Publications by authors named "K J Gurski"
Article Synopsis
- The text describes a complex HIV population model that categorizes individuals into three groups: those not taking PrEP, those taking daily PrEP, and those already infected, focusing on different types of partnerships (casual, monogamous, non-monogamous).
- It addresses the mix of high and low-risk individuals in the PrEP-using population, reflecting real-world prescription practices in the U.S.
- The study uses Markov chain theory for calculating infection rates among non-monogamous partnerships and finds that improving adherence to PrEP significantly reduces new HIV infections.
The transmission dynamics of HIV are closely tied to the duration and overlap of sexual partnerships. We develop an autonomous population model that can account for the possibilities of an infection from either a casual sexual partner or a long-term partner who was either infected at the start of the partnership or has been newly infected since the onset of the partnership. The impact of the long-term partnerships on the rate of infection is captured by calculating the expected values of the rate of infection from these extended contacts.
View Article and Find Full Text PDFA model with both casual and long-term partnerships is considered with respect to the impact of a pre-exposure prophylaxis (PrEP) on the spread of HIV. We consider the effect of the effectiveness of PrEP, the rate that susceptible individuals choose to take PrEP, and compliance with the daily dose of the pre-exposure prophylaxis. The rate of infection in long-term partnerships is computed using a linearized expected value as a means for including the nonlocal effects of long-term partnerships while maintaining computational feasibility.
View Article and Find Full Text PDFOn February 5 the Japanese government ordered the passengers and crew on the Diamond Princess to start a two week quarantine after a former passenger tested positive for COVID-19. During the quarantine the virus spread rapidly throughout the ship. By February 20, there were 651 cases.
View Article and Find Full Text PDFArticle Synopsis
- Dispersal is important for plants, but we still don’t fully understand how it affects their survival and spread.
- It’s tough to predict how seeds move around because it depends on many different factors like the environment and time.
- To really get better at studying seed dispersal, we need to consider all the different ways plants grow and change over time, and work together across different fields of science.