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Vector-borne diseases in a changing world
Case studies of Japanese encephalitis virus and East African
arboviruses
Kansas ...
How to squeeze as much as possible into a
presentation:
1. International Livestock Research Institute
i. What does ILRI do...
International Livestock Research Institute
Consultative Group for International Agricultural Research
2 Departments:
Integ...
CGIAR Research Program
Agriculture for Nutrition
and Health (A4NH)
Agriculture
for Improved
Nutrition & Health
12 CGIAR centers
IFPRI
ILRI
BIOVERSITY
CIAT
CIMMYT
CIP
ICARDA
ICRAF
ICRISAT
II...
1. Food Safety
2. Emerging infectious disease
3. Neglected zoonoses
Agriculture associated disease
CGIAR Research Program
Livestock and fish-
More meat, milk and fish for and by the poor
My main projects
1. DDDAC
i. RVF
ii. Cross-sectional socio-economic
2. Aflatoxins
i. Aflatoxin risk assessment Kenya
ii. M...
An introduction to disease emergence and
anthropogenic influences
• Humans are affecting every part of this planet
– Direc...
2 billion hidden hunger
The world today
7 billion people
One billion
hungry
1.7 billion
overweight/obese
Livestock is important
• 24 billion livestock
After rice, second most important source of food
19 billion in developing ...
There is a business case for one health
• Zoonoses sicken 2.4 billion people, kill
2.2 million people and affect more
than...
Infectious diseases- historically important
• In 1918-1920 Spanish flu:
• 50- 100 million humans
• Late 19th century Rinde...
HIV, TB, malaria
Other infectious
Mat//peri/nutritional
Cardiovascular
diseases
Cancers
Other NCD
Road traffic accidents
O...
Disease emergence?
• Which diseases?
• EID twice as likely to be zoonotic than non-zoonotic (zoonotic viruses
and protozoa...
Livestock
WildlifeHumans
Ecosystem
LH
WL
WH
X
Disease transmission Spillover event
WNV
WNV
Malaria
Malaria
Malaria
Dengue
Dengue
Dengue
Dengue
Yellow fever
Yellow fever
TBE
TBE
RSSE
Borrelia
Borrelia
RSSE
...
k= Probability that a vector feeding on an infected host gets infected.
Pf = Probability that a vector survives from one m...
Japanese Encephalitis Virus-
risks with urban agriculture
Infectious disease driver:
urbanization
Is urbanization important? Why is it a
driver of disease?
• 7 billion people
• 50% urban inhabitants – continuous urbanisa...
Urban inhabitants need food
• Large problems to supply from rural areas
• Difficulties with cold chain
Johanna Lindahl
The benefits and problems
• Local markets with living and dead animals
• Possibility to use urban wastes and waste water
• Lacking sanitation
The benefits and problems
Pathogens in the urban agriculture
• Food-borne pathogens
• Zoonotic animal diseases
• Vector-borne diseases
Japanese Encephalitis Virus
• Flavivirus, + ssRNA
• Known to be a rural disease
C prM E NS1 NS2A NS2B NS3 NS4A NS4B NS5
No...
Genotypes distribution and spread
1,2,3,4,5
1,2
1,2,3
3
1,3
A Flavivirus
• Culicidae, genus Culex
– Culex pseudovishnui
– Culex tritaeniorhyncus
– Culex gelidus
– Culex fuscocephala
...
Disease in humans and horses
• Often asymptomatic
• Incubation period 6-10
days
• Fever, headache and
meningitis
• Acute f...
JEV and reproduction- pigs
• SMEDI: Stillbirth, Mummification, Embryonal
death, infertility
• Middle third of pregnancy mo...
Geographic spread
Population density
(FAO stat estimates)
•3 billion live in
endemic areas
•50 000 cases per
year
•Case fa...
Our project in Vietnam
• 84 million inhabitants
• 1 600 km from North to South
• Two delta regions
– Red river delta in th...
Ninh Kieu district-Can Tho city
• Around 4800 pigs on 288
pig farms
– <17 pigs per farm
– 1-2 sows
• No poultry reported
•...
Ninh kieu district, ”Can Tho city”
Mosquito collections
Johanna Lindahl
Collected mosquitoes
• Culex tritaeniorhynchus (36%)
• Culex gelidus (24%)
• Culex quinquefasciatus (15%)
Johanna Lindahl
Pigs and mosquitoes
Graphs showing the mosquitoes collected in households with and without pigs in Ninh Kieu district, Can...
Serum from urban pigs
• Competitive ELISA
• All seropositive
Johanna Lindahl
Conclusions
• The urban Ninh Kieu district is densely
populated with extensive pig keeping
• Most mosquitoes at urban hous...
JEV- infected mosquitoes
Johanna Lindahl
Household with JEV-positive
mosquito pool close to pigs
Household with pigs,
no p...
Genotypes
Johanna Lindahl
M18370.1| JaOArS982
U14163.1| SA14
U47032.1| p3
AY585242.1| K87P39
GQ902063.1| KPP82-39-214CT
AF...
JEV infection rate
7885 mosquitoes, 352 mosquito pools, 7 positive
Johanna Lindahl
Positive pool
9 Cx. tritaeniorhynchus
3...
Conclusions
• Important with high sensitivity to circumvent
inhibition in the PCR
• Both genotype I and III can circulate ...
Infectious disease driver:
Land use changes
Rift valley fever-
Risks with irrigation
RVF
• Bunyaviridae, phlebovirus
• High mortality, abortions in ruminants
• Hemorrhagic fever, encephalitis in humans
• Arb...
Why irrigation?
• More and more range lands in Africa are being
converted to crop lands through irrigation to
alleviate fo...
Anthropogenic action:
Increased irrigation
Effect on ecosystem:
Creates more larval habitats
Vector consequence:
More infe...
Hypothesis
• Irrigation in an arid and semi-
arid area increases the risk for
RVF
• But other diseases can also be
affecte...
Solving that problem
• Including possible differential
diagnoses:
– Leptospira, Q-fever, malaria,
WNV, Dengue, Chikungunya...
Components
• Cross-sectional
– Humans
– Livestock
– Mosquitoes
– Wildlife
– Ticks
• Longitudinal
– Human febrile cases
– L...
•The study site:
– Tana River and Garissa
counties, northeastern
Kenya
!.
!.
Bura
Hola
0 10 20 30 405
Kilometers
´
Legend
...
Mosquitoes trapped – relative abundance and species distribution
0.00
0.10
0.20
0.30
0.40
RVF WNV Dengue
Irrigateed area
Non-irrigated area
Disease
Seroprevalence
Samples screened
– 481 s...
Risk factor analysis - findings
For WNV and Dengue model:
I. Males have a higher risk of exposure
than females
II. Farmers...
• Build capacity on
differential diagnosis of
febrile illnesses in the
local health centres.
Currently, most cases
are tre...
DDDAC
Case study: Zambia/ Zimbabwe
• Trypanosomiasis/tse tse
• Land use changes
– Protected area
– Area where livestock has been...
Case study: Ghana
• Henipa virus/ bats
• Urban –rural migration
• Livelihoods, poverty, ecology and the
association with d...
Case study: Sierra Leone
• Lassa fever/ multimammate rats
• Land use changes and rodent ecology
– Urban-rural
– Irrigation...
The perfect model?
Ecosystem
health
Human
health
Animal
health
Not the end….
….but the beginning
Open to questions
Open to discussion
Come visit us
Come visit us
Vector-borne diseases in a changing world: Case studies of Japanese encephalitis virus and East African arboviruses
Vector-borne diseases in a changing world: Case studies of Japanese encephalitis virus and East African arboviruses
Vector-borne diseases in a changing world: Case studies of Japanese encephalitis virus and East African arboviruses
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Vector-borne diseases in a changing world: Case studies of Japanese encephalitis virus and East African arboviruses

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Presented by Johanna Lindahl at Kansas State University, Kansas, USA, 21 August 2014.

Published in: Science
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Vector-borne diseases in a changing world: Case studies of Japanese encephalitis virus and East African arboviruses

  1. 1. Vector-borne diseases in a changing world Case studies of Japanese encephalitis virus and East African arboviruses Kansas State University August 21 2014 Johanna Lindahl
  2. 2. How to squeeze as much as possible into a presentation: 1. International Livestock Research Institute i. What does ILRI do? ii. What do I do for ILRI? 2. Introduction: EIDs, vectors and why we have a problem 3. Japanese Encephalitis Virus in Viet Nam i. Background JEV ii. Studies and results 4. Rift Valley fever virus in Kenya i. Background RVF ii. Studies and results (so far) 5. Dynamic drivers of disease in Africa
  3. 3. International Livestock Research Institute Consultative Group for International Agricultural Research 2 Departments: Integrated sciences • Food safety and zoonoses Biosciences Nairobi Main campus Nairobi Second campus Addis Ababa
  4. 4. CGIAR Research Program Agriculture for Nutrition and Health (A4NH)
  5. 5. Agriculture for Improved Nutrition & Health 12 CGIAR centers IFPRI ILRI BIOVERSITY CIAT CIMMYT CIP ICARDA ICRAF ICRISAT IITA IWMI WORLD FISH 1. Leveraging value chains for nutrition 2. Biofortification of staple crops: Harvest + 3. Agricultural associated diseases 4. Evaluating nutrition outcomes in programs 4 Themes
  6. 6. 1. Food Safety 2. Emerging infectious disease 3. Neglected zoonoses Agriculture associated disease
  7. 7. CGIAR Research Program Livestock and fish- More meat, milk and fish for and by the poor
  8. 8. My main projects 1. DDDAC i. RVF ii. Cross-sectional socio-economic 2. Aflatoxins i. Aflatoxin risk assessment Kenya ii. More aflatoxin stuff (Uganda, Vietnam, India, Senegal, etc) 3. Dairy in India i. Hygiene project ii. Tuberculosis, brucellosis and antibiotic resistance 4. Upcoming i. Vector and water associated diseases in SEA
  9. 9. An introduction to disease emergence and anthropogenic influences • Humans are affecting every part of this planet – Directly or indirectly
  10. 10. 2 billion hidden hunger The world today 7 billion people One billion hungry 1.7 billion overweight/obese
  11. 11. Livestock is important • 24 billion livestock After rice, second most important source of food 19 billion in developing countries 1 billion poor people depend on livestock 600 million in South Asia 300 million in Sub-Saharan Africa 25% urban
  12. 12. There is a business case for one health • Zoonoses sicken 2.4 billion people, kill 2.2 million people and affect more than 1 in 7 livestock each year • Cost $9 billion in lost productivity; $25 billion in animal mortality; and$50 billion in human health
  13. 13. Infectious diseases- historically important • In 1918-1920 Spanish flu: • 50- 100 million humans • Late 19th century Rinderpest: • Death of 2/3 of Maasai population in Tanzania and Kenya • Early 19th century Potato blight: • 25% of Irish population starved or migrated • 1967: "…war against infectious diseases has been won“ • US Surgeon General William H. Steward FAO/G.R. Thomson
  14. 14. HIV, TB, malaria Other infectious Mat//peri/nutritional Cardiovascular diseases Cancers Other NCD Road traffic accidents Other unintentional Intentional injuries 0 5 10 15 20 25 30 2004 2015 2030 2004 2015 2030 2004 2015 2030 Deaths(millions) High-income countries Middle-income countries Low-income countries Mortality: global projection, 2004-2030 US Surgeon General William H. Steward was wrong
  15. 15. Disease emergence? • Which diseases? • EID twice as likely to be zoonotic than non-zoonotic (zoonotic viruses and protozoa had highest proportion) • Quick mutations- typically ssRNA • Where? • rapid intensification, increasing interactions between animals, humans and ecosystems, often rapidly changing habits and practices
  16. 16. Livestock WildlifeHumans Ecosystem LH WL WH X Disease transmission Spillover event
  17. 17. WNV WNV Malaria Malaria Malaria Dengue Dengue Dengue Dengue Yellow fever Yellow fever TBE TBE RSSE Borrelia Borrelia RSSE JEV JEV MVE Ross River SLEV VEE, EEE, WEE VEE Bluetongue Bluetongue African horse sickness African swine fever African swine fever Chikungunya Chikungunya JEV Babesia Babesia Anaplasma, Chikungunya Sleeping sickness Chaga’s disease RVF, WNV Chikungunya Climate & climate changes Globalisation Urbanization Land-use changes Why are vector-borne diseases emerging?
  18. 18. k= Probability that a vector feeding on an infected host gets infected. Pf = Probability that a vector survives from one meal to the next. Pe= Probability that a vector survives the Extrinsic incubation period, EIP Q= Probability that a vector feeds from the right host – blood index for the host. HBr= Host biting rate, the number of vectors feeding from an animal per day. v= Probability of pathogens becoming infectious in the vector C= Vector capacity C= HBr Qvk Pe/(1- Pf) Vector capacity and competence
  19. 19. Japanese Encephalitis Virus- risks with urban agriculture Infectious disease driver: urbanization
  20. 20. Is urbanization important? Why is it a driver of disease? • 7 billion people • 50% urban inhabitants – continuous urbanisation • It involves approximately 800 million people and produces 15- 20 % of the food in the world
  21. 21. Urban inhabitants need food • Large problems to supply from rural areas • Difficulties with cold chain Johanna Lindahl
  22. 22. The benefits and problems • Local markets with living and dead animals
  23. 23. • Possibility to use urban wastes and waste water • Lacking sanitation The benefits and problems
  24. 24. Pathogens in the urban agriculture • Food-borne pathogens • Zoonotic animal diseases • Vector-borne diseases
  25. 25. Japanese Encephalitis Virus • Flavivirus, + ssRNA • Known to be a rural disease C prM E NS1 NS2A NS2B NS3 NS4A NS4B NS5 Non structural proteins
  26. 26. Genotypes distribution and spread 1,2,3,4,5 1,2 1,2,3 3 1,3
  27. 27. A Flavivirus • Culicidae, genus Culex – Culex pseudovishnui – Culex tritaeniorhyncus – Culex gelidus – Culex fuscocephala – Culex annulirostris – Culex quinquefasciatus …and an Arbovirus
  28. 28. Disease in humans and horses • Often asymptomatic • Incubation period 6-10 days • Fever, headache and meningitis • Acute flaccid paralysis • Coma, death
  29. 29. JEV and reproduction- pigs • SMEDI: Stillbirth, Mummification, Embryonal death, infertility • Middle third of pregnancy most affected • Immunocompetence after 65-70 days • Boars: orchitis, aspermia • Can be experimentally venerally transmitted
  30. 30. Geographic spread Population density (FAO stat estimates) •3 billion live in endemic areas •50 000 cases per year •Case fatality 30%
  31. 31. Our project in Vietnam • 84 million inhabitants • 1 600 km from North to South • Two delta regions – Red river delta in the north – Mekong delta in the south • A cooperation with the Can Tho university
  32. 32. Ninh Kieu district-Can Tho city • Around 4800 pigs on 288 pig farms – <17 pigs per farm – 1-2 sows • No poultry reported • Not many paddy fields
  33. 33. Ninh kieu district, ”Can Tho city”
  34. 34. Mosquito collections Johanna Lindahl
  35. 35. Collected mosquitoes • Culex tritaeniorhynchus (36%) • Culex gelidus (24%) • Culex quinquefasciatus (15%) Johanna Lindahl
  36. 36. Pigs and mosquitoes Graphs showing the mosquitoes collected in households with and without pigs in Ninh Kieu district, Can Tho city, Vietnam. Collections made close to humans are shown with thin boxes and collections close to pigs are shown with thick boxes. A; total number of mosquitoes, B; Culex tritaeniorhynchus C; Culex gelidus D; Culex quinquefasciatus. Circles depict outliers > 1.5 x the interquartile range and stars extreme outliers > 3 x the interquartile range.
  37. 37. Serum from urban pigs • Competitive ELISA • All seropositive Johanna Lindahl
  38. 38. Conclusions • The urban Ninh Kieu district is densely populated with extensive pig keeping • Most mosquitoes at urban households are potential vectors • Pig keeping increase the number of vectors
  39. 39. JEV- infected mosquitoes Johanna Lindahl Household with JEV-positive mosquito pool close to pigs Household with pigs, no positive pools Household without pigs, no positive pools
  40. 40. Genotypes Johanna Lindahl M18370.1| JaOArS982 U14163.1| SA14 U47032.1| p3 AY585242.1| K87P39 GQ902063.1| KPP82-39-214CT AF075723.1| GP78 AY508813.1| JaOH0566 AF098735.1| HVI AF098737.1| TL AF069076.1| JaGAr 01 AF254452.1| CH1392 AF254453.1| T1P1 AF080251.1| Vellore P20778 EF571853.1| Nakayama Household D fall Household T fall Household D spring Household S nr 2 L48961.1| Beijing-1 L78128.1| Ling Household I Spring Household S nr 1 GQ902061.1| B-1381-85 Household X rural GQ902058.1| B-0860/82 GQ902059.1| 1070/82 GQ902060.1| 3KP''U''CV569 JF499790.1| TC2009-1 GQ902062.1| 4790-85 JF499789.1| YL2009-4 AB051292.1| JEV AB241119.1| JEV/sw /Mie/41/2002 AY316157.1| KV1899 AF045551.2| K94P05 AF217620.1|FU HQ223286.1| WTP-70-22 AY184212.1| JKT6468 HM596272.1| Muar |JF915894.1| XZ0934 91 86 85 82 68 53 43 37 63 55 48 48 29 19 27 33 32 30 34 48 42 36 34 24 16 39 27 43 99 0.01 III I II IV V
  41. 41. JEV infection rate 7885 mosquitoes, 352 mosquito pools, 7 positive Johanna Lindahl Positive pool 9 Cx. tritaeniorhynchus 39 Cx. tritaeniorhynchus 25 Cx. tritaeniorhynchus 30 Cx. quinquefasciatus 50 unsorted 50 unsorted 50 unsorted Minimum infection rate per 1000 mosquitoes All mosquitoes 0.89 Females (including unidentified mosquitoes) 0.98 Cx. tritaeniorhynchus 1.59 Cx. quinquefasciatus 1.27
  42. 42. Conclusions • Important with high sensitivity to circumvent inhibition in the PCR • Both genotype I and III can circulate within the same city • One per thousand mosquitoes can be infected in an urban area
  43. 43. Infectious disease driver: Land use changes Rift valley fever- Risks with irrigation
  44. 44. RVF • Bunyaviridae, phlebovirus • High mortality, abortions in ruminants • Hemorrhagic fever, encephalitis in humans • Arbovirus- but also directly transmitted
  45. 45. Why irrigation? • More and more range lands in Africa are being converted to crop lands through irrigation to alleviate food insecurity • Results: major trade-offs in ecosystem services More food produced (provisioning services) at the expense of biodiversity and regulatory services (disease, flooding, erosion)
  46. 46. Anthropogenic action: Increased irrigation Effect on ecosystem: Creates more larval habitats Vector consequence: More infected vectors Epidemiologic consequence: More individuals exposed Increased disease Most drivers are desired- and not constantly leading to disease
  47. 47. Hypothesis • Irrigation in an arid and semi- arid area increases the risk for RVF • But other diseases can also be affected by this… • … and the doctors don’t know if it is RVF Study site with stagnant water in irrigation canals – source of water for the locals but also breeding grounds for mosquitoes
  48. 48. Solving that problem • Including possible differential diagnoses: – Leptospira, Q-fever, malaria, WNV, Dengue, Chikungunya, Crimean-Congo hemorrhagic fever, Brucella Pastoralists in the study site
  49. 49. Components • Cross-sectional – Humans – Livestock – Mosquitoes – Wildlife – Ticks • Longitudinal – Human febrile cases – Livestock- shoats – Mosquitoes
  50. 50. •The study site: – Tana River and Garissa counties, northeastern Kenya !. !. Bura Hola 0 10 20 30 405 Kilometers ´ Legend Settlements Irrigation Schemes permanent mixed temporary !. Towns Study Block Tana River County Tana River Other Rivers Riverrine Forests
  51. 51. Mosquitoes trapped – relative abundance and species distribution
  52. 52. 0.00 0.10 0.20 0.30 0.40 RVF WNV Dengue Irrigateed area Non-irrigated area Disease Seroprevalence Samples screened – 481 samples have been screened so far – Questionnaires administered to 430 households Serological tests – WNV and Dengue sero- prevalence apparently higher than that for RVF though confirmatory are yet to be done Relative seroprevalence of RVF, WNV and Dengue
  53. 53. Risk factor analysis - findings For WNV and Dengue model: I. Males have a higher risk of exposure than females II. Farmers have a higher risk compared to pastoralists For RVF model: I Males have a higher risk of exposure than females
  54. 54. • Build capacity on differential diagnosis of febrile illnesses in the local health centres. Currently, most cases are treated as: – Malaria – Brucellosis – Typhoid Intervention points 0 0.1 0.2 0.3 0.4 0.5 0.6 Proportion Communities’ perceptions on diseases that manifest similar signs as malaria – limited knowledge on arboviruses
  55. 55. DDDAC
  56. 56. Case study: Zambia/ Zimbabwe • Trypanosomiasis/tse tse • Land use changes – Protected area – Area where livestock has been increasing – Former large-scale farms with low biodiversity
  57. 57. Case study: Ghana • Henipa virus/ bats • Urban –rural migration • Livelihoods, poverty, ecology and the association with disease – How do humans interact with bats and what perceptions do they have of the risks – Protected/sacred area – Urban area
  58. 58. Case study: Sierra Leone • Lassa fever/ multimammate rats • Land use changes and rodent ecology – Urban-rural – Irrigation and precipitation – Human-rat interaction and risk perceptions
  59. 59. The perfect model? Ecosystem health Human health Animal health
  60. 60. Not the end…. ….but the beginning Open to questions Open to discussion
  61. 61. Come visit us
  62. 62. Come visit us

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