Awareness-driven behavior changes can shift the shape of epidemics away from peaks and toward plateaus, shoulders, and oscillations.
control
epidemics
epidemiology
nonlinear dynamics
public health
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
22 12 2020
22 12 2020
Historique:
pubmed:
3
12
2020
medline:
20
1
2021
entrez:
2
12
2020
Statut:
ppublish
Résumé
The COVID-19 pandemic has caused more than 1,000,000 reported deaths globally, of which more than 200,000 have been reported in the United States as of October 1, 2020. Public health interventions have had significant impacts in reducing transmission and in averting even more deaths. Nonetheless, in many jurisdictions, the decline of cases and fatalities after apparent epidemic peaks has not been rapid. Instead, the asymmetric decline in cases appears, in most cases, to be consistent with plateau- or shoulder-like phenomena-a qualitative observation reinforced by a symmetry analysis of US state-level fatality data. Here we explore a model of fatality-driven awareness in which individual protective measures increase with death rates. In this model, fast increases to the peak are often followed by plateaus, shoulders, and lag-driven oscillations. The asymmetric shape of model-predicted incidence and fatality curves is consistent with observations from many jurisdictions. Yet, in contrast to model predictions, we find that population-level mobility metrics usually increased from low levels before fatalities reached an initial peak. We show that incorporating fatigue and long-term behavior change can reconcile the apparent premature relaxation of mobility reductions and help understand when post-peak dynamics are likely to lead to a resurgence of cases.
Identifiants
pubmed: 33262277
pii: 2009911117
doi: 10.1073/pnas.2009911117
pmc: PMC7768772
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
32764-32771Commentaires et corrections
Type : UpdateOf
Informations de copyright
Copyright © 2020 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
The authors declare no competing interest.
Références
IHME COVID-19 health services utilization forecasting team. 2020.
Bregman D. J., Langmuir A. D.. Farr’s law applied to AIDS projections. JAMA. 1990;263:1522–1525.
Ferguson N. M., et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand.
Kucharski A. J., et al. Early dynamics of transmission and control of COVID-19: A mathematical modeling study. Lancet Infect. Dis.. 2020;20:553–558.
Kissler S. M., Tedijanto C., Goldstein E., Grad Y. H., Lipsitch M.. Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period. Science. 2020;368:eabb5793.
Park S. W., et al. Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: Framework and applications to the novel coronavirus (SARS-CoV-2) outbreak. J. Roy Soc. Interface. 2020;17:20200144.
Kraemer M. U. G., et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020;368:493–497.
Li R., et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19). Science. 2020;368:489–493.
Wu J., et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat. Med.. 2020;26:506–510.
Anderson R. M., May R. M.. Infectious Diseases of Humans: Dynamics and Control. 1991.
Funk S., Gilad E., Watkins C., Jansen V. A.. The spread of awareness and its impact on epidemic outbreaks. Proc. Natl. Acad. Sci. U.S.A.. 2009;106:6872–6877.
Funk S., Salathé M., Jansen V. A.. Modeling the influence of human behavior on the spread of infectious diseases: A review. J. R. Soc. Interface. 2010;7:1247–1256.
Eksin C., Shamma J. S., Weitz J. S.. Disease dynamics in a stochastic network game: A little empathy goes a long way in averting outbreaks. Sci. Rep.. 2017;7:44122.
Eksin C., Paarporn K., Weitz J. S.. Systematic biases in disease forecasting—The role of behavior change. Epidemics. 2019;27:96–105.
Franco E.. A feedback SIR (fSIR) model highlights advantages and limitations of infection-based social distancing. 2020.
Chen H., Xu W., Paris C., Reeson A., Li X.. Social distance and SARS memory: Impact on the public awareness of 2019 novel coronavirus (COVID-19) outbreak. 2020.
Leung K., Wu J. T., Liu D., Leung G. M.. First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: A modeling impact assessment. Lancet. 2020;395:1382–1393.
Adam D., et al. Clustering and superspreading potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Hong Kong. 2020.
Althouse B. M., et al. Superspreading events in the transmission dynamics of SARS-CoV-2: Opportunities for interventions and control. PLOS Biology. 2020.
doi: 10.1371/journal.pbio.3000897
Lau M. S. Y., et al. Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA. Proc. Natl. Acad. Sci. U.S.A.. 2020;117:22430–22435.
Liu Y., Eggo R. M., Kucharski A. J.. Secondary attack rate and superspreading events for SARS-CoV-2. Lancet. 2020;395:e47.
Frieden T. R., Lee C. T.. Identifying and interrupting superspreading events—Implications for control of severe acute respiratory syndrome coronavirus 2. Emerg. Infect. Dis.. 2020;26:1059–1066.
Kain M. P., Childs M. L., Becker A. D., Mordecai E. A.. Chopping the tail: How preventing superspreading can help to maintain COVID-19 control. 2020.
Weinberger D. M., et al. Estimation of excess deaths associated with the COVID-19 pandemic in the United States, March to May 2020. JAMA Intern. Med.. 2020;180:1336–1344.
Meyerowitz-Katz G., Merone L.. A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates. Int. J. Infect. Dis.. 101:138–148.
Flaxman S., et al. Estimating the number of infections and the impact of nonpharmaceutical interventions on COVID-19 in 11 European countries. Nature. 2020;584:257–261.
West R., Michie S., Rubin G. J., Amlôt R.. Applying principles of behavior change to reduce SARS-CoV-2 transmission. Nat. Hum. Behav.. 2020;4:451–459.