Number Of New Infections Research Articles

New prospects in the treatment of HIV infection: results of clinical trials of drugs

HIV infection is one of the most dangerous and widespread infectious diseases in the world. According to the latest update from the Joint United Nations Program on HIV/AIDS, as of 2021, the number of people living with HIV infection was 38.4 million, and the number of new infections reached 1.5 million. [1] In Russia, according to Rosstat, the incidence of HIV infection increased by more than 20 % in January 2023, compared to January 2022. This means that the problem of HIV infection is still relevant, and additional efforts are required to combat this disease. Due to the development of medicine and pharmacology, new prospects have now emerged in the treatment of HIV infections. This paper examines the results of clinical trials of drugs that open up new opportunities in the fight against HIV infection. The authors set out to examine the latest advances in the treatment of HIV infection based on the results of clinical drug trials over the past 5 years.

Predicting COVID-19 pandemic waves including vaccination data with deep learning.

During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear. We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines. We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions. Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.

Incorporating testing volume into estimation of effective reproduction number dynamics.

Branching process inspired models are widely used to estimate the effective reproduction number-a useful summary statistic describing an infectious disease outbreak-using counts of new cases. Case data is a real-time indicator of changes in the reproduction number, but is challenging to work with because cases fluctuate due to factors unrelated to the number of new infections. We develop a new model that incorporates the number of diagnostic tests as a surveillance model covariate. Using simulated data and data from the SARS-CoV-2 pandemic in California, we demonstrate that incorporating tests leads to improved performance over the state of the art.

Artificial intelligence with machine learning and the enigmatic discovery of HIV cure

<p>HIV’s complexity has long presented a problem in the quest for a cure. However, the development of machine learning (ML) and artificial intelligence (AI) technology has opened up promising new directions for HIV cure research. This study investigates the impact of AI and ML on the discovery and development of an HIV cure to shed light on their potential role in hastening advancements in this field. The study employs quantitative methodology, and the execution of the methods is achieved by using AI and ML techniques for analysis processes and presenting the study’s findings by utilizing the Kaggle.com HIV dataset, where pertinent features are found for the machine learning algorithm. Additionally, advanced statistical techniques, such as Structural Equation Modeling (SEM), to investigate the causal link between AI and ML utilization and the development of a cure for HIV is utilized. The robustness of the analysis is enhanced by using Penalized Ridge and Lasso Regressions. The study utilizes logistic regression as the machine learning model, and the mean square error is used to evaluate performance. Control variables, including the year, borough, the Uniform Hospitalization Fund (UHF) code, gender, age, race, concurrent diagnoses, percentage linked to care within three months, the prevalence of (People living with HIV/AIDS) PLWDHI, and percentage of viral suppression, deaths, death rate, and HIV-related death rate are all taken into consideration, to ensure a thorough analysis. This study finds that AI and ML are the future of the healthcare sector, providing promising opportunities for finding a cure for HIV and enhancing patient care. Further, the study confirms that new targets for HIV cure research can be found by utilizing AI and ML, and treatment outcomes and individualized treatment plans can also be developed. AI and ML can also enhance clinical trials, boost HIV prevention efforts, and lower the number of new infections.</p>

Global stability analysis of transmission dynamics in a compartmental model of coronavirus disease with environmental reservoirs

This work proposes and analyzes a non-linear deterministic model which describes the outbreak of Coronavirus disease with fomites (environmental reservoirs) propagating the spread of the infection in human population. Suitable Lyapunov functions are constructed to demonstrate the Global Asymptotic Stability (GAS) of both the Endemic Equilibrium (EE) and Disease Free Equilibrium (DFE) of the system. The EE was also found to be epidemiologically relevant when the basic reproduction number of new infections is greater than unity.

Exploring the dynamic relationship between mobility and the spread of COVID-19, and the role of vaccines

ABSTRACT The novel coronavirus disease 2019 (COVID-19) outbreak has caused tremendous turmoil worldwide and is yet to be contained. Therefore, mitigating the number of people infected by COVID-19 remains a fundamental policy goal for several countries. This study aims to analyse the dynamic relationship between mobility and the rate of change in the number of new infections (NNIs) in Japan. Another goal is to evaluate the effects of various policies, such as mobility control and vaccination, as well as the impact of climate factors on the NNIs. The analysis reveals a strong positive relationship between the growth rate of the NNIs and mobility. Our results also indicate that the government-imposed measure of regulating mobility shows a weaker effect on preventing the spread of infection, as the emergency declaration is repeatedly invoked. By contrast, the recent increase in the infection rate seems to reduce the spread of infection by inducing voluntary restraint, and this phenomenon has become even stronger in the recent period. Regarding the effect of vaccination, the results demonstrate little effect on attenuating the spread of infection. However, it has significantly weakened the mobility – spread relationship, suggesting that it may help in the implementation of economic revitalization policies.

Accelerating Bayesian inference for stochastic epidemic models using incidence data

We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed length. We eschew the most natural Markov jump process representation for reasons of computational efficiency, and focus on a stochastic differential equation representation. This is further approximated to give a tractable Gaussian process, that is, the linear noise approximation (LNA). Unless the observation model linking the LNA to data is both linear and Gaussian, the observed data likelihood remains intractable. It is in this setting that we consider two approaches for marginalising over the latent process: a correlated pseudo-marginal method and analytic marginalisation via a Gaussian approximation of the observation model. We compare and contrast these approaches using synthetic data before applying the best performing method to real data consisting of removal incidence of oak processionary moth nests in Richmond Park, London. Our approach further allows comparison between various competing compartment models.

Hepatocellular Carcinoma Prevention in the Era of Hepatitis C Elimination.

The hepatitis C virus (HCV), a single-stranded RNA virus belonging to the Flaviviridae family, is a major cause of hepatocellular carcinoma (HCC) worldwide. Tumors caused by HCC have an increased mortality rate globally, which is more accentuated in Western countries. The carcinogenic potential of this virus is mediated through a wide range of mechanisms, spanning from the induction of chronic inflammation to oxidative stress and deregulation of cellular pathways by viral proteins. As the number of new infections continues unabated, HCC-related mortality should be prioritized through early detection, continued prevention of HCV transmission, and treatment of HCV with safe and efficacious direct antiviral agents (DAAs). People who inject drugs (PWID) are a significant reservoir of new HCV infections globally, and in order to eliminate hepatitis C as a global health threat, as set out by the World Health Organization, an integrated approach based on the optimization of care delivery and increased access to harm reduction and treatment for PWID is needed. Thanks to the development of safe and effective antiviral agents, eradication of the infection is now possible in almost all treated patients, leading to a significant reduction but not the elimination of the risk for HCC in cured patients. This is particularly relevant among aged populations who have cofactors of morbidity known to accelerate HCC progression, such as diabetes, obesity, and excessive alcohol consumption. Given the restless accumulation of individuals with cured HCV infection, the implementation of risk-stratified surveillance programs becomes impellent from a cost-effectiveness perspective, whereas the availability of a performant biomarker to predict HCC in cured patients remains an unmet clinical need.

Hold the phone! A cross-national analysis of Women's education, mobile phones, and HIV infections in low- and middle-income countries, 1990–2018

Despite remarkable progress in the fight against HIV, the number of new infections remains unacceptably high, epidemics continue to grow in certain communities, and therefore AIDS continues to be one of the deadliest pandemics of our times. This study analyzes the rate of new HIV infections over almost 30 years in low- and middle-income countries. Previous research identifies two critical ways to address HIV prevention in developing countries: educating women and using mobile phones to improve health literacy and access to virtual healthcare. Our study bridges these literatures by evaluating how women's education and mobile technology work together to support the goals of HIV prevention in low- and middle-income countries. Using two-way panel fixed effects regression models of HIV incidence across 76 developing countries, we find that both increasing access to women's education and increasing access to mobile phones are associated with fewer HIV infections over time. Furthermore, we discover that women's education moderates the relationship between mobile phones and HIV. More specifically, mobile phones seem to be more beneficial for HIV prevention in cases where rates of formal schooling are low. However, at higher levels of women's education, the impact of mobile phones on new HIV infections is substantially reduced. Our findings have important policy implications for Information and Communications for Development (ICT4D) programs.

Improvement of the software for modeling the dynamics of epidemics and developing a user-friendly interface.

The challenges humanity is facing due to the Covid-19 pandemic require timely and accurate forecasting of the dynamics of various epidemics to minimize the negative consequences for public health and the economy. One can use a variety of well-known and new mathematical models, taking into account a huge number of factors. However, complex models contain a large number of unknown parameters, the values of which must be determined using a limited number of observations, e.g., the daily datasets for the accumulated number of cases. Successful experience in modeling the COVID-19 pandemic has shown that it is possible to apply the simplest SIR model, which contains 4 unknown parameters. Application of the original algorithm of the model parameter identification for the first waves of the COVID-19 pandemic in China, South Korea, Austria, Italy, Germany, France, Spain has shown its high accuracy in predicting their duration and number of diseases. To simulate different epidemic waves and take into account the incompleteness of statistical data, the generalized SIR model and algorithms for determining the values of its parameters were proposed. The interference of the previous waves, changes in testing levels, quarantine or social behavior require constant monitoring of the epidemic dynamics and performing SIR simulations as often as possible with the use of a user-friendly interface. Such tool will allow predicting the dynamics of any epidemic using the data on the number of diseases over a limited period (e.g., 14 days). It will be possible to predict the daily number of new cases for the country as a whole or for its separate region, to estimate the number of carriers of the infection and the probability of facing such a carrier, as well as to estimate the number of deaths. Results of three SIR simulations of the COVID-19 epidemic wave in Japan in the summer of 2022 are presented and discussed. The predicted accumulated and daily numbers of cases agree with the results of observations, especially for the simulation based on the datasets corresponding to the period from July 3 to July 16, 2022. A user-friendly interface also has to ensure an opportunity to compare the epidemic dynamics in different countries/regions and in different years in order to estimate the impact of vaccination levels, quarantine restrictions, social behavior, etc. on the numbers of new infections, death, and mortality rates. As example, the comparison of the COVID-19 pandemic dynamics in Japan in the summer of 2020, 2021 and 2022 is presented. The high level of vaccinations achieved in the summer of 2022 did not save Japan from a powerful pandemic wave. The daily numbers of cases were about ten times higher than in the corresponding period of 2021. Nevertheless, the death per case ratio in 2022 was much lower than in 2020.

The Optimal Strategies to Be Adopted in Controlling the Co-Circulation of COVID-19, Dengue and HIV: Insight from a Mathematical Model

The pandemic caused by COVID-19 led to serious disruptions in the preventive efforts against other infectious diseases. In this work, a robust mathematical co-dynamical model of COVID-19, dengue, and HIV is designed. Rigorous analyses for investigating the dynamical properties of the designed model are implemented. Under a special case, the stability of the model’s equilibria is demonstrated using well-known candidates for the Lyapunov function. To reduce the co-circulation of the three diseases, optimal interventions were defined for the model and the control system was analyzed. Simulations of the model showed different control scenarios, which could have a positive or detrimental impact on reducing the co-circulation of the diseases. Highlights of the simulations included: (i) Upon implementation of the first intervention strategy (control against COVID-19 and dengue), it was observed that a significant number of single and dual infection cases were averted. (ii) Under the COVID-19 and HIV prevention strategy, a remarkable number of new single and dual infection cases were also prevented. (iii) Under the COVID-19 and co-infection prevention strategy, a significant number of new infections were averted. (iv) Comparing all the intervention measures considered in this study, it is possible to state that the strategy that combined COVID-19/HIV averted the highest number of new infections. Thus, the COVID-19/HIV strategy would be the ideal and optimal strategy to adopt in controlling the co-spread of COVID-19, dengue, and HIV.

A Predictive Vaccination Strategy Based on a Swarm Intelligence Technique for the Case of Saudi Arabia: A Control Engineering Approach

The COVID-19 pandemic caused high damage to health, social, and economic systems globally. Saudi Arabia has conducted a relatively successful experience in mitigating the virus. Saudi authorities have started a vaccination campaign by the end of 2020 with more than 60 million doses being administered to citizens and residents by February 2, 2022. The objective of this study is to propose an optimal vaccination strategy in short and medium terms in order to help the local health authorities to first assess the vaccination campaign and to propose a predictive vaccination plan for eradicating the disease. For this purpose, a control engineering approach was used where the disease dynamics was identified and an optimal control law using the daily number of vaccines as input and the daily number of new infections as output was proposed and evaluated. The vaccination process was modeled as a discrete-time transfer function. The parameters of the transfer function were identified based on the Particle Swarm Optimization (PSO) algorithm while considering the Routh-Hurwitz stability criterion for analyzing the system stability. The final step of this study was dedicated to synthesize three controller variants (P, PI, and PID) for the case study of Saudi Arabia. The obtained results for the modeling and the controllers’ design were found to be promising. The results were found to be generic and can therefore be used to control other diseases or any other occurrence of COVID-19 or similar viruses.

Unifying incidence and prevalence under a time-varying general branching process

Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump–Mode–Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We show that the equations for incidence and prevalence are consistent with the so-called back-calculation relationship. We analyse two particular cases of these integral equations, one that arises from a Bellman–Harris process and one that arises from an inhomogeneous Poisson process model of transmission. We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling. We present a numerical discretisation scheme to solve these equations, and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox.

A coupled experimental and statistical approach for an assessment of SARS-CoV-2 infection risk at indoor event locations

Indoor event locations are particularly affected by the SARS-CoV-2 pandemic. At large venues, only incomplete risk assessments exist, whereby no suitable measures can be derived. In this study, a physical and data-driven statistical model for a comprehensive infection risk assessment has been developed. At venues displacement ventilation concepts are often implemented. Here simplified theoretical assumptions fail for the prediction of relevant airflows for airborne transmission processes. Thus, with locally resolving trace gas measurements infection risks are computed more detailed. Coupled with epidemiological data such as incidences, vaccination rates, test sensitivities, and audience characteristics such as masks and age distribution, predictions of new infections (mean), situational R-values (mean), and individual risks on- and off-seat can be achieved for the first time. Using the Stuttgart State Opera as an example, the functioning of the model and its plausibility are tested and a sensitivity analysis is performed with regard to masks and tests. Besides a reference scenario on 2022-11-29, a maximum safety scenario with an obligation of FFP2 masks and rapid antigen tests as well as a minimum safety scenario without masks and tests are investigated. For these scenarios the new infections (mean) are 10.6, 0.25 and 13.0, respectively. The situational R-values (mean) – number of new infections caused by a single infectious person in a certain situation – are 2.75, 0.32 and 3.39, respectively. Besides these results a clustered consideration divided by age, masks and whether infections occur on-seat or off-seat are presented. In conclusion this provides an instrument that can enable policymakers and operators to take appropriate measures to control pandemics despite ongoing mass gathering events.

Forecasting virus outbreaks with social media data via neural ordinary differential equations

During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.

HIV in Paraná: clinical-epidemiological overview, distribution, and incidence by macroregional and regional health

Objective: to trace the clinical-epidemiological panorama, distribution, and incidence of HIV cases by macroregional and regional health of Paraná. Material and Methods: descriptive epidemiological study, carried out with data obtained by the Notifiable Diseases Information System regarding HIV/AIDS cases registered in the state of Paraná between 2018 and 2019. Data were analyzed by descriptive statistics. The study was authorized by the State Department of Health of Paraná and had a favorable opinion from the Research Ethics Committee. Results: 6,023 cases of HIV/AIDS were reported, with a prevalence of male, white, mean age of 35 years old, complete high school and college, and predominance of sexual transmission. The epidemiological characteristics corroborate the findings in the literature and are similar to the national profile. There were cases of mother-to-child vertical transmission during pregnancy and via transfusion, suggesting possibilities for effective action. East macroregional had the highest number of cases and incidence coefficient. Conclusion: the HIV/AIDS epidemic represents a multidimensional challenge for the health sector, considering its magnitude, severity, costs, and dissemination. Despite the improvement in infection prevention and health promotion strategies, the numbers of new infections per year are relevant and stable. The regionalization of information allows the individualization of epidemiological data, enabling the adoption of more effective strategies that correspond to local needs. Therefore, this study brings great contributions to local managers to direct strategies for assistance, surveillance, and control of this condition of great importance to public health.

Generic drugs of direct antiviral action for the treatment of hepatitis C: features of bioequivalence studies

Introduction . In the last decade, approaches to the treatment of chronic viral hepatitis C (HCV) have undergone significant changes. The new WHO strategy aims to eliminate HCV by 2030 by reducing the number of new infections and deaths by simplifying hepatitis C therapy. The development of drugs for the treatment of patients with chronic HCV has a number of features, which requires compliance with special recommendations for conducting clinical bioequivalence studies. Aim . The study was to analyze the guidelines and protocols of bioequivalence studies of drugs for HCV therapy. Materials and methods . An information and analytical method was used to evaluate recommendations for conducting bioequivalence studies of HCV drugs according to the US Food and Drug Administration, the European Medicines Agency, and WHO. Separately, an analysis of permitted clinical trials was carried out according to the data of the GRLS of the Ministry of Health of the Russian Federation. Results and discussions . According to the results of the study, it was noted that for the WHO guidelines on the bioequivalence of drugs for HCV therapy, it is common to develop common principles for planning and conducting research. Special attention is paid to the preferred design of the study. It is also necessary to analyze data on the variability of the pharmacokinetic parameters of the substance under study, which are used to calculate the required number of volunteers to be included in the study. Conclusions . Direct antiviral drugs under development for HCV therapy, for which clinical bioequivalence studies are currently being conducted in the Russian Federation, cover a large list of INN. The entry of new reproduced drugs into the pharmaceutical market will improve patients’ access to effective treatment of hepatitis C. In order to conduct a high-quality bioequivalence study, it is necessary to thoroughly familiarize yourself with nosology manuals, critical assessment and analysis of information, which will allow you to choose the appropriate design of the study and correctly plan its conduct.

Approach to HIV Prevention in Cuba for Men Who Have Sex with Other Men

This paper describes the main interventions for the prevention of human immunodeficiency virus infection, aimed at men who have sex with men, who constitute a risk group for acquiring the disease. For its preparation, the scientific literature published in national and international sources and documents considered relevant to the subject was selected. The analysis and recommendations focused on the health sector and the group of men who have sexual relations with other men. The approaches to the human immunodeficiency virus in Cuba are considered among the high-impact prevention programs, prepared both by civil society, through subsidies from the World Fund to Fight AIDS, Tuberculosis and Malaria, as well as by technicians from the National Program for Prevention and Control of STIs-HIV/AIDS. They include personalized service packages, which contain structural, biomedical and behavioral elements for key population groups in the social contexts where they are most needed. As is well known, the risk for each individual depends on their practices and not on the group to which they belong. These offer a common base for the development and promotion of effective programs for prevention, which allow organizing efforts directed at key populations, especially men who have sex with other men, and in this way reduce the number of new infections each year.

Emotionalität in der COVID-19-Krisenkommunikation von Behörden und unabhängigen Expert*innen auf Twitter

ZusammenfassungHintergrundZu Beginn der COVID‑19-Pandemie herrschte in Deutschland große Unsicherheit in der Bevölkerung und bei den für die Krisenkommunikation Verantwortlichen. Ein wesentlicher Teil der Kommunikation von Expert*innen und zuständigen Behörden fand in den sozialen Medien statt, insbesondere auf der Plattform Twitter. Die dort mit der Krisenkommunikation transportierten positiven, negativen und neutralen Sentiments (Emotionen) sind für Deutschland bisher nicht vergleichend untersucht worden.Ziel der ArbeitDie Sentiments in Twitter-Meldungen von verschiedenen (Gesundheits‑)Behörden und unabhängigen Expert*innen zu COVID‑19 sollen für das erste Pandemiejahr (01.01.2020–15.01.2021) ausgewertet werden, um eine Wissensgrundlage für die Verbesserung zukünftiger Krisenkommunikation zu schaffen.Material und MethodenVon n = 39 Twitter-Akteur*innen (21 Behörden und 18 Expert*innen) flossen n = 8251 Tweets in die Auswertung ein. Diese erfolgte mit dem sog. Lexikonansatz, einer Methode der Social-Media-Analyse. Es wurden deskriptive Statistiken berechnet u. a. zur Bestimmung der durchschnittlichen Polarität der Sentiments und der Häufigkeiten positiv und negativ besetzter Wörter in 3 Phasen der Pandemie.Ergebnisse und DiskussionDie Entwicklung der Emotionalität in COVID‑19-Tweets und der Anzahl von Neuinfektionen in Deutschland verlaufen in etwa parallel. Die Analyse zeigt, dass die Polarität der Sentiments bei beiden Akteursgruppen im Durchschnitt negativ ist. Expert*innen twittern im Untersuchungszeitraum deutlich negativer über COVID‑19 als Behörden. Behörden kommunizieren in der zweiten Phase nahe der Neutralitätslinie, also weder ausgeprägt positiv noch negativ.

Trends of CNS Cryptococcosis during Pre- and Post-HIV era: A 38 years' retrospective cohort analysis from south India.

Central nervous system cryptococcosis (CNSc) is an AIDS defining opportunistic infection. This retrospective study aimed to analyze the changing epidemiology of CNSc cases from the period of pre- to post-emergence of HIV epidemic in south India. Confirmed cases of CNSc from 1978 to 2015 were analyzed for demographic and clinical details with special reference to the cases diagnosed in south India during the period 1952-1977. Geographical distribution, affected age groups, clinical aspects, and comorbidities in relation to immune status were analysed RESULTS: The highest number of CNSc cases (n=125) were recorded in 2006, with 89.6% HIV positivity. The highest HIV-positivity (93.6%) was documented in the years 2002 and 2009. CNSc cases have majorly changed after the introduction and spread of HIV in terms of predisposing factors, comorbidities, severity, affected age groups and treatment. Notably, an overall rise was observed in non-HIV associated CNSc cases from 1997 (8.1%) to 2015 (16.9%). The peak of CNSc had already reached in south India during 2005-2006. However, the number of new infections has slowly decreased in last ten years. Progressive awareness and, early diagnosis of HIV and cryptococcosis, adequate availability of HAART and potential antifungal therapy has played crucial roles in changing epidemiology of the CNSc and its associated mortality.