Access to healthcare is a requirement for human well-being that is constrained, in part, by the allocation of healthcare resources relative to the geographically dispersed human population 1-3 . Quantifying access to care globally is challenging due to the absence of a comprehensive database of healthcare facilities. We harness major data collection efforts underway by OpenStreetMap, Google Maps and academic researchers to compile the most complete collection of facility locations to date. Leveraging the geographically variable strengths of our facility datasets, we use an established methodology 4 to characterize travel time to healthcare facilities in unprecedented detail. We produce maps of travel time with and without access to motorized transport, thus characterizing travel time to healthcare for populations distributed across the wealth spectrum. We find that just 8.9% of the global population (646 million people) cannot reach healthcare within one hour if they have access to motorized transport, and that 43.3% (3.16 billion people) cannot reach a healthcare facility by foot within one hour. Our maps highlight an additional vulnerability faced by poorer individuals in remote areas and can help to estimate whether individuals will seek healthcare when it is needed, as well as providing an evidence base for efficiently distributing limited healthcare and transportation resources to underserved populations both now and in the future.Access to healthcare is a measure of human well-being that is constrained by numerous geographically varying factors 1-3 , the most immediate of which is the time it takes individuals to travel to a properly equipped and adequately staffed healthcare facility. Due to spatial clustering of healthcare facilities in densely populated areas, individuals living in rural regions often face increased travel times and thus cost when seeking healthcare. This situation can be exacerbated by poor transportation infrastructure and lack of motorized transport, which further increase the time required for travel and could disproportionally affect lower-income populations. As such, people facing long travel times to healthcare facilities are less likely to seek care when it is needed [5][6][7][8][9] , and the consequences of failing to seek care include increased mortality and morbidity from treatable conditions 10,11 .

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 two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two 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.

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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.

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.