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Introduction Methods and Results Conclusions
Dynamic Drivers of Disease in Africa
Integrating our understandings of zoonos...
Introduction Methods and Results Conclusions
Acknowledgments
Andrew A. Cunningham 2
, Elisabeth Fichet-Calvet 3
, Robert F...
Introduction Methods and Results Conclusions
Key Questions
Setting up traps in the mining area
in Sierra Leone
If we know ...
Introduction Methods and Results Conclusions
Key Questions
Setting up traps in the mining area
in Sierra Leone
Blood sampl...
Introduction Methods and Results Conclusions
Key Questions
Setting up traps in the mining area
in Sierra Leone
Blood sampl...
Introduction Methods and Results Conclusions
Key Questions
Setting up traps in the mining area
in Sierra Leone
Blood sampl...
Introduction Methods and Results Conclusions
Key Questions
From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at the
hu...
Introduction Methods and Results Conclusions
Key Questions
From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at the
hu...
Introduction Methods and Results Conclusions
Key Questions
From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at the
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Dynamcs of Lassa Fever
Human-­‐to-­‐Human	
  Transmission	
  through	
  close...
Introduction Methods and Results Conclusions
Modelling the risk of spillover events
Introduction Methods and Results Conclusions
Modelling the risk of spillover events
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Occurrence
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Patterns in the cumulative number of cases
Straight line for pure zoonosis
Co...
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Depletion of susceptibles
Introduction Methods and Results Conclusions
Patterns in the cumulative number of cases
Concave (downward) line when susce...
Introduction Methods and Results Conclusions
Inclusion of human-to-human transmission
Introduction Methods and Results Conclusions
Patterns in the cumulative number of cases
Convex (upward) line when human-to...
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Patterns in the cumulative number of cases
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Constant zoonotic exposure
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A unified framework for spillover and stuttering c...
Introduction Methods and Results Conclusions
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A unified framework for spillover and stuttering c...
Introduction Methods and Results Conclusions
Conclusions and Future Work
A unified framework for spillover and stuttering c...
Introduction Methods and Results Conclusions
Conclusions and Future Work
A unified framework for spillover and stuttering c...
Introduction Methods and Results Conclusions
Conclusions and Future Work
A unified framework for spillover and stuttering c...
Introduction Methods and Results Conclusions
Acknowledgments
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A unified framework for the infection dynamics of zoonotic spillover and spread

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Presentation by Dr Gianni Lo Iacono of Public Health England at the One Health for the Real World: zoonoses, ecosystems and wellbeing symposium, London 17-18 March 2016

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A unified framework for the infection dynamics of zoonotic spillover and spread

  1. 1. Introduction Methods and Results Conclusions Dynamic Drivers of Disease in Africa Integrating our understandings of zoonoses, ecosystems and wellbeing A unified framework for the infection dynamics of zoonotic spillover and spread Gianni Lo Iacono1,2 1 Department Veterinary Medicine University of Cambridge, UK 2 Departmen of Environmental Change, Public Health England, UK
  2. 2. Introduction Methods and Results Conclusions Acknowledgments Andrew A. Cunningham 2 , Elisabeth Fichet-Calvet 3 , Robert F. Garry 4 , Donald S. Grant 5 , Melissa Leach 6 , Lina M. Moses 4 , Gordon Nichols 7 John S. Schieffelin 8 , Jeffrey G. Shaffer 9 , Collen Webb 10 , James L. N. Wood 1 1 Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Cambridge, United Kingdom. 2 Institute of Zoology, Zoological Society of London. United Kingdom 3 Bernhard-Nocht Institute of Tropical Medicine. Hamburg, Germany 4 Department of Microbiology and Immunology, Tulane University, New Orleans, Louisiana, USA 5 Lassa Fever Program, Kenema Government Hospital, Kenema, Sierra Leone 6 Institute of Development Studies, University of Sussex. Brighton, United Kingdom 7 Public Health England, United Kingdom 8 Sections of Infectious Disease, Departments of Pediatrics and Internal Medicine, School of Medicine, Tulane University, New Orleans, LA, USA 9 Department of Biostatistics and Bioinformatics, Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA 10 Department of Biology, Colorado State University, Fort Collins, USA
  3. 3. Introduction Methods and Results Conclusions Key Questions Setting up traps in the mining area in Sierra Leone If we know the abundance of, the reservoir,
  4. 4. Introduction Methods and Results Conclusions Key Questions Setting up traps in the mining area in Sierra Leone Blood sampling in bats in Ghana If we know the abundance of, the infection prevalence in, the reservoir,
  5. 5. Introduction Methods and Results Conclusions Key Questions Setting up traps in the mining area in Sierra Leone Blood sampling in bats in Ghana Participatory mapping in Sierra Leone If we know the abundance of, the infection prevalence in, and exposure to the reservoir,
  6. 6. Introduction Methods and Results Conclusions Key Questions Setting up traps in the mining area in Sierra Leone Blood sampling in bats in Ghana Participatory mapping in Sierra Leone If we know the abundance of, the infection prevalence in, and exposure to the reservoir, can we estimate the likelihood of the next spillover event?
  7. 7. Introduction Methods and Results Conclusions Key Questions From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at the human-animal interface. Science, 326(5958).
  8. 8. Introduction Methods and Results Conclusions Key Questions From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at the human-animal interface. Science, 326(5958). A need for unification How to compare different stages?
  9. 9. Introduction Methods and Results Conclusions Key Questions From: Lloyd-Smith, J et al. (2009). Epidemic dynamics at the human-animal interface. Science, 326(5958). A need for unification How to compare different stages? How to disentangle the contribution of human-to-human transmission from zoonotic spillover?
  10. 10. Introduction Methods and Results Conclusions Dynamcs of Lassa Fever Human-­‐to-­‐Human  Transmission  through  close   contacts,  probably  via  body  fluids.  Previous   es8mates  suggest    ~  20%  of  cases  can    be   a?ributed  to  human-­‐to-­‐human  transmission   Nosocomial   Transmission.   e.g.  exchange  of     infected  needles   Rodent-­‐to-­‐Rodent   Transmission.     Unclear  reasons  for   maintenance.   Transmission  pa?erns   are  further  confounded   due  to    seasonality  in     Mastomys  natalensis.   abundance  and  in   infec8on  prevalence.   These  factors  are  also   affected    by  the  habitat   (rodents  living  near   houses  vs  rodents  living   in  the  proximity  of   villages)   Mastomys  natalensis   Repor6ng  Bias.  Many  cases  are  not  reported   despite  improvement  in  community  outreach   and  surveillance  ac8vi8es.  Infrastructure   quality  (roads  are  oJen  flooded  during  the   rainy  season),  economic  and  social  factors   (people  have  limited  economic  resources  in   the  rainy  season)  might  introduce  seasonal   bias  in  repor8ng.     Rodent-­‐to-­‐Human   Transmission.     Transmission  through  domes8c/ agricultural  exposure.  
  11. 11. Introduction Methods and Results Conclusions Modelling the risk of spillover events
  12. 12. Introduction Methods and Results Conclusions Modelling the risk of spillover events 0.0 0.1 0.2 0.3 0 10 20 30 Occurrence Density From knowedge of mean and variance of abundance, prevalence etc. → Infer the risk of spillover in humans
  13. 13. Introduction Methods and Results Conclusions Patterns in the cumulative number of cases Straight line for pure zoonosis Comparison with Agent Based Model qqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqq 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Day CumulativeNumberofZoonoticInfections
  14. 14. Introduction Methods and Results Conclusions Depletion of susceptibles
  15. 15. Introduction Methods and Results Conclusions Patterns in the cumulative number of cases Concave (downward) line when susceptibles are depleted Comparison with Agent Based Model qqqq q q q q q qqq q q q q q q q q q q q qq q q q q q qq q q q q q q q q q q q q qq qq q q qq qq q q qq q q q qq qqq qq q q q q qqq q qqqq q qq q q q q q q qqq qqq qq q qq qq q q q q qq q q q q q qq q qq qqq q qq qq q qq q q qqqq q qq q q qqqqq qqqqqqqqq qq qq q q qq qqq qq qq q q q qqqqqqqq qqqqq qq qqqq qqqqq qq qqqqqqq qqqqqqqq qq qq q qqq qqq q qqqqqqqqqqqqqqqqqqqqqqqq q qqqqqqqqqqqqqqq qqqqqqqqqqqq qqq qqqqqqqqq qqq qqqq qqqqq qqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqq q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq 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qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq Concave shape 250 500 750 1000 0 100 200 300 400 500 600 700 800 900 1000 Day CumulativeNumberofZoonoticInfections
  16. 16. Introduction Methods and Results Conclusions Inclusion of human-to-human transmission
  17. 17. Introduction Methods and Results Conclusions Patterns in the cumulative number of cases Convex (upward) line when human-to-human transmission Comparison with Agent Based Model qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqq qq q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqq qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqq q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq qqqq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqq qq qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqq qqqqqq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qq q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Convex shape 0 10000 20000 30000 40000 0 40 80 120 160 200 240 280 320 360 Day CumulativeNumberofHuman−to−HumanInfections
  18. 18. Introduction Methods and Results Conclusions Patterns in the cumulative number of cases S-shape line when human-to-human transmission and depletion of susceptibles Comparison with Agent Based Model qq qqqqqqqqqqqqqqqqqq qqqqqqqqq q q q q q qq qqqqqqqqqqqqqq q q q q q q qq qq q qq qqqq q q q q q q q qqq q qq q q q qqq qqqqq q q q q q q qq q q qq q q qq qqq q q qq q q qq q q q q q qq q q qq qq q q q q qq q q q q q q q q q q q q q q q q q q qq q q q q qqq q q q q qq q q q q q q q qqq q q q qq q qq q q q q q q qq q q qq q q qq q q q q qq q q qq q q q q q q q q q qqq q qqqqqqqq qq q qq qqqqq q qqqqqqq qq qqqqqqqq qq qqqqqqqq qqq qqqqqqqq qqqqqqq qqqqqq 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qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q qq qq qq q q qqqqqqqq qq qq qqq q q q qqq qqq qq qqq q q qq qqq q q q qq qq q q q q q qq q q q qqq qq q q q q q qq qqq qqqq q q q q q q q q q qq qq qq q q q q q q q q q q q q q q q q q q q q qq q q q q q q qq q q q q q q q q q q q q qq q q q q q q q q q qq q qqq q q q qq q q qq q q q q q q q q q q qqq q qqqqq q qq q q q q qqqqqqq q qqqqqq qq qq qqqqqqqqqq qqqq qqqq qqqqqq qqqq qqqq qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq Convex shape Concave shape 250 500 750 1000 0 100 200 300 400 500 600 Day CumulativeNumberofHuman−to−HumanInfections
  19. 19. Introduction Methods and Results Conclusions Contribution of human-to-human transmission Comparison with Agent Based Model a" Parameter 1 Parameter 2 0.006 0.007 0.008 0.009 0.010 0.045 0.050 0.055 0.060 0.065 0 2500 5000 7500 10000 0 2500 5000 7500 10000 Iteration value b" Parameter 1 Parameter 2 0 250 500 750 1000 0 250 500 750 1000 0.006 0.007 0.008 0.009 0.010 0.05 0.06 value count ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 200 400 600 0 25 50 75 100 Day CumulativenumberofZoonoticandHuman−to−HumanInfections
  20. 20. Introduction Methods and Results Conclusions Comparison with real data from Sierra Leone Constant zoonotic exposure Red Line − : Kenema Gov. Hospital data Blue Lines − : 5 individual stochastic model realizations Black Line −: Average model predictions Grey dots · · · : 100 stochastic model realizations
  21. 21. Introduction Methods and Results Conclusions Comparison with real data from Sierra Leone Constant zoonotic exposure Piecewise, linearly variable zoonotic exposure Red Line − : Kenema Gov. Hospital data Blue Lines − : 5 individual stochastic model realizations Black Line −: Average model predictions Grey dots · · · : 100 stochastic model realizations
  22. 22. Introduction Methods and Results Conclusions Comparison with real data from Sierra Leone Constant zoonotic exposure Piecewise, linearly variable zoonotic exposure Red Line − : Kenema Gov. Hospital data Blue Lines − : 5 individual stochastic model realizations Black Line −: Average model predictions Grey dots · · · : 100 stochastic model realizations A case of un-identifiability Different assumptions are equally compatible with the empirical data
  23. 23. Introduction Methods and Results Conclusions Comparison with real data from Sierra Leone Constant zoonotic exposure Piecewise, linearly variable zoonotic exposure Red Line − : Kenema Gov. Hospital data Blue Lines − : 5 individual stochastic model realizations Black Line −: Average model predictions Grey dots · · · : 100 stochastic model realizations A case of un-identifiability Different assumptions are equally compatible with the empirical data Such un-identifiabilityis is expected to be removed as soon as more accurate data on exposure rates and rodent infection prevalence become available.
  24. 24. Introduction Methods and Results Conclusions Comparison with real data from Sierra Leone Constant zoonotic exposure Piecewise, linearly variable zoonotic exposure Red Line − : Kenema Gov. Hospital data Blue Lines − : 5 individual stochastic model realizations Black Line −: Average model predictions Grey dots · · · : 100 stochastic model realizations A case of un-identifiability Different assumptions are equally compatible with the empirical data Such un-identifiabilityis is expected to be removed as soon as more accurate data on exposure rates and rodent infection prevalence become available. Only a general knowledge of the time-dependency of these quantities
  25. 25. Introduction Methods and Results Conclusions Conclusions and Future Work A unified framework for spillover and stuttering chain
  26. 26. Introduction Methods and Results Conclusions Conclusions and Future Work A unified framework for spillover and stuttering chain Signature for identify human-to-human transmission, and procedure to quantify it..
  27. 27. Introduction Methods and Results Conclusions Conclusions and Future Work A unified framework for spillover and stuttering chain Signature for identify human-to-human transmission, and procedure to quantify it.. ..but it is a signature that can be forged!
  28. 28. Introduction Methods and Results Conclusions Conclusions and Future Work A unified framework for spillover and stuttering chain Signature for identify human-to-human transmission, and procedure to quantify it.. ..but it is a signature that can be forged! Future Work: Remove Un-identifiability
  29. 29. Introduction Methods and Results Conclusions Conclusions and Future Work A unified framework for spillover and stuttering chain Signature for identify human-to-human transmission, and procedure to quantify it.. ..but it is a signature that can be forged! Future Work: Remove Un-identifiability Future Work: Impact of super-spreaders
  30. 30. Introduction Methods and Results Conclusions Acknowledgments This work for the Dynamic Drivers of Disease in Africa Consortium was funded with support from the Ecosystem Services for Poverty Alleviation (ESPA) programme. The ESPA programme is funded by the Department for International Development (DFID), the Economic and Social Research Council (alertESRC) and the Natural Environment Research Council (alertNERC, Project NE-J001570-1 ). See more at: http://www.espa.ac.uk/ Thanks also to my current employer, Public Health England and the National Institute for Health Research Health Protection Research Unit (NIHR HPRU), for giving me the opportunity to be here today

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