COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

scholarly journals COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

Background: Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. Objective: This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. Methods: The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. Results: Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. Conclusions: We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent Reproduction Numbers (Rt), as well as we demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

scholarly journals Estimation of the Time-dependent Reproduction Number of Influenza A(H1N1)pdm2009 in South Korea

2020 ◽  
Author(s):  
Yunjeong Lee ◽  
Dong Han Lee ◽  
Hee-Dae Kwon ◽  
Changsoo Kim ◽  
Jeehyun Lee
Keyword(s):  
South Korea ◽  
Influenza A ◽  
Reproduction Number ◽  
Time Estimation ◽  
Time Dependent ◽  
Estimation Of Parameters ◽  
Transmission Potential ◽  
Reproduction Numbers ◽  
Influenza A H1n1 ◽  
Feasible Range

Abstract Background: The reproduction number is one of the most crucial parameters in determining disease dynamics, providing a summary measure of the transmission potential. However, estimating this value is particularly challenging owing to the characteristics of epidemic data, including non-reproducibility and incompleteness.Methods: In this study, we propose mathematical models with different population structures; each of these models can produce data on the number of cases of the influenza A(H1N1)pdm09 epidemic in South Korea. These structured models incorporating the heterogeneity of age and region are used to estimate the time-dependent effective reproduction numbers. Subsequently, the age- and region-specific reproduction numbers are also computed to analyze the differences illustrated in the incidence data.Results: The basic SIR fails to provide a reasonable estimation of the reproduction numbers. The estimated values demonstrate a large variation and remains outside of the feasible range for the influenza, regardless of the time period for data. Real-time estimation using age- and region-structured models demonstrated that the effective reproduction number rose sharply during mid-October when the ㅜumber of patients increased dramatically. The reproduction number fell below unity at the end of October and stayed lower than unity indicating that the epidemic starts decreasing, which is consistent with the incidence data.Conclusions: Numerical results reveal that the introduction of heterogeneity into the population to represent the general characteristics of dynamics is essential for the robust estimation of parameters.

scholarly journals COVID-19 pandemic control using restrictions and vaccination

2021 ◽  
Vol 19 (2) ◽  
pp. 1355-1372
Author(s):  
Vinicius Piccirillo ◽  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Real Data ◽  
The United States ◽  
Prevention Policy ◽  
Autoregressive Integrated Moving Average ◽  
Infected People ◽  
Marquardt Algorithm ◽  
The Impact ◽  
Methods Numerical

<abstract><p>This work deals with the impact of the vaccination in combination with a restriction parameter that represents non-pharmaceutical interventions measures applied to the compartmental SEIR model in order to control the COVID-19 epidemic. This restriction parameter is used as a control parameter, and the univariate autoregressive integrated moving average (ARIMA) is used to forecast the time series of vaccination of all individuals of a specific country. Having in hand the time series of the population fully vaccinated (real data + forecast), the Levenberg–Marquardt algorithm is used to fit an analytic function that models this evolution over time. Here, it is used two time series of real data that refer to a slow vaccination obtained from India and Brazil, and two faster vaccination as observed in Israel and the United States of America. Together with vaccination, two different control approaches are presented in this paper, which enable reduces the infected people successfully: namely, the feedback and nonfeedback control methods. Numerical results predict that vaccination can reduce the peaks of infections and the duration of the pandemic, however, a better result is achieved when the vaccination is combined with any restrictions or prevention policy.</p></abstract>

scholarly journals Assessment of Vaccination and Underreporting on COVID-19 Infections in Turkey Based On Effective Reproduction Number

2021 ◽  
Author(s):  
Tuğba Akman Yıldız ◽  
Emek Köse ◽  
Necibe Tuncer
Keyword(s):  
Critical Parameter ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Vaccination Rate ◽  
Future Trend ◽  
Time Dependent ◽  
Modified Model ◽  
Department Of Health ◽  
Number Of Patients ◽  
The Impact

AbstractIn this paper, we introduce a SEIR type COVID-19 model where the infected class is further divided into individuals in intensive care (ICUs) and ventilation units. The model is validated with the COVID-19 cases, deaths, and the number of patients in ICUs and ventilation units as reported by Turkey Department of Health for the period March 11 through May 30 when the nationwide lockdown is in order. COVID-19 interventions in Turkey are incorporated into the model to detect the future trend of the outbreak accurately. The lockdown is lifted on June 1, and the model is modified to include a time dependent transmission rate which is linked to the effective reproduction number ℛt through basic reproduction number ℛ0. The modified model captures the changing dynamics and peaks of the outbreak successfully. With the onset of vaccination on 13 January 2021, we augment the model with the vaccination class to investigate the impact of vaccination rate and efficacy. We observe that vaccination rate is a more critical parameter than the vaccine efficacy to eliminate the disease successfully.

scholarly journals Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study

2020 ◽  
Author(s):  
Lee Worden ◽  
Rae Wannier ◽  
Seth Blumberg ◽  
Alex Y. Ge ◽  
George W. Rutherford ◽  
...  
Keyword(s):  
San Francisco ◽  
Statistical Estimation ◽  
Detection Rate ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Reproduction Numbers ◽  
The Impact ◽  
The City ◽  
Testing Coverage ◽  
Over Time

AbstractThe current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco’s shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, −20.1%–81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.

scholarly journals Estimating the reproduction number and forecasting the impact of COVID-19 in Kuwait using a modified compartmental epidemiological model

2020 ◽  
Author(s):  
Mohammad AlHamli
Keyword(s):  
Reproduction Number ◽  
Intrinsic Growth Rate ◽  
General Hospitals ◽  
Maximum Value ◽  
Reproduction Numbers ◽  
Next Generation Matrix ◽  
The State Of Kuwait ◽  
Using Data ◽  
The Impact ◽  
The Basic Reproduction Number

Abstract A modified compartmental epidemic model was developed to simulate the state of Kuwait protocol in fighting COVID-19 pandemic. The next generation matrix method was used to drive an expression for the basic reproduction number, R0. Basic and effective reproduction numbers were calculated using data from the intrinsic growth rate of the confirmed COVID-19 cases. R0 was found to be 2.18. Three scenarios that varied by effective reproduction number were used to estimate the future course of the disease: a high value of R = 1.98, a middle value of R = 1.62, and a low value of R = 1.2. The maximum number of beds required in general hospitals in each scenario were estimated at 141 184, 85 341, and 16 412, respectively. For intensive care units, the estimated numbers of beds required were 16 461, 9 645, and 1788. Maximum deaths also varied and were estimated to be 29 202, 23 973, and 11 565. For the maximum value of R, it is estimated to peak on August 27, 2020. For the middle value of R, it is estimated to peak on September 20, 2020. For the minimum value of R, it is estimated to peak on December 21, 2020.

scholarly journals Interactions among common non-SARS-CoV-2 respiratory viruses and influence of the COVID-19 pandemic on their circulation in New York City

2021 ◽  
Author(s):  
Haokun Yuan ◽  
Alice Yeung ◽  
Wan Yang
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Cumulative Incidence ◽  
Respiratory Infections ◽  
Moving Average ◽  
Vaccination Campaign ◽  
Respiratory Viruses ◽  
Syncytial Virus ◽  
The Impact

Background Non-pharmaceutical interventions (NPIs) and voluntary behavioral changes during the COVID-19 pandemic have influenced the circulation of non-SARS-CoV-2 respiratory infections. We aimed to examine interactions among common non-SARS-CoV-2 respiratory virus and further estimate the impact of the COVID-19 pandemic on these viruses. Methods We analyzed incidence data for seven groups of respiratory viruses in New York City (NYC) during Oct 2015 - May 2021 (i.e., before and during the COVID-19 pandemic). We first used elastic net regression to identify potential virus interactions and further examined the robustness of the found interactions by comparing the performance of Auto Regressive Integrated Moving Average (ARIMA) models with and without the interactions. We then used the models to compute counterfactual estimates of cumulative incidence and estimate the reduction during the COVID-19 pandemic period from March 2020 to May 2021, for each virus. Results We identified potential interactions for three endemic human coronaviruses (CoV-NL63, CoV-HKU, and CoV-OC43), parainfluenza (PIV)-1, rhinovirus, and respiratory syncytial virus (RSV). We found significant reductions (by ~70-90%) in cumulative incidence of CoV-OC43, CoV-229E, human metapneumovirus, PIV-2, PIV-4, RSV, and influenza virus during the COVID-19 pandemic. In contrast, the circulation of adenovirus and rhinovirus was less affected. Conclusions Circulation of several respiratory viruses has been low during the COVID-19 pandemic, which may lead to increased population susceptibility. It is thus important to enhance monitoring of these viruses and promptly enact measures to mitigate their health impacts (e.g., influenza vaccination campaign and hospital infection prevention) in the coming months.

Efficient Risk Set Sampling when a Time-dependent Exposure Is Present

2009 ◽  
Vol 48 (05) ◽  
pp. 438-443 ◽  
Author(s):  
J. Beyersmann ◽  
P. Gastmeier ◽  
M. Schumacher ◽  
M. Wolkewitz
Keyword(s):  
Simulation Study ◽  
Sampling Methods ◽  
Real Data ◽  
Outcome Data ◽  
Time Dependent ◽  
Study Endpoint ◽  
Time Dependency ◽  
Full Cohort ◽  
Covariate Information ◽  
The Impact

Summary Objectives: The impact of time-dependent exposures on the time until study endpoint may correctly be analyzed with data of a full cohort. Ignoring the time-dependent nature of these exposures leads to time-dependent bias. Matching for time to exposure is often applied to take the time-dependency into account, but prefixed sets of exposed and unexposed may still create bias. This approach is attractive since a subcohort would also save resources, especially when exposure and outcome data are only available in the full cohort but further covariate information is required. The first objective is to show to which extent matching for time to exposure yields biased results. Secondly, exposure density sampling is introduced and explored. Methods: To evaluate how both sampling methods perform, they are compared to the correct method as well as to the approach in which the time-dependent nature of the exposure is ignored. Real data of the SIR-3 study (Germany, 2000–2001) and a simulation study are used. Results: Simulations show that matching may reduce the time-dependent bias but still there is a bias. The matching bias decreases if fewer patients are exposed. Exposure density sampling yields unbiased results. Conclusions: Results from studies in which matching for time to exposure was applied are only tolerable for rare exposures. Whenever subcohorting is the intention in order to save resources, exposure density sampling should be preferred instead.

scholarly journals Stepping out of lockdown should start with school re-openings while maintaining distancing measures. Insights from mixing matrices and mathematical models.

2020 ◽  
Author(s):  
Emma Sue McBryde ◽  
James M Trauer ◽  
Adeshina Adekunle ◽  
Romain Ragonnet ◽  
Michael T Meehan
Keyword(s):  
Spectral Radius ◽  
Reproduction Number ◽  
Positive Outcome ◽  
Attendance Rates ◽  
Considerable Uncertainty ◽  
Effective Contact ◽  
Reproduction Numbers ◽  
The World ◽  
The Impact ◽  
Sensitivity And Uncertainty Analysis

Australia is one of a few countries which has managed to control COVID-19 epidemic before a major epidemic took place. Currently with just under 7000 cases and 100 deaths, Australia is seeing less than 20 new cases per day. This is a positive outcome, but makes estimation of current effective reproduction numbers difficult to estimate. Australia, like much of the world is poised to step out of lockdown and looking at which measures to relax first. We use age-based contact matrices, calibrated to Chinese data on reproduction numbers and difference in infectiousness and susceptibility of children to generate next generation matrices (NGMs) for Australia. These matrices have a spectral radius of 2.49, which is hence our estimated basic reproduction number for Australia. The effective reproduction number (Reff) for Australia during the April/May lockdown period is estimated by other means to be around 0.8. We simulate the impact of lockdown on the NGM by first applying observations through Google Mobility Report for Australia at 3 locations: home (increased contacts by 18%), work (reduced contacts by 34%) and other (reduced contacts by 40%), and we reduce schools to 3% reflecting attendance rates during lockdown. Applying macro-distancing to the NGM leads to a spectral radius of 1.76. We estimate that the further reduction of the reproduction number to current levels of Reff = 0.8 is achieved by a micro-distancing factor of 0.26. That is, in a given location, people are 26% as likely as usual to have an effective contact with another person. We apply both macro and micro-distancing to the NGMs to examine the impact of different exit strategies. We find that reopening schools is estimated to reduce Reff from 0.8 to 0.78. This is because increase in school contact is offset by decrease in home contact. The NGMs all estimate that adults aged 30-50 are responsible for the majority of transmission. We also find that micro-distancing is critically important to maintain Reff <1. There is considerable uncertainty in these estimates and a sensitivity and uncertainty analysis is presented.

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