Evaluation of the Spatiotemporal Epidemiological Modeler (STEM) during the recent COVID-19 pandemic

In early December 2019, some people in China were diagnosed with an unknown pneumonia in Wuhan, in the Hubei province. The responsible of the outbreak was identified in a novel human-infecting coronavirus which differs both from severe acute respiratory syndrome coronavirus and from Middle East respiratory syndrome coronavirus. The new coronavirus, officially named severe acute respiratory syndrome coronavirus 2 by the International Committee on Taxonomy of Viruses, has spread worldwide within few weeks. Only two vaccines have been approved by regulatory agencies and some others are under development. Moreover, effective treatments have not been yet identified or developed even if some potential molecules are under investigation. In a pandemic outbreak, when treatments are not available, the only method that contribute to reduce the virus spreading is the adoption of social distancing measures, like quarantine and isolation. With the intention of better managing emergencies like this, which are a great public health threat, it is important to dispose of predictive epidemiological tools that can help to understand both the virus spreading in terms of people infected, hospitalized, dead and recovered and the effectiveness of containment measures

Висока продуктивність java-сокетів для оперування накопиченими даними в медицині

Computer clouds are using in health science for its data collections, manipulations and providing security needs in communications to exchange. The clouds distribution data character is using in science applications created to evaluate the data of the health-care. The science programs like medical visualization, genetic and protein conclusions, map-drag therapy and clinical decisions systems of support (CDSS) require high performance messaging libraries with minimum computer and communication spends and the effective utilization of the resources. The highperformance Java sockets (HPJS) encapsulate the needs of message high communications between cloud platforms science applications. HPJS effectively uses the Java socket realization for high-performance inner-process communications. With single-copy protocol, re-usability of the thread and communication overhead reduction, HPJS can use the message exchange in two times quickly to conventional buffered communication libraries.Компьютерные накопления данных используются в здравохранении для сохранения данных отдельных личностей, их манипуляции и обеспечения необходимости безопасного обмена. Характер распределения таких накоплений данных может быть разработан для использования в научных приложениях, которые разработаны для формирования оценки данных здравохранения. Такие научные программы як медицинская визуализация, генетические и протеиновые заключения, лечебно-профилактическая терапия та клинические системы поддержки принятия решений (CDSS) требуют библиотек скоростного обмена сообщениями с минимальными компьютерными и коммуникационными рас ходами, а также эффективным разграничением ресурсов. Высокопродуктивные Java-сокеты (HPJS) инкапсулируют необходимость высокопродуктивного обмена сообщениями между научными приложениями для cloud-платформ та эффективно используют Java-сокетную реализацию для образования высокоэффективной связи между процессами. С единой копией протокола и повторном использовании ниток та уменьшении накладных расходов связи высокопродуктивные Java-сокеты могут исполнять обмен сообщениями в два раза быстрее с обыкновенными буферизированными библиотекамисвязи.Комп’ютерні нагромадження даних використовуються в області охорони здоров’я для зберігання даних осіб, їх маніпуляції і забезпечення потреб безпечного обміну. Характер розподілу подібних нагромаджень даних може бути розроблений для застосування в наукових додатках, які розроблені для формування оцінки даних охорони здоров’я. Такі наукові програми як медична візуалізація, генетичні і протеїнові заключення, лікувально-профілактична терапія та клінічні системи підтримки прийняття рішень (CDSS) вимагають бібліотек швидкого обміну повідомленнями з мінімальними комп’ютерними і комунікаційними затратами та ефективним розшаруванням ресурсів. Високопродуктивні Java-сокети (HPJS) інкапсулюють потреби високопродуктивного обміну повідомленнями між науковими додатками для cloud-платформ та ефективно використовують Java-сокетну реалізацію для утворення високоефективного зв’язку між процесами. З єдиною копією протоколу при повторному використанні ниток та зменшенні накладних витрат зв’язку високопродуктивні Java-сокети можуть виконувати обмін повідомленнями в два рази швидше із звичайними буферизованими бібліотеками зв’язку

Simulation of the use of Yersinia pestis as a Biological Weapon in Nigeria

A simulation study using the Spatiotemporal Epidemiological Modeler was carried out on the potential usage of Yersinia pestis, the causative agent of plague ("˜black death'), as a biological weapon of terror. The study revealed widespread infections, incidences and deaths due to the infection all over Nigeria with bioweapon attacks originating from 2 Nigerian cities. Instituting an effective intervention program against the infection could save as many as 3.6 million lives within 10 days of the onset of the intervention program. Intervention programs could include social distancing policies and the use of antibiotics in addition to controlling the rodents and fleas vector population. Because of the relative ease of development of bioweapons and the desperation by terrorists to use any weapon at their disposal to achieve terror, there is an urgent need for an effective preparedness plan that can stop or limit the use of this category A bio-agent for biowarfare

Data Science and Ebola

Data Science---Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating even more data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The science that is transforming this ocean of data into a sea of knowledge is called data science. This lecture will discuss how data science has changed the way in which one of the most visible challenges to public health is handled, the 2014 Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit

Modelling pandemic influenza progression using Spatiotemporal Epidemiological Modeller (STEM)

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69-70).The purpose of this project is to incorporate a Poisson disease model into the Spatiotemporal Epidemiological Modeler (STEM) and visualize the disease spread on Google Earth. It is done through developing a Poisson disease model plug-in using the Eclipse Modeling Framework (EMF), a modeling framework and code generation facility for building tools and other applications based on a structured data model. The project consists of two stages. First, it develops a disease model plug-in of a Poisson disease model of a homogenous population, which is built as an extension of the implemented SI disease model in the STEM. Next, it proposes an algorithm to port a Poisson disease model of a heterogeneous population into the STEM. The development of the two new diseases plugins explores the maximum compatibility of the STEM and sets model for potential users to flexibly construct their own disease model for simulation.by Hui Zhang.S.M

Multimodal Epidemic Visual Analytics and Modeling

The risk of infectious disease increases due to various factors including the dense population, development of various transportations, urbanization, and abnormal weather conditions. Since the speed of epidemic spread is fast, it is necessary to respond quickly in order to prevent the high fatality rate. Therefore, a fast search for the highly accurate spreading model has to be focused on the proper analysis of disease spreading. There have been many studies to understand the disease spreading and the epidemic model is often used to analyze and predict the spread of infectious disease. However, it is limited to apply the epidemic model for the spread analysis because the model captures spreading changes only within the defined area. In this paper, we propose a framework for the disease spreading simulation with multimodal factors in the epidemic model and networks of possible spread routes. Our system provides an interactive simulation environment with the interregional disease spreading according to various spread parameters. Moreover, in order to understand the spreading directions, we extract vector fields over time and visualize the vector fields with the fatality of the disease. Therefore, users are able to understand the disease spreading phenomena and obtain appropriate models through our framework

INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

<p>Abstract</p> <p>Background</p> <p>Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.</p> <p>Results</p> <p>We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.</p> <p>Conclusions</p> <p>The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.</p