More Like this

1. Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading

   https://doi.org/10.1098/rsif.2020.0875  

   Parino, Francesco; Zino, Lorenzo; Porfiri, Maurizio; Rizzo, Alessandro (February 2021, Journal of The Royal Society Interface)

   null (Ed.)

   To date, the only effective means to respond to the spreading of the COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reducing their social and economic consequences. Here, we introduce a meta-population model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and applied to evaluate the outcomes of these two types of NPIs. Our approach combines the advantages of granular spatial modelling of meta-population models with the ability to realistically describe social contacts via activity-driven networks. We focus on disentangling the impact of these two different types of NPIs: those aiming at reducing individuals’ social activity, for instance through lockdowns, and those that enforce mobility restrictions. We provide a valuable framework to assess the effectiveness of different NPIs, varying with respect to their timing and severity. Results suggest that the effects of mobility restrictions largely depend on the possibility of implementing timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritized afterwards. 

   more » « less
2. 2016-2021 State Hazard Mitigation Plans and Social Vulnerability

   https://doi.org/10.17603/ds2-sc34-as63  

   Painter, Mary Angelica; Villarreal, Melissa; Peek, Lori (January 2023, Designsafe-CI)

   This dataset includes information regarding 55 State Hazard Mitigation Plans (SHMPs) for all 50 U.S. states and the 5 inhabited U.S. territories. Washington DC’s SHMP is not publicly available and can only be accessed through signing a Non-Disclosure Agreement. As such, it is not included in our dataset. These plans were approved by FEMA between 2016-2021. This data publication includes: (1) the State Hazard Mitigation Plan dataset; (2) a data dictionary with description of each variable output; and (3) variable definitions for the population groups included in State Hazard Mitigation Plans. The dataset was generated through reviewing publicly available plans and coding for socially vulnerable populations. This dataset allows for quantitative analysis of the inclusion (or exclusion) of vulnerable populations in SHMPs. The envisioned audience for this data and information includes government officials, researchers, and others who are interested in understanding patterns in the inclusion or exclusion of socially vulnerable populations in SHMPs. 

   more » « less
3. Disparities in mobile phone ownership reflect inequities in access to healthcare

   https://doi.org/10.1371/journal.pdig.0000270  

   Blake, Alexandre; Hazel, Ashley; Jakurama, John; Matundu, Justy; Bharti, Nita (July 2023, PLOS Digital Health)

   Lichtner, Valentina (Ed.)

   Human movement and population connectivity inform infectious disease management. Remote data, particularly mobile phone usage data, are frequently used to track mobility in outbreak response efforts without measuring representation in target populations. Using a detailed interview instrument, we measure population representation in phone ownership, mobility, and access to healthcare in a highly mobile population with low access to health care in Namibia, a middle-income country. We find that 1) phone ownership is both low and biased by gender, 2) phone ownership is correlated with differences in mobility and access to healthcare, and 3) reception is spatially unequal and scarce in non-urban areas. We demonstrate that mobile phone data do not represent the populations and locations that most need public health improvements. Finally, we show that relying on these data to inform public health decisions can be harmful with the potential to magnify health inequities rather than reducing them. To reduce health inequities, it is critical to integrate multiple data streams with measured, non-overlapping biases to ensure data representativeness for vulnerable populations. 

   more » « less
4. Characterizing equitable access to grocery stores during disasters using location-based data

   https://doi.org/10.1038/s41598-022-23532-y  

   Esmalian, Amir; Coleman, Natalie; Yuan, Faxi; Xiao, Xin; Mostafavi, Ali (December 2022, Scientific Reports)

   Abstract Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy , rapidity , and proximity . The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches. 

   more » « less
5. Cognitive modeling for computational epidemiology

   Pirolli, P.; Bhatia, A.; Mitsopoulos, K.; Lebiere, C.; Orr, M. (October 2020, 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SPB-BRIMS 2020))

   null (Ed.)

   Strategic response options to the COVID-19 pandemic have been greatly influenced by predictive epidemiological models. Effects of non-pharmaceutical interventions (NPIs; such as mask wearing) unfortunately are based on an abundance of very large uncertainties around the extent to which the population adopts risk reducing behaviors. The effects of NPIs appear to have large heterogeneity across regions, subgroups, and individual mindsets and capabilities. We hypothesize that these uncertainties can be improved with higher-fidelity computational modeling of the social-psychological reactions of individuals, groups, and populations. We build up the ACT-R theory and Instance-Based Learning Theory to formulate psychologically valid agents and develop a framework that integrates multi-level cognitive and social simulation with information networks analysis, and epidemiological predictions. We present initial results from analyses of beliefs and sentiments about COVID-19 NPIs induced from online social media that can provide inputs to seed and validate cognitive agents. We present illustrations of cognitive model hypotheses about the dynamics of behavior change in response to intentions, attitudes, messaging, and source credibility. We present an example of social networks propagating attitude change in response to NPIs. 

   more » « less