Workshop 4: Population Models in the 21st Century

(November 14,2016 - November 18,2016 )

Organizers


Marisa Eisenberg
Department of Epidemiology, University of Michigan
Mark Lewis
Canada Research Chair in Mathematical Biology, University of Alberta
Lauren Meyers
Department of Integrative Biology, University of Texas

Models of populations have played a central role in diverse biological contexts, including in studies of cellular, molecular, organismal, and ecological systems. The study of such models also has a long history in the mathematics community. However, recent advances in the sensing of biological systems have generated extremely large data sets, and these data have only started to be integrated effectively with population models in ways that provide meaningful insight and accurate prediction. Examples of these big data include the genetics of microbial communities over time and space in environments as small as the human gut and as large as river estuaries, cell phone signals that can provide information on human movement patterns with high temporal and spatial resolution, and real-time disease surveillance data during epidemic outbreaks. While the incorporation of these types of data into population models has the potential for improving our understanding of how biological systems function, new intellectual challenges arise from these data, many of which are mathematical and statistical in nature. For example, are there principled approaches for simplifying the data being analyzed to minimize loss of information? How can dependencies between data sets be best handled? Can population models guide the collection of these data sets or should they simply respond to the available data? Will the future see some types of data be replaced by other types of data, or will different types of data act synergistically in guiding our understanding of what biological processes are important in the population dynamics we observe? These questions arise across biological scales of organization. Whether at the scale of cells or the scale of ecosystems, the availability of these new types of data, and the extensiveness of these data, should also enable the design of mathematical models with greater predictive accuracy. However, the design of such predictive models poses difficult new challenges. In tumor biology, how does one integrate probabilistic models for cell evolution with PDE models for the growth of tumors with many competing subpopulations? In epidemiology, how does one bridge the gap between models of intrahost pathogen evolution and epidemic models of the spread of disease within populations? How should we model external pressures, for example antibiotic treatment, on the microbial populations residing in humans? Progress on these challenges is important for the development of excellent policy alternatives for human health and the ecology of the planet. A major goal of this workshop is to bring together the large and active group of mathematicians working in population biology with biological practitioners using large data techniques.

Accepted Speakers

William Aeberhard
Departement of Mathematics and Statistics, Dalhousie University
Allison Aiello
Epidemiology, University of North Carolina at Chapel Hill
Julien Arino
Mathematics, University of Manitoba
Shweta Bansal
Biology, Georgetown University
Noelle Beckman
National Socio-Environmental Synthesis Center (SESYNC), University of Maryland, College-Park, National Socio-Environmental Synthesis Center
Dave Campbell
Dept of Statistics and Actuarial Science, Simon Fraser University
Charmaine Dean
Faculty of Science, University of Western Ontario
Sara del Valle
Information Systems and Modeling, Los Alamos National Laboratory
Matthew Ferrari
Biology, Pennsylvania State University
Rebecca Garabed
Veterinary Preventive Medicine, The Ohio State University
Gabriela Gomes
Clinical Sciences, Liverpool School of Tropical Medicine
Juan Gutierrez
Mathematics, Institute of Bioinformatics, University of Georgia
Alan Hastings
Department of Environmental Science and Policy, University of California, Davis
Michael Johansson
Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health
Subhash Lele
Mathematical and Statistical Sciences, University of Alberta
Alun Lloyd
Biomathematics Graduate Program, Department of Mathematics, North Carolina State University
Andrew Morozov
Mathematics, University of Leicester
Stephanie Peacock
Biological Sciences, University of Alberta
Mason Porter
Mathematical Institute, University of Oxford
Samuel Scarpino
Mathematics & Statistics, University of Vermont
Monday, November 14, 2016
Time Session
07:45 AM

Shuttle to MBI

08:00 AM
08:30 AM

Breakfast

08:30 AM
08:45 AM

Welcome by MBI director and overview

08:45 AM
09:00 AM

Introduction by Workshop Organizers

09:00 AM
09:40 AM
William Aeberhard - Robust fitting of state-space models for reliable fish stock assessment

The sustainable management of fisheries strongly relies on the output of fish stock assessment models fitted to scarce and noisy data. State-space models represent a relevant general framework for accounting for both measurement error and a complex dependence structure of latent (unobserved) random variables. Classical estimation of fixed parameters in such models, for instance by maximizing an approximated marginal likelihood, is known to be highly sensitive to the correct specification of the model. This sensitivity is all the more so problematic since assumptions about latent variables cannot be verified by the data analyst. We introduce robust and consistent estimators for general state-space models which remain stable under deviations from the assumed model. These estimators are shown to yield reliable inference for fish stock assessment in various scenarios.

09:40 AM
10:25 AM
Alan Hastings - Dealing with management

One of the goals of population models as the field matures is to use them in making management decisions. I will review different approaches for developing population models for management, and emphasize both general issues of dealing with uncertainty, and also consider specific systems such as fisheries or invasive species. The issue of management means getting away from asymptotic behavior and focusing on shorter time scales.

10:25 AM
11:00 AM

Break

11:00 AM
11:40 AM
Noelle Beckman - Assessing species' risk under climate change

Global change affects the ecology and evolution of dispersal, limiting the ability of species to move or adapt to global change events. Due to the long-term and spatially-complex dynamics of plant populations, understanding and predicting their responses to global change is empirically and mathematically challenging. I apply recent advances in the study of species€™ movement and develop a general classification scheme to assess the risk of plant extinction in response to climate change in continuous landscapes. Using a Bayesian approach, I synthesize existing data on dispersal, functional traits, and demography to generate virtual species with realistic dispersal kernels and life-history strategies. I sample these virtual species to parameterize integrodifference equations and approximate population spread in continuous landscapes. Using this approach, I obtain predictors of risk that are related to easily measurable functional traits that will inform the types of species least likely to track a shifting climate. In future research, this approach will be extended to predict extinction risk of plant species in fragmented landscapes. This research will help identify species at greatest risk and aid the development of conservation strategies to ensure their persistence under global change.

11:45 AM
12:25 PM
Rebecca Garabed - Modeling for the Data You Have
12:30 PM
02:30 PM

Lunch Break

02:30 PM
03:30 PM

Poster Flash talks

03:30 PM
04:00 PM

Break

04:00 PM
05:00 PM
Charmaine Dean - Forest Fire Risks: Assessing Historical Trends, Insurance Risks and Health Effects

Assessing trends forest fire risk is of significant concern to fire managers as well as for the insurance and health sectors because of impacts of such risks in these areas. In particular, determining trends in forest fire ignition risk as measured by increasing annual trends in ignitions, or the lengthening of the fire season within each year, or both of these factors, requires urgent attention. This talk focuses on annual and seasonal changes in forest fire ignition risk, observed over the last 50 years. How fire is linked to climate effects is also discussed along with an identification of those factors that need to be considered before one can attribute observed changes in forest fire occurrence to climate change. The talk then attends to insurance assessments of such forest fire risk as well as an examination of new tools based on satellite imagery to assist in determining the health risks related to forest fire smoke exposure.

05:00 PM
07:00 PM

Poster session and reception

07:00 PM

Shuttle pick-up from MBI

Tuesday, November 15, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:40 AM
Matthew Ferrari - Managing multiple sources of uncertainty: optimal outbreak response for Foot-and-Mouth Disease

Control of epizootics require that decisions be made in the face of multiple sources of uncertainty: economic, political and logistical uncertainty, dynamical uncertainty about epizootiological processes, and stochastic nature of disease spread. Decision-makers are faced with fundamental trade-off between the learning that will accrue through continued observation of a disease process and the opportunity cost of inaction. Structured decision-making and adaptive management seek to minimize the opportunity cost of inaction by defining an iterative, state-dependent policy for selecting among alternative management actions. In particular, we seek to define an adaptive policy that responds to the changing state of information about competing dynamical models as defined in the posterior distribution and the chaining epizootiological state as defined by the size and spatial extent of an outbreak. We achieve the former through an analysis of the value of information across competing models and sequential analysis of real-time outbreak surveillance from the 2001 foot-and-mouth outbreak in the UK. We achieve the latter by using reinforcement learning to solve for an optimal state-dependent policy for the application of vaccination and culling for a spatially explicit livestock outbreak. We show that adaptive policies can result in significant gains over conventional static management.

09:45 AM
10:25 AM
Marisa Eisenberg - Connecting models with data: identifiability and parameter estimation of multiple transmission pathways

Connecting dynamic models with data to yield predictive results often requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what is possible to estimate from a given model and data set, and help guide new data collection. In this talk, we will discuss approaches to both structural and practical identifiability analysis. Using a range of examples from cholera and the West Africa Ebola epidemic, we illustrate some of the potential difficulties in estimating the relative contributions of different transmission pathways, and show how alternative data collection may help resolve unidentifiability. We also illustrate how even in the presence of large uncertainties in the data and model parameters, it may still be possible to successfully forecast disease dynamics.

10:30 AM
11:00 AM

Break

11:00 AM
11:40 AM
Gabriela Gomes - Modeling selection bias to enable accurate estimation and prediction

Mathematical models for infectious diseases are often contested based on accumulating examples where the impact of public health interventions is over-predicted. We attribute model over-optimism to a lack of account for variation in individual €œfrailty€? €“ a concept that has at least 30 years in demography and statistics, but has been overlooked in infectious disease epidemiology. Like any form of standing variation, distributions of individual risk constitute a basis for selection €“ under the force of infection in the case of infectious diseases €“ which results in the population at risk having a dynamic mean susceptibility. In models that fail to account for realistic risk distributions, the dynamics of mean susceptibility are inaccurate (or disabled in the case of homogeneous models), and resulting predictions are unlikely to be met. This concern applies not only to infectious disease modeling but also, and more simply, to the interpretation of clinical trail outcomes.


We propose specific study designs to overcome this type of selection bias in infectious disease modeling and trial analyses. Rather than trying to avoid it with intricate designs, the approach is to acknowledge the presence of cohort selection in the data and account for it in analytic models to enable more accurate parameter estimation and prediction. The resulting frameworks might also serve as think tanks for innovative interventions that modify disease risk distributions more generally, such as those that address comorbidities, socioeconomic health determinants or biological control of disease vectors.

11:45 AM
12:25 PM
Julien Arino - Modelling-assisted disease surveillance

Internet-based disease surveillance is a tool providing early warning about infectious disease outbreaks. There are variations, but the common idea is to automatically monitor Internet sources (news, blogs, etc.), searching for articles containing keywords related to infectious diseases. Natural language processing is then used to pinpoint the location being mentioned, eliminate duplicates, etc. Some systems additionally have human input to weed out false positives. In all instances, though, these systems produce a large amount of alerts.


I will discuss ongoing work using stochastic metapopulation models for the global spread of infectious pathogens along the global air transportation network. I will show in particular how such models can be used to help filter the large number of alerts generated by Internet-trawling surveillance systems.

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:40 PM
Alun Lloyd - Model-Guided Design of Experiments and Data Collection

I shall discuss the utility of mechanistic mathematical models as aids in the design and development of experiments. The impact of model parameters on model outputs can be assessed using techniques from uncertainty quantification. Thus one can determine those parameters for which additional knowledge would best improve the predictive ability of a model. Furthermore, one can gain understanding of what data is needed, and how much and when it should be collected in order to best achieve this aim. I shall illustrate these ideas using some examples from infectious disease projects on which I have worked, including some in the area of mosquito-borne diseases.

02:45 PM
03:00 PM

Break

03:00 PM
04:30 PM

Open Problem Discussion - breakouts and then come back together

04:30 PM

Shuttle pick-up from MBI

Wednesday, November 16, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:40 AM
Dave Campbell - Diagnostics for Fast Model Estimates

State space models work on two different layers of noise: a noise infused process model and additional measurement noise. A noise infused process model may track the annual population size of salmon, where the noise in this layer may be used to account for un-modelable environmental fluctuations or random perturbations to migratory routes. Subsequently, the population size is observed via noisy measurements, where this may be due to challenges in accurately counting the size of the population of salmon. As a result, estimating parameters through these two layers of noise requires dealing with considerable uncertainty. The widely adopted Integrated Nested Laplace Approximation (INLA) is designed to approximately integrate out some parts of the model, accelerating and simplifying the process of estimating parameters. The INLA approximation lies in the assumption that performing the integral is equivalent to integrating a Gaussian. The alternative to using INLA, and also checking validity of the INLA assumption, typically requires high dimensional and slow but very accurate Monte Carlo integration. This forces the practitioner to chose between the extremes of quick and rough or slow and precise. In this work we devise an INLA diagnostic /alternative model integration approach allowing the user to decide where to stand in a continuous variant of the previously binary speed vs accuracy tradeoff. Additionally, the proposed approach outputs a measure of confidence in the applied approximate integral. The method is based on probabilistic numerics, a new area of research bringing together numerical analysis, applied mathematics, statistics, and computer science. This is joint work with Charlie Zhou (Simon Fraser University) and Oksana Chkrebtii (the Ohio State University).

09:45 AM
10:25 AM
Subhash Lele - Covariates in population models

Population dynamics models are used for projecting effects of climate change or management strategies. Obviously these changes appear as covariates that affect various parameters in the population dynamics models. The covariates are often measured with error either because of simple measurement error or because they are projected in future using some model with associated prediction error. In this paper, I will illustrate the effect of covariate measurement error in population dynamics models on estimation and prediction error. Furthermore, I will show that such covariate measurement error can be accounted for using hierarchical modeling structure.

10:30 AM
11:00 AM

Break

11:00 AM
11:40 AM
Juan Gutierrez - Multiscale Systems Biology: From Genes to Environment

The advent of high-throughput molecular technologies, and the flood of information they have produced, has forced the biomathematical community to rethink how to conceive, build, and validate mathematical models. In this talk I will demonstrate how the integration of molecular and cellular models shape geographic considerations in the mathematical modeling of malaria. The usefulness of models under this light takes on new meanings, and this broad scope requires the cooperation of scientists coming from very different intellectual traditions. This talk will also explain how an adaptive learning system named ALICE (Adaptive Learning for Interdisciplinary Collaborative Environments) is used to train scientists that approach biomathematics from multiple disciplines.

11:45 AM
12:25 PM
Andrew Morozov - Enhancing predictability of biological models with structural sensitivity: how should we proceed?

A fundamental property of mathematical models in ecology and epidemiology is sensitivity of model outcomes to the precise equations used. Indeed, the €˜exact€™ mathematical formulation of model functions is often unknown; however the use of slightly different functions fitting well the same dataset may give significantly different predictions. In this case, the model is said to be €˜structurally sensitive€™ and its implementation may be grossly misleading. Even for a purely deterministic model the uncertainty in model functions (e.g. uncertainty in formulation of growth rates, functional responses, mortality terms, etc) carries through the uncertainty of model predictions and thus it can be a serious obstacle in ecological modelling, especially when making a decision in ecological management based on model prediction. In this talk, I will firstly discuss how the uncertainty in predictions using biological models with structural sensitivity can be quantified and estimated. In the second part of the talk, I will revisit the fundamental question of how empirical data (including model-guided data collection process) should be implemented for enhancing predictability of ecological models with structural sensitivity.

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:40 PM
Mason Porter - Migration of Populations via Marriages in the Past

The study of human mobility is both of fundamental importance and of great potential value. For example, it can be leveraged to facilitate efficient city planning and improve prevention strategies when faced with epidemics. The wealth of rich sources of data --- including banknote flows, mobile phone records, and transportation data --- has led to an explosion of attempts to characterize modern human mobility. Unfortunately, the dearth of comparable historical data makes it much more difficult to study human mobility patterns from the past. In this talk, I present an analysis of long-term human migration, which is important for processes such as urbanization and the spread of ideas. I demonstrate that the data record from Korean family books (called "jokbo") can be used to estimate migration patterns via marriages from the past 750 years. I apply two generative models of long-term human mobility to quantify the relevance of geographical information to human marriage records in the data, and I illustrate that the wide variety in the geographical distributions of the clans poses interesting challenges for the direct application of these models. Using the different geographical distributions of clans, I quantify the ergodicity of clans in terms of how widely and uniformly they have spread across Korea, and I compare these results to those obtained using surname data from the Czech Republic. To examine population flow in more detail, I also construct and examine a population-flow network between regions. Based on the correlation between ergodicity and migration in Korea, I identify two different types of migration patterns: diffusive and convective. I expect the analysis of diffusive versus convective effects in population flows to be widely applicable to the study of mobility and migration patterns across different cultures.

02:45 PM
03:00 PM

Break

03:00 PM
04:30 PM

Open Problem Discussion - breakouts and then come back together

04:30 PM

Shuttle pick-up from MBI

Thursday, November 17, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:40 AM
Sara del Valle - Real-time Social Internet Data to Guide Forecasting Models

Disease spread is major health concern around the world and it is compounded by the increasing globalization of our society. As such, epidemiological modeling approaches need to account for rapid changes in human behavior and community perceptions. Social media has recently played a crucial role in informing and changing people's response to the spread of infectious diseases. I will describe a modeling framework that simulates the movements, activities, and social interactions of millions of individuals, and the dynamics of infectious diseases. The simulation allows for agents' behaviors to be influenced by social media (i.e., Twitter) as well as by their neighbors. This feedback loop allows us to inject emergent attitudes in response to epidemics and quantify their impact. In addition, I will describe how Internet data streams are informing models to better forecast disease spread.

09:45 AM
10:25 AM

TBD

10:30 AM
10:45 AM

Break

10:45 AM
11:25 AM
Samuel Scarpino - The Predictability Horizon for Diseases

Infectious disease outbreaks recapitulate biology, emerging from the multi-level interaction of hosts, pathogens, and their shared environment. Therefore, predicting when and where diseases will spread requires a complex systems approach to modeling. However, it remains to be demonstrated that such complex systems are fundamentally predictable. To investigate this question, we study the intrinsic predicability of a diverse set of diseases. Instead of relying on methods which require an assumed knowledge of the data generating model, we utilize permutation entropy as a model independent metric of predicability. By studying the permutation entropy of a large collection of historical outbreaks--including, chlamydia, gonorrhea, hepatitis A, influenza, dengue, measles, polio, whooping cough, Ebola, and Zika--we identify a fundamental horizon for outbreak forecasts. Specifically, most diseases appear to be unpredictable beyond narrow time-horizons, thus highlighting the importance of dynamic modeling approaches to prediction. Our results have clear implications for the emerging field of disease forecasting and highlight the need for broader studies on the predictability of complex systems.

11:25 AM
11:40 AM

Break

11:45 AM
12:25 PM
Allison Aiello - A social network study of isolation and influenza-like illness in the university setting: the eX-FLU study

Introduction: Most universitiesâ‚„ plans for an influenza pandemic include some form of isolation or quarantine measures. But student willingness to comply is largely unknown, and the effectiveness of these measures has not been tested. The lack of research on the effects of isolation measures on university populations represents a significant gap in knowledge. We hypothesized that university students would be protected when their social contacts and classmates voluntarily self-isolated in their rooms at the onset of influenza-like illness (ILI) symptoms.


Methods: The eX-FLU study was conducted in a large public university during the 2013 influenza season to assess the efficacy of asking undergraduate students with ILI symptoms to voluntarily isolate in their dorm rooms for 3 days from symptom onset to evaluate their compliance with this recommendation and also to measure ILI transmission to their friends and classmates. Study participants were identified from students who were >18 years old and resided in six on-campus residence halls, using a chain-referral, snowball process. ILI symptoms were self-reported at any time during the 10 week study (defined as cough plus one of the following (fever or feverishness, chills, and body aches). To verify the presence of ILI, a research assistant visited students in their dorm room to observe and record symptoms and take a nasal/throat sample for influenza testing (both rapid test and RT-PCR). This clustered randomized intervention study examined an intervention group (3-day voluntary ILI self-isolation) compared with a control group of students with ILI who were not asked to isolate or change any of their illness behaviors. ILI case-patients were asked to report the numbers of hours spent in their rooms for the first 3 days of ILI, and their answers were used to assess intervention compliance. Across the intervention period, participants reported on weekly surveys the quantity and duration of contacts they had with other study participants, and these contacts were used to construct the eX-FLU social network. We examined the impact of having a higher proportion of network members that were ILI cases in the isolation intervention versus the control group on the odds of having ILI over the study period. To quantify this, we calculated the proportion of contacts with ILI who were in the intervention group versus the control group for each participant. We then examined whether a higher proportion of intervention group contacts with ILI was protective of a contact participants odds of having ILI over the 10-week study.


Results: During the 10-week intervention period, there were 132 reported ILI cases in 110 individuals (intervention group: 44, control group: 67,), with the number of illness episodes per individual ranging from one to four. Intervention group participants spent significantly more time in their rooms while ill (72 hours post-ILI onset) than those in the control group (82.2% [SD: 15.2%] vs. 61.5% [SD: 18.9%], p<0.001). Individuals who had contact with a higher proportion of ILI cases who were in the intervention arm compared to the control group, had lower odds of reporting ILI over the study period, although the results did not reach statistical significance (OR: 0.85 [0.41, 1.74]). We also examined the average shortest path lengths between individuals and ILI cases and found increasing average distance along the network path from ILI cases in their social networks was significantly protective against ILI.


Conclusions: The main results of this randomized isolation intervention provides suggestive evidence that contacts of isolated ILI cases have a lower likelihood of becoming an ILI case but these non-statistically significant findings should be replicated in larger studies. Our larger secondary analyses showed that having fewer ILI cases in oneâ‚„s own social network is significantly protective of ILI, supporting the findings from our main aims. Moreover, students are willing to stay home for 3 days while ill on campus, showing that this type of intervention in the university setting during an epidemic is feasible. Further social network intervention studies in larger samples of individuals impacted by ILI are needed.

12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:40 PM
Stephanie Peacock - Data cloning can guide study design to ensure parameter estimability in complex ecological models

The statistical tools available to ecologists are becoming increasingly sophisticated, allowing more complex, mechanistic models to be fit to ecological data. Such models have the potential to provide new insights into the processes underlying ecological patterns, but the inferences made are limited by the information in the data. Statistical inestimability of model parameters due to insufficient information in the data is a problem too-often ignored by ecologists employing complex models. Here, we show how a statistical computing method called data cloning can be used in simulation studies to assess the estimability of model parameters and inform study design before data are collected. A case study of parasite transmission from farmed to wild salmon highlights that assessing the estimability of ecologically relevant parameters should be a key step when designing studies in which fitting complex mechanistic models is the end goal.

02:40 PM
03:00 PM

Break

03:00 PM
04:30 PM

Open Problem Discussion - breakouts and then come back together

04:30 PM

Shuttle pick-up from MBI

06:30 PM
07:00 PM

Cash bar

07:00 PM
07:00 PM

Banquet in the Fusion Room @ Crowne Plaza Hotel

Friday, November 18, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:40 AM
Michael Johansson - Applying models to epidemics

Recent epidemics of pathogens such as H1N1 influenza virus, MERS coronavirus, chikungunya virus, Ebola virus, and Zika virus, highlight the importance of epidemics on local and global scales. Modeling has long been used as a conceptual tool to describe epidemic dynamics and assess possible interventions, yet the direct use of modeling in the public decision making process remains limited. To help close this gap it is essential to build links between the research and decision-making communities to: ensure that modeling targets match specific public health needs, facilitate the sharing of data and knowledge about that data, establish standards for assessing and communicating model skill, identify ways to effectively communicate predictions and especially uncertainties, and develop systems for operationalizing models for repeated use. Efforts to forecast seasonal dengue and influenza outbreaks highlight opportunities to evaluate forecasting models in the context of specific public health needs and advance both the science of infectious disease forecasting and the integration of forecasting into decision-making processes.

09:45 AM
10:25 AM
Mark Lewis - Connecting models to data for animal movement models in ecology

Animal movement patterns have long fascinated mathematicians and ecologists alike. One type of primarily mathematical investigation focuses on pattern formation. How do individual behavioural decision rules translate into macroscale patterns of space use? Here mechanistic models, using random walks, stochastic processes and partial differential equations have connected pattern to process. Another type of primarily ecological investigation correlates space use patterns to underlying environmental features. Here statistical models, based on resource selection have connected patterns to environmental features. In this talk I will build a bridge between mechanism and resource selection using the concept of coupled step selection functions. The approach is based on a mechanistic underpinning for the movement process, but is also amenable to easy statistical inference regarding space use. I will demonstrate how each type of model can be connected to detailed movement data to give new insight about animal behaviour. Applications will be made to a spectrum of different animals ranging from Amazonian birds to caribou to coyotes.

10:25 AM
11:00 AM

Break

11:00 AM
12:00 PM

Informal discussion/workshop wrap up

12:00 PM

Shuttle pick up (one to hotel, one to airport)

Name Email Affiliation
Adebimpe, Olukayode adebimpe.olukayode@lmu.edu.ng Department of Industrial Mathematics, Landmark University, Omuaran, Nigeria
Aeberhard, William William.Aeberhard@Dal.Ca Departement of Mathematics and Statistics, Dalhousie University
Aiello, Allison aaiello@unc.edu Epidemiology, University of North Carolina at Chapel Hill
Arino, Julien arinoj@cc.umanitoba.ca Mathematics, University of Manitoba
Bansal, Shweta shweta@sbansal.com Biology, Georgetown University
Beckman, Noelle National Socio-Environmental Synthesis Center (SESYNC), University of Maryland, College-Park, National Socio-Environmental Synthesis Center
Campbell, Dave dac5@sfu.ca Dept of Statistics and Actuarial Science, Simon Fraser University
Carja, Oana Biology, University of Pennsylvania
Chugunova, Marina marina.chugunova@cgu.edu Institute of Mathematical Sciences, Claremont Graduate University
Chukwu, Angela unnachuks2002@yahoo.co.uk Department of Statistics, University of Ibadan
Cifuentes, Patricia cifuentesgarcia.1@osu.edu College of Public Health - department of Biomedical Informatics, The Ohio State University
Dean, Charmaine cdowdel@uwo.ca Faculty of Science, University of Western Ontario
Del Valle, Sara sdelvall@lanl.gov Information Systems and Modeling, Los Alamos National Laboratory
Eisenberg, Marisa marisae@umich.edu Department of Epidemiology, University of Michigan
Ferrari, Matthew matthewferrari@me.com Biology, Pennsylvania State University
Foss-Grant, Andrew andyfg@umd.edu Biology, University of Maryland
Garabed, Rebecca garabed.1@osu.edu Veterinary Preventive Medicine, The Ohio State University
Gomes, Gabriela gabriela.gomes@lstmed.ac.uk Clinical Sciences, Liverpool School of Tropical Medicine
Govinder, Kesh govinder@ukzn.ac.za Mathematics, Statistics and Computer Science, University of KwaZulu-Natal
Gutierrez, Juan B. juan@math.uga.edu Mathematics, Institute of Bioinformatics, University of Georgia
Haider, Mansoor m_haider@ncsu.edu Mathematics & Biomathematics Graduate Program, North Carolina State University
Handelman, Sam samuel.handelman@gmail.com Obstetrics and Gynecology, Wayne State University School of Medicine
Hastings, Alan amhastings@ucdavis.edu Department of Environmental Science and Policy, University of California, Davis
Hurtado, Paul Mathematics & Statistics, University of Nevada
Hyder, Ayaz College of Public Health, The Ohio State University
Johansson, Michael mjohansson@cdc.gov Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health
Juan, Lourdes lourdes.juan@ttu.edu Mathematics and Statistics, Texas Tech University
Kalies, William wkalies@fau.edu Mathematical Sciences, Florida Atlantic University
Kao, Yu-Han kaoyh@umich.edu Epidemiology, University of Michigan
Kelly, Michael mkelly14@utk.edu Mathematics, University of Tennessee
Kirievskaya, Dubrava dkirievskaya1@udayton.edu Research Center, Novgorod State University
Kong, Jude dzevela@icloud.com Mathematics and Statistical Sciences, University of Alberta
Kong, Liang lkong9@uis.edu Mathematical Science, University of Illinois at Springfield
Korman, Philip philip.korman@uc.edu Mathematical Sciences, University of Cincinnati
Kumar, Sandeep sandeep25789@gmail.com Biosciences and Bioengineering, Indian Institute of Technology Bombay
Ledzewicz, Urszula uledzew@siue.edu Department of Mathematics and Statistics, Southern Illinois University
Lee, Elizabeth ecl48@georgetown.edu Global Infectious Disease, Georgetown University
Lele, Subhash slele@ualberta.ca Mathematical and Statistical Sciences, University of Alberta
Lewis, Mark mlewis@math.ualberta.ca Canada Research Chair in Mathematical Biology, University of Alberta
Lloyd, Alun alun_lloyd@ncsu.edu Biomathematics Graduate Program, Department of Mathematics, North Carolina State University
Malitz, Eric emalit1@uic.edu Mathematics, Statistics, and Computer Science, University of Illinois
Mennicke, Christine cvmennic@ncsu.edu Mathematics, North Carolina State University
Morozov, Andrew am379@leicester.ac.uk Mathematics, University of Leicester
Olobatuyi, Oluwole olobatuy@ualberta.ca Mathematical and Statistical Sciences, University of Alberta
Peacock, Stephanie stephanie.peacock@ualberta.ca Biological Sciences, University of Alberta
Porter, Mason porterm@maths.ox.ac.uk Mathematical Institute, University of Oxford
Scarpino, Samuel scarpino@santafe.edu Mathematics & Statistics, University of Vermont
Sudakov, Ivan isudakov1@udayton.edu Physics, University of Dayton
Taylor, Rachel rataylor@usf.edu Department of Integrative Biology, University of South Florida
Walker, Danielle danielle.walker@springer.com Editorial, Springer
Walsh, Alison walshar@umich.edu Epidemiology, University of Michigan
Xi, Dexen dxi@uwo.ca Department of Statistical and Actuarial Sciences, University of Western Ontario
Robust fitting of state-space models for reliable fish stock assessment

The sustainable management of fisheries strongly relies on the output of fish stock assessment models fitted to scarce and noisy data. State-space models represent a relevant general framework for accounting for both measurement error and a complex dependence structure of latent (unobserved) random variables. Classical estimation of fixed parameters in such models, for instance by maximizing an approximated marginal likelihood, is known to be highly sensitive to the correct specification of the model. This sensitivity is all the more so problematic since assumptions about latent variables cannot be verified by the data analyst. We introduce robust and consistent estimators for general state-space models which remain stable under deviations from the assumed model. These estimators are shown to yield reliable inference for fish stock assessment in various scenarios.

A social network study of isolation and influenza-like illness in the university setting: the eX-FLU study

Introduction: Most universities’ plans for an influenza pandemic include some form of isolation or quarantine measures. But student willingness to comply is largely unknown, and the effectiveness of these measures has not been tested. The lack of research on the effects of isolation measures on university populations represents a significant gap in knowledge. We hypothesized that university students would be protected when their social contacts and classmates voluntarily self-isolated in their rooms at the onset of influenza-like illness (ILI) symptoms.


Methods: The eX-FLU study was conducted in a large public university during the 2013 influenza season to assess the efficacy of asking undergraduate students with ILI symptoms to voluntarily isolate in their dorm rooms for 3 days from symptom onset to evaluate their compliance with this recommendation and also to measure ILI transmission to their friends and classmates. Study participants were identified from students who were >18 years old and resided in six on-campus residence halls, using a chain-referral, snowball process. ILI symptoms were self-reported at any time during the 10 week study (defined as cough plus one of the following (fever or feverishness, chills, and body aches). To verify the presence of ILI, a research assistant visited students in their dorm room to observe and record symptoms and take a nasal/throat sample for influenza testing (both rapid test and RT-PCR). This clustered randomized intervention study examined an intervention group (3-day voluntary ILI self-isolation) compared with a control group of students with ILI who were not asked to isolate or change any of their illness behaviors. ILI case-patients were asked to report the numbers of hours spent in their rooms for the first 3 days of ILI, and their answers were used to assess intervention compliance. Across the intervention period, participants reported on weekly surveys the quantity and duration of contacts they had with other study participants, and these contacts were used to construct the eX-FLU social network. We examined the impact of having a higher proportion of network members that were ILI cases in the isolation intervention versus the control group on the odds of having ILI over the study period. To quantify this, we calculated the proportion of contacts with ILI who were in the intervention group versus the control group for each participant. We then examined whether a higher proportion of intervention group contacts with ILI was protective of a contact participants odds of having ILI over the 10-week study.


Results: During the 10-week intervention period, there were 132 reported ILI cases in 110 individuals (intervention group: 44, control group: 67,), with the number of illness episodes per individual ranging from one to four. Intervention group participants spent significantly more time in their rooms while ill (72 hours post-ILI onset) than those in the control group (82.2% [SD: 15.2%] vs. 61.5% [SD: 18.9%], p<0.001). Individuals who had contact with a higher proportion of ILI cases who were in the intervention arm compared to the control group, had lower odds of reporting ILI over the study period, although the results did not reach statistical significance (OR: 0.85 [0.41, 1.74]). We also examined the average shortest path lengths between individuals and ILI cases and found increasing average distance along the network path from ILI cases in their social networks was significantly protective against ILI.


Conclusions: The main results of this randomized isolation intervention provides suggestive evidence that contacts of isolated ILI cases have a lower likelihood of becoming an ILI case but these non-statistically significant findings should be replicated in larger studies. Our larger secondary analyses showed that having fewer ILI cases in one’s own social network is significantly protective of ILI, supporting the findings from our main aims. Moreover, students are willing to stay home for 3 days while ill on campus, showing that this type of intervention in the university setting during an epidemic is feasible. Further social network intervention studies in larger samples of individuals impacted by ILI are needed.

Modelling-assisted disease surveillance

Internet-based disease surveillance is a tool providing early warning about infectious disease outbreaks. There are variations, but the common idea is to automatically monitor Internet sources (news, blogs, etc.), searching for articles containing keywords related to infectious diseases. Natural language processing is then used to pinpoint the location being mentioned, eliminate duplicates, etc. Some systems additionally have human input to weed out false positives. In all instances, though, these systems produce a large amount of alerts.


I will discuss ongoing work using stochastic metapopulation models for the global spread of infectious pathogens along the global air transportation network. I will show in particular how such models can be used to help filter the large number of alerts generated by Internet-trawling surveillance systems.

Assessing species' risk under climate change

Global change affects the ecology and evolution of dispersal, limiting the ability of species to move or adapt to global change events. Due to the long-term and spatially-complex dynamics of plant populations, understanding and predicting their responses to global change is empirically and mathematically challenging. I apply recent advances in the study of species’ movement and develop a general classification scheme to assess the risk of plant extinction in response to climate change in continuous landscapes. Using a Bayesian approach, I synthesize existing data on dispersal, functional traits, and demography to generate virtual species with realistic dispersal kernels and life-history strategies. I sample these virtual species to parameterize integrodifference equations and approximate population spread in continuous landscapes. Using this approach, I obtain predictors of risk that are related to easily measurable functional traits that will inform the types of species least likely to track a shifting climate. In future research, this approach will be extended to predict extinction risk of plant species in fragmented landscapes. This research will help identify species at greatest risk and aid the development of conservation strategies to ensure their persistence under global change.

Diagnostics for Fast Model Estimates

State space models work on two different layers of noise: a noise infused process model and additional measurement noise. A noise infused process model may track the annual population size of salmon, where the noise in this layer may be used to account for un-modelable environmental fluctuations or random perturbations to migratory routes. Subsequently, the population size is observed via noisy measurements, where this may be due to challenges in accurately counting the size of the population of salmon. As a result, estimating parameters through these two layers of noise requires dealing with considerable uncertainty. The widely adopted Integrated Nested Laplace Approximation (INLA) is designed to approximately integrate out some parts of the model, accelerating and simplifying the process of estimating parameters. The INLA approximation lies in the assumption that performing the integral is equivalent to integrating a Gaussian. The alternative to using INLA, and also checking validity of the INLA assumption, typically requires high dimensional and slow but very accurate Monte Carlo integration. This forces the practitioner to chose between the extremes of quick and rough or slow and precise. In this work we devise an INLA diagnostic /alternative model integration approach allowing the user to decide where to stand in a continuous variant of the previously binary speed vs accuracy tradeoff. Additionally, the proposed approach outputs a measure of confidence in the applied approximate integral. The method is based on probabilistic numerics, a new area of research bringing together numerical analysis, applied mathematics, statistics, and computer science. This is joint work with Charlie Zhou (Simon Fraser University) and Oksana Chkrebtii (the Ohio State University).

Forest Fire Risks: Assessing Historical Trends, Insurance Risks and Health Effects

Assessing trends forest fire risk is of significant concern to fire managers as well as for the insurance and health sectors because of impacts of such risks in these areas. In particular, determining trends in forest fire ignition risk as measured by increasing annual trends in ignitions, or the lengthening of the fire season within each year, or both of these factors, requires urgent attention. This talk focuses on annual and seasonal changes in forest fire ignition risk, observed over the last 50 years. How fire is linked to climate effects is also discussed along with an identification of those factors that need to be considered before one can attribute observed changes in forest fire occurrence to climate change. The talk then attends to insurance assessments of such forest fire risk as well as an examination of new tools based on satellite imagery to assist in determining the health risks related to forest fire smoke exposure.

Real-time Social Internet Data to Guide Forecasting Models

Disease spread is major health concern around the world and it is compounded by the increasing globalization of our society. As such, epidemiological modeling approaches need to account for rapid changes in human behavior and community perceptions. Social media has recently played a crucial role in informing and changing people's response to the spread of infectious diseases. I will describe a modeling framework that simulates the movements, activities, and social interactions of millions of individuals, and the dynamics of infectious diseases. The simulation allows for agents' behaviors to be influenced by social media (i.e., Twitter) as well as by their neighbors. This feedback loop allows us to inject emergent attitudes in response to epidemics and quantify their impact. In addition, I will describe how Internet data streams are informing models to better forecast disease spread.

Connecting models with data: identifiability and parameter estimation of multiple transmission pathways

Connecting dynamic models with data to yield predictive results often requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what is possible to estimate from a given model and data set, and help guide new data collection. In this talk, we will discuss approaches to both structural and practical identifiability analysis. Using a range of examples from cholera and the West Africa Ebola epidemic, we illustrate some of the potential difficulties in estimating the relative contributions of different transmission pathways, and show how alternative data collection may help resolve unidentifiability. We also illustrate how even in the presence of large uncertainties in the data and model parameters, it may still be possible to successfully forecast disease dynamics.

Managing multiple sources of uncertainty: optimal outbreak response for Foot-and-Mouth Disease

Control of epizootics require that decisions be made in the face of multiple sources of uncertainty: economic, political and logistical uncertainty, dynamical uncertainty about epizootiological processes, and stochastic nature of disease spread. Decision-makers are faced with fundamental trade-off between the learning that will accrue through continued observation of a disease process and the opportunity cost of inaction. Structured decision-making and adaptive management seek to minimize the opportunity cost of inaction by defining an iterative, state-dependent policy for selecting among alternative management actions. In particular, we seek to define an adaptive policy that responds to the changing state of information about competing dynamical models as defined in the posterior distribution and the chaining epizootiological state as defined by the size and spatial extent of an outbreak. We achieve the former through an analysis of the value of information across competing models and sequential analysis of real-time outbreak surveillance from the 2001 foot-and-mouth outbreak in the UK. We achieve the latter by using reinforcement learning to solve for an optimal state-dependent policy for the application of vaccination and culling for a spatially explicit livestock outbreak. We show that adaptive policies can result in significant gains over conventional static management.

Modeling for the Data You Have
Modeling selection bias to enable accurate estimation and prediction

Mathematical models for infectious diseases are often contested based on accumulating examples where the impact of public health interventions is over-predicted. We attribute model over-optimism to a lack of account for variation in individual “frailty� – a concept that has at least 30 years in demography and statistics, but has been overlooked in infectious disease epidemiology. Like any form of standing variation, distributions of individual risk constitute a basis for selection – under the force of infection in the case of infectious diseases – which results in the population at risk having a dynamic mean susceptibility. In models that fail to account for realistic risk distributions, the dynamics of mean susceptibility are inaccurate (or disabled in the case of homogeneous models), and resulting predictions are unlikely to be met. This concern applies not only to infectious disease modeling but also, and more simply, to the interpretation of clinical trail outcomes.


We propose specific study designs to overcome this type of selection bias in infectious disease modeling and trial analyses. Rather than trying to avoid it with intricate designs, the approach is to acknowledge the presence of cohort selection in the data and account for it in analytic models to enable more accurate parameter estimation and prediction. The resulting frameworks might also serve as think tanks for innovative interventions that modify disease risk distributions more generally, such as those that address comorbidities, socioeconomic health determinants or biological control of disease vectors.

Multiscale Systems Biology: From Genes to Environment

The advent of high-throughput molecular technologies, and the flood of information they have produced, has forced the biomathematical community to rethink how to conceive, build, and validate mathematical models. In this talk I will demonstrate how the integration of molecular and cellular models shape geographic considerations in the mathematical modeling of malaria. The usefulness of models under this light takes on new meanings, and this broad scope requires the cooperation of scientists coming from very different intellectual traditions. This talk will also explain how an adaptive learning system named ALICE (Adaptive Learning for Interdisciplinary Collaborative Environments) is used to train scientists that approach biomathematics from multiple disciplines.

Dealing with management

One of the goals of population models as the field matures is to use them in making management decisions. I will review different approaches for developing population models for management, and emphasize both general issues of dealing with uncertainty, and also consider specific systems such as fisheries or invasive species. The issue of management means getting away from asymptotic behavior and focusing on shorter time scales.

Applying models to epidemics

Recent epidemics of pathogens such as H1N1 influenza virus, MERS coronavirus, chikungunya virus, Ebola virus, and Zika virus, highlight the importance of epidemics on local and global scales. Modeling has long been used as a conceptual tool to describe epidemic dynamics and assess possible interventions, yet the direct use of modeling in the public decision making process remains limited. To help close this gap it is essential to build links between the research and decision-making communities to: ensure that modeling targets match specific public health needs, facilitate the sharing of data and knowledge about that data, establish standards for assessing and communicating model skill, identify ways to effectively communicate predictions and especially uncertainties, and develop systems for operationalizing models for repeated use. Efforts to forecast seasonal dengue and influenza outbreaks highlight opportunities to evaluate forecasting models in the context of specific public health needs and advance both the science of infectious disease forecasting and the integration of forecasting into decision-making processes.

Covariates in population models

Population dynamics models are used for projecting effects of climate change or management strategies. Obviously these changes appear as covariates that affect various parameters in the population dynamics models. The covariates are often measured with error either because of simple measurement error or because they are projected in future using some model with associated prediction error. In this paper, I will illustrate the effect of covariate measurement error in population dynamics models on estimation and prediction error. Furthermore, I will show that such covariate measurement error can be accounted for using hierarchical modeling structure.

Connecting models to data for animal movement models in ecology

Animal movement patterns have long fascinated mathematicians and ecologists alike. One type of primarily mathematical investigation focuses on pattern formation. How do individual behavioural decision rules translate into macroscale patterns of space use? Here mechanistic models, using random walks, stochastic processes and partial differential equations have connected pattern to process. Another type of primarily ecological investigation correlates space use patterns to underlying environmental features. Here statistical models, based on resource selection have connected patterns to environmental features. In this talk I will build a bridge between mechanism and resource selection using the concept of coupled step selection functions. The approach is based on a mechanistic underpinning for the movement process, but is also amenable to easy statistical inference regarding space use. I will demonstrate how each type of model can be connected to detailed movement data to give new insight about animal behaviour. Applications will be made to a spectrum of different animals ranging from Amazonian birds to caribou to coyotes.

Model-Guided Design of Experiments and Data Collection

I shall discuss the utility of mechanistic mathematical models as aids in the design and development of experiments. The impact of model parameters on model outputs can be assessed using techniques from uncertainty quantification. Thus one can determine those parameters for which additional knowledge would best improve the predictive ability of a model. Furthermore, one can gain understanding of what data is needed, and how much and when it should be collected in order to best achieve this aim. I shall illustrate these ideas using some examples from infectious disease projects on which I have worked, including some in the area of mosquito-borne diseases.

Enhancing predictability of biological models with structural sensitivity: how should we proceed?

A fundamental property of mathematical models in ecology and epidemiology is sensitivity of model outcomes to the precise equations used. Indeed, the ‘exact’ mathematical formulation of model functions is often unknown; however the use of slightly different functions fitting well the same dataset may give significantly different predictions. In this case, the model is said to be ‘structurally sensitive’ and its implementation may be grossly misleading. Even for a purely deterministic model the uncertainty in model functions (e.g. uncertainty in formulation of growth rates, functional responses, mortality terms, etc) carries through the uncertainty of model predictions and thus it can be a serious obstacle in ecological modelling, especially when making a decision in ecological management based on model prediction. In this talk, I will firstly discuss how the uncertainty in predictions using biological models with structural sensitivity can be quantified and estimated. In the second part of the talk, I will revisit the fundamental question of how empirical data (including model-guided data collection process) should be implemented for enhancing predictability of ecological models with structural sensitivity.

Data cloning can guide study design to ensure parameter estimability in complex ecological models

The statistical tools available to ecologists are becoming increasingly sophisticated, allowing more complex, mechanistic models to be fit to ecological data. Such models have the potential to provide new insights into the processes underlying ecological patterns, but the inferences made are limited by the information in the data. Statistical inestimability of model parameters due to insufficient information in the data is a problem too-often ignored by ecologists employing complex models. Here, we show how a statistical computing method called data cloning can be used in simulation studies to assess the estimability of model parameters and inform study design before data are collected. A case study of parasite transmission from farmed to wild salmon highlights that assessing the estimability of ecologically relevant parameters should be a key step when designing studies in which fitting complex mechanistic models is the end goal.

Migration of Populations via Marriages in the Past

The study of human mobility is both of fundamental importance and of great potential value. For example, it can be leveraged to facilitate efficient city planning and improve prevention strategies when faced with epidemics. The wealth of rich sources of data --- including banknote flows, mobile phone records, and transportation data --- has led to an explosion of attempts to characterize modern human mobility. Unfortunately, the dearth of comparable historical data makes it much more difficult to study human mobility patterns from the past. In this talk, I present an analysis of long-term human migration, which is important for processes such as urbanization and the spread of ideas. I demonstrate that the data record from Korean family books (called "jokbo") can be used to estimate migration patterns via marriages from the past 750 years. I apply two generative models of long-term human mobility to quantify the relevance of geographical information to human marriage records in the data, and I illustrate that the wide variety in the geographical distributions of the clans poses interesting challenges for the direct application of these models. Using the different geographical distributions of clans, I quantify the ergodicity of clans in terms of how widely and uniformly they have spread across Korea, and I compare these results to those obtained using surname data from the Czech Republic. To examine population flow in more detail, I also construct and examine a population-flow network between regions. Based on the correlation between ergodicity and migration in Korea, I identify two different types of migration patterns: diffusive and convective. I expect the analysis of diffusive versus convective effects in population flows to be widely applicable to the study of mobility and migration patterns across different cultures.

The Predictability Horizon for Diseases

Infectious disease outbreaks recapitulate biology, emerging from the multi-level interaction of hosts, pathogens, and their shared environment. Therefore, predicting when and where diseases will spread requires a complex systems approach to modeling. However, it remains to be demonstrated that such complex systems are fundamentally predictable. To investigate this question, we study the intrinsic predicability of a diverse set of diseases. Instead of relying on methods which require an assumed knowledge of the data generating model, we utilize permutation entropy as a model independent metric of predicability. By studying the permutation entropy of a large collection of historical outbreaks--including, chlamydia, gonorrhea, hepatitis A, influenza, dengue, measles, polio, whooping cough, Ebola, and Zika--we identify a fundamental horizon for outbreak forecasts. Specifically, most diseases appear to be unpredictable beyond narrow time-horizons, thus highlighting the importance of dynamic modeling approaches to prediction. Our results have clear implications for the emerging field of disease forecasting and highlight the need for broader studies on the predictability of complex systems.

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Managing multiple sources of uncertainty: optimal outbreak response for Foot-and-Mouth Disease
Matthew Ferrari

Control of epizootics require that decisions be made in the face of multiple sources of uncertainty: economic, political and logistical uncertainty, dynamical uncertainty about epizootiological processes, and stochastic nature of disease spr

video image

Connecting models with data: identifiability and parameter estimation of multiple transmission pathways
Marisa Eisenberg

Connecting dynamic models with data to yield predictive results often requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what is possible to estimate

video image

Modelling-assisted disease surveillance
Julien Arino

Internet-based disease surveillance is a tool providing early warning about infectious disease outbreaks. There are variations, but the common idea is to automatically monitor Internet sources (news, blogs, etc.), searching for articles cont

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Model-Guided Design of Experiments and Data Collection
Alun Lloyd

I shall discuss the utility of mechanistic mathematical models as aids in the design and development of experiments. The impact of model parameters on model outputs can be assessed using techniques from uncertainty quantification. Thus one c

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Diagnostics for Fast Model Estimates
Dave Campbell

State space models work on two different layers of noise: a noise infused process model and additional measurement noise. A noise infused process model may track the annual population size of salmon, where the noise in this layer may be used

video image

Multiscale Systems Biology: From Genes to Environment
Juan B. Gutierrez

The advent of high-throughput molecular technologies, and the flood of information they have produced, has forced the biomathematical community to rethink how to conceive, build, and validate mathematical models. In this talk I will demonstr

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Enhancing predictability of biological models with structural sensitivity: how should we proceed?
Andrew Morozov

A fundamental property of mathematical models in ecology and epidemiology is sensitivity of model outcomes to the precise equations used. Indeed, the €˜exact€™ mathematical formulation of model functions i

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Migration of Populations via Marriages in the Past
Mason Porter

The study of human mobility is both of fundamental importance and of great potential value. For example, it can be leveraged to facilitate efficient city planning and improve prevention strategies when faced with epidemics. The wealth of ric

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Real-time Social Internet Data to Guide Forecasting Models
Sara Del Valle

Disease spread is major health concern around the world and it is compounded by the increasing globalization of our society. As such, epidemiological modeling approaches need to account for rapid changes in human behavior and community perce

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The Predictability Horizon for Diseases
Samuel Scarpino

Infectious disease outbreaks recapitulate biology, emerging from the multi-level interaction of hosts, pathogens, and their shared environment. Therefore, predicting when and where diseases will spread requires a complex systems approach to

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Applying models to epidemics
Michael Johansson

Recent epidemics of pathogens such as H1N1 influenza virus, MERS coronavirus, chikungunya virus, Ebola virus, and Zika virus, highlight the importance of epidemics on local and global scales. Modeling has long been used as a conceptual tool

video image

Connecting models to data for animal movement models in ecology
Mark Lewis

Animal movement patterns have long fascinated mathematicians and ecologists alike. One type of primarily mathematical investigation focuses on pattern formation. How do individual behavioural decision rules translate into macroscale patterns