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> Virus Respiratorio Sincitial (VRS), Моделирование поведения респираторно-синцитиального вируса человека
Rilian
Jul 9 2010, 00:23
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[img_]Логотип[/img_]

Проект "Virus Respiratorio Sincitial (VRS)"

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ТОП-20 участников:
[img_]картинка[/img_]
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Дата основания команды - 18.06.2010 Капитан - distributed.org.ua
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Для присоединения к команде Украины:
1. Загрузите BOINC менеджер (Если его у Вас еще нет!)
2. Перейдите в "расширенный вид"
3. Выберите сервис ---> добавить проект
4. Введите адрес проекта http://falua.cesfelipesegundo.com/VRS/
5. Введите свои регистрационные данные.
6. Найдите нашу команду. Она называется Ukraine и адрес ее статистики вы могли видеть выше.
7. Если есть доступные для загрузки задания Вы их получите и начнете расчеты.
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Новичкам: статья со скриншотами, как поставить и настроить BOINC-менеджер
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Полезная информация:
Для идентификации пользователя в BOINC могут служить 2 вещи:
1) пара e-mail/пароль
2) межпроектный идентификационный ID (Cross-project ID) - 32значное шестнадцатиричное число.

Если Вы пожелаете подключится ещё и к другому BOINC-проекту, то помните: чтобы не плодить новых аккаунтов при подключении к новому проекту или команде, нужно обязательно везде регистрироваться с одним и тем же Именем и EMAIL. если при регистрации в проекте указать другой e-mail , BOINC создаст новый аккаунт с тем же именем! В этом случае рекомендуется зайти во все ваши аккаунты и во все проекты и где надо поменять емейл на нужный. Через некоторое время ваши аккаунты сольются в один с одним cross-project-id.
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О проекте:
VRS (from the Spanish Virus Respiratorio Sincitial) is a BOINC-based project which main aim is to simulate the behaviour of the human Respiratory Syncytial Virus (RSV). Main facts:
It is the major cause of lower respiratory tract infection and hospital visits during infancy and childhood.
In Spain, it causes more than 15.000 padiatrician visits per year.
It is responsible for more than 18% of the pneumonia elderly hospitalizations.
In the United States, 60% of infants are infected during their first RSV season. Nearly all children will have been infected with the virus by 2-3 years of age. Of those infected with RSV, 23% will develop bronchiolitis necessitating hospitalization.
Our project simulations of the RSV contagion mechanisms will allow:

Better understanding of the contagion evolution and its relevant parameters.
Faster predictions of the virus population evolution.
Designing more accurate vaccination campaigns.
You can contact the researcher on charge for additional information:
Doctor Javier Villanueva
Email jvillanueva AT cesfelipesegundo.com
Phone number +34 91 809 92 00 extension 210
Universidad Complutense de Madrid - CES Felipe II
Aranjuez - Madrid - Spain
Project Web Page HERE.
Project Status Page http://falua.cesfelipesegundo.com/VRS/server_status.php

Ссылки по теме:
Кто знает испанский - вэллкам:
http://www.pediatraldia.cl/01BODEGA1/virus...o_sincicial.htm
http://www.comtf.es/pediatria/Bol-2004-2_3...S_ACallejon.pdf
http://www.sup.org.uy/Archivos/adp76-3/pdf/adp76-3_8.pdf
http://www.aibarra.org/ucip/temas/tema20/tema20.html


График ППД команды "Ukraine"
[img_]картинка с проекта http://boincstats.com/stats/бурумбурум[/img_]

Це повідомлення відредагував nikelong: Sep 18 2010, 21:58
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nikelong
Aug 29 2010, 22:41
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Обновил шапку (собственно, СОЗДАЛ шапку smile.gif )

Добавил проект в БТП.

В данный момент результатов к выдаче Зироу.
Но Вы можете подключится, ибо наша родная тим аж на 360-м месте из 799 команд!
Хотя, мі можем считать себя и 71-ми, ибо только 70 команд набрали хоть какие то очки в проекте.

Алсо, РеММеР, прикрути шедулер в БТП к этому проекту!


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tiss
Aug 29 2010, 22:48
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После иберцивиса что-то испанцам не доверяю.


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nikelong
Dec 27 2010, 00:17
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http://falua.cesfelipesegundo.com/VRS/top_...redit&offset=60

Команда на 75-м месте с 59-ю очками.

ЗАданий к выдаче в данный момент ноль, но у многих команд РАК > 0 так что можно подключится сюда и чайной ложкой не дать команде выпасть из ТОП-100


РЕММЕР ПРИКРУТИ ШЕДУЛЕР!


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Rilian
Oct 16 2011, 08:32
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проект завершен. больше заданий не будет

http://falua.cesfelipesegundo.com/?page_id=18&lang=en

NOTE
This project is finished. There will be no need of more distributed computations in the near future.

There were several conclusions extracted from the study of the VRS model. To sum up: First, that it is in fact possible to use a network model to simulate quite good the infection scheme and that the network model fits better than the usual continous model. Second, that once the model is defined, it is possible to figure out what would happend under certain vaccination schedules, and provide a picture of the cost and benefit of each schedule. And three and more shocking (in fact this goes against the general understanding of the disease, and is quite polemic) that the periodic prevalence of the disease is not linked (or at least could be not linked) to a seasonality periodicity but it arrises naturally from the particular way the virus infects people.

Findings have been provided in the following publications
Paper “Random Networks in a Distributed Computing Environment: An Approach to the Transmission Dynamics of Epidemic Diseases” in the Proceedings of the Seventh International Conference on Engineering Computational Technology (doi: 10.4203/ccp.94.25)
Paper “Seasonal Respiratory Syncytial Virus Epidemic in a Random Social Network”, in the Modelling for addictive behaviour, medicine and engineering 2010
Paper “Random networks to study the dynamics of respiratory syncitial virus (RSV) in the Spanish region of Valencia”, in the Mathematical and Computer Modelling (doi:10.1016/j.mcm.2010.11.068)


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общая статистика: BOINCstats * FreeDC команда: BOINC команда Ukraine

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Death
Oct 16 2011, 11:42
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ну результаті прекрасніе. и поєтому они запускают ещё пару проектов насколько я понял.

єто замечательно!! спид и рак єто фигня по сравнению с тем же менингитом.


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nikelong
Mar 18 2012, 00:02
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http://falua.cesfelipesegundo.com/?page_id=23&lang=en

VRS Project report
Project for the study of Respiratory Syncytial Virus (RSV)

Contact Information: Dr. Javier Villanueva, email jvillanueva AT cesfelipesegundo DOT com

RSV Problems

Respiratory Syncytial Virus (RSV) is the most frequent agent of respiratory tract infections in children younger than 2 years old and the major cause of hospitalizations specially for bronchiolitis and pneumonia. RSV is the cause of annual seasonal epidemics with minor variations each year and these are coincident with other widespread viral infections such as influenza or rotavirus, all these infections produce a high number of hospitalizations saturating the National Health Systems. Moreover, its transmission is very easy and nosocomial infections are frequent.

In Spain, there are 15,000 – 20,000 visits to primary care due to RSV each year. In the Spanish region of Valencia, 1,500 children younger than five years old are hospitalized annually with an average of 6 days of hospitalization per case. The cost of pediatric hospitalization for the Valencian Health System is about 3.5 million euros per year. To estimate the total pediatric cost of the illness it would be necessary to add the costs of primary care attention and the social (indirect) costs.

Therefore, it is important to have tools that allow to adapt to the health requirements, establish epidemic markers and predictors that help Health organizations to design control strategies against RSV and a reliable preparation of Health System. Researchers are also studying vaccines to protect individuals at early ages, when the immune system is not completely developed and, what is more important, to modulate the immune response responsible for the severity of the disease in young children.

Virus Modelling

The above facts lead us to propose the development of a dynamic model of RSV transmission and infection fitting real values of hospitalization for children younger than one year in the Spanish

region of Valencia. Once developed the model, our goal is to design several prevention strategies, vaccination included, study their effectiveness, perform pharmaco-economic analysis in order to obtain more efficient strategies to reduce RSV incidence. Finally, as a consequence, to propose Public Health guidelines and to extend the model to Spain.

The team which is working in this disease has developed a network model able to simulate the inter-person contacts and the RSV transmission better than the already existing models. The starting point is the segmentation of population into the situations an individual can face with respect to the disease: susceptible or healthy, sick and recovered, with transitions among all these status as detailed in the following figure:



Then we build a network or graph where each node is an individual with specific characteristics independent of the other nodes (age, health status, sex, etc.). The links between among nodes represent relationships among individuals the disease uses for transmission. If the relationships are chosen randomly when building the network, then we have a so called "random network". Furthermore, depending on the probability distribution chosen to set-up the links among nodes, we can distinguish different random networks (Poisson, exponential, potential). Once we have built the network and the evolution rules have been defined, we can simulate the model studying, for each individual, the relationships he/she has and how is affected by them. With this approach it is easy to study situations such as the disease’s behaviour when a vaccine is applied on a selected group of the population (i.e. only children, only elderly), or if some treatment is applied to specific group of sick persons. This is represented by the following figure.



The odds of this model is that parameter estimation (what we call "model fitting") is highly CPU demanding. Apart from a few specific exceptions, the fitting process implies a brute force search. What this means is that we are forced to test every possible parameter combination (in our case they are the infection rate, the number of relationships and the recovering time after infection), being each one of these combinations a model. The test or "evolution" of the model consists on analyse, in a day-by-day basis for a period of several years, what happens to each individual, if he/she becomes ill, recovers, dies, etc. After that, the simulation results must be inspected and check how well they fit with the previously known data.

In practice this leads to the network models being used with great restrictions, such as reducing the network’s size (either in number of nodes, relationships or both) or the range in the parameter’s values to be tried. Lets think in a model for the whole population of Spain (about 45,000,000 nodes). The network would be so huge that usually we cut it off and use, say 10,000 or 100,000 nodes, and then extrapolate the results. This is valid sometimes but many others is, at least, questionable.

At the end, once the model has been validated and improved, the main aim is to test different vaccination strategies and to perform a cost-benefit analysis of each strategy.

Project’s Phases

In this project we have used distributed computation to carry on with the model calculation in a reasonable period of time, splitting it into three phases.

PHASE I: PRIMARY FITTING
In this phase we have done a coarse adjustment of the infection rates and number of relationships which better approach to real data. We have used a proprietary distributed computation system called "SISIFO", suitable for small distributed computation in local networks. We used for the computations a changing number of computers (usually over 20 with peaks of about 100 during weekends) and computed over 60,000 models of 1,000,000 nodes each for a total of 3 years of CPU time. We got interesting results and found a model able to imitate reasonably the virus evolution.

FASE II: SECONDARY FITTING
In this phase we have done the fine tuning of the model’s infection rate, number of relationships and immunity time. This is done so to determinate the way the infection is affected with respect to the time the individuals become susceptible once they have recovered. This implies the computation of about 140,000,000 models of 1,000,000 nodes each. This is bigger than what our SISIFO system can handle so we changed to BOINC and built our own BOINC server. In the following figure we show the evolution:



From 18/05 to 26/06 we started computations using the computers of CES Felipe II and IMM but the computation power was not enough and would need about 8 months to end the computation. On 27/06 we make the project public among the BOINC community and the Word-of-mouth makes the rest. In the following 3 weeks we reach over 850 active hosts and we are able to compute all the models we had still pending, reaching 21 years of CPU time and more than 500 Gigabyte of results.

PHASE III: TERTIARY FITTING
In this phase we will use the best model we can find in Phase II to evaluate different vaccination strategies. We hope to do this around September. It is to be defined if we would distributed computation again.

Thanks

We wishes to thank the BOINC community for their support, and among the English speaking teams, thank the Team Starfire and SETI.USA. The list is far longer (and it is available in the statistics area of the project) but we show here the teams over 10,000 credits:

1 Team Starfire World BOINC
2 SETI.USA
3 CANAL@Boinc
4 Crunchers@Freiburg
5 BOINC@Poland
6 SeriousCrunchers
7 TitanesDC
8 SETI.Germany
9 BOINC@MIXI
10 AMD Users
11 Team 2ch
12 SaveTheWorld
13 L’Alliance Francophone
14 BOINC@Heidelberg
15 Free-DC
16 The Knights Who Say Ni!
17 Team Norway
18 Team England (Boinc)


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