2018
DOI: 10.1101/349506
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Systematic biases in disease forecasting - the role of behavior change

Abstract: In a simple susceptible-infected-recovered (SIR) model, the initial speed at which infected cases increase is indicative of the long-term trajectory of the outbreak. Yet during real-world outbreaks, individuals may modify their behavior and take preventative steps to reduce infection risk. As a consequence, the relationship between the initial rate of spread and the final case count may become tenuous. Here, we evaluate this hypothesis by comparing the dynamics arising from a simple SIR epidemic model with tho… Show more

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Cited by 24 publications
(32 citation statements)
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“…• Finally, although the model does not account explicitly for biases due to behavioral changes [20,21], population clustering and super spreading events [22], we argue that our approach to lockdowns and relaxation events is a proxy model of these more general events.…”
Section: Contributions and Limitationsmentioning
confidence: 95%
“…• Finally, although the model does not account explicitly for biases due to behavioral changes [20,21], population clustering and super spreading events [22], we argue that our approach to lockdowns and relaxation events is a proxy model of these more general events.…”
Section: Contributions and Limitationsmentioning
confidence: 95%
“…See for example,Eksin et al (2019) who also explore assumptions where x(I(t), R(t)) is decreasing in both variables, that they argue is a model of 'long-term awareness' in contrast to 'short-term awareness' where x is a function of I(t) alone.4 This might be termed an 'old-timey' macro approach.5Kremer (1996) also included behavioural elements in an SI model but his focus was on equilibrium outcomes in a broader matching game.6 Chen (2012) uses an SIR model where agents can reduce their physical interactions and infected agents may be debilitated and so interact less. He focuses on myopic agents and analyses the impact of different matching functions on the resulting equilibrium outcomes.…”
mentioning
confidence: 99%
“…Este documento contribuye a esa discusión proponiendo una adecuación al modelo canónico usado en epidemiología (SIR), adaptando el enfoque de estado-espacio usado regularmente en el ámbito de las proyecciones econométricas. Esto, en sí mismo, no es una novedad en términos metodológicos (Sebastian & Victor, 2017) y (Eksin et al, 2019), pero sí lo es en cuanto a su aplicación, dado que se emplea para diagnosticar la dinámica del COVID-19 en la República Dominicana, contrastándola con la de una selección de países.…”
Section: Introductionunclassified