Modeling the spread of annual influenza epidemics in the U.S.: the potential role of air travel

Analysis of Factors Affecting the Sustainable Success of Airlines During the COVID-19 Pandemic

The COVID-19 pandemic increased the risk of financial distress, bankruptcy, or both, in the airline industry. Whether airlines can survive or not during and/or after the pandemic is closely related to their decisions and actions which will enable their success by increasing their resilience. In crisis periods such as COVID-19, the decisions taken by airlines are strategically important for achieving sustainable success. Thus, it is critical to understand which factors are more important for airlines to shape their actions and make correct decisions. This paper investigates the sustainable success factors on which airlines should focus to provide resilience during the COVID-19 pandemic crisis. It provides a robust model using the interval type-2 fuzzy analytic hierarchy process (IT2FAHP) and interval type-2 fuzzy Decision Making Trial and Evaluation Laboratory (IT2FDEMATEL) to identify and rank success factors. The findings indicate that financial and operational factors are extremely important to ensure resilience for airlines. In addition, the results of the study reveal that operational factors and information sharing factors have an impact on financial factors and customer satisfaction.

Will COVID-19 be the end for the public transit? Investigating the impacts of public health crisis on transit mode choice

COVID-19 had an unprecedented impact on transit demand and usage. Stiff and vigilant hygiene safety requirements changed travellers' mode choice preferences during the COVID-19 pandemic. Specifically, transit modal share is radically impacted. Therefore, quantitative measurements on transit demand impact are urgently needed to facilitate evidence-based policy responses to COVID-19. Thus, we collected 1000 random samples through a web-based survey in the Greater Toronto Area (GTA), Canada, on traveler's modal choices behavior during the COIVD-19 pandemic. The paper presents an analysis with this firsthand dataset to understand transit users' behavioral adaptation resulting from the spreading of COVID-19 in 2020. We found that the transit frequency dropped by 21% to 71% for various socioeconomic groups in the GTA during the pandemic. The transit modal share dipped for all trip purposes. For private vehicle owners, around 70% of transit users switched to their private vehicles. More than 60% of those without cars switched to active transport for all travel purposes. Also, ride-hailing services are the second most popular substitution of transit for them. More than 80% of the respondents agreed with all transit safety policies, such as mandatory face-covering listed in the survey. Moreover, a similar proportion of the respondents agreed to return to public transit in the future. Multinomial, nested, and mixed logit models are estimated to capture relationships between modal choices and various factors. We found that the daily number of new COVID-19 cases impacts the choice of transit negatively. However, vaccine availability and mandatory face-covering onboard positively affect travellers' choices of riding transit during the pandemic.

Spatial Variation in Humidity and the Onset of Seasonal Influenza Across the Contiguous United States

In recent years, environmental factors, particularly humidity, have been used to inform influenza prediction models. This study aims to quantify the relationship between humidity and influenza incidence at the state-level in the contiguous United States. Piecewise segmented regressions were performed on specific humidity data from NASA's Atmospheric Infrared Sounder (AIRS) and incident influenza estimates from Google Flu Trends to identify threshold values of humidity that signal the onset of an influenza outbreak. Our results suggest that influenza incidence increases after reaching a humidity threshold that is state-specific. A linear regression showed that the state-specific thresholds were associated with annual average humidity conditions (R 2 = 0.9). Threshold values statistically significantly varied by region (F-statistic = 8.274, p < 0.001) and of their 36 pairwise combinations, 13 pairs had at least marginally statistically significant differences in their means. All of the significant comparisons included either the South or Southeast region, which had higher humidity threshold values. Results from this study improve our understanding of the significance of humidity in the transmission of influenza and reinforce the need for local and regional conditions to be considered in this relationship. Ultimately this could help researchers to produce more accurate forecasts of seasonal influenza onset and provide health officials with better information prior to outbreaks.

Non-Communicable Diseases and Social Media: A Heart Disease Symptoms Application

Social media platforms have become ubiquitous and allow users to share information in real-time. Our study uses data analytics as an approach to explore non-communicable diseases on social media platforms and to identify trends and patterns of related disease symptoms. Exploring epidemiological patterns of non-communicable diseases is vital given that they have become prevalent in low-income communities, accounting for about 38 million deaths worldwide. We collected data related to multiple disease conditions from the Twitter microblogging platform and zoomed into symptoms related to heart diseases. As part of our analyses, we focussed on the mechanism and trends of disease occurrences. Our results show that specific symptoms may be attributed to multiple disease conditions and it is viable to identify trends and patterns of their occurrences using a structured analytics approach. This can then act as a supplementary tool to support epidemiological initiatives that monitor non-communicable diseases. Based on the study’s results, we identify that non-communicable disease surveillance approach using social media analytics could support the design of effective health intervention strategies.

A model for social spreading of Covid-19: Cases of Mexico, Finland and Iceland

The shocking severity of the Covid-19 pandemic has woken up an unprecedented interest and accelerated effort of the scientific community to model and forecast epidemic spreading to find ways to control it regionally and between regions. Here we present a model that in addition to describing the dynamics of epidemic spreading with the traditional compartmental approach takes into account the social behaviour of the population distributed over a geographical region. The region to be modelled is defined as a two-dimensional grid of cells, in which each cell is weighted with the population density. In each cell a compartmental SEIRS system of delay difference equations is used to simulate the local dynamics of the disease. The infections between cells are modelled by a network of connections, which could be terrestrial, between neighbouring cells, or long range, between cities by air, road or train traffic. In addition, since people make trips without apparent reason, noise is considered to account for them to carry contagion between two randomly chosen distant cells. Hence, there is a clear separation of the parameters related to the biological characteristics of the disease from the ones that represent the spatial spread of infections due to social behaviour. We demonstrate that these parameters provide sufficient information to trace the evolution of the pandemic in different situations. In order to show the predictive power of this kind of approach we have chosen three, in a number of ways different countries, Mexico, Finland and Iceland, in which the pandemics have followed different dynamic paths. Furthermore we find that our model seems quite capable of reproducing the path of the pandemic for months with few initial data. Unlike similar models, our model shows the emergence of multiple waves in the case when the disease becomes endemic.

TLQP: Early-stage transportation lock-down and quarantine problem

The advent of COVID-19 is a sensible reminder of the vulnerability of our society to pandemics. We need to be better prepared for finding ways to stem such outbreaks. Except from social distancing and wearing face masks, restricting the movement of people is one important measure necessary to control the spread. Such decisions on the lock-down/reduction of movement should be made in an informed way and, accordingly, modeled as an optimization problem. We propose the Early-stage Transportation Lock-down and Quarantine Problem (TLQP), which can help to decide which parts of the transportation infrastructure of a country should be restricted in early stages. On top of the network-based Susceptible-Exposed-Infectious-Recovered (SEIR) model, we establish a decision recommendation framework, which considers the lock-down of cross-border traffic, internal traffic, and movement inside individual populations. The combinatorial optimization problem aims to find the best set of actions which minimize the social cost of a lock-down. Given the inherent intractability of this problem, we develop a highly-efficient heuristic based on the Effective Distance (ED) path and the Cost-Effective Lazy Forward (CELF) algorithm. We perform and report experiments on the global spread of COVID-19 and show how individual countries may protect their population by taking appropriate measures against the threatening pandemic. We believe that our study contributes to the orchestration of measures for dealing with current and future epidemic outbreaks.

Modeling COVID-19 Nonpharmaceutical Interventions: Exploring periodic NPI strategies

We developed a COVID-19 transmission model used as part of RAND's web-based COVID-19 decision support tool that compares the effects of nonpharmaceutical public health interventions (NPIs) on health and economic outcomes. An interdisciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health/economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). This paper provides further details of our model, describes extensions, presents sensitivity analyses, and analyzes strategies that periodically switch between a base NPI level and a higher NPI level. We find that a periodic strategy, if implemented with perfect compliance, could have produced similar health outcomes as static strategies but might have produced better outcomes when considering other measures of social welfare. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies.

Radiological, epidemiological and clinical patterns of pulmonary viral infections

Respiratory viruses are the most common causes of acute respiratory infections. However, identification of the underlying viral pathogen may not always be easy. Clinical presentations of respiratory viral infections usually overlap and may mimic those of diseases caused by bacteria. However, certain imaging morphologic patterns may suggest a particular viral pathogen as the cause of the infection. Although definitive diagnosis cannot be made on the basis of clinical or imaging features alone, the use of a combination of clinical and radiographic findings can substantially improve the accuracy of diagnosis. The purpose of this review is to present the clinical, epidemiological and radiological patterns of lower respiratory tract viral pathogens providing a comprehensive approach for their diagnosis and identification in hospitals and community outbreaks.

COVID-19 in comparison with other emerging viral diseases: risk of geographic spread via travel

Purpose of review

The COVID-19 pandemic poses a major global health threat. The rapid spread was facilitated by air travel although rigorous travel bans and lockdowns were able to slow down the spread. How does COVID-19 compare with other emerging viral diseases of the past two decades?

Recent findings

Viral outbreaks differ in many ways, such as the individuals most at risk e.g. pregnant women for Zika and the elderly for COVID-19, their vectors of transmission, their fatality rate, and their transmissibility often measured as basic reproduction number. The risk of geographic spread via air travel differs significantly between emerging infectious diseases.

Summary

COVID-19 is not associated with the highest case fatality rate compared with other emerging viral diseases such as SARS and Ebola, but the combination of a high reproduction number, superspreading events and a globally immunologically naïve population has led to the highest global number of deaths in the past 20 decade compared to any other pandemic.

Forcing Seasonality of Influenza-like Epidemics with Daily Solar Resonance

Seasonality of acute viral respiratory diseases is a well-known and yet not fully understood phenomenon. Several models have been proposed to explain the regularity of yearly recurring outbreaks and the phase differences observed at different latitudes on the Earth. Such models consider known internal causes, primarily the periodic emergence of new virus variants that evade the host immune response. Yet, this alone is generally unable to explain the regularity of recurrences and the observed phase differences. Here we show that seasonality of viral respiratory diseases, as well as its distribution with latitude on the Earth, can be fully explained by the virucidal properties of UV-B and UV-A solar photons through a daily, minute-scale, resonant forcing mechanism. Such an induced periodicity can last, virtually unperturbed, from tens to hundreds of cycles, and even in the presence of internal dynamics (host's loss of immunity) much slower than seasonal will, on a long period, generate seasonal oscillations.

The link between bike sharing and subway use during the COVID-19 pandemic: The case-study of New York's Citi Bike

The full societal impact COVID-19 pandemic is laid bare in urban mobility patterns. This research explored the recently published data on the operation of subway and bike share systems (BSS) during the COVID-19 outbreak in New York city, providing evidence on its impact over urban transport systems, but also on how its different components can work in conjunction. The BSS has proved to be more resilient than the subway system, with a less significant ridership drop (71% vs 90% ridership drop and 50% decrease on the ridership ratio) and an increase on its trips' average duration (from 13 min to 19 min per trip). Moreover, the study found evidence of a modal transfer from some subway users to the bike sharing system. The first effects of the free BSS programs aimed at essential service workers were also evaluated. BSS can improve the resilience of urban transport systems to disruptive events. Overall, this paper offers clues on how bike sharing, and cycling in general, can support the transition to a post-coronavirus society.

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

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

Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China

There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.

Important changes in the timing of influenza epidemics in the WHO European Region over the past 20 years: virological surveillance 1996 to 2016

The global epidemiology of many infectious diseases is changing, but little attention has been paid to whether the timing of seasonal influenza epidemics changed in recent years. This study investigated whether the timing of the peak of influenza epidemics has changed in countries of the World Health Organization (WHO) European Region between 1996 and 2016. 
Methods: Surveillance data were obtained from the WHO FluNet database. For each country and season (July to June of the next year), the peak was defined as the week with the highest 3-week moving average for reported cases. Linear regression models were used to test for temporal trends in the timing of the epidemic peak in each country and to determine whether this differed geographically. 
Results: More than 600,000 influenza cases were included from 38 countries of the WHO European Region. The timing of the epidemic peak changed according to a longitudinal gradient, occurring progressively later in Western Europe (e.g. by 2.8 days/season in Spain) and progressively earlier in Eastern Europe (e.g. by 3.5 days/season in the Russian Federation). 
Discussion: These results were confirmed in several sensitivity analyses. Our findings have implications for influenza control and prevention measures in the WHO European Region, for instance for the implementation of influenza vaccination campaigns.

A generic framework for spatial quantitative risk assessments of infectious diseases: Lumpy skin disease case study

The increase in availability of spatial data and the technological advances to handle such data allow for subsequent improvements in our ability to assess risk in a spatial setting. We provide a generic framework for quantitative risk assessments of disease introduction that capitalizes on these new data. It can be adopted across multiple spatial scales, for any pathogen, method of transmission or location. The framework incorporates the risk of initial infection in a previously uninfected location due to registered movement (e.g., trade) and unregistered movement (e.g., daily movements of wild animals). We discuss the steps of the framework and the data required to compute it. We then outline how this framework is applied for a single pathway using lumpy skin disease as a case study, a disease which had an outbreak in the Balkans in 2016. We calculate the risk of initial infection for the rest of Europe in 2016 due to trade. We perform the risk assessment on 3 spatial scales-countries, regions within countries and individual farms. We find that Croatia (assuming no vaccination occurred) has the highest mean probability of infection, with Italy, Hungary and Spain following. Including import detection of infected trade does reduce risk but this reduction is proportionally lower for countries with highest risk. The risk assessment results are consistent across the spatial scales, while in addition, at the finer spatial scales, it highlights specific areas or individual locations of countries on which to focus surveillance.

Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel

Greater human mobility, largely driven by air travel, is leading to an increase in the frequency and reach of infectious disease epidemics. Air travel can rapidly connect any two points on the planet, and this has the potential to cause swift and broad dissemination of emerging and re-emerging infectious diseases that may pose a threat to global health security. Investments to strengthen surveillance, build robust early-warning systems, improve predictive models, and coordinate public health responses may help to prevent, detect, and respond to new infectious disease epidemics.

Spatio-temporal patterns of proportions of influenza B cases

We studied the spatio-temporal patterns of the proportions of influenza B cases out of all typed cases, with data from 139 countries and regions downloaded from the FluNet compiled by the World Health Organization, from January 2006 to October 2015. We restricted our analysis to 34 countries that reported more than 2,000 confirmations for each of types A and B over the study period. Globally, we found that Pearson’s correlation is greater than 0.6 between effective distance from Mexico and the proportions of influenza B cases among the countries during the post-pandemic era (i.e. Week 1, 2010 to Week 40, 2015). Locally, in the United States, the proportions of influenza B cases in the pre-pandemic period (2003–2008) negatively correlated with that in the post-pandemic era (2010–2015) at the regional level. Our study limitations are the country-level variations in both surveillance methods and testing policies. The proportions of influenza B cases displayed wide variations over the study period. Our findings suggest that the 2009 influenza pandemic has an evident impact on the relative burden of the two influenza types. Future studies should examine whether there are other additional factors. This study has potential implications in prioritizing public health control measures.

Global epidemic invasion thresholds in directed cattle subpopulation networks having source, sink, and transit nodes

Through the characterization of a metapopulation cattle disease model on a directed network having source, transit, and sink nodes, we derive two global epidemic invasion thresholds. The first threshold defines the conditions necessary for an epidemic to successfully spread at the global scale. The second threshold defines the criteria that permit an epidemic to move out of the giant strongly connected component and to invade the populations of the sink nodes. As each sink node represents a final waypoint for cattle before slaughter, the existence of an epidemic among the sink nodes is a serious threat to food security. We find that the relationship between these two thresholds depends on the relative proportions of transit and sink nodes in the system and the distributions of the in-degrees of both node types. These analytic results are verified through numerical realizations of the metapopulation cattle model.

Characterising two-pathogen competition in spatially structured environments

Different pathogens spreading in the same host population often generate complex co-circulation dynamics because of the many possible interactions between the pathogens and the host immune system, the host life cycle, and the space structure of the population. Here we focus on the competition between two acute infections and we address the role of host mobility and cross-immunity in shaping possible dominance/co-dominance regimes. Host mobility is modelled as a network of traveling flows connecting nodes of a metapopulation, and the two-pathogen dynamics is simulated with a stochastic mechanistic approach. Results depict a complex scenario where, according to the relation among the epidemiological parameters of the two pathogens, mobility can either be non-influential for the competition dynamics or play a critical role in selecting the dominant pathogen. The characterisation of the parameter space can be explained in terms of the trade-off between pathogen's spreading velocity and its ability to diffuse in a sparse environment. Variations in the cross-immunity level induce a transition between presence and absence of competition. The present study disentangles the role of the relevant biological and ecological factors in the competition dynamics, and provides relevant insights into the spatial ecology of infectious diseases.

A marginal benefit approach for vaccinating influenza "superspreaders"

BACKGROUND

There is widespread recognition that interventions targeting "superspreaders" are more effective at containing epidemics than strategies aimed at the broader

POPULATION

However, little attention has been devoted to determining optimal levels of coverage for targeted vaccination strategies, given the nonlinear relationship between program scale and the costs and benefits of identifying and successfully administering vaccination to potential superspreaders.

METHODS

We developed a framework for such an assessment derived from a transmission model of seasonal influenza parameterized to emulate typical seasonal influenza epidemics in the US. We used this framework to estimate how the marginal benefit of expanded targeted vaccination changes with the proportion of the target population already vaccinated.

RESULTS

The benefit of targeting additional superspreaders varies considerably as a function of both the baseline vaccination coverage and proximity to the herd immunity threshold. The general form of the marginal benefit function starts low, particularly for severe epidemics, increases monotonically until its peak at the point of herd immunity, and then plummets rapidly. We present a simplified transmission model, primarily designed to convey qualitative insight rather than quantitative precision. With appropriate contact data, future work could address more complex population structures, such as age structure and assortative mixing patterns. Our illustrative example highlights the general economic and epidemiological findings of our method but does not address intervention design, policy, and resource allocation issues related to practical implementation of this particular scenario.

CONCLUSIONS

Our approach offers a means of estimating willingness to pay for search costs associated with targeted vaccination of superspreaders, which can inform policies regarding whether a targeted intervention should be implemented and, if so, up to what levels.

Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.

What explains the geographic dispersal of emerging pathogens? Reconstructions of evolutionary history from pathogen gene sequences offer qualitative descriptions of spatial spread, but current approaches are poorly equipped to formally test and quantify the contribution of different potential explanatory factors, such as human mobility and demography. Here, we use a novel phylogeographic method to evaluate multiple potential predictors of viral spread in human influenza dynamics. We identify air travel as the predominant driver of global influenza migration, whilst also revealing the contribution of other mobility processes at more local scales. We demonstrate the power of our inter-disciplinary approach by using it to predict the global pandemic expansion of H1N1 influenza in 2009. Our study highlights the importance of integrating evolutionary and ecological information when studying the dynamics of infectious disease.

Effect of the one-child policy on influenza transmission in China: a stochastic transmission model

BACKGROUND

China's one-child-per-couple policy, introduced in 1979, led to profound demographic changes for nearly a quarter of the world's population. Several decades later, the consequences include decreased fertility rates, population aging, decreased household sizes, changes in family structure, and imbalanced sex ratios. The epidemiology of communicable diseases may have been affected by these changes since the transmission dynamics of infectious diseases depend on demographic characteristics of the population. Of particular interest is influenza because China and Southeast Asia lie at the center of a global transmission network of influenza. Moreover, changes in household structure may affect influenza transmission. Is it possible that the pronounced demographic changes that have occurred in China have affected influenza transmission?

METHODS AND FINDINGS

To address this question, we developed a continuous-time, stochastic, individual-based simulation model for influenza transmission. With this model, we simulated 30 years of influenza transmission and compared influenza transmission rates in populations with and without the one-child policy control. We found that the average annual attack rate is reduced by 6.08% (SD 2.21%) in the presence of the one-child policy compared to a population in which no demographic changes occurred. There was no discernible difference in the secondary attack rate, -0.15% (SD 1.85%), between the populations with and without a one-child policy. We also forecasted influenza transmission over a ten-year time period in a population with a two-child policy under a hypothesis that a two-child-per-couple policy will be carried out in 2015, and found a negligible difference in the average annual attack rate compared to the population with the one-child policy.

CONCLUSIONS

This study found that the average annual attack rate is slightly lowered in a population with a one-child policy, which may have resulted from a decrease in household size and the proportion of children in the population.

Human mobility and time spent at destination: impact on spatial epidemic spreading

Host mobility plays a fundamental role in the spatial spread of infectious diseases. Previous theoretical works based on the integration of network theory into the metapopulation framework have shown that the heterogeneities that characterize real mobility networks favor the propagation of epidemics. Nevertheless, the studies conducted so far assumed the mobility process to be either Markovian (in which the memory of the origin of each traveler is lost) or non-Markovian with a fixed traveling time scale (in which individuals travel to a destination and come back at a constant rate). Available statistics however show that the time spent by travelers at destination is characterized by wide fluctuations, ranging from a single day up to several months. Such varying length of stay crucially affects the chance and duration of mixing events among hosts and may therefore have a strong impact on the spread of an emerging disease. Here, we present an analytical and a computational study of epidemic processes on a complex subpopulation network where travelers have memory of their origin and spend a heterogeneously distributed time interval at their destination. Through analytical calculations and numerical simulations we show that the heterogeneity of the length of stay alters the expression of the threshold between local outbreak and global invasion, and, moreover, it changes the epidemic behavior of the system in case of a global outbreak. Additionally, our theoretical framework allows us to study the effect of changes in the traveling behavior in response to the infection, by considering a scenario in which sick individuals do not leave their home location. Finally, we compare the results of our non-Markovian framework with those obtained with a classic Markovian approach and find relevant differences between the two, in the estimate of the epidemic invasion potential, as well as of the timing and the pattern of its spatial spread. These results highlight the importance of properly accounting for host trip duration in epidemic models and open the path to the inclusion of such an additional layer of complexity to the existing modeling approaches.

Host mobility drives pathogen competition in spatially structured populations

Interactions among multiple infectious agents are increasingly recognized as a fundamental issue in the understanding of key questions in public health regarding pathogen emergence, maintenance, and evolution. The full description of host-multipathogen systems is, however, challenged by the multiplicity of factors affecting the interaction dynamics and the resulting competition that may occur at different scales, from the within-host scale to the spatial structure and mobility of the host population. Here we study the dynamics of two competing pathogens in a structured host population and assess the impact of the mobility pattern of hosts on the pathogen competition. We model the spatial structure of the host population in terms of a metapopulation network and focus on two strains imported locally in the system and having the same transmission potential but different infectious periods. We find different scenarios leading to competitive success of either one of the strain or to the codominance of both strains in the system. The dominance of the strain characterized by the shorter or longer infectious period depends exclusively on the structure of the population and on the the mobility of hosts across patches. The proposed modeling framework allows the integration of other relevant epidemiological, environmental and demographic factors, opening the path to further mathematical and computational studies of the dynamics of multipathogen systems.

Estimating the reproductive number in the presence of spatial heterogeneity of transmission patterns

Background

Estimates of parameters for disease transmission in large-scale infectious disease outbreaks are often obtained to represent large groups of people, providing an average over a potentially very diverse area. For control measures to be more effective, a measure of the heterogeneity of the parameters is desirable.

Methods

We propose a novel extension of a network-based approach to estimating the reproductive number. With this we can incorporate spatial and/or demographic information through a similarity matrix. We apply this to the 2009 Influenza pandemic in South Africa to understand the spatial variability across provinces. We explore the use of five similarity matrices to illustrate their impact on the subsequent epidemic parameter estimates.

Results

When treating South Africa as a single entity with homogeneous transmission characteristics across the country, the basic reproductive number, R₀, (and imputation range) is 1.33 (1.31, 1.36). When fitting a new model for each province with no inter-province connections this estimate varies little (1.23-1.37). Using the proposed method with any of the four similarity measures yields an overall R₀ that varies little across the four new models (1.33 to 1.34). However, when allowed to vary across provinces, the estimated R₀ is greater than one consistently in only two of the nine provinces, the most densely populated provinces of Gauteng and Western Cape.

Conclusions

Our results suggest that the spatial heterogeneity of influenza transmission was compelling in South Africa during the 2009 pandemic. This variability makes a qualitative difference in our understanding of the epidemic. While the cause of this fluctuation might be partially due to reporting differences, there is substantial evidence to warrant further investigation.

Predictive validation of an influenza spread model

BACKGROUND

Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread.

METHODS AND FINDINGS

We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic.

CONCLUSIONS

Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability.

An open-access modeled passenger flow matrix for the global air network in 2010

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.

Understanding the dynamics of seasonal influenza in Italy: incidence, transmissibility and population susceptibility in a 9-year period

OBJECTIVES

Influenza surveillance systems have been established in many countries in the world, yielding timely information about the intensity and features of seasonal outbreaks. Such data have also been used to estimate epidemiological parameters and to evaluate the effect of factors on infection dynamics. However, little is known about the extent of under-reporting in surveillance data, and thus of the true influenza incidence in the population.

DESIGN

Through mathematical and statistical modelling, we analysed Italian epidemiological and virological surveillance data collected together with serological data derived from influenza vaccine clinical trials performed in Italy.

RESULTS

Depending on the season, the reporting rate estimates ranged between 20% and 33% of the total incidence with higher reporting rates in seasons dominated by A/H3N2 virus. Despite a generally higher number of individuals immune against A/H3N2 viruses, effective reproduction ratios were quite similar in all seasons varying between 1·2 and 1·4. We observed an age-dependent transmissibility for different subtypes: susceptible children were more likely than susceptible adults and elderly to get infected when A/H1N1 or B strains were circulating, while no clear age-dependence was found for A/H3N2. We also perform sensitivity analysis under different assumptions for vaccine effectiveness, generation time (GT) and model variants; we found that the overall results in predicted patterns were extremely similar, with a slightly better fit obtained with shorter GTs.

CONCLUSIONS

Our results provide relevant information on the influenza dynamics to fine-tune intervention strategies and for data collection improvement.

Skip the trip: air travelers' behavioral responses to pandemic influenza

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over "swine flu," as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm

BACKGROUND

Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches.

METHODS

We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability.

RESULTS

Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model.

CONCLUSIONS

Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.

Seasonality of viral infections: mechanisms and unknowns

Seasonality is a long-recognized attribute of many viral infections of humans, but the mechanisms underlying seasonality, particularly for person-to-person communicable diseases, remain poorly understood. Better understanding of drivers of seasonality could provide insights into the relationship between the physical environment and infection risk, which is particularly important in the context of global ecological change in general, and climate change in particular. In broad terms, seasonality represents oscillation in pathogens' effective reproductive number, which, in turn, must reflect oscillatory changes in infectiousness, contact patterns, pathogen survival, or host susceptibility. Epidemiological challenges to correct identification of seasonal drivers of risk include failure to adjust for predictable correlation between disease incidence and seasonal exposures, and unmeasured confounding. The existing evidence suggests that the seasonality of some enteric and respiratory viral pathogens may be driven by enhanced wintertime survival of pathogens, and also by increased host susceptibility resulting from relative 'wintertime immune suppression'. For vector-borne diseases and zoonoses, environmental influences on vector or reservoir abundance, and vector biting rates, are probably more important. However, numerous areas of uncertainty exist, making this an exciting area for future research.

Invasion threshold in structured populations with recurrent mobility patterns

In this paper we develop a framework to analyze the behavior of contagion and spreading processes in complex subpopulation networks where individuals have memory of their subpopulation of origin. We introduce a metapopulation model in which subpopulations are connected through heterogeneous fluxes of individuals. The mobility process among communities takes into account the memory of residence of individuals and is incorporated with the classical susceptible-infectious-recovered epidemic model within each subpopulation. In order to gain analytical insight into the behavior of the system we use degree-block variables describing the heterogeneity of the subpopulation network and a time-scale separation technique for the dynamics of individuals. By considering the stochastic nature of the epidemic process we obtain the explicit expression of the global epidemic invasion threshold, below which the disease dies out before reaching a macroscopic fraction of the subpopulations. This threshold is not present in continuous deterministic diffusion models and explicitly depends on the disease parameters, the mobility rates, and the properties of the coupling matrices describing the mobility across subpopulations. The results presented here take a step further in offering insight into the fundamental mechanisms controlling the spreading of infectious diseases and other contagion processes across spatially structured communities.

Control of epidemic spreading on complex networks by local traffic dynamics

Despite extensive work on traffic dynamics and epidemic spreading on complex networks, the interplay between these two types of dynamical processes has not received adequate attention. We study the effect of local-routing-based traffic dynamics on epidemic spreading. For the case of unbounded node-delivery capacity, where the traffic is free of congestion, we obtain analytic and numerical results indicating that the epidemic threshold can be maximized by an optimal routing protocol. This means that epidemic spreading can be effectively controlled by local traffic dynamics. For the case of bounded delivery capacity, numerical results and qualitative arguments suggest that traffic congestion can suppress epidemic spreading. Our results provide quantitative insight into the nontrivial role of traffic dynamics associated with a local-routing scheme in the epidemic spreading.

Towards a characterization of behavior-disease models

The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.

Validation of the gravity model in predicting the global spread of influenza

The gravity model is often used in predicting the spread of influenza. We use the data of influenza A (H1N1) to check the model's performance and validation, in order to determine the scope of its application. In this article, we proposed to model the pattern of global spread of the virus via a few important socio-economic indicators. We applied the epidemic gravity model for modelling the virus spread globally through the estimation of parameters of a generalized linear model. We compiled the daily confirmed cases of influenza A (H1N1) in each country as reported to the WHO and each state in the USA, and established the model to describe the relationship between the confirmed cases and socio-economic factors such as population size, per capita gross domestic production (GDP), and the distance between the countries/states and the country where the first confirmed case was reported (i.e., Mexico). The covariates we selected for the model were all statistically significantly associated with the global spread of influenza A (H1N1). However, within the USA, the distance and GDP were not significantly associated with the number of confirmed cases. The combination of the gravity model and generalized linear model provided a quick assessment of pandemic spread globally. The gravity model is valid if the spread period is long enough for estimating the model parameters. Meanwhile, the distance between donor and recipient communities has a good gradient. Besides, the spread should be at the early stage if a single source is taking into account.

On the treatment of airline travelers in mathematical models

The global spread of infectious diseases is facilitated by the ability of infected humans to travel thousands of miles in short time spans, rapidly transporting pathogens to distant locations. Mathematical models of the actual and potential spread of specific pathogens can assist public health planning in the case of such an event. Models should generally be parsimonious, but must consider all potentially important components of the system to the greatest extent possible. We demonstrate and discuss important assumptions relative to the parameterization and structural treatment of airline travel in mathematical models. Among other findings, we show that the most common structural treatment of travelers leads to underestimation of the speed of spread and that connecting travel is critical to a realistic spread pattern. Models involving travelers can be improved significantly by relatively simple structural changes but also may require further attention to details of parameterization.

Equal graph partitioning on estimated infection network as an effective epidemic mitigation measure

Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics.

Mass commuting and influenza vaccination prevalence in new york city: protection in a mixing environment

OBJECTIVE

Assess influenza vaccination among commuters using mass transit in New York City (NYC).

METHODS

We used the 2006 NYC Community Health Survey (CHS) to analyze the prevalence of influenza immunization by commuting behaviors and to understand what socioeconomic and geographic factors may explain any differences found.

RESULTS

Vaccination prevalence is significantly lower for New Yorkers who commute on public transportation compared to other New Yorkers. This difference is largely attenuated after adjusting for socio-demographic characteristics and neighborhood of residence.

CONCLUSIONS

The analysis identified a low prevalence of immunization among commuters, and given the transmissibility in that setting, targeting commuters for vaccination campaigns may impede influenza spread.

Defending a moving target: H1N1 preparedness training for the transit industry

To stem the spread of the novel H1N1 virus, U.S. public health officials put forth a variety of recommendations, ranging from practicing social distancing and frequent hand washing at the individual level, to furloughs and continual cleaning of commonly touched surfaces at the level of the organization. Although these steps are amenable to implementation in an office, school or hospital setting, they are nearly impossible to apply in the public transit environment, where large numbers of people remain in close quarters, with no running water and limited opportunities for disinfection. Recognizing the need to offer adequate protection from infection to employees and customers alike, transit officials expressed the need for H1N1-specific training, tailored to industry needs and limitations, to Rutgers University's Center for Transportation Safety, Security and Risk. The resulting course, which was informed through a combination of literature-based and primary research, combined the most current public health data with best practices gleaned from some of the nation's largest transit agencies, in a just-in-time format.

Modelling environmentally-mediated infectious diseases of humans: transmission dynamics of schistosomiasis in China

Macroparasites of humans are sensitive to a variety of environmental variables, including temperature, rainfall and hydrology, yet current comprehension of these relationships is limited. Given the incomplete mechanistic understanding of environment-disease interactions, mathematical models that describe them have seldom included the effects of time-varying environmental processes on transmission dynamics and where they have been included, simple generic, periodic functions are usually used. Few examples exist where seasonal forcing functions describe the actual processes underlying the environmental drivers of disease dynamics. Transmission of human schistosomes, which involves multiple environmental stages, offers a model for applying our understanding of the environmental determinants of the viability, longevity, infectivity and mobility of these stages to controlling disease in diverse environments. Here, a mathematical model of schistosomiasis transmission is presented which incorporates the effects of environmental variables on transmission. Model dynamics are explored and several key extensions to the model are proposed.

Modelling seasonality and viral mutation to predict the course of an influenza pandemic

As the 2009 H1N1 influenza pandemic (H1N1) has shown, public health decision-makers may have to predict the subsequent course and severity of a pandemic. We developed an agent-based simulation model and used data from the state of Georgia to explore the influence of viral mutation and seasonal effects on the course of an influenza pandemic. We showed that when a pandemic begins in April certain conditions can lead to a second wave in autumn (e.g. the degree of seasonality exceeding 0.30, or the daily rate of immunity loss exceeding 1% per day). Moreover, certain combinations of seasonality and mutation variables reproduced three-wave epidemic curves. Our results may offer insights to public health officials on how to predict the subsequent course of an epidemic or pandemic based on early and emerging viral and epidemic characteristics and what data may be important to gather.

Fluctuation effects in metapopulation models: percolation and pandemic threshold

Metapopulation models provide the theoretical framework for describing disease spread between different populations connected by a network. In particular, these models are at the basis of most simulations of pandemic spread. They are usually studied at the mean-field level by neglecting fluctuations. Here we include fluctuations in the models by adopting fully stochastic descriptions of the corresponding processes. This level of description allows to address analytically, in the SIS and SIR cases, problems such as the existence and the calculation of an effective threshold for the spread of a disease at a global level. We show that the possibility of the spread at the global level is described in terms of (bond) percolation on the network. This mapping enables us to give an estimate (lower bound) for the pandemic threshold in the SIR case for all values of the model parameters and for all possible networks.

Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model

Here we present the Global Epidemic and Mobility (GLEaM) model that integrates sociodemographic and population mobility data in a spatially structured stochastic disease approach to simulate the spread of epidemics at the worldwide scale. We discuss the flexible structure of the model that is open to the inclusion of different disease structures and local intervention policies. This makes GLEaM suitable for the computational modeling and anticipation of the spatio-temporal patterns of global epidemic spreading, the understanding of historical epidemics, the assessment of the role of human mobility in shaping global epidemics, and the analysis of mitigation and containment scenarios.

Seasonal synchronization of influenza in the United States older adult population

BACKGROUND

In temperate regions, influenza epidemics occur annually with the highest activity occurring during the winter months. While seasonal dynamics of the influenza virus, such as time of onset and circulating strains, are well documented by the Centers for Disease Control and Prevention Influenza Surveillance System, an accurate prediction of timing, magnitude, and composition of circulating strains of seasonal influenza remains elusive. To facilitate public health preparedness for seasonal influenza and to obtain better insights into the spatiotemporal behavior of emerging strains, it is important to develop measurable characteristics of seasonal oscillation and to quantify the relationships between those parameters on a spatial scale. The objectives of our research were to examine the seasonality of influenza on a national and state level as well as the relationship between peak timing and intensity of influenza in the United States older adult population.

METHODOLOGY/PRINCIPAL FINDINGS

A total of 248,889 hospitalization records were extracted from the Centers for Medicare and Medicaid Services for the influenza seasons 1991-2004. Harmonic regression models were used to quantify the peak timing and absolute intensity for each of the 48 contiguous states and Washington, DC. We found that individual influenza seasons showed spatial synchrony with consistent late or early timing occurring across all 48 states during each influenza season in comparison to the overall average. On a national level, seasons that had an earlier peak also had higher rates of influenza (r(s) = -0.5). We demonstrated a spatial trend in peak timing of influenza; western states such as Nevada, Utah, and California peaked earlier and New England States such as Rhode Island, Maine, and New Hampshire peaked later.

CONCLUSIONS/SIGNIFICANCE

Our findings suggest that a systematic description of influenza seasonal patterns is a valuable tool for disease surveillance and can facilitate strategies for prevention of severe disease in the vulnerable, older adult population.

Optimizing influenza sentinel surveillance at the state level

Influenza-like illness data are collected via an Influenza Sentinel Provider Surveillance Network at the state level. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. The purpose of this study was to determine the "best" locations for sentinel providers in Iowa by using a maximal coverage model (MCM) and to compare the population coverage obtained with that of the current sentinel network. The authors used an MCM to maximize the Iowa population located within 20 miles (32.2 km) of 1-143 candidate sites and calculated the coverage provided by each additional site. The first MCM location covered 15% of the population; adding a second increased coverage to 25%. Additional locations provided more coverage but with diminishing marginal returns. In contrast, the existing 22 Iowa sentinel locations covered 56% of the population, the same coverage achieved with just 10 MCM sites. Using 22 MCM sites covered more than 75% of the population, an improvement over the current site placement, adding nearly 600,000 Iowa residents. Given scarce public health resources, MCMs can help surveillance efforts by prioritizing recruitment of sentinel locations.

The transmissibility and control of pandemic influenza A (H1N1) virus

Pandemic influenza A (H1N1) 2009 (pandemic H1N1) is spreading throughout the planet. It has become the dominant strain in the Southern Hemisphere, where the influenza season has now ended. Here, on the basis of reported case clusters in the United States, we estimated the household secondary attack rate for pandemic H1N1 to be 27.3% [95% confidence interval (CI) from 12.2% to 50.5%]. From a school outbreak, we estimated that a typical schoolchild infects 2.4 (95% CI from 1.8 to 3.2) other children within the school. We estimated the basic reproductive number, R0, to range from 1.3 to 1.7 and the generation interval to range from 2.6 to 3.2 days. We used a simulation model to evaluate the effectiveness of vaccination strategies in the United States for fall 2009. If a vaccine were available soon enough, vaccination of children, followed by adults, reaching 70% overall coverage, in addition to high-risk and essential workforce groups, could mitigate a severe epidemic.

Traffic-driven epidemic spreading in finite-size scale-free networks

The study of complex networks sheds light on the relation between the structure and function of complex systems. One remarkable result is the absence of an epidemic threshold in infinite-size, scale-free networks, which implies that any infection will perpetually propagate regardless of the spreading rate. The vast majority of current theoretical approaches assumes that infections are transmitted as a reaction process from nodes to all neighbors. Here we adopt a different perspective and show that the epidemic incidence is shaped by traffic-flow conditions. Specifically, we consider the scenario in which epidemic pathways are defined and driven by flows. Through extensive numerical simulations and theoretical predictions, it is shown that the value of the epidemic threshold in scale-free networks depends directly on flow conditions, in particular on the first and second moments of the betweenness distribution given a routing protocol. We consider the scenarios in which the delivery capability of the nodes is bounded or unbounded. In both cases, the threshold values depend on the traffic and decrease as flow increases. Bounded delivery provokes the emergence of congestion, slowing down the spreading of the disease and setting a limit for the epidemic incidence. Our results provide a general conceptual framework for the understanding of spreading processes on complex networks.

Survey of vaccine distribution and delivery issues in the USA: from pediatrics to pandemics

Vaccine distribution and delivery has become an issue of significant interest, given the threat of a pandemic influenza outbreak and the resulting need for coordinated efforts to distribute and deliver pandemic influenza vaccines into the hands of healthcare workers responsible for administering them. This review provides an overview of the issues that are most relevant to vaccine distribution and delivery, including routine pediatric immunization, combination vaccines, vaccine shortages and stockpiling, seasonal influenza vaccines and, of most current interest, a discussion on pandemic influenza outbreak issues and a list of future distribution and delivery challenges that may be faced during such an event.