Predicting intervention effect for COVID-19 in Japan: state space modeling approach

Kobayashi, Genya; Tamae, Hiromasa; Ozu, Takayuki

doi:10.5582/bst.2020.03133

Cited by 41 publications

(36 citation statements)

References 12 publications

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“…(2020) 1.954 (95%CI, 1.851–2.025) Japan Chen et al. (2020) 1.49 (95%CI, 1.30–1.70) Japan Kobayashi et al. (2020) 2.86 (95%CI, 2.73–2.97) Japan Kurita et al.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, our attention is on whether the 80% reduction of the contact rate was successfully achieved in Japan during the period of the state of emergency. For some prior studies on the effect of the control strategies for COVID-19 in Japan, see ( Chen et al., 2020 ; Kobayashi et al., 2020 ; Kurita et al., 2020 ; Sugishita et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

Kuniya¹

2020

Infectious Disease Modelling

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In this paper, we evaluate the effect of the state of emergency for the first wave of COVID-19 in Japan, 2020 from the viewpoint of mathematical modelling. In Japan, it was announced during the period of the state of emergency from April 7 to May 25, 2020 that the 80% reduction of the contact rate is needed to control the outbreak. By numerical simulation, we show that the reduction rate seems to have reached up to 86%. Moreover, we estimate the control reproduction number during the period of the state of emergency as (95%CI, 0.34–0.39), and show that the effective reproduction number after the lifting of the state of emergency could be greater than 1. This result suggests us that the second wave of COVID-19 in Japan could possibly occur if any effective intervention will not be taken again.

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“…(2020) 1.954 (95%CI, 1.851–2.025) Japan Chen et al. (2020) 1.49 (95%CI, 1.30–1.70) Japan Kobayashi et al. (2020) 2.86 (95%CI, 2.73–2.97) Japan Kurita et al.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

Kuniya¹

2020

Infectious Disease Modelling

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“…For the empirical perspectives, several studies analyze the effect of Japan's non-legally enforceable emergency declarations on the population. For example, Kobayashi et al (2020) use a state-space model that combines susceptible-infected-recovered models to predict the evolution of infectious diseases and includes the magnitude and timing of the peak of the epidemic, following the emergency declaration in Japan. They confirm that the issuance and extension of the state of emergency declaration has, to some extent, been successful in controlling the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 with Stigma: Theory and Evidence from Mobility Data

2020

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This study conducts both theoretical and empirical analyses of how non-legally-binding COVID-19 policies affect people's going-out behavior. The theoretical analysis assumes that under a declared state of emergency, the individual going out suffers psychological costs arising from both the risk of infection and the stigma of going out. Our hypothesis states that under a declared state of emergency people refrain from going out because it entails a strong psychological cost. Then, this study estimates a model using regional mobility data and emergency declarations data to analyze self-restraint behavior under a non-legally binding emergency declaration. The results show that, compared with before the declaration of the state of emergency, going-out behavior was suppressed under the state of emergency and after it was lifted even when going out did not result in penalties, which is consistent with the theoretical analysis. Keywords COVID-19 • Stigma • Self-restraint behavior • Non-legally binding policy • Regional mobility This article is part of the Topical Collection on Economics of COVID-19 The authors acknowledge the support from JSPS KAKENHI grant numbers JP19K23194 and JP20K13486, and of Feasibility Project 14200138 of Research Institute for Humanity and Nature. We should like to thank editors of Economics of Disasters and Climate Change as well as anonymous reviewers. We also appreciate valuable comments from Keisuke Hattori, Kosuke Hirose, and Jumpei Hamamura.

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“…This can be suspected by the decreased peak of GT trend for the “COVID” keyword in the second wave (Fig 2, in Australia, Japan, and the United States). In future studies, state space modeling [22] to incorporate potentially time-varying effects may be useful to overcome the potential weakness of the VAR model, especially when the included period becomes so long. In addition, the keywords’ media coverage was adjusted only in Japanese regional data, which makes the obtained results slightly less generalizable to other countries.…”

Section: Discussionmentioning

confidence: 99%

Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

Sato

Mano

Iwata

et al. 2021

Preprint

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Introduction: Google Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results. Methods: We extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data. Results: Our Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by the Pearson correlation-based approach. 'Sense of smell' and 'loss of smell' were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan). Conclusions: Our results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.

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Predicting intervention effect for COVID-19 in Japan: state space modeling approach

Cited by 41 publications

References 12 publications

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

COVID-19 with Stigma: Theory and Evidence from Mobility Data

Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

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