FIC MISMS workshop
Influenza epidemiology, digital surveillance and evolutionary dynamics
March 12-13, 2020
ANISE meeting, Livingstone, Zambia
Link for Registration
This training workshop is held in conjunction with the March 2020 meeting of the ANISE network in Zambia and follows scientific research sessions and training modules led by the GISAID and WHO teams earlier in the week.
Information on influenza epidemiology and evolutionary dynamics is particularly useful to guide intervention strategies, optimize surveillance, and vaccine design, but such studies remain scare in low-income settings. Digital surveillance uses novel data streams (Google searches, Twitter, data from participatory surveillance systems) to help monitor and forecast influenza activity in settings where traditional surveillance data may be lacking, sparse, or lag by several weeks. Phylogenetic analyses of influenza sequence data are important to shed light on the global, regional and local migration of influenza viruses, their evolution, and the match of circulating strains with available vaccines.
The objective of this two-day MISMS workshop is to train epidemiologists and virologists from Africa (particularly from countries with available data) on the use of the quantitative epidemiological methods for digital surveillance and forecasting, and on phylogenetic analyses for phylodynamics and phylogeography using the BEAST package.
This workshop will include two separate modules devoted to (i) epidemiology and (ii) phylogenetics. Both modules will include theory and practice sessions. Participants will be invited to work with their own data and with publicly available sample datasets.
Participants in the epidemiological module will be taught how to use different R packages for statistical analyses of influenza time series (lab-confirmed cases, ILI or SARI) and develop models using digital surveillance signals (Google, Twitter). They will learn how to use and analyse various Google tools, such as Google searches, Google correlates, and Google Trends. Short-term influenza forecasting models will also be presented. Participants will be walked through examples using sample datasets, and if time allows, a few hours will be devoted to the analysis of the participants’ own data. There will be a live demo of InfluenzaNet, an on-line platform for influenza participatory surveillance.
Participants in the phylogenetic module will learn how to prepare a sequence dataset, explored it using maximum likelihood phylogenies and use the BEAST platform to perform advanced phylogenetic analyses to understand the spatial and evolutionary dynamics of influenza viruses. BEAST is a cross-platform program for Bayesian analysis of molecular sequences using Markov chain Monte Carlo (MCMC) algorithms. Outputs include rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability.
Participants will be provided a sample FASTA influenza datasets and walked through an example of BEAST analysis. An overview of the possible uses of BEAST will be discussed. If time allows, a few hours will be devoted to the analysis of the participants’ own data.
Trevor Bedford, Fred Hutchinson Cancer Institute, WA, USA (TBC)
Daniela Paolotti, Institute for Scientific Interchange, Italy
Mauricio Santillana, Boston Children Hospital, MA, USA (TBC)
Kaiyuan Sun, NIH-Fogarty, Bethesda, MD, USA
Nídia Sequeira Trovão, NIH-Fogarty, Bethesda, MD, USA
Cécile Viboud, NIH-Fogarty, Bethesda, MD, USA