scholarly journals Extension of a SIR model for modelling the propagation of Covid-19 in several countries.

2020 ◽  
Author(s):  
Marc Lavielle ◽  
Matthieu Faron ◽  
jeremie lefevre ◽  
Jean-David Zeitoun
Keyword(s):  
Compartmental Model ◽  
Transmission Rate ◽  
Sir Model ◽  
Decision Makers ◽  
Short Term ◽  
Early Evaluation ◽  
The Public ◽  
Containment Measures ◽  
High Level ◽  
Over Time

Background Several epidemiologic models have been published to forecast the spread of the COVID-19 pandemic yet there are still uncertainties regarding their accuracy. We report the main features of the development of a novel freely accessible model intended to urgently help researchers and decision makers to predict the evolution of the pandemic in their country. Methods and findings We built a SIR-type compartmental model with additional compartments and features. We made the hypothesis that the number of contagious individuals in the population was negligible as compared to the population size. We introduced a compartment D corresponding to the deceased patients and a compartment L representing the group of individuals who will die but who will not infect anybody (due to social or medical isolation). Our model integrated a time-dependent transmission rate, whose variations can be thought to be related to the public measures taken by each country and a cosine function to incorporate a periodic weekly component linked to the way in which numbers of cases and deaths are counted and reported, which can change from day to day. The model was able to accurately capture the different changes in the dynamics of the pandemic for nine different countries whatever the type of pandemic spread or containment measures. The model provided very accurate forecasts in the relatively short term (10 days). Conclusions In early evaluation of the performance of our model, we found a high level of accuracy between prediction and observed data, regardless of the country. The model should be used by the community to help public health decisions as we will refine it over time and further investigate its performance.

scholarly journals Predicting the propagation of COVID-19 at an international scale: extension of an SIR model

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e041472
Author(s):  
Marc Lavielle ◽  
Matthieu Faron ◽  
Jérémie H Lefevre ◽  
Jean-David Zeitoun
Keyword(s):  
Web Application ◽  
Compartmental Model ◽  
Transmission Rate ◽  
Sir Model ◽  
Time Dependent ◽  
Epidemiological Models ◽  
Containment Measures ◽  
Early Performance ◽  
High Level ◽  
Novel Model

ObjectivesSeveral epidemiological models have been published to forecast the spread of the COVID-19 pandemic, yet many of them have proven inaccurate for reasons that remain to be fully determined. We aimed to develop a novel model and implement it in a freely accessible web application.DesignWe built an SIR-type compartmental model with two additional compartments: D (deceased patients); L (individuals who will die but who will not infect anybody due to social or medical isolation) and integration of a time-dependent transmission rate and a periodical weekly component linked to the way in which cases and deaths are reported.ResultsThe model was implemented in a web application (as of 2 June 2020). It was shown to be able to accurately capture the changes in the dynamics of the pandemic for 20 countries whatever the type of pandemic spread or containment measures: for instance, the model explains 97% of the variance of US data (daily cases) and predicts the number of deaths at a 2-week horizon with an error of 1%.ConclusionsIn early performance evaluation, our model showed a high level of accuracy between prediction and observed data. Such a tool might be used by the global community to follow the spread of the pandemic.

VENTURE CAPITAL, THE PUBLIC SECTOR AND THE HIGH-TECHNOLOGY INDUSTRY

2008 ◽  
Vol 05 (01) ◽  
pp. 105-122 ◽  
Author(s):  
AVI MESSICA ◽  
TAMIR AGMON
Keyword(s):  
Public Sector ◽  
Venture Capital ◽  
Decision Makers ◽  
Stock Index ◽  
Short Term ◽  
The Public ◽  
Technology Industry ◽  
Highly Correlated ◽  
Decision Making Model ◽  
Practical Implications

We studied the optimal funding of the public sector for the Hi-Tech industry in the presence of short-term, cyclical, venture capital (VC) funding by constructing a decision-making model that results in the optimal governmental support and a model that accounts for the dynamics of the VC industry. We found that the VC industry is highly correlated with the NASDAQ stock index and that the optimal public policy for funding the Hi-Tech sector should be anti-cyclical, dynamic, and conditioned on the VC investments. The models and their validation are discussed as well as the practical implications for policy and decision makers.

Growing without Pains? Explaining Liberal Democrat MPs' Behaviour

2009 ◽  
Vol 11 (2) ◽  
pp. 259-279 ◽  
Author(s):  
Matt Cole
Keyword(s):  
Group Composition ◽  
National Party ◽  
Short Term ◽  
Contextual Data ◽  
Liberal Democrats ◽  
Composition Structure ◽  
High Level ◽  
Over Time

Recent academic studies and wider commentary on the behaviour of Liberal Democrat MPs have recognised their relatively high level of cohesiveness on whipped votes when compared to that of Labour and the Conservatives, and to the Liberal Democrats' own reputation; but while this trend continues, few studies have focused upon its causes. This article uses the MPs' voting records, personal papers, interviews and wider contextual data to chart the extent of that unity over time, and to explore its origins, including group composition, structure, patronage, relations with the extra-parliamentary party and other parties as well as national party image. It finds the key to this unity in a combination of medium and long-term features of the Liberal and Liberal Democrat group of MPs, rather than a short-term singular determinant.

scholarly journals Modelling the transmission dynamics of COVID-19 in six high burden countries

2020 ◽  
Author(s):  
Azizur Rahman ◽  
Md Abdul Kuddus
Keyword(s):  
Disease Transmission ◽  
Compartmental Model ◽  
Transmission Rate ◽  
Control Strategies ◽  
Rank Correlation ◽  
Decision Makers ◽  
High Burden ◽  
Epidemic Period ◽  
Partial Rank Correlation ◽  
Coefficient Method

AbstractThe new coronavirus disease, officially known as COVID-19, originated in China in 2019 and has since spread around the globe. We presented a modified Susceptible-Latent-Infected-Removed (SLIR) compartmental model of COVID-19 disease transmission with nonlinear incidence during the epidemic period. We provided the model calibration to estimate parameters with day wise corona virus (COVID-19) data i.e. reported cases by worldometer from the period of 15th February to 30th March, 2020 in six high burden countries including Australia, Italy, Spain, USA, UK and Canada. We estimate transmission rates for each countries and found that the highest transmission rate country in Spain, which may be increase the new cases and deaths in Spain than the other countries. Sensitivity analysis was used to identify the most important parameters through the partial rank correlation coefficient method. We found that the transmission rate of COVID-19 had the largest influence on the prevalence. We also provides the prediction of new cases in COVID-19 until May 18, 2020 using the developed model and recommends, control strategies of COVID-19. The information that we generated from this study would be useful to the decision makers of various organizations across the world including the Ministry of Health in Australia, Italy, Spain, USA, UK and Canada to control COVID-19.

Pendekatan Kabur untuk Mengenal Pasti Kriteria Mempengaruhi Pelajar Memilih Institusi Pengajian Tinggi Awam (IPTA)

2012 ◽  
Author(s):  
Muhammad Mat Yusof ◽  
Abdul Aziz Jemain
Keyword(s):  
Fuzzy Set ◽  
Higher Learning ◽  
Decision Makers ◽  
The Public ◽  
Fuzzy Preference Relations ◽  
Criteria Weights ◽  
High Level ◽  
Learning Institutions ◽  
Level Of Importance ◽  
Fuzzy Preference

Saban tahun, ramai pelajar yang berkelayakan memohon untuk meneruskan pengajian ke institusi pengajian tinggi awam. Lantaran itu, suatu penunjuk perlu dicari bagi membantu pihak berwajib mendapat gambaran tentang kriteria pemilihan ke universiti untuk tujuan perancangan, pemantauan, pemulihan, pemuliharaan atau penaiktarafan selanjutnya. Dalam kajian ini, teori set kabur digunakan. Maklumat kecenderungan pemilihan boleh digambarkan dengan menggunakan vektor tertib, vektor kepuasan, vektor linguistik atau vektor pemilihan subset. Para pelajar sebagai pembuat keputusan akan memberi kecenderungan mereka dalam format yang berbeza-beza mengikut idea, sikap, motivasi dan personaliti masing-masing. Perbezaan format ini kemudiannya diseragamkan dan diagregatkan menggunakan format hubungan kecenderungan kabur. Pemberat setiap kriteria akan diperolehi dengan menggunakan operasi pemurata tertib berpemberat (Ordered Weighting Averaging – OWA). Berdasarkan kajian ini, didapati bahawa pelajar memilih kriteria penawaran kursus, kualiti pengajaran pensyarah dan kemudahan pinjaman/biasiswa sebagai kriteria utama untuk meneruskan pengajian di Institusi Pengajian Tinggi Awam. Kata kunci: Teori set kabur, vektor tertib, vektor kepuasan, vektor linguistik, operasi pemurata tertib berpemberat (OWA) Every year, many qualified students apply tu further their study to the public higher learning institution. For that reason, indicators are needed in assisting the proper authority to get the current situation in the country regarding criterias used by students in seeking proper places for them to pursue their higher education. Thus, the selection criteria should be treated with a high level of importance, which will provide information useful for planning, monitoring and upgrading in order to ensure all public higher intuitions are able to satisfy the demand from the society. In this study, a fuzzy set theory was applied when the imprecise information was represented in fuzzy terms. Preference information can be represented by means of preference orderings, utility functions, vector of linguistic terms or a selected subset. In this approach, four different preference formats are provided from students as decision makers to express their individual preferences, taking into consideration the decision makers different attitudes, motivation, and personalities. The different formats of preferences are transformed into uniform fuzzy preference relations and aggregated. Ordered Weighting Averaging (OWA) was used to assess the criteria weights. From this study, it was found that offering courses, teaching qualities and availability of financial aid are the most important criteria considered by the students to further their study in public higher learning institutions. Key words: Fuzzy set, fuzzy consensus, multi-criteria decision, fuzzy preference

scholarly journals A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations, and deaths

2021 ◽  
Author(s):  
Eamon B O'Dea ◽  
John M Drake
Keyword(s):  
State Space ◽  
Compartmental Model ◽  
Hospital Admissions ◽  
Transmission Rate ◽  
Parameter Estimates ◽  
Data Sets ◽  
Short Term ◽  
High Performing ◽  
Kinetics Of ◽  
Better Than

Short-term forecasts of the dynamics of COVID-19 in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. A major obstacle has been capturing variations in the underlying kinetics of transmission resulting from changes in public policy, individual behaviors, and evolution of the virus. However, the availability of standardized forecasts and versioned data sets from this period allows for continued work in this area. Here we introduce the Gaussian Infection State Space with Time-dependence (GISST) forecasting model. We evaluate its performance in 1-4 week ahead forecasts of COVID-19 cases, hospital admissions, and deaths in the state of California made with official reports of COVID-19, Google's mobility reports, and vaccination data available each week from June 29, 2020 to April, 26, 2021. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate, and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability, and applicability to large multivariate data sets that may prove useful in improving the accuracy of infectious disease forecasts.

scholarly journals Inhomogeneous Transmission and Asynchronic Mixing in the Spread of COVID-19 Epidemics

2021 ◽  
Vol 9 ◽  
Author(s):  
Carlos I. Mendoza
Keyword(s):  
Reproduction Number ◽  
Transmission Rate ◽  
Real Data ◽  
Epidemiological Models ◽  
Subexponential Growth ◽  
The Public ◽  
Transmission Rates ◽  
Proposed Model ◽  
Containment Measures ◽  
Mitigation Policies

The ongoing epidemic of COVID-19 first found in China has reinforced the need to develop epidemiological models capable of describing the progression of the disease to be of use in the formulation of mitigation policies. Here, this problem is addressed using a metapopulation approach to consider the inhomogeneous transmission of the spread arising from a variety of reasons, like the distribution of local epidemic onset times or of the transmission rates. We show that these contributions can be incorporated into a susceptible-infected-recovered framework through a time-dependent transmission rate. Thus, the reproduction number decreases with time despite the population dynamics remaining uniform and the depletion of susceptible individuals is small. The obtained results are consistent with the early subexponential growth observed in the cumulated number of confirmed cases even in the absence of containment measures. We validate our model by describing the evolution of COVID-19 using real data from different countries, with an emphasis in the case of Mexico, and show that it also correctly describes the longtime dynamics of the spread. The proposed model yet simple is successful at describing the onset and progression of the outbreak, and considerably improves the accuracy of predictions over traditional compartmental models. The insights given here may prove to be useful to forecast the extent of the public health risks of the epidemics, thus improving public policy-making aimed at reducing such risks.

scholarly journals On a Coupled Time-Dependent Sir Models Fitting with New York and New-Jersey States COVID-19 Data

2020 ◽  
Author(s):  
Benjamin Ambrosio ◽  
M.A. Aziz-Alaoui
Keyword(s):  
New York ◽  
New Jersey ◽  
Death Rate ◽  
Transmission Rate ◽  
Hot Spot ◽  
Model Fitting ◽  
Coupled Model ◽  
Sir Model ◽  
Exponential Increase ◽  
Over Time

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of march 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.

The Regulation of Legacy Situations Where Reduction in Long Term Radiation Hazard Requires an Increase in Short Term Detriments

2003 ◽  
Author(s):  
Steve Fisher ◽  
David Turton
Keyword(s):  
Waste Treatment ◽  
Radiation Hazard ◽  
Short Term ◽  
The Public ◽  
Nuclear Site ◽  
Regulatory Approach ◽  
High Level ◽  
Regulatory Practice ◽  
Practical Implications

Legacy situations on nuclear sites usually occur at the end of the life of a facility or site. These situations, such as decommissioning or historic waste treatment, are often responsible for long term hazards to workers and the public. Reducing the magnitude of these long-term hazards will generate waste disposals which may impact on the environment and may result in workers incurring additional doses. The increase in these detriments seems at first glance to be in conflict with the usual aims of the regulators. However by putting the reduction of long term radiation hazard from legacy situations in the context of Government policy and guidance, the approach of current regulatory practice adopted for such situations is considered. The regulatory framework is presented from the high level policy, then through the application of the principles of ALARA, reducing risks — protecting people, Best Practicable Environmental Option (BPEO) and Best Practicable Means (BPM). Issues that the regulators expect the nuclear site operator to address are discussed from both the protection of the public and the environment and examples of the practical implications of the regulatory approach are described. ‘Softer’, but important essentials for the operator to adopt in the handling of legacy situations are also raised. These essentials such as openness and stakeholder dialogue, have in the past been poorly performed leading to a lack of trust and understanding by the public.

scholarly journals Forecasting Transmission Dynamics of COVID-19 Epidemic in India under Various Containment Measures- A Time-Dependent State-Space SIR Approach

2020 ◽  
Author(s):  
Vishal Deo ◽  
Anuradha Rajkonwar Chetiya ◽  
Barnali Deka ◽  
Gurprit Grover
Keyword(s):  
State Space ◽  
Transmission Rate ◽  
Sir Model ◽  
Primary Objective ◽  
Time Dependent ◽  
Infectious Period ◽  
Total Incidence ◽  
Comparison Results ◽  
Containment Measures ◽  
The Impact

Objectives Our primary objective is to predict the dynamics of COVID-19 epidemic in India while adjusting for the effects of various progressively implemented containment measures. Apart from forecasting the major turning points and parameters associated with the epidemic, we intend to provide an epidemiological assessment of the impact of these containment measures in India. Methods We propose a method based on time-series SIR model to estimate time-dependent modifiers for transmission rate of the infection. These modifiers are used in state-space SIR model to estimate reproduction number R0, expected total incidence, and to forecast the daily prevalence till the end of the epidemic. We consider four different scenarios, two based on current developments and two based on hypothetical situations for the purpose of comparison. Results Assuming gradual relaxation in lockdown post 17 May 2020, we expect the prevalence of infecteds to cross 9 million, with at least 1 million severe cases, around the end of October 2020. For the same case, estimates of R0 for the phases no-intervention, partial-lockdown and lockdown are 4.46 (7.1), 1.47 (2.33), and 0.817 (1.29) respectively, assuming 14-day (24-day) infectious period. Conclusions Estimated modifiers give consistent estimates of unadjusted R0 across different scenarios, demonstrating precision. Results corroborate the effectiveness of lockdown measures in substantially reducing R0. Also, predictions are highly sensitive towards estimate of infectious period.

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