Publications by authors named "M Suzanne Kraemer"
East, South, and Southeast Asia (together referred to as Southeastern Asia hereafter) have been recognized as critical areas fuelling the global circulation of seasonal influenza. However, the seasonal influenza migration network within Southeastern Asia remains unclear, including how pandemic-related disruptions altered this network. We leveraged genetic, epidemiological, and airline travel data between 2007-2023 to characterise the dispersal patterns of influenza A/H3N2 and B/Victoria viruses both out of and within Southeastern Asia, including during perturbations by the 2009 A/H1N1 and COVID-19 pandemics.
View Article and Find Full Text PDFBackground And Objectives: Magnetic resonance imaging (MRI) and neurohistopathology are important correlates for evaluation of disease progression in multiple sclerosis (MS). Here we used experimental autoimmune encephalomyelitis (EAE) as an animal model of MS to determine the correlation between clinical EAE severity, MRI and histopathological parameters.
Methods: N = 11 female C57BL/6J mice were immunized with human myelin oligodendrocyte glycoprotein 1-125, while N = 9 remained non-immunized.
Objective: In this multicentric study, we were interested in the vision-related quality of life and its association with visual impairment in neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) in comparison to multiple sclerosis (MS) and healthy controls.
Methods: We analysed extracted data from the German NEMOS registry including National Eye Institute Visual Function Questionnaire (NEI-VFQ) scores, high and low contrast visual acuity (HCVA, LCVA), visually evoked potentials (VEP) and the scores for the expanded disability status scale (EDSS) and other neurological tests which assessed their disease-related impairment. The mean follow-up time of our patients was 1.
Toward optimal disease surveillance with graph-based active learning.
Proc Natl Acad Sci U S A
December 2024
Article Synopsis
- Tracking emerging pathogens is essential for effective public health responses, and this study models resource allocation for testing as a decision-making problem involving locations as nodes on a graph.
- The researchers evaluate different active learning policies for selecting testing locations, comparing their effectiveness in various outbreak scenarios through simulations on both synthetic and real-world networks.
- A new policy that considers the distance-weighted average entropy shows improved performance over existing methods, emphasizing the importance of balancing exploration and exploitation in developing surveillance strategies for pathogen monitoring.
Background: Dengue is a significant global public health concern that poses a threat in Africa. Particularly, African countries are at risk of viral introductions through air travel connectivity with areas of South America and Asia in which explosive dengue outbreaks frequently occur. Limited reporting and diagnostic capacity hinder a comprehensive assessment of continent-wide transmission dynamics and deployment of surveillance strategies in Africa.
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