Publications by authors named "B Lopman"
Background: Understanding healthcare personnel's (HCP) contact patterns are important to mitigate healthcare-associated infectious disease transmission. Little is known about how HCP contact patterns change over time or during outbreaks such as the COVID-19 pandemic.
Methods: This study in a large United States healthcare system examined the social contact patterns of HCP via standardized social contact diaries.
Rotavirus vaccine appears to perform sub-optimally in countries with higher rotavirus burden. We hypothesized that differences in the magnitude of rotavirus exposures may bias vaccine efficacy (VE) estimates, so true differences in country-specific rotavirus VE would be exaggerated without accommodating differences in exposure. We estimated VE against any-severity and severe rotavirus gastroenteritis (RVGE) using Poisson regression models fit to pooled individual-level data from Phase II and III monovalent rotavirus vaccine trials conducted between 2000 and 2012.
View Article and Find Full Text PDFFew sources have reported empirical social contact data from resource-poor settings. To address this shortfall, we recruited 1,363 participants from rural and urban areas of Mozambique during the COVID-19 pandemic, determining age, sex, and relation to the contact for each person. Participants reported a mean of 8.
View Article and Find Full Text PDFThere is currently limited evidence regarding how the rotavirus vaccine dosing schedule might be adjusted to improve vaccine performance. We quantified the impact of the previously implemented 6/10-week Rotarix vaccine (RV1) in Ghana to the model-predicted impact for other vaccine dosing schedules across three hospitals and the entire country. Compared to no vaccination, the model-estimated median percentage reductions in rotavirus ranged from 28 to 85% and 12 to 71% among children <1 and <5 years old, respectively.
View Article and Find Full Text PDFArticle Synopsis
- Measles is a complex infectious disease that poses significant public health challenges, and traditional models often fail to effectively capture its intricate dynamics during outbreaks.
- This study introduces a high-dimensional neural network model (SFNN) that demonstrates better forecasting accuracy compared to a classical mechanistic model (TSIR) when analyzing measles data from England and Wales between 1944-1965.
- The findings suggest that combining mechanistic and machine learning approaches, like using TSIR to enhance Physics-Informed-Neural-Networks (PINN), can further improve predictions and understand underlying disease dynamics without rigid assumptions.