Seven Epidemic Waves of COVID-19 in a Hospital in Madrid: Analysis of Severity and Associated Factors.

(1) Background: COVID-19 has evolved during seven epidemic waves in Spain. Our objective was to describe changes in mortality and severity in our hospitalized patients. (2) Method: This study employed a descriptive, retrospective approach for COVID-19 patients admitted to the Hospital de Fuenlabrada (Madrid, Spain) until 31 December 2022. (3) Results: A total of 5510 admissions for COVID-19 were recorded. The first wave accounted for 1823 (33%) admissions and exhibited the highest proportion of severe patients: 65% with bilateral pneumonia and 83% with oxygen saturation under 94% during admission and elevated levels of CRP, IL-6, and D-dimer. In contrast, the seventh wave had the highest median age (79 years) and comorbidity (Charlson: 2.7), while only 3% of patients had bilateral pneumonia and 3% required intubation. The overall mortality rate was 10.3%. The first wave represented 39% of the total. The variables related to mortality were age (OR: 1.08, 1.07-1.09), cancer (OR: 1.99, 1.53-2.60), dementia (OR: 1.82, 1.20-2.75), the Charlson index (1.38, 1.31-1.47), the need for high-flow oxygen (OR: 6.10, 4.94-7.52), mechanical ventilation (OR: 11.554, 6.996-19.080), and CRP (OR: 1.04, 1.03-1.06). (4) Conclusions: The variables associated with mortality included age, comorbidity, respiratory failure, and inflammation. Differences in the baseline characteristics of admitted patients explained the differences in mortality in each wave. Differences observed between patients admitted in the latest wave and the earlier ones suggest that COVID-19 has evolved into a distinct disease, requiring a distinct approach.

Getting Jab or Regular Test: Observations from an Impulsive Epidemic COVID-19 Model.

Several safe and effective vaccines are available to prevent individuals from experiencing severe illness or death as a result of COVID-19. Widespread vaccination is widely regarded as a critical tool in the fight against the disease. However, some individuals may choose not to vaccinate due to vaccine hesitancy or other medical conditions. In some sectors, regular compulsory testing is required for such unvaccinated individuals. Interestingly, different sectors require testing at various frequencies, such as weekly or biweekly. As a result, it is essential to determine the optimal testing frequency and identify underlying factors. This study proposes a population-based model that can accommodate different personal decision choices, such as getting vaccinated or undergoing regular tests, as well as vaccine efficacies and uncertainties in epidemic transmission. The model, formulated as impulsive differential equations, uses time instants to represent the reporting date for the test result of an unvaccinated individual. By employing well-accepted indices to measure transmission risk, including the basic reproduction number, the peak time, the final size, and the number of severe infections, the study shows that an optimal testing frequency is highly sensitive to parameters involved in the transmission process, such as vaccine efficacy, disease transmission rate, test accuracy, and existing vaccination coverage. The testing frequency should be appropriately designed with the consideration of all these factors, as well as the control objectives measured by epidemiological quantities of great concern.

A perturbative approach to the analysis of many-compartment models characterized by the presence of waning immunity.

The waning of immunity after recovery or vaccination is a major factor accounting for the severity and prolonged duration of an array of epidemics, ranging from COVID-19 to diphtheria and pertussis. To study the effectiveness of different immunity level-based vaccination schemes in mitigating the impact of waning immunity, we construct epidemiological models that mimic the latter's effect. The total susceptible population is divided into an arbitrarily large number of discrete compartments with varying levels of disease immunity. We then vaccinate various compartments within this framework, comparing the value of [Formula: see text] and the equilibria locations for our systems to determine an optimal immunization scheme under natural constraints. Relying on perturbative analysis, we establish a number of results concerning the location, existence, and uniqueness of the system's endemic equilibria, as well as results on disease-free equilibria. We use numerical techniques to supplement our analytical ones, applying our model to waning immunity dynamics in pertussis, among other diseases. Our analytical results are applicable to a wide range of systems composed of arbitrarily many ODEs.

Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season.

Seasonal peaks in infectious disease incidence put pressures on health services. Therefore, early warning of the timing and magnitude of peak activity during seasonal epidemics can provide information for public health practitioners to take appropriate action. Whilst many infectious diseases have predictable seasonality, newly emerging diseases and the impact of public health interventions can result in unprecedented seasonal activity. We propose a Machine Learning process for generating short-term forecasts, where models are selected based on their ability to correctly forecast peaks in activity, and can be useful during atypical seasons. We have validated our forecasts using typical and atypical seasonal activity, using respiratory syncytial virus (RSV) activity during 2019-2021 as an example. During the winter of 2020/21 the usual winter peak in RSV activity in England did not occur but was 'deferred' until the Spring of 2021. We compare a range of Machine Learning regression models, with alternate models including different independent variables, e.g. with or without seasonality or trend variables. We show that the best-fitting model which minimises daily forecast errors is not the best model for forecasting peaks when the selection criterion is based on peak timing and magnitude. Furthermore, we show that best-fitting models for typical seasons contain different variables to those for atypical seasons. Specifically, including seasonality in models improves performance during typical seasons but worsens it for the atypical seasons.

Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection.

Human monkeypox is a very unusual virus that can devastate society. Early identification and diagnosis are essential to treat and manage an illness effectively. Human monkeypox disease detection using deep learning models has attracted increasing attention recently. The virus that causes monkeypox may be passed to people, making it a zoonotic illness. The latest monkeypox epidemic has hit more than 40 nations. Computer-assisted approaches using Deep Learning techniques for automatically identifying skin lesions have shown to be a viable alternative in light of the fast proliferation and ever-growing problems of supplying PCR (Polymerase Chain Reaction) Testing in places with limited availability. In this research, we introduce a deep learning model for detecting human monkeypoxes that is accurate and resilient by tuning its hyper-parameters. We employed a mixture of convolutional neural networks and transfer learning strategies to extract characteristics from medical photos and properly identify them. We also used hyperparameter optimization strategies to fine-tune the Model and get the best possible results. This paper proposes a Yolov5 model-based method for differentiating between chickenpox and Monkeypox lesions on skin pictures. The Roboflow skin lesion picture dataset was subjected to three different hyperparameter tuning strategies: the SDG optimizer, the Bayesian optimizer, and Learning without Forgetting. The proposed Model had the highest classification accuracy (98.18%) when applied to photos of monkeypox skin lesions. Our findings show that the suggested Model surpasses the current best-in-class models and may be used in clinical settings for actual Human Monkeypox disease detection and diagnosis.

Damage control resuscitation in adult trauma patients: What you need to know.

ABSTRACT: Death after injury is a worldwide epidemic. Hemorrhage as a cause of death represents the leading potentially preventable condition. Based on hard-won experience from the recent wars, and two decades of military and civilian research, damage-control resuscitation (DCR) is now widely used. This article will briefly describe the history of blood transfusion, outline "why we do DCR," and then discuss "how we do DCR." Modern DCR occurs both prehospital and in the hospital and has several main tenants. Currently, DCR focuses on the liberal use of temporary hemorrhage-control adjuncts, early use of whole blood or balanced blood product-based transfusions, mitigation of crystalloid use, hypotensive resuscitation to promote hemostasis and decrease coagulopathy, and correction of ongoing metabolic derangements, followed by rapid definitive hemorrhage control. These concepts have evolved from a series of lessons learned over time from both civilian and military trauma casualties, and DCR is now the standard of care in trauma resuscitation.

The spectral underpinnings of pathogen spread on animal networks.

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies.

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Age-differentiated incentives for adaptive behavior during epidemics produce oscillatory and chaotic dynamics.

Heterogeneity in contact patterns, mortality rates, and transmissibility among and between different age classes can have significant effects on epidemic outcomes. Adaptive behavior in response to the spread of an infectious pathogen may give rise to complex epidemiological dynamics. Here we model an infectious disease in which adaptive behavior incentives, and mortality rates, can vary between two and three age classes. The model indicates that age-dependent variability in infection aversion can produce more complex epidemic dynamics at lower levels of pathogen transmissibility and that those at less risk of infection can still drive complexity in the dynamics of those at higher risk of infection. Policymakers should consider the interdependence of such heterogeneous groups when making decisions.

The lead time and geographical variations of Baidu Search Index in the early warning of COVID-19.

Internet search data was a useful tool in the pre-warning of COVID-19. However, the lead time and indicators may change over time and space with the new variants appear and massive nucleic acid testing. Since Omicron appeared in late 2021, we collected the daily number of cases and Baidu Search Index (BSI) of seven search terms from 1 January to 30 April, 2022 in 12 provinces/prefectures to explore the variation in China. Two search peaks of "COVID-19 epidemic", "Novel Coronavirus" and "COVID-19" can be observed. One in January, which showed 3 days lead time in Henan and Tianjin. Another on early March, which occurred 0-28 days ahead of the local epidemic but the lead time had spatial variation. It was 4 weeks in Shanghai, 2 weeks in Henan and 5-8 days in Jilin Province, Jilin and Changchun Prefecture. But it was only 1-3 days in Tianjin, Quanzhou Prefecture, Fujian Province and 0 day in Shenzhen, Shandong Province, Qingdao and Yanbian Prefecture. The BSI was high correlated (rs:0.70-0.93) to the number of cases with consistent epidemiological change trend. The lead time of BSI had spatial and temporal variation and was close related to the strength of nucleic acid testing. The case detection ability should be strengthened when perceiving BSI increase.

Analysis and prediction of improved SEIR transmission dynamics model: taking the second outbreak of COVID-19 in Italy as an example.

This study aimed to predict the transmission trajectory of the 2019 Corona Virus Disease (COVID-19) and analyze the impact of preventive measures on the spread of the epidemic. Considering that tracking a long-term epidemic trajectory requires explanatory modeling with more complexities than short-term predictions, an improved Susceptible-Exposed-Infected-Removed (SEIR) transmission dynamic model is established. The model depends on defining various parameters that describe both the virus and the population under study. However, it is likely that several of these parameters will exhibit significant variations among different states. Therefore, regression algorithms and heuristic algorithms were developed to effectively adapt the population-dependent parameters and ensure accurate fitting of the SEIR model to data for any specific state. In this study, we consider the second outbreak of COVID-19 in Italy as a case study, which occurred in August 2020. We divide the epidemic data from February to September of the same year into two distinct stages for analysis. The numerical results demonstrate that the improved SEIR model effectively simulates and predicts the transmission trajectories of the Italian epidemic during both periods before and after the second outbreak. By analyzing the impact of anti-epidemic measures on the spread of the disease, our findings emphasize the significance of implementing anti-epidemic preventive measures in COVID-19 modeling.

Hepatitis C Elimination in Moldova Is Feasible and Cost-Saving: A Modeling Study.

BACKGROUND: Moldova, an upper-middle-income country in Eastern Europe, is facing a high burden of hepatitis C virus (HCV). Our objective was to assist the National Agency of Public Health of Moldova in planning to achieve the World Health Organization's HCV elimination goals by 2030. METHODS: This study adapted a previously developed microsimulation model to simulate the HCV epidemic in Moldova from 2004 to 2050. Model outcomes included temporal trends in HCV infection, prevalence, mortality, and total cost of care, including screening and treatment. We evaluated scenarios that could eliminate HCV by 2030. RESULTS: Multiple strategies could lead to HCV elimination in Moldova by 2030. A realistic scenario of a 20% annual screening and 80% treatment rate would require 2.75 million individuals to be screened and 65 000 treated by 2030. Compared to 2015, this program will reduce HCV incidence by 98% and HCV-related deaths by 72% in 2030. Between 2022 and 2030, this strategy would cost $17.5 million for HCV screening and treatment. However, by 2050, the health system would save >$85 million compared to no investment in elimination efforts. CONCLUSIONS: HCV elimination in Moldova is feasible and can be cost saving, but requires resources to scale HCV screening and treatment.

[Polizeistaat and police against pestilence: Russian authorities and law in the fight against the threat of epidemics in the 1st third of the 18th century].

The beginning of the 18th century in the history of the Russian state and society was marked by the beginning of large-scale transformations initiated by Peter I. As a result of these reforms, the patriarchal Moscow kingdom turned into a "regular" Russian empire, the political, legal and administrative structure of which differed significantly from what it was before. It is no coincidence that Peter is often referred to as a «revolutionary on the throne.¼ In his actions, Peter was guided by the idea of the "common good" and the ideas of the "regular" state and Polizeistaat. He drew them in Western Europe, in the countries of the Protestant circle - Prussia, Denmark, Sweden. One of the leading ideas of the Polizeistaat doctrine was the state's active policy to form a welfare society, which was supposed to be achieved through the active intervention of the authorities in the daily life of citizens, its regulation and regulation through various regulations, instructions and instructions. A regular, properly organized police force was one of the most important instruments of such intervention, which had as its ultimate goal the creation of conditions for the growth of the well-being of subjects and their number. This growth in the number of subject population was also achieved through the creation of a system of measures to prevent epidemics and their consequences. In this article, the authors, relying on the materials of the Petrine legislation, reconstruct the course of the gradual formation of sanitary legislation in Petrine Russia.

[A survey of performance of public health risk assessment in emergencies of institutions for disease control and prevention at different levels in China].

Objective: To understand the performance of public health risk assessment in emergencies of institutions for disease control and prevention at different levels in China, and provide suggestions for the improvement of public health risk assessment. Methods: A self-administered survey was conducted in professionals involved in public health risk assessment in emergencies from national institution, provincial institutions and some prefectural institutions for disease control and prevention (1-2 prefectural institutions were selected using convenience sampling in each province) between March and April in 2021. Results: A total of 79 institutions for disease control and prevention were investigated, including 1 national institution, 32 provincial institutions and 46 prefectural institutions. By April 2021, all the 79 institutions surveyed had conducted risk assessment of public health emergencies, in which 61 (77.2%) had established departments responsible for the public health risk assessment, i.e. emergency management office or communicable disease prevention and control office (section), and regular risk assessment mechanisms. The main sources of information for public health risk assessment were public health surveillance systems, including the National Notifiable Diseases Reporting System (100.0%) and Public Health Emergencies Management Information System (97.5%). Compared with the provincial institutions, the prefectural institutions were more likely to use specific disease surveillance systems (84.8% vs. 62.5%; χ2=5.09, P=0.024). The risk management recommendations made by 43 institutions for disease control and prevention (54.4%) after the risk assessment were accepted by the superior health administrative departments and used in epidemic prevention and control. Conclusions: Public health risk assessment in emergencies has been widely carried out by national, provincial and prefectural institutions for disease control and prevention in China. Specialized departments and mechanisms have been established, but the information sources are still confined to public health surveillance systems and the application of the risk assessment results still needs to be further improved.

[Epidemiological characteristics of reinfection of 2019-nCoV and influencing factors in Ningbo].

Objective: To analyze the epidemiological characteristics of reinfection of 2019-nCoV and influencing factors, and provide evidence for effective prevention and control of COVID-19 epidemic. Methods: The incidence data of COVID-19 in Ningbo from January 1, 2020 to November 30, 2022 were collected from the infectious disease surveillance system of Chinese information system for disease control and prevention. The incidence of reinfection of 2019-nCoV was investigated by using questionnaire. logistic regression analysis was used to analyze the influences of gender, age, time interval from the first infection, history of underlying disease, 2019-nCoV vaccination dose and disease severity on the reinfection. Results: A total of 897 previous 2019-nCoV infection cases were investigated, of which 115 experienced the reinfection of 2019-nCoV, the reinfection rate was 12.82%. The interval between the two infections M(Q1, Q3) was 1 052 (504, 1 056) days. Univariate analysis showed that age, 2019-nCoV vaccination dose, history of underlying disease, type of 2019-nCoV variant causing the first infection, time interval from the first infection and severity of the first infection were associated with the reinfection rate (all P<0.05). Multivariate logistic regression analysis showed that the risk for reinfection in age group 30- years was higher than that in age group ≥60 years (OR=2.10, 95%CI: 1.11-3.97). No reinfection occurred in those with time interval from the first infection of <6 months, and the risk for reinfection was higher in those with the time interval of ≥12 months than in those with the time interval of 6- months (OR=6.68, 95%CI: 3.46-12.90). The risk for reinfection was higher in the common or mild cases than in the asymptomatic cases (OR=2.64, 95%CI: 1.18-5.88; OR=2.79, 95%CI: 1.27-6.11). Conclusion: The time interval from the first infection was an important influencing factor for the reinfection of 2019-nCoV, and the probability of the reinfection within 6 months was low.

[Epidemiological characteristics of mpox epidemic in Guangzhou].

Objective: To understand the epidemiological characteristics of mpox epidemic in Guangzhou and provide scientific evidence for the prevention and control of the disease. Methods: Based on the mpox surveillance system in Guangzhou, suspected mpox cases with fever and rash were reported by local hospitals at all levels to centers for disease control and prevention in Guangzhou for sampling, investigation and diagnosis. Descriptive epidemiological analysis was conducted on the clinical characteristics and treatment of the mpox cases and positive detection rate reported in Guangzhou as of 24:00 on June 23. Whole genome sequencing of the virus isolates was performed using Illumina Miniseq high-throughput sequencing platform. Results: The first mpox case in Guangzhou was reported on June 10 in 2023. As of 24:00 on June 23, a total of 25 confirmed mpox cases were reported. All the mpox cases were men with a M(Q1,Q3) of 32 (26, 36) years, the majority of the cases were MSM (96.0%). The main clinical features were rash (100.0%, 25/25), lymphadenectasis (100.0%, 25/25) and fever (52.0%, 13/25). Rash usually occurred near the genitals (88.0%, 22/25). The close contacts, mainly family members (40.4%, 23/57), showed no similar symptoms, such as fever or rash. The positive rate of mpox virus in household environment samples was 30.5%. The analyses on 3 complete gene sequences of mpox virus indicated that the strains belonged to West African type Ⅱb clade, B.1.3 lineage. Conclusions: Hidden transmission of mpox virus had occurred in MSM in Guangzhou. However, the size of affected population is relatively limited, and the possibility of wide spread of the virus is low.

[Analysis of big data characteristics of allergic rhinitis patients in Beijing City from 2016 to 2021].

To explore the characteristics of big data of patients with allergic rhinitis, including the time, population and spatial distribution of allergic rhinitis in Beijing from 2016 to 2021, so as to provide reference for the prevention and treatment of this disease. Descriptive epidemiological methods were used to analyze the distribution (including gender, age and location)and trend of allergic rhinitis patients in 30 pilot hospitals from January 2016 to December 2021, T test and Kruskal-Wallis rank sum test were used to test the statistical differences. The results showed that the number of patients with allergic rhinitis in 30 hospitals increased year by year from 2016 to 2019, with an increase of 97.9%. In 2020, the number of patients decreased. In 2021, the number of visits returned to the pre-epidemic level (461 332); The number of patients with allergic rhinitis was the highest in September, with a seasonal index of 177.6%, while the lowest number was in February, accounting for only 47.2%; a significant difference was observed in the number of patients in different age groups(H=45 319.48, P<0.05), and patients under 15 years old accounted for the highest proportion(819 284 visits); There were significant differences between patients of different genders in the 45-59 year old group (t=-4.26, P<0.05).There were relatively more patients with allergic rhinitis in Dongcheng District(31.1%) than in Huairou District and Miyun District (0.4%). In conclusion, since 2016, the number of patients increased significantly, with a varied trend in different seasons. Most patients were children. There were more patients in the central urban area than in the outer suburbs.

The coming Omicron waves and factors affecting its spread after China reopening borders.

The Chinese government relaxed the Zero-COVID policy on Dec 15, 2022, and reopened the border on Jan 8, 2023. Therefore, COVID prevention in China is facing new challenges. Though there are plenty of prior studies on COVID, none is regarding the predictions on daily confirmed cases, and medical resources needs after China reopens its borders. To fill this gap, this study innovates a combination of the Erdos Renyl network, modified computational model [Formula: see text], and python code instead of only mathematical formulas or computer simulations in the previous studies. The research background in this study is Shanghai, a representative city in China. Therefore, the results in this study also demonstrate the situation in other regions of China. According to the population distribution and migration characteristics, we divided Shanghai into six epidemic research areas. We built a COVID spread model of the Erodos Renyl network. And then, we use python code to simulate COVID spread based on modified [Formula: see text] model. The results demonstrate that the second and third waves will occur in July-September and Oct-Dec, respectively. At the peak of the epidemic in 2023, the daily confirmed cases will be 340,000, and the cumulative death will be about 31,500. Moreover, 74,000 hospital beds and 3,700 Intensive Care Unit (ICU) beds will be occupied in Shanghai. Therefore, Shanghai faces a shortage of medical resources. In this simulation, daily confirmed cases predictions significantly rely on transmission, migration, and waning immunity rate. The study builds a mixed-effect model to verify further the three parameters' effect on the new confirmed cases. The results demonstrate that migration and waning immunity rates are two significant parameters in COVID spread and daily confirmed cases. This study offers theoretical evidence for the government to prevent COVID after China opened its borders.

The long-term impact and value of curative therapy for HIV: a modelling analysis.

INTRODUCTION: Curative therapies (CTx) to achieve durable remission of HIV disease without the need for antiretroviral therapy (ART) are currently being explored. Our objective was to model the long-term health and cost outcomes of HIV in various countries, the impact of future CTx on those outcomes and the country-specific value-based prices (VBPs) of CTx. METHODS: We developed a decision-analytic model to estimate the future health economic impacts of a hypothetical CTx for HIV in countries with pre-existing access to ART (CTx+ART), compared to ART alone. We modelled populations in seven low-and-middle-income countries and five high-income countries, accounting for localized ART and other HIV-related costs, and calibrating variables for HIV epidemiology and ART uptake to reproduce historical HIV outcomes before projecting future outcomes to year 2100. Health was quantified using disability-adjusted life-years (DALYs). Base case, pessimistic and optimistic scenarios were modelled for CTx+ART and ART alone. Based on long-term outcomes and each country's estimated health opportunity cost, we calculated the country-specific VBP of CTx. RESULTS: The introduction of a hypothetical CTx lowered HIV prevalence and prevented future infections over time, which increased life-years, reduced the number of individuals on ART, reduced AIDS-related deaths, and ultimately led to fewer DALYs versus ART-alone. Our base case estimates for the VBP of CTx ranged from $5400 (Kenya) up to $812,300 (United States). Within each country, the VBP was driven to be greater primarily by lower ART coverage, lower HIV incidence and prevalence, and higher CTx cure probability. The VBP estimates were found to be greater in countries where HIV prevalence was higher, ART coverage was lower and the health opportunity cost was greater. CONCLUSIONS: Our results quantify the VBP for future curative CTx that may apply in different countries and under different circumstances. With greater CTx cure probability, durability and scale up, CTx commands a higher VBP, while improvements in ART coverage may mitigate its value. Our framework can be utilized for estimating this cost given a wide range of scenarios related to the attributes of a given CTx as well as various parameters of the HIV epidemic within a given country.