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Tomer Shekel

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    Comparing access to urban parks across six OECD countries
    Talia Kaufmann
    Swapnil Vispute
    Mansi Kansal
    Daniel T. O'Brien
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    Lewis Dijkstra
    Paolo Veneri
    oecd-ilibrary.org (2023)
    Preview abstract This work leverages globally consistent data on parks from Google Maps, in combination with the computational power of Google Maps Directions API to quantify accessibility to parks across nearly 500 metropolitan areas in six countries: Estonia, France, Greece, Mexico, Sweden, and the United States. We combined high resolution population data from Worldpop with parks data and navigation estimates to measure: (1) Fraction of the population with access to parks within a 10-minute walk; and (2) the median walking time to the closest park. We find large differences in access to parks between countries, as well as large variability across cities and their respective commuting zones. To demonstrate how this framework can support cross country comparisons and efforts to track progress towards SDG11, we assessed access to parks by income group in selected countries, finding that the median walking time to a park is shorter for residents of low income neighbourhoods both in French and American metropolitan areas. View details
    Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic
    Kristina Gligoric
    Chaitanya Kamath
    Daniel Weiss
    Shailesh Bavadekar
    Kevin Schulman
    Evgeniy Gabrilovich
    Nature Communications Medicine (2023)
    Preview 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. View details
    Identifying COVID-19 Vaccine Deserts and Ways to Reduce Them: A Digital Tool to Support Public Health Decision-Making
    Rebecca L. Weintraub
    Kate Miller
    Benjamin Rader
    Julie Rosenberg
    Shreyas Srinath
    Samuel R. Woodbury
    Marinanicole Schultheiss
    Mansi Kansal
    Swapnil Vispute
    Stelios Serghiou
    Gerardo Flores
    Akim Kumok
    Evgeniy Gabrilovich
    Iman Ahmad
    Molly E. Chiang
    John S. Brownstein
    American Journal of Public Health (2023)
    Preview abstract A private–academic partnership built the Vaccine Equity Planner (VEP) to help decision-makers improve geographic access to COVID-19 vaccinations across the United States by identifying vaccine deserts and facilities that could fill those deserts. The VEP presented complex, updated data in an intuitive form during a rapidly changing pandemic situation. The persistence of vaccine deserts in every state as COVID-19 booster recommendations develop suggests that vaccine delivery can be improved. Underresourced public health systems benefit from tools providing real-time, accurate, actionable data. (Am J Public Health. 2023;113(4):363–367. https://doi.org/10.2105/AJPH.2022.307198) Public health leaders can make better, more equitable decisions when they can clearly see and understand the problems. Being presented with potential solutions based on evidence further supports their decision-making and can aid in supporting health equity. View details
    A geospatial database of close to reality travel times to obstetric emergency care in 15 Nigerian conurbations
    Peter M. Macharia
    Kerry L. M. Wong
    Tope Olubodun
    Lenka Beňová
    Charlotte Stanton
    Narayanan Sundararajan
    Yash Shah
    Mansi Kansal
    Swapnil Vispute
    Uchenna Gwacham-Anisiobi
    Olakunmi Ogunyemi
    Jia Wang
    Ibukun-Oluwa Omolade Abejirinde
    Prestige Tatenda Makanga
    Bosede B. Afolabi
    Aduragbemi Banke-Thomas
    Scientific Data, vol. TBD (2023), TBD
    Preview abstract Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing such services is essential for improving maternal and neonatal health outcomes. Current understanding of travel time to care is particularly inadequate in urban areas where short distances obscure long travel times, and also in low-resource settings. Here, we describe a database of travel times to facilities that can provide comprehensive EmOC in the 15 most populated extended urban areas (conurbations) in Nigeria. The travel times from cells of approximately 0.6 x 0.6km to facilities were derived based on Google Maps Platform’s internal Directions Application Programming Interface (API). The API incorporates estimates of traffic to provide closer-to-reality estimates of travel time. Computations were done to the first, second and third nearest public or private facilities. Travel time estimates for eight traffic scenarios (including peak and non-peak periods) and number of facilities within specific time thresholds were estimated. The database offers a plethora of opportunities for research and planning towards improving EmOC accessibility. View details
    High Resolution Building and Road Segmentation from Sentinel-2 Imagery
    Abdoulaye Diack
    Abel Tesfaye Korme
    Emmanuel Asiedu Brempong
    Jason Hickey
    Juliana Marcos
    Krishna Sapkota
    Mohammed Alewi Hassen
    Wojciech Sirko
    arXiv, https://arxiv.org/abs/2310.11622 (2023)
    Preview abstract Mapping buildings and roads automatically with remote sensing typically requires imagery of at least 50 cm resolution, which is expensive to obtain and often sparsely available. In this work we demonstrate how public, worldwide imagery from the Sentinel-2 Earth observation mission can be used to carry out this task at a much higher level of detail than the 10 m raw pixel resolution would suggest. To do this, we employ a teacher-student method in which a model with access to a temporal stack of Sentinel-2 images is trained to make the same predictions as a high-resolution model with access to corresponding 50 cm imagery. Evaluating at 50cm resolution, we achieve mIOU of 0.78, equivalent in accuracy to applying a single-frame high resolution model with imagery of 4m resolution. This work opens up new possibilities for using freely available Sentinel-2 imagery for a range of downstream tasks that previously could only be done with high resolution satellite imagery. The model will be made available soon to non-commercial, non-governmental entities at https://sites.research.google/open-buildings/ upon request. View details
    An evaluation of Internet searches as a marker of trends in population mental health in the US
    Uma Vaidyanathan
    Yuantong Sun
    Katherine Chou
    Sandro Galea
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    Scientific Reports (2022)
    Preview abstract The absence of continuous, real-time mental health assessment has made it challenging to quantify the impacts of the COVID-19 pandemic on population mental health. We examined publicly available, anonymized, aggregated data on weekly trends in Google searches related to anxiety, depression, and suicidal ideation from 2018 to 2020 in the US. We correlated these trends with (1) emergency department (ED) visits for mental health problems and suicide attempts, and (2) surveys of self-reported symptoms of anxiety, depression, and mental health care use. Search queries related to anxiety, depression, and suicidal ideation decreased sharply around March 2020, returning to pre-pandemic levels by summer 2020. Searches related to depression were correlated with the proportion of individuals reporting receiving therapy (r = 0.73), taking medication (r = 0.62) and having unmet mental healthcare needs (r = 0.57) on US Census Household Pulse Survey and modestly correlated with rates of ED visits for mental health conditions. Results were similar when considering instead searches for anxiety. Searches for suicidal ideation did not correlate with external variables. These results suggest aggregated data on Internet searches can provide timely and continuous insights into population mental health and complement other existing tools in this domain. View details
    Evaluation of US State-Based Policy Interventions on Social Distancing Using Aggregated Mobility Data during the COVID-19 Pandemic
    Gregory Alexander Wellenius
    Swapnil Suresh Vispute
    Valeria Espinosa
    Thomas Tsai
    Jonathan Hennessy
    Krishna Kumar Gadepalli
    Adam Boulanger
    Adam Pearce
    Chaitanya Kamath
    Arran Schlosberg
    Catherine Bendebury
    Chinmoy Mandayam
    Charlotte Stanton
    Shailesh Bavadekar
    Christopher David Pluntke
    Damien Desfontaines
    Benjamin H. Jacobson
    Zan Armstrong
    Katherine Chou
    Andrew Nathaniel Oplinger
    Ashish K. Jha
    Evgeniy Gabrilovich
    Nature Communications (2021)
    Preview abstract Social distancing has emerged as the primary mitigation strategy to combat the COVID-19 pandemic in the United States. However, large-scale evaluation of the effectiveness of social distancing policies are lacking. We used aggregated mobility data to quantify the impact of social distancing policies on observed changes in mobility. Declarations of states of emergency resulted in approximately a 10% reduction in time spent outside places of residence and an increase in visits to grocery stores and pharmacies. Subsequent implementation of ≥1 social distancing policies resulted in an additional 25% reduction in mobility in the following week. The seven states that subsequently ordered residents to shelter in place on or before March 23, 2020 observed an additional 29% reduction in time spent outside the residence. Our findings suggest that state-wide mandates are highly effective in achieving the goals of social distancing to minimize the transmission of COVID-19. View details
    Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020
    Liana R. Woskie
    Jonathan Hennessy
    Valeria Espinosa
    Thomas Tsai
    Swapnil Vispute
    Ciro Cattuto
    Laetitia Gauvin
    Michele Tizzoni
    Krishna Gadepalli
    Adam Boulanger
    Adam Pearce
    Chaitanya Kamath
    Arran Schlosberg
    Charlotte Stanton
    Shailesh Bavadekar
    Matthew Abueg
    Michael Hogue
    Andrew Oplinger
    Katherine Chou
    Ashish K. Jha
    Greg Wellenius
    Evgeniy Gabrilovich
    PLOS ONE (2021)
    Preview abstract 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. View details
    Vaccine Search Patterns Provide Insights into Vaccination Intent
    Sean Malahy
    Keith Spangler
    Jessica Leibler
    Kevin J. Lane
    Shailesh Bavadekar
    Chaitanya Kamath
    Akim Kumok
    Yuantong Sun
    Tague Griffith
    Adam Boulanger
    Mark Young
    Charlotte Stanton
    Yael Mayer
    Karen Lee Smith
    Kat Chou
    Jonathan I. Levy
    Adam A.Szpiro
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    arXiv (2021), TBD
    Preview abstract Despite ample supply of COVID-19 vaccines, the proportion of fully vaccinated individuals remains suboptimal across much of the US. Rapid vaccination of additional people will prevent new infections among both the unvaccinated and the vaccinated, thus saving lives. With the rapid rollout of vaccination efforts this year, the internet has become a dominant source of information about COVID-19 vaccines, their safety and efficacy, and their availability. We sought to evaluate whether trends in internet searches related to COVID-19 vaccination - as reflected by Google's Vaccine Search Insights (VSI) index - could be used as a marker of population-level interest in receiving a vaccination. We found that between January and August of 2021: 1) Google's weekly VSI index was associated with the number of new vaccinations administered in the subsequent three weeks, and 2) the average VSI index in earlier months was strongly correlated (up to r = 0.89) with vaccination rates many months later. Given these results, we illustrate an approach by which data on search interest may be combined with other available data to inform local public health outreach and vaccination efforts. These results suggest that the VSI index may be useful as a leading indicator of population-level interest in or intent to obtain a COVID-19 vaccine, especially early in the vaccine deployment efforts. These results may be relevant to current efforts to administer COVID-19 vaccines to unvaccinated individuals, to newly eligible children, and to those eligible to receive a booster shot. More broadly, these results highlight the opportunities for anonymized and aggregated internet search data, available in near real-time, to inform the response to public health emergencies. View details
    Google COVID-19 Vaccination Search Insights: Anonymization Process Description
    Adam Boulanger
    Akim Kumok
    Arti Patankar
    Benjamin Miller
    Chaitanya Kamath
    Charlotte Stanton
    Chris Scott
    Damien Desfontaines
    Evgeniy Gabrilovich
    Gregory A. Wellenius
    John S. Davis
    Karen Lee Smith
    Krishna Kumar Gadepalli
    Mark Young
    Shailesh Bavadekar
    Tague Griffith
    Yael Mayer
    Arxiv.org (2021)
    Preview abstract This report describes the aggregation and anonymization process applied to the COVID-19 Vaccination Search Insights~\cite{vaccination}, a publicly available dataset showing aggregated and anonymized trends in Google searches related to COVID-19 vaccination. The applied anonymization techniques protect every user’s daily search activity related to COVID-19 vaccinations with $(\varepsilon, \delta)$-differential privacy for $\varepsilon = 2.19$ and $\delta = 10^{-5}$. View details
    Global maps of travel time to healthcare facilities
    Daniel Weiss
    Allie Lieber
    Chaitanya Kamath
    Evgeniy Gabrilovich
    Kristina Gligoric
    Shailesh Bavadekar
    Nature Medicine (2020)
    Preview abstract Access to medical care is a fundamental human right that is constrained, in part, by the realities of the geographically dispersed human population. Using an established methodology, we map travel time to hospitals and clinics globally by utilizing major data collection efforts underway by OpenStreetMap, Google Maps, and researchers who have published continental-scale datasets. While no comprehensive database of global healthcare facilities exists, by leveraging the geographically variable strengths of our facility datasets we characterize global travel time to healthcare facilities in unprecedented detail. We produce travel time maps for both with and without ready access to motorized transport, thus providing upper and lower bounds that characterize travel time to healthcare for populations distributed across the wealth spectrum. We found that 93.3% of humans can reach a hospital or clinic within 60 minutes if they have access to motorized transport, while just 58.4% can reach healthcare within one hour by walking alone. As such, our maps highlight an additional vulnerability faced by poorer individuals in many remote areas. By enumerating travel time, we provide a needed input for accurately estimating the likelihood individuals well seek healthcare when they fall ill, which in turn improves our ability to estimate the burden of numerous diseases experienced by humanity. Furthermore, the maps of travel time to healthcare provide an evidence base for more efficiently allocating limited healthcare and transportation resources to underserved populations, and thus have the potential to provide a substantial contribution to global public health. View details
    Lymelight: forecasting Lyme disease risk using web search data
    Adam Sadilek
    Yulin Hswen
    Shailesh Bavadekar
    John Brownstein
    Evgeniy Gabrilovich
    npj Digital Medicine (2020)
    Preview 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. View details
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