Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Using CINET presentation as part of the CINET Workshop on July 10th, 2015 in Blacksburg, VA. CINET applications include Granite, GDS Calculator, and EDISON.
Use of CINET in Education and Research as part of the CINET Workshop on July 10th, 2015 in Blacksburg, VA. This presentation includes an overview of institutions using CINET in their courses.
Madhav Marathe and Anil Vullikanti will present a tutorial on computational epidemiology, along with Thomas L. Phillips Professor of Engineering Naren Ramakrishnan, at the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining on August 24th, 2014 in New York City.
In this tutorial, the researchers will approach epidemics based on diffusion processes on complex networks, which are able to capture more realistic problems. They will provide a state of the art overview of computational epidemiology, a multi-disciplinary research area that overlaps different areas in computer science, including data mining, machine learning, high performance computing and theoretical computer science, as well as mathematics, economics and statistics.
Dr. Bryan Lewis and Dr. Madhav Marathe (both at Virginia Tech) will present a data driven multi-scale approach for modeling the Ebola epidemic in West Africa. We will discuss how the models and tools were used to study a number of important analytical questions, such as:
(i) computing weekly forecasts, (ii) optimally placing emergency treatment units and more generally health care facilities, and (iii) carrying out a comprehensive counter-factual analysis related to allocation of scarce pharmaceutical and non-pharmaceutical resources. The role of big-data and behavioral adaptation in developing the computational models will be highlighted.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Using CINET presentation as part of the CINET Workshop on July 10th, 2015 in Blacksburg, VA. CINET applications include Granite, GDS Calculator, and EDISON.
Use of CINET in Education and Research as part of the CINET Workshop on July 10th, 2015 in Blacksburg, VA. This presentation includes an overview of institutions using CINET in their courses.
Madhav Marathe and Anil Vullikanti will present a tutorial on computational epidemiology, along with Thomas L. Phillips Professor of Engineering Naren Ramakrishnan, at the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining on August 24th, 2014 in New York City.
In this tutorial, the researchers will approach epidemics based on diffusion processes on complex networks, which are able to capture more realistic problems. They will provide a state of the art overview of computational epidemiology, a multi-disciplinary research area that overlaps different areas in computer science, including data mining, machine learning, high performance computing and theoretical computer science, as well as mathematics, economics and statistics.
Dr. Bryan Lewis and Dr. Madhav Marathe (both at Virginia Tech) will present a data driven multi-scale approach for modeling the Ebola epidemic in West Africa. We will discuss how the models and tools were used to study a number of important analytical questions, such as:
(i) computing weekly forecasts, (ii) optimally placing emergency treatment units and more generally health care facilities, and (iii) carrying out a comprehensive counter-factual analysis related to allocation of scarce pharmaceutical and non-pharmaceutical resources. The role of big-data and behavioral adaptation in developing the computational models will be highlighted.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
The prospects for Nextgen surveillance of pathogens: A view from a Public Hea...nist-spin
"The prospects for Nextgen surveillance of pathogens: A view from a Public Health Lab" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by National Institute for Standards and Technology in October 2014 by William Wolfgang, PhD from Wadsworth Center NYSDOH.
Using Mobile Technology to Facilitate Reactive Case Detection of MalariaRTI International
This presentation will share findings from more than three years of using mobile technology for reactive case detection (RACD) to help eliminate malaria in a well-defined geographic area. It will review the concepts of RACD, the application of mobile technology, lessons learned from more than three years of application, and considerations in applying this technology in other malaria elimination contexts.
Panel - Putting the Principles of PRRS Control into Practice - Tools and Thei...John Blue
Putting the Principles of PRRS Control into Practice - Tools and Their Application to Coordinated Disease Control - Dr. Erin Lowe, ARC coordinator, Boehringer Ingelheim Vetmedica; Dr. Meghann Pierdon - PA ARC; Carrie Pollard - NC IL ARC; Sonya Maas - SE IA ARC; and Kayla Donald - SW IA ARC, from the 2015 North American PRRS Symposium, December 4 - 5, 2015, Chicago, IL, USA.
More presentations at http://www.swinecast.com/2015-north-american-prrs-symposium
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
ISI 2024: Application Form (Extended), Exam Date (Out), EligibilitySciAstra
The Indian Statistical Institute (ISI) has extended its application deadline for 2024 admissions to April 2. Known for its excellence in statistics and related fields, ISI offers a range of programs from Bachelor's to Junior Research Fellowships. The admission test is scheduled for May 12, 2024. Eligibility varies by program, generally requiring a background in Mathematics and English for undergraduate courses and specific degrees for postgraduate and research positions. Application fees are ₹1500 for male general category applicants and ₹1000 for females. Applications are open to Indian and OCI candidates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Unveiling the Energy Potential of Marshmallow Deposits.pdf
Modeling the Ebola Outbreak in West Africa, November 18th 2014 update
1. Modeling
the
Ebola
Outbreak
in
West
Africa,
2014
November
18th
Update
Bryan
Lewis
PhD,
MPH
(blewis@vbi.vt.edu)
presen2ng
on
behalf
of
the
Ebola
Response
Team
of
Network
Dynamics
and
Simula2on
Science
Lab
from
the
Virginia
Bioinforma2cs
Ins2tute
at
Virginia
Tech
Technical
Report
#14-‐122
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
2. NDSSL
Ebola
Response
Team
Staff:
Abhijin
Adiga,
Kathy
Alexander,
Chris
Barre.,
Richard
Beckman,
Keith
Bisset,
Jiangzhuo
Chen,
Youngyoun
Chungbaek,
Stephen
Eubank,
Sandeep
Gupta,
Maleq
Khan,
Chris
Kuhlman,
Eric
Lofgren,
Bryan
Lewis,
Achla
Marathe,
Madhav
Marathe,
Henning
Mortveit,
Eric
Nordberg,
Paula
Stretz,
Samarth
Swarup,
Meredith
Wilson,Mandy
Wilson,
and
Dawen
Xie,
with
support
from
Ginger
Stewart,
Maureen
Lawrence-‐Kuether,
Kayla
Tyler,
Kathy
Laskowski,
Bill
Marmagas
Students:
S.M.
Arifuzzaman,
Aditya
Agashe,
Vivek
Akupatni,
Caitlin
Rivers,
Pyrros
Telionis,
Jessie
Gunter,
Elisabeth
Musser,
James
Schli.,
Youssef
Jemia,
Margaret
Carolan,
Bryan
Kaperick,
Warner
Rose,
Kara
Harrison
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
2
3. Currently
Used
Data
● Data
from
WHO,
MoH
Liberia,
and
MoH
Sierra
Leone,
available
at
h.ps://github.com/cmrivers/ebola
● MoH
and
WHO
have
reasonable
agreement
● Sierra
Leone
case
counts
censored
up
to
4/30/14.
● Time
series
was
filled
in
with
missing
dates,
and
case
counts
were
interpolated.
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
3
Cases
Deaths
Guinea
1919
1166
Liberia
6909
2836
Sierra
Leone
5586
1510
Total
14,436
5520
4. Liberia
–
Case
Loca2ons
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
4
5. Liberia
–
County
Case
Incidence
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
5
6. Liberia
Forecast
–
Original
Model
52%
of
Infected
are
hospitalized
Reproduc2ve
Number
Community
1.3
Hospital
0.4
Funeral
0.5
Overall
2.2
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
6
8/9/08
to
9/14
9/15
to
9/21
9/22
to
9/28
9/29
to
10/05
10/06
to
10/12
10/13
to
10/19
10/20
to
10/26
10/27
to
11/02
11/03
to
11/09
Reported
639
560
416
261
298
446
1604*
227
298
Forecast
(classic
model)
697
927
1232
1636
2172
2883
3825
5070
6741
*
Repor2ng
change
7. Learning
from
Lofa
-‐
Summary
Fit
reduc2on
seen
in
Lofa
Apply
to
Liberia
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
7
Model
fit
to
Lofa
case
with
a
change
in
behaviors
resul2ng
in
reduced
transmission
sta2ng
mid-‐Aug
(blue),
compared
with
observed
data
(green)
Model
fit
to
Liberia
case
with
a
change
in
behaviors
resul2ng
in
reduced
transmission
sta2ng
Sept
21st
(green),
compared
with
observed
data
(blue)
8. Liberia
Forecast
–
Prelim
New
Model
Reproduc2ve
Number
Community
0.5
Hospital
0.2
Funeral
0.2
Overall
1.0
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
8
9/16
to
9/21
9/22
to
9/28
9/29
to
10/05
10/06
to
10/12
10/13
to
10/19
10/20
to
10/26
10/27
to
11/02
11/03
to
11/09
11/10
to
11/16
11/17
to
11/23
Reported
560
416
261
298
446
1604*
227
298
-‐-‐
-‐-‐
Reported
396
251
245
490
back
log
adjusted
New
model
757
603
541
580
598
608
617
625
633
638
9. Prevalence
of
Cases
–
New
model
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
9
Date
People
in
H+I
9/7/14
523
9/14/14
695
9/20/14
887
9/27/14
1051
10/4/14
1119
10/11/14
1152
10/18/14
1174
10/25/14
1192
11/1/14
1208
11/8/14
1224
11/15/14
1239
11/22/14
1255
11/29/14
1271
12/6/14
1288
12/13/14
1304
12/20/14
1320
12/27/14
1337
10. Sierra
Leone
–
County
Data
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
10
11. Sierra
Leone
–
Contact
A.ack
Rate
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
11
12. Sierra
Leone
Forecasts
35%
of
cases
are
hospitalized
ReproducPve
Number
Community
1.20
Hospital
0.29
Funeral
0.15
Overall
1.63
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
12
9/6
to
9/14
9/14
to
9/21
9/22
to
9/28
9/29
to
10/05
10/06
to
10/12
10/13
to
10/19
10/20
to
10/26
10/27
to
11/02
11/03
to
11/09
11/10
to
11/16
11/17
to
11/23
Reported
246
285
377
467
468
454
494
486
580
-‐-‐
-‐-‐
Forecast
256
312
380
464
566
690
841
1025
1250
1523
1856
13. Prevalence
in
SL
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
13
10/6/14
456.6
10/13/14
556.7
10/20/14
678.8
10/27/14
827.5
11/3/14
1008.8
11/10/14
1229.8
11/17/14
1498.9
11/24/14
1826.8
12/1/14
2226.1
12/8/14
2712.2
12/15/14
3303.7
12/22/14
4023.3
12/29/14
4898.1
14. Agent-‐based
Model
Progress
• Synthe2c
Informa2on
Viewer
for
Ebola
affected
Countries
– Assist
in
troubleshoo2ng
simula2on
results
– Aid
in
calibra2on
issues
• Calibra2on
–
Rainy
Season
altera2on
• Considera2on
of
Mali
and
Senegal
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
14
15. Synthe2c
Informa2on
Viewer
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
15
Interface
for
exploring
details
of
the
popula2on
and
their
ac2vi2es
16. Synthe2c
Informa2on
Viewer
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
16
Zoom
down
to
the
household
level
to
see
rela2ve
densi2es
and
selected
details
17. Synthe2c
Informa2on
Viewer
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
17
Zoom
down
to
the
individual
and
look
at
their
ac2vity
pa.ern
18. Calibra2on
–
Previous
Steps
• Disease
Model
representa2on
• Flowminder
data
used
for
travel
• Ini2alize
simula2on
in
Lofa
• Road
map
with
travel
status
used
for
prelim
es2mate
of
travel
altera2on
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
18
Bomi
Bong
Gbarpolu
Grand
Bassa
Grand
Cape
Mount
Grand
Gedeh
Grand
Kru
Lofa
Margibi
Maryland
Montserrado
Nimba
River
Cess
River
Gee
Sinoe
Green
1
Bomi
0.55
0.5
1
0.5
0.425
0.425
0.1
1
0.425
1
0.55
0.1
0.425
0.3
Red
0.5
Bong
0.55
0.3
0.55
0.3
1
1
1
1
1
1
1
0.1
1
0.366666667
Black
0.1
Gbarpolu
0.5
0.3
0.5
0.5
0.425
0.425
0.1
0.5
0.425
0.5
0.3
0.1
0.425
0.3
Grand
Bassa
1
0.55
0.5
0.5
0.3
0.3
0.4
1
0.3
1
0.55
0.1
0.3
0.3
Based
on
traveling
from
county
capital
to
county
capital
Grand
Cape
Mount
0.5
0.3
0.5
0.5
0.3
0.3
0.3
0.5
0.3
0.5
0.233333333
0.1
0.3
0.3
Based
on
17SEPT2014
data
Grand
Gedeh
0.425
1
0.425
0.3
0.3
1
1
0.55
1
0.55
1
0.1
1
0.5
Grand
Kru
0.425
1
0.425
0.3
0.3
1
1
0.533333333
1
0.533333333
1
0.1
1
0.5
Lofa
0.1
1
0.1
0.4
0.3
1
1
0.55
1
0.55
1
0.1
1
0.214285714
Margibi
1
1
0.5
1
0.5
0.55
0.533333333
0.55
0.533333333
1
1
0.1
0.533333333
0.3
Maryland
0.425
1
0.425
0.3
0.3
1
1
1
0.533333333
0.533333333
1
0.1
1
0.5
Montserrado
1
1
0.5
1
0.5
0.55
0.533333333
0.55
1
0.533333333
0.55
0.1
0.533333333
0.3
Nimba
0.55
1
0.3
0.55
0.233333333
1
1
1
1
1
0.55
0.1
1
0.233333333
River
Cess
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
River
Gee
0.425
1
0.425
0.3
0.3
1
1
1
0.533333333
1
0.533333333
1
0.1
0.5
Sinoe
0.3
0.366666667
0.3
0.3
0.3
0.5
0.5
0.2142857
14
0.3
0.5
0.3
0.233333333
0.1
0.5
19. Calibra2on
–
Spa2al
Spread
Simula2on
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
19
21. Simula2on
Comparison
–
spread
from
Lofa
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
21
Cases
per
100k
popula2on
Mean
simula2on
Normal
Travel
Ministry
of
Health
Data
22. Simula2on
Comparison
–
Rainy
Season
Travel
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
22
Cases
per
100k
popula2on
Mean
simula2on
Rainy
Travel
Ministry
of
Health
Data
23. Simula2on
Comparison
–
spread
from
Lofa
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
23
Total
Cases
Single
Simula2on
result
–
Normal
Travel
Ministry
of
Health
Data
24. Simula2on
Comparison
–
spread
from
Lofa
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
24
Total
Cases
Single
Simula2on
result
–
Rainy
Travel
Ministry
of
Health
Data
25. Calibra2on
Next
Steps
• Determine
“right”
2me
of
rainy
travel
– Pursue
more
real-‐2me
and
comprehensive
data
• Combine
all
condi2ons
and
a.empt
calibra2on
– Lofa-‐based
introduc2on
– Lofa
and
other
county
temporal
changes
in
txm
– Regional
travel
–
affected
by
rainy
season
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
25
26. Agent-‐based
Next
Steps
• Planned
Experiments:
– Impact
of
hospitals
with
geo-‐spa2al
disease
• Study
design
/
implementa2on
under
construc2on
– Vaccina2on
campaign
effec2veness
• Framework
under
development
– Es2ma2on
of
surveillance
coverage
requirements
• Simulate
zoono2c
and
human
introduc2on
scenarios,
look
at
“gold
standard”
transmission
trees
with
varying
level
of
completeness
to
represent
different
levels
of
surveillance
• Address
ques2on
of
needed
resources
for
eventual
final
stages
of
“stamp
out”
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
26
27. Suppor2ng
material
describing
model
structure,
and
addi2onal
results
APPENDIX
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
27
28. Legrand
et
al.
Model
Descrip2on
Susceptible
Exposed
not infectious
Infectious
Symptomatic
Hospitalized
Infectious
Funeral
Infectious
Removed
Recovered and immune
or dead and buried
Legrand,
J,
R
F
Grais,
P
Y
Boelle,
A
J
Valleron,
and
A
Flahault.
“Understanding
the
Dynamics
of
Ebola
Epidemics”
Epidemiology
and
Infec1on
135
(4).
2007.
Cambridge
University
Press:
610–21.
doi:10.1017/S0950268806007217.
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
28
29. Compartmental
Model
• Extension
of
model
proposed
by
Legrand
et
al.
Legrand,
J,
R
F
Grais,
P
Y
Boelle,
A
J
Valleron,
and
A
Flahault.
“Understanding
the
Dynamics
of
Ebola
Epidemics”
Epidemiology
and
Infec1on
135
(4).
2007.
Cambridge
University
Press:
610–21.
doi:10.1017/S0950268806007217.
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
29
30. Legrand
et
al.
Approach
• Behavioral
changes
to
reduce
transmissibili2es
at
specified
days
• Stochas2c
implementa2on
fit
to
two
historical
outbreaks
– Kikwit,
DRC,
1995
– Gulu,
Uganda,
2000
• Finds
two
different
“types”
of
outbreaks
– Community
vs.
Funeral
driven
outbreaks
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
30
31. Parameters
of
two
historical
outbreaks
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
31
32. NDSSL
Extensions
to
Legrand
Model
• Mul2ple
stages
of
behavioral
change
possible
during
this
prolonged
outbreak
• Op2miza2on
of
fit
through
automated
method
• Experiment:
– Explore
“degree”
of
fit
using
the
two
different
outbreak
types
for
each
country
in
current
outbreak
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
32
33. Op2mized
Fit
Process
• Parameters
to
explored
selected
– Diag_rate,
beta_I,
beta_H,
beta_F,
gamma_I,
gamma_D,
gamma_F,
gamma_H
– Ini2al
values
based
on
two
historical
outbreak
• Op2miza2on
rou2ne
– Runs
model
with
various
permuta2ons
of
parameters
– Output
compared
to
observed
case
count
– Algorithm
chooses
combina2ons
that
minimize
the
difference
between
observed
case
counts
and
model
outputs,
selects
“best”
one
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
33
34. Fi.ed
Model
Caveats
• Assump2ons:
– Behavioral
changes
effect
each
transmission
route
similarly
– Mixing
occurs
differently
for
each
of
the
three
compartments
but
uniformly
within
• These
models
are
likely
“overfi.ed”
– Many
combos
of
parameters
will
fit
the
same
curve
– Guided
by
knowledge
of
the
outbreak
and
addi2onal
data
sources
to
keep
parameters
plausible
– Structure
of
the
model
is
supported
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
34
35. Model
parameters
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
35
Sierra&Leone
alpha 0.1
beta_F 0.111104
beta_H 0.079541
beta_I 0.128054
dx 0.196928
gamma_I 0.05
gamma_d 0.096332
gamma_f 0.222274
gamma_h 0.242567
delta_1 0.75
delta_2 0.75
Liberia
alpha 0.083
beta_F 0.489256
beta_H 0.062036
beta_I 0.1595
dx 0.2
gamma_I 0.066667
gamma_d 0.075121
gamma_f 0.496443
gamma_h 0.308899
delta_1 0.5
delta_2 0.5
All
Countries
Combined
36. Learning
from
Lofa
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
36
Model
fit
to
Lofa
case
series
up
Aug
18th
(green)
then
from
Aug
19
–
Oct
21
(blue),
compared
with
real
data
(red)
37. Learning
from
Lofa
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
37
Model
fit
to
Lofa
case
with
a
change
in
behaviors
resul2ng
in
reduced
transmission
sta2ng
mid-‐Aug
(blue),
compared
with
observed
data
(green)
38. Learning
from
Lofa
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
38
Model
fit
to
Liberian
case
data
up
to
Sept
20th
(current
model
in
blue),
reduc2on
in
transmissions
observed
in
Lofa
applied
from
Sept
21st
on
(green),
and
observed
cases
(red)
39. Learning
from
Lofa
DRAFT
–
Not
for
a.ribu2on
or
distribu2on
39
Model
fit
to
Liberia
case
with
a
change
in
behaviors
resul2ng
in
reduced
transmission
sta2ng
Sept
21st
(green),
compared
with
observed
data
(blue)