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How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka–Volterra analysis of google trends data

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  • Chiara Sotis

    (London School of Economics and Political Science)

Abstract

In this paper I exploit Google searches for the topics “symptoms”, “unemployment” and “news” as a proxy for how much attention people pay to the health and economic situation and the amount of news they consume, respectively. I then use an integrable nonautonomous Lotka–Volterra model to study the interactions among these searches in three U.S. States (Mississippi, Nevada and Utah), the District of Columbia and in the U.S. as a whole. I find that the results are very similar in all areas analyzed, and for different specifications of the model. Prior to the pandemic outbreak, the interactions among health searches, unemployment searches and news consumption are very weak, i.e. an increase in searches for one of these topics does not affect the amount of searches for the others. However, from around the beginning of the pandemic these interactions intensify greatly, suggesting that the pandemic has created a tight link between the health and economic situation and the amount of news people consume. I observe that from March 2020 unemployment predates searches for news and for symptoms. Consequently, whenever searches for unemployment increase, all the other searches decrease. Conversely, when searches for any of the other topics considered increase, searches for unemployment also increase. This underscores the importance of mitigating the impact of COVID-19 on unemployment to avoid that this issue swallows all others in the mind of the people.

Suggested Citation

  • Chiara Sotis, 2021. "How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka–Volterra analysis of google trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2001-2016, December.
    • Handle: RePEc:spr:qualqt:v:55:y:2021:i:6:d:10.1007_s11135-020-01089-0
    DOI: 10.1007/s11135-020-01089-0
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    1. Addolorata Marasco & Alessandro Romano, 2018. "Deterministic modeling in scenario forecasting: estimating the effects of two public policies on intergenerational conflict," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2345-2371, September.
    2. Scott R. Baker & Andrey Fradkin, 2017. "The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 756-768, December.
    3. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    4. Eighmey, John & McCord, Lola, 1998. "Adding Value in the Information Age: Uses and Gratifications of Sites on the World Wide Web," Journal of Business Research, Elsevier, vol. 41(3), pages 187-194, March.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
    6. Goran Dominioni & Alessandro Romano & Chiara Sotis, 2019. "A Quantitative Study of the Interactions between Oil Price and Renewable Energy Sources Stock Prices," Energies, MDPI, vol. 12(9), pages 1-11, May.
    7. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    8. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    9. Marasco, A. & Picucci, A. & Romano, A., 2016. "Market share dynamics using Lotka–Volterra models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 49-62.
    10. Marasco, Addolorata & Romano, Alessandro, 2018. "Inter-port interactions in the Le Havre-Hamburg range: A scenario analysis using a nonautonomous Lotka Volterra model," Journal of Transport Geography, Elsevier, vol. 69(C), pages 207-220.
    11. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    12. Andrew Atkeson, 2020. "What Will be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," Staff Report 595, Federal Reserve Bank of Minneapolis.
    13. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    14. Dominioni, Goran & Romano, Alessandro & Sotis, Chiari, 2019. "A quantitative study of the interactions between oil price and renewable energy sources stock prices," LSE Research Online Documents on Economics 100548, London School of Economics and Political Science, LSE Library.
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    1. Antoni Wiliński & Łukasz Kupracz & Aneta Senejko & Grzegorz Chrząstek, 2022. "COVID-19: average time from infection to death in Poland, USA, India and Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4729-4746, December.
    2. Chiara Natalie Focacci, 2023. "Old versus young: How much do countries spend on social benefits? Deterministic modeling for government expenditure," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 363-377, February.
    3. Gamze Bayın Donar & Seda Aydan, 2022. "Association of COVID‐19 with lifestyle behaviours and socio‐economic variables in Turkey: An analysis of Google Trends," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(1), pages 281-300, January.
    4. Fernando Delbianco & Andrés Fioriti & Fernando Tohmé & Federico Contiggiani, 2022. "A Tale of two narratives: assessing the sociological hypothesis of the appeal of the US dollar in Argentina," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3519-3537, October.

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