Transmission dynamics, border entry screening, and school holidays during the 2009 influenza A (H1N1) pandemic, China

Cross-sectional survey of SARS-CoV-2 testing at US airports and one health department’s proactive management of travelers

Background

Many health departments and private enterprises began offering SARS-CoV-2 testing to travelers at US airports in 2020. Persons with positive SARS-CoV-2 test results who have planned upcoming travel may be subject to US federal public health travel restrictions.

We assessed availability of testing for SARS-CoV-2 at major US airports. We then describe the management of cases and close contacts at Denver International Airport’s testing site.

Methods

We selected 100 US airports. Online surveys were conducted during November–December 2020 and assessed availability of testing for air travelers, flight crew, and airport employees. Respondents included health department (HD) staff or airport directors.

We analyzed testing data and management practices for persons who tested positive and their close contacts at one airport (Denver International) from 12/21/2020 to 3/31/2021.

Results

Among the 100 selected airports, we received information on 77 airports; 38 (49%) had a testing site and several more planned to offer one (N = 7; 9%). Most sites began testing in the fall of 2020. The most frequently offered tests were RT-PCR or other NAAT tests (N = 28).

Denver International Airport offered voluntary SARS-CoV-2 testing. Fifty-four people had positive results among 5724 tests conducted from 12/21/2020 to 3/31/2021 for a total positivity of < 1%. Of these, 15 were travelers with imminent flights. The Denver HD issued an order requiring the testing site to immediately report cases and notify airlines to cancel upcoming flight itineraries for infected travelers and their traveling close contacts, minimizing the use of federal travel restrictions.

Conclusions

As of December 2020, nearly half of surveyed US airports had SARS-CoV-2 testing sites. Such large-scale adoption of airport testing for a communicable disease is unprecedented and presents new challenges for travelers, airlines, airports, and public health authorities. This assessment was completed before the US and other countries began enforcing entry testing requirements; testing at airports will likely increase as travel demand returns and test requirements for travel evolve.

Lessons from Denver demonstrate how HDs can play a key role in engaging airport testing sites to ensure people who test positive for SARS-CoV-2 immediately before travel do not travel on commercial aircraft.

Principles of risk decision-making

Risk management decisions in public health require consideration of a number of complex, often conflicting factors. The aim of this review was to propose a set of 10 fundamental principles to guide risk decision-making. Although each of these principles is sound in its own right, the guidance provided by different principles might lead the decision-maker in different directions. For example, where the precautionary principle advocates for preemptive risk management action under situations of scientific uncertainty and potentially catastrophic consequences, the principle of risk-based decision-making encourages decision-makers to focus on established and modifiable risks, where a return on the investment in risk management is all but guaranteed in the near term. To evaluate the applicability of the 10 principles in practice, one needs to consider 10 diverse risk issues of broad concern and explore which of these principles are most appropriate in different contexts. The 10 principles presented here afford substantive insight into the process of risk management decision-making, although decision-makers will ultimately need to exercise judgment in reaching appropriate risk decisions, accounting for all of the scientific and extra-scientific factors relevant to the risk decision at hand.

A global assessment of the impact of school closure in reducing COVID-19 spread

Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19-59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.

Human Influenza Epidemiology

Influenza virus infections are common in people of all ages. Epidemics occur in the winter months in temperate locations and at varying times of the year in subtropical and tropical locations. Most influenza virus infections cause mild and self-limiting disease, and around one-half of all infections occur with a fever. Only a small minority of infections lead to serious disease requiring hospitalization. During epidemics, the rates of influenza virus infections are typically highest in school-age children. The clinical severity of infections tends to increase at the extremes of age and with the presence of underlying medical conditions, and impact of epidemics is greatest in these groups. Vaccination is the most effective measure to prevent infections, and in recent years influenza vaccines have become the most frequently used vaccines in the world. Nonpharmaceutical public health measures can also be effective in reducing transmission, allowing suppression or mitigation of influenza epidemics and pandemics.

Travel-related control measures to contain the COVID-19 pandemic: an evidence map

OBJECTIVES

To comprehensively map the existing evidence assessing the impact of travel-related control measures for containment of the SARS-CoV-2/COVID-19 pandemic.

DESIGN

Rapid evidence map.

DATA SOURCES

MEDLINE, Embase and Web of Science, and COVID-19 specific databases offered by the US Centers for Disease Control and Prevention and the WHO.

ELIGIBILITY CRITERIA

We included studies in human populations susceptible to SARS-CoV-2/COVID-19, SARS-CoV-1/severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus/Middle East respiratory syndrome or influenza. Interventions of interest were travel-related control measures affecting travel across national or subnational borders. Outcomes of interest included infectious disease, screening, other health, economic and social outcomes. We considered all empirical studies that quantitatively evaluate impact available in Armenian, English, French, German, Italian and Russian based on the team's language capacities.

DATA EXTRACTION AND SYNTHESIS

We extracted data from included studies in a standardised manner and mapped them to a priori and (one) post hoc defined categories.

RESULTS

We included 122 studies assessing travel-related control measures. These studies were undertaken across the globe, most in the Western Pacific region (n=71). A large proportion of studies focused on COVID-19 (n=59), but a number of studies also examined SARS, MERS and influenza. We identified studies on border closures (n=3), entry/exit screening (n=31), travel-related quarantine (n=6), travel bans (n=8) and travel restrictions (n=25). Many addressed a bundle of travel-related control measures (n=49). Most studies assessed infectious disease (n=98) and/or screening-related (n=25) outcomes; we found only limited evidence on economic and social outcomes. Studies applied numerous methods, both inferential and descriptive in nature, ranging from simple observational methods to complex modelling techniques.

CONCLUSIONS

We identified a heterogeneous and complex evidence base on travel-related control measures. While this map is not sufficient to assess the effectiveness of different measures, it outlines aspects regarding interventions and outcomes, as well as study methodology and reporting that could inform future research and evidence synthesis.

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. 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.

Implementing social distancing policy measures in the battle against the coronavirus: protocol of a comparative study of Denmark and Sweden

BACKGROUND

Social distancing policies to ensure physical distance between people have become a crucial strategy in the battle against the spread of the coronavirus. The aim of this project is to analyze and compare social distancing policies implemented in Denmark and Sweden in 2020. Despite many similarities between the two countries, their response to the coronavirus pandemic differed markedly. Whereas authorities in Denmark initiated mandatory regulations and many severe restrictions, Swedish authorities predominantly promoted voluntary recommendations.

METHODS

The project is an interdisciplinary collaboration between researchers in Denmark and Sweden with different disciplinary backgrounds. The project is based on a comparative analysis, an approach that attempts to reach conclusions beyond single cases and to explain differences and similarities between objects of analysis and relations between objects against the backdrop of their contextual conditions. Data will be gathered by means of document analysis, qualitative interviews, and a questionnaire survey to address three research questions: (1) What social distancing policies regarding the coronavirus have been formulated and implemented, who are the policymakers behind the policy measures, which implementers are expected to implement the measures, and who are the targets that the measures ultimately seek to influence? (2) How have the social distancing policies and policy measures been justified, and what types of knowledge form the basis for the measures? and (3) What are the differences and similarities in citizens' perceptions of acceptability and compliance with social distancing policy measures in relation to the coronavirus?

DISCUSSION

To create a structure for addressing the three research questions, the project applies a theoretical framework informed by the policy and implementation science literatures. The framework consists of five interdependent domains that have an impact on policy implementation: (1) policymakers, (2) policy characteristics, (3) implementers, (4) targets, and (5) policy environment. Details of the framework are provided in the article.

Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings-International Travel-Related Measures

International travel–related nonpharmaceutical interventions (NPIs), which can include traveler screening, travel restrictions, and border closures, often are included in national influenza pandemic preparedness plans. We performed systematic reviews to identify evidence for their effectiveness. We found 15 studies in total. Some studies reported that NPIs could delay the introduction of influenza virus. However, no available evidence indicated that screening of inbound travelers would have a substantial effect on preventing spread of pandemic influenza, and no studies examining exit screening were found. Some studies reported that travel restrictions could delay the start of local transmission and slow international spread, and 1 study indicated that small Pacific islands were able to prevent importation of pandemic influenza during 1918–19 through complete border closure. This limited evidence base indicates that international travel-related NPIs would have limited effectiveness in controlling pandemic influenza and that these measures require considerable resources to implement.

Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks

Coronaviruses (CoVs) are RNA viruses that have become a major public health concern since the Severe Acute Respiratory Syndrome-CoV (SARS-CoV) outbreak in 2002. The continuous evolution of coronaviruses was further highlighted with the emergence of the Middle East Respiratory Syndrome-CoV (MERS-CoV) outbreak in 2012. Currently, the world is concerned about the 2019 novel CoV (SARS-CoV-2) that was initially identified in the city of Wuhan, China in December 2019. Patients presented with severe viral pneumonia and respiratory illness. The number of cases has been mounting since then. As of late February 2020, tens of thousands of cases and several thousand deaths have been reported in China alone, in addition to thousands of cases in other countries. Although the fatality rate of SARS-CoV-2 is currently lower than SARS-CoV, the virus seems to be highly contagious based on the number of infected cases to date. In this review, we discuss structure, genome organization, entry of CoVs into target cells, and provide insights into past and present outbreaks. The future of human CoV outbreaks will not only depend on how the viruses will evolve, but will also depend on how we develop efficient prevention and treatment strategies to deal with this continuous threat.

Mechanistic modelling of multiple waves in an influenza epidemic or pandemic

Multiple-wave outbreaks have been documented for influenza pandemics particularly in the temperate zone, and occasionally for seasonal influenza epidemics in the tropical zone. The mechanisms shaping multiple-wave influenza outbreaks are diverse but are yet to be summarized in a systematic fashion. For this purpose, we described 12 distinct mechanistic models, among which five models were proposed for the first time, that support two waves of infection in a single influenza season, and classified them into five categories according to heterogeneities in host, pathogen, space, time and their combinations, respectively. To quantify the number of infection waves, we proposed three metrics that provide robust and intuitive results for real epidemics. Further, we performed sensitivity analyses on key parameters in each model and found that reducing the basic reproduction number or the transmission rate, limiting the addition of susceptible people who are to get the primary infection to infected areas, and limiting the probability of replenishment of people who are to be reinfected in the short term, could decrease the number of infection waves and clinical attack rate. Finally, we introduced a modelling framework to infer the mechanisms driving two-wave outbreaks. A better understanding of two-wave mechanisms could guide public health authorities to develop and implement preparedness plans and deploy control strategies.

Comparison of methods to Estimate Basic Reproduction Number (R 0) of influenza, Using Canada 2009 and 2017-18 A (H1N1) Data

Background

The basic reproduction number (R ₀) has a key role in epidemics and can be utilized for preventing epidemics. In this study, different methods are used for estimating R ₀'s and their vaccination coverage to find the formula with the best performance.

Materials and Methods

We estimated R ₀ for cumulative cases count data from April 18 to July 6, 2009 and 35-2017 to 34-2018 weeks in Canada: maximum likelihood (ML), exponential growth rate (EG), time-dependent reproduction numbers (TD), attack rate (AR), gamma-distributed generation time (GT), and the final size of the epidemic. Gamma distribution with mean and standard deviation 3.6 ± 1.4 is used as GT.

Results

The AR method obtained a R _{0 (}95% confidence interval [CI]) value of 1.116 (1.1163, 1.1165) and an EG (95%CI) value of 1.46 (1.41, 1.52). The R ₀ (95%CI) estimate was 1.42 (1.27, 1.57) for the obtained ML, 1.71 (1.12, 2.03) for the obtained TD, 1.49 (1.0, 1.97) for the gamma-distributed GT, and 1.00 (0.91, 1.09) for the final size of the epidemic. The minimum and maximum vaccination coverage were related to AR and TD methods, respectively, where the TD method has minimum mean squared error (MSE). Finally, the R ₀ (95%CI) for 2018 data was 1.52 (1.11, 1.94) by TD method, and vaccination coverage was estimated as 34.2%.

Conclusion

For the purposes of our study, the estimation of TD was the most useful tool for computing the R ₀, because it has the minimum MSE. The estimation R ₀ > 1 indicating that the epidemic has occurred. Thus, it is required to vaccinate at least 41.5% to prevent and control the next epidemic.

Impacts of Road Traffic Network and Socioeconomic Factors on the Diffusion of 2009 Pandemic Influenza A (H1N1) in Mainland China

The 2009 pandemic influenza virus caused the majority of the influenza A virus infections in China in 2009. It arrived in several Chinese cities from imported cases and then spread as people travelled domestically by all means of transportation, among which road traffic was the most commonly used for daily commuting. Spatial variation in socioeconomic status not only accelerates migration across regions but also partly induces the differences in epidemic processes and in responses to epidemics across regions. However, the roles of both road travel and socioeconomic factors have not received the attention they deserve. Here, we constructed a national highway network for and between 333 cities in mainland China and extracted epidemiological variables and socioeconomic factors for each city. We calculated classic centrality measures for each city in the network and proposed two new measures (SumRatio and Multicenter Distance). We evaluated the correlation between the centrality measures and epidemiological features and conducted a spatial autoregression to quantify the impacts of road network and socioeconomic factors during the outbreak. The results showed that epidemics had more significant relationships with both our new measures than the classic ones. Higher population density, higher per person income, larger SumRatio and Multicenter Distance, more hospitals and college students, and lower per person GDP were associated with higher cumulative incidence. Higher population density and number of slaughtered pigs were found to advance epidemic arrival time. Higher population density, more colleges and slaughtered pigs, and lower Multicenter Distance were associated with longer epidemic duration. In conclusion, road transport and socioeconomic status had significant impacts and should be considered for the prevention and control of future pandemics.

A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan

BACKGROUND

Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems.

METHODS

We applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week.

RESULTS

The MCM and SRM yielded complete estimates for each of Japan's 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM.

CONCLUSIONS

The Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.

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.

Characterising seasonal influenza epidemiology using primary care surveillance data

Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons. It can be challenging to obtain high-quality data of influenza cases specifically, as clinical presentations with influenza-like symptoms may instead be cases of one of a number of alternate respiratory viruses. We use a new dataset of confirmed influenza virological data from 2011-2016, along with high-quality denominators informing a hierarchical observation process, to model seasonal influenza dynamics in New South Wales, Australia. We use approximate Bayesian computation to estimate parameters in a climate-driven stochastic epidemic model, including the basic reproduction number R₀, the proportion of the population susceptible to the circulating strain at the beginning of the season, and the probability an infected individual seeks treatment. We conclude that R₀ and initial population susceptibility were strongly related, emphasising the challenges of identifying these parameters. Relatively high R₀ values alongside low initial population susceptibility were among the results most consistent with these data. Our results reinforce the importance of distinguishing between R₀ and the effective reproduction number (R_e) in modelling studies.

When patients present to their doctor with influenza-like symptoms, they may have influenza, or some other respiratory virus. The only way to discriminate between these viruses is with an expensive test, which is not performed in many cases. Additionally, results other than influenza may not be reported. This means that it can be difficult to determine how much influenza is circulating in the population each season. We used a unique dataset of confirmed influenza with denominators to fit models for seasonal influenza in New South Wales, Australia. Knowing the denominators allowed us to estimate population level trends. We found that the relationship between influenza transmission rates and immunity due to previous infections was critical, with relatively high transmission corresponding to substantial preexisting immunity likely. This existing immunity is critical to understanding and effectively modeling influenza dynamics.

Assessing the State of Knowledge Regarding the Effectiveness of Interventions to Contain Pandemic Influenza Transmission: A Systematic Review and Narrative Synthesis

BACKGROUND

Influenza pandemics occur when a novel influenza strain, to which humans are immunologically naïve, emerges to cause infection and illness on a global scale. Differences in the viral properties of pandemic strains, relative to seasonal ones, can alter the effectiveness of interventions typically implemented to control seasonal influenza burden. As a result, annual control activities may not be sufficient to contain an influenza pandemic.

PURPOSE

This study seeks to inform pandemic policy and planning initiatives by reviewing the effectiveness of previous interventions to reduce pandemic influenza transmission and infection. Results will inform the planning and design of more focused in-depth systematic reviews for specific types of interventions, thus providing the most comprehensive and current understanding of the potential for alternative interventions to mitigate the burden of pandemic influenza.

METHODS

A systematic review and narrative synthesis of existing systematic reviews and meta-analyses examining intervention effectiveness in containing pandemic influenza transmission was conducted using information collected from five databases (PubMed, Medline, Cochrane, Embase, and Cinahl/EBSCO). Two independent reviewers conducted study screening and quality assessment, extracting data related to intervention impact and effectiveness.

RESULTS AND DISCUSSION

Most included reviews were of moderate to high quality. Although the degree of statistical heterogeneity precluded meta-analysis, the present systematic review examines the wide variety of interventions that can impact influenza transmission in different ways. While it appears that pandemic influenza vaccination provides significant protection against infection, there was insufficient evidence to conclude that antiviral prophylaxis, seasonal influenza cross-protection, or a range of non-pharmaceutical strategies would provide appreciable protection when implemented in isolation. It is likely that an optimal intervention strategy will employ a combination of interventions in a layered approach, though more research is needed to substantiate this proposition.

TRIAL REGISTRATION

PROSPERO 42016039803.

Dynamic patterns of circulating influenza virus from 2005 to 2012 in Shandong Province, China

To identify circulating emerging/reemerging viral strains and epidemiological trends, an influenza sentinel surveillance network was established in Shandong Province, China, in 2005. Nasal and/or throat swabs from patients with influenza-like-illness were collected at sentinel hospitals. Influenza viruses were detected by reverse transcription polymerase chain reaction (RT-PCR) or virus isolation. From October 2005 to March 2012, 7763 (21.44 %) of 36,209 swab samples were positive for influenza viruses, including 5221 (67.25 %) influenza A and 2542 (32.75 %) influenza B. While the influenza viruses were detected year-round, their type/subtype distribution varied significantly. Peak influenza activity was observed from November to February. The proportion of laboratory-confirmed influenza cases was highest among participants aged 0-4 years (14.97 %) in the 2005-2009 and 2010-2012 influenza seasons and the positivity rate of influenza A(H1N1)pdm09 was highest in the 15 to 24 year age group during the 2009-2010 influenza season. Genetic analysis of hemagglutinin (HA) and neuraminidase (NA) genes revealed that the viruses matched seasonal influenza vaccine strains in general, with some amino acid mutations. Influenza A(H1N1)pdm09 strains isolated in Shandong Province were characterized by an S203T mutation that is specific to clade 7 isolates. This report illustrates that the Shandong Provincial influenza surveillance system was sensitive in detecting influenza virus variability by season and by genetic composition. This system will help official public health target interventions such as education programs and vaccines.

Model-Based Comprehensive Analysis of School Closure Policies for Mitigating Influenza Epidemics and Pandemics

School closure policies are among the non-pharmaceutical measures taken into consideration to mitigate influenza epidemics and pandemics spread. However, a systematic review of the effectiveness of alternative closure policies has yet to emerge. Here we perform a model-based analysis of four types of school closure, ranging from the nationwide closure of all schools at the same time to reactive gradual closure, starting from class-by-class, then grades and finally the whole school. We consider policies based on triggers that are feasible to monitor, such as school absenteeism and national ILI surveillance system. We found that, under specific constraints on the average number of weeks lost per student, reactive school-by-school, gradual, and county-wide closure give comparable outcomes in terms of optimal infection attack rate reduction, peak incidence reduction or peak delay. Optimal implementations generally require short closures of one week each; this duration is long enough to break the transmission chain without leading to unnecessarily long periods of class interruption. Moreover, we found that gradual and county closures may be slightly more easily applicable in practice as they are less sensitive to the value of the excess absenteeism threshold triggering the start of the intervention. These findings suggest that policy makers could consider school closure policies more diffusely as response strategy to influenza epidemics and pandemics, and the fact that some countries already have some experience of gradual or regional closures for seasonal influenza outbreaks demonstrates that logistic and feasibility challenges of school closure strategies can be to some extent overcome.

An Epidemic Patchy Model with Entry-Exit Screening

A multi-patch SEIQR epidemic model is formulated to investigate the long-term impact of entry-exit screening measures on the spread and control of infectious diseases. A threshold dynamics determined by the basic reproduction number R₀ is established: The disease can be eradicated if R₀ < 1, while the disease persists if R₀ > 1. As an application, six different screening strategies are explored to examine the impacts of screening on the control of the 2009 influenza A (H1N1) pandemic. We find that it is crucial to screen travelers from and to high-risk patches, and it is not necessary to implement screening in all connected patches, and both the dispersal rates and the successful detection rate of screening play an important role on determining an effective and practical screening strategy.

Evidence compendium and advice on social distancing and other related measures for response to an influenza pandemic

The role of social distancing measures in mitigating pandemic influenza is not precisely understood. To this end, we have conducted a systematised review, particularly in light of the 2009 pandemic influenza, to better inform the role of social distancing measures against pandemic influenza. Articles were identified from relevant databases and the data were synthesised to provide evidence on the role of school or work place-based interventions, case-based distancing (self-isolation, quarantine), and restriction of mobility and mass gatherings. School closure, whether proactive or reactive, appears to be moderately effective and acceptable in reducing the transmission of influenza and in delaying the peak of an epidemic but is associated with very high secondary costs. Voluntary home isolation and quarantine are also effective and acceptable measures but there is an increased risk of intra-household transmission from index cases to contacts. Work place-related interventions like work closure and home working are also modestly effective and are acceptable, but likely to be economically disruptive. Internal mobility restriction is effective only if prohibitively high (50% of travel) restrictions are applied and mass gatherings occurring within 10 days before the epidemic peak are likely to increase the risk of transmission of influenza.

Usefulness and applicability of infectious disease control measures in air travel: a review

BACKGROUND

Air travel has opened up opportunities for world transportation, but has also increased infectious disease transmission and public health risks. To control disease spread, airlines and governments are able to implement control measures in air travel. This study inventories experiences and applicability of infectious disease control measures.

METHODS

A literature search was performed in PubMed, including studies between 1990 and 2013. Search terms included air travel terms and intervention terms. Interventions were scored according outcome, required resources, preparation, passenger inconvenience and passenger compliance.

RESULTS

Provision of information to travelers, isolation, health monitoring, hygiene measures and vector control reportedly prevent disease spread and are well applicable. Contact tracing can be supportive in controlling disease spread but depend on disease characteristics. Exit and entry screening, quarantine and travel restrictions are unlikely to be very effective in preventing disease spread, while implementation requires extensive resources or travel implications.

CONCLUSIONS

Control measures should focus on providing information towards travelers, isolation, health monitoring and hygiene measures. Appropriateness of measures depends on disease characteristics, and the required resources. As most studies analyze one type of measure in a particular situation, further research comparing the effectiveness of measures is recommended.

Estimation of force of infection based on different epidemiological proxies: 2009/2010 Influenza epidemic in Malta

Information about infectious disease outbreaks is often gathered indirectly, from doctor's reports and health board records. It also typically underestimates the actual number of cases, but the relationship between the observed proxies and the numbers that drive the diseases is complicated, nonlinear and potentially time- and state-dependent. We use a combination of data collection from the 2009-2010 H1N1 outbreak in Malta, compartmental modelling and Bayesian inference to explore the effect of using various sources of information (consultations, doctor's diagnose, swabbing and molecular testing) on estimation of the effective basic reproduction ratio, R(t). Different proxies and different sampling rates (daily and weekly) lead to similar behaviour of R(t) as the epidemic unfolds, although individual parameters (force of infection, length of latent and infectious period) vary. We also demonstrate that the relationship between different proxies varies as epidemic progresses, with the first period characterised by high ratio of consultations and influenza diagnoses to actual confirmed cases of H1N1. This has important consequences for modelling that is based on reconstructing influenza cases from doctor's reports.

Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature

BACKGROUND

The potential impact of an influenza pandemic can be assessed by calculating a set of transmissibility parameters, the most important being the reproduction number (R), which is defined as the average number of secondary cases generated per typical infectious case.

METHODS

We conducted a systematic review to summarize published estimates of R for pandemic or seasonal influenza and for novel influenza viruses (e.g. H5N1). We retained and summarized papers that estimated R for pandemic or seasonal influenza or for human infections with novel influenza viruses.

RESULTS

The search yielded 567 papers. Ninety-one papers were retained, and an additional twenty papers were identified from the references of the retained papers. Twenty-four studies reported 51 R values for the 1918 pandemic. The median R value for 1918 was 1.80 (interquartile range [IQR]: 1.47-2.27). Six studies reported seven 1957 pandemic R values. The median R value for 1957 was 1.65 (IQR: 1.53-1.70). Four studies reported seven 1968 pandemic R values. The median R value for 1968 was 1.80 (IQR: 1.56-1.85). Fifty-seven studies reported 78 2009 pandemic R values. The median R value for 2009 was 1.46 (IQR: 1.30-1.70) and was similar across the two waves of illness: 1.46 for the first wave and 1.48 for the second wave. Twenty-four studies reported 47 seasonal epidemic R values. The median R value for seasonal influenza was 1.28 (IQR: 1.19-1.37). Four studies reported six novel influenza R values. Four out of six R values were <1.

CONCLUSIONS

These R values represent the difference between epidemics that are controllable and cause moderate illness and those causing a significant number of illnesses and requiring intensive mitigation strategies to control. Continued monitoring of R during seasonal and novel influenza outbreaks is needed to document its variation before the next pandemic.

Transmission characteristics of different students during a school outbreak of (H1N1) pdm09 influenza in China, 2009

Many outbreaks of A(H1N1)pdm09 influenza have occurred in schools with a high population density. Containment of school outbreaks is predicted to help mitigate pandemic influenza. Understanding disease transmission characteristics within the school setting is critical to implementing effective control measures. Based on a school outbreak survey, we found almost all (93.7%) disease transmission occurred within a single grade, only 6.3% crossed grades. Transmissions originating from freshmen exhibited a star-shaped network; other grades exhibited branch- or line-shaped networks, indicating freshmen have higher activity and are more likely to cause infection. R₀ for freshmen, calculated as 2.04, estimated as 2.76, was greater than for other grades (P < 0.01). Without intervention, the estimated number of cases was much greater when the outbreak was initiated by freshmen than by other grades. Furthermore, the estimated number of cases required to be under quarantine and isolation for freshmen was less than that of equivalent other grades. So we concluded that different grades have different transmission mode. Freshmen were the main facilitators of the spread of A(H1N1)pdm09 influenza during this school outbreak, so control measures (e.g. close contact isolation) priority used for freshmen would likely have effectively reduced spread of influenza in school settings.

Impacts of a mass vaccination campaign against pandemic H1N1 2009 influenza in Taiwan: a time-series regression analysis

OBJECTIVES

A multicenter, hospital-wide, clinical and epidemiological study was conducted to assess the effectiveness of the mass influenza vaccination program during the 2009 H1N1 influenza pandemic, and the impact of the prioritization strategy among people at different levels of risk.

METHODS AND RESULTS

Among the 34 359 medically attended patients who displayed an influenza-like illness and had a rapid influenza diagnostic test (RIDT) at one of the three participating hospitals, 21.0% tested positive for influenza A. The highest daily number of RIDT-positive cases in each hospital ranged from 33 to 56. A well-fitted multiple linear regression time-series model (R(2)=0.89) showed that the establishment of special community flu clinics averted an average of nine cases daily (p=0.005), and an increment of 10% in daily mean level of population immunity against pH1N1 through vaccination prevented five cases daily (p<0.001). Moreover, the regression model predicted five-fold or more RIDT-positive cases if the mass influenza vaccination program had not been implemented, and 39.1% more RIDT-positive cases if older adults had been prioritized for vaccination above school-aged children.

CONCLUSIONS

Mass influenza vaccination was an effective control measure, and school-aged children should be assigned a higher priority for vaccination than older adults during an influenza pandemic.

Assessing the impact of public health interventions on the transmission of pandemic H1N1 influenza a virus aboard a Peruvian navy ship

Background

Limited data exist on transmission dynamics and effectiveness of control measures for influenza in confined settings.

Objectives

To investigate the transmission dynamics of a 2009 pandemic H1N1 influenza A outbreak aboard a Peruvian Navy ship and quantify the effectiveness of the implemented control measures.

Methods

We used surveillance data and a simple stochastic epidemic model to characterize and evaluate the effectiveness of control interventions implemented during an outbreak of 2009 pandemic H1N1 influenza A aboard a Peruvian Navy ship.

Results

The serological attack rate for the outbreak was 49·1%, with younger cadets and low-ranking officers at greater risk of infection than older, higher-ranking officers. Our transmission model yielded a good fit to the daily time series of new influenza cases by date of symptom onset. We estimated a reduction of 54·4% in the reproduction number during the period of intense control interventions.

Conclusion

Our results indicate that the patient isolation strategy and other control measures put in place during the outbreak reduced the infectiousness of isolated individuals by 86·7%. Our findings support that early implementation of control interventions can limit the spread of influenza epidemics in confined settings.

Epidemiology of human infections with avian influenza A(H7N9) virus in China

BACKGROUND

The first identified cases of avian influenza A(H7N9) virus infection in humans occurred in China during February and March 2013. We analyzed data obtained from field investigations to describe the epidemiologic characteristics of H7N9 cases in China identified as of December 1, 2013.

METHODS

Field investigations were conducted for each confirmed case of H7N9 virus infection. A patient was considered to have a confirmed case if the presence of the H7N9 virus was verified by means of real-time reverse-transcriptase-polymerase-chain-reaction assay (RT-PCR), viral isolation, or serologic testing. Information on demographic characteristics, exposure history, and illness timelines was obtained from patients with confirmed cases. Close contacts were monitored for 7 days for symptoms of illness. Throat swabs were obtained from contacts in whom symptoms developed and were tested for the presence of the H7N9 virus by means of real-time RT-PCR.

RESULTS

Among 139 persons with confirmed H7N9 virus infection, the median age was 61 years (range, 2 to 91), 71% were male, and 73% were urban residents. Confirmed cases occurred in 12 areas of China. Nine persons were poultry workers, and of 131 persons with available data, 82% had a history of exposure to live animals, including chickens (82%). A total of 137 persons (99%) were hospitalized, 125 (90%) had pneumonia or respiratory failure, and 65 of 103 with available data (63%) were admitted to an intensive care unit. A total of 47 persons (34%) died in the hospital after a median duration of illness of 21 days, 88 were discharged from the hospital, and 2 remain hospitalized in critical condition; 2 patients were not admitted to a hospital. In four family clusters, human-to-human transmission of H7N9 virus could not be ruled out. Excluding secondary cases in clusters, 2675 close contacts of case patients completed the monitoring period; respiratory symptoms developed in 28 of them (1%); all tested negative for H7N9 virus.

CONCLUSIONS

Most persons with confirmed H7N9 virus infection had severe lower respiratory tract illness, were epidemiologically unrelated, and had a history of recent exposure to poultry. However, limited, nonsustained human-to-human H7N9 virus transmission could not be ruled out in four families.

Epidemiology of human respiratory viruses in children with acute respiratory tract infections in Jinan, China

The viral etiologies of UTRIs and LTRIs in children in Jinan city were investigated between July 2009 and June 2010. Nasal and throat swabs were collected from 397 children with URTIs and bronchoalveolar lavage fluid specimens were collected from 323 children with LRTIs. RT-PCR/PCR was used to examine all samples for IFV, PIV, RSV, RV, hMPV, HBoV, CoV, ADV, RSV, and EV. Viral pathogens were detected in 47.10% of URTI samples and 66.57% samples, and the incidence of viral coinfection was 5.29% and 21.05%, respectively. IFV was the most common virus in URTIs, with a detection rate of 19.40%, followed by PIV (10.83%), RV (10.58%), and EV (6.30%). For LRTIs, PIV and RV were both detected in 27% of samples, followed by RSV (9.91%), HBoV (8.36%), IFV (5.57%), and hMPV (5.57%). RSV and HBoV were more prevalent in the youngest children of no more than six months. Meanwhile, RV, PIV, and RSV were the most frequent viruses combined with bacterial pathogens in LRTIs. In conclusion, the spectrum of respiratory virus infections in URTIs and LRTIs differed in terms of the most common pathogens, seasonal distribution, and coinfection rate.

Transmission dynamics of the 2009 influenza A (H1N1) pandemic in India: the impact of holiday-related school closure

The role of social-distancing measures, such as school closures, is a controversial aspect of pandemic mitigation planning. However, the timing of 2009 pandemic provides a natural experiment for evaluating the impact of school closure during holidays on influenza transmission. To quantify the transmission intensity of the influenza A (H1N1) pdm'09 in India, by estimating the time varying reproduction number (Rt) and correlating the temporal changes in the estimates of Rt for different regions of India with the timing of school holidays. We used daily lab-confirmed case reports of influenza A (H1N1) pdm'09 in India (during 17 May'09 to 17 May'10), stratified by regions. We estimated the transmissibility of the pandemic for different regions from these time-series, using Bayesian methods applied to a branching process model of disease spread and correlated the resulting estimates with the timing of school holidays in each region. The North-west region experienced two notable waves, with the peak of the first wave coinciding with the start of a 4 week school holiday (September-October'09). In the southern region the two waves were less clear cut, though again the first peak of the first wave coincided with the start of school holidays--albeit of less than 2 weeks duration (August'09). Our analysis suggests that the school holidays had a significant influence on the epidemiology of the 2009 pandemic in India. We estimate that school holidays reduced the reproduction number by 14-27% in different regions of India, relative to levels seen outside holiday periods. The estimates of the reproduction number obtained (with peak R values below 1.5) are compatible with those reported from other regions of the world. This work reinforces past studies showing the significant impact of school holidays on spread of 2009 pandemic virus, and by inference the role of contact patterns in children on transmission.

Epidemiology of pandemic influenza A/H1N1 virus during 2009-2010 in Taiwan

Outbreak of swine-origin influenza A/H1N1 virus (pdmH1N1) occurred in 2009. Taiwanese authorities implemented nationwide vaccinations with pdmH1N1-specific inactivated vaccine as of November 2009. This study evaluates prevalence, HA phylogenetic relationship, and transmission dynamic of influenza A and B viruses in Taiwan in 2009-2010. Respiratory tract specimens were analyzed for influenza A and B viruses. The pdmH1N1 peaked in November 2009, was predominant from August 2009 to January 2010, then sharply dropped in February 2010. Significant prevalence peaks of influenza B in April-June of 2010 and H3N2 virus in July and August were observed. Highest percentage of pdmH1N1- and H3N2-positive cases appeared among 11-15-year-olds; influenza B-positive cases were dominant among those 6-10 years old. Maximum likelihood phylogenetic trees showed 11 unique clusters of pdmH1N1, seasonal H3N2 influenza A and B viruses, as well as transmission clusters and mixed infections of influenza strains in Taiwan. The 2009 pdmH1N1 virus was predominant in Taiwan from August 2009 to January 2010; seasonal H3N2 influenza A and B viruses exhibited small prevalence peaks after nationwide vaccinations. Phylogenetic evidence indicated transmission clusters and multiple independent clades of co-circulating influenza A and B strains in Taiwan.

Cytokine responses in patients with mild or severe influenza A(H1N1)pdm09

BACKGROUND

Influenza virus affects millions of people worldwide each year. More severe infection occurs in the elderly, very young and immunocompromised. In 2009, a new variant of swine origin (influenza A(H1N1)pdm09 virus) emerged that produced severe disease in young healthy adults.

OBJECTIVES

The aim of this study was to determine whether cytokine concentrations are associated with clinical outcome in patients infected influenza A(H1N1)pdm09 virus.

STUDY DESIGN

Plasma concentration of 32 cytokines and growth factors were measured using a multiplex bead immunoassay and conventional ELISA in four patient groups. Patients with severe and mild influenza A(H1N1)pdm09 virus infection, rhinovirus infection and healthy volunteers were investigated. In addition, serial samples of respiratory secretions from five patients with severe influenza A(H1N1)pdm09 virus infection were examined.

RESULTS

The majority of cytokines measured were elevated in patients with viral respiratory infections compared to the healthy controls. Concentrations of IL-6, IL-10, IL-15, IP-10, IL-2R, HGF, ST2 and MIG were significantly higher (p<0.05) and EGF significantly lower (p=0.0001) in patients with severe influenza A(H1N1)pdm09 virus infection compared to those with mild influenza A(H1N1)pdm09 virus and rhinovirus infection.

CONCLUSIONS

A number of cytokines were found to be substantially elevated in patients with severe influenza A(H1N1)pdm09 virus infection. This supports and extends other published work suggesting a role for proinflammatory cytokines in influenza-induced lung pathology. Interestingly, EGF was significantly lower in patients with severe infection suggesting it is actively suppressed. As EGF has a role in role in cell proliferation and tissue repair, it may protect the lung from host or virus mediated damage.

Regional variation in mortality impact of the 2009 A(H1N1) influenza pandemic in China

BACKGROUND

Laboratory-confirmed deaths grossly underestimate influenza mortality burden, so that reliable burden estimates are derived from indirect statistical studies, which are scarce in low- and middle-income settings.

OBJECTIVES

Here, we used statistical excess mortality models to estimate the burden of seasonal and pandemic influenza in China.

METHODS

We modeled data from a nationally representative population-based death registration system, combined with influenza virological surveillance data, to estimate influenza-associated excess mortality for the 2004-2005 through 2009-2010 seasons, by age and region.

RESULTS

The A(H1N1) pandemic was associated with 11·4-12·1 excess respiratory and circulatory (R&C) deaths per 100,000 population in rural sites of northern and southern China during 2009-2010; these rates were 2·2-2·8 times higher than those of urban sites (P<0·01). Influenza B accounted for a larger proportion of deaths than pandemic A(H1N1) in 2009-2010 in some regions. Nationally, we attribute 126,200 (95% CI, 61,000-248,400) excess R&C deaths (rate of 9·4/100,000) and 2,323,000 (1,166,000-4,533,000) years of life lost (YLL) to the first year of A(H1N1)pdm circulation.

CONCLUSIONS

The A(H1N1) pandemic posed a mortality and YLL burden comparable to that of interpandemic influenza in China. Our high burden estimates in rural areas highlight the need to enhance epidemiological surveillance and healthcare services, in underdeveloped and remote areas.

Trends in parameterization, economics and host behaviour in influenza pandemic modelling: a review and reporting protocol

BACKGROUND

The volume of influenza pandemic modelling studies has increased dramatically in the last decade. Many models incorporate now sophisticated parameterization and validation techniques, economic analyses and the behaviour of individuals.

METHODS

We reviewed trends in these aspects in models for influenza pandemic preparedness that aimed to generate policy insights for epidemic management and were published from 2000 to September 2011, i.e. before and after the 2009 pandemic.

RESULTS

We find that many influenza pandemics models rely on parameters from previous modelling studies, models are rarely validated using observed data and are seldom applied to low-income countries. Mechanisms for international data sharing would be necessary to facilitate a wider adoption of model validation. The variety of modelling decisions makes it difficult to compare and evaluate models systematically.

CONCLUSIONS

We propose a model Characteristics, Construction, Parameterization and Validation aspects protocol (CCPV protocol) to contribute to the systematisation of the reporting of models with an emphasis on the incorporation of economic aspects and host behaviour. Model reporting, as already exists in many other fields of modelling, would increase confidence in model results, and transparency in their assessment and comparison.

Effectiveness of a school district closure for pandemic influenza A (H1N1) on acute respiratory illnesses in the community: a natural experiment

BACKGROUND

Following detection of pandemic influenza A H1N1 (pH1N1) in Dallas/Fort Worth, Texas, a school district (intervention community, [IC]) closed all public schools for 8 days to reduce transmission. Nearby school districts (control community [CC]) mostly remained open.

METHODS

We collected household data to measure self-reported acute respiratory illness (ARI), before, during, and after school closures. We also collected influenza-related visits to emergency departments (ED(flu)).

RESULTS

In both communities, self-reported ARIs and ED(flu) visits increased from before to during the school closure, but the increase in ARI rates was 45% lower in the IC (0.6% before to 1.2% during) than in the CC (0.4% before to 1.5% during) (RRR(During)(/Before) = 0.55, P < .001; adjusted OR(During/Before) = 0.49, P < .03). For households with school-aged children only (no children 0-5 years), IC had even lower increases in adjusted ARI than in the CC (adjusted OR(During/Before) = 0.28, P < .001). The relative increase of total ED(flu) visits in the IC was 27% lower (2.8% before to 4.4% during) compared with the CC (2.9% before to 6.2% during). Among children aged 6-18 years, the percentage of ED(flu) in IC remained constant (5.1% before vs 5.2% during), whereas in the CC it more than doubled (5.2% before vs 10.9% during). After schools reopened, ARI rates and ED(flu) visits decreased in both communities.

CONCLUSIONS

Our study documents a reduction in ARI and ED(flu) visits in the intervention community. Our findings can be used to assess the potential benefit of school closures during pandemics.