ARGONet: the most accurate estimates of influenza activity available to date

AGRONet leverages its  information from electronic health records, flu-related Google searches and historical flu activity in a given location. Improved accuracy has been achieved by adding a second model, which draws on spatial-temporal patterns of flu spread in neighbouring areas [1]. Furthermore, the machine learning system was “trained” by feeding it flu predictions from both models as well as actual flu data, helping to reduce errors in the predictions.

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What should we learn from Zika?

The 2016 Zika outbreak in the Americas was a public health emergency of international concern. Alongside traditional approaches, several digital technologies were used to tackle this rapidly spreading global health threat. A recent review, identified several domains of digital technologies which were utilised during the Zika outbreak, such as computational modelling , big data , mobile health , and other novel technologies [1].

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Spinal implant helps paraplegics walk again

This week’s blogpost focuses on the development of a rather remarkable piece of homegrown innovation. Stories of restoring a paraplegic’s ability to walk was something previously confined to the pages of ancient divine texts, yet scientists from EPFL based at Campus Biotech in Geneva have managed to achieve the seemingly miraculous. Their success has been a combination of brilliant scientific minds, innovative technology and dedication to a common goal, which has led to this breakthrough.

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AccessMod 5

“Supporting Universal Health Coverage by modelling physical accessibility to health care”

AccessMod 5.0 is a World Health Organisation (WHO) tool, a free and open-source standalone software to model how physically accessible existing health services are to the target population, to estimate the part of the target population that would not receive care despite being physically accessible due to shortage of capacity in these services (human or equipment), to measure referral times and distances between health facilities, and to identify where to place new health facilities to increase population coverage through the scaling up analysis [1].

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A model partnership in transforming health intelligence for the eradication of Malaria

Expose. Explore. Explain. Empower. 

PATH has been involved in Visualise No Malaria  since 2014, an initiative dedicated to eradicate Malaria in Zambia [1] . Visualise No Malaria is a private sector, governmental and social sector collaboration, hoping to transform health intelligence by working together. The dream team is compromised of PATH, Zambia’s Ministry of Health and eight promising tech companies, who with access to better health data already produced a 92% reduction in malaria-related deaths in Southern Zambia [2].

 

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