of-care HCV testing improved HCV diagnosis to 90.9%, HCV treatment to 87.7% and HCV cure to 81.5% among MSM living with HCV. Compared to status quo, prioritized simplified HCV testing for PrEP users and MSM living with diagnosed HIV had considerable impact on the broader HCV epidemic among MSM. A sensitivity analysis suggests that reinfection risk would have a large impact on the effectiveness of each point-of-care testing scenario. CONCLUSIONS: Simplified HCV diagnostic strategies could control the ongoing HCV epidemic and improve HCV testing and treatment among Taiwanese MSM. Single-visit point-of-care RNA testing would result in large reductions in HCV incidence and prevalence among MSM. Efficient risk-reduction strategies will need to be implemented alongside point-of-care testing to achieve HCV elimination among MSM in Taiwan.

Modelling the impact of precaution on disease dynamics and its evolution.

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number R 0 < 1 . For R 0 > 1 , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics.

Tackling the non-communicable disease epidemic: a framework for policy action in low- and middle-income countries.

Health policy frameworks for the prevention and control of non-communicable diseases have largely been developed for application in high-income countries. Limited attention has been given to the policy exigencies in lower- and middle-income countries where the impacts of these conditions have been most severe, and further clarification of the policy requirements for effective prevention is needed. This paper presents a policy approach to prevention that, although relevant to high-income countries, recognizes the peculiar situation of low-and middle-income countries. Rather than a narrow emphasis on the implementation of piecemeal interventions, this paper encourages policymakers to utilize a framework of four embedded policy levels, namely health services, risk factors, environmental, and global policies. For a better understanding of the non-communicable disease challenge from a policy standpoint, it is proposed that a policy framework that recognizes responsible health services, addresses key risk factors, tackles underlying health determinants, and implements global non-communicable disease conventions, offers the best leverage for prevention.

Flip bifurcation analysis and mathematical modeling of cholera disease by taking control measures.

To study the dynamical system, it is necessary to formulate the mathematical model to understand the dynamics of various diseases which are spread in the world wide. The objective of the research study is to assess the early diagnosis and treatment of cholera virus by implementing remedial methods with and without the use of drugs. A mathematical model is built with the hypothesis of strengthening the immune system, and a ABC operator is employed to turn the model into a fractional-order model. A newly developed system SEIBR, which is examined both qualitatively and quantitatively to determine its stable position as well as the verification of flip bifurcation has been made for developed system. The local stability of this model has been explored concerning limited observations, a fundamental aspect of epidemic models. We have derived the reproductive number using next generation method, denoted as " R 0 ", to analyze its impact rate across various sub-compartments, which serves as a critical determinant of its community-wide transmission rate. The sensitivity analysis has been verified according to its each parameters to identify that how much rate of change of parameters are sensitive. Atangana-Toufik scheme is employed to find the solution for the developed system using different fractional values which is advanced tool for reliable bounded solution. Also the error analysis has been made for developed scheme. Simulations have been made to see the real behavior and effects of cholera disease with early detection and treatment by implementing remedial methods without the use of drugs in the community. Also identify the real situation the spread of cholera disease after implementing remedial methods with and without the use of drugs. Such type of investigation will be useful to investigate the spread of virus as well as helpful in developing control strategies from our justified outcomes.

Evaluation of age-structured vaccination strategies for curbing the disease spread.

Age structure is one of the crucial factors in characterizing the heterogeneous epidemic transmission. Vaccination is regarded as an effective control measure for prevention and control epidemics. Due to the shortage of vaccine capacity during the outbreak of epidemics, how to design vaccination policy has become an urgent issue in suppressing the disease transmission. In this paper, we make an effort to propose an age-structured SVEIHR model with the disease-caused death to take account of dynamics of age-related vaccination policy for better understanding disease spread and control. We present an explicit expression of the basic reproduction number R 0 , which determines whether or not the disease persists, and then establish the existence and stability of endemic equilibria under certain conditions. Numerical simulations are illustrated to show that the age-related vaccination policy has a tremendous influence on curbing the disease transmission. Especially, vaccination of people over 65 is better than for people aged 21-65 in terms of rapid eradication of the disease in Italy.

A study of the attenuation stage of a global infectious disease.

Introduction: Differences in control measures and response speeds between regions may be responsible for the differences in the number of infections of global infectious diseases. Therefore, this article aims to examine the decay stage of global infectious diseases. We demonstrate our method by considering the first wave of the COVID-19 epidemic in 2020. Methods: We introduce the concept of the attenuation rate into the varying coefficient SEIR model to measure the effect of different cities on epidemic control, and make inferences through the integrated adjusted Kalman filter algorithm. Results: We applied the varying coefficient SEIR model to 136 cities in China where the total number of confirmed cases exceeded 20 after the implementation of control measures and analyzed the relationship between the estimated attenuation rate and local factors. Subsequent analysis and inference results show that the attenuation rate is significantly related to the local annual GDP and the longitude and latitude of a city or a region. We also apply the varying coefficient SEIR model to other regions outside China. We find that the fitting curve of the average daily number of new confirmed cases simulated by the variable coefficient SEIR model is consistent with the real data. Discussion: The results show that the cities with better economic development are able to control the epidemic more effectively to a certain extent. On the other hand, geographical location also affected the effectiveness of regional epidemic control. In addition, through the results of attenuation rate analysis, we conclude that China and South Korea have achieved good results in controlling the epidemic in 2020.

Overview of the US National HIV Strategy and Ending the HIV Epidemic Initiative.

The US National HIV/AIDS Strategy (NHAS) is a comprehensive plan that outlines specific goals for Ending the HIV Epidemic in the United States (EHE) by 2025. The strategy also provides specific strategies to prevent new HIV infections and improve health outcomes for people with HIV. The EHE is a companion document which focuses on achieving the goals of the NHAS in specific US jurisdictions where the HIV epidemic is concentrated. This article provides an overview of the NHAS and EHE and provides examples of programs and strategies that can be used to end the HIV epidemic in the United States.

HIV Care Continuum and Preexposure Prophylaxis Program in Federal Bureau of Prisons, United States.

In 2019, the US Department of Health and Human Services launched the Ending the HIV Epidemic in the US initiative (EHE) with the goal of reducing new HIV infections by 90% by 2030. This initiative identifies 4 pillars (diagnose, treat, prevent, and respond) to address the HIV epidemic in the United States. To advance the EHE goals, the Federal Bureau of Prisons (FBOP) has implemented interventions at all points of the HIV care continuum. The FBOP has addressed the EHE pillar of prevention through implementing preexposure prophylaxis, developing a strategy to decrease the risk of new HIV infection, and providing guidance to FBOP healthcare providers. This article describes the implementation of programs to improve the HIV care continuum and end the epidemic of HIV within the FBOP including a review of methodology to implement an HIV preexposure prophylaxis program.

Use of bio-information and communication technology shortens time to peak at a lower height of the epidemic curve: An alternative to flattening for countries with early COVID-19 outbreaks.

INTRODUCTION: The traditional approach to epidemic control has been to slow down the rate of infection while building up healthcare capacity, resulting in a flattened epidemic curve. Advancements in bio-information-communication technology (BICT) have enabled the preemptive isolation of infected cases through efficient testing and contact tracing. This study aimed to conceptualize the BICT-enabled epidemic control (BICTEC) and to document its relationships with epidemic curve shaping and epidemic mitigation performance. METHODS: Daily COVID-19 incidences were collected from outbreak to Aug. 12, 2020, for nine countries reporting the first outbreak on or before Feb. 1, 2020. Key epidemic curve determinants-peak height (PH), time to peak (TTP), and area under the curve (AUC)-were estimated for each country, and their relationships were analyzed to test if epidemic curves peak quickly at a shorter height. CFR (Case Fatality Rate) and CI (Cumulative Incidence) were compared across the countries to identify relationships between epidemic curve shapes and epidemic mitigation performance. RESULTS: China and South Korea had the quickest TTPs (40.70 and 45.37 days since outbreak, respectively) and the shortest PHs (2.95 and 4.65 cases per day, respectively). Sweden, known for its laissez-faire approach, had the longest TTP (120.36) and the highest PH (279.74). Quicker TTPs were correlated with shorter PHs (ρ = 0·896, p = 0·0026) and lower AUCs (0.790, p = 0.0028), indicating that epidemic curves do not follow a flattened trajectory. During the study period, countries with quicker TTPs tended to have lower CIs (ρ = .855, P = .006) and CFRs (ρ = 0.684, P = .061). For example, South Korea, with the second-quickest TTP, reported the second lowest CI and the lowest CFR. CONCLUSIONS: Countries that experienced early COVID-19 outbreaks demonstrated the epidemic curves that quickly peak at a shorter height, indicating a departure from the traditional flattened trajectory. South Korea's BICTEC was found to be at least as effective as most lockdowns in reducing CI and CFR.

Fractional epidemic model of coronavirus disease with vaccination and crowding effects.

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.

Informing policy via dynamic models: Cholera in Haiti.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

How individuals' opinions influence society's resistance to epidemics: an agent-based model approach.

BACKGROUND: Protecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration. METHODS: We propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis. RESULTS: Our results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor. CONCLUSIONS: Our results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

COMOKIT v2: A multi-scale approach to modeling and simulating epidemic control policies.

The COVID-19 crisis demonstrated the importance of using models to understand, predict, and manage epidemics, in particular by assessing in advance the effect of different intervention policies. Numerous models have been proposed to answer a wide range of questions, from the impact of open borders to the effectiveness of neighborhood containment to the role of building ventilation in virus dispersion. However, the vast majority of these models are only suited to a scale of representation, analysis, or experimentation. In this article, we present the latest version of the COMOKIT toolbox, which is based on the integration of 3 models (COMOKIT-micro, COMOKIT-meso, and COMOKIT-macro) enabling these questions to be addressed at different geographical scales of analysis and exploration, from the building scale to the scale of entire countries. An application of these 3 models to various questions concerning public health policies against COVID-19 is presented and discussed.

Modelling the impact of human behavior using a two-layer Watts-Strogatz network for transmission and control of Mpox.

PURPOSE: This study aims to evaluate the effectiveness of mitigation strategies and analyze the impact of human behavior on the transmission of Mpox. The results can provide guidance to public health authorities on comprehensive prevention and control for the new Mpox virus strain in the Democratic Republic of Congo as of December 2023. METHODS: We develop a two-layer Watts-Strogatz network model. The basic reproduction number is calculated using the next-generation matrix approach. Markov chain Monte Carlo (MCMC) optimization algorithm is used to fit Mpox cases in Canada into the network model. Numerical simulations are used to assess the impact of mitigation strategies and human behavior on the final epidemic size. RESULTS: Our results show that the contact transmission rate of low-risk groups and susceptible humans increases when the contact transmission rate of high-risk groups and susceptible humans is controlled as the Mpox epidemic spreads. The contact transmission rate of high-risk groups after May 18, 2022, is approximately 20% lower than that before May 18, 2022. Our findings indicate a positive correlation between the basic reproduction number and the level of heterogeneity in human contacts, with the basic reproduction number estimated at 2.3475 (95% CI: 0.0749-6.9084). Reducing the average number of sexual contacts to two per week effectively reduces the reproduction number to below one. CONCLUSION: We need to pay attention to the re-emergence of the epidemics caused by low-risk groups when an outbreak dominated by high-risk groups is under control. Numerical simulations show that reducing the average number of sexual contacts to two per week is effective in slowing down the rapid spread of the epidemic. Our findings offer guidance for the public health authorities of the Democratic Republic of Congo in developing effective mitigation strategies.