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Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics
Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics
SummaryDuring the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO;N= 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.
- University of Mary United States
- Inserm France
- Washington State University United States
- University of Toronto Canada
- University of Erfurt Germany
Microsoft Academic Graph classification: Public economics business.industry Distancing Psychological intervention Disease Risk perception Health care Pandemic Societal Factors business Psychology Psychosocial
[SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], [MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS], [SHS.PSY]Humanities and Social Sciences/Psychology, [SHS.PSY] Humanities and Social Sciences/Psychology, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
[SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], [MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS], [SHS.PSY]Humanities and Social Sciences/Psychology, [SHS.PSY] Humanities and Social Sciences/Psychology, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Microsoft Academic Graph classification: Public economics business.industry Distancing Psychological intervention Disease Risk perception Health care Pandemic Societal Factors business Psychology Psychosocial
4515
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5. Betsch, Cornelia; Wieler, Lothar; Bosnjak, Michael; Ramharter, Michael; Stollorz, Volker; Omer, Saad; Korn, Lars; Sprengholz, Philipp; Felgendreff, Lisa; Eitze, Sarah; Schmid P. German COVID-19 Snapshot MOnitoring (COSMO Germany). PsychArchives. 2020;
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!- University of Mary United States
- Inserm France
- Washington State University United States
- University of Toronto Canada
- University of Erfurt Germany
- EBRAINS Belgium
- University of Washington United States
- Aix-Marseille University France
- University of Copenhagen Denmark
SummaryDuring the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO;N= 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.