Evaluating ChatGPT's Multilingual Performance in Clinical Nutrition Advice Using Synthetic Medical Text: Insights from Central Asia.

Background: While large language models like ChatGPT-4 have demonstrated competency in English, their performance for minority groups speaking underrepresented languages, as well as their ability to adapt to specific socio-cultural nuances and regional cuisines, such as those in Central Asia (e.g., Kazakhstan), still requires further investigation.

A Central Asian Food Dataset for Personalized Dietary Interventions.

Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region.

COVID-19 Vaccination Strategies Considering Hesitancy Using Particle-Based Epidemic Simulation.

Vaccine hesitancy is one of the critical factors in achieving herd immunity and suppressing the COVID-19 epidemic. Many countries face this as an acute public health issue that diminishes the efficacy of their vaccination campaigns. Epidemic modeling and simulation can be used to predict the effects of different vaccination strategies.

A Particle-Based COVID-19 Simulator With Contact Tracing and Testing.

The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths.

A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases.

In this work, we present a particle-based SEIR epidemic simulator as a tool to assess the impact of different vaccination strategies on viral propagation and to model sterilizing and effective immunization outcomes. The simulator includes modules to support contact tracing of the interactions amongst individuals and epidemiological testing of the general population. The particles are distinguished by age to represent more accurately the infection and mortality rates.

A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 With Region-Specific Policies.

In this work, we present an open-source stochastic epidemic simulator calibrated with extant epidemic experience of COVID-19. The simulator models a country as a network representing each node as an administrative region. The transportation connections between the nodes are modeled as the edges of this network.