Journal
APPLIED MATHEMATICS AND COMPUTATION
Volume 411, Issue -, Pages -Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2021.126531
Keywords
Epidemic dynamics; Resource allocation; Information of epidemic state; Metapopulation network
Categories
Funding
- Fundamental Research Funds for the Central Universities [JBK2101048]
- National Natural Science Foundation of China [61903266]
- Special Cultivation Project of the University Level Scientific Research Project of Aba Teachers University [ASZ21-03]
- Science and Technology Department of Sichuan Province [2020YJ0048]
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During a pandemic, effective disease control can be achieved through unbiased resource allocation, especially when resources are allocated without bias between neighboring nodes and other nodes. Information-aware resource allocation may increase the epidemic threshold, while an appropriate small mobility rate is conducive to disease control.
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. (C) 2021 Elsevier Inc. All rights reserved.
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