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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020
摘要
Most models of 流行病學c spread, including many designed specifically for COVID-19, implicitly assume that 社會 網絡s are undirected, i.e., that the infection is equally likely to spread in either direction whenever a contact occurs. In particular, this assumption implies that the individuals most likely to spread the 疾病 are also the most likely to receive it from others. Here, we review results from the theory of random directed graphs which show that many important quantities, including the reproductive number and the 流行病學c size, depend sensitively on the joint distribution of in- and out-degrees (“risk” and “spread”), including their heterogeneity and the correlation between them. By considering joint distributions of various kinds we elucidate why some types of heterogeneity cause a deviation
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