No abstract

The recent emergence of the SARS-CoV-2 in China has raised the spectre of a novel, potentially catastrophic pandemic in both scientific and lay communities throughout the world. In this particular context, people have been accused of being excessively pessimistic regarding the future consequences of this emerging health threat. However, consistent with previous research in social psychology, a large survey conducted in Europe in the early stage of the COVID-19 epidemic shows that the majority of respondents was actually overly optimistic about the risk of infection.

Objective To evaluate ascertainment of the onset of community transmission of influenza A/H1N1 2009 (swine flu) in England during the earliest phase of the epidemic through comparing data from two surveillance systems.Design Cross sectional opportunistic survey.Study samples Results from self samples by consenting patients who had called the NHS Direct telephone health line with cold or flu symptoms, or both, and results from Health Protection Agency (HPA) regional microbiology laboratories on patients tested according to the clinical algorithm for the management of suspected cases of swine flu.Setting Six regions of England between 24 May and 30 June 2009. Main outcome measure Proportion of specimens with laboratory evidence of influenza A/H1N1 2009.Results Influenza A/H1N1 2009 infections were detected in 91 (7%) of the 1385 self sampled specimens tested. In addition, eight instances of influenza A/H3 infection and two cases of influenza B infection were detected. The weekly rate of change in the proportions of infected individuals according to self obtained samples closely matched the rate of increase in the proportions of infected people reported by HPA regional laboratories. Comparing the data from both systems showed that local community transmission was occurring in London and the West Midlands once HPA regional laboratories began detecting 100 or more influenza A/H1N1 2009 infections, or a proportion positive of over 20% of those tested, each week.Conclusions Trends in the proportion of patients with influenza A/H1N1 2009 across regions detected through clinical management were mirrored by the proportion of NHS Direct callers with laboratory confirmed infection. The initial concern that information from HPA regional laboratory reports would be too limited because it was based on testing patients with either travel associated risk or who were contacts of other influenza cases was unfounded. Reports from HPA regional laboratories could be used to recognise the extent to which local community transmission was occurring.

The emergence and spread of gonorrhoea with reduced susceptibility to ceftriaxone seems a realistic prospect, most likely in those involved in 'rapid-transmission' or bridging sexual networks.

Background The full reopening of schools in September 2020 was associated with an increase in COVID-19 cases and outbreaks in educational settings across England. Methods Primary and secondary schools reporting an outbreak (≥2 laboratory-confirmed cases within 14 days) to Public Health England (PHE) between 31 August and 18 October 2020 were contacted in November 2020 to complete an online questionnaire. Interpretation There were 969 school outbreaks reported to PHE, comprising 2% ( n = 450) of primary schools and 10% ( n = 519) of secondary schools in England. Of the 369 geographically-representative schools contacted, 179 completed the questionnaire (100 primary schools, 79 secondary schools) and 2,314 cases were reported. Outbreaks were larger and across more year groups in secondary schools than in primary schools. Teaching staff were more likely to be the index case in primary (48/100, 48%) than secondary (25/79, 32%) school outbreaks ( P = 0.027). When an outbreak occurred, attack rates were higher in staff (881/17,362; 5.07; 95%CI, 4.75–5.41) than students, especially primary school teaching staff (378/3852; 9.81%; 95%CI, 8.90–10.82%) compared to secondary school teaching staff (284/7146; 3.97%; 95%CI, 3.79–5.69%). Secondary school students (1105/91,919; 1.20%; 95%CI, 1.13–1.28%) had higher attack rates than primary school students (328/39,027; 0.84%; 95%CI, 0.75–0.94%). Conclusions A higher proportion of secondary schools than primary schools reported a COVID-19 outbreak and experienced larger outbreaks across multiple school year groups. The higher attack rate among teaching staff during an outbreak, especially in primary schools, suggests that additional protective measures may be needed. Funding PHE

Although gonococcal isolates were available from almost half of the NAAT-positive patients, culture was not attempted or may have failed in the remainder. Patients with culture-positive isolates were not representative of all NAAT-positive patients. Routine culture is necessary for monitoring emerging antimicrobial resistance and to inform gonorrhoea treatment guidelines.

Unrealistic optimism, the underestimation of one’s risk of experiencing harm, has been investigated extensively to understand better and predict behavioural responses to health threats. Prior to the COVID-19 pandemic, a relative dearth of research existed in this domain regarding epidemics, which is surprising considering that this optimistic bias has been associated with a lack of engagement in protective behaviours critical in fighting twenty-first-century, emergent, infectious diseases. The current study addresses this gap in the literature by investigating whether people demonstrated optimism bias during the first wave of the COVID-19 pandemic in Europe, how this changed over time, and whether unrealistic optimism was negatively associated with protective measures. Taking advantage of a pre-existing international participative influenza surveillance network (n = 12,378), absolute and comparative unrealistic optimism were measured at three epidemic stages (pre-, early, peak), and across four countries—France, Italy, Switzerland and the United Kingdom. Despite differences in culture and health response, similar patterns were observed across all four countries. The prevalence of unrealistic optimism appears to be influenced by the particular epidemic context. Paradoxically, whereas absolute unrealistic optimism decreased over time, comparative unrealistic optimism increased, suggesting that whilst people became increasingly accurate in assessing their personal risk, they nonetheless overestimated that for others. Comparative unrealistic optimism was negatively associated with the adoption of protective behaviours, which is worrying, given that these preventive measures are critical in tackling the spread and health burden of COVID-19. It is hoped these findings will inspire further research into sociocognitive mechanisms involved in risk appraisal.

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes . The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.