AIMC Topic: SARS-CoV-2

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A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns.

American journal of epidemiology
When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies or the opening or closing of an industrial facility, careful statistical analysis is needed to separate causal changes from o...

Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19.

Frontiers in public health
BACKGROUND: Disease presentation and progression can vary greatly in heterogeneous diseases, such as COVID-19, with variability in patient outcomes, even within the hospital setting. This variability underscores the need for tailored treatment approa...

IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective.

Frontiers in public health
COVID-19 has not only changed the way people live but has also altered the way all organizations operate. The most effective precautionary measure against the spread of the virus that caused the COVID-19 pandemic SARS-CoV-2, is to use face coverings ...

Artificial intelligence in vaccine research and development: an umbrella review.

Frontiers in immunology
BACKGROUND: The rapid development of COVID-19 vaccines highlighted the transformative potential of artificial intelligence (AI) in modern vaccinology, accelerating timelines from years to months. Nevertheless, the specific roles and effectiveness of ...

Surface-Enhanced Raman Scattering Nanotags: Design Strategies, Biomedical Applications, and Integration of Machine Learning.

Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
Surface-enhanced Raman scattering (SERS) is a transformative technique for molecular identification, offering exceptional sensitivity, signal specificity, and resistance to photobleaching, making it invaluable for disease diagnosis, monitoring, and s...

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

PloS one
BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles a...

A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2.

PloS one
BACKGROUND: Patient symptoms, crucial for disease progression and diagnosis, are often captured in unstructured clinical notes. Large language models (LLMs) offer potential advantages in extracting patient symptoms compared to traditional rule-based ...

Construction and application of SARS-CoV-2 protein ontology (CoVPO).

PloS one
The emergence of the SARS-CoV-2 virus and the resulting COVID-19 pandemic brought forth an urgent need for an in-depth molecular understanding, organization, and data integration to expedite therapeutic and preventive strategies. An essential approac...

What drives the effectiveness of social distancing in combating COVID-19 across U.S. states?

PloS one
We propose a new theory of information-based voluntary social distancing in which people's responses to disease prevalence depend on the credibility of reported cases and fatalities and vary locally. We embed this theory into a new pandemic predictio...

A conceptual and computational framework for modeling the complex, adaptive dynamics of epidemics: The case of the SARS-CoV-2 pandemic in Mexico.

PloS one
In the quest to ensure adequate preparedness for health emergencies caused by infectious disease pandemics, there is a need for tools that can address the myriad relevant questions related to the spread and trajectory of pandemics. A hybrid intellige...