Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

The spread of infectious diseases crucially depends on the pattern of contacts among individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. Few empirical studies are however available that provide estimates of the number and duration of contacts among social groups. Moreover, their space and time resolution are limited, so that data is not explicit at the person-to-person level, and the dynamical aspect of the contacts is disregarded. Here, we want to assess the role of data-driven dynamic contact patterns among individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. We consider high resolution data of face-to-face interactions between the attendees of a conference, obtained from the deployment of an infrastructure based on Radio Frequency Identification (RFID) devices that assess mutual face-to-face proximity. The spread of epidemics along these interactions is simulated through an SEIR model, using both the dynamical network of contacts defined by the collected data, and two aggregated versions of such network, in order to assess the role of the data temporal aspects. We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation which retains only the topology of the contact network fails in reproducing the size of the epidemic. These results have important implications in understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics

The Implementation of Managed Entry Agreements in Central and Eastern Europe : Findings and Implications

Funding Information: In Bosnia and Herzegovina, both The Federation of Bosnia and Herzegovina and the Republic of Srpska, also have special funds and budgets in place for the financing of expensive medicines, which are innovative and under patent. Similar earmarked funds are available in Scotland (the New Medicines Fund funded by the Pharmaceutical Price Regulation Scheme [PPRS] rebates) [35] and England (the Cancer Drugs Fund) [36]. However, support for such earmarked funds is mixed. While they facilitate access, critics raised issues about fairness towards other disease areas and patient groups that are not eligible for special funding [3, 39]. Further, the views of a Patient and Clinician Engagement meeting in Scotland [37] and the end-of-life criteria in England [38] offer opportunities for special considerations affecting medicines for end-of-life and very rare conditions to be taken into account in the health technology assessment process. Funding Information: The authors would like to acknowledge Dr. Jan Jones from the Scottish Medicines Consortium, Scotland, for contributing to the discussion with information on Scotland, Drs. Lyudmila Bezmelnitsyna and Anastasia Isaeva for contributing to data collection in Russia and Dr. Kate?ina Podrazilov? from SZP ?R for providing information on the Czech Republic. Alessandra Ferrario was a Research Officer at the LSE Health at the time this research was conducted. She is now a postdoctoral Research Fellow at the Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA. Email: [email protected] No sources of funding were used for this study. The authors declare they have no conflicts of interest. However, Di?na Ar?ja, Maria Dimitrova, Jurij F?rst, Ieva Grei?i?t?-Kuprijanov, Iris Hoxha, Arianit Jakupi, Erki Laidm?e, Vanda Markovic-Pekovic, Dmitry Meshkov, Guenka Petrova, Maciej Pomorski and Patricia Vella Bonanno work directly for national health authorities or are advisers to them. Alessandra Ferrario, Tomasz Bochenek, Ileana Mardare, Dominik Tomek, Luka Voncina, Alan Haycox, Panos Kanavos,?Olga L?blov?, and Brian Godman are academics and independent researchers also working with national and regional health authorities and others to improve the quality and efficiency of prescribing, and Tarik Catic, D?vid Dank?,and Tanja Novakovic are involved with pharmaceutical, pharmacoeconomics and outcomes research groups in their countries. Olga L?blov? has also carried out remunerated consultancy activities for A&R Partners, Baxter AG and Instytut Arcana and Ileana Mardare has signed a consulting contract with Ewopharma A.G. Romania. The content of the paper and the conclusions are those of each author and may not necessarily reflect those of any organisation that employs them. Publisher Copyright: © 2017, The Author(s).Background: Managed entry agreements (MEAs) are a set of instruments to facilitate access to new medicines. This study surveyed the implementation of MEAs in Central and Eastern Europe (CEE) where limited comparative information is currently available. Method: We conducted a survey on the implementation of MEAs in CEE between January and March 2017. Results: Sixteen countries participated in this study. Across five countries with available data on the number of different MEA instruments implemented, the most common MEAs implemented were confidential discounts (n = 495, 73%), followed by paybacks (n = 92, 14%), price-volume agreements (n = 37, 5%), free doses (n = 25, 4%), bundle and other agreements (n = 19, 3%), and payment by result (n = 10, >1%). Across seven countries with data on MEAs by therapeutic group, the highest number of brand names associated with one or more MEA instruments belonged to the Anatomical Therapeutic Chemical (ATC)-L group, antineoplastic and immunomodulating agents (n = 201, 31%). The second most frequent therapeutic group for MEA implementation was ATC-A, alimentary tract and metabolism (n = 87, 13%), followed by medicines for neurological conditions (n = 83, 13%). Conclusions: Experience in implementing MEAs varied substantially across the region and there is considerable scope for greater transparency, sharing experiences and mutual learning. European citizens, authorities and industry should ask themselves whether, within publicly funded health systems, confidential discounts can still be tolerated, particularly when it is not clear which country and party they are really benefiting. Furthermore, if MEAs are to improve access, countries should establish clear objectives for their implementation and a monitoring framework to measure their performance, as well as the burden of implementation.publishersversionPeer reviewe

Effectiveness and Cost Effectiveness of Expanding Harm Reduction and Antiretroviral Therapy in a Mixed HIV Epidemic: A Modeling Analysis for Ukraine

A cost-effectiveness study by Sabina Alistar and colleagues evaluates the effectiveness and cost effectiveness of different levels of investment in methadone, ART, or both, in the mixed HIV epidemic in Ukraine

The equivalence of numbers: The social value of avoiding health decline: An experimental web-based study

BACKGROUND: Health economic analysis aimed at informing policy makers and supporting resource allocation decisions has to evaluate not only improvements in health but also avoided decline. Little is known however, whether the "direction" in which changes in health are experienced is important for the public in prioritizing among patients. This experimental study investigates the social value people place on avoiding (further) health decline when directly compared to curative treatments in resource allocation decisions. METHODS: 127 individuals completed an interactive survey that was published in the World Wide Web. They were confronted with a standard gamble (SG) and three person trade-off tasks, either comparing improvements in health (PTO-Up), avoided decline (PTO-Down), or both, contrasting health changes of equal magnitude differing in the direction in which they are experienced (PTO-WAD). Finally, a direct priority ranking of various interventions was obtained. RESULTS: Participants strongly prioritized improving patients' health rather than avoiding decline. The mean substitution rate between health improvements and avoided decline (WAD) ranged between 0.47 and 0.64 dependent on the intervention. Weighting PTO values according to the direction in which changes in health are experienced improved their accuracy in predicting a direct prioritization ranking. Health state utilities obtained by the standard gamble method seem not to reflect social values in resource allocation contexts. CONCLUSION: Results suggest that the utility of being cured of a given health state might not be a good approximation for the societal value of avoiding this health state, especially in cases of competition between preventive and curative interventions

A Spoonful of Math Helps the Medicine Go Down: An Illustration of How Healthcare can Benefit from Mathematical Modeling and Analysis

<p>Abstract</p> <p>Objectives</p> <p>A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility.</p> <p>Method</p> <p>Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery.</p> <p>Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization.</p> <p>As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission.</p> <p>Results</p> <p>We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times.</p> <p>Conclusions</p> <p>One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes.</p

Can modeling of HIV treatment processes improve outcomes? Capitalizing on an operations research approach to the global pandemic

<p>Abstract</p> <p>Background</p> <p>Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment. We describe the application of classic operations research (OR) techniques to address logistical and resource management challenges in HIV treatment scale-up activities in resource-limited countries.</p> <p>Methods</p> <p>We review and categorize several of the major logistical and operational problems encountered over the last decade in the global scale-up of HIV care and antiretroviral treatment for people with AIDS. While there are unique features of HIV care and treatment that pose significant challenges to effective modeling and service improvement, we identify several analogous OR-based solutions that have been developed in the service, industrial, and health sectors.</p> <p>Results</p> <p>HIV treatment scale-up includes many processes that are amenable to mathematical and simulation modeling, including forecasting future demand for services; locating and sizing facilities for maximal efficiency; and determining optimal staffing levels at clinical centers. Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces.</p> <p>Conclusion</p> <p>The modeling techniques developed in the engineering field of operations research have wide potential application to the variety of logistical problems encountered in HIV treatment scale-up in resource-limited settings. Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.</p

Models of epidemics: when contact repetition and clustering should be included

Background The spread of infectious disease is determined by biological factors, e.g. the duration of the infectious period, and social factors, e.g. the arrangement of potentially contagious contacts. Repetitiveness and clustering of contacts are known to be relevant factors influencing the transmission of droplet or contact transmitted diseases. However, we do not yet completely know under what conditions repetitiveness and clustering should be included for realistically modelling disease spread. Methods We compare two different types of individual-based models: One assumes random mixing without repetition of contacts, whereas the other assumes that the same contacts repeat day-by-day. The latter exists in two variants, with and without clustering. We systematically test and compare how the total size of an outbreak differs between these model types depending on the key parameters transmission probability, number of contacts per day, duration of the infectious period, different levels of clustering and varying proportions of repetitive contacts. Results The simulation runs under different parameter constellations provide the following results: The difference between both model types is highest for low numbers of contacts per day and low transmission probabilities. The number of contacts and the transmission probability have a higher influence on this difference than the duration of the infectious period. Even when only minor parts of the daily contacts are repetitive and clustered can there be relevant differences compared to a purely random mixing model. Conclusion We show that random mixing models provide acceptable estimates of the total outbreak size if the number of contacts per day is high or if the per-contact transmission probability is high, as seen in typical childhood diseases such as measles. In the case of very short infectious periods, for instance, as in Norovirus, models assuming repeating contacts will also behave similarly as random mixing models. If the number of daily contacts or the transmission probability is low, as assumed for MRSA or Ebola, particular consideration should be given to the actual structure of potentially contagious contacts when designing the model.ISSN:1742-468

Cost-Effectiveness of Pooled Nucleic Acid Amplification Testing for Acute HIV Infection after Third-Generation HIV Antibody Screening and Rapid Testing in the United States: A Comparison of Three Public Health Settings

Angela Hutchinson and colleagues conducted a cost-effectiveness analysis of pooled nucleic acid amplification testing following HIV testing and show that it is not cost-effective at recommended antibody testing intervals for high-risk persons except in very high-incidence settings

Recommendations for increasing the use of HIV/AIDS resource allocation models

The article of record as published may be found at: http://dx.doi.org/10.1186/1471-2458-9-S1-S8Background: Resource allocation models have not had a substantial impact on HIV/AIDS resource allocation decisions in spite of the important, additional insights they may provide. In this paper, we highlight six difficulties often encountered in attempts to implement such models in policy settings; these are: model complexity, data requirements, multiple stakeholders, funding issues, and political and ethical considerations. We then make recommendations as to how each of these difficulties may be overcome. Results: To ensure that models can inform the actual decision, modellers should understand the environment in which decision-makers operate, including full knowledge of the stakeholders' key issues and requirements. HIV/AIDS resource allocation model formulations should be contextualized and sensitive to societal concerns and decision-makers' realities. Modellers should provide the required education and training materials in order for decision-makers to be reasonably well versed in understanding the capabilities, power and limitations of the model. Conclusion: This paper addresses the issue of knowledge translation from the established resource allocation modelling expertise in the academic realm to that of policymaking