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.
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.
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.
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.
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.
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.
• The highest point for Deaths/Day was 1281 on 15th September. This peak has
held till now (67 days)
• Deaths/Day have crossed 1000 on only 1 day after 3rd October. Declining trend
had set in followed by a plateau and a slow decline post the Diwali spike
• New/Active cases have also peaked and were declining.
• The highest no of cases was on 16th September at 97,856. That peak has held till now.
• Active Cases peaked at 10,17,718 on 17th September
• Both New and Active cases are plateauing/declining now
• Likely trend in Deaths/Day for the next 30 days is a plateau/slow decline
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.
PERTUSSIS PROTECTION - CURRENT SCHEDULES IN EUROPEWAidid
Slide set by Professor Susanna Esposito, president WAidid, presented at the 3rd ESCMID Conference on Vaccines, held in Lisbon (Portugal), 6- 8 March 2015. Learn more: http://goo.gl/8GUwwL
The co-relative model presented on 24.05.20 has been reasonably successful in predicting the date for first decline in deaths/day to start. Decline commenced on 15.09.20
The decline has been faster than anticipated. After a plateau in November and early December a declining trend is visible currently
North India’s spike after Diwali has come under control. As of now all states are stable/declining
In the next 30 days we may expect Deaths/Day to slowly decline further
71 cases of the new UK variant have been observed in India – no indications of local spread as of now. Genome has been mapped in UK and India. Implications for vaccine effectiveness awaited.
Vaccination logistics and process seem comprehensive and well thought through
Vaccination should start within a week or ten days
Key Highlights:
1. India Deaths/Day continue to decline. 7 DMA now stands at 343.
2. New mutant discovered in UK is an imponderable. There is considerable movement between the 2 countries.
3. Vaccine status in India is unchanged. No EUA has been given yet.
North India’s spike after Diwali has come under control. As of now all states are declining
In the next 30 days we may expect Deaths/Day to slowly decline further
150 cases of the new UK variant have been observed in India – no indications of local spread as of now. Genome has been mapped in UK and India. Implications for vaccine effectiveness awaited
Sero positive study in Delhi has come up with 50% positive in Delhi. Significant jump in a few months. This may hasten the progress to herd immunity. Results awaited
Vaccination has got off to a slow start with numbers picking up gradually. India cumulative upto 24.01.21 is 1,615,504 jabs in 9 days. Average of 179,500 per day. USA 20.54 Mn from 14th Dec (42 days) = 489,047 per day
Key risk is of a second wave (possible but unlikely) caused either by a the existing variant or possibly a new one. Vaccination is too slow to provide herd immunity in the near future. India will have to rely on social distancing, masking etc for the foreseeable future
Real-time Surveillance and Response for Malaria EliminationRTI International
Coconut Surveillance is a proven, ground-breaking mobile application designed by malaria epidemiologists and program managers. In Zanzibar it is helping to prevent the resurgence of the disease. Can it be useful in other malaria elimination contexts?
SA’s Covid-19 epidemic: Trends & Next stepsSABC News
Why is SA different - new cases declining to a plateau:
• Are we missing cases due to low or declining testing coverage?
• Are there missing cases in poor communities due to skewed
higher private lab testing?
• Is the reduction genuine and due to the interventions in SA’s
Covid-19 response?
The 11th Update of Covid Stats in India was presented by Debu Bhatnagar on 3.11.20. Neeraj Chandra presented a model that seeks to understand the shapes of the Covid curves for different countries.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
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.
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.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
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/
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Modeling the Ebola Outbreak in West Africa January 6th 2015 update
1. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Modeling
the
Ebola
Outbreak
in
West
Africa,
2014
January
6th
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
#15-‐001
2. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
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
2
3. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Currently
Used
Data
(as
of
Dec
22nd,
2014)
● 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.
3
Cases
Deaths
Guinea
2,730
1,739
Liberia
8,115
3,423
Sierra
Leone
9,633
2,827
Total
20,478
7,989
4. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia
–
Case
Loca2ons
4
5. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia
infec2on
rate
5
6. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia
Forecast
6
11/24
to
11/30
12/1
to
12/8
12/9
to
12/16
12/15
to
12/21
12/22
to
12/28
12/29
to
1/04
1/05
to
1/11
1/12
to
1/18
1/19-‐1
/25
1/26-‐2
/01
Reported
246
362
99
35
101
Newer
model
303
285
270
255
240
226
214
201
190
180
Reproduc2ve
Number
Community
0.23
Hospital
0.3
Funeral
0.2
Overall
0.8
7. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia
long
term
forecasts
7
Date
Weekly
forecast
12/08
270
12/15
255
12/22
240
12/29
227
1/05
213
1/12
202
1/19
190
1/26
179
2/2
169
2/9
160
2/16
150
2/23
142
3/02
134
3/09
126
8. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Liberia-‐
Prevalence
8
Date
People
in
H
+
I
12/08
652
12/15
616
12/22
580
12/29
548
1/05
517
1/12
488
1/19
460
1/26
434
2/2
410
2/9
386
2/16
365
2/23
344
3/02
325
3/09
306
9. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
–
County
Data
9
10. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
infec2on
rate
10
11. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
Forecast
11
35%
of
cases
are
hospitalized
ReproducRve
Number
Community
0.8
Hospital
0.3
Funeral
0.1
Overall
1.1
11/24
to
11/30
12/1
to
12/8
12/9
to
12/16
12/15
to
12/21
12/22
to
12/28
12/29
to
1/04
1/05
to
1/11
1/12
to
1/18
1/19-‐1
/25
1/26-‐2
/01
Reported
641
598
702
531
651
467
Newer
model
612
635
660
686
713
740
769
799
830
862
12. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
SL
longer
term
forecast
12
Sierra
Leone
–
Newer
Model
fit
–
Weekly
Incidence
Date
Weekly
forecast
12/08
660
12/15
686
12/22
713
12/29
740
1/05
769
1/12
799
1/19
830
1/26
862
2/2
895
2/9
929
2/16
965
2/23
1002
3/02
1040
3/09
1080
13. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
-‐
Prevalence
13
Date
People
in
H
+
I
12/08
898
12/15
932
12/22
968
12/29
1006
1/05
1045
1/12
1085
1/19
1128
1/26
1171
2/2
1216
2/9
1263
2/16
1319
2/23
1370
3/02
1423
3/09
1477
14. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Guinea
Forecasts
14
40%
of
cases
are
hospitalized
ReproducRve
Number
Community
0.7
Hospital
0.1
Funeral
0.1
Overall
0.9
11/24
to
11/30
12/1
to
12/8
12/9
to
12/16
12/15
to
12/21
12/22
to
12/28
12/29
to
1/04
1/05
to
1/11
1/12
to
1/18
1/19-‐1
/25
1/26-‐2
/01
Reported
129
44
127
132
166
106
Newer
model
89
85
81
78
75
72
68
66
60
58
15. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Guinea
–
longer
term
forecast
15
Date
Weekly
forecast
12/08
81
12/15
78
12/22
75
12/29
72
1/05
69
1/12
66
1/19
63
1/26
60
2/2
58
2/9
55
2/16
53
2/23
51
3/02
48
16. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Guinea
Prevalence
16
Date
People
in
H+I
12/08
98
12/15
94
12/22
90
12/29
86
1/05
82
1/12
79
1/19
76
1/26
72
2/2
69
2/9
66
2/16
64
2/23
61
3/02
58
17. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Agent-‐based
model
progress
• Sierra
Leone
calibra2on
– Behavioral
Shil
of
17%
in
Mid-‐Oct
– Debugging
of
HPC
environment
– 2nd
round
of
calibra2on
is
be.er,
s2ll
not
sa2sfied
• Fundamental
science
of
calibra2on
– Methods
for
iden2fying
epidemic
instances
being
developed
– Extensions
to
simula2on
engine
to
generate
an
“enriched”
set
of
instances
underway
17
18. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
Sierra
Leone
Calibra2on
18
19. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
App
Development
• EpiViewer
enhancements
– Added
more
filters
– Improving
visualiza2on
before
broader
release
19
20. DRAFT
–
Not
for
a.ribu2on
or
distribu2on
App
Development
• Eyes-‐on-‐the-‐Ground
– Sierra
Leone
road
network
analysis
underway
– Support
for
older
browser
being
inves2gated
• Older
version
of
std
didn’t
support
current
implementa2on
20