Renaud Lancelot is a veterinary epidemiologist with 20-year experience in field research, mostly in continental Africa and Madagascar. His research focuses on livestock infectious diseases of tropical origin and their vectors. Renaud explains the differences and appropriate applications of Generalized linear mixed models (GLMMs) - extensions to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects - and marginal models which are used when estimating fixed effects. He also discusses different model types as they relate to a case study on COVID-19 mortality rates and lockdown measures.

Marvin, Computer Engineer and Biostatistician, is the head of the Emmy Noether research group on interpretable machine learning, funded by the German Research Foundation, at the Leibniz Institute for Prevention Research and Epidemiology – BIPS in Bremen, Germany. Since February 2021, he is also Professor of Machine Learning in Statistics at the University of Bremen. He has a research focus on statistical learning and interpretable machine learning and is interested in epidemiological applications to high-dimensional genetic data and longitudinal register data. Marvin is also author of several R packages, including the random forest package ranger. Marvin presented the results of his latest paper, just accepted in Machine Learning journal, explaining the conditional predictive impact (CPI), a model-agnostic interpretable machine learning method which can handle correlated predictor variables and adjust for confounders. The method builds on the knockoff framework of Candès et al. (2018) and works in conjunction with any valid knockoff sampler, supervised learning algorithm, and loss function. Marvin briefly described the method, show selected simulation results and give an example (with R code) of the application. The CPI has been implemented in an R package, cpi, which can be downloaded from this https URL.

Theresa Smith is a Lecturer in Statistics at the University of Bath. She received her PhD in Statistics from the University of Washington and went on to work as a postdoctoral researcher in spatial epidemiology in the Center for Health Informatics, Computing and Statistics at Lancaster University from 2014 to 2016. In current role at the University of Bath, Theresa specialises in working collaboratively with multidisciplinary teams to develop predictive analytics tools with applications to clinical and public health. In this talk, Theresa discussed the statistics and data science challenges arising from her ongoing work to develop community-scale monitoring systems for COVID-19 and other diseases using regular sampling and testing of wastewater. More on this project can be found at https://ewsresearch.com/our-projects/ews-c19/

John Kinsman’s work has been focussing on behaviour change interventions since 1996, when he joined the UK’s Medical Research Council (MRC) Programme on AIDS in Uganda as a behavioural scientist. Since then, he has worked as an action-oriented researcher on behaviour change issues: through much of the early 2000s, John focused on issues relating to HIV testing and counselling, and adherence to antiretroviral therapy in a number of African countries, while subsequently he worked on several WHO-designated Public Health Emergencies of International Concern (PHEICs). In 2019 John moved to the European Centre for Disease Prevention and Control (ECDC), taking up a position as their in-house expert on social and behaviour change. Since the emergence of the COVID-19 pandemic, his work has been focused exclusively on the response, with direct support to EU/EEA Member States as well as regular input on behavioural and risk communication issues into ECDC technical reports and rapid risk assessments. John has also led or been closely involved with projects on addressing pandemic fatigue in the population, examining Behavioural Insights research in the Member States to support the response to COVID-19, supporting socially vulnerable populations, preparedness and implementation support for the COVID-19 vaccines, and countering online vaccine misinformation. John presented his work on "Social listening and the use of qualitative data for monitoring health behaviours and trust", exploring the role of social listening via social media, and its related challenges, in support of the "infodemic" and COVID-19 outbreaks responses. See the following link for the ECDC publication on countering online vaccine misinformation: “Countering online vaccine misinformation in the EU/EEA”

William Wint is Senior Analyst for the Environmental Research Group of Oxford - ERGO, and an SRA at the Department of Zoology, University of Oxford. For MOOD, William focuses on providing covariate and disease driver datasets for risk assessment, and on modelling the distributions of the vectors and hosts of a range of MOOD’s target diseases. During the Science Webinar, William illustrated the latest developments about his reserch about vector and host modelling.

Esther van Kleef is a senior epidemiologist at the Institute of Tropical Medicine, Antwerp and holds a PhD in infectious disease epidemiology from the London School of Hygiene & Tropical Medicine. During her work experiences at national public health institutes (PHE, RIVM) and Oxford university, she has developed a keen interest in understanding the transmission-dynamics of pathogens and effectiveness of interventions, having largely focused on (modelling) antimicrobial resistance. Within the MOOD Project, Esther is working, together with MOOD partners, on identifying how to improve the integration of the threat of disease X in existing procedures of epidemic intelligence. She has supported analyses on ECDC mortality data related to different countries' Covid-19 measures in the context of MOOD's focus on early detection, preparedness, and monitoring of infectious diseases. She was recently selected among other scientists to join the Technical Advisory Group (TAG) created by WHO and UN DESA. The objective of the TAG is to obtain more accurate data on Covid-19 mortality cases, to help review WHO’s current methods, to come up with more reliable analytical methods, and to standardise the existing methods for surveillance data. Esther illustrates the effects of physical distancing and school reopening on cases reporting and age-specific SARS-CoV-2 transmission patterns, as discussed in the recently-published paper she co-authored (https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.7.2100065). For this study, the group of researchers employed operational data from the COVID-19 contact tracing system of the Brussels region and case reports made available via the Belgian institute for health, Sciensano.

Pierre Nouvellet is a quantitative biologist and epidemiologist focused on data science and modeling. He uses mathematical formalisation to resolve concrete ecological and epidemiological problems. Specifically, he develops statistical methods to estimate parameters within complex ecological settings by integrating various sources of information in order to better understand the dynamics of diseases’ transmission and mitigate their public health risk. Currently he is focused on examining vector-borne and zoonotic diseases, emerging diseases, and rapid response to outbreaks. Pierre’s presentation “Reduction in mobility and Covid-19 transmission” focuses on his team’s research (https://spiral.imperial.ac.uk/handle/10044/1/85559) in which they examine mobility data in order to analyze the relationship between transmission and mobility for 52 countries around the world.

Diana Inkpen is a Professor at the University of Ottawa, in the School of Electrical Engineering and Computer Science. She obtained her Ph.D. from the University of Toronto, Department of Computer Science. She has a M.Sc. and B.Eng. degree in Computer Science and Engineering from the Technical University of Cluj-Napoca, Romania. Her research is in applications of Natural Language Processing and Text Mining. She organized seven international workshops and she was a program co-chair for the 25th Canadian Conference on Artificial Intelligence (AI 2012, Toronto, ON, May 2012) conference. She is the editor-in-chief of the Computational Intelligence journal and the associate. Diana presented some of the methodological and ethical aspects behind her latest book “Natural Language Processing for Social Media” (3rd Ed.), focusing on NLP health care applications, NLP-based user modelling and event detection in text mining from Social Media.

Mattia Manicai is a researcher at Fondazione Edmund Mach. His presentation will be centered around one of the most recent publications he has contributed to, titled “Spring temperature shapes West Nile Virus (WNV) transmission in Europe” (https://doi.org/10.1016/j.actatropica.2020.105796). Mattia explains how spatio-temporal conditions will shape WNV transmissions, why WNV circulation tends to be higher in warmer regions, how the impact of change of temperatures due to approaching spring time on WNV transmissions can be predicted, and why new results in conjunction with previous findings can serve as indicators for the reliability of surveillance systems and WNV’s overwintering capacity.

Mathieu Roche, Senior Research Scientist and currently co-leader of the MISCA group (i.e. Spatial Information, Modelling, Data Mining, and Knowledge Extraction) at TETIS (CIRAD - France) presented the results of his latest analysis on how to use terminology and text-mining for event-based surveillance systems (i.e. disease-based and symptom-based surveillance). In this presentation Mathieu discussed the use of different datasets related to COVID-19, e.g. scientific publications, news data (PADI-web, MedISys), social media data (Twitter). The extracted terminology has been used (i) for surveillance systems (i.e. web crawling and information extraction tasks) and (ii) for spatio-temporal analysis of tweets dealing with COVID-19.

Dr. Laura Kramer has 50 years’ experience studying arboviruses in the field and laboratory, from both experimental and observational approaches, using both classical and molecular tools. She was Director of the Arbovirus Laboratory, Wadsworth Center, New York State Department of Health from 2000 – Dec 2020 when she retired, and Professor of Biomedical Sciences, State University of New York (SUNY) School of Public Health, Albany, NY. She is also an Adjunct Professor in the Biology department at SUNY Albany. Dr. Kramer also is a virology moderator of ProMED-mail [Program for Monitoring Emerging Diseases] where she reports on COVID-19 and Ebola as well as vaccine-preventable diseases. In this MOOD Science Webinar, Laura discussed her comprehensive research paper "Introduction, Spread, and Establishment of West Nile Virus in the Americas" published on the Journal of Medical Entomology, illustrating the complex relationships among West Nile Virus (WNV), vector mosquitoes and vertebrate hosts, as well as how certain environmental variables - including land use and climate - affected the spillover risk to humans in the United States since its introduction.

Alan Radford is a professor of veterinary health informatics at the University of Liverpool. He is the coordinator of the Small Animal Veterinary Surveillance Network (SAVSNET), which exploits electronic health records from veterinary practitioners across the United Kingdom, and identifies significant trends in diseases. Alan shared his experience in big data analytics across the network.