Infectious Diseases Spreading on an Adaptive Metapopulation Network

Feng, Shanshan; Jin, Zhen

doi:10.1109/access.2020.3016016

Cited by 15 publications

(5 citation statements)

References 19 publications

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“…In addition to predicting superspreader risks within a metapopulation network, the model discussed in this paper may help predict the relative effectiveness of various epidemic mitigation strategies [ 51 , 55 ]. For example, closing airports or blocking highways has the effect of reducing a node’s degree of connectivity and centrality, although the effectiveness of these methods is under question [ 56 ], and they require substantial social data to develop an accurate mobility model [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

“…The second form consists of a few hotspot nodes of high R, surrounded by a network with low to moderate R. This is relevant to diseases that are prevalent in high-population centers [49], and these locales tend to be particularly effective superspreaders relative to their neighbors. The third and fourth forms, which are not addressed in this paper, are cases in which R can vary over time, usually as a response to changes in policy during an ongoing epidemic [50,51], and cases in which R can vary within a node, such as diseases that have varying R values among different species [52]. Both these cases require alternative methods of measuring superspreader capacity than what is considered in this article.…”

Section: Plos Computational Biologymentioning

confidence: 99%

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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%

Section: Plos Computational Biologymentioning

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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“…The last few years have seen a significant increase in interest in epidemic mathematical models inside various formal frameworks [14]- [15]. Some of these models are expressed using dynamic systems, control theory, differential, difference, and hybrid equations [16]- [23], information theory, [24], etc. Modeling infectious illnesses is a fascinating area of mathematical biology.…”

Section: Mathematical Model Of Sirmentioning

confidence: 99%

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Abougarair,

Elwefati

2023

IJRCS

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Developing effective strategies to contain the spread of infectious diseases, particularly in the case of rapidly evolving outbreaks like COVID-19, remains a pressing challenge. The Susceptible-Infected-Recovery (SIR) model, a fundamental tool in epidemiology, offers insights into disease dynamics. The SIR system exhibits complex nonlinear relationships between the input variables (e.g., population, infection rate, recovery rate) and the output variables (e.g., the number of infected individuals over time). We employ Recurrent Neural Networks (RNNs) to model the SIR system due to their ability to capture sequential dependencies and handle time-series data effectively. RNNs, with their ability to model nonlinear functions, can capture these intricate relationships, enabling accurate predictions and understanding of the dynamics of the system. Additionally, we apply the Pontryagin Minimum Principle (PMP) based different control strategies to formulate an optimal control approach aimed at maximizing the recovery rate while minimizing the number of affected individuals and achieving a balance between minimizing costs and satisfying constraints. This can include optimizing vaccination strategies, quarantine measures, treatment allocation, and resource allocation. The findings of this research indicate that the proposed modeling and control approach shows potential for a comprehensive analysis of viral spread, providing valuable insights and strategies for disease management on a global level. By integrating epidemiological modeling with intelligent control techniques, we contribute to the ongoing efforts aimed at combating infectious diseases on a larger scale.

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“…There is a long history of using metapopulation models to encode the spatial and coupled structure between populations in epidemiology [39] and also specifically for COVID-19 [40][41][42]. This literature includes models accounting for how individuals might adapt their mobility in response to an epidemic [43][44][45]. Importantly, this adaptive behavior change is always a bottom-up response to the epidemic itself (i.e., one individual choosing to avoid infectious contacts or move due to the local prevalence of a disease).…”

Section: A Closir Model On Network Of Interconnected Populationsmentioning

confidence: 99%

The unintended consequences of inconsistent closure policies and mobility restrictions during epidemics

Althouse,

Wallace,

Case

et al. 2023

BMC Global Public Health

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Background Controlling the spread of infectious diseases―even when safe, transmission-blocking vaccines are available―may require the effective use of non-pharmaceutical interventions (NPIs), e.g., mask wearing, testing, limits on group sizes, venue closure. During the SARS-CoV-2 pandemic, many countries implemented NPIs inconsistently in space and time. This inconsistency was especially pronounced for policies in the United States of America (US) related to venue closure. Methods Here, we investigate the impact of inconsistent policies associated with venue closure using mathematical modeling and high-resolution human mobility, Google search, and county-level SARS-CoV-2 incidence data from the USA. Specifically, we look at high-resolution location data and perform a US-county-level analysis of nearly 8 million SARS-CoV-2 cases and 150 million location visits, including 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7662 grocery stores, and 13.5 million gym visitors across 5483 gyms. Results Analyzing the interaction between venue closure and changing mobility using a mathematical model shows that, across a broad range of model parameters, inconsistent or partial closure can be worse in terms of disease transmission as compared to scenarios with no closures at all. Importantly, changes in mobility patterns due to epidemic control measures can lead to increase in the future number of cases. In the most severe cases, individuals traveling to neighboring jurisdictions with different closure policies can result in an outbreak that would otherwise have been contained. To motivate our mathematical models, we turn to mobility data and find that while stay-at-home orders and closures decreased contacts in most areas of the USA, some specific activities and venues saw an increase in attendance and an increase in the distance visitors traveled to attend. We support this finding using search query data, which clearly shows a shift in information seeking behavior concurrent with the changing mobility patterns. Conclusions While coarse-grained observations are not sufficient to validate our models, taken together, they highlight the potential unintended consequences of inconsistent epidemic control policies related to venue closure and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic.

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Infectious Diseases Spreading on an Adaptive Metapopulation Network

Cited by 15 publications

References 19 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

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

The unintended consequences of inconsistent closure policies and mobility restrictions during epidemics

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