Molecular surveillance to monitor the prevalence of tetracycline resistance in .

Doxycycline post-exposure prophylaxis (Doxy-PEP) reduces bacterial sexually transmitted infections (STIs) but may select for tetracycline resistance in Neisseria gonorrhoeae and co-resistance to other antibiotics, including ceftriaxone.. The implementation of doxy-PEP should be accompanied by monitoring doxycycline resistance, but the optimal strategy to detect changes in the prevalence of resistance has not been established.

Optimal environmental testing frequency for outbreak surveillance.

Public health surveillance for pathogens presents an optimization problem: we require enough sampling to identify intervention-triggering shifts in pathogen epidemiology, such as new introductions or sudden increases in prevalence, but not so much that costs due to surveillance itself outweigh those from pathogen-associated illness. To determine this optimal sampling frequency, we developed a general mathematical model for the introduction of a new pathogen that, once introduced, increases in prevalence exponentially. Given the relative cost of infection vs.

Estimating changes in antibiotic consumption with the introduction of doxycycline post-exposure prophylaxis in the United States.

Doxycycline as post-exposure prophylaxis (doxy-PEP) reduces the risk of gonorrhea, chlamydia, and syphilis in studies of men who have sex with men (MSM) and transgender women (TGW) on HIV Pre-exposure Prophylaxis (PrEP) and people living with HIV (PLWH)). Doxy-PEP is an important tool to address the increasing burden of sexually transmitted infections (STIs), but there is concern that increased consumption of doxycycline may drive antimicrobial resistance. We estimated the expected increase in antibiotic use in the US under several doxy-PEP prescribing scenarios.

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.

Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data.

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments.

Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca.

Forecasting new diseases in low-data settings using transfer learning.

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak.

Machine-Learning-Based Forecasting of Dengue Fever in Brazilian Cities Using Epidemiologic and Meteorological Variables.

Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease.

Country Case Studies in Economic Fitness: Mexico and Brazil.

We leverage a new complexity framework called Economic Fitness, which characterizes an economy's level of diversification and its capabilities to produce more complex products. It can be used to predict economic growth and competitiveness. This paper describes an application of Economic Fitness called the Country Opportunity Spotlight (COS) that assesses a country's current level of capabilities and demonstrates which industries have upgrade and diversification potential given those capabilities.