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

Kuniya, Toshikazu

doi:10.1016/j.idm.2020.08.004

Cited by 41 publications

(44 citation statements)

References 25 publications

(31 reference statements)

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“…Published studies have estimated the R(t) of COVID-19 and assessed the impact of PHSM on R(t) at the country or city/district levels [3,4,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Because R ( t ) reflects the frequency of transmission over time, it has been recognized as epidemiologically useful in assessing transmission dynamics in populations with varying susceptibility levels and experiencing various public health interventions. Published studies have estimated the R ( t ) of COVID-19 and assessed the impact of PHSM on R ( t ) at the country or city/district levels [ 3 , 4 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

Nakajo

Nishiura

2021

JCM

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Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts.

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“…Published studies have estimated the R(t) of COVID-19 and assessed the impact of PHSM on R(t) at the country or city/district levels [3,4,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

Nakajo

Nishiura

2021

JCM

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“…We assume that average incubation and infectious periods ℓ ε and ℓ γ were assumed as 5 days and 10 days, respectively. 2,33–35, The infectiousness probability of an exposed individual was given as ε = 1/ ℓ ε , and the recovery probability of an infected individual was given by γ = 1/ ℓ γ . The transmission rate was determined as β = R 0 γ based on the standard SEIR model.…”

Section: Methodsmentioning

confidence: 99%

Simulating the Impacts of Interregional Mobility Restriction on the Spatial Spread of COVID-19 in Japan

Kondo

2021

Preprint

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This study develops a spatial Susceptible–Exposed–Infectious–Recovered (SEIR) model that analyzes the effect of interregional mobility on the spatial spread of the coronavirus disease 2019 (COVID-19) outbreak in Japan. National and local governments have requested that residents refrain from traveling between 47 prefectures during the state of emergency. However, the extent to which restricting the interregional mobility prevents infection expansion has not been elucidated. Our spatial SEIR model describes the spatial spread pattern of COVID-19 when people commute to a prefecture where they work or study during the daytime and return to their residential prefecture at night. We assume that people are exposed to infection risk during their daytime activities. According to our simulation results, interregional mobility restriction can prevent geographical expansion of the infection. However, in prefectures with many infectious individuals, residents are exposed to higher infection risk when their mobility is restricted. Our simulation results also show that interregional mobility restriction plays a limited role in reducing the national total number of infected individuals.

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“…However, the authors focus on the data until early 2020. Kuniya ( 4 ) also examines the impact of an emergency state for the first wave of the COVID-19 in Japan for the period from April 7 to May 25, 2020. The author finds that the state of emergency has provided to 80% decline in the contact rate.…”

Section: Introductionmentioning

confidence: 99%

Clusters in the Spread of the COVID-19 Pandemic: Evidence From the G20 Countries

Tian

2021

Front. Public Health

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This study tests the validity of the club convergence clustering hypothesis in the G20 countries using four measures of the spread of the COVID-19 pandemic: total number of confirmed cases per million people, new cases per million people, total deaths per million people, and new deaths per million people. The empirical analysis is based on the daily data from March 1, 2020, to October 10, 2020. The results indicate three clusters for the per capita income, two clusters for total cases per million people, and new cases per million people. Besides, there are only one and two clusters for total deaths per million people and new deaths per million people. Potential policy implications are also discussed in detail.

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

Cited by 41 publications

References 25 publications

Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

Simulating the Impacts of Interregional Mobility Restriction on the Spatial Spread of COVID-19 in Japan

Clusters in the Spread of the COVID-19 Pandemic: Evidence From the G20 Countries

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