scholarly journals Improving the reproduction number calculation by treating for daily variations of SARS-CoV-2 cases

2021 ◽  
Author(s):  
Harry Drewes ◽  
Gotthold Flaeschner ◽  
Peter Moeller
Keyword(s):  
Decision Making ◽  
Disease Control ◽  
Reproduction Number ◽  
Human Life ◽  
Time Lag ◽  
Time Dependent ◽  
Daily Variations ◽  
Key Indicator ◽  
Statistical Knowledge ◽  
Alternative Method

The Covid-19 pandemic impacted the human life all over the globe, starting in the year of its emergence, 2019, and in the following years. A epidemiological key indicator that gained particular recognition in politics and decision making is the time-dependent reproduction number R_t, which is commonly calculated by institutions responsible for disease control following a method presented by Cori et. al. Here, we propose an improved as well as an alternative method, which makes the calculation more stable against oscillations arising from daily variations in testing. Both methods can be used without great statistical knowledge or effort. The methods provide a smoother result without increasing the time-lag, and provides an advantage particularly in the timeframe of weeks, which might serve as a better ground for forecasts and the raising of alarms.

scholarly journals Exploring COVID-19 Daily Records of Diagnosed Cases and Fatalities Based on Simple Nonparametric Methods

2021 ◽  
Vol 13 (2) ◽  
pp. 302-328
Author(s):  
Hans H. Diebner ◽  
Nina Timmesfeld
Keyword(s):  
Time Series ◽  
Reproduction Number ◽  
Time Lag ◽  
The United States ◽  
Nonparametric Methods ◽  
Time Dependent ◽  
Series Data ◽  
Periodic Patterns ◽  
Temporal Behaviour ◽  
Time Courses

Containment strategies to combat epidemics such as SARS-CoV-2/COVID-19 require the availability of epidemiological parameters, e.g., the effective reproduction number. Parametric models such as the commonly used susceptible-infected-removed (SIR) compartment models fitted to observed incidence time series have limitations due to the time-dependency of the parameters. Furthermore, fatalities are delayed with respect to the counts of new cases, and the reproduction cycle leads to periodic patterns in incidence time series. Therefore, based on comprehensible nonparametric methods including time-delay correlation analyses, estimates of crucial parameters that characterise the COVID-19 pandemic with a focus on the German epidemic are presented using publicly available time-series data on prevalence and fatalities. The estimates for Germany are compared with the results for seven other countries (France, Italy, the United States of America, the United Kingdom, Spain, Switzerland, and Brazil). The duration from diagnosis to death resulting from delay-time correlations turns out to be 13 days with high accuracy for Germany and Switzerland. For the other countries, the time-to-death durations have wider confidence intervals. With respect to the German data, the two time series of new cases and fatalities exhibit a strong coherence. Based on the time lag between diagnoses and deaths, properly delayed asymptotic as well as instantaneous fatality–case ratios are calculated. The temporal median of the instantaneous fatality–case ratio with time lag of 13 days between cases and deaths for Germany turns out to be 0.02. Time courses of asymptotic fatality–case ratios are presented for other countries, which substantially differ during the first half of the pandemic but converge to a narrow range with standard deviation 0.0057 and mean 0.024. Similar results are obtained from comparing time courses of instantaneous fatality–case ratios with optimal delay for the 8 exemplarily chosen countries. The basic reproduction number, R0, for Germany is estimated to be between 2.4 and 3.4 depending on the generation time, which is estimated based on a delay autocorrelation analysis. Resonances at about 4 days and 7 days are observed, partially attributable to weekly periodicity of sampling. The instantaneous (time-dependent) reproduction number is estimated from the incident (counts of new) cases, thus allowing us to infer the temporal behaviour of the reproduction number during the epidemic course. The time course of the reproduction number turns out to be consistent with the time-dependent per capita growth.

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 Fractional SIR-Model for Estimating Transmission Dynamics of COVID-19 in India

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 86-100
Author(s):  
Nita H. Shah ◽  
Ankush H. Suthar ◽  
Ekta N. Jayswal ◽  
Ankit Sikarwar
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Sir Model ◽  
Time Dependent ◽  
Contact Rate ◽  
Distribution Maps ◽  
Fundamental Parameters ◽  
Different Order ◽  
The Basic Reproduction Number

In this article, a time-dependent susceptible-infected-recovered (SIR) model is constructed to investigate the transmission rate of COVID-19 in various regions of India. The model included the fundamental parameters on which the transmission rate of the infection is dependent, like the population density, contact rate, recovery rate, and intensity of the infection in the respective region. Looking at the great diversity in different geographic locations in India, we determined to calculate the basic reproduction number for all Indian districts based on the COVID-19 data till 7 July 2020. By preparing district-wise spatial distribution maps with the help of ArcGIS 10.2, the model was employed to show the effect of complete lockdown on the transmission rate of the COVID-19 infection in Indian districts. Moreover, with the model's transformation to the fractional ordered dynamical system, we found that the nature of the proposed SIR model is different for the different order of the systems. The sensitivity analysis of the basic reproduction number is done graphically which forecasts the change in the transmission rate of COVID-19 infection with change in different parameters. In the numerical simulation section, oscillations and variations in the model compartments are shown for two different situations, with and without lockdown.

scholarly journals Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2084
Author(s):  
Raman Kumar ◽  
Rohit Dubey ◽  
Sehijpal Singh ◽  
Sunpreet Singh ◽  
Chander Prakash ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Femoral Component ◽  
Human Life ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Biomedical Science ◽  
Human Beings ◽  
Weighted Sum Method ◽  
Selection Of

Total knee replacement (TKR) is a remarkable achievement in biomedical science that enhances human life. However, human beings still suffer from knee-joint-related problems such as aseptic loosening caused by excessive wear between articular surfaces, stress-shielding of the bone by prosthesis, and soft tissue development in the interface of bone and implant due to inappropriate selection of TKR material. The choice of most suitable materials for the femoral component of TKR is a critical decision; therefore, in this research paper, a hybrid multiple-criteria decision-making (MCDM) tactic is applied using the degree of membership (DoM) technique with a varied system, using the weighted sum method (WSM), the weighted product method (WPM), the weighted aggregated sum product assessment method (WASPAS), an evaluation based on distance from average solution (EDAS), and a technique for order of preference by similarity to ideal solution (TOPSIS). The weights of importance are assigned to different criteria by the equal weights method (EWM). Furthermore, sensitivity analysis is conducted to check the solidity of the projected tactic. The weights of importance are varied using the entropy weights technique (EWT) and the standard deviation method (SDM). The projected hybrid MCDM methodology is simple, reliable and valuable for a conflicting decision-making environment.

scholarly journals The Role of Religiosity on Fashion Store Patronage Intention of Muslim Consumers in Indonesia

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402092703
Author(s):  
Andriani Kusumawati ◽  
Sari Listyorini ◽  
Suharyono ◽  
Edy Yulianto
Keyword(s):  
Decision Making ◽  
Human Life ◽  
Consumer Decision Making ◽  
Fashion Products ◽  
Managerial Implications ◽  
Research Findings ◽  
Store Patronage ◽  
Life Values ◽  
Patronage Intention

Religiosity covers all aspects of human life values. Consumer decision-making in Muslim product purchase needs to involve religiosity. Muslim fashion is increasingly popular and becomes a potential business for fashion entrepreneurs in Indonesia. This condition evokes a dilemma for the consumers as Muslim fashion users on whether they have to conform to the religious sharia or follow the trend. The purpose of this article is to identify the role of religiosity as a factor affecting Muslim consumers to revisit Muslim fashion stores. This research involved 243 Muslim consumers of several Muslim fashion stores. The results showed that religiosity of Muslim consumers had a direct effect on patronage intention and indirect effect on patronage intention of Muslim fashion stores through Customer Satisfaction. The research findings are directed to managerial implications for Muslim fashion entrepreneurs in relation to consumer religiosity and marketing of Indonesian Muslim fashion products.

scholarly journals Progress and Regress of Time Dependent Data and Application in Bank Branch

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
F. Hosseinzadeh Lotfi ◽  
Z. Taeb ◽  
S. Abbasbandy
Keyword(s):  
Decision Making ◽  
Commercial Bank ◽  
Spline Function ◽  
Time Dependent ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Dependent Data ◽  
Bank Branch ◽  
Decision Making Unit ◽  
Cubic Spline Function

To evaluate each decision making unit having time dependent inputs and outputs data, a new method has been developed and reported here. This method uses the Malmquist productivity index, and is a very simple function based on Cubic Spline function to determine the progress and regress of that unit. To show the capability of this developed method, the data of 9 branches of a commercial bank has been used, evaluated, and reported.

The Demand for, and Avoidance of, Information

2021 ◽  
Author(s):  
Russell Golman ◽  
George Loewenstein ◽  
Andras Molnar ◽  
Silvia Saccardo
Keyword(s):  
Decision Making ◽  
Time Lag ◽  
Relevant Information ◽  
Behavioral Theory ◽  
Bad News ◽  
Good News ◽  
Focus Attention ◽  
New Information ◽  
Attention Demand ◽  
Aid Decision

Management scientists recognize that decision making depends on the information people have but lack a unified behavioral theory of the demand for (and avoidance of) information. Drawing on an existing theoretical framework in which utility depends on beliefs and the attention paid to them, we develop and test a theory of the demand for information encompassing instrumental considerations, curiosity, and desire to direct attention to beliefs one feels good about. We decompose an individual’s demand for information into the desire to refine beliefs, holding attention constant, and the desire to focus attention on anticipated beliefs, holding these beliefs constant. Because the utility of resolving uncertainty (i.e., refining beliefs) depends on the attention paid to it and more important or salient questions capture more attention, demand for information depends on the importance and salience of the question(s) it addresses. In addition, because getting new information focuses attention on one’s beliefs and people want to savor good news and ignore bad news, the desire to obtain or avoid information depends on the valence (i.e., goodness or badness) of anticipated beliefs. Five experiments (n = 2,361) test and find support for these hypotheses, looking at neutrally valenced as well as ego-relevant information. People are indeed more inclined to acquire information (a) when it feels more important, even if it cannot aid decision making (Experiments 1A and 2A); (b) when a question is more salient, manipulated through time lag (Experiments 1B and 2B); and (c) when anticipated beliefs have higher valence (Experiment 2C). This paper was accepted by Yan Chen, behavioral economics and decision analysis.

Different approach to medical decision-making in difficult circumstances: Kittay’s Ethics of Care

2021 ◽  
pp. medethics-2021-107521
Author(s):  
Liam Butchart ◽  
Kristin Krumenacker ◽  
Aymen Baig
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Ethics Of Care ◽  
Resource Scarcity ◽  
Medical Decision ◽  
Alternative Method

The onset of the COVID-19 pandemic has necessitated advances in bioethical approaches to medical decision-making. This paper develops an alternative method for rationing care during periods of resource scarcity. Typical approaches to triaging rely on utilitarian calculations; however, this approach introduces a problematic antihumanist sentiment, inviting the proposition of alternative schemata. As such, we suggest a feminist approach to medical decision-making, founded in and expanding upon the framework of Eva Kittay’s Ethics of Care. We suggest that this new structure addresses the issue of medical decision-making during times of resource scarcity just as well as pure utilitarian approaches while better attending to their significant theoretical concerns, forming a coherent alternative to the current bioethical consensus.

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

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