Between 7 and 25 May, 86 monkeypox cases were confirmed in the United Kingdom (UK). Only one case is known to have travelled to a monkeypox virus (MPXV) endemic country. Seventy-nine cases with information were male and 66 reported being gay, bisexual, or other men who have sex with men. This is the first reported sustained MPXV transmission in the UK, with human-to-human transmission through close contacts, including in sexual networks. Improving case ascertainment and onward-transmission preventive measures are ongoing.

Since April 2015, whole genome sequencing (WGS) has been the routine test for Salmonella identification, surveillance and outbreak investigation at the national reference laboratory in England and Wales. In May 2015, an outbreak of Salmonella Enteritidis cases was detected using WGS data and investigated. UK cases were interviewed to obtain a food history and links between suppliers were mapped to produce a food chain network for chicken eggs. The association between the food chain network and the phylogeny was explored using a network comparison approach. Food and environmental samples were taken from premises linked to cases and tested for Salmonella. Within the outbreak single nucleotide polymorphism defined cluster, 136 cases were identified in the UK and 18 in Spain. One isolate from a food containing chicken eggs was within the outbreak cluster. There was a significant association between the chicken egg food chain of UK cases and phylogeny of outbreak isolates. This is the first published Salmonella outbreak to be prospectively detected using WGS. This outbreak in the UK was linked with contemporaneous cases in Spain by WGS. We conclude that UK and Spanish cases were exposed to a common source of Salmonella-contaminated chicken eggs.

Background: Households appear to be the highest risk setting for transmission of COVID-19. Large household transmission studies were reported in the early stages of the pandemic in Asia with secondary attack rates ranging from 5-30% but few large scale household transmission studies have been conducted outside of Asia. Methods: A prospective case ascertained study design based on the World Health Organization FFX protocol was undertaken in the UK following the detection of the first case in late January 2020. Household contacts of cases were followed using enhanced surveillance forms to establish whether they developed symptoms of COVID-19, became confirmed cases and their outcomes. Household secondary attack rates and serial intervals were estimated. Individual and household basic reproduction numbers were also estimated. The incubation period was estimated using known point source exposures that resulted in secondary cases. Results: A total of 233 households with two or more people were included with a total of 472 contacts. The overall household SAR was 37% (95% CI 31-43%) with a mean serial interval of 4.67 days, an R0 of 1.85 and a household reproduction number of 2.33. We find lower secondary attack rates in larger households. SARs were highest when the primary case was a child. We estimate a mean incubation period of around 4.5 days. Conclusions: High rates of household transmission of COVID-19 were found in the UK emphasising the need for preventative measures in this setting. Careful monitoring of schools reopening is needed to monitor transmission from children.

Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more.

Abstract The COVID-19 pandemic is exerting major pressures on society, health and social care services and science. Understanding the progression and current impact of the pandemic is fundamental to planning, management and mitigation of future impact on the population. Surveillance is the core function of any public health system, and a multi-component surveillance system for COVID-19 is essential to understand the burden across the different strata of any health system and the population. Many countries and public health bodies utilise ‘syndromic surveillance’ (using real-time, often non-specific symptom/preliminary diagnosis information collected during routine healthcare provision) to supplement public health surveillance programmes. The current COVID-19 pandemic has revealed a series of unprecedented challenges to syndromic surveillance including: the impact of media reporting during early stages of the pandemic; changes in healthcare-seeking behaviour resulting from government guidance on social distancing and accessing healthcare services; and changes in clinical coding and patient management systems. These have impacted on the presentation of syndromic outputs, with changes in denominators creating challenges for the interpretation of surveillance data. Monitoring changes in healthcare utilisation is key to interpreting COVID-19 surveillance data, which can then be used to better understand the impact of the pandemic on the population. Syndromic surveillance systems have had to adapt to encompass these changes, whilst also innovating by taking opportunities to work with data providers to establish new data feeds and develop new COVID-19 indicators. These developments are supporting the current public health response to COVID-19, and will also be instrumental in the continued and future fight against the disease.