The impact of prophylaxis of healthcare workers on influenza pandemic burden.

Several models have rationalized the use of antiviral drugs as an early control measure for delaying the progression and limiting the size of outbreaks during an influenza pandemic. However, the strategy for use of these drugs is still under debate. We evaluated the impact of prophylaxis of healthcare workers (HCWs) through a mathematical model that considers attack rates in a range of 25-35% in the general population and 25-50% among HCWs. Simulations and uncertainty analysis using the demographics of the province of Ontario, Canada show that increasing prophylaxis coverage of HCWs has little impact on reducing the reproduction number of disease transmission and may not prevent the occurrence of an outbreak if expected. However, it does enable a high level of treatment, which substantially reduces morbidity and mortality in the population as a whole. Therefore, prophylaxis of HCWs should be considered an important part of public health efforts for minimizing influenza pandemic burden and its socio-economic disruption.     

Strategies for mitigating an influenza pandemic with pre-pandemic H5N1 vaccines

The recent worldwide spread of the swine-origin H1N1 2009 influenza outbreak has resulted in its designation as a pandemic by the World Health Organization. While it appears to result in mild symptoms, concern still exists that a more severe influenza pandemic with a high case fatality ratio might arise by reassortment or mutation of the currently circulating avian influenza (H5N1) virus. Given that recently developed candidate pre-pandemic H5N1 vaccines have shown potential for cross-strain protection, we investigated alternative vaccination strategies that exploit such vaccines using an agent-based simulation model of an actual community of approximately 30 000 people in a developed country. Assuming that a two-dose vaccination regimen would be required, we examined three vaccination strategies: pre-emptive, with vaccination applied prior to emergence of human-transmissible H5N1 influenza; reactive, where vaccination was initiated immediately after the first cases in the community were diagnosed; and a ‘split’ strategy where the first dose was administered pre-emptively during the pre-pandemic phase, with the second dose administered reactively. We showed that by effectively moving the delay between first and second doses into the pre-pandemic period, the split vaccination strategy achieved a substantially better attack rate reduction than the reactive strategy. Our results for an influenza strain with a reproduction number of 1.5 suggest reactive vaccination strategies that may be applicable to the current H1N1 2009 pandemic.     

The effect of opinion clustering on disease outbreaks

Many high-income countries currently experience large outbreaks of vaccine-preventable diseases such as measles despite the availability of highly effective vaccines. This phenomenon lacks an explanation in countries where vaccination rates are rising on an already high level. Here, we build on the growing evidence that belief systems, rather than access to vaccines, are the primary barrier to vaccination in high-income countries, and show how a simple opinion formation process can lead to clusters of unvaccinated individuals, leading to a dramatic increase in disease outbreak probability. In particular, the effect of clustering on outbreak probabilities is strongest when the vaccination coverage is close to the level required to provide herd immunity under the assumption of random mixing. Our results based on computer simulations suggest that the current estimates of vaccination coverage necessary to avoid outbreaks of vaccine-preventable diseases might be too low.     

Targeting vaccination against novel infections: risk,age and spatial structure for pandemic influenza in Great Britain

The emergence of a novel strain of H1N1 influenza virus in Mexico in 2009, and its subsequent worldwide spread, has focused attention to the question of optimal deployment of mass vaccination campaigns. Here, we use three relatively simple models to address three issues of primary concern in the targeting of any vaccine. The advantages of such simple models are that the underlying assumptions and effects of individual parameters are relatively clear, and the impact of uncertainty in the parametrization can be readily assessed in the early stages of an outbreak. In particular, we examine whether targeting risk-groups, age-groups or spatial regions could be optimal in terms of reducing the predicted number of cases or severe effects; and how these targeted strategies vary as the epidemic progresses. We examine the conditions under which it is optimal to initially target vaccination towards those individuals within the population who are most at risk of severe effects of infection. Using age-structured mixing matrices, we show that targeting vaccination towards the more epidemiologically important age groups (5–14 year olds and then 15–24 year olds) leads to the greatest reduction in the epidemic growth and hence reduces the total number of cases. Finally, we consider how spatially targeting the vaccine towards regions of country worst affected could provide an advantage. We discuss how all three of these priorities change as both the speed at which vaccination can be deployed and the start of the vaccination programme is varied.     

Estimation of measles vaccine efficacy and critical vaccination coverage in a highly vaccinated population

Measles is a highly infectious disease that has been targeted for elimination from four WHO regions. Whether and under which conditions this goal is feasible is, however, uncertain since outbreaks have been documented in populations with high vaccination coverage (more than 90%). Here, we use the example of a large outbreak in a German public school to show how estimates of key epidemiological parameters such as the basic reproduction number (R₀), vaccine efficacy (VE_S) and critical vaccination coverage (p_c) can be obtained from partially observed outbreaks in highly vaccinated populations. Our analyses rely on Bayesian methods of inference based on the final size distribution of outbreak size, and use data which are easily collected. For the German public school the analyses indicate that the basic reproduction number of measles is higher than previously thought (, 95% credible interval: 23.6–40.4), that the vaccine is highly effective in preventing infection (, 95% credible interval: 0.993–0.999), and that a vaccination coverage in excess of 95 per cent may be necessary to achieve herd immunity (, 95% credible interval: 0.961–0.978). We discuss the implications for measles elimination from highly vaccinated populations.     

Transmissibility of swine flu at Fort Dix, 1976.

The 1976 outbreak of A/New Jersey/76 influenza in Fort Dix is a rare example of an influenza virus with documented human to human transmission that failed to spread widely. Despite extensive epidemiological investigation, no attempt has been made to quantify the transmissibility of this virus. The World Health Organization and the United States Government view containment of emerging influenza strains as central to combating pandemic influenza. Computational models predict that it may be possible to contain an emergent pandemic influenza if virus transmissibility is low. The A/New Jersey/76 outbreak at the United States Army Training Center at Fort Dix, New Jersey in January 1976 caused 13 hospitalizations, 1 death and an estimated 230 cases. To characterize viral transmission in this epidemic, we estimated the basic reproductive number and serial interval using deterministic epidemic models and stochastic simulations. We estimated the basic reproductive number for this outbreak to be 1.2 (supported interval 1.1-1.4), the serial interval to be 1.9 days (supported interval 1.6-3.8 days), and that the virus had at least six serial human to human transmissions. This places the transmissibility of A/New Jersey/76 virus at the lower end of circulating flu strains, well below the threshold for control.     

The role of heterogeneity in contact timing and duration in network models of influenza spread in schools

Influenza poses a significant health threat to children, and schools may play a critical role in community outbreaks. Mathematical outbreak models require assumptions about contact rates and patterns among students, but the level of temporal granularity required to produce reliable results is unclear. We collected objective contact data from students aged 5–14 at an elementary school and middle school in the state of Utah, USA, and paired those data with a novel, data-based model of influenza transmission in schools. Our simulations produced within-school transmission averages consistent with published estimates. We compared simulated outbreaks over the full resolution dynamic network with simulations on networks with averaged representations of contact timing and duration. For both schools, averaging the timing of contacts over one or two school days caused average outbreak sizes to increase by 1–8%. Averaging both contact timing and pairwise contact durations caused average outbreak sizes to increase by 10% at the middle school and 72% at the elementary school. Averaging contact durations separately across within-class and between-class contacts reduced the increase for the elementary school to 5%. Thus, the effect of ignoring details about contact timing and duration in school contact networks on outbreak size modelling can vary across different schools.     

An avian influenza model with latency and vaccination

Avian influenza, caused by influenza A viruses, has received worldwide attention in recent years. The viruses can spread from birds to humans as well as transmit among human hosts. In this study, we formulate a mathematical model for avian influenza that includes bird-human interaction and that incorporates the effects of infection latency and human vaccination. We investigate the essential dynamics of the model through an equilibrium analysis. Meanwhile, we explore effective vaccination strategy to control avian influenza outbreaks using optimal control theory. Our results indicate that strategically deployed human vaccination can significantly reduce the numbers of exposed and infectious people.     

Distribution of vaccine/antivirals and the ‘least spread line’ in a stratified population

We describe a prioritization scheme for an allocation of a sizeable quantity of vaccine or antivirals in a stratified population. The scheme builds on an optimal strategy for reducing the epidemic''s initial growth rate in a stratified mass-action model. The strategy is tested on the EpiSims network describing interactions and influenza dynamics in the population of Utah, where the stratification we have chosen is by age (0–6, 7–13, 14–18, adults). No prior immunity information is available, thus everyone is assumed to be susceptible—this may be relevant, possibly with the exception of persons over 50, to the 2009 H1N1 influenza outbreak. We have found that the top priority in an allocation of a sizeable quantity of seasonal influenza vaccinations goes to young children (0–6), followed by teens (14–18), then children (7–13), with the adult share being quite low. These results, which rely on the structure of the EpiSims network, are compared with the current influenza vaccination coverage levels in the US population.     

Epidemicity of cholera spread and the fate of infection control measures

The fate of ongoing infectious disease outbreaks is predicted through reproduction numbers, defining the long-term establishment of the infection, and epidemicity indices, tackling the reactivity of the infectious pool to new contagions. Prognostic metrics of unfolding outbreaks are of particular importance when designing adaptive emergency interventions facing real-time assimilation of epidemiological evidence. Our aim here is twofold. First, we propose a novel form of the epidemicity index for the characterization of cholera epidemics in spatial models of disease spread. Second, we examine in hindsight the survey of infections, treatments and containment measures carried out for the now extinct 2010–2019 Haiti cholera outbreak, to suggest that magnitude and timing of non-pharmaceutical and vaccination interventions imply epidemiological responses recapped by the evolution of epidemicity indices. Achieving negative epidemicity greatly accelerates fading of infections and thus proves a worthwhile target of containment measures. We also show that, in our model, effective reproduction numbers and epidemicity indices are explicitly related. Therefore, providing an upper bound to the effective reproduction number (significantly lower than the unit threshold) warrants negative epidemicity and, in turn, a rapidly fading outbreak preventing coalescence of sparse local sub-threshold flare-ups.     

Outbreaks of H5N1 in poultry in Thailand: the relative role of poultry production types in sustaining transmission and the impact of active surveillance in control

H5N1, highly pathogenic avian influenza, continues to pose a public health risk in the countries of southeast Asia where it has become endemic. However, in Thailand, which experienced two of the largest recorded epidemics in 2004–2005, the disease has been successfully reduced to very low levels. We fitted a spatio-temporal model of the spread of infection to outbreak data collected during the second wave of outbreaks to assess the extent to which different poultry types were responsible for propagating infection. Our estimates suggest that the wave of outbreaks would not have been possible without the contribution of backyard flocks to the susceptibility of a sub-district. However, we also estimated that outbreaks involving commercial poultry, a much larger sector in Thailand than in neighbouring countries, were disproportionately infectious, a factor which was also crucial in sustaining the wave. As a result, implemented measures that aim to reduce the role of commercial farms in the spread of infection, such as the drive to bring aspects of the supply chain ‘in house’, may help to explain the subsequent success in controlling H5N1 in Thailand. We also found that periods of active surveillance substantially improved the rate of outbreak detection.     

The influence of altruism on influenza vaccination decisions

Game theory is based on the assumption that individuals act according to self-interest and make decisions that maximize their personal payoffs. To test this fundamental assumption, we conducted a survey study in the context of influenza vaccination decisions. Contrary to the assumption of self-interest, we found that altruism plays an important role in vaccination decisions. Nevertheless, altruistic motivation has not yet been considered in epidemiological models, in predictions of vaccination decisions or in the design of vaccination policies. To determine the impact of altruism on the adherence to optimal vaccination policies and on resulting disease burden, we incorporated altruism into a game-theoretic epidemiological model of influenza vaccination. We found that altruism significantly shifted vaccination decisions away from individual self-interest and towards the community optimum, greatly reducing the total cost, morbidity and mortality for the community. Therefore, promoting altruism could be a potential strategy to improve public health outcomes.     

How necessary is a fast testkit for mitigation of pandemic flu?

It is widely feared that a novel, highly pathogenic, human transmissible influenza virus may evolve that could cause the next global pandemic. Mitigating the spread of such an influenza pandemic would require not only the timely administration of antiviral drugs to those infected, but also the implementation of suitable intervention policies for stunting the spread of the virus. Towards this end, mathematical modelling and simulation studies are crucial as they allow us to evaluate the predicted effectiveness of the various intervention policies before enforcing them. Diagnosis plays a vital role in the overall pandemic management framework by detecting and distinguishing the pathogenic strain from the less threatening seasonal strains and other influenza-like illnesses. This allows treatment and intervention to be deployed effectively, given limited antiviral supplies and other resources. However, the time required to design a fast and accurate testkit for novel strains may limit the role of diagnosis. Herein, we aim to investigate the cost and effectiveness of different diagnostic methods using a stochastic agent-based city-scale model, and then address the issue of whether conventional testing approaches, when used with appropriate intervention policies, can be as effective as fast testkits in containing a pandemic outbreak. We found that for mitigation purposes, fast and accurate testkits are not necessary as long as sufficient medication is given, and are generally recommended only when used with extensive contact tracing and prophylaxis. Additionally, in the event of insufficient medication and fast testkits, the use of slower, conventional testkits together with proper isolation policies while waiting for the diagnostic results can be an equally effective substitute.     

Control of equine influenza: scenario testing using a realistic metapopulation model of spread

We present a metapopulation model of the spread of equine influenza among thoroughbred horses parametrized with data from a 2003 outbreak in Newmarket, UK. The number of horses initially susceptible is derived from a threshold theorem and a published statistical model. Two simulated likelihood-based methods are used to find the within- and between-yard transmissions using both exponential and empirical latent and infectious periods. We demonstrate that the 2003 outbreak was largely locally driven and use the parametrized model to address important questions of control. The chance of a large epidemic is shown to be largely dependent on the size of the index yard. The impact of poor responders to vaccination is estimated under different scenarios. A small proportion of poor responders strongly influences the efficiency of vaccine policies, which increases risk further when the vaccine and infecting strains differ following antigenic drift. Finally, the use of vaccinating in the face of an outbreak is evaluated at a global and individual management group level. The benefits for an individual horse trainer are found to be substantial, although this is influenced by the behaviour of other trainers.     

Forecast the Influenza Pandemic Using Machine Learning

Forecasting future outbreaks can help in minimizing their spread. Influenza is a disease primarily found in animals but transferred to humans through pigs. In 1918, influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population. Afterwards, that influenza became a pandemic several times on a local and global levels. In 2009, influenza ‘A’ subtype H1N1 again took many human lives. The disease spread like in a pandemic quickly. This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network (MSDII-FFNN). This model helps us predict the outbreak, and determines which type of influenza becomes a pandemic, as well as which geographical area is infected. Data collection for the model is done by using IoT devices. This model is divided into 2 phases: The training phase and the validation phase, both being connected through the cloud. In the training phase, the model is trained using FFNN and is updated on the cloud. In the validation phase, whenever the input is submitted through the IoT devices, the system model is updated through the cloud and predicts the pandemic alert. In our dataset, the data is divided into an 85% training ratio and a 15% validation ratio. By applying the proposed model to our dataset, the predicted output precision is 90%.     

Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza and Streptococcus pneumoniae

A significant fraction of seasonal and in particular pandemic influenza deaths are attributed to secondary bacterial infections. In animal models, influenza virus predisposes hosts to severe infection with both Streptococcus pneumoniae and Staphylococcus aureus. Despite its importance, the mechanistic nature of the interaction between influenza and pneumococci, its dependence on the timing and sequence of infections as well as the clinical and epidemiological consequences remain unclear. We explore an immune-mediated model of the viral–bacterial interaction that quantifies the timing and the intensity of the interaction. Taking advantage of the wealth of knowledge gained from animal models, and the quantitative understanding of the kinetics of pathogen-specific immunological dynamics, we formulate a mathematical model for immune-mediated interaction between influenza virus and S. pneumoniae in the lungs. We use the model to examine the pathogenic effect of inoculum size and timing of pneumococcal invasion relative to influenza infection, as well as the efficacy of antivirals in preventing severe pneumococcal disease. We find that our model is able to capture the key features of the interaction observed in animal experiments. The model predicts that introduction of pneumococcal bacteria during a 4–6 day window following influenza infection results in invasive pneumonia at significantly lower inoculum size than in hosts not infected with influenza. Furthermore, we find that antiviral treatment administered later than 4 days after influenza infection was not able to prevent invasive pneumococcal disease. This work provides a quantitative framework to study interactions between influenza and pneumococci and has the potential to accurately quantify the interactions. Such quantitative understanding can form a basis for effective clinical care, public health policies and pandemic preparedness.     

Estimating antiviral effectiveness against pandemic influenza using household data

Current estimates of antiviral effectiveness for influenza are based on the existing strains of the virus. Should a pandemic strain emerge, strain-specific estimates will be required as early as possible to ensure that antiviral stockpiles are used optimally and to compare the benefits of using antivirals as prophylaxis or to treat cases. We present a method to measure antiviral effectiveness using early pandemic data on household outbreak sizes, including households that are provided with antivirals for prophylaxis and those provided with antivirals for treatment only. We can assess whether antiviral drugs have a significant impact on susceptibility or on infectivity with the data from approximately 200 to 500 households with a primary case. Fewer households will suffice if the data can be collected before case numbers become high, and estimates are more precise if the study includes data from prophylaxed households and households where no antivirals are provided. Rates of asymptomatic infection and the level of transmissibility of the virus do not affect the accuracy of these estimates greatly, but the pattern of infectivity in the individual strongly influences the estimate of the effect of antivirals on infectivity. An accurate characterization of the infectiousness profile—informed by strain-specific data—is essential for measuring antiviral effectiveness.     

Detecting Periods of Significant Increased Communication Levels for Subgroups of Targeted Individuals

This paper investigates detecting significant increases in communication patterns and levels between small groups of individuals within a moderate‐size targeted group. Potential applications range from trying to establish emerging thought leaders within an organisation to the detection of the planning stages of a crime. The scan statistic is a popular choice for monitoring and detecting spatio‐temporal outbreaks, but it is difficult to apply to large‐scale target groups because of the computational effort required. When monitoring communication levels between thousands of people, the number of combinations of people whose communication may have increased is very high, and to scan through all of these to find which combinations have increased communications significantly is an enormous task. A successful surveillance plan will have early communication outbreak detection properties and good diagnostic capabilities for identifying individuals contributing to this outbreak. This paper proposes a new computationally feasible approach for detecting communication outbreaks based on exponentially weighted moving average smoothed communication counts between individuals within the network. We apply a cumulative sum of ordered signal‐to‐noise (SN) ratios for communication counts to flag significant departures from their respective median values. This plan is demonstrated to be efficient at detecting changes in communication levels for a small part of the network and diagnosing who is involved in the outbreak. Copyright © 2015 John Wiley & Sons, Ltd.     

On spatially explicit models of cholera epidemics

We generalize a recently proposed model for cholera epidemics that accounts for local communities of susceptibles and infectives in a spatially explicit arrangement of nodes linked by networks having different topologies. The vehicle of infection (Vibrio cholerae) is transported through the network links that are thought of as hydrological connections among susceptible communities. The mathematical tools used are borrowed from general schemes of reactive transport on river networks acting as the environmental matrix for the circulation and mixing of waterborne pathogens. Using the diffusion approximation, we analytically derive the speed of propagation for travelling fronts of epidemics on regular lattices (either one-dimensional or two-dimensional) endowed with uniform population density. Power laws are found that relate the propagation speed to the diffusion coefficient and the basic reproduction number. We numerically obtain the related, slower speed of epidemic spreading for more complex, yet realistic river structures such as Peano networks and optimal channel networks. The analysis of the limit case of uniformly distributed population sizes proves instrumental in establishing the overall conditions for the relevance of spatially explicit models. To that extent, the ratio between spreading and disease outbreak time scales proves the crucial parameter. The relevance of our results lies in the major differences potentially arising between the predictions of spatially explicit models and traditional compartmental models of the susceptible–infected–recovered (SIR)-like type. Our results suggest that in many cases of real-life epidemiological interest, time scales of disease dynamics may trigger outbreaks that significantly depart from the predictions of compartmental models.     

Antiviral treatment for the control of pandemic influenza: some logistical constraints.

Disease control programmes for an influenza pandemic will rely initially on the deployment of antiviral drugs such as Tamiflu, until a vaccine becomes available. However, such control programmes may be severely hampered by logistical constraints such as a finite stockpile of drugs and a limit on the distribution rate. We study the effects of such constraints using a compartmental modelling approach.We find that the most aggressive possible antiviral programme minimizes the final epidemic size, even if this should lead to premature stockpile run-out. Moreover, if the basic reproductive number R(0) is not too high, such a policy can avoid run-out altogether. However, where run-out would occur, such benefits must be weighed against the possibility of a higher epidemic peak than if a more conservative policy were followed.Where there is a maximum number of treatment courses that can be dispensed per day, reflecting a manpower limit on antiviral distribution, our results suggest that such a constraint is unlikely to have a significant impact (i.e. increasing the final epidemic size by more than 10%), as long as drug courses sufficient to treat at least 6% of the population can be dispensed per day.