Spatiotemporal infectious disease modeling: a BME-SIR approach.

Spatiotemporal infectious disease modeling: a BME-SIR approach.
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DOI:
10.1371/journal.pone.0072168
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发表时间:
2013
期刊:
影响因子:
3.7
通讯作者:
Christakos G
Christakos G
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Angulo J;Yu HL;Langousis A;Kolovos A;Wang J;Madrid AE;Christakos G

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研究了不确定条件下传染病在复合时空域中传播的建模问题。我们专注于随机建模,它解释了疾病分布的基本机制和多源的原位不确定性。该模型从人口迁移动力学的一般公式以及传播和恢复率的规范出发,研究了易感感染-康复个体部分的演化的泛函公式。所建议的方法能够:a)对地区内和跨地区的人口动态进行建模,b)将疾病表示(即易受感染的恢复者)与不同地理位置的观察时间序列和其他信息来源(例如,硬数据和软数据、经验关系、二次信息)相结合,以及c)在考虑模型和观察的不确定性的同时,实时产生对疾病传播和相关参数的预测。通过模拟(即综合研究)和使用中国的手足口病数据的真实应用,说明了所提出的方法的关键方面。
This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.
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