Suchergebnisse

Abstract
Machine learning was applied to predict evacuation rates for all census tracts affected by Hurricane Laura. The evacuation ground truth was derived from cellular telephone–based mobility data. Twitter data, census data, geographical data, COVID-19 case rates, the social vulnerability index from the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a mean absolute percent error of 4.9% on testing data. Feature importance for prediction was analyzed using Shapley additive explanations and it was found that previous evacuation, rainfall forecasts, COVID-19 case rates, and Twitter data rank highly in terms of importance. Social vulnerability indices were also found to show a very consistent relationship with evacuation rates, such that higher vulnerability consistently implies lower evacuation rates. These findings can help with hurricane evacuation preparedness and planning as well as real-time assessment.

Significance Statement
This study evaluates the usefulness of Twitter data, COVID-19 case rates, and the social vulnerability index from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry in predicting evacuation rates during Hurricane Laura, in the context of other relevant geographic, and weather-related variables. All three are found to be useful, to different extents, and this work suggests important directions for future research in understanding the reasons behind their relevance to predicting evacuation rates.

Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.

OBJECTIVE: To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines. BACKGROUND: Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood. METHODS: We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment. RESULTS: The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits. CONCLUSION: Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination ...

This work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions -- county, state, country -- using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km (2) . We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell -- often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

Abstract
The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a result of conflict events during the initial phase of the Ukraine war. We discuss policy use cases for the proposed framework by demonstrating how it can leverage refugee demographic details to answer pressing policy questions. We also show how to incorporate conflict forecast scenarios to predict future conflict-induced migration flows. Detailed future migration estimates across various conflict scenarios can both help to reduce policymaker uncertainty and improve allocation and staging of limited humanitarian resources in crisis settings.