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Tracking and Predicting
Ebola Outbreaks
Colleen M. Farrelly, Chief Mathematician at Staticlysm/Quantopo
Who am I?
 Social scientist who started
the data science journey as
an MD/PhD student
 Data scientist and machine
learning researcher for 10+
years now
 Entrepreneur within
healthcare and applied
physics
 Author of scientific articles,
lay audience articles, and a
forthcoming machine learning
book
What is Ebola?
 Caused by a single-stranded RNA filovirus
 Transmitted through direct contact with bodily fluids, particularly at later
stages of infection
 Produces severe symptoms:
 Starts with sudden flu-like symptoms
 Progresses to high fever with severe diarrhea and vomiting
 Some with symptoms of chest pain or trouble breathing in the later stages
 Some with severe bleeding in the later stages
 Death from hypovolemia for 50%+ of patients
 Ebola is mostly survivable with sufficient medical resources per patient,
though.
2014 West African Outbreak
 March 2014 outbreak in Guinea
traced to a single case in December
2013 (death of a one-year-old)
 Quickly spread to Sierra Leone and
Liberia
 Isolated cases spread to other
countries, prompting international
panic.
 Outbreak was officially declared
under control in March 2016.
 As of May 2016, the death toll stood
at 11,323.
Sociopolitical Context
 Guinea had endured periods of unrest internally in the years preceding the
outbreak but been connected to other African nations and outside investors;
Guinea is a mostly Muslim country, following Islamic tradition with respect to
burials.
 Liberia, a mostly Christian country, is Africa’s oldest republic, and following
unrest in the early 2000s, Liberia had returned to stability, albeit at high
poverty rates.
 Sierra Leone, a mixed Muslim and Christian nation, weathered a lengthy civil
war and path to peace prior to the epidemic; however, women remain
vulnerable in Sierra Leone.
 Poverty, religious burial practices, and gender inequality created disparities in
vulnerability and response to the epidemic.
Engaging Partners
 Within Sierra Leone, Liberia, and Guinea:
 Large NGOs to send personnel
 Meeting supply provision
 Addressing cultural practices
 Within countries bordering the most-impacted countries:
 Drivers were:
 Fear of Ebola spread
 Impacts on economy and population health
 My involvement started with an entrepreneur friend in Mali, who then engaged the
health ministry and local NGOs.
Methodology
 Methods
 Simple scenario models
 Run with an Excel macro
 Using a susceptible-infected-
recovered model
 At both local and country levels
 This gave:
 Locations most at-risk for initial case
in country
 Locations leading to the biggest
epidemic potential
 Transportation risk for initial case
entry
Response
 Models were able to pinpoint most
vulnerable targets within Mali (including
Kayes, where the first case crossed into
Mali), prompting:
 Diversion of resources and international aid
 Movement of personnel to vulnerable areas
 Phone lines to disseminate information to
illiterate populations
 In all, very few people were infected in
Mali after two incidents of sick travelers
occurred.
2018 DRC Outbreak
 Current outbreak in the
Democratic Republic of Congo
traced to a cluster of four cases in
August 2018.
 Quickly spread across North Kivu
and Ituri provinces
 Isolated cases in South Kivu and
Uganda
 As of May 2020, the outbreak is
ongoing, with new cases and
deaths reported.
 New outbreak declared June 2020
Sociopolitical Context
 The DRC is a former Belgian colony that has seen a lot of exploitation and war
for the last 150 years.
 The Kivu Conflict has been ongoing since 2004 in the affected area.
 Funded by exploiting natural resources like cobalt, gold, and diamonds
 Includes Congolese rebels and exiled Hutus who had fled to the DRC after the
genocide in Rwanda
 Has involved child soldiers, human trafficking, and rape of civilians (estimated to
be 400,000+/year in the impacted region)
 Attacks within the past days when the outbreak started
 Death toll estimates in the millions in this conflict alone
Engaging Partners
 International aid workers were already in the region when the outbreak
started.
 Vaccines had already been developed in response to the 2014 outbreak and have
been tested in the DRC outbreak.
 Most of the international coverage has shown attacks on humanitarian workers
trying to contain the outbreak.
 Given the proximity to US interests, military and humanitarian groups have
been mobilized.
 One disaster response organization contacted Quantopo regarding predictive
modeling for resource planning purposes (Feb 2019).
 Action has been complicated by the ongoing Kivu conflict.
Methodology
 Methods:
 Data from Humanitarian Exchange
 Geographic data on travel routes between cities
in Ituri and Kivu
 Topology-based methods along with traditional
forecast models
 Curvature-based analyses to forecast areas at
risk for major increases in case loads and attacks
against aid workers
 Calculated using custom R code
 This gave:
 Cities at risk for large
increases in numbers of
cases, including Katwa,
Beni, and Goma
 Identification of violence-
prone regions (including
Katwa and Beni, where
most attacks have
happened)
Response
 Within two weeks of our results, attacks picked up in Katwa and Beni, forcing
most international aid organizations to pull out remaining personnel in the
region.
 This has complicated the response to the current outbreak.
 Numbers of new cases doubled between April and June of 2019, within weeks our
analysis.
 Since then, cases have begun appearing in major cities and across borders,
including cases in Goma and Uganda.
 The United Nations and World Health Organization have declared this an
international emergency.
 So far, cities and bordering countries have been relatively unaffected, and
efforts have focused on limiting the spread of the outbreak.
Complicating Issues
 The conflict is ongoing, forcing aid
workers to evacuate.
 There’s widespread misinformation
about the source of the outbreak
(biological warfare from white aid
workers…).
 New cases are popping up with no
known contact with an infected person.
 There was a major scare in Tanzania
regarding potential travelers infected
with Ebola. The government has not
been forthcoming with test results.
 This outbreak is complex, and
containment strategies that worked
well in West Africa are not working in
the DRC.
Current Situation in the USA
Pandemic, deep politics-based mistrust, violent clashes in major cities
Lessons: Build Bridges
 Need to predict at-risk areas not
impacted yet and areas at risk for
epidemic worsening
 Need for ongoing international
response in conjunction with local
governments to meet the needs of
local populations at risk
 Need for infrastructure to support
aid workers (armed protection…)
 Need to disseminate accurate
information to local populations
with limited technological access
and education

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Tracking and predicting ebola outbreaks

  • 1. Tracking and Predicting Ebola Outbreaks Colleen M. Farrelly, Chief Mathematician at Staticlysm/Quantopo
  • 2. Who am I?  Social scientist who started the data science journey as an MD/PhD student  Data scientist and machine learning researcher for 10+ years now  Entrepreneur within healthcare and applied physics  Author of scientific articles, lay audience articles, and a forthcoming machine learning book
  • 3. What is Ebola?  Caused by a single-stranded RNA filovirus  Transmitted through direct contact with bodily fluids, particularly at later stages of infection  Produces severe symptoms:  Starts with sudden flu-like symptoms  Progresses to high fever with severe diarrhea and vomiting  Some with symptoms of chest pain or trouble breathing in the later stages  Some with severe bleeding in the later stages  Death from hypovolemia for 50%+ of patients  Ebola is mostly survivable with sufficient medical resources per patient, though.
  • 4. 2014 West African Outbreak  March 2014 outbreak in Guinea traced to a single case in December 2013 (death of a one-year-old)  Quickly spread to Sierra Leone and Liberia  Isolated cases spread to other countries, prompting international panic.  Outbreak was officially declared under control in March 2016.  As of May 2016, the death toll stood at 11,323.
  • 5. Sociopolitical Context  Guinea had endured periods of unrest internally in the years preceding the outbreak but been connected to other African nations and outside investors; Guinea is a mostly Muslim country, following Islamic tradition with respect to burials.  Liberia, a mostly Christian country, is Africa’s oldest republic, and following unrest in the early 2000s, Liberia had returned to stability, albeit at high poverty rates.  Sierra Leone, a mixed Muslim and Christian nation, weathered a lengthy civil war and path to peace prior to the epidemic; however, women remain vulnerable in Sierra Leone.  Poverty, religious burial practices, and gender inequality created disparities in vulnerability and response to the epidemic.
  • 6. Engaging Partners  Within Sierra Leone, Liberia, and Guinea:  Large NGOs to send personnel  Meeting supply provision  Addressing cultural practices  Within countries bordering the most-impacted countries:  Drivers were:  Fear of Ebola spread  Impacts on economy and population health  My involvement started with an entrepreneur friend in Mali, who then engaged the health ministry and local NGOs.
  • 7. Methodology  Methods  Simple scenario models  Run with an Excel macro  Using a susceptible-infected- recovered model  At both local and country levels  This gave:  Locations most at-risk for initial case in country  Locations leading to the biggest epidemic potential  Transportation risk for initial case entry
  • 8. Response  Models were able to pinpoint most vulnerable targets within Mali (including Kayes, where the first case crossed into Mali), prompting:  Diversion of resources and international aid  Movement of personnel to vulnerable areas  Phone lines to disseminate information to illiterate populations  In all, very few people were infected in Mali after two incidents of sick travelers occurred.
  • 9. 2018 DRC Outbreak  Current outbreak in the Democratic Republic of Congo traced to a cluster of four cases in August 2018.  Quickly spread across North Kivu and Ituri provinces  Isolated cases in South Kivu and Uganda  As of May 2020, the outbreak is ongoing, with new cases and deaths reported.  New outbreak declared June 2020
  • 10. Sociopolitical Context  The DRC is a former Belgian colony that has seen a lot of exploitation and war for the last 150 years.  The Kivu Conflict has been ongoing since 2004 in the affected area.  Funded by exploiting natural resources like cobalt, gold, and diamonds  Includes Congolese rebels and exiled Hutus who had fled to the DRC after the genocide in Rwanda  Has involved child soldiers, human trafficking, and rape of civilians (estimated to be 400,000+/year in the impacted region)  Attacks within the past days when the outbreak started  Death toll estimates in the millions in this conflict alone
  • 11. Engaging Partners  International aid workers were already in the region when the outbreak started.  Vaccines had already been developed in response to the 2014 outbreak and have been tested in the DRC outbreak.  Most of the international coverage has shown attacks on humanitarian workers trying to contain the outbreak.  Given the proximity to US interests, military and humanitarian groups have been mobilized.  One disaster response organization contacted Quantopo regarding predictive modeling for resource planning purposes (Feb 2019).  Action has been complicated by the ongoing Kivu conflict.
  • 12. Methodology  Methods:  Data from Humanitarian Exchange  Geographic data on travel routes between cities in Ituri and Kivu  Topology-based methods along with traditional forecast models  Curvature-based analyses to forecast areas at risk for major increases in case loads and attacks against aid workers  Calculated using custom R code  This gave:  Cities at risk for large increases in numbers of cases, including Katwa, Beni, and Goma  Identification of violence- prone regions (including Katwa and Beni, where most attacks have happened)
  • 13. Response  Within two weeks of our results, attacks picked up in Katwa and Beni, forcing most international aid organizations to pull out remaining personnel in the region.  This has complicated the response to the current outbreak.  Numbers of new cases doubled between April and June of 2019, within weeks our analysis.  Since then, cases have begun appearing in major cities and across borders, including cases in Goma and Uganda.  The United Nations and World Health Organization have declared this an international emergency.  So far, cities and bordering countries have been relatively unaffected, and efforts have focused on limiting the spread of the outbreak.
  • 14. Complicating Issues  The conflict is ongoing, forcing aid workers to evacuate.  There’s widespread misinformation about the source of the outbreak (biological warfare from white aid workers…).  New cases are popping up with no known contact with an infected person.  There was a major scare in Tanzania regarding potential travelers infected with Ebola. The government has not been forthcoming with test results.  This outbreak is complex, and containment strategies that worked well in West Africa are not working in the DRC.
  • 15. Current Situation in the USA Pandemic, deep politics-based mistrust, violent clashes in major cities
  • 16. Lessons: Build Bridges  Need to predict at-risk areas not impacted yet and areas at risk for epidemic worsening  Need for ongoing international response in conjunction with local governments to meet the needs of local populations at risk  Need for infrastructure to support aid workers (armed protection…)  Need to disseminate accurate information to local populations with limited technological access and education