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THE POWER OF CENTRAL GOVERNMENT IN BRITAIN INCREASED SIGNIFICANTLY IN THE INTER-WAR PERIOD, BUT DID BRITAIN BECOME BUREAUCRATIC? THIS ARTICLE EXAMINES THE EXPANSION OF THE CIVIL SERVICE AS A WHOLE THROUGH A DETAILED CASE STUDY OF ONE PARTICULAR DEPARTMENT (THE MINISTRY OF LABOUR) IN THE LIGHT OF TRADITIONAL CRITIQUES OF BUREAUCRACY. IT CONCLUDES THAT PREVIOUS OVERCONCENTRATION ON THE ROLE OF THE TREASURY HAS DISTORTED ANALYSIS. THE INTER-WAR CIVIL SERVICE BECAME BUREAUCRATIC IN TERMS NOT OF RATIONALITY, INEFFICIENCY OR POWER BUT ONLY OF SIZE AND INCREASING COMPLEXITY. IN THIS IT REFLECTED A GENERAL TREND IN BRITISH SOCIETY, WHICH ALSO AFFECTED INDUSTRY AND THE TRADE UNION MOVEMENT.

Energy demand models are central to the efforts of many governments to reduce carbon emissions from buildings. The lack of empirical research to ensure the appropriate use of predictions from the models has implications for building regulations and evaluating policy initiatives. We present three recent examples from the UK that highlight challenges: the discovery of a heat by-pass in party walls, trends in household gas consumption and the impact of condensing boilers, and inter-model variation in the non-domestic sector. We emphasise and contrast the approach of health sciences to support policy, and suggest that a far more systematic and integrated approach between empirical research, model development, and policy evaluation is needed.

This paper presents a novel method to analyse policy performance, using the example of legislation in the UK to require domestic boilers fitted since 1 April 2005 to be condensing. A technological uptake model based on the logistic equation is combined with four physical and economic models; Bayesian techniques are used for data analysis. Projections of energy savings are presented and the impact of different policy implementation dates investigated.

The UK Government has unveiled an ambitious retrofit programme that seeks significant improvement to the energy efficiency of the housing stock. High quality data on the energy efficiency of buildings and their related energy demand is critical to supporting and targeting investment in energy efficiency. Using existing home improvement programmes over the past 15 years, the UK Government has brought together data on energy efficiency retrofits in approximately 13 million homes into the Homes Energy Efficiency Database (HEED), along with annual metered gas and electricity use for the period of 2004–2007. This paper describes the HEED sample and assesses its representativeness in terms of dwelling characteristics, the energy demand of different energy performance levels using linked gas and electricity meter data, along with an analysis of the impact retrofit measures has on energy demand. Energy savings are shown to be associated with the installation of loft and cavity insulation, and glazing and boiler replacement. The analysis illustrates this source of 'in-action' data can be used to provide empirical estimates of impacts of energy efficiency retrofit on energy demand and provides a source of empirical data from which to support the development of national housing energy efficiency retrofit policies.

Little detailed evidence has previously been available regarding the uptake rate or prevalence of energy efficiency interventions among specific household groups. This study uses the Home Energy Efficiency Database (HEED) to investigate both the combination of measures that have been installed, and in which dwellings, according to key neighbourhood socio-demographic variables, including income and tenure. Analysis of 2000–07 data indicates that approximately 40% (9.3 million) dwellings in England had approximately 23.7 million efficiency measures installed, with an average of 2.5 measures per dwelling. Building fabric-related measures were the most frequent (e.g. cavity wall insulation, loft insulation and glazing) with an average of 2.1 million installed each year. Dwellings with the highest number of fabric interventions (the top 20%) were more likely to be found in areas with low income, with more owner-occupied dwellings, experiencing lower winter temperatures, having a lower proportion of flats, and having a slightly higher proportion of older adults and children. Energy efficiency installations have tended to occur among specific types of households or parts of the building stock. These findings have implications for the design of future government programmes for targeting energy efficiency measures to specific household groups or dwelling types.

A moderately-developed tidal creek feeding the U.S. Atlantic Intracoastal Waterway was closed to shellfishing for a number of years because of high fecal coliform counts. An investigation was unable to determine an anthropogenic source for the contamination. Because dung piles and other wild animal signs were concentrated in the most affected area of the creek animals were assumed to be a major source of the coliform pollution. Over the years a sandbar had accumulated across the mouth of the creek, impeding flow into the creek. A coalition consisting of a concerned citizen's group, local government, and university scientists obtained permits to conduct limited creek-mouth dredging to improve flushing and increase salinity in polluted areas. Following dredging, fecal coliform counts decreased 35 to 60% creek-wide, with greatest decreases in stations midway up the creek. Nitrate concentrations decreased significantly and turbidity increased significantly at two creek stations. A continuous monitor located midway up the creek recorded higher salinities and less intertidal salinity variability following dredging than before dredging. The North Carolina Division of Marine Fisheries reopened the creek to shellfishing a year after dredging. While dredging proved to lower fecal coliform counts to acceptable levels, it did not remove the source of contamination; rather, it alleviated the symptoms.

Reducing space heating energy demand supports the UK's legislated carbon emission reduction targets and requires the effective characterisation of the UK's existing housing stock to facilitate retrofitting decision-making. Approximately 6.6 million UK dwellings pre-date 1919 and are predominantly of suspended timber ground floor construction, the thermal performance of which has not been extensively investigated. This paper examines suspended timber ground floor heat-flow by presenting high resolution in-situ heat-flux measurements undertaken in a case study house at 15 point locations on the floor. The results highlight significant variability in observed heat-flow: point U-values range from 0.56 ± 0.05 to 1.18 ± 0.11 Wm−2 K−1. This highlights that observing only a few measurements is unlikely to be representative of the whole floor heat-flow and the extrapolation from such point values to whole floor U-value estimates could lead to its over- or under- estimation. Floor U-value models appear to underestimate the actual measured floor U-value in this case study. This paper highlights the care with which in-situ heat-flux measuring must be undertaken to enable comparison with models, literature and between studies and the findings support the unique, high-resolution in-situ monitoring methodology used in this study for further research in this area.

© The Author(s) 2021. There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission. ; D.R. was supported by a postdoctoral research fellowship of the Xunta de Galicia (Spain). A.G. was funded by the Medical Research Council-UK (Grant ID: MR/R013349/1), the Natural Environment Research Council UK (Grant ID: NE/R009384/1) and the European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). R.L. was supported by a Royal Society Dorothy Hodgkin Fellowship. S.A. and S.M. were funded by the Wellcome Trust (grant 210758/Z/18/Z210758/Z/18/Z). The following funding sources are acknowledged as providing funding for the MCC Collaborative Research Network authors: J.K. and A.U. were supported by the Czech Science Foundation, project 18-22125S. S.T. was supported by the Shanghai Municipal Science and Technology Commission (Grant 18411951600). N.S. is supported by the NIEHS-funded HERCULES Center (P30ES019776). H.K. was supported by the National Research Foundation of Korea (BK21 Center for Integrative Response to Health Disasters, Graduate School of Public Health, Seoul National University). A.S., F.D.R. and S.R. were funded by the European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). Each member of the CMMID COVID-19 Working Group contributed to processing, cleaning and interpretation of data, interpreted findings, contributed to the manuscript and approved the work for publication. The following funding sources are acknowledged as providing funding for the CMMID COVID-19 working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: M.Q; INV-003174: K.P., M.J., Y.L., J.L.; NTD Modelling Consortium OPP1184344: C.A.B.P., G.M.; OPP1180644: S.R.P.; OPP1183986: E.S.N.). BMGF (OPP1157270: K.E.A.). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: C.A.B.P.). EDCTP2 (RIA2020EF-2983-CSIGN: H.P.G.). ERC Starting Grant (#757699: M.Q.). This project has received funding from the European Union's Horizon 2020 research and innovation programme—project EpiPose (101003688: K.P., M.J., P.K., R.C.B., W.J.E., Y.L.). This research was partly funded by the Global Challenges Research Fund (GCRF) project 'RECAP' managed through RCUK and ESRC (ES/P010873/1: A.G., C.I.J., T.J.). HDR UK (MR/S003975/1: R.M.E.). MRC (MR/N013638/1: N.R.W.; MR/V027956/1: W.W.). Nakajima Foundation (A.E.). This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care (16/136/46: B.J.Q.; 16/137/109: B.J.Q., F.Y.S., M.J., Y.L.; Health Protection Research Unit for Immunisation NIHR200929: N.G.D.; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: T.J.; NIHR200908: R.M.E.; NIHR200929: F.G.S., M.J.; PR-OD-1017-20002: A.R., W.J.E.). Royal Society (Dorothy Hodgkin Fellowship: R.L.; RP\EA\180004: P.K.). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: H.P.G.). UK MRC (MC_PC_19065—Covid 19: Understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: A.G., N.G.D., R.M.E., S.C., T.J., W.J.E., Y.L.; MR/P014658/1: G.M.K.). Authors of this research receive funding from the UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care (T.J.). Wellcome Trust (206250/Z/17/Z: A.J.K., T.W.R.; 206471/Z/17/Z: O.B.; 208812/Z/17/Z: S.C.; 210758/Z/18/Z: J.D.M., J.H., N.I.B.; UNS110424: F.K.). No funding (A.M.F., A.S., C.J.V.-A., D.C.T., J.W., K.E.A., Y.-W.D.C.). LSHTM, DHSC/UKRI COVID-19 Rapid Response Initiative (MR/V028456/1: Y.L.). Innovation Fund of the Joint Federal Committee (01VSF18015: F.K.). Foreign, Commonwealth and Development Office/Wellcome Trust (221303/Z/20/Z: M.K.).

Abstract There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th-95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.

Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology. ; C.E.B. was supported by a PhD fellowship from the EU-FP7 project EpiTrain (316758). J.H. was supported by the UCL Cancer Institute Research Trust. V.K.R. was supported by BLUEPRINT (282510). K.D. was funded as a HSST trainee by NHS Health Education England. M.F. was supported by the BHF Cambridge Centre of Excellence (RE/13/6/30180). Research in W.H.O.'s laboratory was supported by EU-FP7 project BLUEPRINT (282510) and by program grants from the National Institute for Health Research (NIHR, http://www.nihr.ac.uk) and the British Heart Foundation under numbers RP-PG-0310-1002 and RG/09/12/28096 (https://www.bhf.org.uk/). W.H.O.'s laboratory receives funding from NHS Blood and Transplant for facilities. We gratefully acknowledge the participation of all NIHR Cambridge BioResource volunteers. We thank the Cambridge BioResource staff for their help with volunteer recruitment. We thank members of the Cambridge BioResource SAB and Management Committee for their support of our study and the National Institute for Health Research Cambridge Biomedical Research Centre for funding. R.S. and his group were supported by the European Union in the framework of the BLUEPRINT Project (HEALTH-F5-2011-282510) and the German Ministry of Science and Education (BMBF) in the framework of the MMML-MYC-SYS project (036166B). We thank Deborah Winter (Weizmann Institute) for supplying a set of microglial enhancers from Lavin et al. (2014). Research in S.B.'s laboratory was supported by the Wellcome Trust (99148), Royal Society Wolfson Research Merit Award (WM100023), and EU-FP7 projects EpiTrain (316758), EPIGENESYS (257082), and BLUEPRINT (282510).