The Science Webinars
Every last Wednesday of the month, every two months, 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?
Would you like to participate? The Webinars are accessible upon invitation. Sign up below to receive them.
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.
Knowledge, attitudes and practices (KAP) towards endemic vector-borne diseases in Finland
By Henna Mäkelä
Keywords: vector-borne diseases ; Finland ; knowledge ; attitude ; practices
Henna Mäkelä is a researcher working in public health, focusing on vector-borne diseases and their surveillance. With a background in geography, she is currently doing her PhD in epidemiology on a topic of vector-borne diseases. Henna will present her work conducted within a Finnish VECLIMIT-consortium and shares results from a national level KAP-survey related to endemic vector-borne diseases in Finland. The aim for the questionnaire study was to assess knowledge, attitudes and practices of Finnish residents concerning these diseases and to reveal misconceptions that may represent obstacles in behaviour change. The study showed that Finnish residents have obvious gaps in and misconceptions in knowledge and practices for vector-borne diseases, especially tick-borne diseases.
Vector-Borne Diseases Stakeholder Mapping in Finland: A One Health Approach
By Kyottinen Anniina
Keywords: Vector-Borne Diseases ; Stakeholder ; Finland ; OneHealth
Anniina Kyöttinen is a master’s student of Public and Global Health from Tampere University, Finland, with a bachelor’s degree in Dental Sciences. She worked as a visiting researcher at THL concentrating on vector-borne diseases, the One Health approach and stakeholder networks. During the MOOD Science Webinars, Anniina presented her master’s thesis work: Vector-Borne Diseases Stakeholder Mapping in Finland: A One Health Approach. The aim of this qualitative semi-structured interview research was to map and analyze the current and missing stakeholder/ actor interactions and information flow related to vector-borne diseases and their management in Finland, within a One Health context. In addition, the objective was also to discuss and reflect on the future of a possible formal VBD/One Health-network in Finland and what were the chances, challenges and means of establishing one.
State of the art of epidemic intelligence activities among national public and animal health agencies in Europe: A large-scale cross-sectional study
By Timothée Dub
Keywords: Epidemiology ; Disease surveillance ; Epidemic intelligence ; Europe
Timothée Dub is a senior expert in the department of Health security of the Finnish Institute for Health and Welfare (THL). His activities mainly focus on vector-borne, emerging infectious and vaccine-preventable diseases, as well as outbreaks investigations. Timothée presented his latest work on a large-scale sectional study conducted in 2021 about the state-of-art of epidemic intelligence activities among national public and animal health agencies in Europe. The study was conducted as part of the MOnitoring Outbreaks for Disease surveillance in a Data science context (MOOD – Horizon-2020). The study will be published in 2023.
Decision making and Covid 19
By Alexandre Hobeika
Keywords: Modelling ; Political Science ; Sociology ; Finland ; France
Alexandre Hobeika is a researcher in political science and sociology. He works on the coordination of actors for the governance of health risks, using the One Health approach. He has recently focused on Covid-19, antimicrobial resistance, and African swine fever. During MOOD science webinar, he presented the results of a socio-political study about the relationships between modelers and decision-makers during the Covid-19 crisis, at the national level in Finland and France. While these countries have been affected by and responded to the pandemic in different manners, many reported points of tension between science and policy have been similar: a narrow political framing and governance of the crisis, claims of political instrumentalization of science, fraught debates among scientists. Then he layed out possible avenues for a better preparation of the science-policy collaboration, based on the stakeholders’ feedbacks of these results.
Potential endemisation of West Nile and Usutu viruses in the South of France
By Serafin Gutierrez
Keywords: West Nile virus ; Usutu virus ; Veterinary health ; Public health ; France ; Epidemiology
Serafin Gutierrez’s main interests are virus ecology and evolution. Along mhis career, he has worked with different viral pathogens of insects, plants and vertebrates. Currently, his group works on various aspects of the ecology and evolution of arthropod-borne viruses, including the discovery of new zoonotic viruses. Moreover, his team explores the viral community or virome of insect vectors and its potential influence on arbovirus epidemiology. In this webinar, Serafin presented a collaborative work on West Nile virus (WNV) and Usutu virus (USUV), including teams involved in both human and veterinary health. These viruses follow a similar enzootic cycle involving mosquitoes and birds. However, they can also infect humans and other mammals, leading to severe disease. Their epidemiological situation may have shifted from irregular epidemics to endemicity in several European regions. This potential change requires confirmation, as it could have implications for risk assessment and surveillance strategies. To this end, the team has evaluated the prevalence and genetics of WNV and USUV in a cross-sectional study in humans, dogs, horses, birds and mosquitoes in the French Camargue area, between 2016 and 2020. Their findings support endemisation in the study region. Serafin also presented future research directions in his group.
Understanding Outbreak Data Dissemination In Event Based Surveillance Systems. Application On Avian Influenza Using PADI-web
By Bahdja Boudoua
Keywords: Epidemic intelligence ; infectious diseases ; health information, signal ; source ; transmitter ; source ; EI system
Boudoua Bahdja is a second year PhD student at the UMR TETIS. She received a veterinary medicine training from the higher school of veterinary medicine (ENSV) of Algiers, and holds a master’s degree in Epidemiology from Paul-Sabatier University, Toulouse, France. Her work is part of the MOOD project H2020 and focuses on The Identification, qualification and integration of epidemiological indicators from multi-source textual data. The work she presented was conducted during her Master’s degree Internship. Its context and purpose are described below: Epidemic intelligence (EI) has been adopted by several countries to reach fast detection of new and emerging infectious diseases. EI collects information from two types of sources: official sources (i.e. health reports from OIE or FAO) and unofficial sources (i.e. online media outlets, scientific publications, etc.). In France, the EI system PADI-web (Platform for Automated extraction of Disease Information from the Web) is used since 2014 to detect signals of animal health events with risk of introduction to France. The objective of this work was to understand how health information (signal) is disseminated from a primary source (transmitter) to a final source (EI system) through quantitative and qualitative network analysis methods.
Data, AI and Health: how to manage a that cares for everyone
Dr Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas Consulting, where she is responsible for leading the management, strategic direction and execution of the Eticas vision.
One of the sectors where AI has been embed into rapidly, especially in the past few years is Healthcare. The advantages that might come from this are enormous in terms of time management and efficiency, so enormous that we are missing the point: caring. It is critical to apply ethics and oversight when handling individuals’ data that is so crucial and impactful. During this session, Gemma goes through the complexity behind this data and systems and the best practices to ensure success.
Importance of Relative Spatial Information (RSI) in EBS
By Mehtab Alam Syed
Keywords: Event-based surveillance, infectious diseases, zoonosis, epidemiology, text mining, spatial information, health
Mehtab Alam Syed is doing his PhD on topic “Generic methods for epidemiological monitoring based on the integration of heterogeneous textual data” funded by H2020 MOOD project. His PhD work focuses on the development of generic methods in order to extract new and relevant event in heterogeneous data textual in a One Health context. Previously, he was Research Associate in University of Bozen-Bolzano, Italy in which he mainly contributed in European funded project “COCkPiT” from 2018-2020. Since 2010, he was a part of different software organizations in which he was involved in different software development project across different domains i.e. Healthcare, European Taxi Systems, Mobile TV systems etc. The presentation is about the importance of relative spatial information (RSI) in EBS. Mehtab described the approach to extract RSI from different unofficial media sources and how to accurately geographically map this information for different events in EBS.
PADI-web: a health monitoring system to analyze the emergence and spread of animal diseases
By Julien Rabatel
Keywords: Computer science, PADI-web, Event-based surveillance, epidemic intelligence, media mining, outbreak, health
Julien Rabatel is a freelance developer with a PhD in Computer Science. Nowadays, he uses his development and research experience to participate as a freelancer in various projects, frequently involving public institutions such as CIRAD or universities. PADI-web is an online tool that collects and processes documents from the Web. Its goal is to allow daily surveillance in epidemiology, by exploiting unofficial sources such as local news. This talk will describe how PADI-web is working, and what it can provide to the end-user. Also, we will take a look at the future of PADI-web and how we can address its current limitations.
COVID-19 Scatterplots: Waves as circles
By Julia Haas
Keywords: COVID-19; Big data; Germany; Scatterplots; Data Visualization; Incidence
Julia Haas is a data analyst and developer at mundialis. She holds an MSc in Geography. Her work usually focuses on the analysis of remotely sensed geospatial data and the processing of voluminous geodata. However, for the COVID-19 Scatterplots statistical data (COVID-19 incidence data) is being analysed. In this talk, Julia presented a newly developed application showing graphs for COVID-19 “status” in Germany. The graphs refer to incidence values and change of new cases in different regions of Germany.
The scatterplots can be visualized on this website.
Building a web portal to improve the availability of data on ticks and tick borne diseasese
By Xavier Bailly
Keywords: data science; tick; tick-borne disease; web portal; veterinary health; public health; one health
Xavier Bailly leads a research unit that focuses on the epidemiology of animal and zoonotic diseases, with a particular interest in tick dynamics and the epidemiology of tick-borne pathogens. He is a molecular epidemiologist working on various tick-borne pathogens such as Borrelia burgdorferi and Anaplasma phagocytophilum. The aim of this talk is to share ideas about the implementation of a web portal that could be used to centralize data on tick dynamics and infection by tick-borne pathogens. These data could be used to model the spatial and temporal distribution of ticks and in the longer term, the distribution of tick-borne diseases.
Game Animal Density, Climate, and Tick-Borne Encephalitis in Finland, 2007–2017
By Timothée Dub
Keywords: One Health, Tick-borne encephalitis, infectious disease, Finland, Epidemic Intelligene, Epidemic Surveillance, flavovirus
Tick-borne encephalitis (TBE) has become a growing public health challenge in Europe and other parts of the world. The number of human cases of TBE in all endemic regions of Europe has increased by almost 400% in the last 30 years; with spreading risk areas and new foci across Europe and worldwide, TBE has been included as one of the biggest health threats arising from environmental change. Novel assessment and monitoring strategies are therefore needed to face current and future outbreaks. For our first MOOD science webinar of 2022, we invited Timothee Dub, Research Manager in the department of Health security of the Finnish Institute for Health and Welfare (THL) and MOOD Case Study Facilitator, to discuss the results of his paper published in Emerging Infectious Disease ‘Game Animal Density, Climate, and Tick-Borne Encephalitis in Finland, 2007–2017’.
Modeling environmental impacts on questing activity of Ixodes ricinus nymphs in France
By Karine Chalvet-Monfray 10.5446/56289 (DOI)
Keywords: Ticks, tick-borne, model, France, disease ecology, vector-borne disease
Karine Chalvet-Monfray is a professor of Biostatistics and Epidemiology at VetAgro Sup (Lyon). She is a veterinarian with a PhD and post-doc in biomathematics and statistics. Her research mainly focuses on the modelling of weather-sensitive diseases such as vectorial diseases. Since 2014, Karine has been coordinating the tick network observatories in France (ClimaTick) and in 2009 she participated in the design of FleaTickRisk, a forecasting model that uses meteorological data to predict ectoparasite activity in different climates.
In this talk, Karine illustrated the results of 7 years of standardized monthly observations of tick activity and the associated model taking into account various environmental and meteorological factors, now available as pre-print on Research Square.
The Covid-19 Data Portal
By Nadim Rahman
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.
Modelling spatio temporal COVID-19 trends through wastewater surveillance
By Theresa Smith
Keywords: COVID-19, wastewater, modelling, predictive analytics
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
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.
Monitoring behaviours and perceptions of
By John Kinsman
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
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.
Overview of MOOD Vector and Host modelling - where we are now
By William Wint
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
Keywords: Models, agnostic, biostatistic, machine learning,
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.
Text mining on COVID19 datasets - Terminology extraction
By Mathieu Roche
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
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.
Reducing contacts to stop SARS-CoV-2 transmission during the second pandemic wave in Brussels, Belgium, August to November 2020
By van Kleef, Esther
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
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.
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
By Manica, Mattia
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.