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

2021 ◽  
Vol 10 (6) ◽  
pp. 1256
Author(s):  
Ko Nakajo ◽  
Hiroshi Nishiura
Keyword(s):  
Model Comparison ◽  
Reproduction Number ◽  
State Of Emergency ◽  
Illness Onset ◽  
Epidemic Dynamics ◽  
Osaka Prefecture ◽  
Time Of Infection ◽  
Single Intervention ◽  
Second Wave ◽  
Combined Interventions

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.

scholarly journals Effect of a one-month lockdown on the epidemic dynamics of COVID-19 in France

2020 ◽  
Author(s):  
Lionel Roques ◽  
Etienne Klein ◽  
Julien Papaïx ◽  
Antoine Sar ◽  
Samuel Soubeyrand
Keyword(s):  
Reproduction Number ◽  
Early Stage ◽  
Herd Immunity ◽  
Equation Model ◽  
Differential Equation Model ◽  
Epidemic Dynamics ◽  
Data Collection Process ◽  
Statistical Formalism ◽  
Type Differential Equation ◽  
Second Wave

AbstractThe COVID-19 epidemic started in the Hubei province in China in December 2019 and then spread around the world reaching the pandemic stage at the beginning of March 2020. Since then, several countries went into lockdown. We estimate the effect of the lockdown in France on the contact rate and the effective reproduction number Re of the COVID-19. We obtain a reduction by a factor 7 (Re = 0.47, 95%-CI: 0.45-0.50), compared to the estimates carried out in France at the early stage of the epidemic. We also estimate the fraction of the population that would be infected by the beginning of May, at the official date at which the lockdown should be relaxed. We find a fraction of 3.7% (95%-CI: 3.0-4.8%) of the total French population, without taking into account the number of recovered individuals before April 1st, which is not known. This proportion is seemingly too low to reach herd immunity. Thus, even if the lockdown strongly mitigated the first epidemic wave, keeping a low value of Re is crucial to avoid an uncontrolled second wave (initiated with much more infectious cases than the first wave) and to hence avoid the saturation of hospital facilities. Our approach is based on the mechanistic-statistical formalism, which uses a probabilistic model to connect the data collection process and the latent epidemiological process, which is described by a SIR-type differential equation model.

scholarly journals Quantifying the relationship between lockdowns, mobility, and effective reproduction number (Rt) during the COVID-19 pandemic in the Greater Toronto Area

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christopher Dainton ◽  
Alexander Hay
Keyword(s):  
Public Health ◽  
Secular Trend ◽  
Reproduction Number ◽  
Time Lag ◽  
Correlation Coefficients ◽  
Greater Toronto Area ◽  
Global Mobility ◽  
The Relationship ◽  
Second Wave ◽  
Over Time

Abstract Background The effectiveness of lockdowns in mitigating the spread of COVID-19 has been the subject of intense debate. Data on the relationship between public health restrictions, mobility, and pandemic growth has so far been conflicting. Objective We assessed the relationship between public health restriction tiers, mobility, and COVID-19 spread in five contiguous public health units (PHUs) in the Greater Toronto Area (GTA) in Ontario, Canada. Methods Weekly effective reproduction number (Rt) was calculated based on daily cases in each of the five GTA public health units between March 1, 2020, and March 19, 2021. A global mobility index (GMI) for each PHU was calculated using Google Mobility data. Segmented regressions were used to assess changes in the behaviour of Rt over time. We calculated Pearson correlation coefficients between GMI and Rt for each PHU and mobility regression coefficients for each mobility variable, accounting for time lag of 0, 7, and 14 days. Results In all PHUs except Toronto, the most rapid decline in Rt occurred in the first 2 weeks of the first province-wide lockdown, and this was followed by a slight trend to increased Rt as restrictions decreased. This trend reversed in all PHUs between September 6th and October 10th after which Rt decreased slightly over time without respect to public health restriction tier. GMI began to increase in the first wave even before restrictions were decreased. This secular trend to increased mobility continued into the summer, driven by increased mobility to recreational spaces. The decline in GMI as restrictions were reintroduced coincides with decreasing mobility to parks after September. During the first wave, the correlation coefficients between global mobility and Rt were significant (p < 0.01) in all PHUs 14 days after lockdown, indicating moderate to high correlation between decreased mobility and decreased viral reproduction rates, and reflecting that the incubation period brings in a time-lag effect of human mobility on Rt. In the second wave, this relationship was attenuated, and was only significant in Toronto and Durham at 14 days after lockdown. Conclusions The association between mobility and COVID-19 spread was stronger in the first wave than the second wave. Public health restriction tiers did not alter the existing secular trend toward decreasing Rt over time.

scholarly journals Design of Vaccine Trials during Outbreaks with and without a Delayed Vaccination Comparator

2016 ◽  
Author(s):  
Natalie E. Dean ◽  
M. Elizabeth Halloran ◽  
Ira M. Longini
Keyword(s):  
Immune Response ◽  
Incubation Period ◽  
Vaccine Efficacy ◽  
Emerging Pathogens ◽  
Illness Onset ◽  
Efficacy Trials ◽  
Randomized Design ◽  
Time Of Infection ◽  
Cluster Randomized ◽  
Time Required

AbstractConducting vaccine efficacy trials during outbreaks of emerging pathogens poses particular challenges. The ‘Ebola ça suffit’ trial in Guinea used a novel ring vaccination cluster randomized design to target populations at highest risk of infection. Another key feature of the trial was the use of a delayed vaccination arm as a comparator, in which clusters were randomized to immediate vaccination or vaccination 21 days later. This approach, chosen to improve ethical acceptability of the trial, complicates the statistical analysis as participants in the comparison arm are eventually protected by vaccine. Furthermore, for infectious diseases, we observe time of illness onset and not time of infection, and we may not know the time required for the vaccinee to develop a protective immune response. As a result, including events observed shortly after vaccination may bias the per protocol estimate of vaccine efficacy. We provide a framework for approximating the bias and power of any given per protocol analysis period as functions of the background infection hazard rate, disease incubation period, and vaccine immune response. We use this framework to provide recommendations for designing standard vaccine efficacy trials and trials with a delayed vaccination comparator. Briefly, narrower analysis periods within the correct window can minimize or eliminate bias but may suffer from reduced power. Designs should be reasonably robust to misspecification of the incubation period and time to develop a vaccine immune response.

scholarly journals Optimal Control of a Dengue-Dengvaxia Model: Comparison Between Vaccination and Vector Control

2021 ◽  
Vol 9 (1) ◽  
pp. 198-212
Author(s):  
Cheryl Q. Mentuda
Keyword(s):  
Optimal Control ◽  
Vector Control ◽  
Basic Reproduction Number ◽  
Human Population ◽  
Model Comparison ◽  
Reproduction Number ◽  
Control Strategies ◽  
Minimum Principle ◽  
Transmitted Disease ◽  
The Basic Reproduction Number

Abstract Dengue is the most common mosquito-borne viral infection transmitted disease. It is due to the four types of viruses (DENV-1, DENV-2, DENV-3, DENV-4), which transmit through the bite of infected Aedes aegypti and Aedes albopictus female mosquitoes during the daytime. The first globally commercialized vaccine is Dengvaxia, also known as the CYD-TDV vaccine, manufactured by Sanofi Pasteur. This paper presents a Ross-type epidemic model to describe the vaccine interaction between humans and mosquitoes using an entomological mosquito growth population and constant human population. After establishing the basic reproduction number ℛ0, we present three control strategies: vaccination, vector control, and the combination of vaccination and vector control. We use Pontryagin’s minimum principle to characterize optimal control and apply numerical simulations to determine which strategies best suit each compartment. Results show that vector control requires shorter time applications in minimizing mosquito populations. Whereas vaccinating the primary susceptible human population requires a shorter time compared to the secondary susceptible human.

Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics

2021 ◽  
Author(s):  
Kayode Oshinubi ◽  
◽  
Fahimah Al-Awadhi ◽  
Mustapha Rachdi ◽  
Jacques Demongeot ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Moving Average ◽  
Arima Model ◽  
Exponential Model ◽  
Spreading Rate ◽  
Support Vector ◽  
Global Threat ◽  
Log Linear ◽  
Second Wave

Coronavirus (COVID-19) has continued to be a global threat to public health. When the coronavirus pandemic began early in 2020, experts wondered if there would be waves of cases, a pattern seen in other virus pandemics. The overall pattern so far has been one of increasing cases of COVID-19 followed by a decline, and we observed a second wave of increased cases and yet we are still exploring this pandemic. Hence, updating the prediction model for the new cases of COVID-19 for different waves is essential to monitor the spreading of the virus and control the disease. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modelling approach for predicting new cases of coronavirus (COVID-19). We propose a deterministic method to predict the basic reproduction number Ro of first and second wave transition of COVID-19 cases in Kuwait and also to forecast the daily new cases and deaths of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holt’s method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait. The results show that support vector regression has achieved the best performance for prediction while a simple exponential model without trend gives good optimal results for forecasting of Kuwait COVID-19 data.

Evolution of SARS-CoV-2 in the state of Alagoas-Brazil via an adaptive SIR model

2020 ◽  
pp. 2150040
Author(s):  
I. F. F. Dos Santos ◽  
G. M. A. Almeida ◽  
F. A. B. F. De Moura
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Sir Model ◽  
The State ◽  
Epidemic Dynamics ◽  
Historical Series ◽  
Input Parameters ◽  
Number Formula ◽  
Near Future ◽  
The Basic Reproduction Number

We investigate the spreading of SARS-CoV-2 in the state of Alagoas, northeast of Brazil, via an adaptive susceptible-infected-removed (SIR) model featuring dynamic recuperation and propagation rates. Input parameters are defined based on data made available by Alagoas Secretary of Health from April 19, 2020 on. We provide with the evolution of the basic reproduction number [Formula: see text] and reproduce the historical series of the number of confirmed cases with less than [Formula: see text] error. We offer predictions, from November 16 forward, over the epidemic situation in the near future and show that it will keep decelerating. Furthermore, the same model can be used to study the epidemic dynamics in other countries with great easiness and accuracy.

scholarly journals Backcalculating the Incidence of Infection with COVID-19 on the Diamond Princess

2020 ◽  
Vol 9 (3) ◽  
pp. 657 ◽  
Author(s):  
Hiroshi Nishiura
Keyword(s):  
Close Contact ◽  
Peak Time ◽  
Risk Of Infection ◽  
Movement Restriction ◽  
Illness Onset ◽  
Epidemic Curve ◽  
Time Period ◽  
Restriction Policy ◽  
Time Of Infection ◽  
Novel Coronavirus

To understand the time-dependent risk of infection on a cruise ship, the Diamond Princess, I estimated the incidence of infection with novel coronavirus (COVID-19). The epidemic curve of a total of 199 confirmed cases was drawn, classifying individuals into passengers with and without close contact and crew members. A backcalculation method was employed to estimate the incidence of infection. The peak time of infection was seen for the time period from 2 to 4 February 2020, and the incidence has abruptly declined afterwards. The estimated number of new infections among passengers without close contact was very small from 5 February on which a movement restriction policy was imposed. Without the intervention from 5 February, it was predicted that the cumulative incidence with and without close contact would have been as large as 1373 (95% CI: 570, 2176) and 766 (95% CI: 587, 946) cases, respectively, while these were kept to be 102 and 47 cases, respectively. Based on an analysis of illness onset data on board, the risk of infection among passengers without close contact was considered to be very limited. Movement restriction greatly reduced the number of infections from 5 February onwards.

scholarly journals The Impact of Public Health Interventions on The First and Second Waves of COVID-19 in Trinidad And Tobago – A Simple Epidemic Model

2020 ◽  
Author(s):  
Loren De Freitas ◽  
Han-I Wang
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Trinidad And Tobago ◽  
Specific Treatment ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Support Methods ◽  
The Impact ◽  
Second Wave ◽  
Combined Interventions

Introduction The COVID-19 pandemic has resulted in more than 35 million confirmed cases worldwide. Currently, there is no specific treatment for the disease or available vaccine to reduce the spread of COVID-19. As such, countries rely on a range of public health interventions to assist in halting the spread of transmission. Caribbean countries have also adopted many public health interventions. In this paper, we use mathematical modelling to demonstrate the impact of public health interventions on the progression of COVID-19 in order to provide timely decision support. Methods A cohort Markov model, based on the concept of the SEIR model, was built to reflect the characteristics of the COVID-19 virus. Five possible public health interventions in the first wave and a projection of current second wave were simulated using the constructed model. Results The model results indicate that the strictest combined interventions of complete border closure and lockdown were the most effective with the number of deaths less than ten in the first wave. For the current second wave, it will take around 30 days for the pandemic to pass its peak after implementing the wearing of face masks policy. Conclusions This paper shows the impact of common public health interventions on the COVID-19 pandemic, using Trinidad and Tobago as an example. Such impacts may be useful in reducing delays in decision-making and improving compliance by populations. However, given the limitations associated with mathematical models, decision-making should be guided by economic assessments, infectious disease and public health expertise.

scholarly journals A Second Wave? What Do People Mean By COVID Waves? – A Working Definition of Epidemic Waves

2021 ◽  
Author(s):  
Stephen X. Zhang ◽  
Francisco Arroyo Marioli ◽  
Renfei Gao
Keyword(s):  
Common Ground ◽  
Reproduction Number ◽  
Resource Planning ◽  
Healthcare Organizations ◽  
The Public ◽  
Working Definition ◽  
Public Data ◽  
Definition Of ◽  
Second Wave ◽  
Epidemic Wave

AbstractPolicymakers and researchers describe the COVID-19 epidemics by waves without a common vocabulary on what constitutes an epidemic wave, either in terms of a working definition or operationalization, causing inconsistencies and confusions. A working definition and operationalization can be helpful to characterize and communicate about epidemics. We propose a working definition of epidemic waves in the ongoing COVID-19 pandemic and an operationalization based on the public data of the effective reproduction number R. Our operationalization characterizes the numbers and durations of waves (upward and downward) in 178 countries and reveals patterns that can enable healthcare organizations and policymakers to make better description and assessment of the COVID crisis to make more informed resource planning, mobilization, and allocation temporally in the continued COVID-19 pandemic.One Sentence SummaryA working definition and operationalization of waves to enable common ground to understand and communicate COVID-19 crisis.

scholarly journals The risk for a new COVID-19 wave – and how it depends on R0, the current immunity level and current restrictions

2020 ◽  
Author(s):  
Tom Britton ◽  
Pieter Trapman ◽  
Frank Ball
Keyword(s):  
Preventive Measures ◽  
Reproduction Number ◽  
Social Activity ◽  
Herd Immunity ◽  
Time Varying ◽  
The World ◽  
Two Factors ◽  
Second Wave ◽  
Different Parts ◽  
Future Outbreak

AbstractThe COVID-19 pandemic has hit different parts of the world differently: some regions are still in the rise of the first wave, other regions are now facing a decline after a first wave, and yet other regions have started to see a second wave. The current immunity level î in a region is closely related to the cumulative fraction infected, which primarily depends on two factors: a) the initial potential for COVID-19 in the region (often quantified by the basic reproduction number R0), and b) the timing, amount and effectiveness of preventive measures put in place. By means of a mathematical model including heterogeneities owing to age, social activity and susceptibility, and allowing for time-varying preventive measures, the risk for a new epidemic wave and its doubling time, and how they depend on R0, î and the overall effect of the current preventive measures, are investigated. Focus lies on quantifying the minimal overall effect of preventive measures pMin needed to prevent a future outbreak. The first result shows that the current immunity level î plays a more influential roll than when immunity is obtained from vaccination. Secondly, by comparing regions with different R0 and î it is shown that regions with lower R0 and low î may now need higher preventive measures (pMin) compared with other regions having higher R0 but also higher î, even when such immunity levels are far from herd immunity.

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