scholarly journals An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis (Preprint)

2020 ◽  
Author(s):  
Motoaki Utamura ◽  
Makoto Koizumi ◽  
Seiichi Kirikami
Keyword(s):  
Public Health ◽  
Transmission Rate ◽  
Time Lag ◽  
Sir Model ◽  
Epidemiological Model ◽  
Basic Reproductive Number ◽  
Polymerase Chain Reaction Test ◽  
Series Data ◽  
Reproductive Number ◽  
Alternative Formulation

BACKGROUND 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 population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies. OBJECTIVE This study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count. METHODS Time-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model. RESULTS In a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag <i>T</i> from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of <i>T</i>=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19. CONCLUSIONS A novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

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

10.2196/23624 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e23624
Author(s):  
Motoaki Utamura ◽  
Makoto Koizumi ◽  
Seiichi Kirikami
Keyword(s):  
Public Health ◽  
Transmission Rate ◽  
Time Lag ◽  
Sir Model ◽  
Epidemiological Model ◽  
Basic Reproductive Number ◽  
Polymerase Chain Reaction Test ◽  
Series Data ◽  
Reproductive Number ◽  
Alternative Formulation

Background 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 population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies. Objective This study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count. Methods Time-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model. Results In a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag T from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of T=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19. Conclusions A novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

scholarly journals Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan

2020 ◽  
Author(s):  
Motoaki Utamura ◽  
Makoto Koizumi ◽  
Seiichi Kirikami
Keyword(s):  
Public Health ◽  
Public Health Intervention ◽  
Epidemiological Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Alternative Formulation ◽  
Global Public Health ◽  
Economic Activities ◽  
Delay Differential ◽  
Spread Of Infection

Background: Coronavirus Disease 2019 (COVID19) 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 and its derivatives predict susceptible, infectious, and removed (recovered/deaths) cases but ignore isolation of confirmed cases. Predicting COVID19 trends with hospitalized and infectious people in field separately is important to prepare beds and develop quarantine strategies. Methods: Time-series COVID19 data from February 28 to May 23, 2020 in Tokyo were adopted for this study. A novel epidemiological model based on delay differential equation was proposed. The model can evaluate patients in hospitals and infectious cases in the field. Various data such as daily new cases, cumulative infections, patients in hospital, and PCR test positivity ratios were used to examine the model. This approach derived an alternative formulation equivalent to the standard SIR model. Its results were compared quantitatively with those of the present isolation model. Results: The basic reproductive number, inferred as 2.30, is a dimensionless parameter composed of modeling parameters. Effects of intervention to mitigate the epidemic spread were assessed a posteriori. An exit policy of how and when to release a statement of emergency was also assessed using the model. Furthermore, results suggest that the rapid isolation of infectious cases has a large potential to effectively mitigate the spread of infection and restores social and economic activities safely. Conclusions: A novel mathematical model was proposed and examined using COVID19 data for Tokyo. Results show that shortening the period from infection to hospitalization is effective against outbreak without rigorous public health intervention and control. Faster and precise case cluster detection and wider and quicker introduction of testing measures are strongly recommended.

scholarly journals Susceptible-Infected-Recovered model study using free particle dynamics

2021 ◽  
Vol 67 (4 Jul-Aug) ◽  
Author(s):  
Fernando Garzón ◽  
Olvera Orozco ◽  
Jorge Castro ◽  
Aldo Figueroa
Keyword(s):  
Particle Density ◽  
Transmission Rate ◽  
Free Particle ◽  
Sir Model ◽  
Particle Dynamics ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Closed Domain ◽  
Health Crisis ◽  
Social Distancing

A study on the epidemiologic Susceptible-Infected-Recovered (SIR) model is presented using free particle dynamics. The study is performed using a computational model consisting of randomly allocated particles in a closed domain which are free to move inrandom directions with the ability to collide into each other. The transmission rules for the particle–particle interactions are based on the main viral infection mechanisms, resulting in real–time results of the number of susceptible, infected, and recovered particles within a population of N= 200 particles. The results are qualitatively compared with a differential equation SIR model in terms of the transmission rate β, recovery rate γ, and the basic reproductive number R0, yielding overall good results. The effect of the particle density ρ on R0 is also studied to analyze how an infectious disease spreads over different types of populations. The versatility of the proposed free–particle–dynamics SIR model allows to simulate different scenarios, such as social distancing, commonly referredto as quarantine, no social distancing measures, and a mixture of the former and the latter. It is found that by implementing early relaxation of social distancing measures before the number of infected particles reaches zero, could lead to subsequent outbreaks such as the particular events observed in different countries due to the ongoing COVID–19 health crisis

scholarly journals Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China

2020 ◽  
Author(s):  
Jia Wangping ◽  
Han Ke ◽  
Song Yang ◽  
Cao Wenzhe ◽  
Wang Shengshu ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series Data ◽  
Credible Interval ◽  
Basic Reproductive Number ◽  
Series Data ◽  
Reproductive Number ◽  
Global Public Health ◽  
Health Measures ◽  
Intervention Measures ◽  
Public Health Strategies

AbstractBackgroundCoronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the most suffering countries with the COVID-19 epidemic. It is important to predict the epidemics trend of COVID-19 epidemic in Italy to help develop public health strategies.MethodsWe used time-series data of COVID-19 from Jan 22,2020 to Mar 16,2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with similar total number of populations in Italy, was used as a comparative item.ResultsIn the eSIR model, we estimated that the basic reproductive number for COVID-19 was respectively 4.10 (95% CI: 2.15–6.77) in Italy and 3.15(95% CI: 1.71–5.21) in Hunan. There would be totally 30 086 infected cases (95%CI:7920-81 869) under the current country blockade and the endpoint would be Apr 25 (95%CI: Mar 30 to Aug 07) in Italy. If the country blockade is imposed 5 day later, the total number of infected cases would expand the infection scale 1.50 times.ConclusionItaly’s current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.

AN EPIDEMIOLOGICAL MODEL WITH MULTIPLE ENDEMIC STATES

2015 ◽  
Vol 23 (supp01) ◽  
pp. S17-S31
Author(s):  
GEISER VILLAVICENCIO-PULIDO ◽  
IGNACIO BARRADAS ◽  
LUNA BEATRIZ
Keyword(s):  
Infectious Disease ◽  
Backward Bifurcation ◽  
Epidemiological Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Force Of Infection

We present a model describing the dynamics of an infectious disease for which the force of infection is diminished through a reaction of the susceptible to the number of infected individuals. We show that, even though the structure of the model is a simple one, different kinds of backward bifurcation can appear for values of the basic reproductive number bigger than one. Under some conditions on the parameters, multiple endemic equilibria may appear for values of the basic reproductive number less or greater than one.

scholarly journals Universal scaling law for COVID-19 propagation in urban centers

2020 ◽  
Author(s):  
Ben-Hur Francisco Cardoso ◽  
Sebastián Gonçalves
Keyword(s):  
New York ◽  
Population Density ◽  
Urban Areas ◽  
Transmission Rate ◽  
Scaling Law ◽  
City Size ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Continuous Change ◽  
Urban Centers

Due to the COVID-19 pandemic, there is a high demand for Susceptible-Infective-Recovered (SIR) models to adjust and predict the number of cases in urban areas. Forecasting, however, is a difficult task, because the change in people’s behavior reflects in a continuous change in the parameters of the model. An important question is what we can use from one city to another; if what happened in Madrid could have been applied to New York and then, if what we have learned from this city would be useful for São Paulo. To answer this question, we present an analysis of the transmission rate of COVID-19 as a function of population density and population size for US counties, cities of Brazil, German, and Portugal. Contrary to the common hypothesis in epidemics modeling, we observe a higher disease transmissibility for higher city’s population density/size –with the latter showing more predicting power. We present a contact rate scaling theory that explain the results, predicting that the basic reproductive number R0 of epidemics scales as the logarithm of the city size.

scholarly journals The effects of the clinical symptoms pneumonia-confirmation strategy of the COVID-19 epidemic in Wuhan, China

2020 ◽  
Author(s):  
Yanjin Wang ◽  
Pei Wang ◽  
Shudao Zhang ◽  
Hao Pan
Keyword(s):  
Turning Point ◽  
Clinical Symptoms ◽  
Transmission Rate ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
The Novel ◽  
Wuhan City ◽  
Start Date ◽  
Rapid Spread ◽  
Novel Coronavirus

Abstract Motivated by the quick control in Wuhan, China, and the rapid spread in other countries of COVID-19, we investigate the questions that what is the turning point in Wuhan by quantifying the variety of basic reproductive number after the lockdown city. The answer may help the world to control the COVID-19 epidemic. A modified SEIR model is used to study the COVID-19 epidemic in Wuhan city. Our model is calibrated by the hospitalized cases. The modeling result gives out that the means of basic reproductive numbers are 1.5517 (95% CI 1.1716-4.4283) for the period from Jan 25 to Feb 11, 2020, and 0.4738(95% CI 0.0997-0.8370) for the period from Feb 12 to Mar 10. The transmission rate fell after Feb 12, 2020 as a result of China’s COVID-19 strategy of keeping society distance and the medical support from all China, but principally because of the clinical symptoms to be used for the novel coronavirus pneumonia (NCP) confirmation in Wuhan since Feb 12, 2020. Clinical diagnosis can quicken up NCP-confirmation such that the COVID-19 patients can be isolated without delay. So the clinical symptoms pneumonia-confirmation is the turning point of the COVID-19 battle of Wuhan. The measure of clinical symptoms pneumonia-confirmation in Wuhan has delayed the growth and reduced size of the COVID-19 epidemic, decreased the peak number of the hospitalized cases by 96% in Wuhan. Our modeling also indicates that the earliest start date of COVID-19 in Wuhan may be Nov 2, 2019.

scholarly journals Epidemic spreading model in heterogeneous conditions

2021 ◽  
pp. 1-12
Author(s):  
Andrey Viktorovich Podlazov
Keyword(s):  
Equilibrium Position ◽  
Sir Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Epidemic Spread ◽  
Epidemic Spreading ◽  
Oscillatory Dynamics ◽  
Spreading Model ◽  
Multiple Contacts ◽  
The Social

I propose two modifications of the SIR model of the epidemic spread, taking into account the social and space heterogeneity of the population. Social hetero¬geneity associated with differences in the intensity of paired contacts between people qualitatively changes the basic reproductive number. Space heterogeneity associated with differences in the intensity of multiple contacts between people significantly shifts the equilibrium position, increases the characteristic times and leads to the emergence of oscillatory dynamics of finite duration.

scholarly journals Estimation of the Basic Reproduction Number of Novel Influenza A (H1N1) pdm09 in Elementary Schools Using the SIR Model

2017 ◽  
Vol 11 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Daisuke Furushima ◽  
Shoko Kawano ◽  
Yuko Ohno ◽  
Masayuki Kakehashi
Keyword(s):  
Elementary Schools ◽  
Infection Rate ◽  
School Size ◽  
Influenza A ◽  
Missing Values ◽  
Sir Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Influenza A H1n1 ◽  
Novel Influenza A

Background: The novel influenza A (H1N1) pdm09 (A/H1N1pdm) pandemic of 2009-2010 had a great impact on society. Objective: We analyzed data from the absentee survey, conducted in elementary schools of Oita City, to evaluate the A/H1N1pdm pandemic and to estimate the basic reproductive number (R0 ) of this novel strain. Method: We summarized the overall absentee data and calculated the cumulative infection rate. Then, we classified the data into 3 groups according to school size: small (<300 students), medium (300–600 students), and large (>600 students). Last, we estimated the R0 value by using the Susceptible-Infected-Recovered (SIR) mathematical model. Results: Data from 60 schools and 27,403 students were analyzed. The overall cumulative infection rate was 44.4%. There were no significant differences among the grades, but the cumulative infection rate increased as the school size increased, being 37.7%, 44.4%, and 46.6% in the small, medium, and large school groups, respectively. The optimal R0 value was 1.33, comparable with that previously reported. The data from the absentee survey were reliable, with no missing values. Hence, the R0 derived from the SIR model closely reflected the observed R0 . The findings support previous reports that school children are most susceptible to A/H1N1pdm virus infection and suggest that the scale of an outbreak is associated with the size of the school. Conclusion: Our results provide further information about the A/H1N1pdm pandemic. We propose that an absentee survey should be implemented in the early stages of an epidemic, to prevent a pandemic.

scholarly journals Transmission dynamics of SARS-COV-2 in China: impact of public health interventions

2020 ◽  
Author(s):  
Wenbao Wang ◽  
Yiqin Chen ◽  
Qi Wang ◽  
Ping Cai ◽  
Ye He ◽  
...  
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Public Health Interventions ◽  
The Public ◽  
Global Pandemic ◽  
Actual Growth ◽  
Global Threat ◽  
The Impact

AbstractCOVID-19 has become a global pandemic. However, the impact of the public health interventions in China needs to be evaluated. We established a SEIRD model to simulate the transmission trend of China. In addition, the reduction of the reproductive number was estimated under the current forty public health interventions policies. Furthermore, the infection curve, daily transmission replication curve, and the trend of cumulative confirmed cases were used to evaluate the effects of the public health interventions. Our results showed that the SEIRD curve model we established had a good fit and the basic reproductive number is 3.38 (95% CI, 3.25–3.48). The SEIRD curve show a small difference between the simulated number of cases and the actual number; the correlation index (H2) is 0.934, and the reproductive number (R) has been reduced from 3.38 to 0.5 under the current forty public health interventions policies of China. The actual growth curve of new cases, the virus infection curve, and the daily transmission replication curve were significantly going down under the current public health interventions. Our results suggest that the current public health interventions of China are effective and should be maintained until COVID-19 is no longer considered a global threat.

Sign in / Sign up

Export Citation Format

Share Document