Epidemiology. Social factors in epidemiology

Adaptive human behaviour modulates the impact of immune life history and vaccination on long-term epidemic dynamics

The multiple immunity responses exhibited in the population and co-circulating variants documented during pandemics show a high potential to generate diverse long-term epidemiological scenarios. Transmission variability, immune uncertainties and human behaviour are crucial features for the predictability and implementation of effective mitigation strategies. Nonetheless, the effects of individual health incentives on disease dynamics are not well understood. We use a behavioural-immuno-epidemiological model to study the joint evolution of human behaviour and epidemic dynamics for different immunity scenarios. Our results reveal a trade-off between the individuals' immunity levels and the behavioural responses produced. We find that adaptive human behaviour can avoid dynamical resonance by avoiding large outbreaks, producing subsequent uniform outbreaks. Our forward-looking behaviour model shows an optimal planning horizon that minimizes the epidemic burden by balancing the individual risk-benefit trade-off. We find that adaptive human behaviour can compensate for differential immunity levels, equalizing the epidemic dynamics for scenarios with diverse underlying immunity landscapes. Our model can adequately capture complex empirical behavioural dynamics observed during pandemics. We tested our model for different US states during the COVID-19 pandemic. Finally, we explored extensions of our modelling framework that incorporate the effects of lockdowns, the emergence of a novel variant, prosocial attitudes and pandemic fatigue.

Host behaviour driven by awareness of infection risk amplifies the chance of superspreading events

We demonstrate that heterogeneity in the perceived risks associated with infection within host populations amplifies chances of superspreading during the crucial early stages of epidemics. Under this behavioural model, individuals less concerned about dangers from infection are more likely to be infected and attend larger sized (riskier) events, where we assume event sizes remain unchanged. For directly transmitted diseases such as COVID-19, this leads to infections being introduced at rates above the population prevalence to those events most conducive to superspreading. We develop an interpretable, computational framework for evaluating within-event risks and derive a small-scale reproduction number measuring how the infections generated at an event depend on transmission heterogeneities and numbers of introductions. This generalizes previous frameworks and quantifies how event-scale patterns and population-level characteristics relate. As event duration and size grow, our reproduction number converges to the basic reproduction number. We illustrate that even moderate levels of heterogeneity in the perceived risks of infection substantially increase the likelihood of disproportionately large clusters of infections occurring at larger events, despite fixed overall disease prevalence. We show why collecting data linking host behaviour and event attendance is essential for accurately assessing the risks posed by invading pathogens in emerging stages of outbreaks.

Finding influential subjects in a network using a causal framework

Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.

Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Social network structure and the spread of complex contagions from a population genetics perspective

Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a nonlinear function of the fraction of the individuals' affected neighbors, such a spreading process becomes a "complex contagion." This nonlinearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g., for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g., for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex contagions on real social networks, we present a framework for analyzing the statistics of contagions with arbitrary nonlinear adoption probabilities based on the mathematical tools of population genetics. The central idea is to use an effective lower-dimensional diffusion process to approximate the statistics of the contagion. This leads to a tradeoff between the effects of "selection" (microscopic tendencies for an idea to spread or die out), random drift, and network structure. Our framework illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection compared to random drift. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global contagions become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate.

COVID-19, Genetics, and Risk: Content Analysis of Facebook Posts Early in the Coronavirus Pandemic

The COVID-19 pandemic represented a unique information seeking and sharing context for billions of Internet users across the globe. Although previous research has established that people often seek health information on the Internet, including through social media platforms, there was a political element to pandemic responses that is not typical of health issues such as seasonal influenza or heart conditions. This unique context, in conjunction with the public posting of the novel coronavirus DNA by Chinese scientists in January 2020, begs for analysis of how people used social media in the early phase of the pandemic to make sense of the role of genetics in COVID-19. This study represents such an analysis as a qualitative content analysis of Facebook posts concerning genetics and COVID-19. Data were collected from March through August of 2020 to identify how genetics issues were being shared on Facebook and the types of accounts that were sharing that information. Through analysis, four themes emerged representing Facebook posts about genetics and COVID-19: disease risk, testing, vaccines, and virus characteristics. These posts appeared on eight types of accounts, with five of those representing 88% of the data: education, health, lifestyle, news, and political. Results are interpreted with constructs from media dependency theory and implications for future research are presented.

COVID-19 compliance among urban trail users: Behavioral insights and environmental implications

Public green spaces provide physical and mental respite, which have become essential and elevated services during the COVID-19 pandemic. As visitation to public parks and recreation areas increased during the pandemic, the challenge of maintaining visitor safety and protecting environmental resources was exacerbated. A key visitor safety practice during the COVID-19 onset was maintaining a physical distance of six feet (1.8 m) between groups. A novel data set documented and compared physical distancing compliance and off-trail behavior on multiple-use trails across multiple states and within select U.S. communities, attending to the impact of select environmental factors. Nearly 6000 observations revealed physical distancing compliance varied and the environmental factors of trail width, density, and signage influenced its variability. Similarly, off-trail movement was related to trail width and density. Clearly the environment matters as people negotiate the 'new normal' of physical distancing during physical activity and outdoor recreation participation. Given the ongoing COVID-19 pandemic and likelihood of future health crises, this project provides important information and insight for trail and other public green space management, monitoring, and modelling moving forward.

Management implications

As both trail width and visitor density impacted physical distancing, a combination of trail design that accommodates distancing requirements and density management practices that provide sufficient trail user spacing is essential to retain safe and active trail use.Off-trail movement was influenced by both trail width and density, so ensuring safe off-trail spaces exist and using durable off-trail materials can minimize disturbance and protect visitors.Signage is inconsistently significant to influence trail-compliant distancing behavior, but optimizing its placement and content may improve effectiveness.Compliant trail behavior varied by trail width, visitor density, and trail location; therefore, site-specific information is necessary to understand possible visitor behavior and design/implement mitigation strategies.

Cascades in capacity constrained agents

Many sorts of contagious phenomenon, such as music, do not exist in isolation but as part of a competitive marketplace. In these settings there are often superstars with out-sized popularity along with a large number of flops with little popularity. It could be the case that superstars are more popular because they are higher quality but I suggest that capacity constraints may be a structural factor that influences these disparities. In this agent-based model, there are multiple potentially cascading states that the agent can potentially occupy. The agents have a certain capacity of states that they can occupy at once. For example, suppose someone has a workout playlist that lasts 1 hour. As they discover new music to add to the playlist, they have to remove songs currently in the playlist to keep the playlist 1 hour. Thus, in this setting, the states indirectly trade off with each other by virtue of the capacity constraint. The key question is whether the indirect trade offs imposed by the capacity constraint are enough to induce disparities in popularity, even when the states are otherwise identical. I find that increasing the number of states in excess of capacity increases the disparities between popular and unpopular states. This suggests that capacity constraints may be a structural factor in explaining market concentration and superstar phenomenon.

Covering the Crisis: Evolution of Key Topics and Actors in COVID-19 News Coverage in Switzerland

Objectives: The goal of this study is to map the share of COVID-related news articles over time, to investigate key subtopics and their evolution throughout the pandemic, and to identify key actors and their relationship with different aspects of the discourse around the pandemic.

Methods: This study uses a large-scale automated content analysis to conduct a within-country comparison of news articles (N = 1,171,114) from two language regions of Switzerland during the first 18 months of the pandemic.

Results: News media coverage of the pandemic largely mirrors key epidemiological developments in terms of the volume and content of coverage. Key actors in COVID-related reporting tend to be included in news articles that relate to their respective area of expertise.

Conclusion: Balanced news coverage of the pandemic facilitates effective dissemination of pandemic-related information by health authorities.

Willingness to receive a vaccine is influenced by adverse events following immunisation experienced by others

An adverse event following immunization (AEFI) can have consequences for an individual's future decision making and may contribute to vaccine hesitancy. AEFIs vary in severity and can be experienced directly (by an individual themselves) or indirectly (through witnessed or recounted events). We sought to measure the prevalence of specific AEFIs and understand which AEFIs have the greatest associations with reduced willingness to receive a vaccine and how injection anxiety may moderate the relationship. We conducted a cross-sectional online survey with both qualitative and quantitative elements in a sample of adults aged 18 years and over in Australia. Nineteen percent of the 1050 respondents reported experiencing an AEFI that they found stressful. Those who experienced an AEFI reported significantly higher levels of injection anxiety than those who did not. Within the group who reported experiencing an AEFI, respondents were significantly less likely to be willing to receive a COVID-19 vaccine if they reported: indirect exposure to an uncommon/rare AEFI compared with other AEFIs (aOR:0.39; 95% CI: 0.18-0.87); indirect exposure to a scientifically unsupported AEFI compared with other AEFIs (aOR:0.18; 95% CI: 0.05-0.57). Direct exposure to an AEFI was not associated with willingness to receive a COVID-19 vaccine. For those who reported experiencing an AEFI, the odds of willingness to receive a COVID-19 vaccine decreased significantly with an increase in injection anxiety (aOR:0.94; 95% CI: 0.9-0.98). Our results suggest that more is needed to mitigate the consequences of AEFIs on vaccine willingness. Empathically acknowledging at a community level, the experience of both real and perceived AEFIs and incorporating accounts of positive vaccination experiences in vaccine hesitancy interventions may be useful.

Mathematical Models for Cholera Dynamics-A Review

Cholera remains a significant public health burden in many countries and regions of the world, highlighting the need for a deeper understanding of the mechanisms associated with its transmission, spread, and control. Mathematical modeling offers a valuable research tool to investigate cholera dynamics and explore effective intervention strategies. In this article, we provide a review of the current state in the modeling studies of cholera. Starting from an introduction of basic cholera transmission models and their applications, we survey model extensions in several directions that include spatial and temporal heterogeneities, effects of disease control, impacts of human behavior, and multi-scale infection dynamics. We discuss some challenges and opportunities for future modeling efforts on cholera dynamics, and emphasize the importance of collaborations between different modeling groups and different disciplines in advancing this research area.

Machine learning based regional epidemic transmission risks precaution in digital society

The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.

Rural-Urban Disparities in Vaccine Hesitancy among Adults in South Tyrol, Italy

BACKGROUND

The demographic determinants of hesitancy in Coronavirus Disease-2019 (COVID-19) vaccination include rurality, particularly in low- and middle-income countries. In the second year of the pandemic, in South Tyrol, Italy, 15.6 percent of a representative adult sample reported hesitancy. Individual factors responsible for greater vaccination hesitancy in rural areas of central Europe are poorly understood.

METHODS

A cross-sectional survey on a probability-based sample of South Tyrol residents in March 2021 was analyzed. The questionnaire collected information on sociodemographic characteristics, comorbidities, COVID-19-related experiences, conspiracy thinking, and the likelihood of accepting the national vaccination plan. A logistic regression analysis was performed.

RESULTS

Among 1426 survey participants, 17.6% of the rural sample (n = 145/824) reported hesitancy with COVID-19 vaccination versus 12.8% (n = 77/602) in urban residents (p = 0.013). Rural residents were less likely to have post-secondary education, lived more frequently in households with children under six years of age, and their economic situation was worse than before the pandemic. Chronic diseases and deaths due to COVID-19 among close relatives were less frequently reported, and trust in pandemic management by national public health institutions was lower, as was trust in local authorities, civil protection, and local health services. Logistic regression models confirmed the most well-known predictors of hesitancy in both urban and rural populations; overall, residency was not an independent predictor.

CONCLUSION

Several predictors of COVID-19 vaccine hesitancy were more prevalent in rural areas than in urban areas, which may explain the lower vaccine uptake in rural areas. Rurality is not a determinant of vaccine hesitancy in the economically well-developed North of Italy.

Examining perceived risk to bovine tuberculosis through factorial survey to inform policymaking for zoonotic diseases control and surveillance

Prevention and control of infectious diseases in livestock is dependent upon perceived risk and susceptibility, including the prevention of between-herd transmission of bovine tuberculosis through introductions of cattle to susceptible herds. To examine how perceived risk and susceptibility can help to inform policymaking in disease surveillance and control, we used factorial surveys to profile risk perceptions of cattle producers. We found that government indemnity and slaughtering policy did not impact the cattle purchasing behavior of producers who responded to our survey, but rather through other attributes such as the reliability or reputation of the seller. In addition, we identified significant production type and gender differences in purchasing behavior and risk perception. Finally, clustering analysis revealed a group of high-risk respondents characterized as experienced and very dedicated owners of established medium to large size herds. With the increasing availability of business data, assessment of producer's behavior, personalities and attitudes allows policymakers to understand the needs of cattle producers and develop tailored programs that will improve producer cooperation with government agencies.

Transmission dynamics of disease spreading in multilayer networks with mass media

On the basis of existing disease spreading research, in this paper we propose a Hesitant-Taken-Unaware-Aware-Susceptible-Asymptomatic-Symptomatic-Recovered (HTUA-SI^{a}I^{s}R) model with mass media in a two-layer network, which consists of a virtual communication layer and a physical contact layer. Based on the UAU-SIR model, we additionally consider three practical factors, including whether individuals will disseminate information or not, the influence of unaware individuals on aware individuals, and the direct recovery of asymptomatic infected individuals. Based on the microscopic Markov chain approach (MMCA), for the proposed HTUA-SI^{a}I^{s}R model, MMCA equations are generated and the analytical expression of the epidemic threshold is obtained. Compared with Monte Carlo techniques, numerical simulations show the feasibility and effectiveness of the MMCA equations, as well as the HTUA-SI^{a}I^{s}R model theoretically. Meanwhile, extensive simulations demonstrate that the acceleration of the awareness dissemination in the virtual communication layer can effectively block the epidemic spreading and raise the epidemic threshold. However, under certain conditions, the increasing of T-state individuals will increase the U-state individuals because the T-state and U-state individuals can influence the A-state individuals losing their awareness of protection, and then promote the epidemic spreading and decrease the epidemic threshold. In addition, reducing asymptomatic infections can hinder the epidemic spreading. But, when the recovery rate of asymptomatic infections is greater than that of symptomatic infections, decreasing the tendency of individuals acquiring asymptomatic infections will lower the epidemic threshold.

How reported outbreak data can shape individual behavior in a social world

Agencies reporting on disease outbreaks face many choices about what to report and the scale of its dissemination. Reporting impacts an epidemic by influencing individual decisions directly, and the social network in which they are made. We simulated a dynamic multiplex network model-with coupled infection and communication layers-to examine behavioral impacts from the nature and scale of epidemiological information reporting. We explored how adherence to protective behaviors (social distancing) can be facilitated through epidemiological reporting, social construction of perceived risk, and local monitoring of direct connections, but eroded via social reassurance. We varied reported information (total active cases, daily new cases, hospitalizations, hospital capacity exceeded, or deaths) at one of two scales (population level or community level). Total active and new case reporting at the population level were the most effective approaches, relative to the other reporting approaches. Case reporting, which synergizes with test-trace-and-isolate and vaccination policies, should remain a priority throughout an epidemic.

Coevolution of vaccination behavior and perceived vaccination risk can lead to a stag-hunt-like game

Voluntary vaccination is effective to prevent infectious diseases from spreading. Both vaccination behavior and cognition of the vaccination risk play important roles in individual vaccination decision making. However, it is not clear how the coevolution of the two shapes population-wide vaccination behavior. We establish a coupled dynamics of epidemic, vaccination behavior, and perceived vaccination risk with three different time scales. We assume that the increase of vaccination level inhibits the rise of perceived vaccination risk, and the increase of perceived vaccination risk inhibits the rise of vaccination level. It is shown that the resulting vaccination behavior is similar to the stag-hunt game, provided that the basic reproductive ratio is moderate and that the epidemic dynamics evolves sufficiently fast. This is in contrast with the previous view that vaccination is a snowdriftlike game. And we find that epidemic breaks out repeatedly and eventually leads to vaccine scares if these three dynamics evolve on a similar time scale. Furthermore, we propose some ways to promote vaccination behavior, such as controlling side-effect bias and perceived vaccination costs. Our work sheds light on epidemic control via vaccination by taking into account the coevolutionary dynamics of cognition and behavior.

The effect of information-driven resource allocation on the propagation of epidemic with incubation period

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible-exposed-infected-susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

Individualism Versus Collectivism and the Early-Stage Transmission of COVID-19

We propose a perspective based on the individualism versus collectivism (IC) cultural distinction to understand the diverging early-stage transmission outcomes of COVID-19 between countries. Since individualism values personal freedom, people in such cultures would be less likely to make the collective action of staying at home and less likely to support compulsory measures. As a reaction to the public will, governments of individualistic societies would be more hesitant to take compulsory measures, leading to the delay of necessary responses. With processed COVID-19 data that can provide a fair comparison, we find that COVID-19 spread much faster in more individualistic societies than in more collectivistic societies. We further use pronoun drop and absolute latitude as the instruments for IC to address reverse causality and omitted variable bias. The results are robust to different measures. We propose to consider the role of IC not only for understanding the current pandemic but also for thinking about future trends in the world.

Research Participation Influences Willingness to Reduce Zoonotic Exposure in Uganda

The majority of emerging and re-emerging infectious diseases in people are zoonotic. Despite substantial research in communities adjacent to protected areas with high levels of biodiversity, limited data exist on people's knowledge, attitudes, and practices to avoid exposure to infections from domestic and wild animals. We used a modified grounded-theory framework in QS NVivo to develop a Knowledge, Attitude, and Practices (KAP) survey administered at two time points, KAPT1 (April-July 2016) and KAPT2 (February-May 2018) to participants living at the edge of Kibale National Park, Uganda. We measured the difference in willingness to engage in protective behaviors around zoonotic exposure between an Intervention group (n = 61) and a Comparison group (n = 125). Prior to KAPT1, the Intervention group engaged in a human-centered design (HCD) activity identifying behaviors that reduce zoonotic exposure (March-May 2016). Using a difference-in-difference approach, we compared the Intervention and Comparison groups to assess sustained willingness and use of protective behaviors against domestic and wild animal exposures. At KAPT1, Comparison group participants had a significantly lower (p < 0.05) level of willingness to engage in behaviors that increase exposure to zoonoses from domestic animals; Intervention group participants had a significantly higher (p < 0.01) level of willingness to engage in behaviors that increase exposure to zoonoses from wild animals. At KAPT2, the treatment effect was significant (p < 0.01) for sustained willingness to engage in protective behaviors for domestic animal exposure in the Intervention group. There were no significant differences in practices to avoid domestic and wild animal zoonotic exposure between the Intervention and Comparison groups.

The Polarization of the Coupling Strength of Interdependent Networks Stimulates Cooperation

We introduce a mixed network coupling mechanism and study its effects on how cooperation evolves in interdependent networks. This mechanism allows some players (conservative-driven) to establish a fixed-strength coupling, while other players (radical-driven) adjust their coupling strength through the evolution of strategy. By means of numerical simulation, a hump-like relationship between the level of cooperation and conservative participant density is revealed. Interestingly, interspecies interactions stimulate polarization of the coupling strength of radical-driven players, promoting cooperation between two types of players. We thus demonstrate that a simple mixed network coupling mechanism substantially expands the scope of cooperation among structured populations.

Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China

COVID-19, the disease caused by the novel coronavirus 2019, has caused grave woes across the globe since it was first reported in the epicentre of Wuhan, Hubei, China, in December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that rendered more than 900 million people housebound for more than two months since the lockdown of Wuhan, and elsewhere, on 23 January 2020. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns across and within provinces, before and during the lockdown period. We calibrate movement flow between provinces with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicentre Hubei. Moreover, we show that synchronous lockdowns and consequent reduced mobility lag a certain time to elicit an actual impact on suppressing the spread. Such highly coordinated nationwide lockdowns, applied via a top-down approach along with high levels of compliance from the bottom up, are central to mitigating and controlling early-stage outbreaks and averting a massive health crisis.

Intralayer Synchronization of Multiplex Dynamical Networks via Pinning Impulsive Control

These days, the synchronization of multiplex networks is an emerging and important research topic. Grounded framework and theory about synchronization and control on multiplex networks are yet to come. This article studies the intralayer synchronization on a multiplex network (i.e., a set of networks connected through interlayer edges), via the pinning impulsive control method. The topologies of different layers are independent of each other, and the individual dynamics of nodes in different layers are different as well. Supra-Laplacian matrices are adopted to represent the topological structures of multiplex networks. Two cases are considered according to impulsive sequences of multiplex networks: 1) pinning controllers are applied to all the layers simultaneously at the instants of a common impulse sequence and 2) pinning controllers are applied to each layer at the instants of distinct impulse sequences. Using the Lyapunov stability theory and the impulsive control theory, several intralayer synchronization criteria for multiplex networks are obtained, in terms of the supra-Laplacian matrix of network topology, self-dynamics of nodes, impulsive intervals, and the pinning control effect. Furthermore, the algorithms for implementing pinning schemes at every impulsive instant are proposed to support the obtained criteria. Finally, numerical examples are presented to demonstrate the effectiveness and correctness of the proposed schemes.

Analyzing an Epidemic of Human Infections with Two Strains of Zoonotic Virus

Due to the existence and variation of various viruses, an epidemic in which different strains spread at the same time will occur. here, an avian–human epidemic model with two strain viruses are established and analyzed. Both theoretical and simulation results reveal that the mixed infections intensify the epidemic and the dynamics become more complex and sensitive. There are six equilibria. The trivial equilibrium point is a high-order singular point and will undergo the transcritical bifurcations to bifurcate three equilibria. The existence and stability of equilibria mainly depend on five thresholds. A bifurcation portrait for the existence and stability of equilibria is presented. Simulations suggest that the key control measure is to develop the identification technology to eliminate the poultry infected with a high pathogenic virus preferentially, then the infected poultry with a low pathogenic virus in the recruitment and on farms. Controlling contact between human and poultry can effectively restrain the epidemic and controlling contagions in poultry can avoid great infection in humans.

Highly coordinated nationwide massive travel restrictions are central to effective mitigation and control of COVID-19 outbreaks in China

The COVID-19, the disease caused by the novel coronavirus 2019 (SARS-CoV-2), has caused graving woes across the globe since first reported in the epicenter Wuhan, Hubei, China, December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that put more than 900 million people housebound for more than two months since the lockdown of Wuhan on 23 January 2020 when other provinces in China followed suit. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns before and during the lockdown period. We quantify the synchrony of mobility patterns across provinces and within provinces. Using these mobility data, we calibrate movement flow between provinces in combination with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicenter Hubei. As such, infections can propagate further into other interconnected places both near and far, thereby necessitating synchronous lockdowns. Moreover, our data-driven modeling analysis shows that lockdowns and consequently reduced mobility lag a certain time to elicit an actual impact on slowing down the spreading and ultimately putting the epidemic under check. In spite of the vastly heterogeneous demographics and epidemiological characteristics across China, mobility data shows that massive travel restrictions have been applied consistently via a top-down approach along with high levels of compliance from the bottom up.

The impact of information dissemination on vaccination in multiplex networks

The impact of information dissemination on epidemic control is essentially subject to individual behaviors. Vaccination is one of the most effective strategies against the epidemic spread, whose correlation with the information dissemination should be better understood. To this end, we propose an evolutionary vaccination game model in multiplex networks by integrating an information-epidemic spreading process into the vaccination dynamics, and explore how information dissemination influences vaccination. The spreading process is described by a two-layer coupled susceptible-alert-infected-susceptible (SAIS) model, where the strength coefficient between two layers characterizes the tendency and intensity of information dissemination. We find that the impact of information dissemination on vaccination decision-making depends on not only the vaccination cost and network topology, but also the stage of the system evolution. For instance, in a two-layer BA scale-free network, information dissemination helps to improve vaccination density only at the early stage of the system evolution, as well as when the vaccination cost is smaller. A counter-intuitive conclusion that more information transmission cannot promote vaccination is obtained when the vaccination cost is larger. Moreover, we study the impact of the strength coefficient and individual sensitivity on the fraction of infected individuals and social cost, and unveil the role of information dissemination in controlling the epidemic.

Sustained Effects of Government Response on the COVID-19 Infection Rate in China: A Multiple Mediation Analysis

Many scholars have considered the relationship between the government response to COVID-19, an important social intervention strategy, and the COVID-19 infection rate. However, few have examined the sustained impact of an early government response on the COVID-19 infection rate. The current paper fills this gap by investigating a national survey performed in February 2020 and infection data from Chinese cities surveyed 1.5 years after the outbreak of COVID-19. The results suggest that the Chinese government's early response to COVID-19 significantly and sustainedly reduced China's COVID-19 infection rate, and that this impact worked through risk perception, the adoption of protective action recommendations (PARs), and the chain-mediating effects of risk perception and the adoption of PARs, respectively. These findings have important practical value. In demonstrating how government response and infection rate at the macro level are connected to the behaviour of individuals at the micro level, they suggest feasible directions for curbing the spread of diseases such as COVID-19. When facing such public health emergencies, the focus should be on increasing the public's risk perception and adoption of PARs.

Mathematical modeling applied to epidemics: an overview

This work presents an overview of the evolution of mathematical modeling applied to the context of epidemics and the advances in modeling in epidemiological studies. In fact, mathematical treatments have contributed substantially in the epidemiology area since the formulation of the famous SIR (susceptible-infected-recovered) model, in the beginning of the 20th century. We presented the SIR deterministic model and we also showed a more realistic application of this model applying a stochastic approach in complex networks. Nowadays, computational tools, such as big data and complex networks, in addition to mathematical modeling and statistical analysis, have been shown to be essential to understand the developing of the disease and the scale of the emerging outbreak. These issues are fundamental concerns to guide public health policies. Lately, the current pandemic caused by the new coronavirus further enlightened the importance of mathematical modeling associated with computational and statistical tools. For this reason, we intend to bring basic knowledge of mathematical modeling applied to epidemiology to a broad audience. We show the progress of this field of knowledge over the years, as well as the technical part involving several numerical tools.

Dynamical SEIR Model With Information Entropy Using COVID-19 as a Case Study

Social network information is a measure of the number of infections. Understanding the effect of social network information on disease spread can help improve epidemic forecasting and uncover preventive measures. Many driving factors for the transmission mechanism of infectious diseases remain unclear. Some experts believe that redundant information on social media may increase people's panic to evade the restrictions or refuse to report their symptoms, which increases the actual infection rate. We analyze the engagement in the COVID-19 topics on the Internet and find that the infection rate is not only related to the total amount of information. In our research, information entropy is introduced into the quantification of the impact of social network information. We find that the amount of information with different distributions has different effects on disease transmission. Furthermore, we build a new dynamic susceptible-exposed-infected-recovered (SEIR) model with information entropy to simulate the epidemic situation in China. Simulation results show that our modified model is effective in predicting the COVID-19 epidemic peaks and sizes.

Protective population behavior change in outbreaks of emerging infectious disease

BACKGROUND

During outbreaks of emerging and re-emerging infections, the lack of effective drugs and vaccines increases reliance on non-pharmacologic public health interventions and behavior change to limit human-to-human transmission. Interventions that increase the speed with which infected individuals remove themselves from the susceptible population are paramount, particularly isolation and hospitalization. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are zoonotic viruses that have caused significant recent outbreaks with sustained human-to-human transmission.

METHODS

This investigation quantified changing mean removal rates (MRR) and days from symptom onset to hospitalization (DSOH) of infected individuals from the population in seven different outbreaks of EVD, SARS, and MERS, to test for statistically significant differences in these metrics between outbreaks.

RESULTS

We found that epidemic week and viral serial interval were correlated with the speed with which populations developed and maintained health behaviors in each outbreak.

CONCLUSIONS

These findings highlight intrinsic population-level changes in isolation rates in multiple epidemics of three zoonotic infections with established human-to-human transmission and significant morbidity and mortality. These data are particularly useful for disease modelers seeking to forecast the spread of emerging pathogens.

Accounting for the spread of vaccination behavior to optimize influenza vaccination programs

Vaccination is the most efficient means of preventing influenza infection and its complications. While previous studies have considered the externalities of vaccination that arise from indirect protection against influenza infection, they have often neglected another key factor-the spread of vaccination behavior among social contacts. We modeled influenza vaccination as a socially contagious process. Our model uses a contact network that we developed based on aggregated and anonymized mobility data from the cellphone devices of ~1.8 million users in Israel. We calibrated the model to high-quality longitudinal data of weekly influenza vaccination uptake and influenza diagnoses over seven years. We demonstrate how a simple coupled-transmission model accurately captures the spatiotemporal patterns of both influenza vaccination uptake and influenza incidence. Taking the identified complex underlying dynamics of these two processes into account, our model determined the optimal timing of influenza vaccination programs. Our simulation shows that in regions where high vaccination coverage is anticipated, vaccination uptake would be more rapid. Thus, our model suggests that vaccination programs should be initiated later in the season, to mitigate the effect of waning immunity from the vaccine. Our simulations further show that optimally timed vaccination programs can substantially reduce disease transmission without increasing vaccination uptake.

Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities

The distinct ways the COVID-19 pandemic has been unfolding in different countries and regions suggest that local societal and governmental structures play an important role not only for the baseline infection rate, but also for short and long-term reactions to the outbreak. We propose to investigate the question of how societies as a whole, and governments in particular, modulate the dynamics of a novel epidemic using a generalization of the SIR model, the reactive SIR (short-term and long-term reaction) model. We posit that containment measures are equivalent to a feedback between the status of the outbreak and the reproduction factor. Short-term reaction to an outbreak corresponds in this framework to the reaction of governments and individuals to daily cases and fatalities. The reaction to the cumulative number of cases or deaths, and not to daily numbers, is captured in contrast by long-term reaction. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short and long-term control parameters. We find increased contributions of long-term control for countries and regions in which the outbreak was suppressed substantially together with a strong correlation between the strength of societal and governmental policies and the time needed to contain COVID-19 outbreaks. Furthermore, for numerous countries and regions we identified a predictive relation between the number of fatalities within a fixed period before and after the peak of daily fatality counts, which allows to gauge the cumulative medical load of COVID-19 outbreaks that should be expected after the peak. These results suggest that the proposed model is applicable not only for understanding the outbreak dynamics, but also for predicting future cases and fatalities once the effectiveness of outbreak suppression policies is established with sufficient certainty. Finally, we provide a web app (https://itp.uni-frankfurt.de/covid-19/) with tools for visualising the phase space representation of real-world COVID-19 data and for exporting the preprocessed data for further analysis.

Containment efficiency and control strategies for the corona pandemic costs

The rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of measuring the effectiveness of containment strategies, balancing public health considerations with the economic costs of social distancing measures. We introduce a modified epidemic model that we name the controlled-SIR model, in which the disease reproduction rate evolves dynamically in response to political and societal reactions. An analytic solution is presented. The model reproduces official COVID-19 cases counts of a large number of regions and countries that surpassed the first peak of the outbreak. A single unbiased feedback parameter is extracted from field data and used to formulate an index that measures the efficiency of containment strategies (the CEI index). CEI values for a range of countries are given. For two variants of the controlled-SIR model, detailed estimates of the total medical and socio-economic costs are evaluated over the entire course of the epidemic. Costs comprise medical care cost, the economic cost of social distancing, as well as the economic value of lives saved. Under plausible parameters, strict measures fare better than a hands-off policy. Strategies based on current case numbers lead to substantially higher total costs than strategies based on the overall history of the epidemic.

How do social media and individual behaviors affect epidemic transmission and control?

In the outbreak of infectious diseases such as COVID-19, social media channels are important tools for the public to obtain information and form their opinions on infection risk, which can affect their disease prevention behaviors and the consequent disease transmission processes. However, there has been a lack of theoretical investigation into how social media and human behaviors jointly affect the spread of infectious diseases. In this study, we develop an agent-based modeling framework that couples (1) a general opinion dynamics model that describes how individuals form their opinions on epidemic risk with various information sources, (2) a behavioral adoption model that simulates the adoption of disease prevention behaviors, and (3) an epidemiological SEIR model that simulates the spread of diseases in a host population. Through simulating the spread of a coronavirus-like disease in a hypothetical residential area, the modeling results show that social media can make a community more sensitive to external drivers. Social media can increase the public's awareness of infection risk, which is beneficial for epidemic containment, when high-quality epidemic information exists at the early stage of pandemics. However, fabricated and fake news on social media, after a "latent period", can lead to a significant increase in infection rate. The modeling results provide scientific evidence for the intricate interplay between social media and human behaviors in epidemic dynamics and control, and highlight the importance of public education to promote behavioral changes and the need to correct misinformation and fake news on social media in a timely manner.

Social distancing compliance: A video observational analysis

PURPOSE

Virus epidemics may be mitigated if people comply with directives to stay at home and keep their distance from strangers in public. As such, there is a public health interest in social distancing compliance. The available evidence on distancing practices in public space is limited, however, by the lack of observational data. Here, we apply video observation as a method to examine to what extent members of the public comply with social distancing directives.

DATA

Closed Circuit Television (CCTV) footage of interactions in public was collected in inner-city Amsterdam, the Netherlands. From the footage, we observed instances of people violating the 1.5-meter distance directives in the weeks before, during, and after these directives were introduced to mitigate the COVID-19 pandemic.

RESULTS

We find that people complied with the 1.5-meter distance directives when these directives were first introduced, but that the level of compliance started to decline soon after. We also find that violation of the 1.5-meter distance directives is strongly associated with the number of people observed on the street and with non-compliance to stay-at-home directives, operationalized with large-scale aggregated location data from cell phones. All three measures correlate to a varying extent with temporal patterns in the transmission of the COVID-19 virus, temperature, COVID-19 related Google search queries, and media attention to the topic.

CONCLUSION

Compliance with 1.5 meter distance directives is short-lived and coincides with the number of people on the street and with compliance to stay-at-home directives. Potential implications of these findings are that keep- distance directives may work best in combination with stay-at-home directives and place-specific crowd-control strategies, and that the number of people on the street and community-wide mobility as captured with cell phone data offer easily measurable proxies for the extent to which people keep sufficient physical distance from others at specific times and locations.

Adaptive social contact rates induce complex dynamics during epidemics

Epidemics may pose a significant dilemma for governments and individuals. The personal or public health consequences of inaction may be catastrophic; but the economic consequences of drastic response may likewise be catastrophic. In the face of these trade-offs, governments and individuals must therefore strike a balance between the economic and personal health costs of reducing social contacts and the public health costs of neglecting to do so. As risk of infection increases, potentially infectious contact between people is deliberately reduced either individually or by decree. This must be balanced against the social and economic costs of having fewer people in contact, and therefore active in the labor force or enrolled in school. Although the importance of adaptive social contact on epidemic outcomes has become increasingly recognized, the most important properties of coupled human-natural epidemic systems are still not well understood. We develop a theoretical model for adaptive, optimal control of the effective social contact rate using traditional epidemic modeling tools and a utility function with delayed information. This utility function trades off the population-wide contact rate with the expected cost and risk of increasing infections. Our analytical and computational analysis of this simple discrete-time deterministic strategic model reveals the existence of an endemic equilibrium, oscillatory dynamics around this equilibrium under some parametric conditions, and complex dynamic regimes that shift under small parameter perturbations. These results support the supposition that infectious disease dynamics under adaptive behavior change may have an indifference point, may produce oscillatory dynamics without other forcing, and constitute complex adaptive systems with associated dynamics. Implications for any epidemic in which adaptive behavior influences infectious disease dynamics include an expectation of fluctuations, for a considerable time, around a quasi-equilibrium that balances public health and economic priorities, that shows multiple peaks and surges in some scenarios, and that implies a high degree of uncertainty in mathematical projections.

Oscillatory dynamics in the dilemma of social distancing

Social distancing as one of the main non-pharmaceutical interventions can help slow down the spread of diseases, like in the COVID-19 pandemic. Effective social distancing, unless enforced as drastic lockdowns and mandatory cordon sanitaire, requires consistent strict collective adherence. However, it remains unknown what the determinants for the resultant compliance of social distancing and their impact on disease mitigation are. Here, we incorporate into the epidemiological process with an evolutionary game theory model that governs the evolution of social distancing behaviour. In our model, we assume an individual acts in their best interest and their decisions are driven by adaptive social learning of the real-time risk of infection in comparison with the cost of social distancing. We find interesting oscillatory dynamics of social distancing accompanied with waves of infection. Moreover, the oscillatory dynamics are dampened with a non-trivial dependence on model parameters governing decision-makings and gradually cease when the cumulative infections exceed the herd immunity. Compared to the scenario without social distancing, we quantify the degree to which social distancing mitigates the epidemic and its dependence on individuals’ responsiveness and rationality in their behaviour changes. Our work offers new insights into leveraging human behaviour in support of pandemic response.

Fake news and science denier attacks on vaccines. What can you do?

Misinformation and disinformation ("fake news") about vaccines are contagious-travelling faster and farther than truth. The consequences are serious; leading to negative impacts on health decisions, including vaccine acceptance, and on trust in immunization advice from public health and/or healthcare professional. This article provides a brief overview of evidence-based strategies to address vaccine deniers in public, in clinical practice and in social situations. As well, a strategy to help differentiate between vaccine deniers and simple vaccine refusers in a practice or clinic is provided. Five tactics are widely used by vaccine deniers: conspiracy; fake experts; selectivity; impossible expectations; and misrepresentation and false logic. Recognizing and understanding these tactics can help protect against misinformation and science denialism propaganda. Highlighting the strong medical science consensus on the safety and effectiveness of vaccines also helps. Carefully and wisely choosing what to say and speaking up-whether you are at a dinner party, out with friends or in your medical office or clinic-is crucial. Not speaking up implies you agree with the misinformation. Having healthcare providers recognize and address misinformation using evidence-based strategies is of growing importance as the arrival of the coronavirus disease 2019 (COVID-19) vaccines is expected to further ramp up the vaccine misinformation and disinformation rhetoric. Healthcare providers must prepare themselves and act now to combat the vaccine misinformation tsunami.

Prophylactic host behaviour discourages pathogen exploitation

Much work has considered the evolution of pathogens, but little is known about how they respond to changes in host behaviour. We build a model of sublethal disease effects where hosts are able to choose to engage in prophylactic measures that reduce the likelihood of disease transmission. This choice is mediated by utility costs and benefits associated with prophylaxis, and the fraction of hosts engaged in prophylaxis is also affected by population dynamics. When prophylactic host behaviour occurs, we find that the level of pathogen host exploitation is reduced, by the action of selection, relative to the level that would otherwise be predicted in the absence of prophylaxis. Our work emphasizes the significance of the transmission-recovery trade-off faced by the pathogen and the ability of the pathogen to influence host prophylactic behaviour.

Sociocultural, behavioural and political factors shaping the COVID-19 pandemic: the need for a biocultural approach to understanding pandemics and (re)emerging pathogens

Although there has been increasing focus in recent years on interdisciplinary approaches to health and disease, and in particular the dimension of social inequalities in epidemics, infectious diseases have been much less focused on. This is especially true in the area of cultural dynamics and their effects on pathogen behaviours, although there is evidence to suggest that this relationship is central to shaping our interactions with infectious disease agents on a variety of levels. This paper makes a case for a biocultural approach to pandemics such as COVID-19. It then uses this biocultural framework to examine the anthropogenic dynamics that influenced and continue to shape the COVID-19 pandemic, both during its initial phase and during critical intersections of the pandemic. Through this understanding of biocultural interactions between people, animals and pathogens, a broader societal and political dimension is drawn as a function of population level and international cultures, to reflect on the culturally mediated differential burden of the pandemic. Ultimately, it is argued that a biocultural perspective on infectious disease pandemics will allow for critical reflection on how culture shapes our behaviours at all levels, and how the effects of these behaviours are ultimately foundational to pathogen ecology and evolution.

Inferring evolutionary pathways and directed genotype networks of foodborne pathogens

Modelling the emergence of foodborne pathogens is a crucial step in the prediction and prevention of disease outbreaks. Unfortunately, the mechanisms that drive the evolution of such continuously adapting pathogens remain poorly understood. Here, we combine molecular genotyping with network science and Bayesian inference to infer directed genotype networks-and trace the emergence and evolutionary paths-of Salmonella Typhimurium (STM) from nine years of Australian disease surveillance data. We construct networks where nodes represent STM strains and directed edges represent evolutionary steps, presenting evidence that the structural (i.e., network-based) features are relevant to understanding the functional (i.e., fitness-based) progression of co-evolving STM strains. This is argued by showing that outbreak severity, i.e., prevalence, correlates to: (i) the network path length to the most prevalent node (r = -0.613, N = 690); and (ii) the network connected-component size (r = 0.739). Moreover, we uncover distinct exploration-exploitation pathways in the genetic space of STM, including a strong evolutionary drive through a transition region. This is examined via the 6,897 distinct evolutionary paths in the directed network, where we observe a dominant 66% of these paths decrease in network centrality, whilst increasing in prevalence. Furthermore, 72.4% of all paths originate in the transition region, with 64% of those following the dominant direction. Further, we find that the length of an evolutionary path strongly correlates to its increase in prevalence (r = 0.497). Combined, these findings indicate that longer evolutionary paths result in genetically rare and virulent strains, which mostly evolve from a single transition point. Our results not only validate our widely-applicable approach for inferring directed genotype networks from data, but also provide a unique insight into the elusive functional and structural drivers of STM bacteria.

Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve

In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, "counties"). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.

Trending on Social Media: Integrating Social Media into Infectious Disease Dynamics

Social media plays an important role in alerting and educating the public during disease outbreaks. By increasing awareness of the disease and its prevention, it can lead to a modification of behaviour which then affects contact/incidence rates. Social media data may also be used when formulating, developing and parameterising models. As mobile technology continues to evolve and proliferate, social media is expected to occupy an increasingly prominent role in the field of infectious disease modelling to improve their predictive power. This article presents a review of existing models incorporating media in general and highlights opportunities for social media to enhance traditional compartmental models so as to make the best use of this resource in controlling the spread of disease.