J
Jennifer A. Flegg
Researcher at University of Melbourne
Publications - 75
Citations - 4037
Jennifer A. Flegg is an academic researcher from University of Melbourne. The author has contributed to research in topics: Malaria & Medicine. The author has an hindex of 24, co-authored 60 publications receiving 3445 citations. Previous affiliations of Jennifer A. Flegg include Monash University & University of Oxford.
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Journal ArticleDOI
Optimal sampling designs for accurate estimation of parasite clearance in the context of artemisinin resistance
Jennifer A. Flegg,Philippe J Guerin,François Nosten,François Nosten,Arjen M. Dondorp,Arjen M. Dondorp,Rick M. Fairhurst,Duong Socheat,Steffen Borrmann,Anders Björkman,Andreas Mårtensson,Mayfong Mayxay,Paul N. Newton,Paul N. Newton,Delia Bethell,Youry Se,Harald Noedl,Abdoulaye Djimde,Nicholas J. White,Nicholas J. White,Kasia Stepniewska +20 more
TL;DR: This indicator remains critically important to monitor the extent of the problem in the absence of molecular marker(s) associated with artemisinin resistance and lack of sensitivity of current in vitro tests.
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COVID-19 morbidity in Afghanistan: a nationwide, population-based seroepidemiological study
Sayed Ataullah Saeedzai,Mohammad Nadir Sahak,Fatima Arifi,Eman Abdelkreem Aly,Margo van Gurp,Lisa J. White,Siyu Chen,Amal Barakat,Giti Azim,Bahara Rasoly,Soraya Safi,Jennifer A. Flegg,Nasar Ahmed,Mohmmad Jamaluddin Ahadi,Niaz M. Achakzai,A Abouzeid +15 more
TL;DR: The survey revealed that, to July 2020, around 10 million people in Afghanistan (31.5% of the population) had either current or previous COVID-19 infection, which implies that most of thepopulation remained at risk of infection.
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How quickly does a wound heal? Bayesian calibration of a mathematical model of venous leg ulcer healing.
TL;DR: This work calibrates an existing mathematical model using a Bayesian approach with clinical data for individual patients to explore which clinical factors may impact the rate of wound healing for individuals and highlights the challenges of using Bayesian methods to calibrate partial differential equation models to individual patient clinical data.
Posted ContentDOI
Free and interfacial boundaries in individual-based models of multicellular biological systems
TL;DR: In this article , the impact of cell boundary descriptions on individual-based models of cell dynamics has been investigated in the context of biological simulation, and the authors concluded that the appropriate choice of boundary description depends on the biological phenomenon being studied and the metrics of interest.
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Physiological factors leading to a successful vaccination: A computational approach
TL;DR: The results show that increasing T cell inflow through high endothelial venules, restricting cellular egress via the efferent lymph and increasing the total dendritic cell count by improving vaccinations are the among the most important physiological factors leading to an improved immune response.