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Since the beginning of the epidemic sex workers have experienced a heightened burden of HIV across settings, despite their higher levels of HIV protective behaviors (UNAIDS, 2009). Unfairly, sex workers have often been framed as 'vectors of disease' and 'core transmitters' rather than workers and human beings with rights in terms of HIV prevention and beyond. By gaining a deeper understanding of the epidemiologic and broader policy and social context within which sex work is set one begins to quickly gain a sense of the complex backdrop for increased risk to HIV among sex workers. This backdrop includes the critical role of stigma, discrimination and violence faced by sex workers, as well as, the importance of community empowerment and mobilization among sex workers to address these regressive forces. The eight country case studies work to highlight the experiences of diverse populations of and contexts for sex work across settings. Given the limited epidemiologic and intervention evaluation data available among male and transgender sex workers, however, our collaborative team (Johns Hopkins University, or JHU, World Bank, United Nations Population Fund (UNFPA), and Global Network of Sex Work Projects, or NSWP) determined that the systematic review, mathematical modeling and cost-effective analyses would focus on female sex workers. Throughout the process of this analysis as a whole, the participation of sex worker perspectives and sex worker organizations such as NSWP and their regional partners has been critical by providing documents and resources, input and consultation throughout the analytical process.

This publication addresses research questions related to an increase in the levels of access and utilization for four key interventions that have the potential to significantly reduce HIV infections among People Who Inject Drugs (PWID) and their sexual and injecting partners, and hence morbidity and mortality in low and middle-income countries (LMIC). These interventions are drawn from nine consensus interventions that comprise a 'comprehensive package' for PWID. The four interventions are: Needle and Syringe Programs (NSP), Medically Assisted Therapy (MAT), HIV Counseling and Testing (HCT), and Antiretroviral Therapy (ART). The book summarizes the results from several recent reviews of studies related to the effectiveness of the four key interventions in reducing risky behaviors in the context of transmitting or acquiring HIV infection. Overall, the four key interventions have strong effects on the risk of HIV infection among PWID via different pathways, and this determination is included in the documents proposing the comprehensive package of interventions. In order to attain the greatest effect from these interventions, structural issues must be addressed, especially the removal of punitive policies targeting PWID in many countries. The scientific evidence presented here, the public health rationale, and the human rights imperatives are all in accord: we can and must do better for PWID. The available tools are evidence-based, right affirming, and cost effective. What are required now are political will and a global consensus that this critical component of global HIV can no longer be ignored and under-resourced.

A critical analysis of the World Bank's strategy to combat HIV/AIDS in Africa. The World Bank's Commitment to HIV/AIDS in Africa examines the development challenges posed by HIV/AIDS in Sub-Saharan Africa and outlines a comprehensive agenda for action. This report reaffirms the World Bank's dedication to supporting African countries in achieving their Universal Access targets, integrating AIDS into national development plans, and strengthening national systems. This agenda provides a roadmap for policymakers, development practitioners, and researchers seeking to understand and address the complexities of the HIV/AIDS epidemic in Africa. Discover how the World Bank is working with partners to: * Provide sustainable funding for HIV/AIDS programs * Promote evidence-based strategies for prevention and treatment * Strengthen governance and accountability * Build capacity in key sectors This report is essential reading for anyone committed to global health and development in Africa.

'The Africa Multi-Country AIDS Program 2000-2006' shows that the funding made available through the World Bank's Multi-Country AIDS Program (MAP) has dramatically increased access to HIV prevention, care, and treatment across Africa.

In this paper, a neural network model is constructed to predict the number of new infections, and on the basis of this model, the demand for medical resources of new infections is further predicted. Firstly, the traditional Back Propagation neural network is optimized by genetic algorithm, which solves the problem that Back Propagation neural network is easy to fall into the local optimal solution in the prediction. Taking the number of severe patients, the number of cured patients, the number of deaths, and the number of suspected patients on the first day of the new crown period as the input variables, and the number of newly diagnosed patients on the second day as the output variable, a new Back Propagation neural network is constructed Novel coronavirus pneumonia model is constructed. Secondly, according to the official documents on the new crown pneumonia research and the guide for the use of medical resources, the linear function of the infection proportion and the normal distribution function of each symptom duration are constructed. Finally, combined with the above two contents, a complete process is designed to achieve the medical treatment for the newly infected patients. Prediction of the demand for medical resources.

Our objective was to estimate the effects of the first occurrence of pathogen-specific clinical mastitis (CM) on milk yield in 3071 dairy cows in 2 New York State farms. The pathogens studied were Streptococcus spp.,Staphylococcus aureus, Staphylococcus spp., Escherichia coli, Klebsiella spp., Arcanobacterium pyogenes, other pathogens grouped together, and “no pathogen isolated.” Data were collected from October 1999 to July 2001. Milk samples were collected from cows showing signs of CM and were sent to the Quality Milk Production Services laboratory at Cornell University for microbiological culture. The SAS statistical procedure PROC MIXED, with an autoregressive covariance structure, was used to quantify the effect of CM and several other control variables (herd, calving season, parity, month of lactation, J-5 vaccination status, and other diseases) on weekly milk yield. Separate models were fitted for primipara and multipara, because of the different shapes of their lactation curves. To observe effects of mastitis, milk weights were divided into several periods both pre- and postdiagnosis, according to when they were measured in relation to disease occurrence. Another category contained cows without the type of CM being modeled. Because all pathogens were modeled simultaneously, a control cow was one without CM. Among primipara, Staph. aureus, E. coli, Klebsiella spp., and “no pathogen isolated” caused the greatest losses. Milk yield generally began to drop 1 or 2 wk before diagnosis; the greatest loss occurred immediately following diagnosis. Mastitic cows often never recovered their potential yield. Among older cows, Streptococcus spp., Staph. aureus, A. pyogenes, E. coli, and Klebsiella spp. caused the most significant losses. Many multipara that developed CM were actually higher producers before diagnosis than their nonmastitic herd-mates. As in primipara, milk yield in multipara often began to decline shortly before diagnosis; the greatest loss occurred immediately following diagnosis. Milk loss persisted until at least 70 d after diagnosis for Streptococcus spp., Klebsiella spp., and A. pyogenes. The tendency for higher producing cows to contract CM may mask its impact on cow health and production. These findings provide dairy producers with more information on which pathogen-specific CM cases should receive treatment and how to manage these cows, thereby reducing CM impact on cow well being and profitability.

Despite a fair amount of progress on understanding human immunodeficiency virus (HIV) epidemiology globally, the Middle East and North Africa (MENA) region is the only region where knowledge of the epidemic continues to be very limited, and subject to much controversy. It has been more than 25 years since the discovery of HIV, but no scientific study has provided a comprehensive data-driven synthesis of HIV/AIDS (acquired immunodeficiency syndrome) infectious spread in this region. The current report provides the first comprehensive scientific assessment and data-driven epidemiological synthesis of HIV spread in MENA since the beginning of the epidemic. It is based on a literature review and analysis of thousands of widely unrecognized publications, reports, and data sources extracted from scientific literature or collected from sources at the local, national, and regional levels. The recommendations provided here focus on key strategies related to the scope of this report and its emphasis on understanding HIV epidemiology in MENA as a whole. The recommendations are based on identifying the status of the HIV epidemic in MENA, through this synthesis, as a low HIV prevalence setting with rising concentrated epidemics among priority populations. General directions for prevention interventions as warranted by the outcome of this synthesis are also discussed briefly, but are not delineated because they are beyond the scope of this report. This report was not intended to provide intervention recommendations for each MENA country.

The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13 to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with presymptomatic ones. If asymptomatic infections transmit at similar rates as symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, presymptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection subpopulations have reproductive numbers lower than three. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) should be reconsidered.

Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.

This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.