TY - JOUR
T1 - Impact of public sentiments on the transmission of COVID-19 across a geographical gradient
AU - Agusto, Folashade B.
AU - Numfor, Eric
AU - Srinivasan, Karthik
AU - Iboi, Enahoro A.
AU - Fulk, Alexander
AU - Saint Onge, Jarron M.
AU - Townsend Peterson, A.
N1 - Funding Information:
This research was supported by the National Science Foundation under the grant number DMS 2028297. Enahoro A. Iboi had additional support from the National Science Foundation with award number #176194. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
Copyright 2023 Agusto et al.
PY - 2023/2
Y1 - 2023/2
N2 - COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.
AB - COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.
KW - COVID-19
KW - Human behavior
KW - Sensitivity analysis
KW - Sentiment analysis
KW - Twitter tweets
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U2 - 10.7717/peerj.14736
DO - 10.7717/peerj.14736
M3 - Article
AN - SCOPUS:85150879784
SN - 2167-8359
VL - 11
JO - PeerJ
JF - PeerJ
M1 - e14736
ER -