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Addressing Vector-Borne Disease Challenges: Malaria and Insecticide Resistance in Sub-Saharan Africa

Vector-borne diseases, particularly malaria and dengue, impose significant health burdens globally, especially in Sub-Saharan Africa. Despite existing treatments, drug resistance and lack of vaccines highlight the urgent need for effective control strategies. This overview focuses on malaria’s incidence rates, the challenges posed by insecticide resistance, and potential solutions, including innovative insecticide development and environmental management. With collaboration among researchers and ongoing monitoring, advancements in vector control can improve malaria management and support public health initiatives.

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Addressing Vector-Borne Disease Challenges: Malaria and Insecticide Resistance in Sub-Saharan Africa

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  1. Ontologies & thing entity continuant dependent_continuant specifically_dependent_continuant realizable_entity disposition vector_borne_disease

  2. Tropical disease burden in SSA

  3. Global malaria burden

  4. Malaria incidence rate

  5. Dengue incidence

  6. VBD in the USA

  7. Malaria

  8. The Malaria triad

  9. The Malaria problem • - Good drugs exist, but pathogens are • increasingly resistant. • Most drugs are too expensive: a typical disease of poverty. • No vaccine available. • Chemotherapy works well for individual • patient but not for area-wide control.

  10. With the exception of the yellow fever, for which an effective vaccine exists, vector-borne diseases have only been controlled through the control of their arthropod vectors (insecticides, physical protection, environmental management). Examples range from Malaria (Anopheline mosquitoes - e.g. Europe, Latin America, etc.), to American trypanosomiasis (Chagas’ disease - kissing bugs), to Onchocerciasis (River blindness - simulid flies), to…

  11. The historic solution: DDT (and other insecticides)

  12. Spread of Insecticide Resistance

  13. Insecticide Resistance is a heritable change in the sensitivity of a pest population that is reflected in the repeated failure of a product to achieve the expected level of control when used according to the label recommendation for that pest species. Insecticide resistance is a population phenotype, it is the result of natural selection!

  14. Insecticide Resistance: management • (Develop new insecticides with a different mode of action). • Use more than one insecticides with a different mode of action (alternating, combinatorial, chessboard). • Efficient monitoring in combination with above.

  15. MIRO & IRBase • Originally non-BFO, now also integrated into IDOMAL. Stand-alone MIRO is being expanded to cover agricultural pests. • Domains: Insecticides (ChEBI), biological material, resistance mechanisms, methods (future: OBI?), Geo and environment (GAZ & ENVO) • IRBase: has been integrated into the new PopGen section of VectorBase (cross-talk to transmission, population genetics, genome, etc.)

  16. IRBase

  17. The example of Insecticide Resistance: Aedes aegypti, 2098 records

  18. The example of Insecticide Resistance: Aedes aegypti, 2098 records

  19. The example of Insecticide Resistance: Aedes aegypti - DDT, 133 records

  20. IDO EuPathDB IDOCHA MDSS IDOMAL VectorBase DDSS VIPR IDODEN GO, ChEBI, GAZ, ENVO, OBI, etc.

  21. vector control malaria immunology insecticide resistance geography GAZ environment ENVO symptoms & signs antimalarial drugs, resistance insecticidal substances IDOMAL vector biology etc... IR methodology remedies & natural products malaria transmission vector pop. biol. & genet. malaria epidemiology and more…

  22. is_a participates_in MIRO: resistance assay CHEBI: insecticide OBI/MIRO: assay IDOMAL: Transmission detection assay is_a has_output is_a MIRO: resistance PATO: Phenotype has_input has_output has_quality MIRO: Organism VB_CV: genotype IDOMAL: Human biting rate Sporozoite rate Parous rate Vectorial capacity IDOMAL: Human population has_member VB_CV/MIRO: Mosquito population Season + Geolocation + Human population + Mosq. population located_in has_output is_a GAZ: Geolocation OBI/MIRO: collection assay is_a has_quality is_about has_input ENVO : Environmental conditions e.g. Rice field, Irrigation scheme, swamp, altitude, av. temperature, annual rainfall, av. humidity GAZ: Country, Province, District, GPS coordinates IDOMAL: Season IDOMAL: Malaria control measures

  23. is_a participates_in MIRO: resistance assay CHEBI: insecticide OBI/MIRO: assay IDOMAL: Transmission detection assay is_a has_output is_a MIRO: resistance PATO: Phenotype has_input has_output has_quality MIRO: Organism VB_CV: genotype IDOMAL: Human biting rate Sporozoite rate Parous rate Vectorial capacity IDOMAL: Human population has_member VB_CV/MIRO: Mosquito population Season + Geolocation + Human population + Mosq. population located_in has_output is_a “Oth_Ont1”: Additional data models GAZ: Geolocation OBI/MIRO: collection assay is_a has_quality is_about has_input ENVO : Environmental conditions e.g. Rice field, Irrigation scheme, swamp, altitude, av. temperature, annual rainfall, av. humidity GAZ: Country, Province, District, GPS coordinates IDOMAL: Season IDOMAL: Malaria control measures "IDOVBD": Additional data models “Oth_Ont2”: Additional data models

  24. Chris Stoeckert, David Rooset al., pers. communic.

  25. Acknowledgements • The people who did the work: • IoanaBujila, Elena Deligianni, Emmanuel Dialynas, Vicky Dritsou, Elvira Mitraka, Inga Siden-Kiamos, PantelisTopalis • Our collaborators: • All members of VectorBase (esp. Frank Collins), MaritaTroye-Blomberg (malaria immunology/U. Stockholm), all PIs of Transmalariabloc (esp. George Christophides), John Vontas (Insecticide resistance/U. Crete) • The hands that fed us: • VectorBase (NIAID), BioMalPar, EVIMalR and Transmalariabloc (EU-DG XII)

  26. Τέλος Ευχαριστώ!

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