Virus-virus interactions impact the population dynamics of influenza and the common cold.

The human respiratory tract hosts a diverse community of cocirculating viruses that are responsible for acute respiratory infections. This shared niche provides the opportunity for virus-virus interactions which have the potential to affect individual infection risks and in turn influence dynamics of infection at population scales. However, quantitative evidence for interactions has lacked suitable data and appropriate analytical tools. Here, we expose and quantify interactions among respiratory viruses using bespoke analyses of infection time series at the population scale and coinfections at the individual host scale. We analyzed diagnostic data from 44,230 cases of respiratory illness that were tested for 11 taxonomically broad groups of respiratory viruses over 9 y. Key to our analyses was accounting for alternative drivers of correlated infection frequency, such as age and seasonal dependencies in infection risk, allowing us to obtain strong support for the existence of negative interactions between influenza and noninfluenza viruses and positive interactions among noninfluenza viruses. In mathematical simulations that mimic 2-pathogen dynamics, we show that transient immune-mediated interference can cause a relatively ubiquitous common cold-like virus to diminish during peak activity of a seasonal virus, supporting the potential role of innate immunity in driving the asynchronous circulation of influenza A and rhinovirus. These findings have important implications for understanding the linked epidemiological dynamics of viral respiratory infections, an important step towards improved accuracy of disease forecasting models and evaluation of disease control interventions.

An evaluation of community assessment tools (CATs) in predicting use of clinical interventions and severe outcomes during the A(H1N1)pdm09 pandemic.

During severe influenza pandemics healthcare demand can exceed clinical capacity to provide normal standards of care. Community Assessment Tools (CATs) could provide a framework for triage decisions for hospital referral and admission. CATs have been developed based on evidence that supports the recognition of severe influenza and pneumonia in the community (including resource limited settings) for adults, children and infants, and serious feverish illness in children. CATs use six objective criteria and one subjective criterion, any one or more of which should prompt urgent referral and admission to hospital. A retrospective evaluation of the ability of CATs to predict use of hospital-based interventions and patient outcomes in a pandemic was made using the first recorded routine clinical assessment on or shortly after admission from 1520 unselected patients (800 female, 480 children <16 years) admitted with PCR confirmed A(H1N1)pdm09 infection (the FLU-CIN cohort). Outcome measures included: any use of supplemental oxygen; mechanical ventilation; intravenous antibiotics; length of stay; intensive or high dependency care; death; and "severe outcome" (combined: use of intensive or high dependency care or death during admission). Unadjusted and multivariable analyses were conducted for children (age <16 years) and adults. Each CATs criterion independently identified both use of clinical interventions that would in normal circumstances only be provided in hospital and patient outcome measures. "Peripheral oxygen saturation ≤ 92% breathing air, or being on oxygen" performed well in predicting use of resources and outcomes for both adults and children; supporting routine measurement of peripheral oxygen saturation when assessing severity of disease. In multivariable analyses the single subjective criterion in CATs "other cause for clinical concern" independently predicted death in children and in adults predicted length of stay, mechanical ventilation and "severe outcome"; supporting the role of clinical acumen as an important independent predictor of serious illness.

The comparative clinical course of pregnant and non-pregnant women hospitalised with influenza A(H1N1)pdm09 infection.

INTRODUCTION: The Influenza Clinical Information Network (FLU-CIN) was established to gather detailed clinical and epidemiological information about patients with laboratory confirmed A(H1N1)pdm09 infection in UK hospitals. This report focuses on the clinical course and outcomes of infection in pregnancy. METHODS: A standardised data extraction form was used to obtain detailed clinical information from hospital case notes and electronic records, for patients with PCR-confirmed A(H1N1)pdm09 infection admitted to 13 sentinel hospitals in five clinical 'hubs' and a further 62 non-sentinel hospitals, between 11th May 2009 and 31st January 2010.Outcomes were compared for pregnant and non-pregnant women aged 15-44 years, using univariate and multivariable techniques. RESULTS: Of the 395 women aged 15-44 years, 82 (21%) were pregnant; 73 (89%) in the second or third trimester. Pregnant women were significantly less likely to exhibit severe respiratory distress at initial assessment (OR = 0.49 (95% CI: 0.30-0.82)), require supplemental oxygen on admission (OR = 0.40 (95% CI: 0.20-0.80)), or have underlying co-morbidities (p-trend <0.001). However, they were equally likely to be admitted to high dependency (Level 2) or intensive care (Level 3) and/or to die, after adjustment for potential confounders (adj. OR = 0.93 (95% CI: 0.46-1.92). Of 11 pregnant women needing Level 2/3 care, 10 required mechanical ventilation and three died. CONCLUSIONS: Since the expected prevalence of pregnancy in the source population was 6%, our data suggest that pregnancy greatly increased the likelihood of hospital admission with A(H1N1)pdm09. Pregnant women were less likely than non-pregnant women to have respiratory distress on admission, but severe outcomes were equally likely in both groups.

Comparison of CATs, CURB-65 and PMEWS as triage tools in pandemic influenza admissions to UK hospitals: case control analysis using retrospective data.

Triage tools have an important role in pandemics to identify those most likely to benefit from higher levels of care. We compared Community Assessment Tools (CATs), the CURB-65 score, and the Pandemic Medical Early Warning Score (PMEWS); to predict higher levels of care (high dependency--Level 2 or intensive care--Level 3) and/or death in patients at or shortly after admission to hospital with A/H1N1 2009 pandemic influenza. This was a case-control analysis using retrospectively collected data from the FLU-CIN cohort (1040 adults, 480 children) with PCR-confirmed A/H1N1 2009 influenza. Area under receiver operator curves (AUROC), sensitivity, specificity, positive predictive values and negative predictive values were calculated. CATs best predicted Level 2/3 admissions in both adults [AUROC (95% CI): CATs 0.77 (0.73, 0.80); CURB-65 0.68 (0.64, 0.72); PMEWS 0.68 (0.64, 0.73), p<0.001] and children [AUROC: CATs 0.74 (0.68, 0.80); CURB-65 0.52 (0.46, 0.59); PMEWS 0.69 (0.62, 0.75), p<0.001]. CURB-65 and CATs were similar in predicting death in adults with both performing better than PMEWS; and CATs best predicted death in children. CATs were the best predictor of Level 2/3 care and/or death for both adults and children. CATs are potentially useful triage tools for predicting need for higher levels of care and/or mortality in patients of all ages.

Predictors of clinical outcome in a national hospitalised cohort across both waves of the influenza A/H1N1 pandemic 2009-2010 in the UK.

BACKGROUND: Although generally mild, the 2009-2010 influenza A/H1N1 pandemic caused two major surges in hospital admissions in the UK. The characteristics of patients admitted during successive waves are described. METHODS: Data were systematically obtained on 1520 patients admitted to 75 UK hospitals between May 2009 and January 2010. Multivariable analyses identified factors predictive of severe outcome. RESULTS: Patients aged 5-54 years were over-represented compared with winter seasonal admissions for acute respiratory infection, as were non-white ethnic groups (first wave only). In the second wave patients were less likely to be school age than in the first wave, but their condition was more likely to be severe on presentation to hospital and they were more likely to have delayed admission. Overall, 45% had comorbid conditions, 16.5% required high dependency (level 2) or critical (level 3) care and 5.3% died. As in 1918-1919, the likelihood of severe outcome by age followed a W-shaped distribution. Pre-admission antiviral drug use decreased from 13.3% to 10% between the first and second waves (p=0.048), while antibiotic prescribing increased from 13.6% to 21.6% (p<0.001). Independent predictors of severe outcome were age 55-64 years, chronic lung disease (non-asthma, non-chronic obstructive pulmonary disease), neurological disease, recorded obesity, delayed admission (≥5 days after illness onset), pneumonia, C-reactive protein ≥100 mg/litre, and the need for supplemental oxygen or intravenous fluid replacement on admission. CONCLUSIONS: There were demographic, ethnic and clinical differences between patients admitted with pandemic H1N1 infection and those hospitalised during seasonal influenza activity. Despite national policies favouring use of antiviral drugs, few patients received these before admission and many were given antibiotics.

A new sentinel surveillance system for severe influenza in England shows a shift in age distribution of hospitalised cases in the post-pandemic period.

BACKGROUND: The World Health Organization and European Centre for Disease Prevention and Control have highlighted the importance of establishing systems to monitor severe influenza. Following the H1N1 (2009) influenza pandemic, a sentinel network of 23 Trusts, the UK Severe Influenza Surveillance System (USISS), was established to monitor hospitalisations due to confirmed seasonal influenza in England. This article presents the results of the first season of operation of USISS in 2010/11. METHODOLOGY/PRINCIPAL FINDINGS: A case was defined as a person hospitalised with confirmed influenza of any type. Weekly aggregate numbers of hospitalised influenza cases, broken down by flu type and level of care, were submitted by participating Trusts. Cases in 2010/11 were compared to cases during the 2009 pandemic in hospitals with available surveillance data for both time periods (n = 19). An unexpected resurgence in seasonal A/H1N1 (2009) influenza activity in England was observed in December 2010 with reports of severe disease. Reported cases over the period of 4 October 2010 to 13 February 2011 were mostly due to influenza A/H1N1 (2009). One thousand and seventy-one cases of influenza A/H1N1 (2009) occurred over this period compared to 409 at the same Trusts over the 2009/10 pandemic period (1 April 2009 to 6 January 2010). Median age of influenza A/H1N1 (2009) cases in 2010/11 was 35 years, compared with 20 years during the pandemic (p = <0.0001). CONCLUSIONS/SIGNIFICANCE: The Health Protection Agency successfully established a sentinel surveillance system for severe influenza in 2010/11, detecting a rise in influenza cases mirroring other surveillance indicators. The data indicate an upward shift in the age-distribution of influenza A/H1N1 (2009) during the 2010/11 influenza season as compared to the 2009/10 pandemic. Systems to enable the ongoing surveillance of severe influenza will be a key component in understanding and responding to the evolving epidemiology of influenza in the post-pandemic era.

Origin and fate of A/H1N1 influenza in Scotland during 2009.

The spread of influenza has usually been described by a 'density' model, where the largest centres of population drive the epidemic within a country. An alternative model emphasizing the role of air travel has recently been developed. We have examined the relative importance of the two in the context of the 2009 H1N1 influenza epidemic in Scotland. We obtained genome sequences of 70 strains representative of the geographical and temporal distribution of H1N1 influenza during the summer and winter phases of the pandemic in 2009. We analysed these strains, together with another 128 from the rest of the UK and 292 globally distributed strains, using maximum-likelihood phylogenetic and bayesian phylogeographical methods. This revealed strikingly different epidemic patterns within Scotland in the early and late parts of 2009. The summer epidemic in Scotland was characterized by multiple independent introductions from both international and other UK sources, followed by major local expansion of a single clade that probably originated in Birmingham. The winter phase, in contrast, was more diverse genetically, with several clades of similar size in different locations, some of which had no particularly close phylogenetic affinity to strains sampled from either Scotland or England. Overall there was evidence to support both models, with significant links demonstrated between North American sequences and those from England, and between England and East Asia, indicating that major air-travel routes played an important role in the pattern of spread of the pandemic, both within the UK and globally.

Sero-prevalence and incidence of A/H1N1 2009 influenza infection in Scotland in winter 2009-2010.

BACKGROUND: Sero-prevalence is a valuable indicator of prevalence and incidence of A/H1N1 2009 infection. However, raw sero-prevalence data must be corrected for background levels of cross-reactivity (i.e. imperfect test specificity) and the effects of immunisation programmes. METHODS AND FINDINGS: We obtained serum samples from a representative sample of 1563 adults resident in Scotland between late October 2009 and April 2010. Based on a microneutralisation assay, we estimate that 44% (95% confidence intervals (CIs): 40-47%) of the adult population of Scotland were sero-positive for A/H1N1 2009 influenza by 1 March 2010. Correcting for background cross-reactivity and for recorded vaccination rates by time and age group, we estimated that 34% (27-42%) were naturally infected with A/H1N1 2009 by 1 March 2010. The central estimate increases to >40% if we allow for imperfect test sensitivity. Over half of these infections are estimated to have occurred during the study period and the incidence of infection in late October 2009 was estimated at 4.3 new infections per 1000 people per day (1.2 to 7.2), falling close to zero by April 2010. The central estimate increases to over 5.0 per 1000 if we allow for imperfect test specificity. The rate of infection was higher for younger adults than older adults. Raw sero-prevalences were significantly higher in more deprived areas (likelihood ratio trend statistic = 4.92,1 df, P = 0.03) but there was no evidence of any difference in vaccination rates. CONCLUSIONS: We estimate that almost half the adult population of Scotland were sero-positive for A/H1N1 2009 influenza by early 2010 and that the majority of these individuals (except in the oldest age classes) sero-converted as a result of natural infection with A/H1N1 2009. Public health planning should consider the possibility of higher rates of infection with A/H1N1 2009 influenza in more deprived areas.

Nosocomial pandemic (H1N1) 2009, United Kingdom, 2009-2010.

To determine clinical characteristics of patients hospitalized in the United Kingdom with pandemic (H1N1) 2009, we studied 1,520 patients in 75 National Health Service hospitals. We characterized patients who acquired influenza nosocomially during the pandemic (H1N1) 2009 outbreak. Of 30 patients, 12 (80%) of 15 adults and 14 (93%) of 15 children had serious underlying illnesses. Only 12 (57%) of 21 patients who received antiviral therapy did so within 48 hours after symptom onset, but 53% needed escalated care or mechanical ventilation; 8 (27%) of 30 died. Despite national guidelines and standardized infection control procedures, nosocomial transmission remains a problem when influenza is prevalent. Health care workers should be routinely offered influenza vaccine, and vaccination should be prioritized for all patients at high risk. Staff should remain alert to the possibility of influenza in patients with complex clinical problems and be ready to institute antiviral therapy while awaiting diagnosis during influenza outbreaks.