scholarly journals SIR Model Parameters Estimation with COVID-19 Data

2021 ◽  
pp. 97-117
Author(s):  
Nilson C. Roberty ◽  
Lucas S. F. de Araujo
Keyword(s):  
Removal Rate ◽  
Sir Model ◽  
Parameters Estimation ◽  
Systems Science ◽  
Model Parameters ◽  
Science And Engineering ◽  
University Center ◽  
Johns Hopkins University ◽  
System Solution ◽  
Rate Of Decay

Based on the SIR model that divides the population into susceptible, infected and removed individuals, data about the evolution of the pandemic compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHUCSSE) are integrated into the numerical system solution. The system parameters Rate of Contact β, Basic Reproduction Number R0 and Removal Rate γ, also named Rate of Decay, are determined according to a ridge regression approach and a mobile statistical scheme with different averages. Data is automatically downloaded from https://raw.githubusercontent.com/CSSEGISandData/COVID-19. The main Python libraries used are Numpy, Pandas, Skit-Learn, Requests and Urllib.

scholarly journals Country transition index based on hierarchical clustering to predict next COVID-19 waves

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ricardo A. Rios ◽  
Tatiane Nogueira ◽  
Danilo B. Coimbra ◽  
Tiago J. S. Lopes ◽  
Ajith Abraham ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Temporal Analysis ◽  
Systems Science ◽  
Collateral Damage ◽  
Science And Engineering ◽  
University Center ◽  
Main Challenge ◽  
Johns Hopkins University ◽  
The World ◽  
Transition Index

AbstractCOVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries’ movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository

2020 ◽  
pp. 47-51
Author(s):  
Meg Miller
Keyword(s):  
Academic Library ◽  
Systems Science ◽  
Data Repository ◽  
Science And Engineering ◽  
University Center ◽  
Johns Hopkins University ◽  
Novel Coronavirus

This review provides an overview of 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository compiled by the Johns Hopkins University Center for Systems Science and Engineering. It provides a background of how the repository was compiled, the data included and how the repo is being made use of in a Canadian academic library context.

scholarly journals Measuring Time-Varying Effective Reproduction (Rt) Numbers for COVID-19 and Their Relationship with Movement Control Order (MCO) in Malaysia

2021 ◽  
Author(s):  
Musa Kamarul Imran ◽  
Wan Nor Ariffin ◽  
Mohd Mohd Hafiz ◽  
Subhi Jamiluddin ◽  
Noor Atinah Ahmad ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Movement Control ◽  
Systems Science ◽  
Time Varying ◽  
Science And Engineering ◽  
University Center ◽  
Incidence Data ◽  
Johns Hopkins University ◽  
Control Order ◽  
Local Transmission

To quantify the time-varying reproduction number (Rt) for Malaysia using the COVID-19 incidence data., we used data the from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository. Day 1 was taken from the first assumed local transmission of COVID-19. Data was split into four intervals: a) Interval 1: from Day 1 to Day 10 MCO 1, b) Interval 2: from Day 1 to Day 10 MCO 2, c) Interval 3: from Day 1 to Day 10 MCO 3 and d) Interval 4: from Day 1 to Day 10 MCO 4. We estimated the Rt using the EpiEstim package. The means for Rt at Day 1, Day 5 and Day 10 for all MCOs ranged between 0.665 to 1.147. The average Rt gradually decreased in MCO 1 and MCO 2. However, Rt increased in MCO 3 before stabilized around 0.8 in MCO 4. MCO 1 and MCO 2 which were stricter coincide with the gradual reduction of Rt. However, the more relaxed MCO 3 and MCO 4 correspond to a slight increase in the Rt before it stabilized.

scholarly journals Interim Analysis of Pandemic Coronavirus Disease 2019 (COVID-19) and the SARS-CoV-2 virus in Latin America and the Caribbean: Morbidity, Mortality and Molecular Testing Trends in the Region

2020 ◽  
Author(s):  
Katherine Simbaña-Rivera ◽  
Lenin Gómez-Barreno ◽  
Jhon Guerrero ◽  
Fernanda Simbaña-Guaycha ◽  
Raúl Fernández ◽  
...  
Keyword(s):  
Latin American ◽  
Case Fatality ◽  
Incidence Rates ◽  
Systems Science ◽  
Molecular Testing ◽  
Science And Engineering ◽  
Johns Hopkins University ◽  
Significant Burden ◽  
Similar Disease ◽  
Novel Coronavirus

AbstractBackgroundThe relentless advance of the SARS-CoV-2 virus pandemic has resulted in a significant burden on countries, regardless of their socio-economic conditions. The virus has infected more than 2.5 million people worldwide, causing to date more than 150,000 deaths in over 210 countries.ObjectiveThe aim of this study was to describe the trends in cases, tests and deaths related to novel coronavirus disease (COVID-19) in Latin American and Caribbean (LAC) countries.MethodologyData were retrieved from the WHO-Coronavirus Disease (COVID-2019) situation reports and the Center for Systems Science and Engineering (CSSE) databases from Johns Hopkins University. Descriptive statistics including death rates, cumulative mortality and incidence rates, as well as testing rates per population at risk were performed. A comparison analysis among countries with ≥50 confirmed cases was performed from February 26th, 2020 to April 8th, 2020.ResultsBrazil had the greatest number of cases and deaths in the region. Panama experienced a rapid increase in the number of confirmed cases with Trinidad and Tobago, Bolivia and Honduras having the highest case fatality rates. Panama and Chile conducted more tests per million inhabitants and more tests per day per million inhabitants, followed by Uruguay and El Salvador. Dominican Republic, Bolivia, Ecuador and Brazil had the highest positive test rates.ConclusionsThe COVID-19 disease pandemic caused by the SARS-CoV-2 virus has progressed rapidly in LAC countries. Some countries have been affected more severely than others, with some adopting similar disease control methods to help slow down the spread of the virus. With limited testing and other resources, social distancing is needed to help alleviate the strain on already stretched health systems.

scholarly journals Test-Driven Development pada Pengembangan Aplikasi Android untuk Memantau COVID-19

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Federick Jonathan ◽  
Magdalena Ariance Ineke Pakereng
Keyword(s):  
Mobile Application ◽  
Systems Science ◽  
Test Case ◽  
Android Application ◽  
Science And Engineering ◽  
Test Driven Development ◽  
Writing Test ◽  
Johns Hopkins University ◽  
Reliable Software

Dalam pengembangan perangkat lunak, terdapat banyak teknik dan pendekatan yang digunakan untuk menghasilkan perangkat lunak yang handal. Kualitas perangkat lunak sangat bergantung pada pengujian perangkat lunak. Namun tidak semua pengembang peduli dengan tahapan pengujian pada sebuah perangkat lunak. Penelitian ini bertujuan untuk mengetahui pengaruh dari menerapkan proses pengujian dalam mengembangkan perangkat lunak dengan menggunakan metode TDD. Pada Metode TDD, pengembangan perangkat lunak dimulai dengan menulis test case terlebih dahulu lalu kemudian menulis kode. Pada artikel ini, dikembangkan aplikasi mobile dengan menerapkan metode TDD. Perangkat lunak yang dikembangkan adalah berupa sistem informasi mengenai data laporan kasus COVID-19. Data diambil dari Johns Hopkins University The Center of Systems Science and Engineering (JHU CSSE). Hasil penerapan metode TDD menunjukkan bahwa fungsi dan fitur dari perangkat lunak yang dibangun dapat bekerja dan terintegrasi dengan baik antar satu sama lain. Kode yang dihasilkan dari penerapan TDD juga menjadi rapih karena dilakukannya proses refactoring. In Software Engineering, there are many techniques and approaches that can be used to build a reliable software. The quality of a software relies mostly on the software testing process. However, not many developers are bothered with the testing step of a software. The purpose of this article is to learn the results from implementing a testing process on software developmenty. In TDD, the development is started by writing test case first and then writing code. This article developed a mobile application by applying TDD in the process. The android application that had been developed is an information system about report cases on COVID-19. The cases are coming from Johns Hopkins University The Center of Systems Science and Engineering (JHU CSSE). The result of using TDD in development proves that all functions and features of the developed application are working and integrated well.

scholarly journals Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation

10.2196/19907 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e19907 ◽  
Author(s):  
Se Young Jung ◽  
Hyeontae Jo ◽  
Hwijae Son ◽  
Hyung Ju Hwang
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Influenza Pandemic ◽  
The United States ◽  
Sir Model ◽  
Time Dependent ◽  
Systems Science ◽  
Model Parameters ◽  
Viral Spread ◽  
Spread Model

Background The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. Objective The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. Methods In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. Results We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. Conclusions The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.

scholarly journals BeCaked: An Explainable Artificial Intelligence Model For COVID-19 Forecasting

2021 ◽  
Author(s):  
Duc Nguyen ◽  
Nghia Vo ◽  
Thinh Nguyen ◽  
Khuong Nguyen ◽  
Quang Nguyen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Future Trend ◽  
World Health ◽  
Systems Science ◽  
University Center ◽  
Country Level ◽  
Johns Hopkins University ◽  
Variational Autoencoder ◽  
The World ◽  
Health Organization

Abstract From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people’s lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 R2 and 0.012 MAPE at world level with 31-step forecast and up to 0.99 R2 and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on-time right decisions.

scholarly journals Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Se Young Jung ◽  
Hyeontae Jo ◽  
Hwijae Son ◽  
Hyung Ju Hwang
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Influenza Pandemic ◽  
The United States ◽  
Sir Model ◽  
Time Dependent ◽  
Systems Science ◽  
Model Parameters ◽  
Viral Spread ◽  
Spread Model

BACKGROUND The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. OBJECTIVE The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. METHODS In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. RESULTS We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. CONCLUSIONS The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.

scholarly journals Epidemiology of COVID-19 in Africa - daily cumulative index and mortality rate

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Kenneth Bitrus David ◽  
Naomi Thomas ◽  
Joan Kuyet Solomon
Keyword(s):  
Mortality Rate ◽  
Human Life ◽  
Epidemiological Data ◽  
Systems Science ◽  
Science And Engineering ◽  
Cumulative Index ◽  
Johns Hopkins University ◽  
The World

COVID-19 outbreak which originated from Wuhan, a city in China has spread to over 180 countries in the world, disrupting several sectors of the human life, and causing deaths. This unprecedented event has affected 55 countries in Africa in different ways. This study aims to outline the current epidemiological data of COVID-19 in Africa. The number of confirmed cases and deaths in Africa was obtained from COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Mortality rate and daily cumulative index were calculated for each country. The mortality rate in Africa is low compared to other Continents regardless of the high Daily Cumulative Index recorded.

Adaptive control of time-varying non-linear plants by speed-gradient algorithms

2019 ◽  
pp. 37-44 ◽  
Author(s):  
O. P. Tomchina ◽  
D. N. Polyakhov ◽  
O. I. Tokareva ◽  
A. L. Fradkov
Keyword(s):  
Adaptive Control ◽  
Adaptive Systems ◽  
Reference Model ◽  
Maximum Deviation ◽  
Model Parameters ◽  
Time Varying ◽  
System Solution ◽  
Non Linear ◽  
Initial Uncertainty ◽  
Speed Gradient

Introduction: The motion of many real world systems is described by essentially non-linear and non-stationary models. A number of approaches to the control of such plants are based on constructing an internal model of non-stationarity. However, the non-stationarity model parameters can vary widely, leading to more errors. It is only assumed in this paper that the change rate of the object parameters is limited, while the initial uncertainty can be quite large.Purpose: Analysis of adaptive control algorithms for non-linear and time-varying systems with an explicit reference model, synthesized by the speed gradient method.Results: An estimate was obtained for the maximum deviation of a closed-loop system solution from the reference model solution. It is shown that with sufficiently slow changes in the parameters and a small initial uncertainty, the limit error in the system can be made arbitrarily small. Systems designed by the direct approach and systems based on the identification approach are both considered. The procedures for the synthesis of an adaptive regulator and analysis of the synthesized system are illustrated by an example.Practical relevance: The obtained results allow us to build and analyze a broad class of adaptive systems with reference models under non-stationary conditions.

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