Media impact switching surface during an infectious disease outbreak

Global Hopf bifurcation of a cholera model with media coverage

We propose a model for cholera under the impact of delayed mass media, including human-to-human and environment-to-human transmission routes. First, we establish the extinction and uniform persistence of the disease with respect to the basic reproduction number. Then, we conduct a local and global Hopf bifurcation analysis by treating the delay as a bifurcation parameter. Finally, we carry out numerical simulations to demonstrate theoretical results. The impact of the media with the time delay is found to not influence the threshold dynamics of the model, but is a factor that induces periodic oscillations of the disease.

Media impact research: a discrete SIR epidemic model with threshold switching and nonlinear infection forces

The media's coverage has the potential to impact human behavior and aid in the control of emergent infectious diseases. We aim to quantify and evaluate the extent to which media coverage can influence infectious disease control through a mathematical model, thus proposing a switching epidemic model that considers the effect of media coverage. The threshold strategy incorporates media influence only when the number of infected cases surpasses a specific threshold; otherwise, it is disregarded. When conducting qualitative analysis of two subsystems, focusing on the existence and stability of equilibria. Using numerical methods, the codimension-2 bifurcation analysis is adopted here to investigate the various types of equilibria within the switching system that play a vital role in pest control. On the other hand, codimension-1 bifurcation analysis reveals the existence of periodic, chaotic solutions, period-doubling bifurcations, multiple attractors and other complexities within the proposed model, which could pose challenges in disease control. Additionally, the impact of key parameters on epidemic outbreaks is analyzed, such as the initial values of susceptible and infective individuals, and discuss the potential benefits of mass media coverage in preventing emerging infectious diseases. The modeling and analytical techniques developed for threshold control strategies can be applied to other disease control efforts.

A COVID-19 Infection Model Considering the Factors of Environmental Vectors and Re-Positives and Its Application to Data Fitting in Japan and Italy

COVID-19, which broke out globally in 2019, is an infectious disease caused by a novel strain of coronavirus, and its spread is highly contagious and concealed. Environmental vectors play an important role in viral infection and transmission, which brings new difficulties and challenges to disease prevention and control. In this paper, a type of differential equation model is constructed according to the spreading functions and characteristics of exposed individuals and environmental vectors during the virus infection process. In the proposed model, five compartments were considered, namely, susceptible individuals, exposed individuals, infected individuals, recovered individuals, and environmental vectors (contaminated with free virus particles). In particular, the re-positive factor was taken into account (i.e., recovered individuals who have lost sufficient immune protection may still return to the exposed class). With the basic reproduction number R0 of the model, the global stability of the disease-free equilibrium and uniform persistence of the model were completely analyzed. Furthermore, sufficient conditions for the global stability of the endemic equilibrium of the model were also given. Finally, the effective predictability of the model was tested by fitting COVID-19 data from Japan and Italy.

Long-term effect of childhood pandemic experience on medical major choice: Evidence from the 2003 severe acute respiratory syndrome outbreak in China

This study examines the long-term effect of a pandemic on a crucial human capital decision, namely college major choice. Using China's 2008-2016 major-level National College Entrance Examination (Gaokao) entry grades, we find that the 2003 severe acute respiratory syndrome (SARS) had a substantial deterrent effect on the choice of majoring in medicine among high school graduates who experienced the pandemic in their childhood. In provinces with larger intensities of SARS impact, medical majors become less popular as the average Gaokao grades of enrolled students decline. Further evidence from a nationally representative survey shows that the intensity of the SARS impact significantly decreases children's aspirations to pursue medical occupations, but does not affect their parents' expectations for their children to enter the medical profession. Our discussion on the effect mechanism suggests that the adverse influence of SARS on the popularity of medical majors likely originates from students' childhood experiences.

Modeling COVID-19 transmission dynamics incorporating media coverage and vaccination

The COVID-19 pandemic has caused widespread concern around the world. In order to study the impact of media coverage and vaccination on the spread of COVID-19, we establish an SVEAIQR infectious disease model, and fit the important parameters such as transmission rate, isolation rate and vaccine efficiency based on the data from Shanghai Municipal Health Commission and the National Health Commission of the People's Republic of China. Meanwhile, the control reproduction number and the final size are derived. Moreover, through sensitivity analysis by PRCC (partial rank correlation coefficient), we discuss the effects of both the behavior change constant $ k $ according to media coverage and the vaccine efficiency $ \varepsilon $ on the transmission of COVID-19. Numerical explorations of the model suggest that during the outbreak of the epidemic, media coverage can reduce the final size by about 0.26 times. Besides that, comparing with $ 50\% $ vaccine efficiency, when the vaccine efficiency reaches $ 90\% $, the peak value of infected people decreases by about 0.07 times. In addition, we simulate the impact of media coverage on the number of infected people in the case of vaccination or non-vaccination. Accordingly, the management departments should pay attention to the impact of vaccination and media coverage.

Adaptive behaviors and vaccination on curbing COVID-19 transmission: Modeling simulations in eight countries

Current persistent outbreak of COVID-19 is triggering a series of collective responses to avoid infection. To further clarify the impact mechanism of adaptive protection behavior and vaccination, we developed a new transmission model via a delay differential system, which parameterized the roles of adaptive behaviors and vaccination, and allowed to simulate the dynamic infection process among people. By validating the model with surveillance data during March 2020 and October 2021 in America, India, South Africa, Philippines, Brazil, UK, Spain and Germany, we quantified the protection effect of adaptive behaviors by different forms of activity function. The modeling results indicated that (1) the adaptive activity function can be used as a good indicator for fitting the intervention outcome, which exhibited short-term awareness in these countries, and it could reduce the total human infections by 3.68, 26.16, 15.23, 4.23, 7.26, 1.65, 5.51 and 7.07 times, compared with the reporting; (2) for complete prevention, the average proportions of people with immunity should be larger than 90%, 92%, 86%, 71%, 92%, 84%, 82% and 76% with adaptive protection behaviors, or 91%, 97%, 94%, 77%, 92%, 88%, 85% and 90% without protection behaviors; and (3) the required proportion of humans being vaccinated is a sub-linear decreasing function of vaccine efficiency, with small heterogeneity in different countries. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Sliding dynamics and bifurcations of a human influenza system under logistic source and broken line control strategy

This paper proposes a non-smooth human influenza model with logistic source to describe the impact on media coverage and quarantine of susceptible populations of the human influenza transmission process. First, we choose two thresholds $ I_{T} $ and $ S_{T} $ as a broken line control strategy: Once the number of infected people exceeds $ I_{T} $, the media influence comes into play, and when the number of susceptible individuals is greater than $ S_{T} $, the control by quarantine of susceptible individuals is open. Furthermore, by choosing different thresholds $ I_{T} $ and $ S_{T} $ and using Filippov theory, we study the dynamic behavior of the Filippov model with respect to all possible equilibria. It is shown that the Filippov system tends to the pseudo-equilibrium on sliding mode domain or one endemic equilibrium or bistability endemic equilibria under some conditions. The regular/virtulal equilibrium bifurcations are also given. Lastly, numerical simulation results show that choosing appropriate threshold values can prevent the outbreak of influenza, which implies media coverage and quarantine of susceptible individuals can effectively restrain the transmission of influenza. The non-smooth system with logistic source can provide some new insights for the prevention and control of human influenza.

Dynamic analysis and optimal control of a class of SISP respiratory diseases

In this paper, the actual background of the susceptible population being directly patients after inhaling a certain amount of PM2.5 is taken into account. The concentration response function of PM2.5 is introduced, and the SISP respiratory disease model is proposed. Qualitative theoretical analysis proves that the existence, local stability and global stability of the equilibria are all related to the daily emission P0 of PM2.5 and PM2.5 pathogenic threshold K. Based on the sensitivity factor analysis and time-varying sensitivity analysis of parameters on the number of patients, it is found that the conversion rate β and the inhalation rate η has the largest positive correlation. The cure rate γ of infected persons has the greatest negative correlation on the number of patients. The control strategy formulated by the analysis results of optimal control theory is as follows: The first step is to improve the clearance rate of PM2.5 by reducing the PM2.5 emissions and increasing the intensity of dust removal. Moreover, such removal work must be maintained for a long time. The second step is to improve the cure rate of patients by being treated in time. After that, people should be reminded to wear masks and go out less so as to reduce the conversion rate of susceptible people becoming patients.

Mobility was a significant determinant of reported COVID-19 incidence during the Omicron Surge in the most populous U.S. Counties

BACKGROUND

Significant immune escape by the Omicron variant, along with the emergence of widespread worry fatigue, have called into question the robustness of the previously observed relation between population mobility and COVID-19 incidence.

METHODS

We employed principal component analysis to construct a one-dimensional summary indicator of six Google mobility categories. We related this mobility indicator to case incidence among 111 of the most populous U.S. counties during the Omicron surge from December 2021 through February 2022.

RESULTS

Reported COVID-19 incidence peaked earlier and declined more rapidly among those counties exhibiting more extensive decline in mobility between December 20 and January 3. Based upon a fixed-effects, longitudinal cohort model, we estimated that every 1% decline in mobility between December 20 and January 3 was associated with a 0.63% decline in peak incidence during the week ending January 17 (95% confidence interval, 0.40-0.86%). Based upon a cross-sectional analysis including mean household size and vaccination participation as covariates, we estimated that the same 1% decline in mobility was associated with a 0.36% decline in cumulative reported COVID-19 incidence from January 10 through February 28 (95% CI, 0.18-0.54%).

CONCLUSION

Omicron did not simply sweep through the U.S. population until it ran out of susceptible individuals to infect. To the contrary, a significant fraction managed to avoid infection by engaging in risk-mitigating behaviors. More broadly, the behavioral response to perceived risk should be viewed as an intrinsic component of the natural course of epidemics in humans.

Effects of Media Coverage on Global Stability Analysis and Optimal Control of an Age-Structured Epidemic Model with Multi-Staged Progression

In this paper, a hybrid SEIAM model for infectious disease with a continuous age structure is established, where combined dynamic effects of media coverage and multi-staged infected progression on threshold dynamics are discussed. Sufficient conditions for uniform persistence of the solution are studied by using the basic reproduction number. By constructing appropriate Lyapunov functions, the global stability analysis of endemic equilibrium is investigated based on Lyapunov–LaSalle’s stability theorem. In order to minimize costs incurred due to applied controls and infectious disease burden, an optimal cost-effective control strategy associated with disease treatment and media coverage is discussed. Numerical simulations are carried out to show consistency with theoretical analysis.

Analysis of a diffusive epidemic system with spatial heterogeneity and lag effect of media impact

We considered an SIS functional partial differential model cooperated with spatial heterogeneity and lag effect of media impact. The wellposedness including existence and uniqueness of the solution was proved. We defined the basic reproduction number and investigated the threshold dynamics of the model, and discussed the asymptotic behavior and monotonicity of the basic reproduction number associated with the diffusion rate. The local and global Hopf bifurcation at the endemic steady state was investigated theoretically and numerically. There exists numerical cases showing that the larger the number of basic reproduction number, the smaller the final epidemic size. The meaningful conclusion generalizes the previous conclusion of ordinary differential equation.

Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19)

With the rapid development of the Mobile Internet in China, epidemic information is real-time and holographic, and the role of information diffusion in epidemic control is increasingly prominent. At the same time, the publicity of all kinds of big data also provides the possibility to explore the impact of media information diffusion on disease transmission. We explored the mechanism of the influence of information diffusion on the transmission of COVID-19, developed a model of the interaction between information diffusion and disease transmission based on the Susceptible-Infected-Recovered (SIR) model, and conducted an empirical test by using econometric methods. The benchmark result showed that there was a significant negative correlation between the information diffusion and the transmission of COVID-19. The result of robust test showed that the diffusion of both epidemic information and protection information hindered the further transmission of the epidemic. Heterogeneity test results showed that the effect of epidemic information on the suppression of COVID-19 is more significant in cities with weak epidemic control capabilities and higher Internet development levels.

Asymptotic behavior of the solutions for a stochastic SIRS model with information intervention

In this paper, a stochastic SIRS epidemic model with information intervention is considered. By constructing an appropriate Lyapunov function, the asymptotic behavior of the solutions for the proposed model around the equilibria of the deterministic model is investigated. We show the average in time of the second moment of the solutions of the stochastic system is bounded for a relatively small noise. Furthermore, we find that information interaction response rate plays an active role in disease control, and as the intensity of the response increases, the number of infected population decreases, which is beneficial for disease control.

Media coverage of COVID-19 health information in India: a content analysis

Effective response to the COVID-19 pandemic is dependent on individual understanding of the disease and compliance to prevention measures. Early media depiction of health information about COVID-19 may influence public perceptions and behaviour. Media should ensure coverage is relevant, timely and actionable to encourage individuals to respond appropriately. India has been particularly affected by a large COVID-19 caseload. We analysed online reporting in India to assess how well the media represented health information about COVID-19 as per the World Health Organization's Strategic Risk Communications guidelines. This included media coverage of symptoms, transmission and prevention. We found that limited articles (18.8%) provided actionable suggestions to readers, including urging people to stay at home and social distance. Most articles were relevant as per WHO COVID-19 updates, accurately covering symptoms, risk factors for severe symptoms, transmission and prevention. However, 40% of media coverage of treatments options provided misleading information, such as suggesting plasma therapy or chloroquine, were effective. In addition, only 1.9% of articles included discussion of equity issues, where many prevention activities such as distancing are less applicable in lower-income households. Sixty-seven per cent of articles quoting sources of information quoted credible sources such as public health agencies and researchers. Media coverage also did not appear to reflect WHO updates in a timely manner, with most of the coverage preceding these updates. The findings show that Indian media should focus on actionable and relevant reporting that provides guidance for individual response. Media should also endeavour to report on evidence-based prevention and treatment options to avert the spread of misinformation.

A new perspective on infection forces with demonstration by a DDE infectious disease model

In this paper, we revisit the notion of infection force from a new angle which can offer a new perspective to motivate and justify some infection force functions. Our approach can not only explain many existing infection force functions in the literature, it can also motivate new forms of infection force functions, particularly infection forces depending on disease surveillance of the past. As a demonstration, we propose an SIRS model with delay. We comprehensively investigate the disease dynamics represented by this model, particularly focusing on the local bifurcation caused by the delay and another parameter that reflects the weight of the past epidemics in the infection force. We confirm Hopf bifurcations both theoretically and numerically. The results show that, depending on how recent the disease surveillance data are, their assigned weight may have a different impact on disease control measures.

Complex dynamics of an epidemic model with saturated media coverage and recovery

During the outbreak of emerging infectious diseases, media coverage and medical resource play important roles in affecting the disease transmission. To investigate the effects of the saturation of media coverage and limited medical resources, we proposed a mathematical model with extra compartment of media coverage and two nonlinear functions. We theoretically and numerically investigate the dynamics of the proposed model. Given great difficulties caused by high nonlinearity in theoretical analysis, we separately considered subsystems with only nonlinear recovery or with only saturated media impact. For the model with only nonlinear recovery, we theoretically showed that backward bifurcation can occur and multiple equilibria may coexist under certain conditions in this case. Numerical simulations reveal the rich dynamic behaviors, including forward-backward bifurcation, Hopf bifurcation, saddle-node bifurcation, homoclinic bifurcation and unstable limit cycle. So the limitation of medical resources induces rich dynamics and causes much difficulties in eliminating the infectious diseases. We then investigated the dynamics of the system with only saturated media impact and concluded that saturated media impact hardly induces the complicated dynamics. Further, we parameterized the proposed model on the basis of the COVID-19 case data in mainland China and data related to news items, and estimated the basic reproduction number to be 2.86. Sensitivity analyses were carried out to quantify the relative importance of parameters in determining the cumulative number of infected individuals at the end of the first month of the outbreak. Combining with numerical analyses, we suggested that providing adequate medical resources and improving media response to infection or individuals' response to mass media may reduce the cumulative number of the infected individuals, which mitigates the transmission dynamics during the early stage of the COVID-19 pandemic.

Global dynamics for a Filippov system with media effects

In the process of spreading infectious diseases, the media accelerates the dissemination of information, and people have a deeper understanding of the disease, which will significantly change their behavior and reduce the disease transmission; it is very beneficial for people to prevent and control diseases effectively. We propose a Filippov epidemic model with nonlinear incidence to describe media's influence in the epidemic transmission process. Our proposed model extends existing models by introducing a threshold strategy to describe the effects of media coverage once the number of infected individuals exceeds a threshold. Meanwhile, we perform the stability of the equilibriua, boundary equilibrium bifurcation, and global dynamics. The system shows complex dynamical behaviors and eventually stabilizes at the equilibrium points of the subsystem or pseudo equilibrium. In addition, numerical simulation results show that choosing appropriate thresholds and control intensity can stop infectious disease outbreaks, and media coverage can reduce the burden of disease outbreaks and shorten the duration of disease eruptions.

Discrete epidemic modelling of COVID-19 transmission in Shaanxi Province with media reporting and imported cases

The large-scale infection of COVID-19 has led to a significant impact on lives and economies around the world and has had considerable impact on global public health. Social distancing, mask wearing and contact tracing have contributed to containing or at least mitigating the outbreak, but how public awareness influences the effectiveness and efficiency of such approaches remains unclear. In this study, we developed a discrete compartment dynamic model to mimic and explore how media reporting and the strengthening containment strategies can help curb the spread of COVID-19 using Shaanxi Province, China, as a case study. The targeted model is parameterized based on multi-source data, including the cumulative number of confirmed cases, recovered individuals, the daily number of media-reporting items and the imported cases from the rest of China outside Shaanxi from January 23 to April 11, 2020. We carried out a sensitivity analysis to investigate the effect of media reporting and imported cases on transmission. The results revealed that reducing the intensity of media reporting, which would result in a significant increasing of the contact rate and a sizable decreasing of the contact-tracing rate, could aggravate the outbreak severity by increasing the cumulative number of confirmed cases. It also demonstrated that diminishing the imported cases could alleviate the outbreak severity by reducing the length of the epidemic and the final size of the confirmed cases; conversely, delaying implementation of lockdown strategies could prolong the length of the epidemic and magnify the final size. These findings suggest that strengthening media coverage and timely implementing of lockdown measures can significantly reduce infection.

Impact of information intervention on stochastic hepatitis B model and its variable-order fractional network

This paper aims at analyzing the dynamical behavior of a SIR hepatitis B epidemic stochastic model via a novel approach by incorporating the effect of information interventions and random perturbations. Initially, we demonstrate the positivity and global existence of the solutions. Afterward, we derive the stochastic threshold parameter R s , followed by the fact that this number concludes the transmission of hepatitis B from the population. By increasing the intensity of noise, we get R s less than one, inferring that ultimately hepatitis B will lapse. While decreasing the intensity of noise to a sufficient level, we have R s > 1 . For the case R s > 1 , adequate results for the presence of stationary distribution are achieved, showing the prevalence of hepatitis B. The present study also involves the derivation of the necessary conditions for the persistence of the epidemic. Finally, the main theoretical solutions are plotted through simulations. Discussion on theoretical and numerical results shows that utilizing random perturbations and information interventions have a pronounced impact on the syndrome's dynamics. Furthermore, since most communities interact with each other, and the disease spread rate is affected by this factor, a new variable-order fractional network of the stochastic hepatitis B model is offered. Subsequently, this study will provide a robust theoretical basis for comprehending worldwide SIR stochastic and variable-order fractional network-related case studies.

On the effectiveness of communication strategies as non-pharmaceutical interventions to tackle epidemics

When pharmaceutical interventions are unavailable to deal with an epidemic outbreak, adequate management of communication strategies can be key to reduce the contagion risks. On the one hand, accessibility to trustworthy and timely information, whilst on the other, the adoption of preventive behaviors may be both crucial. However, despite the abundance of communication strategies, their effectiveness has been scarcely evaluated or merely circumscribed to the scrutiny of public affairs. To study the influence of communication strategies on the spreading dynamics of an infectious disease, we implemented a susceptible-exposed-infected-removed-dead (SEIRD) epidemiological model, using an agent-based approach. Agents in our systems can obtain information modulating their behavior from two sources: (i) through the local interaction with other neighboring agents and, (ii) from a central entity delivering information with a certain periodicity. In doing so, we highlight how global information delivered from a central entity can reduce the impact of an infectious disease and how informing even a small fraction of the population has a remarkable impact, when compared to not informing the population at all. Moreover, having a scheme of delivering daily messages makes a stark difference on the reduction of cases, compared to the other evaluated strategies, denoting that daily delivery of information produces the largest decrease in the number of cases. Furthermore, when the information spreading relies only on local interactions between agents, and no central entity takes actions along the dynamics, then the epidemic spreading is virtually independent of the initial amount of informed agents. On top of that, we found that local communication plays an important role in an intermediate regime where information coming from a central entity is scarce. As a whole, our results highlight the importance of proper communication strategies, both accurate and daily, to tackle epidemic outbreaks.

Media bias exposure and the incidence of COVID-19 in the USA

The role of the media as a source of reliable health information during the COVID-19 pandemic has come under intense scrutiny, with claims of misinformation and partisanship coming from all sides of the political divide. This paper seeks to understand the relationship between exposure to biased media outlets and the likelihood of testing positive for COVID-19 in the USA. I use detailed household data extracted from the 2020 American National Election Study in order to gauge media consumption patterns, coupled with data on media bias scores for different outlets and programmes. I combine these variables to compute media bias exposure values for each respondent, and relate these to the likelihood of a positive COVID-19 test within each respondent’s household, controlling for a variety of other factors including partisanship, social media use, trust in the media and several socioeconomic and demographic variables. The results indicate that media bias exposure is significantly related to COVID-19 incidence, and in particular the coefficients show that a 1% increase in exposure to left-wing media is associated with a 0.2% decrease in the probability of a positive COVID-19 test. Conversely, I find no significant relationship between right-wing media exposure and COVID-19 infection rates. I also find a significantly higher likelihood of contracting COVID-19 among low socioeconomic status households, suggesting a disproportionate impact of the pandemic on such cohorts. These findings are robust to a number of tests, and emphasise the importance of aligning media messages with those advocated by leading medical experts during public health crises.

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.

Mathematical modeling of intervention and low medical resource availability with delays: Applications to COVID-19 outbreaks in Spain and Italy

Infectious diseases have been one of the major causes of human mortality, and mathematical models have been playing significant roles in understanding the spread mechanism and controlling contagious diseases. In this paper, we propose a delayed SEIR epidemic model with intervention strategies and recovery under the low availability of resources. Non-delayed and delayed models both possess two equilibria: the disease-free equilibrium and the endemic equilibrium. When the basic reproduction number $ R_0 = 1 $, the non-delayed system undergoes a transcritical bifurcation. For the delayed system, we incorporate two important time delays: $ \tau_1 $ represents the latent period of the intervention strategies, and $ \tau_2 $ represents the period for curing the infected individuals. Time delays change the system dynamics via Hopf-bifurcation and oscillations. The direction and stability of delay induced Hopf-bifurcation are established using normal form theory and center manifold theorem. Furthermore, we rigorously prove that local Hopf bifurcation implies global Hopf bifurcation. Stability switching curves and crossing directions are analyzed on the two delay parameter plane, which allows both delays varying simultaneously. Numerical results demonstrate that by increasing the intervention strength, the infection level decays; by increasing the limitation of treatment, the infection level increases. Our quantitative observations can be useful for exploring the relative importance of intervention and medical resources. As a timing application, we parameterize the model for COVID-19 in Spain and Italy. With strict intervention policies, the infection numbers would have been greatly reduced in the early phase of COVID-19 in Spain and Italy. We also show that reducing the time delays in intervention and recovery would have decreased the total number of cases in the early phase of COVID-19 in Spain and Italy. Our work highlights the necessity to consider the time delays in intervention and recovery in an epidemic model.

Huanglongbing Model under the Control Strategy of Discontinuous Removal of Infected Trees

By using differential equations with discontinuous right-hand sides, a dynamic model for vector-borne infectious disease under the discontinuous removal of infected trees was established after understanding the transmission mechanism of Huanglongbing (HLB) disease in citrus trees. Through calculation, the basic reproductive number of the model can be attained and the properties of the model are discussed. On this basis, the existence and global stability of the calculated equilibria are verified. Moreover, it was found that different I0 in the control strategy cannot change the dynamic properties of HLB disease. However, the lower the value of I0, the fewer HLB-infected citrus trees, which provides a theoretical basis for controlling HLB disease and reducing expenditure.

Linking the disease transmission to information dissemination dynamics: An insight from a multi-scale model study

During the outbreak of emerging infectious diseases, information dissemination dynamics significantly affects the individuals’ psychological and behavioral changes, and consequently influences on the disease transmission. To investigate the interaction of disease transmission and information dissemination dynamics, we proposed a multi-scale model which explicitly models both the disease transmission with saturated recovery rate and information transmission to evaluate the effect of information transmission on dynamic behaviors. Considering time variation between information dissemination, epidemiological and demographic processes, we obtained a slow-fast system by reasonably introducing a sufficiently small quantity. We carefully examined the dynamics of proposed system, including existence and stability of possible equilibria and existence of backward bifurcation, by using the fast-slow theory and directly investigating the full system. We then compared the dynamics of the proposed system and the essential thresholds based on two methods, and obtained the similarity between the basic dynamical behaviors of the slow system and that of the full system. Finally, we parameterized the proposed model on the basis of the COVID-19 case data in mainland China and data related to news items, and estimated the basic reproduction number to be 3.25. Numerical analysis suggested that information transmission about COVID-19 pandemic caused by media coverage can reduce the peak size, which mitigates the transmission dynamics during the early stage of the COVID-19 pandemic.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travel bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

Joint impacts of media, vaccination and treatment on an epidemic Filippov model with application to COVID-19

A non-smooth SIR Filippov system is proposed to investigate the impacts of three control strategies (media coverage, vaccination and treatment) on the spread of an infectious disease. We synthetically consider both the number of infected population and its changing rate as the switching condition to implement the curing measures. By using the properties of the Lambert W function, we convert the proposed switching condition to a threshold value related to the susceptible population. The classical epidemic model involving media coverage, linear functions describing injecting vaccine and treatment strategies is examined when the susceptible population exceeds the threshold value. In addition, we consider another SIR model accompanied with the vaccination and treatment strategies represented by saturation functions when the susceptible population is smaller than the threshold value. The dynamics of these two subsystems and the sliding domain are discussed in detail. Four types of local sliding bifurcation are investigated, including boundary focus, boundary node, boundary saddle and boundary saddle-node bifurcations. In the meantime, the global bifurcation involving the appearance of limit cycles is examined, including touching bifurcation, homoclinic bifurcation to the pseudo-saddle and crossing bifurcation. Furthermore, the influence of some key parameters related to the three treatment strategies is explored. We also validate our model by the epidemic data sets of A/H1N1 and COVID-19, which can be employed to reveal the effects of media report and existing strategy related to the control of emerging infectious diseases on the variations of confirmed cases.

Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada

A mathematical model of COVID-19 is presented where the decision to increase or decrease social distancing is modelled dynamically as a function of the measured active and total cases as well as the perceived cost of isolating. Along with the cost of isolation, we define an overburden healthcare cost and a total cost. We explore these costs by adjusting parameters that could change with policy decisions. We observe that two disease prevention practices, namely increasing isolation activity and increasing incentive to isolate do not always lead to optimal health outcomes. We demonstrate that this is due to the fatigue and cost of isolation. We further demonstrate that an increase in the number of lock-downs, each of shorter duration can lead to minimal costs. Our results are compared with case data in Ontario, Canada from March to August 2020 and details of expanding the results to other regions are presented.

Dynamics of a stage-structured SI model for food adulteration with media-induced response function

In this work, an eco-epidemic predator-prey model with media-induced response function for the interaction of humans with adulterated food is developed and studied. The human population is divided into two main compartments, namely, susceptible and infected. This system has three equilibria; trivial, disease-free and endemic. The trivial equilibrium is forever an unstable saddle position, while the disease-free state is locally asymptotically stable under a threshold of delay parameter τ as well as R 0 < 1 . The sufficient conditions for the local stability of the endemic equilibrium point are further explored when min { R 0 , R 0 ∗ } > 1 . The conditions for the occurrence of the stability switching are also determined by taking infection delay time as a critical parameter, which concludes that the delay can produce instability and small amplitude oscillations of population masses via Hopf bifurcations. Further, we study the stability and direction of the Hopf bifurcations using the center manifold argument. Furthermore, some numerical simulations are conducted to validate our analytical findings and discuss their biological inferences. Finally, the normalized forward sensitivity index is used to perform the sensitivity analysis of R 0 and R 0 ∗ .

Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak

Building an effective and highly usable epidemiology model presents two main challenges: finding the appropriate, realistic enough model that takes into account complex biological, social and environmental parameters and efficiently estimating the parameter values with which the model can accurately match the available outbreak data, provide useful projections. The reproduction number of the novel coronavirus (SARS-CoV-2) has been found to vary over time, potentially being influenced by a multitude of factors such as varying control strategies, changes in public awareness and reaction or, as a recent study suggests, sensitivity to temperature or humidity changes. To take into consideration these constantly evolving factors, the paper introduces a time dynamic, humidity-dependent SEIR-type extended epidemiological model with range-defined parameters. Using primarily the historical data of the outbreak from Northern and Southern Italy and with the help of stochastic global optimization algorithms, we are able to determine a model parameter estimation that provides a high-quality fit to the data. The time-dependent contact rate showed a quick drop to a value slightly below 2. Applying the model for the COVID-19 outbreak in the northern region of Italy, we obtained parameters that suggest a slower shrinkage of the contact rate to a value slightly above 4. These findings indicate that model fitting and validation, even on a limited amount of available data, can provide useful insights and projections, uncover aspects that upon improvement might help mitigate the disease spreading.

The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy

In this paper, we present a mathematical model of an infectious disease according to the characteristics of the COVID-19 pandemic. The proposed enhanced model, which will be referred to as the SEIR (Susceptible-Exposed-Infectious-Recovered) model with population migration, is inspired by the role that asymptomatic infected individuals, as well as population movements can play a crucial role in spreading the virus. In the model, the infected and the basic reproduction numbers are compared under the influence of intervention policies. The experimental simulation results show the impact of social distancing and migration-in rates on reducing the total number of infections and the basic reproductions. And then, the importance of controlling the number of migration-in people and the policy of restricting residents' movements in preventing the spread of COVID-19 pandemic are verified.

Global dynamics for a Filippov epidemic system with imperfect vaccination

Given imperfect vaccination we extend the existing non-smooth models by considering susceptible and vaccinated individuals enhance the protection and control strategies once the number of infected individuals exceeds a certain level. On the basis of global dynamics of two subsystems, for the formulated Filippov system, we examine the sliding mode dynamics, the boundary equilibrium bifurcations, and the global dynamics. Our main results show that it is possible that the pseudo-equilibrium exists and is globally stable, or the pseudo-equilibrium, the disease-free equilibrium and the real equilibrium are tri-stable, or the pseudo-equilibrium and the real equilibrium are bi-stable, or the pseudo-equilibrium and disease-free equilibrium are bi-stable, which depend on the threshold value and other parameter values. The global stability of the disease-free equilibrium or pseudo-equilibrium reveals that we may eradicate the disease or maintain the number of infected individuals at a previously given value. Further, the bi-stability and tri-stability imply that whether the number of infected individuals tends to zero or a previously given value or other positive values depends on the parameter values and the initial states of the system. This emphasizes the importance of threshold policy and challenges in the control of infectious diseases if without perfect vaccines.

The effect of control measures on COVID-19 transmission in Italy: Comparison with Guangdong province in China

BACKGROUND

COVID-19 has spread all around the world. Italy is one of the worst affected countries in Europe. Although there is a trend of relief, the epidemic situation hasn't stabilized yet. This study aims to investigate the dynamics of the disease spread in Italy and provide some suggestions on containing the epidemic.

METHODS

We compared Italy's status at the outbreak stage and control measures with Guangdong Province in China by data observation and analysis. A modified autonomous SEIR model was used to study the COVID-19 epidemic and transmission potential during the early stage of the outbreak in Italy. We also utilized a time-dependent dynamic model to study the future disease dynamics in Italy. The impact of various non-pharmaceutical control measures on epidemic was investigated through uncertainty and sensitivity analyses.

RESULTS

The comparison of specific measures implemented in the two places and the time when the measures were initiated shows that the initial prevention and control actions in Italy were not sufficiently timely and effective. We estimated parameter values based on available cumulative data and calculated the basic reproduction number to be 4.32 before the national lockdown in Italy. Based on the estimated parameter values, we performed numerical simulations to predict the epidemic trend and evaluate the impact of contact limitation, detection and diagnosis, and individual behavior change due to media coverage on the epidemic.

CONCLUSIONS

Italy was in a severe epidemic status and the control measures were not sufficiently timely and effective in the beginning. Non-pharmaceutical interventions, including contact restrictions and improvement of case recognition, play an important role in containing the COVID-19 epidemic. The effect of individual behavior changes due to media update of the outbreak cannot be ignored. For policy-makers, early and strict blockade measures, fast detection and improving media publicity are key to containing the epidemic.

Grappling With the COVID-19 Health Crisis: Content Analysis of Communication Strategies and Their Effects on Public Engagement on Social Media

Background

The coronavirus disease (COVID-19) has posed an unprecedented challenge to governments worldwide. Effective government communication of COVID-19 information with the public is of crucial importance.

Objective

We investigate how the most-read state-owned newspaper in China, People’s Daily, used an online social networking site, Sina Weibo, to communicate about COVID-19 and whether this could engage the public. The objective of this study is to develop an integrated framework to examine the content, message style, and interactive features of COVID-19–related posts and determine their effects on public engagement in the largest social media network in China.

Methods

Content analysis was employed to scrutinize 608 COVID-19 posts, and coding was performed on three main dimensions: content, message style, and interactive features. The content dimension was coded into six subdimensions: action, new evidence, reassurance, disease prevention, health care services, and uncertainty, and the style dimension was coded into the subdimensions of narrative and nonnarrative. As for interactive features, they were coded into links to external sources, use of hashtags, use of questions to solicit feedback, and use of multimedia. Public engagement was measured in the form of the number of shares, comments, and likes on the People’s Daily’s Sina Weibo account from January 20, 2020, to March 11, 2020, to reveal the association between different levels of public engagement and communication strategies. A one-way analysis of variance followed by a post-hoc Tukey test and negative binomial regression analysis were employed to generate the results.

Results

We found that although the content frames of action, new evidence, and reassurance delivered in a nonnarrative style were predominant in COVID-19 communication by the government, posts related to new evidence and a nonnarrative style were strong negative predictors of the number of shares. In terms of generating a high number of shares, it was found that disease prevention posts delivered in a narrative style were able to achieve this purpose. Additionally, an interaction effect was found between content and style. The use of a narrative style in disease prevention posts had a significant positive effect on generating comments and likes by the Chinese public, while links to external sources fostered sharing.

Conclusions

These results have implications for governments, health organizations, medical professionals, the media, and researchers on their epidemic communication to engage the public. Selecting suitable communication strategies may foster active liking and sharing of posts on social media, which in turn, might raise the public’s awareness of COVID-19 and motivate them to take preventive measures. The sharing of COVID-19 posts is particularly important because this action can reach out to a large audience, potentially helping to contain the spread of the virus.

Emotional Responses and Self-Protective Behavior Within Days of the COVID-19 Outbreak: The Promoting Role of Information Credibility

Due to changes in the information environment since the last global epidemic, high WHO officials have spoken about the need to fight not only the current COVID-19 pandemic but also the related infodemic. We thus explored how people search for information, how they perceive its credibility, and how all this relates to their engagement in self-protective behaviors in the crucial period right after the onset of COVID-19 epidemic. The online questionnaire was circulated within 48 h after the first case of COVID-19 was confirmed in Slovenia. We gathered information on participants' demographics, perception of the situation, their emotional and behavioral responses to the situation (i.e., self-protective behavior), perceived subjective knowledge, perceived credibility of different sources of information, and their level of trust. We looked into the relationships between perceived credibility and trust, and self-protective behavior of 1,718 participants and found that mass media, social media, and officials received relatively low levels of trust. Conversely, medical professionals and scientists were deemed the most credible. The perceived credibility of received information was linked not only with lower levels of negative emotional responses but also with higher adherence to much needed self-protective measures, which aim to contain the spread of the disease. While results might vary between societies with different levels of trust in relevant governmental and professional institutions, and while variances in self-protective behavior scores explained by our model are modest, even a small increase in self-protective behavior could go a long way in viral epidemics like the one we are facing today.

A study of SARS-CoV-2 epidemiology in Italy: from early days to secondary effects after social distancing

BACKGROUND

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 101,739 confirmed cases, in Italy, as of March 30th, 2020. While the analogous event in China appears to be under control at the moment, the outbreaks in western countries are still at an early stage of development. Italy, at present, is playing a major role in understanding the transmission dynamics of these new infections and evaluating the effectiveness of control measures in a western social context.

METHODS

We combined a quarantined model with early-stage development data in Italy (during the period February 20th-March 30th) to predict longer-term progression (from March 30th, till June 25th, 2020 in a long-term view) with different control measures. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies leading to faster confirmation of the SARS-CoV-2 infections, we made use of time-dependent contact and diagnosis rates to estimate when the effective daily reproduction ratio can fall below 1. Within the same framework, we analyze the possible secondary infection event after relaxing the isolation measures.

OUTCOMES AND INTERPRETATION

We study two simplified scenarios compatible with the observation data and the effects of two stringent measures on the evolution of the epidemic. On one side, the contact rate must be kept as low as possible, but it is also clear that, in a modern developed country, it cannot fall under certain minimum levels and for a long time. The complementary parameter tuned is the transition rate of the symptomatic infected individuals to the quarantined class, a parameter δ I I connected with the time t _I = 1/δ_I   needed to perform diagnostic tests. Within the conditions of the outbreak in Italy, this time must fall under 12-8 h in order to make the reproduction number less than 1 to minimize the case numbers. Moreover, we show how the same parameter plays an even more important role in mitigating the effects of a possible secondary infection event.

The coronavirus disease (COVID-19) pandemic: simulation-based assessment of outbreak responses and postpeak strategies

It is critical to understand the impact of distinct interventions on the ongoing coronavirus disease pandemic. I develop a behavioral dynamic epidemic model for multifaceted policy analysis comprising endogenous virus transmission (from severe or mild/asymptomatic cases), social contacts, and case testing and reporting. Calibration of the system dynamics model to the ongoing outbreak (31 December 2019-15 May 2020) using multiple time series data (reported cases and deaths, performed tests, and social interaction proxies) from six countries (South Korea, Germany, Italy, France, Sweden, and the United States) informs an explanatory analysis of outbreak responses and postpeak strategies. Specifically, I demonstrate, first, how timing and efforts of testing-capacity expansion and social-contact reduction interplay to affect outbreak dynamics and can explain a large share of cross-country variation in outbreak pathways. Second, absent at-scale availability of pharmaceutical solutions, postpeak social contacts must remain well below prepandemic values. Third, proactive (targeted) interventions, when complementing general deconfinement readiness, can considerably increase admissible postpeak social contacts. © 2020 System Dynamics Society.

Switching dynamics analysis of forest-pest model describing effects of external periodic disturbance

A periodically forced Filippov forest-pest model incorporating threshold policy control and integrated pest management is proposed. It is very natural and reasonable to introduce Filippov non-smooth system into the ecosystem since there were many disadvantageous factors in pest control at fixed time and the threshold control according to state variable showed rewarding characteristics. The main aim of this paper is to quest the association between pests dynamics and system parameters especially the economical threshold ET, the amplitude and frequency of periodic forcing term. From the view of pest control, if the maximum amplitude of the sliding periodic solution does not exceed economic injury level(EIL), the sliding periodic solution is a desired result for pest control. The Filippov forest-pest model exhibits the rich dynamic behaviors including multiple attractors coexistence, period-adding bifurcation, quasi-periodic feature and chaos. At certain frequency of periodic forcing, the varying system initial densities trigger the system state switch between different attractors with diverse amplitudes and periods. Besides, parameters sensitivity analysis shows that the pest could be controlled at a certain level by choosing suitable parameters.

Modelling the Measles Outbreak at Hong Kong International Airport in 2019: A Data-Driven Analysis on the Effects of Timely Reporting and Public Awareness

Background

Measles, a highly contagious disease, still poses a huge burden worldwide. Lately, a trend of resurgence threatened the developed countries. A measles outbreak occurred in the Hong Kong International Airport (HKIA) between March and April 2019, which infected 29 airport staff. During the outbreak, multiple measures were taken including daily situation updates, setting up a public enquiry platform on March 23, and an emergent vaccination program targeting unprotected staff. The outbreak was put out promptly. The effectiveness of these measures was unclear.

Methods

We quantified the transmissibility of outbreak in HKIA by the effective reproduction number, R_eff(t), and basic reproduction number, R₀(t). The reproduction number was modelled as a function of its determinants that were statistically examined, including lags in hospitalization, situation update, and level of public awareness. Then, we considered a hypothetical no-measure scenario when improvements in reporting and public enquiry were absent and calculated the number of infected airport staff.

Results

Our estimated average R₀ is 10.09 (95% CI: 1.73−36.50). We found that R₀(t) was positively associated with lags in hospitalization and situation update, while negatively associated with the level of public awareness. The average predicted basic reproduction number, r₀, was 14.67 (95% CI: 9.01−45.32) under the no-measure scenario, which increased the average R₀ by 77.57% (95% CI: 1.71−111.15). The total number of infected staff would be 179 (IQR: 90−339, 95% CI: 23−821), namely the measure induced 8.42-fold (95% CI: 0.21−42.21) reduction in the total number of infected staff.

Conclusion

Timely reporting on outbreak situation and public awareness measured by the number of public enquiries helped to control the outbreak.

Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019

Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of −5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.

The impact of media on the spatiotemporal pattern dynamics of a reaction-diffusion epidemic model

In this paper, a reaction-diffusion SI epidemic model with media impact is considered. The boundedness of system and the existence of the state are given. The local stabilities of the endemic states are analyzed. Sufficient conditions of the occurrence of the Turing pattern are obtained by the center manifold theorem and normal form method. Some numerical simulations are given to check in the theoretical results. We find that the influence of media not only inhibits the spread of infectious diseases, but also effects the spatial steady-state of model.

A Multidimensional Model of Public Health Approaches Against COVID-19

COVID-19 is appearing as one of the most fetal disease of the world's history and has caused a global health emergency. Therefore, this study was designed with the aim to address the issue of public response against COVID-19. The literature lacks studies on social aspects of COVID-19. Therefore, the current study is an attempt to investigate its social aspects and suggest a theoretical structural equation model to examine the associations between social media exposure, awareness, and information exchange and preventive behavior and to determine the indirect as well as direct impact of social media exposure on preventive behavior from the viewpoints of awareness and information exchange. The current empirical investigation was held in Pakistan, and the collected survey data from 500 respondents through social media tools were utilized to examine the associations between studied variables as stated in the anticipated study model. The findings of the study indicate that social media exposure has no significant and direct effect on preventive behavior. Social media exposure influences preventive behavior indirectly through awareness and information exchange. In addition, awareness and information exchange have significant and direct effects on preventive behavior. Findings are valuable for health administrators, governments, policymakers, and social scientists, specifically for individuals whose situations are like those in Pakistan. This research validates how social media exposure indirectly effects preventive behavior concerning COVID-19 and explains the paths of effect through awareness or information exchange. To the best of our knowledge, there is no work at present that covers this gap, for this reason the authors propose a new model. The conceptual model offers valuable information for policymakers and practitioners to enhance preventive behavior through the adoption of appropriate awareness strategies and information exchange and social media strategies.

Coupling the Macroscale to the Microscale in a Spatiotemporal Context to Examine Effects of Spatial Diffusion on Disease Transmission

There are many challenges to coupling the macroscale to the microscale in temporal or spatial contexts. In order to examine effects of an individual movement and spatial control measures on a disease outbreak, we developed a multiscale model and extended the semi-stochastic simulation method by linking individual movements to pathogen’s diffusion, linking the slow dynamics for disease transmission at the population level to the fast dynamics for pathogen shedding/excretion at the individual level. Numerical simulations indicate that during a disease outbreak individuals with the same infection status show the property of clustering and, in particular, individuals’ rapid movements lead to an increase in the average reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0, the final size and the peak value of the outbreak. It is interesting that a high level of aggregation the individuals’ movement results in low new infections and a small final size of the infected population. Further, we obtained that either high diffusion rate of the pathogen or frequent environmental clearance lead to a decline in the total number of infected individuals, indicating the need for control measures such as improving air circulation or environmental hygiene. We found that the level of spatial heterogeneity when implementing control greatly affects the control efficacy, and in particular, an uniform isolation strategy leads to low a final size and small peak, compared with local measures, indicating that a large-scale isolation strategy with frequent clearance of the environment is beneficial for disease control.

New estimates of the Zika virus epidemic attack rate in Northeastern Brazil from 2015 to 2016: A modelling analysis based on Guillain-Barré Syndrome (GBS) surveillance data

BACKGROUND

Between January 2015 and August 2016, two epidemic waves of Zika virus (ZIKV) disease swept the Northeastern (NE) region of Brazil. As a result, two waves of Guillain-Barré Syndrome (GBS) were observed concurrently. The mandatory reporting of ZIKV disease began region-wide in February 2016, and it is believed that ZIKV cases were significantly under-reported before that. The changing reporting rate has made it difficult to estimate the ZIKV infection attack rate, and studies in the literature vary widely from 17% to > 50%. The same applies to other key epidemiological parameters. In contrast, the diagnosis and reporting of GBS cases were reasonably reliable given the severity and easy recognition of the disease symptoms. In this paper, we aim to estimate the real number of ZIKV cases (i.e., the infection attack rate) and their dynamics in time, by scaling up from GBS surveillance data in NE Brazil.

METHODOLOGY

A mathematical compartmental model is constructed that makes it possible to infer the true epidemic dynamics of ZIKV cases based on surveillance data of excess GBS cases. The model includes the possibility that asymptomatic ZIKV cases are infectious. The model is fitted to the GBS surveillance data and the key epidemiological parameters are inferred by using a plug-and-play likelihood-based estimation. We make use of regional weather data to determine possible climate-driven impacts on the reproductive number [Formula: see text], and to infer the true ZIKV epidemic dynamics.

FINDINGS AND CONCLUSIONS

The GBS surveillance data can be used to study ZIKV epidemics and may be appropriate when ZIKV reporting rates are not well understood. The overall infection attack rate (IAR) of ZIKV is estimated to be 24.1% (95% confidence interval: 17.1%-29.3%) of the population. By examining various asymptomatic scenarios, the IAR is likely to be lower than 33% over the two ZIKV waves. The risk rate from symptomatic ZIKV infection to develop GBS was estimated as ρ = 0.0061% (95% CI: 0.0050%-0.0086%) which is significantly less than current estimates. We found a positive association between local temperature and the basic reproduction number, [Formula: see text]. Our analysis revealed that asymptomatic infections affect the estimation of ZIKV epidemics and need to also be carefully considered in related modelling studies. According to the estimated effective reproduction number and population wide susceptibility, we comment that a ZIKV outbreak would be unlikely in NE Brazil in the near future.

Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions

Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71-7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.

Public awareness, news promptness and the measles outbreak in Hong Kong from March to April, 2019

Background: Globally, a resurgence of measles during the last decade may be attributed to many factors. An unexpected measles outbreak occurred in Hong Kong, and infected 29 airport staff between March and April 2019. The authority updated public on new cases daily, a public enquiry telephone/online platform was set up on March 23, and an emergent vaccination programme was launched targeting unvaccinated airport staff. We aimed to study this measles outbreak and its related factors.Methods: We quantified the transmissibility of the outbreak by the time-varying effective reproduction number, R_eff(t), and inferred the time-varying basic reproduction number, R₀(t). We examined the statistical associations between local public awareness or reporting delay and the R₀(t).Results: Our estimated average R₀ is 10.7 with 95% CI of 6.0-29.2. We found that R₀(t) was negatively associated with the level of public awareness and the level of promptness of situation updates on new cases.Conclusion: Public awareness via situation updates helped to control the outbreak. The medical effects of the vaccination programme was not soon enough to cause the immediate shutting down of the outbreak, but it boosted herd immunity to prevent future airport outbreaks in the next few years.

Mathematical modeling and analysis of meningococcal meningitis transmission dynamics

Meningococcal meningitis (MCM) is one of the serious public health threats in the tropical and sub-tropical regions. In this paper, we propose an epidemic model to study the transmission dynamics of MCM with high- and low-risk susceptible populations. The model considers two different groups of susceptible individuals depending on the availability of medical resources (MR, including hospitals, health workers, etc.), which varies the infection risk. We find that the model exhibits the phenomenon of backward bifurcation (BB), which increases the difficulty of MCM control since the dynamics are not merely relying on the basic reproduction number, [Formula: see text]. This study explores the effects of MR on the MCM epidemics by mathematical analysis and shows the existence of BB on MCM disease. Our findings suggest that providing adequate MR in a community is crucial in mitigating MCM incidences and deaths, especially, in the MCM endemic regions.

A stochastic SIR epidemic model with Lévy jump and media coverage

A stochastic susceptible-infectious-recovered epidemic model with temporary immunity and media coverage is proposed. The effects of Lévy jumps on the dynamics of the model are considered. A unique global positive solution for the epidemic model is obtained. Sufficient conditions are derived to guarantee that the epidemic disease is extinct and persistent in the mean. The threshold behavior is discussed. Numerical simulations are given to verify our theoretical results.