A Probabilistic Infection Model for Efficient Trace-Prediction of Disease Outbreaks in Contact Networks
A Probabilistic Infection Model for Efficient Trace-Prediction of Disease Outbreaks in Contact Networks
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DOI：
10.1007/978-3-030-50371-0_50
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发表时间：
2020-05-26
期刊：
影响因子：
--
通讯作者：
Mikler AR
中科院分区：
文献类型：
--
作者：
Qian W;Bhowmick S;O’Neill M;Ramisetty-Mikler S;Mikler AR
我们提出了一种称为概率感染模型（PIM）的新方法。 PIM 不是一次随机地为每个代理分配一个状态，而是跟踪每个代理处于特定状态的可能性。因此，特定的药剂可以同时存在于多种疾病状态中。我们的模型提供了状态之间转换的改进分辨率，并允许更全面地了解个体层面的疫情动态。此外，通过使用概率方法，我们的模型可以对模拟爆发的整体轨迹有代表性的理解，而无需进行大量（数百次）重复的蒙特卡洛模拟。我们通过使用大学生注册数据构建的联系网络来模拟我们的模型。我们对三种疾病进行建模；麻疹和两种流感病毒。我们将 PIM 获得的结果与通过在同一接触网络上模拟随机 SEIR 模型获得的结果进行比较。结果表明，PIM 可以在一次确定性模拟中成功复制随机模型的多次模拟的平均结果。
We propose a novel method which we call the Probabilistic Infection Model (PIM). Instead of stochastically assigning exactly one state to each agent at a time, PIM tracks the likelihood of each agent being in a particular state. Thus, a particular agent can exist in multiple disease states concurrently. Our model gives an improved resolution of transitions between states, and allows for a more comprehensive view of outbreak dynamics at the individual level. Moreover, by using a probabilistic approach, our model gives a representative understanding of the overall trajectories of simulated outbreaks without the need for numerous (order of hundreds) of repeated Monte Carlo simulations. We simulate our model over a contact network constructed using registration data of university students. We model three diseases; measles and two strains of influenza. We compare the results obtained by PIM with those obtained by simulating stochastic SEIR models over the same the contact network. The results demonstrate that the PIM can successfully replicate the averaged results from numerous simulations of a stochastic model in a single deterministic simulation.
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DOI：
10.1073/pnas.0706849105
发表时间：
2008-03-25
影响因子：
11.1
作者：
Halloran, M. Elizabeth;Ferguson, Neil M.;Cooley, Philip
通讯作者：
Cooley, Philip
影响因子：
11.8
作者：
Jain, Seema;Benoit, Stephen R.;Finelli, Lyn
通讯作者：
Finelli, Lyn
影响因子：
3.7
作者：
Ajelli M;Gonçalves B;Balcan D;Colizza V;Hu H;Ramasco JJ;Merler S;Vespignani A
通讯作者：
Vespignani A
与传染病的传播相关的社会接触和混合模式。
影响因子：
15.8
作者：
Mossong, Joeel;Hens, Niel;Jit, Mark;Beutels, Philippe;Auranen, Kari;Mikolajczyk, Rafael;Massari, Marco;Salmaso, Stefania;Tomba, Gianpaolo Scalia;Wallinga, Jacco;Heijne, Janneke;Sadkowska-Todys, Malgorzata;Rosinska, Magdalena;Edmunds, W. John
通讯作者：
Edmunds, W. John
影响因子：
1.7
作者：
Holme, Petter;Rocha, Luis E. C.
通讯作者：
Rocha, Luis E. C.