Vargas Soto Juan S, Kosiewska Justin R, Grove Dan, Metts Dailee, Muller Lisa I, Wilber Mark Q
School of Natural Resources, University of Tennessee Institute of Agriculture, Knoxville, TN, 37996, USA.
Mov Ecol. 2025 Feb 26;13(1):11. doi: 10.1186/s40462-025-00539-4.
Despite decades of epidemiological theory making relatively simple assumptions about host movements, it is increasingly clear that non-random movements drastically affect disease transmission. To better predict transmission risk, theory is needed that quantifies the contributions of both fine-scale host space use and non-independent, correlated host movements to epidemiological dynamics.
We developed and applied new theory that quantifies relative contributions of fine-scale space use and non-independent host movements to spatio-temporal transmission risk. Our theory decomposes pairwise spatio-temporal transmission risk into two components: (i) spatial overlap of hosts-a classic metric of spatial transmission risk - and (ii) pairwise correlations in space use - a component of transmission risk that is almost universally ignored. Using analytical results, simulations, and empirical movement data, we ask: under what ecological and epidemiological conditions do non-independent movements substantially alter spatio-temporal transmission risk compared to spatial overlap?
Using theory and simulation, we found that for directly transmitted pathogens even weak pairwise correlations in space use among hosts can increase contact and transmission risk by orders of magnitude compared to independent host movements. In contrast, non-independent movements had reduced importance for transmission risk for indirectly transmitted pathogens. Furthermore, we found that if the scale of pathogen transmission is smaller than the scale where host social decisions occur, host movements can be highly correlated but this correlation matters little for transmission. We applied our theory to GPS movement data from white-tailed deer (Odocoileus virginianus). Our approach predicted highly seasonally varying contributions of the spatial and social drivers of transmission risk - with social interactions augmenting transmission risk between hosts by greater than a factor of 10 in some cases, despite similar degrees of spatial overlap. Moreover, social interactions could lead to a distinct shift in the predicted locations of transmission hotspots, compared to joint space use.
Our theory provides clear expectations for when non-independent movements alter spatio-temporal transmission risk, showing that correlated movements can reshape epidemiological landscapes, creating transmission hotspots whose magnitude and location are not necessarily predictable from spatial overlap.
尽管数十年来流行病学理论对宿主移动做出了相对简单的假设，但越来越明显的是，非随机移动会极大地影响疾病传播。为了更好地预测传播风险，需要一种理论来量化精细尺度的宿主空间利用以及非独立、相关的宿主移动对流行病学动态的贡献。
我们开发并应用了新理论，该理论量化了精细尺度空间利用和非独立宿主移动对时空传播风险的相对贡献。我们的理论将成对的时空传播风险分解为两个组成部分：（i）宿主的空间重叠——空间传播风险的经典指标，以及（ii）空间利用中的成对相关性——几乎普遍被忽视的传播风险组成部分。利用分析结果、模拟和实证移动数据，我们提出问题：与空间重叠相比，在哪些生态和流行病学条件下非独立移动会显著改变时空传播风险？
通过理论和模拟，我们发现对于直接传播的病原体，与独立的宿主移动相比，即使宿主之间在空间利用上存在微弱的成对相关性，也能将接触和传播风险提高几个数量级。相比之下，非独立移动对间接传播病原体的传播风险影响较小。此外，我们发现如果病原体传播的尺度小于宿主社会决策发生的尺度，宿主移动可能高度相关，但这种相关性对传播影响不大。我们将我们的理论应用于白尾鹿（弗吉尼亚鹿）的GPS移动数据。我们的方法预测了传播风险的空间和社会驱动因素的季节性变化很大——在某些情况下，社会互动使宿主之间的传播风险增加了10倍以上，尽管空间重叠程度相似。此外，与联合空间利用相比，社会互动可能导致预测的传播热点位置发生明显变化。
我们的理论明确了非独立移动何时会改变时空传播风险，表明相关移动可以重塑流行病学格局，创造出其规模和位置不一定能从空间重叠预测的传播热点。
