Pharmageek

Academics and public health practitioners studying communicable disease dynamics have

long advocated for open-access data to better inform risk assessments. During any

evolving outbreak, the collection, aggregation, visualisation, and analysis of granular

data is paramount to developing appropriate public health interventions.1 The COVID-19

pandemic has underscored the need for this type of information, especially in relation

to context (eg, timing and intensity of interventions) and epidemiology (eg, spatially

resolved and age-specific case counts).

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UK researchers say an algorithm can identify people who remain at risk of developing serious COVID-19, despite having one or two doses of vaccine for the coronavirus.



The team – from multiple UK universities – developed the QCovid3 algorithm using data from almost seven million vaccinated people, including around five million who had two doses, as well as the results of SARS-CoV-2 tests, hospital admissions, various treatments, and national death and cancer registries.



They found key risk factors included Down’s syndrome, kidney transplantation, sickle cell disease, care home residency, chemotherapy, bone marrow transplantation or solid organ transplant, HIV/AIDS, dementia, Parkinson’s disease, neurological conditions, and liver cirrhosis.

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Les données personnelles d'environ 1,4 million de personnes, qui ont effectué un test de dépistage du Covid-19 en Ile-de-France mi-2020, ont été dérobées

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Quid des changements structurels : moyens pour l’hôpital public, lutte contre la pollution, prévention des maladies environnementales ? Contre le Covid, le gouvernement a préféré tout miser sur la vaccination et le traçage numérique, rage notre chroniqueuse. Pour le bonheur des tenants des industriels de l’e-santé, de l’intelligence artificielle et de la big data.



Celia Izoard, journaliste et membre de la revue Z, a fait des études de philosophie et traduit des ouvrages critiques de la technologie (...)

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