Legionnaires’ disease, a form of pneumonia which can be fatal, is transmitted via the inhalation of water droplets containing Legionella bacteria. These droplets can be dispersed in the atmosphere several kilometers from their source. The most common such sources are contaminated water within cooling towers and other air-conditioning systems but other sources such as ornamental fountains and spa pools have also caused outbreaks of the disease in the past. There is an obvious need to locate and eliminate any such sources as quickly as possible. Here a maximum likelihood model estimating the source of an outbreak from case location data has been developed and implemented. Unlike previous models, the average dose exposure sub-model is formulated using a atmospheric dispersion model. How the uncertainty in inferred parameters can be estimated is discussed. The model is applied to the 2012 Edinburgh Legionnaires’ disease outbreak.

Understanding the scale of the threat posed by SARS-CoV2 B.1.1.529, or Omicron , variant formed a key problem in public health in the early part of 2022. Early evidence indicated that the variant was more transmissible and less severe than previous variants. As the virus was expected to spread quickly through the population of England, it was important that some understanding of the immunological landscape of the country was developed. This paper attempts to estimate the number of people with good immunity to the Omicron variant, defined as either recent infection with two doses of vaccine, or two doses of vaccine with a recent booster dose. To achieve this, we use a process of iterative proportional fitting to estimate the cell values of a contingency table, using national immunisation records and real-time model infection estimates as marginal values. Our results indicate that, despite the increased risk of immune evasion with the Omicron variant, a high proportion of England’s population had good immunity to the virus, particularly in older age groups. However, low rates of immunity in younger populations may allow endemic infection to persist for some time.

The roll-out of COVID-19 vaccines in the England has generally been very good – with over 80% of over 12s having received two doses of vaccine by the start of February 2022, and 67% having received a further booster dose. Despite this, there is a small section of the population who remain unvaccinated, either due to lack of access, hesitancy or resistant to vaccination. In this report we estimate that, during 2021, there were approximately 3,500 deaths in unvaccinated people, who could otherwise have reasonably been expected to receive a vaccination. Further, we show that if all of the remaining unvaccinated population in England were to become infected (or reinfected) with the Omicron variant of COVID-19, we would expect to see approximately 11,700 further deaths and 29,600 hospitalisations. These number could fall to 5,300 and 19,600 respectively, if all but the most vaccine resistant individuals become fully vaccinated.

Reverse epidemiology is a mathematical modelling tool used to ascertain information about the source of a pathogen, given the spatial and temporal distribution of cases, hospitalisations and deaths. In the context of a deliberately released pathogen, such asBacillus anthracis(the disease-causing organism of anthrax), this can allow responders to quickly identify the location and timing of the release, as well as other factors such as the strength of the release, and the realized wind speed and direction at release. These estimates can then be used to parameterise a predictive mechanistic model, allowing for estimation of the potential scale of the release, and to optimise the distribution of prophylaxis. In this paper we present two novel approaches to reverse epidemiology, and demonstrate their utility in responding to a simulated deliberate release ofB. anthracisin ten locations in the UK and compare these to the standard grid-search approach. The two methods - a modified MCMC and a Recurrent Convolutional Neural Network - are able to identify the source location and timing of the release with significantly better accuracy compared to the grid-search approach. Further, the neural network method is able to do inference on new data significantly quicker than either the grid-search or novel MCMC methods, allowing for rapid deployment in time-sensitive outbreaks.

Understanding the scale of the threat posed by SARS-CoV2 B.1.1.529, or Omicron, variant formed a key problem in public health in the early part of 2022. Early evidence indicated that the variant was more transmissible and less severe than previous variants. As the virus was expected to spread quickly through the population of England, it was important that some understanding of the immunological landscape of the country was developed. This paper attempts to estimate the number of people with good immunity to the Omicron variant, defined as either recent infection with two doses of vaccine, or two doses of vaccine with a recent booster dose. To achieve this, we use a process of iterative proportional fitting to estimate the cell values of a contingency table, using national immunisation records and real-time model infection estimates as marginal values. Our results indicate that, despite the increased risk of immune evasion with the Omicron variant, a high proportion of England’s population had good immunity to the virus, particularly in older age groups. However, low rates of immunity in younger populations may allow endemic infection to persist for some time.

ObjectiveTo determine if the sensitivity of the lateral flow test is dependent on the viral load and on the location of swabbing in the respiratory tract in children.DesignPhase 1: Routinely performed reverse transcriptase PCR (RT-PCR) using nose and throat (NT) swabs or endotracheal (ET) aspirates were compared with Innova lateral flow tests (LFTs) using anterior nasal (AN) swabs. Phase 2: RT-PCR-positive children underwent paired AN RT-PCR and LFT and/or paired AN RT-PCR and buccal LFT.SettingTertiary paediatric hospitals.PatientsChildren under the age of 18 years. Phase 1: undergoing routine testing, phase 2: known SARS-CoV-2 positive.ResultsPhase 1: 435 paired swabs taken in 431 asymptomatic patients resulted in 8 positive RT-PCRs, 9 PCR test failures and 418 negative RT-PCRs from NT or ET swabs. The test performance of AN LFT demonstrated sensitivity: 25% (4%–59%), specificity: 100% (99%–100%), positive predictive value (PPV): 100% (18%–100%) and negative predictive value (NPV): 99% (97%–99%).Phase 2: 14 AN RT-PCR-positive results demonstrated a sensitivity of 77% (50%–92%) of LFTs performed on AN swabs. 15/16 paired buccal LFT swabs were negative.ConclusionThe NPV, PPV and specificity of LFTs are excellent. The sensitivity of LFTs compared with RT-PCR is good when the samples are colocated but may be reduced when the LFT swab is taken from the AN. Buccal swabs are not appropriate for LFT testing. Careful consideration of the swabbing reason, the tolerance of the child and the requirements for test processing (eg, rapidity of results) should be undertaken within hospital settings.Trial registration numberNCT04629157.