The Innovative Use of HoloLens2 in the fetal cardiac servcies during COVID-19 pandemic

Introduction: COVID-19 prevent expectant parents of babies with congenital heart disease (CHD) from visiting cardiac surgical units prior to the birth of their baby who may be separated early for treatment. Pre-recorded video-clips or telephony consultation are generic and cannot deliver individual needs, especially in complex care pathways. We demonstrate our pilot experience using HoloLens 2 (Microsoft, Washington, USA) to deliver bespoke one-to-one virtual tour of the hospital and acute cardiac service environment. Methods: Families with antenatal diagnosis of significant CHD were offered the virtual tour in their third trimester. Parents and Fetal Nurse Specialists communicate via Microsoft Teams (Microsoft 365, Washington, USA) on portable electronic devices. HoloLens was deployed by the Nurse Specialists to provide oneto- one audio-visual experience, with opportunity to interact and raise questions. All parents participated remotely from the hospital site. The first 5 sets of parents had company of a second Nurse Specialist during the tour, in case of adverse psychological reaction. Due to zero negative event, the presence of the 2nd Nurse was optional subsequently. All were asked to complete a questionnaire. Results: Over the 3-month period, 25 sets of parents participated in the quality improvement survey. Respond rate was 96%. 85% said the virtual visit met or exceeded their expectations for preparedness, 80% said they liked that it allows them to 'visit' the hospital without physical attendance and associated costs of childcare/leave from work/travel time, 80% said it is a great use of technology, 70% liked that they were able to interact with the nurse specialist during the visit, 80% would recommend a virtual visit to other families and only 10% said they would have preferred to visit the hospital in person. Conclusions: The innovative use of HoloLens in cardiac fetal services is proven useful for the family, enabling preparedness for the arrival and early separation of their babies. Its deployment met the needs to continue high quality service in critical time. Whilst not intended to replace physical consultation;however, this technology proves to be an effective option for parents once the pandemic has passed.

Disruptions to agricultural activities, income loss and food insecurity during the COVID-19 pandemic: evidence from farm households in a developing country

Purpose In the early stage of the COVID-19 pandemic, Vietnam imposed many drastic restrictions to curb the outbreak of this virus. Such restrictions interrupted the normal functioning of various economic sectors, including agriculture. This research examined disruptions to agricultural activities, income loss and perceived food insecurity among farm households during the pandemic, and then explored the relationships among these economic factors. Design/methodology/approach Household data from Vietnam and Generalized Structural Equation Model (GSEM) were used for empirical analysis. Findings Descriptive analyses found that only a small proportion of farm households suffered from the COVID-19 disruptions to their agricultural activities, a large percentage experienced income loss, and a medium number were worried about their food insecurity. GSEM results also revealed that the COVID-19 disruptions to agricultural activities significantly increased the likelihood of worrying about food insecurity, mediated by income loss. Research limitations/implications Due to data limitations, the authors could not use better indicators to define and measure the variables of interest (e.g. COVID-19 disruptions to agricultural activities, income loss and food insecurity). Another similar concern was that our models did not account for unobservables, causing some estimation biases. Originality/value This research is among the first attempts that examined the direct and indirect (mediated by income loss) effects of the COVID-19 disruptions to agricultural activities on food insecurity.

Covid-19 Disease Simulation using GAMA platform

In less than three months after its emergence in China, the Covid-19 pandemic has spread to at least 180 countries. In the absence of previous experience with this new disease, public health authorities have implemented many experiments in a short period and, in a mostly uninformed way, various combinations of interventions at different scales. These include a ban on large gatherings, closure of borders - individual and collective containment, monitoring of population movements, social tracing, social distancing, etc. However, as the pandemic is progressing, data are collected from various sources. On the one hand, authorities allow to make informed adjustments to the current and planned interventions and reveal them. On the other hand, an urgent need for tools and methodologies that enable fast analysis, understanding, comparison, and forecasting of the effectiveness of the responses against COVID-19 across different communities and contexts. In this perspective, computational modeling appears as invaluable leverage as it allows us to explore in silico a range of intervention strategies before the potential phase of field implementation. © 2020 IEEE.