MOOD Science Webinars

Every last Wednesday of the month, MOOD hosts a series of science webinars inviting two leading experts to share their research work on disease surveillance and modelling in data science, the impact of global warming on disease outbreaks, and the building of one-health systems across Europe and the world. With the MOOD science webinars, we aim at bringing the leading scientists and professionals in the field to discuss important recent discoveries and discuss implications of their work.

We especially encourage presentations on published research work focusing on: how was the work implemented? What were the main discoveries? What did and did not work out the way you expected? and what are the implications of the main discoveries, especially in the context of the MOOD project objectives?

All our webinars are hosted on the Leibniz Information Centre for Science and Technology and University Library (TIB AV-Portal), which provides ad-free videos on science, research, industry and business with literature and information. 

 

Join the next MOOD Science webinar!

Every last Wednesday of the month (except August and December), from 3 to 4 PM (CET). Click on the button below to join the Zoom meeting using the passcode.

November 2021

The Covid-19 Data Portal

By Nadim Rahman

10.5446/51628 (DOI)

https://av.tib.eu/media/51628

Keywords: COVID-19, Genomics, Sequence data, Big Data, European Union, VEO project

Nadim Rahman is an Infectious Diseases Project Manager at the European Nucleotide Archive (ENA), EMBL-EBI. He has a background in Biomedical Sciences, before completing an MSc in Bioinformatics at Queen Mary University, London. Following this, he completed a year’s internship at Illumina which entailed full-stack software development, before joining the ENA team initially as a software engineer, focused on pathogen activities, where over the last 1-2 years, this has been focused around the European COVID-19 Data Platform. In this edition, Nadim talked about the COVID-19 data portal and platform, providing background and insight into the challenges and set up of this type of resource.

October 2021

Modelling spatio temporal COVID-19 trends through wastewater surveillance

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 here.

The Small Animal Veterinary Surveillance Network (SAVSNET)

By Alan Radford
10.5446/55548 (DOI)

https://av.tib.eu/media/55548

Keywords: veterinary health; pets; database; big data

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.

September 2021

Monitoring behaviours and perceptions of health measures

By John Kinsman
10.5446/54894
(DOI)

https://av.tib.eu/media/54894

Keywords: Social Science, misinformation, ECDC, Behaviour, Vaccine, COVID-19

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”

Social media text mining

By Diana Inkpen
10.5446/54798(DOI)

https://av.tib.eu/media/54798

Keywords: Text mining, social media, health, event detection,

natural language processing


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.

June 2021

Overview of MOOD Vector and Host modelling - where we are now

By William Wint
10.5446/54144 (DOI)

https://av.tib.eu/media/54144

Keywords: West Nile Virus, Covariates, MOOD, Ecology

William is a Senior Analyst for ERGO, and an SRA at the Department of Zoology, University of Oxford. Originally an ecological entomologist looking at arthropod community ecology, he then spent 15 years developing integrated air and ground survey techniques for agricultural resources throughout Sub Saharan Africa, which eventually morphed into spatial data management, analysis and modelling for animal and human diseases, their vector and hosts.
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.

Model-agnostic Interpretable Machine Learning

By Marvin Wright
10.5446/54137(DOI)

https://av.tib.eu/media/54137

Keywords: Models, agnostic, biostatistic, machine learning,
epidemiology

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 the 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 that 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.

April 2021

Text mining on COVID19 datasets - Terminology extraction

By Mathieu Roche
10.5446/53410(DOI)

https://av.tib.eu/media/53410

Keywords: Data Science; Text Mining; Data Extraction; Social Media; EBS; 

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.

The impact of biotic and abiotic factors on vectorial capacity of Culex mosquitoes for West Nile virus

By Laura Kramer
10.5446/53417(DOI)

https://av.tib.eu/media/53417

Keywords: West Nile Virus; America; Disease Ecology; Vector-borne; Zoonosis; 

Dr. Laura Kramer, PhD 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. Laura expounded her comprehensive research paper published in the Journal of Medical Entomology. Her work reviews current knowledge on several aspects of West Nile Virus ecology and its evolution to highlight key outstanding questions and gaps regarding the introduction, spread, establishment, and ongoing transmission throughout the American continent.

March 2021

Reducing contacts to stop SARS-CoV-2 transmission during the second pandemic wave in Brussels, Belgium, August to November 2020

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. 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. 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 published paper she co-authored

Estimating fixed-effect coefficients in count models - GLMM vs marginal models

By Lancelot, Renaud
10.5446/52154 (DOI)

https://av.tib.eu/media/52154

Keywords: Covid-19; MOOD;rstats

Renaud Lancelot is a veterinary epidemiologist with 20-year experience in field research, mostly in continental Africa and Madagascar. 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.

February 2021

Reduction in mobility and covid-19 transmission

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. 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 in which they examine mobility data to analyze the relationship between transmission and mobility for 52 countries around the world.

Spring temperature shapes West Nile Virus (WNV) transmission in Europe

Mattia Manicai is a researcher at Fondazione Edmund Mach. His presentation revolved  around one of the most recent publications he has contributed to, titled “Spring temperature shapes West Nile Virus (WNV) transmission in Europe”. 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.