How can vector-borne diseases be impacted by non-linear climate instabilities, such as a rapid melting of the ice sheet, that can have a strong impact on climate? We try to answer this question by studying the impact of an accelerated melting of the Greenland ice sheet on climate and malaria in Africa.

Our study illustrates the potential of ML models for accurately distinguishing the hemodynamic presentations of the two viral infections. This may have utility as a diagnostic tool to aid healthcare workers in triaging patients as the viral infections start co-circulating in the communities.

Despite substantial efforts to develop digital biomarkers, benchmarking and validating these algorithms remains difficult. Open, crowd-sourced efforts through DREAM Challenges is one approach to speed the process.

During the height of COVID-19 pandemic, clinicians were forced to make difficult treatment decisions, given the large number of patients and limited resources. To help guide treatment strategies, we evaluated the performance of 18 machine learning models in predicting COVID-19 patient outcomes.

Behind the paper: Rasmy et al: Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

Evaluating the efficacy of government interventions used to mitigate the spread of COVID-19 has been challenging. Mobility data from phones can be used as a low-cost and standardised mechanism to observe the change in the aggregate movement of populations in response to interventions stimuli.

Do seasonal variations in day length and temperature influence sleep in a world of climate control and electricity? Our study combined a year of wearable sleep data with meteorological data. We found that seasons, day length, and temperature influence when and how long individuals sleep.

By Dr. Kirstin Aschbacher and colleagues.  

The combination of clinical and laboratory parameters on admission can accurately predict severe disease amongst COVID-19 patients