New paper: Liu L, Miller HJ, Scheff J (2020) The impacts of COVID-19 pandemic on public transit demand in the United States. PLOS ONE 15(11):e0242476. https://doi.org/10.1371/journal.pone.0242476
The COVID-19 pandemic and related restrictions led to major transit demand decline for many public transit systems in the United States. This paper is a systematic analysis of the dynamics and dimensions of this unprecedented decline. Using transit demand data derived from a widely used transit navigation app, we fit logistic functions to model the decline in daily demand and derive key parameters: base value, the apparent minimal level of demand and cliff and base points, representing the initial date when transit demand decline began and the final date when the decline rate attenuated. Regression analyses reveal that communities with higher proportions of essential workers, vulnerable populations (African American, Hispanic, Female, and people over 45 years old), and more coronavirus Google searches tend to maintain higher levels of minimal demand during COVID-19. Approximately half of the agencies experienced their decline before the local spread of COVID-19 likely began; most of these are in the US Midwest. Almost no transit systems finished their decline periods before local community spread. We also compare hourly demand profiles for each system before and during COVID-19 using ordinary Procrustes distance analysis. The results show substantial departures from typical weekday hourly demand profiles. Our results provide insights into public transit as an essential service during a pandemic.
Epidemics and pandemics such as the COVID-19 outbreak have clear geographic dimensions due to the vector spreading the virus (human contact), demographics and co-morbidity factors that vary geographically, the distributed and heterogeneous nature of health care systems, and the highly variable response and interventions from political authorities and the public-at-large. The decline and shifts in human activity also affect broader social, economic and environmental systems to varying degrees. Geospatial information can play vital roles in crafting effective government and societal responses at the operational, tactical and strategic levels.
On 17 June 2020, the Mapping Science Committee of the National Academies of Science, Engineering and Medicine will host an online workshop on Geospatial Needs for a Pandemic-Resilient World. This workshop will explore the needs of federal agencies, organizations, and scientists for geospatial data science to understand and respond to epidemics/pandemics and developing infrastructure and policies that facilitate effective management and graceful recovery from these types of shocks. This workshop is free and open to the public.
New publication: McHaney-Lindstrom, M., Hebert, C., Miller, H.J., Moffatt-Bruce, S. and Root, E. “Network analysis of intra-hospital transfers and hospital-onset Clostridium Difficile infection,” Health Information and Libraries Journal, https://doi.org/10.1111/hir.12274
Objectives. To explore how SNA can be used to analyse intra‐hospital patient networks of individuals with a HAI for further analysis in a GIS environment.
Methods. A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS.
Results. The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor.
Discussion. This study confirmed the importance that intra‐hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals.
Conclusion. Utilising SNA and GIS analysis in conjunction with one another provided a data‐rich environment in which the risk inherent in intra‐hospital transfer networks was quantified, visualised and interpreted for potential interventions.