Filters








9 Hits in 0.93 sec

Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19 [article]

Vishrawas Gopalakrishnan, Sayali Navalekar, Pan Ding, Ryan Hooley, Jacob Miller, Raman Srinivasan, Ajay Deshpande, Xuan Liu, Simone Bianco, James H. Kaufman
2021 arXiv   pre-print
Pandemic control measures like lock-down, restrictions on restaurants and gatherings, social-distancing have shown to be effective in curtailing the spread of COVID-19. However, their sustained enforcement has negative economic effects. To craft strategies and policies that reduce the hardship on the people and the economy while being effective against the pandemic, authorities need to understand the disease dynamics at the right geo-spatial granularity. Considering factors like the hospitals'
more » ... bility to handle the fluctuating demands, evaluating various reopening scenarios, and accurate forecasting of cases are vital to decision making. Towards this end, we present a flexible end-to-end solution that seamlessly integrates public health data with tertiary client data to accurately estimate the risk of reopening a community. At its core lies a state-of-the-art prediction model that auto-captures changing trends in transmission and mobility. Benchmarking against various published baselines confirm the superiority of our forecasting algorithm. Combined with the ability to extend to multiple client-specific requirements and perform deductive reasoning through counter-factual analysis, this solution provides actionable insights to multiple client domains ranging from government to educational institutions, hospitals, and commercial establishments.
arXiv:2106.02094v1 fatcat:pxulq53jgna55lxro24gtmucqu

Tracking Temporal Community Strength in Dynamic Networks

Nan Du, Xiaowei Jia, Jing Gao, Vishrawas Gopalakrishnan, Aidong Zhang
2015 IEEE Transactions on Knowledge and Data Engineering  
Vishrawas Gopalakrishnan is a Ph.D. candidate in the department of Computer Science and Engineering at State University of New York at Buffalo.  ...  E-mail:nandu,xiaoweij,jing,vishrawa,azhang@buffalo.edu from the outside world.  ... 
doi:10.1109/tkde.2015.2432815 fatcat:u6yp6zdyszghlpqnd5wzysd4he

Globally Local: Hyper-local Modeling for Accurate Forecast of COVID-19 [article]

Vishrawas Gopalakrishnan, Sayali Pethe, Sarah Kefayati, Raman Srinivasan, Paul Hake, Ajay Deshpande, Xuan Liu, Etter Hoang, Marbelly Davila, Simone Bianco, James H Kaufman
2020 medRxiv   pre-print
Multiple efforts to model the epidemiology of SARS-CoV-2 have recently been launched in support of public health response at the national, state, and county levels. While the pandemic is global, the dynamics of this infectious disease varies with geography, local policies, and local variations in demographics. An underlying assumption of most infectious disease compartment modeling is that of a well mixed population at the resolution of the areas being modeled. The implicit need to model at
more » ... spatial resolution is impeded by the quality of ground truth data for fine scale administrative subdivisions. To understand the trade-offs and benefits of such modeling as a function of scale, we compare the predictive performance of a SARS-CoV-2 modeling at the county, county cluster, and state level for the entire United States. Our results demonstrate that accurate prediction at the county level requires hyper-local modeling with county resolution. State level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. As an important use case, leveraging high resolution modeling with public health data and admissions data from Hillsborough County Florida, we performed weekly forecasts of both hospital admission and ICU bed demand for the county. The repeated forecasts between March and August 2020 were used to develop accurate resource allocation plans for Tampa General Hospital.
doi:10.1101/2020.11.16.20232686 fatcat:lpvzgrkvzvatfoliuk5wkeknzq

Matching titles with cross title web-search enrichment and community detection

Nikhil Londhe, Vishrawas Gopalakrishnan, Aidong Zhang, Hung Q. Ngo, Rohini Srihari
2014 Proceedings of the VLDB Endowment  
Title matching refers roughly to the following problem. We are given two strings of text obtained from different data sources. The texts refer to some underlying physical entities and the problem is to report whether the two strings refer to the same physical entity or not. There are manifestations of this problem in a variety of domains, such as product or bibliography matching, and location or person disambiguation. We propose a new approach to solving this problem, consisting of two main
more » ... onents. The first component uses Web searches to "enrich" the given pair of titles: making titles that refer to the same physical entity more similar, and those which do not, much less similar. A notion of similarity is then measured using the second component, where the tokens from the two titles are modelled as vertices of a "social" network graph. A "strength of ties" style of clustering algorithm is then applied on this to see whether they form one cohesive "community" (matching titles), or separately clustered communities (mismatching titles). Experimental results confirm the effectiveness of our approach over existing title matching methods across several input domains.
doi:10.14778/2732977.2732990 fatcat:ne75tc3rjnd7heeryb3cwnidf4

Matching product titles using web-based enrichment

Vishrawas Gopalakrishnan, Suresh Parthasarathy Iyengar, Amit Madaan, Rajeev Rastogi, Srinivasan Sengamedu
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
Matching product titles from different data feeds that refer to the same underlying product entity is a key problem in online shopping. This matching problem is challenging because titles across the feeds have diverse representations with some missing important keywords like brand and others containing extraneous keywords related to product specifications. In this paper, we propose a novel unsupervised matching algorithm that leverages web search engines to (1) enrich product titles by adding
more » ... portant missing tokens that occur frequently in search results, and (2) compute importance scores for tokens based on their ability to retrieve other (enriched title) tokens in search results. Our matching scheme calculates the Cosine similarity between enriched title pairs with tokens weighted by their importance scores. We propose an optimization that exploits the templatized structure of product titles to reduce the number of search queries. In experiments with real-life shopping datasets, we found that our matching algorithm has superior F1 scores compared to IDF-based cosine similarity.
doi:10.1145/2396761.2396839 dblp:conf/cikm/GopalakrishnanIMRS12 fatcat:xbnn2hpruvcgvoix5uoq2ayjnu

On Machine Learning-Based Short-Term Adjustment of Epidemiological Projections of COVID-19 in US [article]

Sarah KEFAYATI, Hu Huang, Prithwish Chakraborty, Fred Roberts, Vishrawas Gopalakrishnan, Raman Srinivasan, Sayali Pethe, Piyush Madan, Ajay Deshpande, Xuan Liu, Jianying Hu, Gretchen Jackson
2020 medRxiv   pre-print
Epidemiological models have provided valuable information for the outlook of COVID-19 pandemic and relative impact of different mitigation scenarios. However, more accurate forecasts are often needed at near term for planning and staffing. We present our early results from a systemic analysis of short-term adjustment of epidemiological modeling of COVID 19 pandemic in US during March-April 2020. Our analysis includes the importance of various types of features for short term adjustment of the
more » ... edictions. In addition, we explore the potential of data augmentation to address the data limitation for an emerging pandemic. Following published literature, we employ data augmentation via clustering of regions and evaluate a number of clustering strategies to identify early patterns from the data. From our early analysis, we used CovidActNow as our underlying epidemiological model and found that the most impactful features for the one-day prediction horizon are population density, workers in commuting flow, number of deaths in the day prior to prediction date, and the autoregressive features of new COVID-19 cases from three previous dates of the prediction. Interestingly, we also found that counties clustered with New York County resulted in best preforming model with maximum of R2= 0.90 and minimum of R2= 0.85 for state-based and COVID-based clustering strategy, respectively.
doi:10.1101/2020.09.11.20180521 fatcat:lgvhst6v35dujhpeqrzoh4hy2i

A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure [article]

Prithwish Chakraborty and Vishrawas Gopalakrishnan and Sharon M.H. Alford and Faisal Farooq
2017 arXiv   pre-print
Electronic medical records (EMR) contain longitudinal information about patients that can be used to analyze outcomes. Typically, studies on EMR data have worked with established variables that have already been acknowledged to be associated with certain outcomes. However, EMR data may also contain hitherto unrecognized factors for risk association and prediction of outcomes for a disease. In this paper, we present a scalable data-driven framework to analyze EMR data corpus in a disease
more » ... way that systematically uncovers important factors influencing outcomes in patients, as supported by data and without expert guidance. We validate the importance of such factors by using the framework to predict for the relevant outcomes. Specifically, we analyze EMR data covering approximately 47 million unique patients to characterize renal failure (RF) among type 2 diabetic (T2DM) patients. We propose a specialized L1 regularized Cox Proportional Hazards (CoxPH) survival model to identify the important factors from those available from patient encounter history. To validate the identified factors, we use a specialized generalized linear model (GLM) to predict the probability of renal failure for individual patients within a specified time window. Our experiments indicate that the factors identified via our data-driven method overlap with the patient characteristics recognized by experts. Our approach allows for scalable, repeatable and efficient utilization of data available in EMRs, confirms prior medical knowledge and can generate new hypothesis without expert supervision.
arXiv:1711.11022v1 fatcat:tklf6jcvlzeshhq26okm37rlya

Continuous top-k query for graph streams

Shirui Pan, Xingquan Zhu
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
Gopalakrishnan (Suny Buffalo) Suresh Parthasarathy Iyengar (Yahoo!  ...  Categorization for e-Commerce (Page 595) Dan Shen (dianping.com) Jean-David Ruvini (eBay Research Labs) Badrul Sarwar (eBay Research Labs) Matching Product Titles using Web-based Enrichment (Page 605) Vishrawas  ... 
doi:10.1145/2396761.2398717 dblp:conf/cikm/PanZ12a fatcat:lugcho6xdbdtzgg5srgcqkwvsa

Data-Centric Epidemic Forecasting: A Survey [article]

Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash
2022 arXiv   pre-print
Nature human behaviour , ( [ ] Vishrawas Gopalakrishnan, Sayali Pethe, Sarah Kefayati, Raman Srinivasan, Paul Hake, Ajay Deshpande, ), -. , ( ), -. [ ] Herbert W Hethcote. .  ...  Gopalakrishnan et al. [ ] proposed to use local mechanistic models at county granularity to obtain more accurate forecasts at the state level.  ... 
arXiv:2207.09370v2 fatcat:x5a7uvmwrbgd7fiskufmn5nlmi