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1. Considerations for first field trials of low-threshold gene drive for malaria vector control

2. Participatory Mathematical Modeling Approach for Policymaking during the First Year of the COVID-19 Crisis, Jordan

5. LLIN Evaluation in Uganda Project (LLINEUP)–effects of a vector control trial on Plasmodium infection prevalence and genotypic markers of insecticide resistance in Anopheles vectors from 48 districts of Uganda

11. LLIN Evaluation in Uganda Project (LLINEUP) -Plasmodiuminfection prevalence and genotypic markers of insecticide resistance inAnophelesvectors from 48 districts of Uganda

13. Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991–2019

16. Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors

18. Additional file 1 of Modelling spatiotemporal trends in the frequency of genetic mutations conferring insecticide target-site resistance in African mosquito malaria vector species

22. Modelling spatiotemporal trends in the frequency of genetic mutations conferring insecticide target-site resistance in African malaria vector species

23. Mapping malaria by sharing spatial information between incidence and prevalence data sets

24. Predicting non-state terrorism worldwide

25. Increasing role of pyrethroid-resistant Anopheles funestus in malaria transmission in the Lake Zone, Tanzania: implications for the evaluation of novel vector control products

26. A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making

27. Additional file 1 of Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991–2019

28. Evaluating the Evidence from Molecular Structure and Population Studies for Cross-Resistance to the Pyrethroids Used in Malaria Vector Control

31. Mapping malaria by sharing spatial information between incidence and prevalence datasets

34. Exploiting the behaviour of wild malaria vectors to achieve high infection with fungal biocontrol agents

36. Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors

38. Additional file 5: Figure S1.3. of Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits

39. Additional file 4: Figure S1.2. of Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits

41. Additional file 6: Figure S2.1. of Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits

42. Additional file 13: Figure S4.1. of Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits

43. Additional file 7: Figure S3.1. of Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits

45. Additional file 9: Figure S5.1. of Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits

48. Predicting the spatial dynamics of Wolbachia infections in Aedes aegypti arbovirus vector populations in heterogeneous landscapes.

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