Neighborhood level chronic respiratory disease prevalence estimation using search query data.

ABSTRACT: Estimation of disease prevalence at sub-city neighborhood scale allows early and targeted interventions that can help save lives and reduce public health burdens. However, the cost-prohibitive nature of highly localized data collection and sparsity of representative signals, has made it challenging to identify neighborhood scale prevalence of disease. To overcome this challenge, we utilize alternative data sources, which are both less sparse and representative of localized disease prevalence: using query data from a large commercial search engine, we identify the prevalence of respiratory illness in the United States, localized to census tract geographic granularity. Focusing on asthma and Chronic Obstructive Pulmonary Disease (COPD), we construct a set of features based on searches for sy

SUBMITTER: Abdur Rehman N 

PROVIDER: S-EPMC8189491 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature