A new approach to pandemic and epidemic intelligence is needed that includes modern approaches to surveillance and risk assessment, as well as improved trust and cooperation between stakeholders and society. Conducting effective pandemic and epidemic intelligence, however, is not straightforward. Gathering, managing, analyzing and interpreting disparate information from the health sector and beyond is complex, in part because of data fragmentation, difficulties with accessing sources on a continuous basis, licensing, ownership and security restrictions, privacy and re-identification risks, and the inherent complexity of working with a wide range of different data types and formats.

Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables.

As patient health information is highly regulated due to privacy concerns, most machine learning (ML)-based healthcare studies are unable to test on external patient cohorts, resulting in a gap between locally reported model performance and cross-site generalizability. Different approaches have been introduced for developing models across multiple clinical sites, however less attention has been given to adopting ready-made models in new settings. We introduce three methods to do this—(1) applying a ready-made model “as-is” (2); readjusting the decision threshold on the model’s output using site-specific data and (3); finetuning the model using site-specific data via transfer learning.

The use of mobile network data has come to attention in response to COVID-19 pandemic leveraged on their ability in capturing people social behaviour. Crucially, we show that there are latent features in irreversibly anonymised and aggregated mobile network data that carry useful information in relation to the spread of SARS-CoV-2 virus. We describe development of the forecast models using such features for near-time prediction of COVID-19 hospital admissions.