In this work we report that the public reacted on social media at an early stage of the SARS-CoV-2 pandemic, the virus responsible of COVID-19 , in a surprisingly accurate way. Early social media activity levels reflected the severity of the disease as registered almost a month later. Specifically, the intensity of COVID-19 related social media activity from different Italian, Spanish and United States regions at the beginning of the epidemic, predicts well the total number of deaths reached almost a month later in each region. More strikingly, at the time of the initial twitter reaction no tabled regional data on the epidemic was readily available in Italy. By the 24th February 2020 only two regions reported death cases and only three reported infected subjects. Our findings could underpin the creation of real-time novelty detection systems aimed at early reporting of SARS-like mortality and thus early activation of control measures in future pandemics .
During the unfolding of a crisis, it is crucial to determine its severity, yet access to reliable data is challenging. We investigate the relation between geolocated Tweet Intensity of initial COVID-19 related tweet at the beginning of the pandemic across Italian, Spanish and USA regions and mortality in the region a month later. We find significant proportionality between early social media reaction and the cumulative number of COVID-19 deaths almost a month later . Our findings suggest that "the crowds" perceived the risk correctly. This is one of the few examples where the "wisdom of crowds" can be quantified and applied in practice. This can be used to create real-time alert systems that could be of help for crisis-management and intervention, especially in developing countries. Such systems could contribute to inform fast-response policy making at early stages of a crisis .
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In this work we report that the public reacted on social media at an early stage of the SARS-CoV-2 pandemic, the virus responsible of
During the unfolding of a crisis, it is crucial to determine its severity, yet access to reliable data is challenging. We investigate the relation between geolocated Tweet Intensity of initial
R
, in a surprisingly accurate way. Early social media activity levels reflected the severity of the disease as registered almost
related tweet at the beginning of the pandemic across Italian, Spanish and USA regions and mortality in the region
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Specifically, the intensity of COVID-19 related social media activity from different Italian, Spanish and United States regions at the beginning of the epidemic, predicts well the total number of deaths reached
We find significant proportionality between early social media reaction and the cumulative number of COVID-19 deaths
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in each region. More strikingly, at the time of the initial twitter reaction no tabled regional data on the epidemic was readily available in Italy. By the 24th February 2020 only two regions reported death cases and only three reported infected subjects. Our findings could underpin the creation of
. Our findings suggest that "the crowds" perceived the risk correctly. This is one of the few examples where the "wisdom of crowds" can be quantified and applied in practice. This can be used to create
R
novelty detection systems aimed at early reporting of SARS-like mortality and thus early activation of control measures in future pandemics
alert systems that could be of help for crisis-management and intervention, especially in developing countries. Such systems could contribute to inform fast-response policy making at early stages of a crisis