Co-evolutionary theory assumes co-adapted characteristics are a positive response to counter those of another species,whereby co-evolved species reach an evolutionarily stable interaction through bilateral adaptation....Co-evolutionary theory assumes co-adapted characteristics are a positive response to counter those of another species,whereby co-evolved species reach an evolutionarily stable interaction through bilateral adaptation.However,evidence from the fig-fig wasp mutualistic system implies very different co-evolutionary selection mechanisms,due to the inherent conflict among interacted partners.Fig plants appear to have discriminatively enforced fig wasps to evolve"adaptation characteristics"that provide greater benefit to the fig,and fig wasps appear to have diversified their evolutionary strategies in response to discriminative enforcement by figs and competition among different fig wasp species.In what appears to be an asymmetric interaction,the prosperity of cooperative pollinating wasps should inevitably lead to population increases of parasitic individuals,thus resulting in localized extinctions of pollinating wasps.In response,the sanctioning of parasitic wasps by the fig should lead to a reduction in the parasitic wasp population.The meta-populations created by such asymmetric interactions may result in each population of coevolved species chaotically oscillated,temporally or evolutionarily.展开更多
We present in this article an epidemic model with saturated in metapopulation setting. We develop the mathematical modelling of HIV transmission among adults in Metapopulation setting. We discussed the positivity of t...We present in this article an epidemic model with saturated in metapopulation setting. We develop the mathematical modelling of HIV transmission among adults in Metapopulation setting. We discussed the positivity of the system. We calculated the reproduction number, If ?for , then each infectious individual in Sub-Population j infects on average less than one other person and the disease is likely to die out. Otherwise, if ?for , then each infectious individual in Sub-Population j infects on average more than one other person;the infection could therefore establish itself in the population and become endemic. An epidemic model, where the presence or absence of an epidemic wave is characterized by the value of ?both ideas of the inner equilibrium point of stability properties are discussed.展开更多
In this paper, we study a stochastic epidemic model in Meta-population setting. The stochastic model is obtained from the deterministic model by set up random perturbations about the endemic equilibrium state. The out...In this paper, we study a stochastic epidemic model in Meta-population setting. The stochastic model is obtained from the deterministic model by set up random perturbations about the endemic equilibrium state. The outcome of random perturbations on the stability actions of endemic equilibrium is discussed. Stability of the two equilibriums is studied using the Lyapunov function.展开更多
Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine...Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine-scale transmission patterns via deep learning.Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.First,in Hong Kong,China,we construct the mobility trajectories of confirmed cases using their visiting records.Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution.Integrating the spatial and temporal information,we represent the TransCode via spatiotemporal transmission networks.Further,we propose a deep transfer learning model to adapt the TransCode of Hong Kong,China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises:New York City,San Francisco,Toronto,London,Berlin,and Tokyo,where fine-scale data are limited.All the data used in this study are publicly available.Results The TransCode of Hong Kong,China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns(e.g.,the imported and exported transmission intensities)at the district and constituency levels during different COVID-19 outbreaks waves.By adapting the TransCode of Hong Kong,China to other data-limited densely populated metropolises,the proposed method outperforms other representative methods by more than 10%in terms of the prediction accuracy of the disease dynamics(i.e.,the trend of case numbers),and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.Conclusions The fine-scale transmission patterns due to the metapopulation level mobility(e.g.,travel across different districts)and contact behaviors(e.g.,gathering in social-economic centers)are one of the main contributors to the rapid spread of the virus.Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.展开更多
基金supported by the National Natural Science Foundation of China (NSFC) (31270433,31170408)National Science Fund for Distinguished Young Scholars (31325005)+2 种基金NSFC-Yunnan United Fund (U1302267)the West Light Foundation of the Chinese Academy of Sciencesthe Special Fund for the Excellent Youth of the Chinese Academy of Sciences (KSCX2-EW-Q-9)
文摘Co-evolutionary theory assumes co-adapted characteristics are a positive response to counter those of another species,whereby co-evolved species reach an evolutionarily stable interaction through bilateral adaptation.However,evidence from the fig-fig wasp mutualistic system implies very different co-evolutionary selection mechanisms,due to the inherent conflict among interacted partners.Fig plants appear to have discriminatively enforced fig wasps to evolve"adaptation characteristics"that provide greater benefit to the fig,and fig wasps appear to have diversified their evolutionary strategies in response to discriminative enforcement by figs and competition among different fig wasp species.In what appears to be an asymmetric interaction,the prosperity of cooperative pollinating wasps should inevitably lead to population increases of parasitic individuals,thus resulting in localized extinctions of pollinating wasps.In response,the sanctioning of parasitic wasps by the fig should lead to a reduction in the parasitic wasp population.The meta-populations created by such asymmetric interactions may result in each population of coevolved species chaotically oscillated,temporally or evolutionarily.
文摘We present in this article an epidemic model with saturated in metapopulation setting. We develop the mathematical modelling of HIV transmission among adults in Metapopulation setting. We discussed the positivity of the system. We calculated the reproduction number, If ?for , then each infectious individual in Sub-Population j infects on average less than one other person and the disease is likely to die out. Otherwise, if ?for , then each infectious individual in Sub-Population j infects on average more than one other person;the infection could therefore establish itself in the population and become endemic. An epidemic model, where the presence or absence of an epidemic wave is characterized by the value of ?both ideas of the inner equilibrium point of stability properties are discussed.
文摘In this paper, we study a stochastic epidemic model in Meta-population setting. The stochastic model is obtained from the deterministic model by set up random perturbations about the endemic equilibrium state. The outcome of random perturbations on the stability actions of endemic equilibrium is discussed. Stability of the two equilibriums is studied using the Lyapunov function.
基金the Ministry of Science and Technology of the People’s Republic of China(2021ZD0112501,2021ZD0112502)the Research Grants Council of Hong Kong SAR(RGC/HKBU12201318,RGC/HKBU12201619,RGC/HKBU12202220)the Guangdong Basic and Applied Basic Research Foundation(2022A1515010124).
文摘Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine-scale transmission patterns via deep learning.Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.First,in Hong Kong,China,we construct the mobility trajectories of confirmed cases using their visiting records.Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution.Integrating the spatial and temporal information,we represent the TransCode via spatiotemporal transmission networks.Further,we propose a deep transfer learning model to adapt the TransCode of Hong Kong,China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises:New York City,San Francisco,Toronto,London,Berlin,and Tokyo,where fine-scale data are limited.All the data used in this study are publicly available.Results The TransCode of Hong Kong,China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns(e.g.,the imported and exported transmission intensities)at the district and constituency levels during different COVID-19 outbreaks waves.By adapting the TransCode of Hong Kong,China to other data-limited densely populated metropolises,the proposed method outperforms other representative methods by more than 10%in terms of the prediction accuracy of the disease dynamics(i.e.,the trend of case numbers),and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.Conclusions The fine-scale transmission patterns due to the metapopulation level mobility(e.g.,travel across different districts)and contact behaviors(e.g.,gathering in social-economic centers)are one of the main contributors to the rapid spread of the virus.Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.
