scholarly journals Data-driven modeling and forecasting of COVID-19 outbreak for public policy making

2020 ◽  
Author(s):  
Agus Hasan ◽  
Endah Putri ◽  
Hadi Susanto ◽  
Nuning Nuraini
Keyword(s):  
Public Policy ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Case Fatality ◽  
Data Driven ◽  
Infectious Individual ◽  
Transmission Index ◽  
Modeling And Forecasting ◽  
Data Driven Approach ◽  
Data Driven Modeling

This paper presents a data-driven approach for COVID-19 outbreak modeling and forecasting, which can be used by public policy and decision makers to control the outbreak through Non-Pharmaceutical Interventions (NPI). First, we apply an extended Kalman filter (EKF) to a discrete-time stochastic augmented compartmental model to estimate the time-varying effective reproduction number Rt. We use daily confirmed cases, active cases, recovered cases, deceased cases, Case-Fatality-Rate (CFR), and infectious time as inputs for the model. Furthermore, we define a Transmission Index (TI) as a ratio between the instantaneous and the maximum value of the effective reproduction number. The value of TI shows the disease transmission in a contact between a susceptible and an infectious individual due to current measures such as physical distancing and lock-down relative to a normal condition. Based on the value of TI, we forecast different scenarios to see the effect of relaxing and tightening public measures. Case studies in three countries are provided to show the practicability of our approach.

scholarly journals Data-Driven Modeling of Mechanical Properties of Cast Iron Using Fuzzy Logic

2020 ◽  
Author(s):  
He Tan ◽  
Vladimir Tarasov ◽  
Vasileios Fourlakidis ◽  
Attila Dioszegi
Keyword(s):  
Experimental Data ◽  
Fuzzy Logic ◽  
Cast Iron ◽  
Materials Science ◽  
Fatigue Behavior ◽  
Relevant Literature ◽  
Data Driven ◽  
Fuzzy Logic Systems ◽  
Data Driven Approach ◽  
Data Driven Modeling

For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

scholarly journals COVID-19 pandemic scenario in India compared to China and rest of the world: a data driven and model analysis

2020 ◽  
Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal
Keyword(s):  
Reproduction Number ◽  
Case Fatality ◽  
Respiratory Illness ◽  
Data Driven ◽  
Daily Growth Rate ◽  
Daily Growth ◽  
Epidemic Size ◽  
Serial Interval ◽  
The World ◽  
Before And After

AbstractThe COVID-19 is a rapidly spreading respiratory illness caused with the infection of SARS-CoV-2. The COVID-19 data from India was compared with China and rest of the world. The average values of daily growth rate (DGR), case recovery rate (CRR), case fatality rate (CFR), serial interval (SI) of COVID-19 in India was 17%, 8.25%, and 1.87%, and 5.76 days respectively, as of April 9, 2020. The data driven estimates of basic reproduction number (R0), average reproduction number (R) and effective reproduction number (Re) were 1.03, 1.73, and 1.35, respectively. The results of exponential and SIR model showed higher estimates of R0, R and Re. The data driven as well as estimated COVID-19 cases reflect the growing nature of the epidemic in India and world excluding China, whereas the same in China reveal the involved population became infected with the disease and moved into the recovered stage. The epidemic size of India was estimated to be ∼30,284 (as of April 15, 2020 with 12,370 infectious cases) with an estimated end of the epidemic on June 9, 2020. The Re values in India before and after lockdown were 1.62 and 1.37 respectively, with SI 5.52 days and 5.98 days, respectively, as of April 17, 2020, reflecting the effectiveness of lockdown strategies. Beyond April 17, 2020, our estimate of 24,431 COVID-19 infected cases with lockdown is 78% lower compared to the 112,042 case estimates in absence of lockdown, on April 27, 2020. To early end of the COVID-19 epidemic, strong social distancing is important.

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2020 ◽  
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Reproduction Number ◽  
Case Reports ◽  
College Campuses ◽  
Data Driven ◽  
Mathematical Epidemiology ◽  
Ongoing Debate ◽  
Risk Period ◽  
Order Of Magnitude ◽  
Data Driven Modeling

ABSTRACTThe COVID-19 pandemic continues to present enormous challenges for colleges and universities and strategies for save reopening remain a topic of ongoing debate. Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and–most importantly–compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.

scholarly journals Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing

2020 ◽  
Author(s):  
Robert Marsland ◽  
Pankaj Mehta
Keyword(s):  
Disease Transmission ◽  
Predictive Power ◽  
Functional Form ◽  
Population Characteristics ◽  
Data Driven ◽  
Random Networks ◽  
Social Distancing ◽  
Scale Free ◽  
Scale Free Networks ◽  
Data Driven Modeling

We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before eventually slowing. In agreement with a recent statistical forecasting model by the IHME, we show that this dynamics is well described by the erf function. Using this functional form, we perform a data collapse across countries and US states with very different population characteristics and social distancing policies, confirming the universal behavior of the COVID-19 outbreak. We show that the predictive power of statistical models is limited until a few days before curves flatten, forecast deaths and infections assuming current policies continue and compare our predictions to the IHME models. We present simulations showing this universal dynamics is consistent with disease transmission on scale-free networks and random networks with non-Markovian transmission dynamics.

Extracting Cylinder Individual Combustion Data From a High Precision Torque Sensor

2010 ◽  
Author(s):  
Hans Aulin ◽  
Per Tunestal ◽  
Thomas Johansson ◽  
Bengt Johansson
Keyword(s):  
High Precision ◽  
Complex Dynamics ◽  
Signal To Noise Ratio ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Combustion Model ◽  
Torque Sensor ◽  
Sensor Position ◽  
Data Driven Approach ◽  
Data Driven Modeling

A high precision torque sensor is used for extracting combustion timing information from cylinder individual pressure estimates constructed from the torque measurements. A combination of physics-based and data driven modeling is used where the physical part of the model is based on equations describing contributions of inertial and gas forces while the flexing of the crankshaft, which has rather complex dynamics, is modeled using the data driven approach. The first part of the study shows the derivation of the models and how well the torque at the sensor position can be estimated from the pressures in the four cylinders. The second part demonstrates how it is possible to reconstruct cylinder individual torque and pressure by inverting the pressure to torque model. Going from measured torque to pressure in each cylinder is not trivial since the inverted model is ill conditioned around top dead centre which causes large errors where the precision is the most needed. A parameterized combustion model is therefore introduced to improve the signal to noise ratio in the estimated parameters. The proposed method for detecting combustion demonstrated good results with a coefficient of determination of 0.95 against “true” combustion phasing.

scholarly journals Improving the estimation of the COVID-19 effective reproduction number using nowcasting

2021 ◽  
pp. 096228022110089
Author(s):  
Joaquin Salas
Keyword(s):  
Reproduction Number ◽  
The Self ◽  
Data Driven ◽  
Statistical Behavior ◽  
Final Count ◽  
Data Driven Approach

As the interactions between people increases, the impending menace of COVID-19 outbreaks materializes, and there is an inclination to apply lockdowns. In this context, it is essential to have easy-to-use indicators for people to employ as a reference. The effective reproduction number of confirmed positives, Rt, fulfills such a role. This document proposes a data-driven approach to nowcast Rt based on previous observations’ statistical behavior. As more information arrives, the method naturally becomes more precise about the final count of confirmed positives. Our method’s strength is that it is based on the self-reported onset of symptoms, in contrast to other methods that use the daily report’s count to infer this quantity. We show that our approach may be the foundation for determining useful epidemy tracking indicators.

THE 2019 NOVEL PANDEMIC OF CORONAVIRUS DISEASE (COVID-19): A STUDY OF EXISTING EVIDENCE IN THE INDIAN POPULATION

2020 ◽  
Vol 7 (2) ◽  
pp. 199-204
Author(s):  
Shrikant Verma ◽  
Mohammad Abbas ◽  
Sushma Verma ◽  
Syed Tasleem Raza ◽  
Farzana Mahdi
Keyword(s):  
Disease Transmission ◽  
Indian Population ◽  
Respiratory Infections ◽  
Clinical Signs ◽  
Case Fatality ◽  
Public Health Emergency ◽  
The World ◽  
And Control ◽  
General Wellbeing ◽  
Different Parts

A novel spillover coronavirus (nCoV), with its epicenter in Wuhan, China's People's Republic, has emerged as an international public health emergency. This began as an outbreak in December 2019, and till November eighth, 2020, there have been 8.5 million affirmed instances of novel Covid disease2019 (COVID-19) in India, with 1,26,611 deaths, resulting in an overall case fatality rate of 1.48 percent. Coronavirus clinical signs are fundamentally the same as those of other respiratory infections. In different parts of the world, the quantity of research center affirmed cases and related passings are rising consistently. The COVID- 19 is an arising pandemic-responsible viral infection. Coronavirus has influenced huge parts of the total populace, which has prompted a global general wellbeing crisis, setting all health associations on high attentive. This review sums up the overall landmass, virology, pathogenesis, the study of disease transmission, clinical introduction, determination, treatment, and control of COVID-19 with the reference to India.

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