Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter.

ABSTRACT:

Background

Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than [Formula: see text] of all vector-borne diseases in North America.

Objective

In this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US.

Methods

The study was done in three stages: (1) Approximately 1.3 million tweets were collected and pre-processed to extract the most relevant Lyme disease tweets with geolocations. A subset of tweets were semi-automatically labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, and the remaining portion were manually labelled, yielding a curated labelled dataset of 77, 500 twe

SUBMITTER: Boligarla S 

PROVIDER: S-EPMC10578027 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature