Note: The findings and conclusions in panels, breakout groups, posters, and discussion are those of the individuals presenting and/or speaking and do not necessarily represent the official position of institutions with which they are affiliated. Thank you to the sponsors:

Mathematical Assessment of the Role of Traditional Beliefs

System and Customs and Health-care Settings on the

Transmission Dynamics of the 2014 Ebola Outbreaks

January 13, 2015

F.B. Agustoa, M.I Teboh-Ewungkemb and A.B. Gumelc,d

a Department of Mathematics and Statistics, Austin Peay State University, Clarksville, TN, 37044, USA.b Department of Mathematics, Lehigh University, Bethlehem, PA, 18015, USA.c Simon A Levin Mathematical, Computational and Modeling Sciences Center, Arizona State University, Tempe,

AZ 85287-1904, USA.d School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069-7100, USA.

Abstract

A new mathematical model is designed and used to assess the population-level impact of ba-sic (non-pharmaceutical) public health control measures on the 2014 outbreaks of the Ebolavirus disease in some parts of Western Africa. Using available data relevant to the diseasedynamics in the three Ebola-stricken countries in Western Africa, numerical simulations ofthe model, which incorporate the role of traditional/cultural beliefs system and customs(which inhibit public health campaigns to curtail the spread of the disease) as well as dis-ease transmission by Ebola-infected deceased individuals, show that, in the absence of suchcontrol measures (worst-case scenario), the distribution of the associated basic reproductionnumber of the model (denoted by R0) lies in the range R0 ∈ [3.5, 6] for the case whentraditional belief system and customs do not result in more unreported Ebola cases. For thecase when the traditional belief system and customs inhibit control efforts, there is furtherincrease in the distribution of R0 (these findings suggest that a catastrophic disease bur-den would have resulted if effective public health control measures were not implemented).Further simulations of the worst-case scenario model shows that while the Ebola-infecteddeceased individuals contribute about 5% of the total number of cases, symptomatic indi-viduals in the early (24%) and later stages of infection (71%) generate the most numberof cases. The model with interventions is simulated under three effectiveness levels of thebasic public health strategy. These simulations, together with a detailed sensitivity analysisto determine the parameters that most affect the disease transmission dynamics, show thateffective control of the Ebola virus in the three West African countries is feasible using basicpublic health measures alone (provided at least moderate effectiveness level is attained). Inparticular, this study shows that a dramatic reduction of new Ebola cases can be achievedby (i) increasing the duration of health-care workers’ daily work shifts from 8 hours to 24hours; (ii) limiting the length of hospital visitation of members of the public (to see theirEbola-infected relatives or acquaintances) to 1 hour and (iii) effectively educating the pop-ulace to desist from practicing traditional/cultural beliefs system and customs that aid thespread of Ebola in the affected regions.

1

Title: Optimizing Allocation of Resources in Ebola Epidemics: (1) Collection of Corpses and (2) Facilities (What and Where)

Student Teams from Senior Capstone Course

H. Milton Stewart School of Industrial and Systems Engineering

Georgia Institute of Technology

Client Advisor: Dr. Brian Gurbaxani (OADS at CDC, Adjunct faculty in ECE Georgia Tech)

Team 1: Alberto Gedaly, Alex Kraemer, Elena Joy, Moussa Hodjat-­‐Shamami, Caterina Selman, Meghna Mehadevan (Faculty Advisor, Dr. Turgay Ayer*)

Team 2: Kimberly Adelaar, Charmaine Chan, Matt Daniels, Javeria Javeria, Caleb Mbuvi, Chu Qian, Ivan Renaldi Jonathan Sutomo (Faculty Advisor, Dr. Julie Swann+)

*turgay.ayer@isye.gatech.edu; +jswann@isye.gatech.edu

Abstract:

Senior design is the capstone course for students graduating from Georgia Tech’s School of Industrial and Systems Engineering (ISyE). Student teams work with clients, delivering recommendations and designing solutions to real-­‐world problems. In Spring 2015, two teams are studying particular problems related to reducing the impact of the Ebola virus in Africa.

Team 1 is examining the best location prioritization and routing to collect deceased Ebola victims in order to prevent as many secondary infections as possible. Team 2 is assessing resources for different types of treatment facilities and diagnostic labs during an Ebola epidemic, especially focusing on what type of facilities are most effective, where, and when, when facilities may differ in their effectiveness and how quickly they can be built. Both teams will be linking models of disease spread with allocation of resources and projecting how many cases can be prevented under different scenarios. Full results will be available by May 2015.

Title:

Predicting Unobserved Contacts from Ebola Epidemic Data

Authors:

Simone Bianco1,*, Kun Hu1, James Kaufman1, and Ira B. Schwartz2

1IBM Almaden Research Center, Department of Industrial and Applied Ge-

nomics, 650 Harry Road, San Jose, CA 95120-6099, United States2US Naval Research Laboratory, Nonlinear Systems Dynamics Section, Plasma

Physics Division, Code 6792, U.S. Naval Re- search Laboratory, Washington,

DC 20375, USA*Presenting author: sbianco@us.ibm.com

Poster abstract:

The epidemiology of the Ebola virus is characterized by a long incubation

period. Exposure to the virus results in symptomatic infection, within 21 days

from direct contact with infected bodily uids. This implies that asymptomatic

individuals are able to travel without showing apparent symptoms, and become

eective carriers to areas which do not directly harbor animal reservoirs, thus

sustaining the epidemics. A correct characterization of the exposure class is

paramount to assessing the risk of spreading the virus as well as implement-

ing successful anticipatory controls. In this work we propose a synchronization

method based on dimension reduction to infer the number of exposed Ebola

individuals from real data. Through our method, we are able to project the

deterministic dynamics of the disease ontoa lower dimensional manifold, and

correctly infer the time course of the population of unobserved exposed indi-

viduals. Our results are able to provide reliable statistical information about

unobserved contacts and may be important for rapid tracing of contacts and

future disease control strategies.

1

Title:Modeling contact tracing in Ebola outbreaks

Authors:Cameron Browne1∗, Hayriye Gulbudak2∗, Glenn Webb1, and Joshua Weitz2

1. Department of Mathematics, Vanderbilt University, Nashville, TN, USA2. School of Biology, Georgia Institute of Technology, Atlanta, GA, USA∗ Presenting author: cameron.j.browne@vanderbilt.edu∗ Presenting author: hgulbudak3@gatech.edu

Poster abstract:Contact tracing is an important control strategy for containing Ebola epi-demics. From a theoretical perspective, even in relatively simple models,contact tracing presents significant dynamic complexities. In this work, weformulate and analyze a mechanistic SEIR type outbreak model which in-corporates the key features of contact tracing. We characterize the impactof contact tracing on the effective reproduction number, Re, of Ebola. Wealso determine how relevant epidemiological properties such as incubationperiod and infectious period, along with varying public health protocols,affect the efficacy of contact tracing. In special cases of our model, we de-rive (solely in the terms of R0): the critical proportion of cases triggeringcontact tracing and critical average number of infected contacts traced per(triggering) case, which is required to bring the effective reproduction num-ber Re below one. Data from the West Africa Ebola outbreak is used toparameterize the model and inform our projections of the impact of contacttracing, and other control measures, on the epidemic trajectory.

1

Title:

Large graph properties for an extension of the configuration model general stochastic epidemic

Authors:

Mark Burch*, Joseph Tien, Grzegorz Rempala

Mathematical Biosciences Institute & Division of Biostatistics

The Ohio State University

Columbus, OH, USA

* Presenting author: burch.126@osu.edu

Poster Abstract:

Recent methodological work in Stochastic modeling of epidemic processes has shifted away from the classical (SIR-

type) compartmental model paradigm towards the contact process taking place on a large random network.

Random network models are of particular interest as they are thought to better reflect true transmission patterns

observed in disease outbreaks. Perhaps surprisingly, it has been shown that certain classes of random graphs yield

deterministic asymptotic properties which are comparable in complexity to compartmental models [1,2,3]. There

remains work to be done in terms of generalization, inference, and computation to improve pragmatic usage of

these models. We propose a generalization of the system of Volz/Miller that reflects Ebola-specific properties such

as disproportionate risk of infection for healthcare workers, changes in contact pattern as symptoms worsen, and

contact clustering (such as within household) [4]. We explore the asymptotic properties of this system by diffusion

approximation and the deterministic limit with numerical simulations. Future work includes rigorous mathematical

analysis of these properties, development of statistical inference procedures from standard data structures, and

software implementation to aid in public health management of future outbreaks.

[1] Erik Volz. SIR dynamics in random networks with heterogeneous connectivity. Journal of Mathematical Biology, 56(3):293–310, 2008.

[2] Joe Miller, Anja Slim, Erik Volz. Edge-based compartmental modeling for infectious disease spread. Journal of the Royal Society Interface, 2011.

[3] Laurent Decreusefond, Jean-Stephane Dhersin, Pascal Moyal, Viet Chi Tran, et al. Large graph limit for an SIR process in random network with heterogeneous connectivity. The Annals of Applied Probability, 22(2):541–575, 2012.

[4] WHO Ebola Response Team. Ebola virus disease in West Africa – the first 9 months of the epidemic and forward projections. New England Journal of Medicine, 371(16):1481–1495, 2014.

Title:

2014 West African Ebola Epidemic

Using a Lattice-Based Network Model to Resolve Hypotheses for Regional Trends

Author:

Maria Kiskowski

Department of Mathematics and Statistics

University of South Alabama

Mobile, AL, USA

Poster Abstract:

The early growth dynamics of the West Africa Ebola virus epidemic have been qualitatively

different for Guinea, Sierra Leona and Liberia, reflecting consistent overall regional differences

that we aim to quantify. In particular, early growth dynamics were sub-exponential for the first

nine months of the epidemic for Guinea and exponential over that time period for Liberia. Here,

we demonstrate that the different regional trends may be quantified in terms of different

community mixing rates.

Using a stochastic network model with three levels of community structure (households and

communities of households within a larger region) we model SEIR transmission dynamics for

the spread of Ebola infection with different community mixing rates. We are able to fit the WHO

Ebola case data by using a higher community mixing rate for Liberia and Sierra Leone compared

with Guinea. We observe that even for Liberia, the effects of local saturation (clustering effects)

are significant, and in the absence of further epidemic control or spread of the epidemic to new

communities, the long term dynamics of epidemic spread is linear. Exponential growth is

predicted only in the early phase of an outbreak and we demonstrate that linear growth phases

followed by exponential growth phases are consistent with seeding of the epidemic to new

communities. An unexpected prediction of the model that requires further investigation is that

the distribution of large-sized epidemics shifts substantially with the community mixing rate so

that the distribution of epidemic sizes becomes increasingly long-tailed with increased

community mixing.

Title:Parameter estimation in a Ebola epidemic model

Authors:Gerardo Chowell Puente. School of Public Health, Georgia State University,Atlanta, GA, USAMarcos A. Capistran∗, J. Andres Christen. CIMAT, Guanajuato, Mexico∗Presenting author: marcos@cimat.mx

Poster abstract:We define a Ebola epidemic model through a continuous time Markov jumpprocess. Later we use the linear noise approximation to obtain approximateequations for the first two moments of the state variables. We constructan approximate likelihood. We use informative prior distributions to obtaina posterior distribution of the model parameters given reports of Ebolaincidence. We use Markov Chain Monte Carlo to sample from the posteriordistribution and discuss our findings.

1

Title: Potential effects of isolation and safe burial on the evolution of Ebola virus virulence

Authors: Daozhou Gao

* and Travis C. Porco

Francis I. Proctor Foundation

University of California, San Francisco

San Francisco, CA, USA

* Presenting Author: Daozhou.Gao@ucsf.edu

Abstract: We assess possible evolutionary changes in virulence during the EVD outbreak based on

simple arguments based on maximization of the reproduction number under constraint.

We extended the mathematical models developed by Lewnard et al. (Lancet Infect. Dis.,

2014) and by Legrand et al. (Epidemiol. Infect., 2007), and parameterized the

transmission pre- and post-death, and the death rate in terms of a virulence parameter. We

then examined the dependence of the value of the virulence for which the basic

reproduction number is maximized, as a function of behavior change (contact rate per

case per unit time, and the probability of safe burial). Consistent with standard theory, we

find that both contact reduction and safe burial practices should promote evolution to

lowered virulence levels. Available data do not yet allow us to assess the nature of

possible evolutionary tradeoffs for Ebola virus at this time, and we note the limitation of

simple R0 maximization arguments in that within-host competition is not considered.

Word count: 160

Title: Implications of asymptomatic immunity for Ebola vaccine trial design Authors: J Dushoff(1), Steve Bellan(2), D Champredon(1)*, S. Cinkovich(5), S. Fox(2), LA Meyers(3, 4), CAB Pearson(5, 6), TC Porco(7), JRC Pulliam(5, 6, 8)

1. McMaster University Department of Biology 2. Center for Computational Biology and Bioinformatics, The University of Texas at Austin 3. Department of Integrative Biology, The University of Texas at Austin 4. The Santa Fe Institute 5. Department of Biology, University of Florida 6. Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA 7. Francis I. Proctor Foundation, University of California San Francisco 8. Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

*Presenting author: david.champredon@gmail.com Poster abstract: Evidence suggests that many Ebola infections are sub­clinical, and that sub­clinical infections may produce immunity to future infection. This is a potential concern for planned vaccine trials ­­ one which might be ameliorated by screening for Ebola­specific antibodies at trial enrollment, and accounting for their possible effects. We use a simulation approach to investigate the effects of sub­clinical immunity on vaccine trial outcome, for a range of assumptions about study design, study­analysis plan and immunological interactions between infection and the vaccine. The presence of sub­clinical immunity will reduce the power to detect vaccine effects, by reducing the number of cases of Ebola virus disease. Controlling for the number of cases observed, however, we found that unobserved sub­clinical immunity has a surprisingly small effect on vaccine­trial power, in both individual­level randomized controlled and stepped­wedge designs. Although accounting for sub­clinical immunity could improve study power in some designs, the effect was small, and the specifics of which analysis design would be beneficial varied by scenario, depending on details about immunological interactions that are currently unknown. We caution that the current study does not account for the possibility that existing sub­clinical immunity could be confounded with current force of infection; such confounding could increase the importance of taking sub­clinical immunity into account.

Title: Changes in transmission during the Ebola epidemic in West Africa Authors: Wayne T.A. Enanoria, Fengchen Liu, Daozhou Gao, Sarah Ackley, James Scott, Michael Deiner, Ernest Mwebaze, Wui Ip, Thomas M. Lietman, Travis C. Porco* Francis I. Proctor Foundation and the Department of Epidemiology & Biostatistics University of California, San Francisco San Francisco, CA, USA * Presenting Author: Travis.Porco@ucsf.edu Poster Abstract: The 2014 Ebola outbreak is the largest and most widespread to date. In order to describe ongoing transmission, we estimated the average number of secondary cases caused by one Ebola infection (basic reproduction number, R0, where a value below 1.0 indicates subcriticality ) in Liberia, Sierra Leone, and Guinea using a stochastic hidden Markov model fitted to weekly confirmed case data from the World Health Organization (WHO). The natural history of Ebola was modeled using an SEIR-type model with two sequential exposure classes and progression rates chosen to provide a gamma-distributed incubation period consistent with a previously published WHO model, and separate states for diagnosed versus undiagnosed (and unknown) cases with different transmission rates. We applied a simple nonlinear particle filtering method to fit the model to the data with the resample/move method to prevent particle diversity depletion. Best fit values of the transmission coefficient, together with assumed values of other model parameters, were then used to calculate weekly R0. The median R0 estimate for Liberia was 0.85 (95% credible interval 0.41, 1.47) two weeks prior to the last available data. Sensitivity analyses suggest the relative transmission for diagnosed cases and the reporting fraction of cases do not greatly affect the R0 estimate. The epidemic had not reached subcriticality in Sierra Leone or Guinea as of November 23, 2014. According to our model, the Ebola epidemic in Liberia may be near subcriticality, while the epidemics in Guinea and Sierra Leone are not. Word Count: 242

Title: Legal Preparedness for Infectious Disease Outbreaks in the US – Gaps and Opportunities Authors: Kevin D. Ashley1, Elizabeth Ferrell Bjerke2, Margaret A. Potter2, Hasan Guclu2*, Jaromir Savelka1, Matthias Grabmair1 University of Pittsburgh 1School of Law 2Department of Health Policy and Management, Graduate School of Public Health *Presenting author: guclu@pitt.edu Poster abstract: We study the public health emergency preparedness laws and regulations of 12 different states in the USA. We specifically focus on surveillance of infectious diseases outbreaks including Ebola and its main components from hospitals to governmental public health agencies such as local health departments. We use the framework and tools of network analysis to investigate to what extent the agents of the public health system in a state communicate with each other. Network analysis provides quantitative measures as well as graphic visualizations for complex patterns embedded in infectious disease emergency and preparedness laws, facilitating interpretation and comparison by non-lawyers. We compared the legal preparedness networks of Texas, Wisconsin, Pennsylvania and Kansas for infectious disease surveillance and found some gaps in the state of Texas where the first Ebola case has been reported. Our work has implications for the public health policy and also has the potential to help modelers to adopt their simulations for different jurisdictions.

Title: Novel designs for cluster randomized trials leveraging contact network structure

Authors: Guy Harling*, Rui Wang, JP Onnela, Victor DeGruttola

Departments of Global Health & Population and Biostatistics Harvard School of Public Health Boston, MA, USA

*Presenting author: gharling@hsph.harvard.edu

Poster abstract:

Randomized trials of products to both treat and prevent (via vaccination) Ebola are currently under development. Such trials may be conducted at the level either of the individual subject, or of a cluster (e.g. all patients or healthcare workers in an Ebola Treatment Center, or all members of a village or neighborhood). Standard randomized cluster-level designs include the Parallel design, in which clusters are randomized to intervention or control, and the Stepped Wedge design, in which all clusters receive the intervention but in a randomized order.

In an acute epidemic setting there is a premium on rapid control of infection as well as evaluating intervention efficacy. We propose two novel cluster-level study designs that may, in some settings, reduce the number of new infections more rapidly than these standard designs. Our proposed designs leverage information about the contact network structure both within and between clusters to determine an optimal order for intervention delivery.

We simulate population contact networks containing variability in within- and between-cluster connectivity, and then simulate Ebola epidemics on these networks using an agent-based SEIR-type state-transition model. This approach allows us to compare our proposed designs to standard approaches regarding both the reduction of overall population incidence rates, and statistical power to detect intervention effects, as well as to evaluate the impact of network structure and other local conditions on each model’s relative performance.

Title: Strategies for Ebola Containment: A Biological-Behavioral-Logistics Computation Decision Framework Authors: Eva K Lee *, Fan Yuan *, Ferdinand Pietz †, Bernard Benecke

‡

*School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA. †Strategic National Stockpile, Centers for Disease Control and Prevention, GA,‡ Global Disease Detection and Emergency Response, Centers for Disease Control and Prevention, GA. *Presenting author: eva.lee@gatech.edu Poster abstract: This work focuses on a computational decision modeling framework that integrates an agent-based biological disease spread model, a dynamic network-based social-behavior model that captures human behavior and interaction, and a stochastic queueing model that describes treatment characteristics, day-to-day hospital and homecare processes, and resource usage (labor, time, and equipment). The computational platform includes an optimization engine that determines the minimum resource requirements needed to contain the Ebola epidemic in West Africa under time and labor constraints. Biological characteristics of Ebola are collected through previous studies as well as using on-the-ground estimates. Data are also collected from confirmed cases by the World Health Organization, interviews of on-the-ground hospital and public health personnel, estimated treatment characteristics and work tasks, social media knowledge of the local population and business organizations, and statistics from Doctors Without Borders. Safe burial is incorporated in the model for locations where such practices have been implemented. We perform analysis for three countries: Guinea, Sierra Leone, and Liberia. In September 2014, we ran our model and estimated an overall infection of 500,000 by January 2015. Performing our analysis in real-time with updated and confirmed data daily, we present here the results obtained on November 1. Our model projects a lower rate of transmission among the population when compared to other models, reflecting that it is more suitable in handling the non-airborne nature of transmission. It returns the number of beds required to contain the epidemic, and to minimize the overall infection by end of year 2015. This work is partially sponsored by CDC and NSF.

Title:

Evolutionary consequences of delaying intervention for Ebola

Authors:

Philip L. F. Johnson1,∗, Carl T. Bergstrom2, Roland R. Regös3, Ira M. Longini4,M. Elizabeth Halloran5,6, Rustom Antia7

1Department of Biology, University of Maryland, College Park2Department of Biology, University of Washington, Seattle3ETH Zürich4Department of Biostatistics, University of Florida, Gainesville5School of Public Health, University of Washington, Seattle6Fred Hutchinson Cancer Research Center7Department of Biology, Emory University*Presenting author: plfj@umd.edu

Abstract:

The basic reproductive number (R0) is viewed as a good predictor both of thespread of existing pathogens and of the probability of emergence of novel zoonoses.We show this view needs to be re-evaluated when considering the global riskposed by Ebola, which currently has a relatively low R0 in the range of 1.2 to2.3. If the virus were to evolve more efficient transmission (e.g. pre-symptomaticor aerosol spread), R0 would increase, greatly complicating control and elimina-tion efforts. What determines the probability that Ebola evolves in this way? Inparticular, how does this probability depend on our strategy for surveillance andcontrol of the incipient epidemic? We find that if significant mobilization of publichealth resources is delayed until the number of current cases reaches a threshold,the very infections that appear least problematic (low R0) pose the greatest threatof evolution. This occurs because, relative to high R0 infections, low R0 infec-tions: (i) infect more individuals prior to reaching the intervention threshold; and(ii) have longer chains of transmission, which make the accumulation of multiplemutations more likely. These results highlight the importance of rapid control anderadication at the initial time of emergence.

Title: A Framework for Assessing the Economic Cost of an Ebola Outbreak

Authors: Marcus Marktanner and Dlorah Jenkins International Conflict Management Coles School of Business & College of Health and Human Sciences Kennesaw State University Kennesaw, GA, USA *Presenting authors: mmarktan@kennesaw.edu; djenki45@kennesaw.edu Poster abstract: This poster presents a simulation framework to estimate the economic cost of the ongoing Ebola

outbreak, using the case of Liberia. The main idea is that the control of an outbreak of this magnitude, in

the absence of an effective vaccination campaign, requires the implementation of quarantines, leading

to a de-facto reduction of the labor force. The optimum quarantine rate of the labor force is determined

by herd-immunization-threshold requirements. We assume moreover that the epidemic reduces the

economy’s overall factor productivity. Both labor force and total factor productivity implications are

linked to a simple Solow economic growth framework. Our model requires input regarding the R0 and

number of infected cases at the time of quarantine implementation, as well as the estimated length of

time to reduce the R0 to one and zero, respectively. The model may be used to compare the economic

costs of alternative courses of the epidemic as well as to illustrate the economic gains from a fast and

effective public health response.

Title:An adaptive decision making algorithm to manage the spread of Ebola in West Africa

Authors:Nicholas J. Meyer1*, Eric B. Laber1, Krishna Pacifici2, Brian J. Reich1, John Drake31: Department of Statistics, North Carolina State University2: Department of Applied Ecology, North Carolina State University3: Odum School of Ecology, University of Georgia*Presenting author: njmeyer@ncsu.edu

Poster abstract:Th Ebola epidemic in West Africa poses a serious threat to regional and global publichealth. Consequently, the ability to contain the spread of Ebola is imperative. Managingthe spread of a disease, such as Ebola, is a complex decision making problem with bothspatial and temporal components. Optimally choosing the times and locations to applytreatment requires a high quality model for the disease dynamics and the ability to utilizethat model to simulate and optimize potential treatment strategies. In some settings thedisease dynamics are non-stationary and the model must be re-estimated as new data becomeavailable. We construct an adaptive sequential treatment strategy that minimizes the diseasespread by a pre-specified finite time horizon. Using observed data, the method estimatesa dynamics model to develop an initial treatment strategy. In real time, as data accrue,the initial strategy is updated using a stochastic approximation algorithm. The method isillustrated using a series of simulated experiments.

1

Title: Assessing the International Spreading Risk Associated with the 2014 West African Ebola Outbreak Authors: Marcelo FC Gomes1, Ana Pastore y Piontti1*, Luca Rossi2, Dennis Chao3, Ira Longini4, M. Elizabeth Halloran3 and Alessandro Vespignani1. 1. Laboratory for the Modeling of Biological and Socio-­‐technical Systems, Northeastern University, Boston, Massachusetts, USA. 2. Institute for Scientific Interchange (ISI), Torino, Italy. 3. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, University of Washington, Seattle, Washington, USA. 4. Department of Biostatistics, University of Florida, Gainesville, Florida, USA. *Presenting author: a.pastoreypiontti@neu.edu Poster abstract: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. As of 31 December 2014, the World Health Organization has reported 20,171 EVD cases in West Africa. As the epidemic made its way to major cities in the region, increased the concern about the risk of international dissemination. We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 9 August to 14 September 2014. The estimates obtained were used to generate a 4-­‐month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in the most affected countries and the probability of international spread considering three different scenarios based on the possible success at curtailing the outbreak (exponential growth, marginal containment and containment scenarios). We model the growth rate of the disease in West Africa and simulated the international spreading of the outbreak. We provide the estimate for the probability of Ebola virus disease case importation in countries across the world for the three different scenarios considered.

Title: Rapid, iterative Ebola modeling for public health decision support Authors: Caitlin Rivers*, Eric Lofgren, Madhav Marathe, Stephen Eubank, Bryan L. Lewis Network Dynamics and Simulation Science Laboratory Virginia Tech Blacksburg, VA, USA *Presenting author: cmrivers@vbi.vt.edu Poster abstract: In March 2014, the World Health Organization announced what was to become the most severe Ebola outbreak in history. During the first year of the epidemic, a dearth of available epidemiological data hampered public health planning and response efforts; modeling helped to fill this gap. We partnered with the Department of Defense to use agile, iterative modeling methods to inform public health planning and response. This poster summarizes strategic methods for rapid model development, and summarizes lessons learned for forging effective partnerships. We implemented a compartmental model of Ebola using a previously published model structure. The model is a variant on the SEIR model, with three infectious compartments representing community, healthcare and funeral settings. Model parameters were derived from the literature where available, and for missing values we used computational inference techniques to identify plausible parameter spaces. These decisions facilitated the development of an accurate Ebola model in a matter of days. As the outbreak progressed, we produced updated forecasts each week that were used by the Department of Defense to plan response operations in Sierra Leone and Liberia. We also modeled various interventions to better understand what effect different countermeasures could have on the outbreak’s future. This work shows modelers using agile and iterative modeling techniques can partner with outbreak responders to answer policy questions and provide evidence-based support for operational decision making.

Title:

A mathematical model of Ebola Virus Disease

Authors:

Danny Salem, Robert Smith?

Department of Biomedical Science and Department of Mathematics

University of Ottawa

Ottawa, ON, Canada

Presenting author: dsale017@uottawa.ca

Ebola virus is a lethal human pathogen that is the cause of Ebola virus disease (EVD), which is the cause

of the ongoing outbreak originating in Western Africa since December 2013. Cultural factors facilitate

the spread of the virus due to unsanitary behavior such as burial rituals and consumption of bush meat.

Several outbreaks have occurred since the discovery of this virus in 1976 with an average mortality rate

of 78%. This most recent outbreak is the most severe in terms of the number of confirmed cases since

the virus’ discovery. A mathematical model of the disease that looks at how the virus spreads through a

population would be very helpful in predicting how the current and future outbreaks may progress. We

propose a model that takes into account the peculiarities of the disease like the infectivity of the

infected dead, using standard incidence and including the dead as a dynamic variable. This poster

describes the analysis of our mathematical model through a surface plot and LHS (Latin hypercube

sampling). We discover that the variables with the most effect on the spread of Ebola is the probability

of transmission and the rate at which infectivity of dead bodies is lost. Solely increasing the rate at which

infectivity of the dead is lost can reduce the basic reproductive ratio (a measure of disease spread) of

the Ebola outbreak below the threshold of 1. It follows that reducing contact with infectious bodies is

critical to managing the outbreak.

Title: Exploring Spatio-temporal Triggers of Ebola

Authors:John Paul Schmidt*, Laura Alexander, and Andrew ParkOdum School of EcologyUniversity of GeorgiaAthens, GA, USA*Presenting author: jps@uga.edu

Poster abstract:The spatial and temporal conditions which have lead to Ebola outbreaks occurring since the 1970s are poorly understood. Resolving the mystery is now more critical given the recent and catastrophic expansion of Ebola in West Africa. Toward this end, we have verified outbreak points and collated geo-referenced data. To capture spatio-temporal climate variability, we assembled a gridded time series of rainfall data modeled from satellite imagery for Central and West Africa since 1980. We quantified changing human demographic factors from gridded estimates of population density for the region for each decade from 1960-2010. We captured biotic and environmental variability spatially fromgrids of mammal, fruit bat, other bat, rodent, and primate diversity, climate averages (WorldClim) , and recent land use data for the region. Interestingly, preliminary boosted regression tree models indicate bat (but not fruit bat) diversity as the most important predictor in combination human population density, and climate variables delimiting the tropical forest climate zone in Africa.

Title: Apparent increased evolutionary rate in the 2014 West African Ebola outbreak signals transient polymorphism and not positive selection Authors: Stephanie J. Spielman*, Austin G. Meyer, and Claus O. Wilke Department of Integrative Biology and Center for Computational Biology and Bioinformatics University of Texas at Austin Austin, TX, USA * Presenting author: stephanie.spielman@gmail.com Poster Abstract: Gire et al. (Science 345:1369-­‐1372, 2014) analyzed 81 complete genomes sampled from the 2014 Zaire ebolavirus (EBOV) outbreak and reported "rapid accumulation of [...] genetic variation" and a substitution rate that was "roughly twice as high within the 2014 outbreak as between outbreaks." These findings have received widespread attention, and many have perceived Gire et al.'s results as implying rapid adaptation of EBOV to humans during the current outbreak. Here, we argue that, on the contrary, sequence divergence in EBOV is rather limited, and that the currently available data contain no robust signal of particularly rapid evolution or adaptation to humans. The doubled substitution rate can be attributed entirely to the application of a molecular-­‐clock model to a population of sequences with minimal divergence and segregating polymorphisms. Our results highlight how subtle technical aspects of sophisticated evolutionary analysis methods may result in highly-­‐publicized, misconstrued statements about an ongoing public health crisis.

Title: An SEIR Model of Potential Vaccination Programs in the 2014 Sierra Leone Ebola Epidemic Authors: Rahul Subramanian1* and Isaac Chun-­‐Hai Fung2

1Department of Electrical Engineering School of Engineering and Applied Science Princeton University Princeton, NJ, USA 2Department of Epidemiology Jiann-Ping Hsu College of Public Health Georgia Southern University Statesboro, GA, USA *Presenting author: rsfive@princeton.edu Poster abstract: The March 2014 Ebola outbreak in Sierra Leone and Liberia has significantly strained public health systems in both countries. Current epidemic models focus on non-vaccine treatment interventions. We modeled the potential consequences of future vaccine deployment in Sierra Leone, specifically the relationship between vaccination rates, date of commencement of a vaccination program, vaccine efficacy, and epidemic size. As a baseline model of the epidemic in the absence of control measures, we utilized a version of the SEIR (susceptible-exposed-infectious-recovered) epidemic model utilized by Chowell et al. to model Ebola outbreaks in the Congo parameterized using September 2014 estimates from the WHO. We utilized a model developed by Althaus using October 2014 epidemic data as a baseline for epidemic sizes in the presence of non-vaccination interventions. We plan to investigate whether the deployment of a moderately effective vaccine at a high vaccination rate can yield a similar reduction in deaths as a highly effective vaccine at a smaller vaccination rate. Preliminary data utilizing the WHO baseline model, assuming the start of a vaccination program concurrent with the start of the epidemic in Sierra Leone, found that at a vaccine efficacy of 30%, a vaccination rate of 1000 individuals per day results in a 64% reduction of cases by November 2014. Data modeled using vaccination rates ranging from 1000 individual per day to 8000 individuals per day, vaccine efficacy values ranging from 30% to 70% efficacy, and various vaccination program commencement dates will be presented.

Title: Stochasticity and the limits to confidence in confidence intervals when estimating R0 Authors: Joshua S. Weitz, Bradford P. Taylor*, Jonathan Dushoff School of Physics Georgia Institute of Technology Atlanta, GA, USA *Presenting author: bradfordptaylor@gmail.com Poster abstract: Deterministic dynamic models are commonly used to estimate the basic reproductive number, R0. Here we ask: what are the limits in confidence of estimating R0 from deterministic models given data of a single epidemic event? The realized growth rate of an epidemic varies significantly between stochastic realizations, even when the underlying rates of transmission are identical in the deterministic model. This leads a potential problem in which many parameter combinations, including a range of R0, are indistinguishable when inferred from case data. Here, we quantify the range of confidence in R0 as a result of fitting stochastic realizations to case data. We do so by simulating an ensemble of stochastic epidemic trajectories and asking: which are compatible with the observed growth rate of the epidemic? We apply our methodology to reported case counts from Guinea, Liberia, and Sierra Leone and present values of R0 and associated confidence intervals resulting from the stochastic process alone. Our results suggests that previous reports of R0 from deterministic fitting alone are likely overconfident and that a hybrid stochastic-­‐deterministic fitting procedure will yield more prudent results.

Title: Post-death Transmission of Ebola Virus Disease (EVD): Challenges for Inference and Opportunities for Control Authors: Joshua S Weitz(1)* & Jonathan Dushoff(2) (1) School of Biology and School of Physics, Georgia Institute of Technology, Atlanta, GA, USA (2) Department of Biology & Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada *Presenting author: jsweitz@gatech.edu Poster abstract: Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. These models include consideration of counter-measures meant to slow and, eventually, stop the spread of the disease. Here, we examine the challenges to inference and control posed by post-death transmission of EVD. We show that underlying disease parameters cannot be inferred with confidence from early-stage incidence data (that is, they are not ``identifiable'') because different parameter combinations can produce virtually the same epidemic trajectory. Despite this identifiability problem, we find robustly that inferences that don't account for post-death transmission tend to underestimate the basic reproductive number. Thus, given the observed rate of epidemic growth, larger amounts of post-death transmission imply larger reproductive numbers. From a control perspective, we explain how improvements in reducing post-death transmission of Ebola can reduce the overall epidemic spread and scope substantially. Increased attention to the proportion of post-death transmission has the potential to aid both in projecting the course of the epidemic and in evaluating a portfolio of control strategies. Adapted from: Weitz, JS and Dushoff, J. http://arxiv.org/abs/1411.3435. (November 13, 2014)