Browsing by Author "Edmunds, John"
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- Pregnancy during COVID-19: social contact patterns and vaccine coverage of pregnant women from CoMix in 19 European countriesPublication . Wong, Kerry L.M.; Gimma, Amy; Paixao, Enny; Paolotti, Daniela; Karch, André; Jäger, Veronika; Baruch, Joaquin; Melillo, Tanya; Hudeckova, Henrieta; Rosińska-Bukowska, Magdalena; Niedźwiedzka-Stadnik, Marta; Fischer, Krista; Vorobjov, Sigrid; Sõnajalg, Hanna; Althaus, Christian; Low, Nicola; Reichmuth, Martina L.; Auranen, Kari; Nurhonen, Markku; Petrović, Goranka; Makaric, Zvjezdana Lovric; Namorado, Sónia; Caetano, Constantino; Santos, Ana João; Röst, Gergely; Oroszi, Beatrix; Karsai, Márton; Fafangel, Mario; Klepac, Petra; Kranjec, Natalija; Vilaplana, Cristina; Casabona-Barbarà, Jordi; FAES, Christel; Beutels, Philippe; Hens, Niel; Jarvis, Christopher; Edmunds, JohnBackground: Evidence and advice for pregnant women evolved during the COVID-19 pandemic. We studied social contact behaviour and vaccine uptake in pregnant women between March 2020 and September 2021 in 19 European countries. Methods: In each country, repeated online survey data were collected from a panel of nationally-representative participants. We calculated the adjusted mean number of contacts reported with an individual-level generalized additive mixed model, modelled using the negative binomial distribution and a log link function. Mean proportion of people in isolation or quarantine, and vaccination coverage by pregnancy status and gender were calculated using a clustered bootstrap. Findings: We recorded 4,129 observations from 1,041 pregnant women, and 115,359 observations from 29,860 non-pregnant individuals aged 18-49. Pregnant women made slightly fewer contacts (3.6, 95%CI = 3.5-3.7) than non-pregnant women (4.0, 95%CI = 3.9-4.0), driven by fewer work contacts but marginally more contacts in non-essential social settings. Approximately 15-20% pregnant and 5% of non-pregnant individuals reported to be in isolation and quarantine for large parts of the study period. COVID-19 vaccine coverage was higher in pregnant women than in non-pregnant women between January and April 2021. Since May 2021, vaccination in non-pregnant women began to increase and surpassed that in pregnant women. Interpretation: Limited social contact to avoid pathogen exposure during the COVID-19 pandemic has been a challenge to many, especially women going through pregnancy. More recognition of maternal social support desire is needed in the ongoing pandemic. As COVID-19 vaccination continues to remain an important pillar of outbreak response, strategies to promote correct information can provide reassurance and facilitate informed pregnancy vaccine decisions in this vulnerable group.
- Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptomsPublication . Kalimeri, Kyriaki; Delfino, Matteo; Cattuto, Ciro; Perrotta, Daniela; Colizza, Vittoria; Guerrisi, Caroline; Turbelin, Clement; Duggan, Jim; Edmunds, John; Obi, Chinelo; Pebody, Richard; Franco, Ana O.; Moreno, Yamir; Meloni, Sandro; Koppeschaar, Carl; Kjelsø, Charlotte; Mexia, Ricardo; Paolotti, DanielaAbstract: 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.