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Infectious disease modelling has played an integral part of the scientific evidence used to guide the response to the COVID-19 pandemic. In the UK, modelling evidence used for policy is reported to the Scientific Advisory Group for Emergencies (SAGE) modelling subgroup, SPI-M-O (Scientific Pandemic Influenza Group on Modelling-Operational). This Special Issue contains 20 articles detailing evidence that underpinned advice to the UK government during the SARS-CoV-2 pandemic in the UK between January 2020 and July 2020. Here, we introduce the UK scientific advisory system and how it operates in practice, and discuss how infectious disease modelling can be useful in policy making. We examine the drawbacks of current publishing practices and academic credit and highlight the importance of transparency and reproducibility during an epidemic emergency. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Intro -- Foreword -- Contents -- 1 Introduction -- 1.1 Intended Readership -- 1.2 Evolutions of ade4 -- 1.2.1 The ade4 Add-On Package for R -- 1.2.2 Previous Versions of ade4 -- 1.3 Using ade4 -- 1.3.1 Computer Hardware -- 1.3.2 Installing R -- 1.3.3 Installing ade4 -- 1.3.4 Dependencies -- 1.3.5 Packages of the ade4 Family -- 1.3.5.1 adegraphics -- 1.3.5.2 ade4TkGUI -- 1.3.5.3 adespatial -- 1.3.5.4 adephylo -- 1.3.6 Version of the Packages Used in This Book -- 1.3.7 The adelist Forum -- 1.3.8 Using the Help System -- 1.4 Interactive Code Snippets -- 1.5 Ecological Data Sets -- 2 Useful R Functions and Data Structures -- 2.1 Introduction -- 2.2 Basic Data Import and Export Functions -- 2.2.1 read.table -- 2.2.2 write.table -- 2.2.3 Data -- 2.3 Vectors -- 2.4 Data Frames -- 2.4.1 Dimensions -- 2.4.2 Row and Column Names -- 2.4.3 Accessing Data Frame Elements -- 2.4.4 Row and Column Sums and Means -- 2.4.5 Row and Column Selection -- 2.4.6 Changing Values -- 2.4.7 Missing Values in Data Frames -- 2.4.8 Data Transformation -- 2.4.9 Apply -- 2.4.10 Summary -- 2.4.11 Other Functions -- 2.5 Factors -- 2.5.1 Using Factors -- 2.5.2 Generating Factors -- 2.5.3 Re-ordering Levels -- 3 The dudi Class -- 3.1 Introduction -- 3.2 Principles of Multivariate Analysis -- 3.3 Structure -- 3.4 Functions -- 3.5 Elements of dudi Objects -- 3.5.1 pca1tab -- 3.5.2 pca1cw and pca1lw -- 3.5.3 pca1eig, pca1rank and pca1nf -- 3.5.4 pca1c1, pca1l1, pca1co and pca1li -- 3.5.5 pca1cent and pca1norm -- 3.6 Using dudi Objects -- 3.6.1 The print and summary Functions -- 3.6.2 The scatter and biplot Functions -- 3.6.3 The score Function -- 3.6.4 The s.label and plot Functions -- 3.6.5 The inertia Function -- 3.6.6 Other Graphical Functions -- 3.7 Exporting dudi Elements -- 4 Multivariate Analysis Graphs -- 4.1 Introduction -- 4.2 Basics of adegraphics.

COVID-19 is but one of many public health crises facing the people of the Democratic Republic of the Congo (DRC). On 25 June 2020, the DRC government announced the end of the country's largest Ebola outbreak on record and the second largest Ebola outbreak worldwide, a mere few weeks after a new outbreak (11th) started on 1 June 2020, in Mbandaka, Equateur Province.1 In 2019, measles claimed the lives of over 6000 people including 4500 children under the age of 5, malaria killed 17000 individuals, and cholera outbreaks affected 20 of 26 provinces, resulting in 31000 cases.

The tenth outbreak of Ebola virus disease (EVD) in North Kivu, the Democratic Republic of the Congo (DRC), was declared 8 days after the end of the ninth EVD outbreak, in the Equateur Province on August 1, 2018. With a total of 3,461 confirmed and probable cases, the North Kivu outbreak was the second largest outbreak after that in West Africa in 2014-2016, and the largest observed in the DRC. This outbreak was difficult to control because of multiple challenges, including armed conflict, population displacement, movement of contacts, community mistrust, and high population density. It took more than 21 months to control the outbreak, with critical innovations and systems put into place. We describe systems that were put into place during the EVD response in the DRC that can be leveraged for the response to the current COVID-19 global pandemic.

The authors acknowledge financial support from the UKCRC Translational Infection Research (TIR) Initiative and the Medical Research Council (Grant number G1000803), with contributions to the grant from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate (to Professor Peacock); from Wellcome Trust grant number 098051 awarded to the Wellcome Trust Sanger Institute; and the NIHR Cambridge Biomedical Research Centre (to Professor Peacock). S.Y.C.T. is an Australian National Health and Medical Research Council Career Development Fellow (1065736). ; Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial infection. Whole-genome sequencing of MRSA has been used to define phylogeny and transmission in well-resourced healthcare settings, yet the greatest burden of nosocomial infection occurs in resource-restricted settings where barriers to transmission are lower. Here, we study the flux and genetic diversity of MRSA on ward and individual patient levels in a hospital where transmission was common. We repeatedly screened all patients on two intensive care units for MRSA carriage over a 3-mo period. All MRSA belonged to multilocus sequence type 239 (ST 239). We defined the population structure and charted the spread of MRSA by sequencing 79 isolates from 46 patients and five members of staff, including the first MRSA-positive screen isolates and up to two repeat isolates where available. Phylogenetic analysis identified a flux of distinct ST 239 clades over time in each intensive care unit. In total, five main clades were identified, which varied in the carriage of plasmids encoding antiseptic and antimicrobial resistance determinants. Sequence data confirmed intra- and interwards transmission events and identified individual patients who were colonized by more than one clade. One patient on each unit was the source of numerous transmission events, and deep sampling of one of these cases demonstrated colonization with a "cloud" of related MRSA variants. The application of whole-genome sequencing and analysis provides novel insights into the transmission of MRSA in under-resourced healthcare settings and has relevance to wider global health. ; Publisher PDF ; Peer reviewed

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018–2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018-2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.

The ongoing Ebola outbreak in the eastern Democratic Republic of the Congo is facing unprecedented levels of insecurity and violence. We evaluate the likely impact in terms of added transmissibility and cases of major security incidents in the Butembo coordination hub. We also show that despite this additional burden, an adapted response strategy involving enlarged ring vaccination around clusters of cases and enhanced community engagement managed to bring this main hotspot under control.

BACKGROUND: To mitigate SARS-CoV-2 transmission risks from international air travellers, many countries implemented a combination of up to 14 days of self-quarantine upon arrival plus PCR testing in the early stages of the COVID-19 pandemic in 2020. AIM: To assess the effectiveness of quarantine and testing of international travellers to reduce risk of onward SARS-CoV-2 transmission into a destination country in the pre-COVID-19 vaccination era. METHODS: We used a simulation model of air travellers arriving in the United Kingdom from the European Union or the United States, incorporating timing of infection stages while varying quarantine duration and timing and number of PCR tests. RESULTS: Quarantine upon arrival with a PCR test on day 7 plus a 1-day delay for results can reduce the number of infectious arriving travellers released into the community by a median 94% (95% uncertainty interval (UI): 89–98) compared with a no quarantine/no test scenario. This reduction is similar to that achieved by a 14-day quarantine period (median > 99%; 95% UI: 98–100). Even shorter quarantine periods can prevent a substantial amount of transmission; all strategies in which travellers spend at least 5 days (mean incubation period) in quarantine and have at least one negative test before release are highly effective (median reduction 89%; 95% UI: 83–95)). CONCLUSION: The effect of different screening strategies impacts asymptomatic and symptomatic individuals differently. The choice of an optimal quarantine and testing strategy for unvaccinated air travellers may vary based on the number of possible imported infections relative to domestic incidence.