Infectious diseases spreading on a metapopulation network coupled with its second-neighbor network

Feng, Shanshan; Jin, Zhen

doi:10.1016/j.amc.2019.05.005

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…The effect of nodal communities on superspreader risk merits further research. In addition, future analysis should consider the case in which movement between adjacent nodes is not symmetric, which would require a directed network graph structure for the metapopulation network, as well as networks with multiple layers of mobility [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Lieberthal

Gardner

2021

PLoS Comput Biol

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Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.

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Section: Discussionmentioning

confidence: 99%

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Lieberthal

Gardner

2021

PLoS Comput Biol

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“…Data on passenger flights between locations can help to track ZIKV spread, 29 , 39 , 40 but limit our understanding of movement to the origin and destination airports and rarely considers connections made between flights and travel made at the individual level. 53 Call data records from mobile phones offer large coverage of populations and areas and can be leveraged to measure individual-level movement in near real time. 22 , 52 Access to call data records is currently restricted to research groups and access is granted via a negotiated agreement with the operator, which makes their widespread use challenging.…”

Section: Using Human Mobility To Model Zikv Spreadmentioning

confidence: 99%

A review of models applied to the geographic spread of Zika virus

Messina

Pybus

et al. 2021

Transactions of the Royal Society of Tropical Medicine and Hygiene

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In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015–2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector–human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.

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“…Their work lays a foundation for future researches on metapopulation networks. From the point of view of network structure, Cao et al studied the rendezvous effects on bipartite metapopulation network and found that rendezvous effects made for the transmission of infectious diseases [3] ; Liu et al investigated time-varying metapopulation networks, which slowed down the spread of infectious diseases [4] ; Mata et al studied local subpopulation structure on metapopulation networks, that is, individuals within a subpopulation were not well-mixed but within a social network [5] ; we defined a second-neighbor network (SNN) and investigated the global spread of infectious disease on a metapopulation network coupled with its SNN [6] . In order to avoid being infected, individuals may respond to infectious diseases.…”

Section: Introductionmentioning

confidence: 99%

Infectious Diseases Spreading on an Adaptive Metapopulation Network

Feng

Jin

2020

IEEE Access

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When an emerging acute infectious disease occurs, travel restrictions, one-way or two-way, are often taken to prevent its global spread. In order to investigate the impact of two-way travel restrictions in the global spread of infectious disease, this paper defines a risk indicator according to the relative infection density and defines an adaptive metapopulation network based on this risk indicator and an intervention time on two-way travel restrictions. Then a susceptible-infectious-removed (SIR) metapopulation model on this network is proposed. The mathematical analysis shows that the basic reproduction number is independent of human mobility. Furthermore, this essay compares the effects of one-way travel restrictions and two-way travel restrictions on the global spread of infectious disease. It is shown that that the adaptive metapopulation network under two-way travel restrictions can effectively suppress the global spread of infectious diseases. We also obtain a threshold of risk indicator to prevent the global spread of infectious disease by simulations. The earlier the intervention time on two-way travel restriction is, the better to curb the global spread of the disease. Even if two-way travel restrictions are not implemented, controlling the mobility of infectious persons would help prevent the global spread of the disease. This work will throw lights on the prevention and control of the globally spreading of disease.

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Infectious diseases spreading on a metapopulation network coupled with its second-neighbor network

Cited by 7 publications

References 24 publications

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

A review of models applied to the geographic spread of Zika virus

Infectious Diseases Spreading on an Adaptive Metapopulation Network

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