9 research outputs found
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification
With the devastating outbreak of COVID-19, vaccines are one of the crucial
lines of defense against mass infection in this global pandemic. Given the
protection they provide, vaccines are becoming mandatory in certain social and
professional settings. This paper presents a classification model for detecting
COVID-19 vaccination related search queries, a machine learning model that is
used to generate search insights for COVID-19 vaccinations. The proposed method
combines and leverages advancements from modern state-of-the-art (SOTA) natural
language understanding (NLU) techniques such as pretrained Transformers with
traditional dense features. We propose a novel approach of considering dense
features as memory tokens that the model can attend to. We show that this new
modeling approach enables a significant improvement to the Vaccine Search
Insights (VSI) task, improving a strong well-established gradient-boosting
baseline by relative +15% improvement in F1 score and +14% in precision.Comment: EMNLP 202
Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: insights from Spring 2020
Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions
Impacts of Social Distancing Policies on Mobility and COVID-19 Case Growth in the US
Social distancing remains an important strategy to combat the COVID-19
pandemic in the United States. However, the impacts of specific state-level
policies on mobility and subsequent COVID-19 case trajectories have not been
completely quantified. Using anonymized and aggregated mobility data from
opted-in Google users, we found that state-level emergency declarations
resulted in a 9.9% reduction in time spent away from places of residence.
Implementation of one or more social distancing policies resulted in an
additional 24.5% reduction in mobility the following week, and subsequent
shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in
mobility were associated with substantial reductions in case growth 2 to 4
weeks later. For example, a 10% reduction in mobility was associated with a
17.5% reduction in case growth 2 weeks later. Given the continued reliance on
social distancing policies to limit the spread of COVID-19, these results may
be helpful to public health officials trying to balance infection control with
the economic and social consequences of these policies.Comment: Co-first Authors: GAW, SV, VE, and AF contributed equally.
Corresponding Author: Dr. Evgeniy Gabrilovich, [email protected] 32 pages
(including supplemental material), 4 figures in the main text, additional
figures in the supplemental materia
Lymelight: forecasting Lyme disease risk using web search data
Abstract Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight—a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease
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Lymelight: forecasting Lyme disease risk using web search data.
Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease
Lymelight: forecasting Lyme disease risk using web search data.
Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease
Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic
Abstract Background Timely access to healthcare is essential but measuring access is challenging. Prior research focused on analyzing potential travel times to healthcare under optimal mobility scenarios that do not incorporate direct observations of human mobility, potentially underestimating the barriers to receiving care for many populations. Methods We introduce an approach for measuring accessibility by utilizing travel times to healthcare facilities from aggregated and anonymized smartphone Location History data. We measure these revealed travel times to healthcare facilities in over 100 countries and juxtapose our findings with potential (optimal) travel times estimated using Google Maps directions. We then quantify changes in revealed accessibility associated with the COVID-19 pandemic. Results We find that revealed travel time differs substantially from potential travel time; in all but 4 countries this difference exceeds 30 minutes, and in 49 countries it exceeds 60 minutes. Substantial variation in revealed healthcare accessibility is observed and correlates with life expectancy (⍴=−0.70) and infant mortality (⍴=0.59), with this association remaining significant after adjusting for potential accessibility and wealth. The COVID-19 pandemic altered the patterns of healthcare access, especially for populations dependent on public transportation. Conclusions Our metrics based on empirical data indicate that revealed travel times exceed potential travel times in many regions. During COVID-19, inequitable accessibility was exacerbated. In conjunction with other relevant data, these findings provide a resource to help public health policymakers identify underserved populations and promote health equity by formulating policies and directing resources towards areas and populations most in need