Dataset Information

Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan: Numerical Study.

ABSTRACT: Background:Coronavirus Disease 2019 (COVID-19) currently poses a global public health threat. Although no exception, Tokyo, Japan was affected at first by only a small epidemic. Medical collapse nevertheless nearly happened because no predictive method existed for counting patients. A standard SIR epidemiological model predicts susceptible, infectious, and removed (recovered /deaths) cases and has been widely used but its applicability is limited often to an early phase of epidemic in the case of a large collective population. Full numerical simulation of entire period from beginning till end-point would be helpful in understanding COVID-19 trends with in-patient and infectious at large separately and also in preparing beds and develop quarantine strategies.

Objective:The study aimed to develop an isolation considered epidemiological model to simulate a whole trend of the first epidemic in Tokyo and allow to count in-patient and those of infectious at large separately. It was also intended to induce important corollaries of governing equation i.e. effective reproductive number and the final size equation.

Methods:Time-series SARS-COV-2 data from February 28 to May 23, 2020 in Tokyo and antibody test conducted by Japan Government were adopted for this study. A novel epidemiological model based on discrete delay differential equation (ATLM) was introduced. The model can predict trends of in-patient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, in-patient, and PCR test positivity ratios were used to examine the model. This approach also derived an alternative formulation equivalent to the standard SIR model.

Results:In a typical parameter setting the present ATLM gave 20% less infectious cases in field than that of standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that time lag T from infected till detected and isolated is 14 days. Based on this, adequate vaccine ratio to avoid outbreak was evaluated 57% of population. The date of release of a statement of emergency was 23rd May was assessed. Taking into consideration of infectious cases in field, 30th May should have been most effective. Furthermore, simulation results with shorter time lag of T=7 and larger transmission rate ?=1.43?0 suggest that infectious at large should reduce to by half and in-patient be much the same of those of the 1st wave of SARS-COV-2 epidemic while public health intervention is applied to enhance social contact by 43% .

Conclusions:A novel mathematical model was proposed and examined using SARS-COV-2 data for Tokyo. Simulation agreed with data from beginning to endemic. Shortening the period from infection to hospitalization is effective against outbreak without rigorous public health intervention and control.

Clinicaltrial:

SUBMITTER: Utamura M

PROVIDER: S-EPMC7746226 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis.

Utamura Motoaki M Koizumi Makoto M Kirikami Seiichi S

JMIR public health and surveillance 20201216 4

<h4>Background</h4>COVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective po ...[more]

PMID: 33259325

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Project description:BackgroundOn April 7, 2020, the Japanese government declared a state of emergency regarding the novel coronavirus (COVID-19). Given the nation-wide spread of the coronavirus in major Japanese cities and the rapid increase in the number of cases with untraceable infection routes, large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan is urgently required.MethodsA chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And AN care seeking) was developed to surveil the Japanese epidemiological situation in real-time. COOPERA asked questions regarding personal information, location, preventive actions, COVID-19 related symptoms and their residence. Empirical Bayes estimates of the age-sex-standardized incidence rate and disease mapping approach using scan statistics were utilized to identify the geographical distribution of the symptoms in Tokyo and their spatial correlation r with the identified COVID-19 cases.FindingsWe analyzed 353,010 participants from Tokyo recruited from 27th March to 6th April 2020. The mean (SD) age of participants was 42.7 (12.3), and 63.4%, 36.4% or 0.2% were female, male, or others, respectively. 95.6% of participants had no subjective symptoms. We identified several geographical clusters with high spatial correlation (r = 0.9), especially in downtown areas in central Tokyo such as Shibuya and Shinjuku.InterpretationWith the global spread of COVID-19, medical resources are being depleted. A new system to monitor the epidemiological situation, COOPERA, can provide insights to assist political decision to tackle the epidemic. In addition, given that Japan has not had a strong lockdown policy to weaken the spread of the infection, our result would be useful for preparing for the second wave in other countries during the next flu season without a strong lockdown.FundingThe present work was supported in part by a grant from the Ministry of Health, Labour and Welfare of Japan (H29-Gantaisaku-ippan-009).

Project description:The COVID-19 pandemic that started at the end of 2019 forced populations around the world to reduce social and economic activities; it is believed that this can prevent the spread of the disease. In this paper, we report an analysis of the seismic noise during such an induced social activity reduction in the Tokyo metropolitan area, Japan. Using seismic data obtained from 18 stations in the Metropolitan Seismic Observation Network (MeSO-net), a two-step seismic noise reduction was observed during the timeline of COVID-19 in Tokyo. The first noise reduction occurred at the beginning of March 2020 in the frequency band of 20-40 Hz. This corresponded with the request by the Prime Minister of Japan for a nationwide shutdown of schools. Although social activity was not reduced significantly at this juncture, local reduction of seismic wave excitation in the high-frequency band, 20-40 Hz, was recorded at some MeSO-net stations located in school properties. The second reduction of seismic noise occurred at the end of March to the beginning of April 2020 in a wider frequency band including lower frequency bands of 1-20 Hz. This timing corresponds to when the Governors of the Tokyo metropolitan area requested citizens to stay home and when the state of emergency was declared for the Tokyo metropolitan area by the government, respectively. Since then, the estimated population at train stations abruptly dropped, which suggests that social activity was severely reduced. Such large-scale changes in social activity affect the seismic noise level in low-frequency bands. The seismic noise level started to increase from the middle of May correlating with increase in population at the train stations. This suggests that social activity restarted even before the state of emergency was lifted at the end of May. The two-step seismic noise reduction observed in this study has not been reported in other cities around the world. Unexpected reduction of social activity due to COVID-19 provided a rare opportunity to investigate the characteristics of seismic noise caused by human activities.

Dataset Information

Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan: Numerical Study.

Publications

An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis.

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