Characterizing Population-level Changes in Human Behavior during the COVID-19 Pandemic in the United States DOI Creative Commons
Tamanna Urmi, Binod Pant, George Dewey

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 22, 2024

Abstract The transmission of communicable diseases in human populations is known to be modulated by behavioral patterns. However, detailed characterizations how population-level behaviors change over time during multiple disease outbreaks and spatial resolutions are still not widely available. We used data from 431,211 survey responses collected the United States, between April 2020 June 2022, provide a description fluctuated first two years COVID-19 pandemic. Our analysis suggests that at national state levels, people’s adherence recommendations avoid contact with others (a preventive behavior) was highest early pandemic but gradually—and linearly—decreased time. Importantly, periods intense mortality, increased—despite overall temporal decrease. These spatial-temporal help improve our understanding bidirectional feedback loop outbreak severity behavior. findings should benefit both computational modeling teams developing methodologies predict dynamics future epidemics policymakers designing strategies mitigate effects outbreaks.

Language: Английский

Formulating human risk response in epidemic models: Exogenous vs endogenous approaches DOI
Leah LeJeune, Navid Ghaffarzadegan, Lauren M. Childs

et al.

European Journal of Operational Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Mathematical Assessment of Wastewater-Based Epidemiology to Predict SARS-CoV-2 Cases and Hospitalizations in Miami-Dade County DOI
Binod Pant,

Salman Safdar,

Calistus N. Ngonghala

et al.

Acta Biotheoretica, Journal Year: 2025, Volume and Issue: 73(1)

Published: Feb. 11, 2025

Language: Английский

Citations

0
Minimal epidemic models with information index: from compartmental to integral formulation DOI Creative Commons
Bruno Buonomo, Eleonora Messina,

Claudia Panico

et al.

Bollettino dell Unione Matematica Italiana, Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

Language: Английский

Citations

0
Optimal control of a reaction-diffusion epidemic model with non-compliance DOI Creative Commons
Marcelo Bongarti, Christian Parkinson, Weinan Wang

et al.

European Journal of Applied Mathematics, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 26

Published: April 14, 2025

Abstract In this paper, we consider an optimal distributed control problem for a reaction-diffusion-based SIR epidemic model with human behavioural effects. We develop wherein non-pharmaceutical intervention methods are implemented, but portion of the population does not comply them, and non-compliance affects spread disease. Drawing from social contagion theory, our allows parallel to The quantities interest reduction in infection rate among compliant population, at which non-compliant individuals become after, e.g., receiving more or better information about underlying prove existence global-in-time solutions fixed controls study regularity properties resulting control-to-state map. is then established abstract framework fairly general class objective functions. Necessary first–order optimality conditions obtained via Lagrangian-based stationarity system. conclude discussion regarding minimisation size infected populations present simulations various parameters values demonstrate behaviour model.

Language: Английский

Citations

0
A Simultaneous Simulation of Human Behavior Dynamics and Epidemic Spread: A Multi-Country Study Amidst the COVID-19 Pandemic DOI
A. A. Osi, Navid Ghaffarzadegan

Mathematical Biosciences, Journal Year: 2024, Volume and Issue: unknown, P. 109368 - 109368

Published: Dec. 1, 2024

Language: Английский

Citations

0
The effect of Behavioral Factors and Intervention Strategies on Pathogen Transmission: Insights from a Two-Week Epidemic Game at Wenzhou-Kean University in China DOI Open Access
Salihu S. Musa, Winnie Mkandawire, Trusting Inekwe

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Abstract Background Effective control of infectious diseases relies heavily on understanding transmission dynamics and implementing interventions that reduce the spread. Non-pharmaceutical (NPIs), such as mask-wearing, social distancing, quarantining, are vital tools in managing outbreaks where vaccines or treatments limited. However, success NPIs is influenced by human behavior, including compliance with guidelines, attitudes beliefs about effectiveness interventions. In this study, we applied an innovative proximitybased experimentation platform to generate empirical data behaviors their effect disease transmission. Our uses a smartphone application enables spread digital pathogen among participants via Bluetooth during open-world “experimental epidemic games”. This creates environment for epidemiology field researchers can mechanics collect full ground-truth datasets. Methods study employed “epidemic” app investigate impact risk perception Involving nearly 1,000 two-weeks long game at Wenzhou-Kean University (WKU) China, generated multimodal dataset, which allowed us develop parameterize Susceptible-Exposed-Infected-Recovered (SEIR) models. We quantified extent behavioral factors, quarantine, strength intervention strategies influence The model incorporates time-varying rates reflect changes calibrated it using from provide critical insights into how variations NPI levels affect outbreak control. Findings findings reveal adherence alone, behavior attitudes, may not result expected reduction transmission, illustrating complex interplay between factors Moreover, further shows coupled could significantly infection well susceptibility. Interpretation highlights usefulness experimental games realistic datasets, importance integrating epidemiological models enhance accuracy predictions public health outbreaks. Research Context Evidence before conducted comprehensive review existing literature evaluate current state knowledge regarding empirically-informed modeling, particular focus role non-pharmaceutical (NPIs) mitigating search spanned databases PubMed, MEDLINE, Web Science, targeting publications up March 1, 2024, keywords “infectious modeling,” “simulation,” game,” “human behavior,” “non-pharmaceutical interventions,” “epidemiology.” While substantial body research explores dynamics, there notable gap studies integrate large-scale mobility collected apps within environments, university campus. Most fail incorporate complexity real-time responses NPIs, crucial accurately modeling contexts. Added value first use our proximity-based conduct setting while mechanistic framework. By employing flexible, rate model, dynamics. novel approach provides more accurate nuanced depiction real-world scenarios, observed experiment. Through integration participants, combined detailed simulations rigorous sensitivity analyses, offer timely coordinated interventions, alongside compliance, trajectory outbreak. underscores necessity adaptive management presents robust framework inform future planning response efforts. Implications all available evidence underscore pivotal computational approaches generating datasets results then become valuable planning. solid foundation refining additional complexities, age-based behaviors, offers optimizing pandemic preparedness.

Language: Английский

Citations

0
Characterizing Population-level Changes in Human Behavior during the COVID-19 Pandemic in the United States DOI Creative Commons
Tamanna Urmi, Binod Pant, George Dewey

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 22, 2024

Abstract The transmission of communicable diseases in human populations is known to be modulated by behavioral patterns. However, detailed characterizations how population-level behaviors change over time during multiple disease outbreaks and spatial resolutions are still not widely available. We used data from 431,211 survey responses collected the United States, between April 2020 June 2022, provide a description fluctuated first two years COVID-19 pandemic. Our analysis suggests that at national state levels, people’s adherence recommendations avoid contact with others (a preventive behavior) was highest early pandemic but gradually—and linearly—decreased time. Importantly, periods intense mortality, increased—despite overall temporal decrease. These spatial-temporal help improve our understanding bidirectional feedback loop outbreak severity behavior. findings should benefit both computational modeling teams developing methodologies predict dynamics future epidemics policymakers designing strategies mitigate effects outbreaks.

Language: Английский

Citations

0