Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Samarth Swarup"'
Autor:
Josh M Colston, Bin Fang, Eric Houpt, Pavel Chernyavskiy, Samarth Swarup, Lauren M Gardner, Malena K Nong, Hamada S Badr, Benjamin F Zaitchik, Venkataraman Lakshmi, Margaret N Kosek
Publikováno v:
PLoS ONE, Vol 19, Iss 2, p e0297775 (2024)
BackgroundDiarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the
Externí odkaz:
https://doaj.org/article/1446f69ef811448b98133213f83ce3f7
Autor:
Swapna Thorve, Young Yun Baek, Samarth Swarup, Henning Mortveit, Achla Marathe, Anil Vullikanti, Madhav Marathe
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-23 (2023)
Abstract Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to pla
Externí odkaz:
https://doaj.org/article/7987c40a691d4de2a377dd96d1abadd9
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract Non-pharmaceutical interventions (NPIs) constitute the front-line responses against epidemics. Yet, the interdependence of control measures and individual microeconomics, beliefs, perceptions and health incentives, is not well understood. Ep
Externí odkaz:
https://doaj.org/article/d81f640fab52495e8741666dfb0ae6ac
Autor:
Balaji Ramesh, Rashida Callender, Benjamin F. Zaitchik, Meredith Jagger, Samarth Swarup, Julia M. Gohlke
Publikováno v:
GeoHealth, Vol 7, Iss 4, Pp n/a-n/a (2023)
Abstract Remotely sensed inundation may help to rapidly identify areas in need of aid during and following floods. Here we evaluate the utility of daily remotely sensed flood inundation measures and estimate their congruence with self‐reported home
Externí odkaz:
https://doaj.org/article/14867ca80bbf4a88b6d220bc00366fd1
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Infections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceiv
Externí odkaz:
https://doaj.org/article/7dd0b1f8a493495e877672d6c09288b4
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
Externí odkaz:
https://doaj.org/article/7c2b1024da86463b86ba494a2ffac2bc
Autor:
Meghendra Singh, Prasenjit Sarkhel, Gloria J. Kang, Achla Marathe, Kevin Boyle, Pamela Murray-Tuite, Kaja M. Abbas, Samarth Swarup
Publikováno v:
BMC Infectious Diseases, Vol 19, Iss 1, Pp 1-13 (2019)
Abstract Background Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distanc
Externí odkaz:
https://doaj.org/article/2faec70e0e434923ab89c0b463b47a37
Autor:
Swapna Thorve, Mandy L. Wilson, Bryan L. Lewis, Samarth Swarup, Anil Kumar S. Vullikanti, Madhav V. Marathe
Publikováno v:
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-10 (2018)
Abstract Background Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data
Externí odkaz:
https://doaj.org/article/419d6b99103f4c1fbe66102a8b981175
Autor:
Nargesalsadat Dorratoltaj, Achla Marathe, Bryan L Lewis, Samarth Swarup, Stephen G Eubank, Kaja M Abbas
Publikováno v:
PLoS Computational Biology, Vol 13, Iss 6, p e1005521 (2017)
The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine
Externí odkaz:
https://doaj.org/article/a92613b232914ad98639d00e605c676c
Autor:
Anna E. Brower, Bianca Corpuz, Balaji Ramesh, Benjamin Zaitchik, Julia M. Gohlke, Samarth Swarup
Publikováno v:
Weather, Climate, and Society. 15:177-193
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