Many social and biological networks periodically change over time with daily, weekly, and other cycles. Thus motivated, we formulate and analyze susceptible-infectious-susceptible (SIS) epidemic models over temporal networks with periodic schedules. More specifically, we assume that the temporal network consists of a cycle of alternately used static networks, each with a given duration. We observe a phenomenon in which two static networks are individually above the epidemic threshold but the alternating network composed of them renders the dynamics below the epidemic threshold, which we refer to as a Parrondo paradox for epidemics. We find that network structure plays an important role in shaping this phenomenon, and we study its dependence on the connectivity between and number of subpopulations in the network. We associate such paradoxical behavior with anti-phase oscillatory dynamics of the number of infectious individuals in different subpopulations.
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Article Synopsis
- Parrondo's paradox shows that mixing two losing strategies can surprisingly lead to a win!
- This idea is useful in many areas of biology, helping us understand how life adapts and survives in changing environments!
- The article wraps up by highlighting important findings and suggesting future research that could help us learn even more!
Influence analysis of network evolution on Parrondo effect.
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February 2024
School of Management Science and Engineering, Anhui University of Technology, Anhui Ma'anshan, 243002, China. Electronic address:
Parrondo's paradox is a scheme used to describe an interesting paradoxical situation that a losing Game A and a losing Game B played randomly or periodically will produce a winning result. Here, a dynamic process of network evolution of Link A + Game B is proposed to yield the Parrondo effect. Game B with two asymmetric branches depends on the relative comparison between the capital of the network node and the average capital of all its neighbors.
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