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      Studies on Influenza Virus Transmission between Ferrets: the Public Health Risks Revisited

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      American Society of Microbiology

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          Abstract

          LETTER Lipsitch and Inglesby recently estimated the potential public health risks associated with research on influenza virus transmission via respiratory droplets or aerosols between ferrets, leading them to conclude that such research is too risky to be conducted (1). The authors of that and other publications (2 – 4) estimated the probability of laboratory-acquired infections (LAIs) and onward transmission of the viruses under investigation, as well as the potential consequence to public health if such events were to occur. Given the weight assigned to these risk estimates, it is important that potential pitfalls in the underlying assumptions in these analyses be rigorously scrutinized. Importantly, the published estimates were based on historical data and did not take into account the numerous risk reduction measures that are in place in the laboratories where the research is conducted. Here, I provide a critical appraisal of the published work, discussing, challenging, and modifying the estimates based on the specific conditions under which the work is performed and the properties of the viruses under investigation. By doing so, the outcome of the risk assessment changes from serious risks to negligible risks for humans and the environment. As a consequence, a more balanced debate about the research on influenza virus transmission via respiratory droplets or aerosols between ferrets is warranted, in particular given the substantial public health benefits assigned to this type of research (5, 6). PROBABILITY OF LABORATORY-ACQUIRED INFECTIONS Initial calculations of the potential risks associated with research on influenza virus transmission via respiratory droplets or aerosols between ferrets (1 – 4) used reports on select agent theft, loss, and release collected by the U.S. Centers for Disease Control and Prevention (CDC) from 2004 to 2010 (7) to calculate the probability of occurrence of LAIs. Although these reports have limitations (1, 4, 7), they provide the most recent account of LAIs in the United States and probably reflect the current state of the art in biosafety and biosecurity practices better than older studies on laboratory incidents (8, 9), e.g., as a consequence of the implementation of the U.S. select agent program and best practices developed in biosafety and biosecurity in general over the last decades. From 2004 to 2010, 11 LAIs in total were reported to the U.S. CDC, 4 of which occurred in biosafety level 3 (BSL3) facilities. During this 7-year period, on average 10,000 individuals per year had access to select agents in an average of 292 laboratories per year, thus totaling 2,044 laboratory-years and 70,000 person-years of follow-up (7). From these data, the probability of occurrence of LAIs under BSL3 conditions was calculated as 4/2,044 (or 2 × 10−3) per laboratory-year, or 4/70,000 (or 5.7 × 10−5) per person-year (1 – 4). These estimates, however, do not take into account specific pathogen types or research settings. This is crucial, because working practices in, e.g., virology and microbiology laboratories are different and because each biosafety laboratory is unique (10, 11). Research facilities and the experiments that are conducted are therefore appraised through targeted risk assessments, in which the planned studies are scrutinized before any experiment is started. On this note, it is important that none of the LAIs reported to the U.S. CDC from 2004 to 2010 involved viruses (7), and the risks of LAIs associated with work on viral pathogens should thus be estimated as less than 1 per 2,044 (<5 × 10−4 per laboratory-year), or less than 1 per 70,000 (<1.4 × 10−5 per person-year). Unfortunately, the report by Henkel et al. (7) does not specify how many of the 2,044 laboratory-years and 70,000 person-years were related to BSL3 facilities versus BSL2 and BSL4 facilities. Thus, using 2,044 and 70,000 as the denominators yields an underestimation of the true probability of LAIs under BSL3 conditions, as discussed previously (1, 4). SOME KEY BIOSAFETY MEASURES AND RISK MITIGATION STRATEGIES AT ERASMUS MC Research on influenza virus transmission via respiratory droplets or aerosols between ferrets is performed in facilities and under conditions that are specifically designed for the purpose of such studies (12 – 16). In ordinary BSL3 laboratories, including diagnostic laboratories, work is performed in open-front class 2 biosafety cabinets with directional airflow, aimed at protecting the environment from release of pathogens and protecting laboratory workers from exposure. Contrary to ordinary BSL3 conditions for work with viruses, all in vivo and in vitro experimental work on influenza virus transmission in the Erasmus MC facility is carried out in class 3 isolators or class 3 biosafety cabinets, which are airtight boxes with negative pressure (<−200 Pa), to ensure inward flow in case of leakage (12, 16). Handling is done through airtight gloves fitted to the front of these cabinets. Air released from the class 3 units is filtered by high efficiency particulate air (HEPA) filters and then leaves directly via the facility ventilation system, again via HEPA filters. Only authorized and experienced personnel that have received extensive training can access the facility. For animal handling, personnel always work in pairs to reduce the chance of human error. Although the laboratory is considered “clean” because all experiments are conducted in closed class 3 cabinets and isolators, special personal protective equipment, including laboratory suits, gloves, and FFP3 (class 3 filtering face piece) facemasks, are used, and all personnel are vaccinated with the homologous A/H5N1 vaccine. All equipment in the facilities is monitored electronically, and alarm systems are employed to ensure that workers do not enter the facilities if equipment is malfunctioning. All personnel have been instructed and trained how to act in case of incidents, which are handled upon consultation between a senior staff member, a clinical microbiologist, the institutional biosafety officers, and the facility management. Antiviral drugs (oseltamivir or zanamivir) are used immediately in the event that an incident should occur. Every incident in the laboratory is followed up by actions to prevent such incidents from happening again. The facilities, personnel, and procedures are inspected by the U.S. CDC every 3 years, in agreement with the U.S. select agent regulations for overseas laboratories and by the Dutch government (Inspectie Leefomgeving en Transport [ILT] inspection) (12, 16). The biosafety conditions in the Erasmus MC facility thus extend well beyond “normal” BSL3 conditions for working with viruses, and a number of these biosafety measures should be considered when the probability of LAIs is inferred from the U.S. CDC report. Unfortunately, an exact number for the effectiveness of individual biosafety measures is not available (9). However, it is reasonable to assume that the effectiveness of the physical separation of personnel from the viruses they work with through the use of class 3 isolator units and class 3 biosafety cabinets, the use of personnel protective equipment, the extensive training program, the use of experienced personnel only, and the application of a two-person rule to reduce human error during animal experiments would yield a decrease in the probability of LAIs. Although the magnitude of this increase in safety is not known, I assume that it is at least a factor of 10. Using the risk analysis done by others and this assumption of reduced risk, the probability of a LAI in the Erasmus MC facility would be reduced to below 0.1 × (1.4 × 10−5), or <1.4 × 10−6 per person-year. The quantitative risk assessment to be performed upon request of the U.S. government (17) will have the challenging task of a better quantitative assessment of the effectiveness for each of the biosafety measures in individual laboratories, to yield exact numbers instead of the conservative estimates used here. The vaccination of laboratory personnel against the homologous A/H5N1 virus under investigation produces another layer of safety that results in further risk mitigation. Given the generally accepted efficacy of influenza vaccine of ~65% for laboratory-confirmed influenza in healthy adults (18), vaccination reduces the probability of an LAI that results in viral escape to below 0.35 × (1.4 × 10−6), or <5 × 10−7 per person-year. The 65% value is almost certainly an underestimate, because this number is taken from general population studies that include individuals with impaired immunity and nonexact matches between the vaccine antigen and the circulating viruses. It is important to note that the antibody titers in our vaccinated laboratory workers are high (geometric mean titer, 987; range, 160 to 10,240) compared to the titers generally accepted as protective against seasonal influenza (≥40) (19) and that individuals are revaccinated if and when their antibody titers decrease (12). As a consequence of the monitoring of equipment both electronically and by visual inspection, potential exposures to virus are unlikely to go unnoticed. Upon any potential exposures, personnel receive oseltamivir treatment upon consultation with various specialists as indicated above. Such early treatment with drugs has been reported to have ~80% efficacy against human influenza virus infection (20) and avian influenza virus infection (21). Here, it is important to note that viruses under study in the Erasmus MC facility are evaluated for their sensitivity to oseltamivir (12). The immediate treatment of laboratory personnel with oseltamivir upon any potential exposure to virus is thus expected to reduce the probability of LAI further, to below 0.2 × (5 × 10−7), or <1× 10 − 7 per person-year, given the average 80% efficacy in preventing laboratory-confirmed influenza. From this analysis, the conservative estimate is that when research is performed on transmission of influenza viruses via respiratory droplets or aerosols between ferrets in the Erasmus MC facility, to which 10 persons have access, 1 LAI would be expected to occur less frequently than once every 1 million years. PROBABILITY OF ONWARD TRANSMISSION FROM A CASE OF LAI The second factor in the equation of the previous risk assessments is the probability of onward transmission from each case of LAI. Previous studies used 5 to 60% as the probability of onward transmission (1, 2), which is based on the unlimited spread of a pandemic influenza virus in the general population. It is important to note that onward transmission from LAIs has so far been uncommon (7 – 9). In the case of research on influenza virus transmission via respiratory droplets or aerosols between ferrets, a substantial, scientifically justified reduction from the probability of 0.05 to 0.6 of onward transmission from an LAI can be made, based on the above-mentioned biosafety measures and risk mitigation strategies that are in place (12). The first factor that needs to be considered is that laboratory personnel that acquired the LAI were vaccinated against the homologous A/H5N1 virus and treated with oseltamivir upon any incident with potential exposure to the virus. Although the vaccination and treatment may have been insufficient to prevent infection altogether (hence the occurrence of the LAI at a frequency of less than once every 1 million years), the virus shedding in H5-vaccinated and oseltamivir-treated individuals is likely to be reduced substantially, compared to the onward transmission in times of spread during an influenza pandemic from untreated immunologically naive individuals. If we assume a conservative 2-log reduction in virus excretion in immunized and treated individuals (20 – 24) compared to untreated immunologically naive individuals, the range of probability of onward transmission from a case of LAI would be reduced to <5 × 10−4 to 6 × 10−3. As an important risk mitigation strategy to reduce onward transmission upon any potential LAI, Erasmus MC policy dictates enforcement of quarantine of any laboratory personnel that are potentially virus exposed. This policy would reduce the exposure of nonlaboratory personnel to one (the partner of the laboratory worker) or nil, rather than the ~100 contacts human adults would ordinarily have during a 5-day time frame (25). As a consequence, the transmission probability can be further reduced ~100-fold, yielding a probability of onward transmission from the case of LAI of <5 × 10−6 to 6 × 10−5. A final factor to consider in the calculation of the probability of onward transmission from each case of LAI is the basic reproduction number (R0) of the influenza virus under investigation. As indicated above, the previous risk assessments were based on R0 of pandemic influenza virus. However, laboratory experiments have shown that the efficiency of transmission of the laboratory-derived influenza viruses was lower than that of the transmission of pandemic and seasonal influenza viruses in ferrets, as could be expected (12, 16, 26). Moreover, given that the viruses are ferret adapted rather than human adapted, even an extremely conservative adjustment of the transmissibility parameter by a factor of 2 would yield a “final” estimation of the probability of onward transmission from a case of LAI of <2.5 × 10−6 to 3 × 10−5. PROBABILITY OF AN LAI FOLLOWED BY ONWARD TRANSMISSION Multiplying the probability of occurrence of an LAI by the probability of onward transmission from each case of LAI, one can estimate that the probability of an LAI resulting in onward transmission would range between (1 × 10−7) × (2.5 × 10−6) (or 2.5 × 10−13) and (1 × 10−7) × (3 × 10−5) (or 3 × 10−12). From this analysis, the estimate is that when research is performed on transmission of influenza viruses via respiratory droplets or aerosols between ferrets in the Erasmus MC facility, to which 10 persons have access, 1 LAI with onward transmission would be expected to occur far less frequently than once every 33 billion years. This probability could be assigned the term “negligible,” given that the age of our planet is only 5 billion years. THE CONSEQUENCE OF AN LAI FOLLOWED BY ONWARD TRANSMISSION In previous work (1 – 4), it was assumed that if a ferret-adapted avian influenza virus caused an LAI and onward transmission, it could cause a pandemic with an attack rate of 24 to 38%, as deduced from previous pandemics, and a case fatality rate ranging from 1 to 60% in a population of 7 billion people, thus leading to millions of, or more than a billion, fatalities. However, I consider an attack rate and case fatality rate of this magnitude to be unrealistic. Given that the avian influenza viruses under investigation are ferret adapted rather than human adapted, it is unlikely that these viruses would spread as efficiently between humans. Of note, this does not mean that the ferret model is therefore useless for studies to increase our fundamental knowledge about airborne virus transmission; it simply means that—just like when the mouse model is used to address fundamentals in immunology—we need to carefully validate any results obtained in animals before extrapolation to humans. Throughout the history of virology, scientists have adapted viruses to cells, chicken embryos, or animal species in order to yield viruses that have increased replication properties in these specific hosts or cells but at the same time lose replication capacity and virulence in others (27). Examples are the passaging of vaccinia virus in chicken embryo fibroblasts to yield modified vaccinia virus Ankara (MVA), which is now in use as a safe vaccine vector (28), the passaging of measles virus, mumps virus, and rubella virus in various cells to yield the live-attenuated MMR vaccine (29), and the passaging of influenza viruses in mice, ferrets, and eggs to yield the vaccine strain A/PR/8/34 (30), all of which are highly attenuated in humans. The higher bound of the range of case fatality rates of 60% stems from the number of deaths recorded among laboratory-confirmed cases of A/H5N1 influenza reported to the WHO (31). Since mild cases of infection—those individuals that do not consult a physician or remain untested—are not recorded, the true case fatality rate of A/H5N1 virus infections in humans is unknown. Due to intrinsic difficulties associated with serology data to estimate the numbers of previously infected individuals, there is no consensus on the incidence of A/H5N1 infections in Southeast Asia (32, 33), but case fatality rates orders of magnitude lower than 60% have been inferred (27). In addition, it is important to note that fatalities in ferrets infected with A/H5N1 virus via respiratory droplets or aerosols have not occurred, contrary to when ferrets received large dosages of A/H5N1 virus directly in the (lower) airways (12, 13, 16). CONCLUDING REMARKS On the topic of intentional or accidental releases of viruses from laboratories involved in influenza virus transmission studies, it is important to note that during a decade of transmission studies on pandemic and epidemic strains derived from the 1918, 1957, 1968, and 2009 pandemics and on various wild-type and laboratory-adapted zoonotic viruses of subtypes H1, H2, H5, H7, and H9 (summarized in reference 16), no LAIs have been recorded. There have also been no recorded intentional or accidental releases during more than a century of research with human and animal influenza viruses, including highly pathogenic avian influenza viruses, even at times when biocontainment measures were largely nonexistent. Some have argued that the 1977 Russian influenza epidemic was the result of a laboratory accident (2), but in 1977, influenza research was done under conditions of limited biocontainment, and attenuated and wild type strains were tested in humans. We do not know what happened in 1977, but we cannot conclude that the virus escaped a BSL3(+) laboratory. Since natural influenza pandemics have occurred on average every 30 years over the last century, the probability that the next pandemic will emerge in nature is orders of magnitude larger than emergence from a laboratory. Given the recently summarized immediate and short-term benefits of research on influenza viruses that are transmitted via respiratory droplets or aerosols between ferrets (5, 6) and the longer-term aims to fully understand and predict and prevent pandemics, combined with the extremely low risk to humans and the environment associated with properly conducted experiments, I conclude that it is sensible and acceptable to restart the research, provided that any laboratory participating in this research adopt biosafety and biosecurity conditions comparable to those that are currently in place (12 – 16).

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          Use of the oral neuraminidase inhibitor oseltamivir in experimental human influenza: randomized controlled trials for prevention and treatment.

          Influenza virus neuraminidase is thought to be essential for virus replication in humans; however, to date, available neuraminidase inhibitors are limited to zanamivir, which is topically administered. To determine the safety, tolerability, and antiviral activity of oral neuraminidase inhibitor oseltamivir (GS4104/Ro64-0796) for prevention and the early treatment of influenza in experimentally infected humans. Two randomized, double-blind, placebo-controlled trials conducted between June and July 1997. Individual hotel rooms; 2 large US university medical schools. A total of 117 healthy adult volunteers (aged 18-40 years; median age, 21 years) who were susceptible (hemagglutination-inhibition antibody titer < or =1:8). All subjects were inoculated intranasally with influenza A/Texas/36/91 (H1N1) virus. For the prophylaxis study, oral oseltamivir (100 mg once daily [n = 12], 100 mg twice daily [n = 12], or matching placebo [n = 13], starting 26 hours before virus inoculation) was administered. For the treatment study, the same drug was given (20 mg, 100 mg, or 200 mg twice daily, 200 mg once daily, or matching placebo [n = 16], in each group starting 28 hours after inoculation). All regimens were continued for 5 days. Comparing placebo groups with pooled treatment groups, for prophylaxis, outcomes included frequency of infection and viral shedding; for treatment, viral shedding in titers. In the prophylaxis study, 8 (67%) of 12 placebo and 8 (38%) of 21 oseltamivir recipients became infected (P = .16; efficacy, 61%); 6 (50%) placebo compared with 0 oseltamivir recipients shed virus (P<.001; efficacy, 100%), and 33% of placebo but no oseltamivir recipient had infection-related respiratory illness (P<.01). Among infected subjects in the treatment study (n = 69), the viral titer area under the curve of the combined oseltamivir groups (n = 56) was lower (median [interquartile range [IQR]], 80 [23-151] vs 273 [79-306] log10 tissue culture-infective doses50 per milliliter x hour; P = .02) than the placebo group (n = 13), and the median (IQR) duration of viral shedding with therapy was reduced from 107 (83-131) to 58 (35-59) hours (P = .003). Oseltamivir treatment also reduced symptom scores (median [IQR] score-hours, 225 [97-349] vs 400 [189-645]; P = .05), and nasal proinflammatory cytokine levels. Transient mild to moderate nausea after dosing was observed in 15 (17%) of 88 oseltamivir and 2 (7%) of 29 placebo recipients (95% confidence interval for difference, -11% to 68%), which was largely prevented by ingestion with food. In these trials, prophylaxis and early treatment with oral oseltamivir were both associated with significant antiviral and clinical effects in experimental human influenza.
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            H5N1 hybrid viruses bearing 2009/H1N1 virus genes transmit in guinea pigs by respiratory droplet.

            In the past, avian influenza viruses have crossed species barriers to trigger human pandemics by reassorting with mammal-infective viruses in intermediate livestock hosts. H5N1 viruses are able to infect pigs, and some of them have affinity for the mammalian type α-2,6-linked sialic acid airway receptor. Using reverse genetics, we systematically created 127 reassortant viruses between a duck isolate of H5N1, specifically retaining its hemagglutinin (HA) gene throughout, and a highly transmissible, human-infective H1N1 virus. We tested the virulence of the reassortants in mice as a correlate for virulence in humans and tested transmissibility in guinea pigs, which have both avian and mammalian types of airway receptor. Transmission studies showed that the H1N1 virus genes encoding acidic polymerase and nonstructural protein made the H5N1 virus transmissible by respiratory droplet between guinea pigs without killing them. Further experiments implicated other H1N1 genes in the enhancement of mammal-to-mammal transmission, including those that encode nucleoprotein, neuraminidase, and matrix, as well as mutations in H5 HA that improve affinity for humanlike airway receptors. Hence, avian H5N1 subtype viruses do have the potential to acquire mammalian transmissibility by reassortment in current agricultural scenarios.
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              Measured Dynamic Social Contact Patterns Explain the Spread of H1N1v Influenza

              Introduction Seasonal changes in patterns of social contacts have a marked influence on the spread of infectious diseases. In particular, the patterns of school terms and holidays affect the incidence of infections with a significant impact on school-age children, including measles, pertussis, and influenza [1]–[6]. Mathematical models can be used to explain and attempt to predict the spread of infectious diseases; however until recently a lack of data about social contact patterns has restricted the applicability of these models. In 2008 results were published from the POLYMOD study, a social contact survey involving participants in 8 European countries [7]; this study described patterns of social mixing, quantifying the tendency of people to mix with others of a similar age, and showing that the highest levels of contact were between children. These data have been used to model close-contact infectious diseases, and have been found useful in explaining observed patterns of incidence [2], [5], [7]–[10]. Important factors are still missing from available datasets. One such factor is good information about how social contact behaviour varies over time. On an individual level, there is day-to-day variation in social behaviour [11], and incidence data suggest that there are population-level changes resulting from events such as school holidays [1]–[4], [6]. As part of the POLYMOD study, some data were collected during the school holidays, demonstrating significant changes in contact patterns during holiday periods [12]. Studies focusing on school-age children have confirmed that children make substantially fewer contacts on average during the holidays and at weekends than when at school [13]–[16]. However, there is a general lack of information about temporal changes in contact patterns, in particular quantifying the impact of school holidays on contact behaviour within the population as a whole. In the absence of these data, mathematical models of disease spread have been obliged to make a range of plausible assumptions about how to model the impact of school holidays [2]–[4], [6], [17]–[19]. Here, we present the results of a longitudinal population-level social mixing survey and use these data to parameterise an age-structured model of H1N1v incidence. In April 2009, H1N1v influenza emerged in the Americas. Over the next few months, this virus spread around the globe, causing millions of cases worldwide. The UK experienced two distinct peaks in incidence, one in July 2009, and another in October 2009 [1], [2]. Serological data collected during the epidemic suggest that, in some parts of the UK, over 40% of children aged 5–14 were infected before the end of the first wave of infection [20], with an estimated cumulative incidence over the second epidemic wave in this group of 59% [21]; these serological data suggest that the great majority of cases were not captured in incidence estimates derived from clinical surveillance [1], even though such estimates may give a good indication of incidence trends. The UK flusurvey (www.flusurvey.org.uk) was developed as an internet-based tool to augment existing influenza surveillance [22], [23], most of which depends on recording healthcare usage by symptomatic individuals [1], [24], [25] and so misses individuals with influenza-like-illness (ILI) who do not seek medical attention. The UK flusurvey is an attempt to record ILI incidence that does not depend on ill individuals seeking healthcare [23]. As well as estimating incidence trends [26], flusurvey data have been used to estimate the effectiveness of influenza vaccination [27]. Flusurvey participants were also asked about their social contact behaviour. Here we describe the results of the social contact survey carried out during the H1N1v epidemic in the UK. We describe changes in contact patterns that took place during the pandemic; in particular, we quantify the impact of school holiday periods. In order to test whether this internet-based social contact survey captures epidemiologically-relevant patterns of social interactions, we use the measured dynamic contact patterns in a simple mathematical model of influenza spread, and explore their ability to explain the observed patterns of incidence. We find that a relatively simple model, parameterised by our age-structured mixing data, gives a good match with observed patterns of incidence. Results Contact Survey The contact survey was completed 9,261 times by 3,338 individuals, many completing it multiple times. 104 surveys were excluded from further analysis because of missing age information; the analysis that follows is based on the remaining 9,157 reports. The data can be found in the Supporting Information, Dataset S1. As expected, the majority of reports were completed by adults during the school term time (Table S1 in Text S1). We have therefore not further subdivided the school-aged groups or to attempted to distinguish between different holiday periods (e.g. summer holiday and autumn half term holiday). Fig. 1 shows the impact of school holidays on the social mixing patterns of the population. Both for conversational and for physical contacts the most obvious change was in the number of interactions between school-aged children. School holidays had a much smaller effect on the number of contacts made by or with other age groups. 10.1371/journal.pcbi.1002425.g001 Figure 1 Social contact matrices. Values and colours show the mean number of contacts per day reported between each age group. In each panel, the participant's age group is shown on the vertical axis, that of their contacts on the horizontal axis. The four panels show patterns of A: conversational contacts during school term time; B: conversational contacts during school holidays; C: physical contacts during school term time; D: physical contacts during school holidays. There was a large, highly significant, reduction during the school holidays in the daily average number of conversational contacts made by those aged 5–18 (from 41.2 during term time to 24.8 during the holidays, p = 0.001; Table 1). Older age groups reported a small, but statistically significant, change. There were fewer physical contacts than conversational contacts reported, and the reduction in the number of physical contacts reported by school children during school holidays (from 11.0 to 8.9) was not statistically significant. 10.1371/journal.pcbi.1002425.t001 Table 1 Daily contact numbers. number of conversational contacts number of physical contacts age group school term time school holidays p school term time school holidays p 0–4 13.8 (14.6) 15.4 (24.1) 0.701 8.2 (6.8) 7.1 (10.3) 0.750 12 [5, 18] 7 [4, 17.5] 7 [3, 12] 3 [2, 9] 5–18 41.2 (62.4) 24.8 (38.9) 0.001 11.0 (21.1) 8.9 (14.0) 0.342 14 [6, 55] 9.5 [6, 26] 6 [3, 13] 5 [2, 9] 19–64 20.5 (32.7) 19.6 (35.6) 0.017 5.0 (14.7) 5.0 (14.0) 0.633 12 [6, 21] 12 [6, 21] 2 [1, 5] 3 [1, 5] 65+ 9.1 (19.6) 7.6 (8.3) 0.014 2.4 (3.3) 2.7 (4.2) 0.525 4 [2, 9] 6 [3, 10] 1 [1, 3] 2 [1, 3] Summary of the number of daily contacts reported by participants in each age group, comparing term time with school holidays. For each age group, the mean (standard deviation), and median [inter-quartile range] are shown. p-values give the significance level for differences in number of contacts reported in school term time and school holidays. Model Performance Models parameterised using these measured mixing patterns were fitted to estimated incidence curves (Fig. 2). While models parameterised using both conversational and physical contact patterns broadly capture observed incidence, the patterns of conversational contacts appear to provide a better fit to incidence data than patterns of physical contacts. In particular, models parameterised using physical contact patterns cannot capture the timing and depth of the trough in incidence at the end of the summer holidays. The model fits are similar whether using Health Protection Agency (HPA) or flusurvey-adjusted incidence estimates. 10.1371/journal.pcbi.1002425.g002 Figure 2 Incidence estimates, comparing models and data. A comparison of estimated per-capita weekly incidence data (black) and best-fitting model output (red). The four panels show A: model using patterns of conversational contacts fitted to HPA incidence estimates; B: model using patterns of conversational contacts fitted to flusurvey-adjusted incidence estimates; C: model using patterns of physical contacts fitted to HPA incidence estimates; D: model using patterns of physical contacts fitted to flusurvey-adjusted incidence estimates. Best-fitting parameter sets and fits using bootstrapped matrices can be found in Table S2 in Text S1, and Figs S1 and S2. An outbreak would have grown more slowly had it begun during the school holidays than during term time. During the holidays, in the absence of prior immunity, the initial growth rate of the epidemic, R, would have been approximately 35% lower than during term time (25% lower in the model using patterns of physical contact) – falling from 1.57 to 1.07. Prior immunity reduced initial growth rate by approximately 10%, to 1.42 in term time and 0.91 in the holidays (Fig. 3). 10.1371/journal.pcbi.1002425.g003 Figure 3 The impact of school holidays on epidemic growth rate. The impact of school holidays and prior immunity on initial epidemic growth rate predicted using the best-fitting model (using patterns of conversational contacts fitted to HPA incidence estimates) considering an epidemic that began during term time or during the school holidays, with and without measured levels of prior immunity. Comparable results from the other models can be found in Table S5 in Text S1. Lines show the range of model predictions using the low-difference and high-difference bootstrapped contact matrices. Estimated parameter values are reasonably consistent across the models used (Table S2 in Text S1), aside from the transmission rate per encounter, τ, which, as expected, is larger in the models using physical contact patterns. The value of the rescaling factor is estimated to be between 9 and 15. The models suggest that around 30% of adults and over 50% of school-aged children had acquired immunity by the end of the outbreak (Table S3 in Text S1). Both models and incidence estimates indicate that incidence during school term time was dominated by those aged under 18, whereas during holidays the majority of cases were in adults (Fig. 4) [1]. The good agreement between the models and the data supports the usefulness of the mixing data obtained. 10.1371/journal.pcbi.1002425.g004 Figure 4 Incidence within younger age groups, over time. The fraction of incidence each week that occurs in younger people, as predicted using the best-fitting model (using patterns of conversational contacts fitted to HPA incidence estimates) and as reported in the HPA incidence estimate data. Incidence data showing the proportion of incidence in those aged under 25 (black, dashed) and under 15 (black, dash-dotted); model predicted fraction of incidence in those aged under 19 is shown in red; model predictions using the low-difference and high-difference bootstrapped contact matrices are shown in green and blue respectively. Mixing matrices generated from bootstrapping the original dataset suggest that the substantial change in contacts between term time and holidays is necessary for the model to be able to fit the incidence data, with low-difference bootstrap matrices resulting in models that fit the observed data less well (Figs S1, S2). Discussion Substantial and significant changes in social contact patterns take place during school holidays. The greatest change is seen in school-aged children, who make approximately 40% fewer conversational contacts (95% CI 22–59%) each day during the school holidays than during term time. These changes in social contact patterns have a large impact on the spread of infections. As the incidence patterns of the 2009 H1N1v epidemic in the UK show, incidence began to fall at the start of the holiday period and began to rise again when schools reopened. Models incorporating these dynamic contact patterns capture the observed dynamics of influenza, suggesting that the social contact patterns reported here are closely correlated to those relevant to the spread of influenza. The large fall in contacts during school holidays generates the observed decline in cases seen during the summer of 2009. The models highlight the impact of prior immunity on epidemic behaviour, and suggest that, had the first cases arrived in the population during the school holidays, existing immunity in the population would have been sufficient to prevent the epidemic from taking off until schools reopened. This work supports previous studies that suggest that school holidays are associated with significant changes in mixing patterns and in epidemic behaviour. The impact of holidays appears larger than some studies suggest [12], though not as large as others [13]–[16]. Different survey tools are likely to give different results: in contrast to surveys that use a detailed contact diary-based approach [7], [12]–[14], [28], [29] the method used here did not require participants to give additional details about each of the people they met, and thus there was no time-saving incentive towards recording fewer contacts; on the other hand, listing one by one all encounters may provide an aid to recall. Several different methods of collecting social contact data have been used in other studies, including self-completed paper contact diaries [7], [11], [28], [29], network studies [8], [30], [31], electronic contact diaries [32], online contact diaries [29] and automated electronic proximity sensors [33]. All have been found to be useful, and none to be perfect. Perfect recall of all encounters is unlikely, especially for short-duration encounters [30]. Some studies have found electronic self-reported contact data to perform similarly to paper diaries [29], while others have found that more encounters are reported when using paper diaries [32]. In our study, we collected aggregate numbers of contacts (by age group and social setting), in order to reduce the time required to complete the surveys; a previous study suggests that this approach gives similar results to contact diaries if, as in our case, the recall period was short [28]. In common with other contact surveys [7], [11], data about the contact patterns of young children could be reported on their behalf by their parents, which may limit its reliability. Collecting contact data from young children is challenging though not impossible [13], [15], [31], and although our survey was designed to be straightforward to complete it was not possible to devise something that would be equally suitable for all age groups. School closure has been suggested as an intervention to control infection, an idea that models have helped to explore [17]–[19]. Although this work demonstrates that scheduled school holidays have a large impact on transmission, school closure as a public health intervention may not have the same effect on social mixing patterns, since child care arrangements during unplanned, short-notice, closures may differ from those during school holidays. Unsurprisingly, there is only limited information available on this subject [15], [16], [34]. Furthermore, as was seen in the UK, it is likely that the epidemic would take off again once schools re-opened; thus school closure is more likely to be useful as a way to delay transmission than to prevent it altogether. The models developed here suggest that a large fraction of the UK population was infected during the 2009 H1N1v epidemic. The same conclusion resulted from serological sampling that reported seropositivity by the end of the first wave of over 45% in children aged 5–14 in the regions of the UK first affected by the epidemic [20], and a cumulative incidence of 59% in this group over the second wave [21]. Interpretation of serological data is difficult, since not all those infected are expected to have seroconverted by the time of the sampling and blood samples used in these studies are not sampled at random [20], [21]. However, the models presented here, available serological data [20], [21], and other modelling work [2] all suggest that the original incidence figures dramatically underestimated the true number of infections. The models suggest that estimated influenza incidence only includes around 7–11% of all people infected. A number of factors may account for this, including mild or asymptomatic infections that would not have been diagnosed as ILI, imperfect test sensitivity, or poor estimates of the fraction of individuals with ILI who seek medical attention. Ideally, there would be perfect incidence data to which to fit epidemic models. However, incidence estimates are not perfect, and serological surveys cannot give fine-scaled information about weekly incidence patterns. Here, we have used models appropriate to the level of incidence and behavioural data available and fitted models to incidence estimated in two different ways, in both cases drawing similar conclusions. The social contact data used here are, likewise, imperfect. Participants in the flusurvey are not a random sample of the UK population, and we are unable to control for all biases in this self-selecting sample [23]. It would be interesting to be able to look at variations in contact patterns at a finer temporal resolution, such as comparing different holiday periods or detecting other temporal variations, but in this case the sample size, particularly of school age children, is not large enough to make this feasible. We cannot reasonably justify splitting up the most interesting and important groups – school-aged children – any further into, for example, primary and secondary school groups. It is planned to continue the UK flusurvey in future years, and it is hoped that wider recruitment will allow these issues to be explored more fully in due course. We found that patterns of conversational encounters provided a better fit to incidence data than patterns of physical encounters. Some other studies have found that models using patterns of physical encounters provide a better fit to serological profiles [8], [9] though other studies do not find a difference between using physical and conversational encounter patterns [10]. Of course, fitting models to serological profiles that are the result of many years of potential exposure is not the same as fitting to short term incidence data. We found that the relatively small school-holiday change in numbers of physical encounters was unable to explain the sharp decline in incidence associated with the summer holiday period, an effect that may be less important when considering cumulative exposure over many years. Or it may simply be the case that conversational encounters provide a better proxy for interactions that led to the transmission of H1N1v than physical encounters. The mathematical model of influenza transmission used here is extremely simple, with a population categorised into broad age groups roughly corresponding to normal patterns of work and school attendance. The model ignores geographical differences in transmission and incidence across the UK. The novel aspect of the model is that it makes use of measured changes in patterns of social contacts taking place between these groups as a result of the opening and closing of schools. The model is parameterised by data collected from an internet-based survey completed by a subset of the population of interest at the time of the epidemic. Despite the caveats, the survey reported here is, to our knowledge, the only large-scale longitudinal study of population-level social contacts to have been carried out. We have shown that internet-based contact surveys can be used in large-scale studies. The fact that the contact data can be used in models to capture observed incidence patterns suggests that we have succeeded in quantifying epidemiologically relevant longitudinal social contact patterns. Methods Ethics Statement Participation in this opt-in study was voluntary, and all analysis was carried out on anonymised data. The study was approved by the ethics committee of the London School of Hygiene and Tropical Medicine. The UK Flusurvey The UK flusurvey was launched in July 2009, based on similar systems used elsewhere in Europe [22]. It ran from July 2009 until March 2010. Members of the public were encouraged to register via the flusurvey website and reported their symptoms (or lack of symptoms) each week. On registration, participants completed a background survey recording information about themselves including age, gender, and vaccination history. Participation in all parts of the flusurvey was entirely voluntary. Participants were prompted to continue to take part with a weekly email reminder. Further details about the flusurvey can be found in [23] and in Text S1. Participants could also take part in a contact survey. This could be completed as often as participants chose; they were reminded of it each week, but its completion was not heavily advocated since the principal interest was in measuring incidence and behavioural response to infection [23], [26]. The contact survey was a simplified version of that used in other contact studies [7], [12]–[14], [16]: participants were asked two main questions: “How many people did you have conversational contact with yesterday?” and “How many people did you have physical contact with yesterday?” In each case, participants were asked to report the numbers of people they met in 4 different age groups (0–4; 5–18; 19–64; 65+), roughly corresponding to normal school and work attendance, and three different social settings (Home, Work/School/College, Other). Participants were asked to approximate larger numbers of contacts using in the following categories: 16–24; 25–49; 50–99; 100 or more; while we would have liked to collect precise numbers, it was decided that this would present an unrealistic recall challenge for participants. For larger numbers of contacts, in the analyses that follow the number of contacts was approximated by midpoint of these categories aside from the category “100 or more”, which was approximated by 150. Further details can be found in Text S1. Statistical Analyses Participants were categorised into the same age groups as contacts (0–4; 5–18; 19–64; and 65+); time period was categorised as term time or school holidays. To explore the influence of school holiday periods on the number and age distribution of contacts, accounting for multiple reports from participants who completed the contact survey multiple times, we used a population averaged negative binomial regression model with robust standard errors [35], [36]. Analyses were carried out separately for each age group of participants. Time period and gender were considered as explanatory variables, but gender was found not to be a significant factor and was subsequently omitted from the analyses. Analyses were carried out in Stata 11. Because the weekly survey reminder email was sent to participants each Wednesday, and the contact survey asked about “yesterday's” contacts, most reports related to Tuesdays. Therefore, although a small number of surveys were completed on other days, day of the week was not included as a variable in the analysis. Dynamic Disease Model A dynamic, differential-equation, age-structured, Susceptible-Exposed-Infectious-Recovered (SEIR) model [3] was used to investigate whether measured changes in contact patterns could explain the observed epidemic dynamics. In this model, susceptible individuals become infected at a rate proportional to the number of contacts they have with infected individuals. Each contact (whether made during term time or holidays) has the same rate of transmission, τ; thus the rate at which a susceptible individual in age group i acquires infection is given by , where Ij is the number of infectious individuals in group j, nj the size of group j, and B i,j the number of contacts per unit time each individual in group i makes with individuals in group j. When infected, an individual enters the exposed (latent) class, during which she is infected but not yet infectious. She then enters the infectious class at rate ν, then recovers at rate g. Because we consider events taking place over only a few months, ageing is not included. The model is described by the following set of differential equations: where Si, Ei, Ii , and Ri are respectively the number of susceptible, exposed, infected, and recovered individuals in group i. The contact matrix {B i,j } is time dependent, representing differences in mixing patterns between school term times and holidays, taking values BT i,j during term time and BH i,j during the school summer holiday. The initial growth rate of the epidemic, R, was calculated as the dominant eigenvalue of the next generation matrix M, with elements {M i,j = (τ/g)B j,iSi /ni } (where, in the early stages of the epidemic Si = ni in the absence of immunity) [3]. Incidence and Immunity Data The model was fitted to weekly incidence data based on individuals with ILI who sought medical attention [1]. Combined with laboratory testing of swabs taken from a subset of those who sought medical attention, these data are thought to give a good estimate of the number of cases of H1N1v with ILI who sought medical attention. To estimate the total number of H1N1v cases these observed cases must be scaled up to account for those individuals with influenza who do not seek medical attention. We fit the model to two different estimates of weekly influenza incidence: one calculated by the HPA, using a scaling factor that was informed by flusurvey data made available to the HPA during the early part of the 2009 H1N1v pandemic [1]; the other using subsequent analysis of healthcare-seeking behaviour recorded by flusurvey users with ILI [27]. In reality, both estimates only provide approximations to true incidence trends. The advantage of the latter, flusurvey-adjusted, estimate is that it uses directly measured differences in healthcare-seeking behaviour between different age groups, and changes in this behaviour over time. A large, unknown, number of people infected with influenza were either asymptomatic or displayed only mild symptoms, and as such would not have been recorded as ILI [2], [21], even if they had sought medical attention. In common with other modeling work, to account for this under-recording we apply a rescaling factor to the case estimates. Previous modeling work considered a rescaling factor of 7.5, 10, and 12.5, and concluded that a rescaling factor of 10 was reasonable [2]; here, we seek a more precise value for this parameter. Model Fitting Two models were used: one using social contact pattern data relating to conversational encounters, and a second using data about physical encounters. Weekly incidence as predicted by the model was fitted to estimated incidence data using a least-squares fit. Five model parameters were estimated: the transmission rate, the rescaling factor, the start of the epidemic and the beginning and end of the school holidays. Because of the rescaling factor included in the model, we fit to the shape of the incidence curve not its absolute value. The best-fitting parameter sets (Table S2 in Text S1) were used to calculate the initial growth rate of the epidemic, R, for an outbreak beginning during term time and for one beginning during the school holidays, in the presence and in the absence of pre-existing immunity. Calculated values of R can be found in Table S5 in Text S1. Bootstrapping Contact Matrices To explore the role of variability in the collected contact data, 1000 bootstrap copies of the dataset were generated, matching the original dataset in the number of responses from each age group in term time and holiday periods. These bootstrapped datasets were used to estimate a range of contact matrices describing term time and school holiday mixing patterns. It is not the absolute number of contacts but rather the change between holiday and term time contact patterns that is important for understanding the observed incidence; therefore, bootstrapped matrices were ranked according to the ratio of the term time and holiday epidemic growth rates. Models were fitted using those bootstrapped datasets that resulted in contact matrices that generated the 5th and 95th percentiles of this ratio (referred to as “low-difference bootstrap” and “high-difference bootstrap” respectively). Parameterisation Serological testing in England indicated that a large number of people, particularly older people, had prior immunity to H1N1v [20]. In common with other interpretations [2], [20], we have assumed that a haemagluttination inhibition titre at or above 1∶32 provides immunity, and that the fraction of the population in each age group with levels greater than this before the epidemic is immune to further H1N1v infection. Values used in the models can be found in Table S4 in Text S1. To match the availability of serological data, the model population is parameterised to represent the population of England. For simplicity, we use a latent period of one day and an infectious period of 1.8 days for H1N1v influenza in the UK, derived from previous modeling work by Baguelin et al [2]. Contact rates between age groups are taken directly from the flusurvey contact survey, and can be found in Text S1. Supporting Information Figure S1 Incidence estimates, comparing models and data. Equivalent to Figure 2, using the low-difference bootstrap contact matrices. Comparison of estimated per-capita weekly incidence data (black) and best-fitting model output (red). The four panels show A: model using patterns of conversational contacts fitted to HPA incidence estimates; B: model using patterns of conversational contacts fitted to flusurvey-adjusted incidence estimates; C: model using patterns of physical contacts fitted to HPA incidence estimates; D: model using patterns of physical contacts fitted to flusurvey-adjusted incidence estimates. Best-fitting parameter sets can be found in Table S2 in Text S1, and values for contact matrices in Table S7 in Text S1. (EPS) Click here for additional data file. Figure S2 Incidence estimates, comparing models and data. Equivalent to Figure 2, using the high-difference bootstrap contact matrices. Comparison of estimated per-capita weekly incidence data (black) and best-fitting model output (red). The four panels show A: model using patterns of conversational contacts fitted to HPA incidence estimates; B: model using patterns of conversational contacts fitted to flusurvey-adjusted incidence estimates; C: model using patterns of physical contacts fitted to HPA incidence estimates; D: model using patterns of physical contacts fitted to flusurvey-adjusted incidence estimates. Best-fitting parameter sets can be found in Table S2 in Text S1, and values for contact matrices in Table S7 in Text S1. (EPS) Click here for additional data file. Figure S3 Contact survey screen shot. Screen shot from the contact survey, showing wording and layout of questions. Each entry in the matrix of encounter numbers consisted of a drop down menu. The number of physical encounters was asked similarly. (TIFF) Click here for additional data file. Text S1 The file Text S1 contains further information and parameters. Section 1 contains the contact matrices (and bootstrapped contact matrices) as measured in the contact survey and as used in the dynamic disease model. Section 2 contains additional details about the survey design and participant recruitment. (DOC) Click here for additional data file. Dataset S1 The file DatasetS1.csv contains the data used in this manuscript. Columns contain the number of reported conversational and physical encounters with each of the four age groups. The column “Term time” takes a value of 1 for surveys completed during school term time, and zero for surveys completed during the school holidays. (CSV) Click here for additional data file.
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                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society of Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                23 January 2015
                Jan-Feb 2015
                : 6
                : 1
                : e02560-14
                Affiliations
                [1]Department of Viroscience, Erasmus MC, Rotterdam, The Netherlands
                Author notes
                Address correspondence to r.fouchier@ 123456erasmusmc.nl .
                Article
                mBio02560-14
                10.1128/mBio.02560-14
                4323420
                25616377
                a097e0b5-4757-4a60-9c67-4809b7000af3
                Copyright © 2015 Fouchier.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                January/February 2015

                Life sciences
                Life sciences

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