Using conservation of pattern to estimate spatial parameters from a single snapshot
Using conservation of pattern to estimate spatial parameters from a single snapshot
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DOI：
10.1073/pnas.0400335101
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发表时间：
2004-06-15
影响因子：
11.1
通讯作者：
Gilligan, CA
中科院分区：
文献类型：
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作者：
Keeling, MJ;Brooks, SP;Gilligan, CA
面对疫情快速反应是有效防控的关键；当疾病造成严重的公共卫生或经济后果时，这一点尤其重要。确定适当的反应水平需要根据有限的疾病分布数据快速估计感染的传播率。一般来说，用于估计此类空间参数的技术需要多个时间点的详细空间数据；收集此类数据通常既耗时又昂贵。在这里，我们提出了一种计算效率高且仅需要单个时间点的空间数据的替代方法，因此在流行病开始时节省了宝贵的时间。通过假设基本空间统计数据接近平衡，可以通过最小化这些统计数据的预期变化率来估计参数，从而保留一般空间模式。虽然适用于生态和流行病学数据，但这里我们重点关注计算机模拟和真实流行病的疾病数据，以表明该方法产生可在实际情况中使用的可靠结果。
Rapid reaction in the face of an epidemic is a key element in effective and efficient control; this is especially important when the disease has severe public health or economic consequences. Determining an appropriate level of response requires rapid estimation of the rate of spread of infection from limited disease distribution data. Generally, the techniques used to estimate such spatial parameters require detailed spatial data at multiple time points; such data are often time-consuming and expensive to collect. Here we present an alternative approach that is computationally efficient and only requires spatial data from a single time point, hence saving valuable time at the start of the epidemic. By assuming that fundamental spatial statistics are near equilibrium, parameters can be estimated by minimizing the expected rate of change of these statistics, hence conserving the general spatial pattern. Although applicable to both ecological and epidemiological data, here we focus on disease data from computer simulations and real epidemics to show that this method produces reliable results that could be used in practical situations.