Using social media to estimate Zika's impact on tourism: #babymoon, 2014-2017

Artificial intelligence in public health: the potential of epidemic early warning systems

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Exploring the experiences of pregnant women in the U.S. during the first year of the Covid-19 pandemic

In this paper, we integrate the stress process model with symbolic interactionism to frame our analysis of interviews with 35 women who were pregnant and/or gave birth during the first year of the Covid-19 pandemic. We detail three stressors, highlight their variation, and discuss how they coped with these stressors. Women reported having to navigate contradictory information about the public health crisis, but Black participants simultaneously endured added strain from a heightened awareness of racialized violence. To cope with an overabundance of precarious and contradictory messages, some women sought out information (i.e., information gatherers), others eschewed information (i.e., information avoiders), and most established protective "bubbles." Next, women experienced disruptions in pregnancy rituals but coped by reframing their expectations. This stressor, however, was less acute for women with a prior birth. Third, women shared feelings of social isolation and reduced social support, which were intensified if pregnancy complications occurred. Women coped by relying on telecommunication and at-home monitoring devices. Our study shows how pregnant women experienced and coped through adversity to mitigate stressors amid pandemonium.