scholarly journals TW-SIR: time-window based SIR for COVID-19 forecasts

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhifang Liao ◽  
Peng Lan ◽  
Zhining Liao ◽  
Yan Zhang ◽  
Shengzong Liu
Keyword(s):  
Prediction Model ◽  
Reproduction Number ◽  
Time Window ◽  
Machine Learning Method ◽  
Exponential Growth Rate ◽  
Learning Method ◽  
Mathematical Epidemiology ◽  
Dynamic Data ◽  
Time Changes ◽  
The Basic Reproduction Number

AbstractSince the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

scholarly journals TW-SIR: Time-window based SIR for COVID-19 forecasts

2020 ◽  
Author(s):  
Zhifang Liao ◽  
Peng Lan ◽  
Zhingning Liao ◽  
Yan Zhang ◽  
Shengzong Liu
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Time Window ◽  
Data Sets ◽  
Machine Learning Method ◽  
The Real ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Time Changes ◽  
Epidemic Diseases

Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In order to reflect the real-time trend of the epidemic in the process of infection for different areas, different policies and different epidemic diseases, a general adapted time- window based SIR model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the Basic reproduction number R0 and the exponential growth rate of the epidemic. Multiple data sets of epidemic diseases are analyzed, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%

scholarly journals Bilateral Globus Pallidus Internus Deep Brain Stimulation for Parkinson’s Disease: Therapeutic Effects and Motor Outcomes Prediction

2020 ◽  
Author(s):  
Dingding Shen ◽  
Linhao Cao ◽  
Yun Ling ◽  
Dianyou Li ◽  
Kang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Prediction Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
One Year ◽  
Deep Brain

Abstract Background: Deep brain stimulation (DBS) has emerged as a highly effective surgical treatment for advanced Parkinson’s disease (PD). Good response in levodopa challenge test has suggested as criterion to identify optimal candidates for surgery. However, the response to levodopa and DBS is not always congruent, and predictive value of the levodopa test remains controversial. This study was set out to identify predictors of response to DBS and develop a novel prediction model evaluating DBS candidacy. Methods: Herein, we retrospectively analyzed 62 consecutive PD patients who underwent bilateral globus pallidus interna (GPi) DBS from 2016 to 2019.  The changes in UPDRS-III (Unified Parkinson’s Disease Rating Scale part III) total and subscores after surgery at one-year follow-up were evaluated and potential predictor variables were also collected. In the training cohort of 29 patients, we developed a novel machine learning method with 5-fold cross validations implementing on these variables to predict GPi DBS treatment outcomes in a multivariate linear analysis. Furthermore, the machine learning model was externally validated with another cohort of 33 GPi DBS PD patients.Results: GPi DBS significantly improved postoperative motor function of PD patients. The overall UPDRS-III scores improved by 30.4%, with highest improvement in tremor (75.0%), followed by limb bradykinesia (27.5%), rigidity (27.3%) and axial bradykinesia (22.4%). Most intriguingly, improvement in tremor can be predicted with high accuracy using this prediction model (adjusted R2= 0.82 for absolute improvement, and adjusted R2 = 0.76 for relative improvement), in which off medication tremor subscore was identified as the most powerful preoperative predictor. In the external validation cohort, the machine learning method showed good predictive performance.Conclusions: We confirmed the effects of bilateral GPi-DBS with a one-year follow-up. The good performance of the present prediction model demonstrated the utility of machine-learning based motor response prediction after GPi DBS, based on clinical preoperative variables.

scholarly journals DINAMIKA LOKAL MODEL EPIDEMI SVIR DENGAN IMIGRASI PADA KOMPARTEMEN VAKSINASI

2020 ◽  
Vol 14 (2) ◽  
pp. 297-304
Author(s):  
Joko Harianto ◽  
Titik Suparwati ◽  
Inda Puspita Sari
Keyword(s):  
Equilibrium Point ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Equilibrium Points ◽  
Asymptotically Stable ◽  
Mathematical Epidemiology ◽  
Unique Equilibrium ◽  
The Stability ◽  
Disease Free ◽  
The Basic Reproduction Number

Abstrak Artikel ini termasuk dalam ruang lingkup matematika epidemiologi. Tujuan ditulisnya artikel ini untuk mendeskripsikan dinamika lokal penyebaran suatu penyakit dengan beberapa asumsi yang diberikan. Dalam pembahasan, dianalisis titik ekuilibrium model epidemi SVIR dengan adanya imigrasi pada kompartemen vaksinasi. Dengan langkah pertama, model SVIR diformulasikan, kemudian titik ekuilibriumnya ditentukan, selanjutnya, bilangan reproduksi dasar ditentukan. Pada akhirnya, kestabilan titik ekuilibirum yang bergantung pada bilangan reproduksi dasar ditentukan secara eksplisit. Hasilnya adalah jika bilangan reproduksi dasar kurang dari satu maka terdapat satu titik ekuilbirum dan titik ekuilbrium tersebut stabil asimtotik lokal. Hal ini berarti bahwa dalam kondisi tersebut penyakit akan cenderung menghilang dalam populasi. Sebaliknya, jika bilangan reproduksi dasar lebih dari satu, maka terdapat dua titik ekuilibrium. Dalam kondisi ini, titik ekuilibrium endemik stabil asimtotik lokal dan titik ekuilibrium bebas penyakit tidak stabil. Hal ini berarti bahwa dalam kondisi tersebut penyakit akan tetap ada dalam populasi. Kata Kunci : Model SVIR, Stabil Asimtotik Lokal Abstract This article is included in the scope of mathematical epidemiology. The purpose of this article is to describe the dynamics of the spread of disease with some assumptions given. In this paper, we present an epidemic SVIR model with the presence of immigration in the vaccine compartment. First, we formulate the SVIR model, then the equilibrium point is determined, furthermore, the basic reproduction number is determined. In the end, the stability of the equilibrium point is determined depending on the number of basic reproduction. The result is that if the basic reproduction number is less than one then there is a unique equilibrium point and the equilibrium point is locally asymptotically stable. This means that in those conditions the disease will tend to disappear in the population. Conversely, if the basic reproduction number is more than one, then there are two equilibrium points. The endemic equilibrium point is locally asymptotically stable and the disease-free equilibrium point is unstable. This means that in those conditions the disease will remain in the population. Keywords: SVIR Model, Locally Asymptotically stable.

scholarly journals Estimation of the basic reproduction number (R0) for the novel coronavirus disease in Sri Lanka

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Samath Dharmaratne ◽  
Supun Sudaraka ◽  
Ishanya Abeyagunawardena ◽  
Kasun Manchanayake ◽  
Mahen Kothalawala ◽  
...  
Keyword(s):  
Growth Rate ◽  
Maximum Likelihood ◽  
Sri Lanka ◽  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Likelihood Method ◽  
Exponential Growth Rate ◽  
Rate Method ◽  
The Basic Reproduction Number

Abstract Background The basic reproduction number (R0) is the number of cases directly caused by an infected individual throughout his infectious period. R0 is used to determine the ability of a disease to spread within a given population. The reproduction number (R) represents the transmissibility of a disease. Objectives We aimed to calculate the R0 of Coronavirus disease-2019 (COVID-19) in Sri Lanka and to describe the variation of R, with its implications to the prevention and control of the disease. Methods Data was obtained from daily situation reports of the Epidemiology Unit, Sri Lanka and a compartmental model was used to calculate the R0 using estimated model parameters. This value was corroborated by using two more methods, the exponential growth rate method and maximum likelihood method to obtain a better estimate for R0. The variation of R was illustrated using a Bayesian statistical inference-based method. Results The R0 calculated by the first model was 1.02 [confidence interval (CI) of 0.75–1.29] with a root mean squared error of 7.72. The exponential growth rate method and the maximum likelihood estimation method yielded an R0 of 0.93 (CI of 0.77–1.10) and a R0 of 1.23 (CI of 0.94–1.57) respectively. The variation of R ranged from 0.69 to 2.20. Conclusion The estimated R0 for COVID-19 in Sri Lanka, calculated by three different methods, falls between 0.93 and 1.23, and the transmissibility R has reduced, indicating that measures implemented have achieved a good control of disease.

scholarly journals Estimation of exponential growth rate and basic reproduction number of the coronavirus disease 2019 (COVID-19) in Africa

2020 ◽  
Author(s):  
Salihu S Musa ◽  
Shi Zhao ◽  
Maggie H Wang ◽  
Abdurrazaq G Habib ◽  
Umar T Mustapha ◽  
...  
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Initial Phase ◽  
Reproduction Number ◽  
Exponential Growth Rate ◽  
Preparedness Planning ◽  
First Case ◽  
Exponential Growth Model ◽  
The Basic Reproduction Number

Abstract Since the first case of coronavirus disease 2019 (COVID-19) was detected on February 14, 2020, the cumulative confirmations reached 834 including 17 deaths by March 19, 2020. We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 19 March 2020, by using the simple exponential growth model. We estimated the exponential growth rate as 0.22 per day (95%CI: 0.20 – 0.24), and the basic reproduction number to be 2.37 (95%CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March, 2020. Our estimates should be useful in preparedness planning.

scholarly journals An Introduction to The Basic Reproduction Number in Mathematical Epidemiology

2018 ◽  
Vol 62 ◽  
pp. 123-138 ◽  
Author(s):  
Antoine Perasso
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Basic Reproduction Number ◽  
Matrix Method ◽  
Reproduction Number ◽  
Mathematical Epidemiology ◽  
Pde Model ◽  
Mathematical Definition ◽  
Next Generation Matrix ◽  
The Basic Reproduction Number

This article introduces the notion of basic reproduction number R0 in mathematical epi-demiology. After an historic reminder describing the steps leading to the statement of its mathematical definition, we explain the next-generation matrix method allowing its calculation in the case of epidemic models described by ordinary differential equations (ODEs). The article then focuses, through four ODEs examples and an infection load structured PDE model, on the usefulness of the R0 to address biological as well mathematical issues.

scholarly journals Estimating the basic reproduction number for COVID-19 in Western Europe

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248731
Author(s):  
Isabella Locatelli ◽  
Bastien Trächsel ◽  
Valentin Rousson
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Cumulative Incidence ◽  
Western Europe ◽  
Reproduction Number ◽  
European Population ◽  
Exponential Growth Rate ◽  
Official Statistics ◽  
The Basic Reproduction Number

Objective To estimate the basic reproduction number (R0) for COVID-19 in Western Europe. Methods Data (official statistics) on the cumulative incidence of COVID-19 at the start of the outbreak (before any confinement rules were declared) were retrieved in the 15 largest countries in Western Europe, allowing us to estimate the exponential growth rate of the disease. The rate was then combined with estimates of the distribution of the generation interval as reconstructed from the literature. Results Despite the possible unreliability of some official statistics about COVID-19, the spread of the disease appears to be remarkably similar in most European countries, allowing us to estimate an average R0 in Western Europe of 2.2 (95% CI: 1.9–2.6). Conclusions The value of R0 for COVID-19 in Western Europe appears to be significantly lower than that in China. The proportion of immune persons in the European population required to stop the outbreak could thus be closer to 50% than to 70%.

scholarly journals Reassessment of contact restrictions and testing campaigns against COVID-19 via spatio-temporal modeling

2021 ◽  
Author(s):  
Naleen Chaminda Ganegoda ◽  
Karunia Putra Wijaya ◽  
Joseph Páez Chávez ◽  
Dipo Aldila ◽  
K. K. W. Hasitha Erandi ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Time Window ◽  
Herd Immunity ◽  
Degree Theory ◽  
Path Following ◽  
Transient Behavior ◽  
Fixed Time ◽  
Mathematical Framework ◽  
The Basic Reproduction Number

AbstractSince the earliest outbreak of COVID-19, the disease continues to obstruct life normalcy in many parts of the world. The present work proposes a mathematical framework to improve non-pharmaceutical interventions during the new normal before vaccination settles herd immunity. The considered approach is built from the viewpoint of decision makers in developing countries where resources to tackle the disease from both a medical and an economic perspective are scarce. Spatial auto-correlation analysis via global Moran’s index and Moran’s scatter is presented to help modulate decisions on hierarchical-based priority for healthcare capacity and interventions (including possible vaccination), finding a route for the corresponding deployment as well as landmarks for appropriate border controls. These clustering tools are applied to sample data from Sri Lanka to classify the 26 Regional Director of Health Services (RDHS) divisions into four clusters by introducing convenient classification criteria. A metapopulation model is then used to evaluate the intra- and inter-cluster contact restrictions as well as testing campaigns under the absence of confounding factors. Furthermore, we investigate the role of the basic reproduction number to determine the long-term trend of the regressing solution around disease-free and endemic equilibria. This includes an analytical bifurcation study around the basic reproduction number using Brouwer Degree Theory and asymptotic expansions as well as related numerical investigations based on path-following techniques. We also introduce the notion of average policy effect to assess the effectivity of contact restrictions and testing campaigns based on the proposed model’s transient behavior within a fixed time window of interest.

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender
Keyword(s):  
Target Prediction ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Protein Target ◽  
Bioactivity Prediction ◽  
Vector Machines ◽  
Scaling Methods ◽  
Data Points ◽  
Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

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