Abstract
An infodemic---an outpouring of information, including misleading and also fake news---is accompanying the current pandemic caused by SARS-CoV-2. In the absence of valid therapeutic approaches, behavioral responses may seriously affect the social dynamics of contagion, so the infodemic may cause confusion and disorientation in the public, leading to possible individually and socially harmful choices. This new phenomenon requires specific modeling efforts to better understand the complex intertwining of the epidemic and infodemic components of a pandemic crisis, with a view to building an integrative public health approach. We propose three models, from epidemiology to game theory, as potential candidates for the onset of the infodemics and statistically assess their accuracy in reproducing real infodemic waves observed in a data set of 390 million tweets collected worldwide. Our results show that evolutionary game-theory models are the most suitable ones to reproduce the observed infodemic modulations around the onset of the local epidemic wave. Furthermore, we find that the number of confirmed COVID-19 reported cases in each country and worldwide are driving the modeling dynamics with opposite effects.
Highlights
In the past two decades, the periodic appearance of highly infectious, severe respiratory syndromes with pandemic potential caused by viral agents of the coronavirus family (SARS-CoV-1 in 2003, MERS-CoV in 2012), for which no reliable therapeutic approach was available at the time, has brought the issue of the social containment of epidemics to the attention of both decision makers and the general public
The double infection (DI) model is the best candidate to explain infodemic data for most of the countries and the bounded rationality (BR) model is instead penalized with respect to other models since, even if it presents comparable or even lower residuals, it requires the estimation of one more parameters with respect to the others
Modeling infodemic waves which spread through online social media during an ongoing epidemic is challenging, and here we provide an exploration of this issue
Summary
In the past two decades, the periodic appearance of highly infectious, severe respiratory syndromes with pandemic potential caused by viral agents of the coronavirus family (SARS-CoV-1 in 2003, MERS-CoV in 2012), for which no reliable therapeutic approach was available at the time, has brought the issue of the social containment of epidemics to the attention of both decision makers and the general public. As a response to the previous crises, several mathematical models have been developed to make reliable predictions on the time evolution of an epidemic and at shedding light on key aspects of its drift and shift dynamics [1] as a basis for the design and evaluation of appropriate management and mitigation strategies [2,3,4] These developments have proven their importance in the extreme case of the pandemic diffusion of the new virus SARS-CoV-2, that is causing a previously unknown severe acute respiratory syndrome [5], which between early January 2020 and August 2021 has led to a global figure of 216 900 000 confirmed cases and more than 5 500 000 deaths. Mathematical models allow for nowcasting epidemic dynamics (see Ref. [7] and references therein) and to better understand the role of human behavior to flatten the curve and prepare to face potential future epidemic waves [8,9]
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