Resilience management during large-scale epidemic outbreaks

Competing control scenarios in probabilistic SIR epidemics on social-contact networks

A probabilistic approach to the epidemic evolution on realistic social-contact networks allows for characteristic differences among subjects, including the individual number and structure of social contacts, and the heterogeneity of the infection and recovery rates according to age or medical preconditions. Within our probabilistic Susceptible-Infectious-Removed (SIR) model on social-contact networks, we evaluate the infection load or activation margin of various control scenarios; by confinement, by vaccination, and by their combination. We compare the epidemic burden for subpopulations that apply competing or cooperative control strategies. The simulation experiments are conducted on randomized social-contact graphs that are designed to exhibit realistic person-person contact characteristics and which follow near homogeneous or block-localized subpopulation spreading. The scalarization method is used for the multi-objective optimization problem in which both the infection load is minimized and the extent to which each subpopulation's control strategy preference ranking is adhered to is maximized. We obtain the compounded payoff matrices for two subpopulations that impose contrasting control strategies, each according to their proper ranked control strategy preferences. The Nash equilibria, according to each subpopulation's compounded objective, and according to their proper ranking intensity, are discussed. Finally, the interaction effects of the control strategies are discussed and related to the type of spreading of the two subpopulations.

Community resilience to pandemics: An assessment framework developed based on the review of COVID-19 literature

The COVID-19 outbreak in 2019 and the challenges it posed to communities around the world, demonstrated the necessity of enhancing the resilience of communities to pandemics. In this regard, assessment frameworks can play an essential role and guide resilience-building efforts. However, the lack of a comprehensive assessment framework has led to a focus on sectoral evaluation. This study aims to propose an integrated framework for assessing the pandemic resilience of communities. For this purpose, we rely on a systematic review of literature indexed in major academic databases. We have thoroughly analyzed a total number of 115 related documents to extract relevant criteria. Findings show that many criteria and factors affect community resilience to pandemics. By inductive content coding in MAXQDA software, we have categorized these criteria into five dimensions of Institutional, Social, Economic, Infrastructural, and Demographic. Good leadership and management, insurance and governmental support, planning and preparation, expertise and labor, and available equipment and technologies are the most important institutional criteria. Communication and collective identity, mutual support, public safety and protection, public awareness, and social justice are the influential social criteria. Economic sustainability and resource availability are criteria of economic resilience. Sufficiency of services, public spaces, housing tenure, and transportation system are the main criteria related to the built environment and infrastructural dimension. Finally, demographic resilience includes physical health, psychological well-being, life quality, and hygiene. Based on these criteria, this study develops an integrated evaluation framework that researchers can implement along with conventional assessment and ranking methods to determine the level of community resilience to pandemics.

A. M, Park A, Parveen S, Kim S. Evolution and consequences of individual responses during the COVID-19 outbreak

In a long-lasting major disease outbreak such as that of COVID-19, the challenge for public health authorities is to keep people motivated and keen on following safety guidelines. In this study, a compartmental model with a heterogeneous transmission rate (based on awareness) is utilized to hypothesize about the public adoption of preventive guidelines. Three subsequent outbreaks in South Korea, Pakistan, and Japan were analyzed as case studies. The transmission, behavior change, and behavioral change ease rates of the disease were measured in these countries. The parameters were estimated using the maximum likelihood method with an additional identifiability analysis performed to determine the uniqueness of the estimated parameters for quantitatively comparing them during the first three waves of COVID-19. The mathematical analysis and simulation results show that individual responses had a significant effect on the outbreak. Individuals declining to follow the public health guidelines in Korea and Japan between the second and third waves contributed to making the third peak the highest of the three peaks. In Pakistan, however, individual responses to following public health guidelines were maintained between the second and third waves, resulting in the third peak being lower than the first, rather than being associated with the highest transmission rate. Thus, maintaining a high level of awareness is critical for containing the spread. Improvised public health campaigns are recommended to sustain individual attention and maintain a high level of awareness.

Creating Quality-Based Smart Sustainable Public Parking Enterprises: A Methodology to Reframe Organizations into Smart Organizations

Enterprise sustainability is a key aim in the fourth industrial revolution era, requiring a new approach based on intelligent technologies that considers the new roles of leadership and sustainability as well as the new trends in emerging smart technologies, with a new focus on Society 5.0. Smart parking has a significant role in fostering the determinants of sustainability in public parking enterprises and achieving adequate mobility in smart cities. Thus, smart parking is the subject of the research presented in this paper. This study defines the vital processes, including leadership processes and technologies needed for smart parking, managed by innovative public parking enterprises. Having this in mind, trends, key facts, the results of present innovative technology enterprises, and methodologies for designing and establishing smart public parking enterprises are analyzed. This paper aims to determine the sustainability of parking enterprises in their current states by developing a MORSO methodology. The MORSO methodology includes independent variables, including the leadership level of the intelligent technologies used, quality of the business processes, and risk related to the business processes, and a dependent variable, the sustainability of smart public parking enterprises. The MORSO methodology also includes steps for the definition of indices related to variables that could be assessed by appropriate techniques such as using questionnaires. Finally, the MORSO methodology introduces steps by which statistical approaches and artificial neural networks (ANN) are applied to test hypotheses regarding correlations between independent and dependent variables. The results of the presented model case study application show that there are strong correlations between smart sustainability and leadership (0.769), quality (0.904), and risk (−0.884), respectively. Additionally, at the level of the presented case study, the results of the application of the ANN indicate that the values of the dependent variable in the following time period can be determined with high accuracy, based on the knowledge of the values from the previous period, with a regression coefficient value of R = 0.99482. Finally, in this way, the transition from existing public enterprises to sustainable smart public parking enterprises is envisioned.

A Perspective towards Multi-Hazard Resilient Systems: Natural Hazards and Pandemics

The concept of resilience has been used extensively across the sciences in engineering and the humanities. It is applied to ecology, medicine, economics, and psychology. The novel coronavirus disease (COVID-19) has posed an extraordinary challenge to the resilience of healthcare systems, communities, and nations and has profoundly altered our previous day-to-day operations. This paper presents a discussion of the definitions and characteristics of resilient systems. Scenarios are utilized to qualitatively explore key relationships, responses, and paths for recovery across different system types. The purpose is to develop an integrated approach that can accommodate simultaneous threats to system resilience, in particular, impacts from a natural hazard in conjunction with COVID-19. This manuscript is the first to advocate for more in-depth and quantitative research utilizing transdisciplinary approaches that can accommodate considerations across our built environment and healthcare system infrastructures in pursuit of designing systems that are resilient to both natural hazards and pandemic impacts.

Accuracy of a one-dimensional reduction of dynamical systems on networks

Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)0028-083610.1038/nature16948] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.

Mobility in times of pandemics: Evidence on the spread of COVID19 in Italy's labour market areas

We investigate the interplay between the local spread of COVID-19 and patterns of individual mobility within and across self-contained geographical areas. Conceptually, we connect the debate on regional development in the presence of shocks with the literature on spatial labour markets and address some research questions about the role of individual mobility in affecting the spread of the disease. By looking at granular flows of Facebook users moving within and across Italian labour market areas (LMAs), we analyse whether their heterogeneous internal and external mobility has had a significant impact on excess mortality. We also explore how individual mobility plays different roles in LMAs hosting industrial districts - characterised by a thicker local labour market and denser business and social interactions - and with a high presence of "essential sectors" - activities not affected by the COVID-19 containment measures taken by the Italian government at the onset of the crisis.

Impact of non-pharmaceutical interventions against COVID-19 in Europe in 2020: a quasi-experimental non-equivalent group and time series design study

IntroductionThe current pandemic of coronavirus disease (COVID-19) is unparalleled in recent history as are the social distancing interventions that have led to a considerable halt on the economic and social life of so many countries.AimWe aimed to generate empirical evidence about which social distancing measures had the most impact in reducing case counts and mortality.MethodsWe report a quasi-experimental (observational) study of the impact of various interventions for control of the outbreak through 24 April 2020. Chronological data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Prevention and Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our complementary analyses were modelled in R using Bayesian generalised additive mixed models and in STATA using multilevel mixed-effects regression models.ResultsFrom both sets of modelling, we found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay-at-home orders and closure of additional non-essential businesses was not associated with any independent additional impact.ConclusionsOur findings are that schools and some non-essential businesses operating 'as normal' as well as allowing mass gatherings were incompatible with suppressing disease spread. Closure of all businesses and stay at home orders are less likely to be required to keep disease incidence low. Our results help identify what were the most effective non-pharmaceutical interventions in this period.

Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

BACKGROUND

As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases.

METHODS

In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan.

RESULTS

We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact.

CONCLUSIONS

To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

System models for resilience in gerontology: application to the COVID-19 pandemic

The care needs for aging adults are increasing burdens on health systems around the world. Efforts minimizing risk to improve quality of life and aging have proven moderately successful, but acute shocks and chronic stressors to an individual's systemic physical and cognitive functions may accelerate their inevitable degradations. A framework for resilience to the challenges associated with aging is required to complement on-going risk reduction policies, programs and interventions. Studies measuring resilience among the elderly at the individual level have not produced a standard methodology. Moreover, resilience measurements need to incorporate external structural and system-level factors that determine the resources that adults can access while recovering from aging-related adversities. We use the National Academies of Science conceptualization of resilience for natural disasters to frame resilience for aging adults. This enables development of a generalized theory of resilience for different individual and structural contexts and populations, including a specific application to the COVID-19 pandemic.

A perspective on early detection systems models for COVID-19 spreading

The ongoing COVID-19 epidemic highlights the need for effective tools capable of predicting the onset of infection outbreaks at their early stages. The tracing of confirmed cases and the prediction of the local dynamics of contagion through early indicators are crucial measures to a successful fight against emerging infectious diseases (EID).

The proposed framework is model-free and applies Early Warning Detection Systems (EWDS) techniques to detect changes in the territorial spread of infections in the very early stages of onset. This study uses publicly available raw data on the spread of SARS-CoV-2 mainly sourced from the database of the Italian Civil Protection Department.

Two distinct EWDS approaches, the Hub-Jones (H&J) and Strozzi-Zaldivar (S&Z), are adapted and applied to the current SARS-CoV-2 outbreak. They promptly generate warning signals and detect the onset of an epidemic at early surveillance stages even if working on the limited daily available, open-source data.

Additionally, EWDS S&Z criterion is theoretically validated on the basis of the epidemiological SIR.

Discussed EWDS successfully analyze self-accelerating systems, like the SARS-CoV-2 scenario, to precociously identify an epidemic spread through the calculation of onset parameters. This approach can also facilitate early clustering detection, further supporting common fight strategies against the spread of EIDs. Overall, we are presenting an effective tool based on solid scientific and methodological foundations to be used to complement medical actions to contrast the spread of infections such as COVID-19.

Regional Resilience in Times of a Pandemic Crisis: The Case of COVID-19 in China

The notion of resilience to analyse how fast systems recover from shocks has been increasingly taken up in economic geography, in which there is a burgeoning literature on regional resilience. Regional resilience is a place-sensitive, multi-layered and multi-scalar, conflict-ridden and highly contingent process. The nature of shocks is one important impact factor on regional resilience. Arguably, so far, most literature on regional resilience has dealt with the financial crisis in 2008/2009. In this research note, we will analyse both the particular characteristics of the current COVID-19 crisis, as well as its effects on regional recovery and potential resilience in China, where it started. We conclude that a complex combination of the characteristics of the current COVID-19 crisis, the institutional experience of dealing with previous pandemic and epidemic crises, government support schemes, as well as regional industrial structures, might potentially affect the recovery and resilience rates of Chinese regions.

Assessing resilience of healthcare infrastructure exposed to COVID-19: emerging risks, resilience indicators, interdependencies and international standards

In the moment of preparation of this paper, the world is still globally in grip of the Corona (COVID-19) crisis, and the need to understand the broader overall framework of the crisis increases. As in similar cases in the past, also with this one, the main interest is on the “first response”. Fully appreciating the efforts of those risking their lives facing pandemics, this paper tries to identify the main elements of the larger, possibly global, framework, supported by international standards, needed to deal with new (emerging) risks resulting from threats like Corona and assess the resilience of systems affected. The paper proposes that future solutions should include a number of new elements, related to both risk and resilience. That should include broadening the scope of attention, currently focused onto preparation and response phases, to the phases of “understanding risks”, including emerging risks, and transformation and adaptation. The paper suggests to use resilience indicators in this process. The proposed approach has been applied in different cases involving critical infrastructures in Europe (energy supply, water supply, transportation, etc., exposed to various threats), including the health system in Austria. The detailed, indicator-based, resilience analysis included mapping resilience, resilience stress-testing, visualization, etc., showing, already before the COVID-19, the resilience (stress-testing) limits of the infrastructures. A simpler (57 indicator based) analysis has, then been done for 11 countries (including Austria). The paper links these results with the options available in the area of policies, standards, guidelines and tools (such as the RiskRadar), with focus on interdependencies and global standards—especially the new ISO 31,050, linking emerging risks and resilience.

Bouncing forward: a resilience approach to dealing with COVID-19 and future systemic shocks

Policy questions are often framed in popular discussion as situations where pulling the right levers will get the economy and society back on track after shocks and crises. This approach ignores how systems interact and how their systemic properties shape socioeconomic outcomes, leading to an over-emphasis on a limited set of characteristics, notably efficiency. We argue that this emphasis on efficiency in the operation, management and outcomes of various economic and social systems is not a conscious collective choice, but rather the response of the whole system to the incentives that individual components face. This has brought much of the world to rely upon complex, nested, and interconnected systems to deliver goods and services around the globe. While this approach has many benefits, the Covid-19 crisis shows how it has also reduced the resilience of key systems to shocks, and allowed failures to cascade from one system to others. This paper reviews the impact of COVID-19 on socioeconomic systems, discusses the notion of resilience, and provides specific recommendations on both integrating resilience analytics for recovery from the current crisis as well as on building resilient infrastructure to address future systemic challenges.

Recovery during a crisis: facing the challenges of risk assessment and resilience management of COVID-19

The paper offers a disaster risk management perspective to analyze the COVID-19 pandemic and to propose and assess non-pharmaceutical mitigation measures for the recovery phase. Three main aspects are tackled: (i) the need to take a scenario-based approach; (i) the need to propose more fine-tuned and context-sensitive mitigation measures, the effectiveness and the cost-benefit of which must be carefully appraised; (iii) better communication as a fundamental pillar of any mitigation measure. Evidence and ideas from the field of natural disasters and man-made technological incidents are applied to tackle the health risk posed by the SARS-COV 2 virus and its rapid spread according to a multi-disciplinary perspective that addresses the health-related challenges and the need to avoid societal and economic breakdown.

Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models

Metapopulation epidemic models describe epidemic dynamics in networks of spatially distant patches connected via pathways for migration of individuals. In the present study, we deal with a susceptible-infected-recovered (SIR) metapopulation model where the epidemic process in each patch is represented by an SIR model and the mobility of individuals is assumed to be a homogeneous diffusion. We consider two types of patches including high-risk and low-risk ones under the assumption that a local patch is changed from a high-risk one to a low-risk one by an intervention. We theoretically analyze the intervention threshold which indicates the critical fraction of low-risk patches for preventing a global epidemic outbreak. We show that an intervention targeted to high-degree patches is more effective for epidemic control than a random intervention. The theoretical results are validated by Monte Carlo simulations for synthetic and realistic scale-free patch networks. The theoretical results also reveal that the intervention threshold depends on the human mobility network and the mobility rate. Our approach is useful for exploring better local interventions aimed at containment of epidemics.

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.

How Cities Cope in Outbreak Events?

An outbreak can cause more problems than just the spread of disease. It can be an antagonistic nemesis to our cities and communities, particularly if we lack preparedness and resilience. Its progress is usually unclear as it can be completely different from case to case, and can react differently in different contexts and with different groups of people. Such reactions may purely relate to climatic conditions, hygienic status, and environmental attributes of the context. Those reactions can also differ from one group of people to another, while the disease has to find its correct host as well the way it can transmit and evolve. Consequently, the magnitude of impacts would depend on many factors, of which the nature of the disease is very important during the whole outbreak progress.

Assessing the interplay between human mobility and mosquito borne diseases in urban environments

Urbanization drives the epidemiology of infectious diseases to many threats and new challenges. In this research, we study the interplay between human mobility and dengue outbreaks in the complex urban environment of the city-state of Singapore. We integrate both stylized and mobile phone data-driven mobility patterns in an agent-based transmission model in which humans and mosquitoes are represented as agents that go through the epidemic states of dengue. We monitor with numerical simulations the system-level response to the epidemic by comparing our results with the observed cases reported during the 2013 and 2014 outbreaks. Our results show that human mobility is a major factor in the spread of vector-borne diseases such as dengue even on the short scale corresponding to intra-city distances. We finally discuss the advantages and the limits of mobile phone data and potential alternatives for assessing valuable mobility patterns for modeling vector-borne diseases outbreaks in cities.

Understanding the temporal pattern of spreading in heterogeneous networks: Theory of the mean infection time

For a reliable prediction of an epidemic or information spreading pattern in complex systems, well-defined measures are essential. In the susceptible-infected model on heterogeneous networks, the cluster of infected nodes in the intermediate-time regime exhibits too large fluctuation in size to use its mean size as a representative value. The cluster size follows quite a broad distribution, which is shown to be derived from the variation of the cluster size with the time when a hub node was first infected. On the contrary, the distribution of the time taken to infect a given number of nodes is well concentrated at its mean, suggesting the mean infection time is a better measure. We show that the mean infection time can be evaluated by using the scaling behaviors of the boundary area of the infected cluster and use it to find a nonexponential but algebraic spreading phase in the intermediate stage on strongly heterogeneous networks. Such slow spreading originates in only small-degree nodes left susceptible, while most hub nodes are already infected in the early exponential-spreading stage. Our results offer a way to detour around large statistical fluctuations and quantify reliably the temporal pattern of spread under structural heterogeneity.

A decision-support framework to optimize border control for global outbreak mitigation

The introduction and spread of emerging infectious diseases is increasing in both prevalence and scale. Whether naturally, accidentally or maliciously introduced, the substantial uncertainty surrounding the emergence of novel viruses, specifically where they may come from and how they will spread, demands robust and quantifiably validated outbreak control policies that can be implemented in real time. This work presents a novel mathematical modeling framework that integrates both outbreak dynamics and outbreak control into a decision support tool for mitigating infectious disease pandemics that spread through passenger air travel. An ensemble of border control strategies that exploit properties of the air traffic network structure and expected outbreak behavior are proposed. A stochastic metapopulation epidemic model is developed to evaluate and rank the control strategies based on their effectiveness in reducing the spread of outbreaks. Sensitivity analyses are conducted to illustrate the robustness of the proposed control strategies across a range of outbreak scenarios, and a case study is presented for the 2009 H1N1 influenza pandemic. This study highlights the importance of strategically allocating outbreak control resources, and the results can be used to identify the most robust border control policy that can be implemented in the early stages of an outbreak.

Percolation and Internet Science

Percolation, in its most general interpretation, refers to the “flow” of something (a physical agent, data or information) in a network, possibly accompanied by some nonlinear dynamical processes on the network nodes (sometimes denoted reaction–diffusion systems, voter or opinion formation models, etc.). Originated in the domain of theoretical and matter physics, it has many applications in epidemiology, sociology and, of course, computer and Internet sciences. In this review, we illustrate some aspects of percolation theory and its generalization, cellular automata and briefly discuss their relationship with equilibrium systems (Ising and Potts models). We present a model of opinion spreading, the role of the topology of the network to induce coherent oscillations and the influence (and advantages) of risk perception for stopping epidemics. The models and computational tools that are briefly presented here have an application to the filtering of tainted information in automatic trading. Finally, we introduce the open problem of controlling percolation and other processes on distributed systems.

Lessons from History

Resilience has a lengthy history of practice and implementation for events of extreme consequence and high uncertainty. One of the clearest cases of embryonic resilience thinking includes Medieval Venice, which was forced to grapple with the recurring threat of plague that threatened to destroy the fabric of European society and cripple the juggernaut of Venetian maritime trade (Linkov et al. 2014a, b, c, d, e; Lane 1973). This early resilience thinking did not fully inoculate Venetian society from the ravages of disease—on the contrary, limitations of medical knowledge and border control allowed for outbreaks throughout the early modern era—yet it did allow Venetian policymakers to begin to address the question of how to combat deadly disease. The cumulative successes in reducing disease incidence and spread throughout the city and its dependent settlements eventually brought policymakers to embrace resilience thinking for other unrelated projects ranging from climate change to land reclamation efforts—all centered on the idea of strengthening Venetian social, economic, and cultural capabilities in the midst of an uncertain future (Vergano and Nunes 2007; Linkov et al. 2014a, b, c, d, e). This all goes to show that while resilience thinking and resilience analysis are growing buzzwords in the early twenty-first century, their roots go back centuries before even the printing press or functional medicine.

Factors influencing message dissemination through social media

Online social networks strongly impact our daily lives. An internet user (a "Netizen") wants messages to be efficiently disseminated. The susceptible-infected-recovered (SIR) dissemination model is the traditional tool for exploring the spreading mechanism of information diffusion. We here test our SIR-based dissemination model on open and real-world data collected from Twitter. We locate and identify phase transitions in the message dissemination process. We find that message content is a stronger factor than the popularity of the sender. We also find that the probability that a message will be forwarded has a threshold that affects its ability to spread, and when the probability is above the threshold the message quickly achieves mass dissemination.