Author Correction: Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Public involvement in research about environmental change and health: A case study.

Involving and engaging the public are crucial for effective prioritisation, dissemination and implementation of research about the complex interactions between environments and health. Involvement is also important to funders and policy makers who often see it as vital for building trust and justifying the investment of public money. In public health research, 'the public' can seem an amorphous target for researchers to engage with, and the short-term nature of research projects can be a challenge. Technocratic and pedagogical approaches have frequently met with resistance, so public involvement needs to be seen in the context of a history which includes contested truths, power inequalities and political activism. It is therefore vital for researchers and policy makers, as well as public contributors, to share best practice and to explore the challenges encountered in public involvement and engagement. This article presents a theoretically informed case study of the contributions made by the Health and Environment Public Engagement Group to the work of the National Institute for Health Research (NIHR) Health Protection Research Unit in Environmental Change and Health (HPRU-ECH). We describe how Health and Environment Public Engagement Group has provided researchers in the HPRU-ECH with a vehicle to support access to public views on multiple aspects of the research work across three workshops, discussion of ongoing research issues at meetings and supporting dissemination to local government partners, as well as public representation on the HPRU-ECH Advisory Board. We conclude that institutional support for standing public involvement groups can provide conduits for connecting public with policy makers and academic institutions. This can enable public involvement and engagement, which would be difficult, if not impossible, to achieve in individual short-term and unconnected research projects.

Pathogen seasonality and links with weather in England and Wales: a big data time series analysis.

BACKGROUND: Many infectious diseases of public health importance display annual seasonal patterns in their incidence. We aimed to systematically document the seasonality of several human infectious disease pathogens in England and Wales, highlighting those organisms that appear weather-sensitive and therefore may be influenced by climate change in the future. METHODS: Data on infections in England and Wales from 1989 to 2014 were extracted from the Public Health England (PHE) SGSS surveillance database. We conducted a weekly, monthly and quarterly time series analysis of 277 pathogen serotypes. Each organism's time series was forecasted using the TBATS package in R, with seasonality detected using model fit statistics. Meteorological data hosted on the MEDMI Platform were extracted at a monthly resolution for 2001-2011. The organisms were then clustered by K-means into two groups based on cross correlation coefficients with the weather variables. RESULTS: Examination of 12.9 million infection episodes found seasonal components in 91/277 (33%) organism serotypes. Salmonella showed seasonal and non-seasonal serotypes. These results were visualised in an online Rshiny application. Seasonal organisms were then clustered into two groups based on their correlations with weather. Group 1 had positive correlations with temperature (max, mean and min), sunshine and vapour pressure and inverse correlations with mean wind speed, relative humidity, ground frost and air frost. Group 2 had the opposite but also slight positive correlations with rainfall (mm, > 1 mm, > 10 mm). CONCLUSIONS: The detection of seasonality in pathogen time series data and the identification of relevant weather predictors can improve forecasting and public health planning. Big data analytics and online visualisation allow the relationship between pathogen incidence and weather patterns to be clarified.