A practical resource allocation strategy is the prerequisite for disease control during a pandemic affected by various external factors, such as the information about the epidemic state, the interregional population mobility, and the geographical factors. Understanding the influence of these factors on resource allocation and epidemic spreading is the premise for designing an optimal resource allocation strategy. To this end, we study the interaction of resource allocation and epidemic spreading in the scope of the metapopulation model by incorporating the factors of geographic proximity, the information of the epidemic state, the willingness of resource allocation, and the population mobility simultaneously. We develop a mathematical framework based on the Markovian chain approach to analyze the dynamical system and obtain the epidemic threshold concerning external factors. Combining extensive Monte Carlo simulations, we find that the disease can be controlled effectively when resources are allocated unbiased in terms of the geographical factor during a pandemic. Specifically, the spreading size is the lowest, and the epidemic threshold is the largest when resources are allocated unbiasedly between neighbor nodes and other nodes. In addition, when studying the effects of resource allocation on the epidemic threshold, we find the same results, i.e., information-aware resource allocation with unbiased in terms of the geographical factor will raise the epidemic threshold. At last, we study the effects of mobility rate on the dynamical property and find an appropriate small value of mobility rate that is propitious to control the disease through numerical analysis and simulations. Our findings will have a direct application in the development of strategies to suppress the spread of the disease and guide the behavior of individuals during a pandemic.

切实可行的资源配置策略是大流行期间疾病控制的前提，受各种外部因素的影响，如疫情信息、区域间人口流动和地理因素。了解这些因素对资源配置和疫情传播的影响是设计最优资源配置策略的前提。为此，我们同时综合考虑地理邻近性、疫情信息、资源配置意愿和人口流动性等因素，在元种群模型范围内研究资源配置与疫情传播的交互作用。我们开发了一个基于马尔可夫链方法的数学框架来分析动态系统并获得有关外部因素的流行阈值。结合广泛的蒙特卡罗模拟，我们发现在大流行期间根据地理因素无偏见地分配资源时，可以有效控制疾病。具体而言，当资源在邻居节点和其他节点之间无偏分配时，传播规模最小，流行阈值最大。此外，在研究资源配置对流行阈值的影响时，我们发现相同的结果，即在地理因素方面无偏的信息感知资源分配会提高流行阈值。最后，我们研究了流动率对动力学特性的影响，并通过数值分析和模拟找到了有利于控制疾病的适当的小流动率值。我们的研究结果将直接应用于制定抑制疾病传播和指导大流行期间个人行为的策略。