A new SEIAR model on small-world networks to assess the intervention measures in the COVID-19 pandemics

Li, Jie; Zhong, Jiu Chang; Ji, Yong-Mao; Yang, Fang

doi:10.1016/j.rinp.2021.104283

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…Chronic wasting disease (CWD), a highly contagious prion disease, is another disease of concern for WTD and other cervids [ 13 ]. Social network analysis has provided communicable disease modeling, which has proven useful for developing control strategies for human contagions such as HIV, the SARS virus, SARS-CoV-2, and even E. coli outbreaks to name just a few [ 29 , 30 , 31 , 32 , 33 ]. Network analysis has also provided insight for management strategies for livestock diseases [ 34 , 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Identifying Potential Super-Spreaders and Disease Transmission Hotspots Using White-Tailed Deer Scraping Networks

Hearst

Huang

Johnson

et al. 2023

Animals

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White-tailed deer (Odocoileus virginianus, WTD) spread communicable diseases such the zoonotic coronavirus SARS-CoV-2, which is a major public health concern, and chronic wasting disease (CWD), a fatal, highly contagious prion disease occurring in cervids. Currently, it is not well understood how WTD are spreading these diseases. In this paper, we speculate that “super-spreaders” mediate disease transmission via direct social interactions and indirectly via body fluids exchanged at scrape sites. Super-spreaders are infected individuals that infect more contacts than other infectious individuals within a population. In this study, we used network analysis from scrape visitation data to identify potential super-spreaders among multiple communities of a rural WTD herd. We combined local network communities to form a large region-wide social network consisting of 96 male WTD. Analysis of WTD bachelor groups and random network modeling demonstrated that scraping networks depict real social networks, allowing detection of direct and indirect contacts, which could spread diseases. Using this regional network, we model three major types of potential super-spreaders of communicable disease: in-degree, out-degree, and betweenness potential super-spreaders. We found out-degree and betweenness potential super-spreaders to be critical for disease transmission across multiple communities. Analysis of age structure revealed that potential super-spreaders were mostly young males, less than 2.5 years of age. We also used social network analysis to measure the outbreak potential across the landscape using a new technique to locate disease transmission hotspots. To model indirect transmission risk, we developed the first scrape-to-scrape network model demonstrating connectivity of scrape sites. Comparing scrape betweenness scores allowed us to locate high-risk transmission crossroads between communities. We also monitored predator activity, hunting activity, and hunter harvests to better understand how predation influences social networks and potential disease transmission. We found that predator activity significantly influenced the age structure of scraping communities. We assessed disease-management strategies by social-network modeling using hunter harvests or removal of potential super-spreaders, which fragmented WTD social networks reducing the potential spread of disease. Overall, this study demonstrates a model capable of predicting potential super-spreaders of diseases, outlines methods to locate transmission hotspots and community crossroads, and provides new insight for disease management and outbreak prevention strategies.

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Section: Discussionmentioning

confidence: 99%

Identifying Potential Super-Spreaders and Disease Transmission Hotspots Using White-Tailed Deer Scraping Networks

Hearst

Huang

Johnson

et al. 2023

Animals

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“…This theory has gained further support and development within the context of social networks and big data analysis [ 39 , 40 ]. Additionally, the small-world network model effectively simulates real-world social networks’ structure, particularly in describing population clustering and social interactions [ 41 , 42 ]; After January 23rd, Wuhan was “closed,” all public transportation was halted, and all long-distance connections were cut off. On February 10th, Wuhan carried out the closed management of the neighborhood, the social contact between the communities was cut off, and all network connections were removed.…”

Section: Methods and Study Designmentioning

confidence: 99%

Modeling COVID-19 spread using multi-agent simulation with small-world network approach

Fan,

Li,

Chen

et al. 2024

BMC Public Health

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Background The rapid global spread of COVID-19 has seriously impacted people’s daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems. Methods We propose a spatiotemporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents’ social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved. Results Experimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating 1 hundred times. Additionally, the model simulates and compares different “city closure” scenarios. The results showed that implementing a ‘lockdown’ 10 days earlier would lead to the peak number of infections occurring 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections. Discussion Our methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data.

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“…For instance, the SEIAR model adds an asymptomatic compartment to the SEIR model (e.g., ref. [53]). In such a model, asymptomatic individuals cause infections at a different rate than symptomatic individuals but everyone is equally susceptible and connectivity is homogeneous.…”

Section: Heterogeneity In Infectiousness and Susceptibilitymentioning

confidence: 99%

Modeling Complex Systems: A Case Study of Compartmental Models in Epidemiology

2022

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Compartmental epidemic models have been widely used for predicting the course of epidemics, from estimating the basic reproduction number to guiding intervention policies. Studies commonly acknowledge these models’ assumptions but less often justify their validity in the specific context in which they are being used. Our purpose is not to argue for specific alternatives or modifications to compartmental models, but rather to show how assumptions can constrain model outcomes to a narrow portion of the wide landscape of potential epidemic behaviors. This concrete examination of well-known models also serves to illustrate general principles of modeling that can be applied in other contexts.

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A new SEIAR model on small-world networks to assess the intervention measures in the COVID-19 pandemics

Cited by 14 publications

References 31 publications

Identifying Potential Super-Spreaders and Disease Transmission Hotspots Using White-Tailed Deer Scraping Networks

Identifying Potential Super-Spreaders and Disease Transmission Hotspots Using White-Tailed Deer Scraping Networks

Modeling COVID-19 spread using multi-agent simulation with small-world network approach

Modeling Complex Systems: A Case Study of Compartmental Models in Epidemiology

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