Disease Prevention Maps

Researchers collaborated with Chilean ministries to integrate socio-economic data with Facebook mobility data, exploring why some districts were not adhering as strongly to social distancing policies... Researchers collaborated with Chilean ministries to integrate socio-economic data with Facebook mobility data, exploring why some districts were not adhering as strongly to social distancing policies, quarantines, and other measures. Integrating the social economic data showed that some socio-economic groups were traveling a longer distance than others. This helped them further understand that people in lower socio-economic status were likely traveling for money and also traveling frequently to access food.

New data streams are being used to track the pandemic of SARS-CoV-2, including genomic data which provides insights into patterns of importation and spatial spread of the virus, as well as population... New data streams are being used to track the pandemic of SARS-CoV-2, including genomic data which provides insights into patterns of importation and spatial spread of the virus, as well as population mobility data obtained from mobile phones. Here, we analyse the emergence and outbreak trajectory of SARS-CoV-2 in Bangladesh using these new data streams, and identify mass population movements as a key early event driving the ongoing epidemic.

When outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for... When outbreaks occur there is a need to anticipate which populations and locations are at heightened risk of exposure. In this work, we evaluate the utility of aggregate human mobility data for estimating the geographical distribution of transmission risk. We present a simple procedure for producing spatial transmission risk assessments from near-real-time population mobility data. We validate our estimates against three well-documented COVID-19 outbreaks in Australia.

We utilize graph representation learning to capitalize on the underlying relationship of population movement with the spread of COVID-19. Specifically, we create a graph where nodes correspond to a... We utilize graph representation learning to capitalize on the underlying relationship of population movement with the spread of COVID-19. Specifically, we create a graph where nodes correspond to a country's regions and the edge weights denote human mobility from one region to another. Then, we employ graph neural networks to predict the number of future cases. Experimental results on 4 European countries demonstrate the superiority of our method.