RESUMO
Pharmacogenomics is increasingly moving into mainstream clinical practice. Careful consideration must be paid to inclusion of diverse populations in research, translation and implementation, in the historical and social context of population stratification, to ensure that this leads to improvements in healthcare for all rather than increased health disparities. This review takes a broad and critical approach to the current role of diversity in pharmacogenomics and addresses potential pitfalls in order to raise awareness for prescribers. It also emphasizes evidence gaps and suggests approaches that may minimize negative consequences and promote health equality.
Assuntos
Promoção da Saúde , Farmacogenética , HumanosAssuntos
Vacinas contra COVID-19 , COVID-19 , Transmissão de Doença Infecciosa , Política de Saúde , Aceitação pelo Paciente de Cuidados de Saúde , Saúde Pública , Criança , Humanos , COVID-19/epidemiologia , COVID-19/prevenção & controle , Vacinas contra COVID-19/administração & dosagem , Transmissão de Doença Infecciosa/prevenção & controle , Pandemias/prevenção & controle , SARS-CoV-2 , Reino Unido/epidemiologiaAssuntos
Controle de Doenças Transmissíveis/métodos , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Betacoronavirus , COVID-19 , Controle de Doenças Transmissíveis/normas , Consenso , Europa (Continente) , Medicina Baseada em Evidências , Humanos , Imunidade Coletiva , SARS-CoV-2RESUMO
The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.