scholarly journals Implementation of An Online COVID-19 Epidemic Calculator for Tracking the Spread of the Coronavirus in Singapore and Other Countries

2020 ◽  
Author(s):  
Fook Fah Yap ◽  
Minglee Yong
Keyword(s):  
Doubling Time ◽  
Reproduction Number ◽  
Growth Parameters ◽  
Circuit Breaker ◽  
The Public ◽  
Slowing Down ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
Case Data

AbstractThis paper describes the methods underlying the development of an online COVID-19 Epidemic Calculator for tracking COVID-19 growth parameters. From publicly available infection case data, the calculator is used to estimate the effective reproduction number, doubling time, final epidemic size, and death toll. As a case study, we analyzed the results for Singapore during the “Circuit breaker” period from April 7, 2020 to the end of May 2020. The calculator shows that the stringent measures imposed have an immediate effect of rapidly slowing down the spread of the coronavirus. After about two weeks, the effective reproduction number reduced to 1.0. Since then, the number has been fluctuating around 1.0.The COVID-19 Epidemic Calculator is available in the form of an online Google Sheet and the results are presented as Tableau Public dashboards at www.cv19.one. By making the calculator readily accessible online, the public can have a tool to meaningfully assess the effectiveness of measures to control the pandemic.

scholarly journals Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study

2021 ◽  
Author(s):  
Sarafa A. Iyaniwura ◽  
Musa Rabiu ◽  
Jummy F. David ◽  
Jude D. Kong
Keyword(s):  
Public Health ◽  
Reproduction Number ◽  
Health Policies ◽  
Public Health Policies ◽  
Indirect Transmission ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
The Impact ◽  
Size Relation ◽  
Pharmaceutical Interventions

AbstractAdherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that direct transmission has more effect on the reproduction number and final epidemic size, relative to indirect transmission. In addition, we showed that there is a significant benefit in adhering to the COVID-19 NPIs.

scholarly journals COVID-19 Epidemic Forecast in Different States of India using SIR Model

2020 ◽  
Author(s):  
Mukesh Jakhar ◽  
P K Ahluwalia ◽  
Ashok Kumar
Keyword(s):  
Tamil Nadu ◽  
Reproduction Number ◽  
Epidemiological Data ◽  
Final Phase ◽  
Statistical Parameters ◽  
Indian States ◽  
Infected People ◽  
Epidemic Curve ◽  
Epidemic Size ◽  
Final Epidemic Size

The epidemiological data up to 12th May 2020 for India and its 24 states has been used to predict COVID-19 outbreak within classical SIR (Susceptible-Infected-Recovered) model. The basic reproduction number R0 of India is calculated to be 1.15, whereas for various states it ranges from 1.03 in Uttarakhand to 7.92 in Bihar. The statistical parameters for most of the states indicates the high significance of the predicted results. It is estimated that the epidemic curve flattening in India will start from the first week of July and epidemic may end in the third week of October with final epidemic size ~1,75,000. The epidemic in Kerala is in final phase and is expected to end by first week of June. Among Indian states, Maharashtra is severely affected where the ending phase of epidemic may occur in the second week of September with epidemic size of ~55,000. The model indicates that the fast growth of infection in Punjab is from 27th April 2020 to 2nd June 2020, thereafter, curve flattening will start and the epidemic is expected to finished by the first week of July with the estimated number of ~3300 infected people. The epidemic size of COVID-19 outbreak in Delhi, West Bengal, Gujrat, Tamil Nadu and Odisha can reach as large as 24,000, 18,000, 16,000, 13,000 and 11,000, respectively, however, these estimations may be invalid if large fluctuation of data occurs in coming days.

scholarly journals Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020

2020 ◽  
Vol 9 (3) ◽  
pp. 789 ◽  
Author(s):  
Toshikazu Kuniya
Keyword(s):  
Compartmental Model ◽  
Reproduction Number ◽  
Least Square ◽  
Time Data ◽  
Long Period ◽  
Epidemic Size ◽  
First Case ◽  
Final Epidemic Size ◽  
Positive Effect ◽  
Day By Day

The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2.6 ( 95 % CI, 2.4 – 2.8 ) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.

scholarly journals Pairwise approximation for SIR -type network epidemics with non-Markovian recovery

2018 ◽  
Vol 474 (2210) ◽  
pp. 20170695 ◽  
Author(s):  
G. Röst ◽  
Z. Vizi ◽  
I. Z. Kiss
Keyword(s):  
Recovery Time ◽  
Reproduction Number ◽  
Mean Field ◽  
Stochastic Network ◽  
Hyperbolic Partial Differential Equation ◽  
Epidemic Size ◽  
Major Benefit ◽  
Pairwise Models ◽  
Final Epidemic Size ◽  
Pairwise Model

We present the generalized mean-field and pairwise models for non-Markovian epidemics on networks with arbitrary recovery time distributions. First we consider a hyperbolic partial differential equation (PDE) system, where the population of infective nodes and links are structured by age since infection. We show that the PDE system can be reduced to a system of integro-differential equations, which is analysed analytically and numerically. We investigate the asymptotic behaviour of the generalized model and provide an implicit analytical expression involving the final epidemic size and pairwise reproduction number. As an illustration of the applicability of the general model, we recover known results for the exponentially distributed and fixed recovery time cases. For gamma- and uniformly distributed infectious periods, new pairwise models are derived. Theoretical findings are confirmed by comparing results from the new pairwise model and explicit stochastic network simulation. A major benefit of the generalized pairwise model lies in approximating the time evolution of the epidemic.

scholarly journals Modeling the COVID-19 spread, a case study of Egypt

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Assem S. Deif ◽  
Sahar A. El-Naggar
Keyword(s):  
Sir Model ◽  
Logistic Growth ◽  
Approximate Estimate ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
Logistic Growth Model ◽  
Reported Data ◽  
High Degree ◽  
Second Wave

AbstractIn this article, the authors applied a logistic growth model explaining the dynamics of the spread of COVID-19 in Egypt. The model which is simple follows well-known premises in population dynamics. Our aim is to calculate an approximate estimate of the total number of infected persons during the course of the disease. The model predicted—to a high degree of correctness—the timing of the pandemic peak$$t_{{\text{m}}}$$ t m and the final epidemic size$$P$$ P ; the latter was foreseen by the model long before it was announced by the Egyptian authorities. The estimated values from the model were also found to match significantly with the nation reported data during the course of the disease. The period in which we applied the model was from the first of April 2020 until the beginning of October of the same year. By the time the manuscript was returned for revision, the second wave swept through Egypt and the authors felt obliged to renew their study. Finally, a comparison is made with the SIR model showing that ours is much simpler; yet leading to the same results.

scholarly journals Early phylodynamics analysis of the COVID-19 epidemics in France

2020 ◽  
Author(s):  
Gonché Danesh ◽  
Baptiste Elie ◽  
Yannis Michalakis ◽  
Mircea T Sofonea ◽  
Antonin Bal ◽  
...  
Keyword(s):  
Public Health ◽  
Doubling Time ◽  
Reproduction Number ◽  
Genomic Data ◽  
Contact Tracing ◽  
Epidemic Spread ◽  
Time Reproduction ◽  
Incidence Data ◽  
Slowing Down ◽  
Epidemic Wave

AbstractFrance was one of the first countries to be reached by the COVID-19 pandemics. Here, we analyse 196 SARS-Cov-2 genomes collected between Jan 24 and Mar 24 2020, and perform a phylodynamics analysis. In particular, we analyse the doubling time, reproduction number (ℛt) and infection duration associated with the epidemic wave that was detected in incidence data starting from Feb 27. We show that a slowing down of the epidemic spread can be detected in Mar, which is consistent with the implementation of the national lock-down on Mar 17. The inferred distributions for the infection duration and ℛt are in line with those estimated from contact tracing data. Overall, this analysis shows the potential to use sequence genomic data to inform public health decisions in an epidemic crisis context.

scholarly journals Dynamical analysis in disease transmission and final epidemic size

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Daifeng Duan ◽  
Cuiping Wang ◽  
Yuan Yuan
Keyword(s):  
Basic Reproduction Number ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Dynamical Analysis ◽  
Health Care Demand ◽  
Future Health ◽  
Epidemic Size ◽  
Future Health Care ◽  
Final Epidemic Size ◽  
The Basic Reproduction Number

<p style='text-indent:20px;'>We propose two compartment models to study the disease transmission dynamics, then apply the models to the current COVID-19 pandemic and to explore the potential impact of the interventions, and try to provide insights into the future health care demand. Starting with an SEAIQR model by combining the effect from exposure, asymptomatic and quarantine, then extending the model to the one with ages below and above 65 years old, and classify the infectious individuals according to their severity. We focus our analysis on each model with and without vital dynamics. In the models with vital dynamics, we study the dynamical properties including the global stability of the disease free equilibrium and the existence of endemic equilibrium, with respect to the basic reproduction number. Whereas in the models without vital dynamics, we address the final epidemic size rigorously, which is one of the common but difficult questions regarding an epidemic. Finally, we apply our models to estimate the basic reproduction number and the final epidemic size of disease by using the data of COVID-19 confirmed cases in Canada and Newfoundland &amp; Labrador province.</p>

scholarly journals Optimal timing for social distancing during an epidemic

2020 ◽  
Author(s):  
Oscar Patterson-Lomba
Keyword(s):  
Reproduction Number ◽  
Decision Makers ◽  
Optimal Time ◽  
Optimal Timing ◽  
Epidemiological Models ◽  
Pharmacological Interventions ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Epidemic Size ◽  
Final Epidemic Size

AbstractSocial distancing is an effective way to contain the spread of a contagious disease, particularly when facing a novel pathogen and no pharmacological interventions are available. In such cases, conventional wisdom suggests that social distancing measures should be introduced as soon as possible after the beginning of an outbreak to more effectively mitigate the spread of the disease. Using a simple epidemiological model we show that, however, there is in fact an optimal time to initiate a temporal social distancing intervention if the goal is to reduce the final epidemic size or “flatten” the epidemic curve. The optimal timing depends strongly on the effective reproduction number (R0) of the disease, such that as the R0 increases, the optimal time decreases non-linearly. Additionally, if pharmacological interventions (e.g., a vaccine) become available at some point during the epidemic, the sooner these interventions become available the sooner social distancing should be initiated to maximize its effectiveness. Although based on a simple model, we hope that these insights inspire further investigations within the context of more complex and data-driven epidemiological models, and can ultimately help decision makers to improve temporal social distancing policies to mitigate the spread of epidemics.

scholarly journals Modelling the coronavirus disease (COVID-19) outbreak on the Diamond Princess ship using the public surveillance data from January 20 to February 20, 2020

2020 ◽  
Author(s):  
Shi Zhao ◽  
Peihua Cao ◽  
Daozhou Gao ◽  
Zian Zhuang ◽  
Marc KC Chong ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Doubling Time ◽  
Reproduction Number ◽  
The Novel ◽  
The Public ◽  
Public Surveillance ◽  
Transmission Process ◽  
Large Dispersion ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

AbstractThe novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess ship has caused over 634 cases as of February 20, 2020. We model the transmission process on the ship with a stochastic model and estimate the basic reproduction number at 2.2 (95%CI: 2.1-2.4). We estimate a large dispersion parameter than other coronaviruses, which implies that the virus is difficult to go extinction. The epidemic doubling time is at 4.6 days (95%CI: 3.0-9.3), and thus timely actions were crucial. The lesson learnt on the ship is generally applicable in other settings.

scholarly journals COVID-19 Transmission Dynamics and Final Epidemic Size

2020 ◽  
Author(s):  
Daifeng Duan ◽  
Cuiping Wang ◽  
Yuan Yuan
Keyword(s):  
Basic Reproduction Number ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Dynamics ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
The Common ◽  
Potential Impact ◽  
Disease Free ◽  
The Basic Reproduction Number

Abstract We propose two kinds of compartment models to study the transmission dynamics of COVID-19 virus and to explore the potential impact of the interventions, to disentangle how transmission is affected in different age group. Starting with an SEAIQR model by combining the effect from exposure, asymptomatic and quarantine, then extending the model to an two groups with ages below and above 65 years old, and classify the infectious individuals according to their severity, we focus our analysis on each model with and without vital dynamics. In the models with vital dynamics, we study the dynamical properties including the global stability of the disease free equilibrium and the existence of endemic equilibrium, with respect to the basic reproduction number. Whereas in the models without vital dynamics, we address the final epidemic size rigorously, which is one of the common but difficult questions regarding an epidemic. Finally, using the data of COVID-19 confirmed cases in Canada and Newfoundland & Labrador province, we can parameterize the models to estimate the basic reproduction number and the final epidemic size of disease transmission.

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