scholarly journals Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA

2020 ◽  
Author(s):  
Raj Dandekar ◽  
Emma Wang ◽  
George Barbastathis ◽  
Chris Rackauckas
Keyword(s):  
Data Driven ◽  
Us States ◽  
Infected Case ◽  
West Central ◽  
Case Count ◽  
Proposed Model ◽  
The Us ◽  
June July ◽  
Central Usa

1SUMMARYIn the wake of the rapid surge in the Covid-19 infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection timeseries, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly co-related with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100, 000 for the states of Florida and Texas. As we continue our fight against Covid-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution; for any region under consideration.

scholarly journals Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Raj Dandekar ◽  
Emma Wang ◽  
George Barbastathis ◽  
Chris Rackauckas
Keyword(s):  
Data Driven ◽  
Us States ◽  
Infected Case ◽  
West Central ◽  
Case Count ◽  
Proposed Model ◽  
The Us ◽  
June July ◽  
Central Usa

In the wake of the rapid surge in the COVID-19-infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel COVID-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection time series, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly corelated with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100,000 for the states of Florida and Texas. As we continue our fight against COVID-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution, for any region under consideration.

scholarly journals COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model

2021 ◽  
Vol 3 (1) ◽  
pp. 57-66
Author(s):  
Akey Sungheetha
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Risk Minimization ◽  
Proposed Model ◽  
Epidemic Intelligence ◽  
Spread Dynamics ◽  
Using Data ◽  
The Impact ◽  
Global Strategies

In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Strategies (DDEIS) are compared in this paper to identify the shortcomings and propose a novel solution to overcome them. In terms of epidemic resurgence risk minimization, guaranteeing public health safety and quick return of cities to normalcy, a social as well as technological solution may be provided by incorporating the key features of DDEIS. The role of human behavior is taken into consideration while assessing its limitations and benefits for policy making as well as individual decision making. The epidemiological model of SEIR (Susceptible–Exposed–Infectious–Recovered) provides preliminary data for the preferences of users in a DPTT. The impact of the proposed model on the spread dynamics of Covid-19 is evaluated and the results are presented.

scholarly journals Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning

2020 ◽  
Author(s):  
Raj Dandekar ◽  
George Barbastathis
Keyword(s):  
Neural Network ◽  
South Korea ◽  
The United States ◽  
Government Interventions ◽  
Infected Case ◽  
Health Measures ◽  
Case Count ◽  
The Us ◽  
Spread Of Infection ◽  
Limited Testing

Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019 (CHP 2020), the disease has spread worldwide and met with a wide variety of social distancing and quarantine policies. The effectiveness of these responses is notoriously difficult to quantify as individuals travel, violate policies deliberately or inadvertently, and infect others without themselves being detected (Li et al. 2020a; Wu & Leung 2020; Wang et al. 2020; Chinazzi et al. 2020; Ferguson et al. 2020; Kraemer et al. 2020). Moreover, the publicly available data on infection rates are themselves unreliable due to limited testing and even possibly under-reporting (Li et al. 2020b). In this paper, we attempt to interpret and extrapolate from publicly available data using a mixed first-principles epidemiological equations and data-driven neural network model. Leveraging our neural network augmented model, we focus our analysis on four locales: Wuhan, Italy, South Korea and the United States of America, and compare the role played by the quarantine and isolation measures in each of these countries in controlling the effective reproduction number Rt of the virus. Our results unequivocally indicate that the countries in which rapid government interventions and strict public health measures for quarantine and isolation were implemented were successful in halting the spread of infection and prevent it from exploding exponentially. In the case of Wuhan especially, where the available data were earliest available, we have been able to test the predicting ability of our model by training it from data in the January 24th till March 3rd window, and then matching the predictions up to April 1st. Even for Italy and South Korea, we have a buffer window of one week (25 March - 1 April) to validate the predictions of our model. In the case of the US, our model captures well the current infected curve growth and predicts a halting of infection spread by 20 April 2020. We further demonstrate that relaxing or reversing quarantine measures right now will lead to an exponential explosion in the infected case count, thus nullifying the role played by all measures implemented in the US since mid March 2020.

Credit and the Labour Share: Evidence from US States

2020 ◽  
Vol 130 (630) ◽  
pp. 1782-1816 ◽  
Author(s):  
Aslı Leblebicioğlu ◽  
Ariel Weinberger
Keyword(s):  
Financial Development ◽  
Financial Frictions ◽  
Us States ◽  
Banking Deregulation ◽  
Interstate Banking ◽  
Percentage Points ◽  
Labour Share ◽  
The Us ◽  
The Relationship

Abstract We investigate the role of credit markets as a cause for changes in the US labour share. Causal evidence is provided that the labour share declined between 0.8 and 1.2 percentage points following the interstate banking deregulation, explaining more than half of the overall reduction during that period. The lower costs of credit and greater bank competition in each state are mechanisms that led to the decline. To quantify the relationship between credit and factor payments, we calibrate a model with financial frictions and highlight financial development as a potential channel for the reduction in labour share observed globally.

scholarly journals Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258824
Author(s):  
Sayanti Mukherjee ◽  
Zhiyuan Wei
Keyword(s):  
Environmental Factors ◽  
Goodness Of Fit ◽  
Metropolitan Areas ◽  
Predictive Accuracy ◽  
Climatic Factors ◽  
Data Driven ◽  
Us States ◽  
Suicide Rates ◽  
Significant Difference ◽  
The Us

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

scholarly journals House price synchronization across the US states: The role of structural oil shocks

2021 ◽  
Vol 56 ◽  
pp. 101372
Author(s):  
Xin Sheng ◽  
Hardik A. Marfatia ◽  
Rangan Gupta ◽  
Qiang Ji
Keyword(s):  
House Price ◽  
Oil Shocks ◽  
Us States ◽  
The Us

scholarly journals Prediction of Covid-19 Infections Through December 2020 for 10 US States Incorporating Outdoor Temperature and School Re-Opening Effects-August Update

2020 ◽  
Author(s):  
Ty A Newell
Keyword(s):  
Human Behavior ◽  
Disease Transmission ◽  
Face Mask ◽  
Transmission Efficiency ◽  
Actual Data ◽  
Outdoor Temperature ◽  
Us States ◽  
The Us ◽  
June July ◽  
The Impact

End-of-August updates for Covid-19 infection case predictions for 10 US States (NY, WA, GA, IL, MN, FL, OH, MI, CA, and NC) are compared to actual data. Several states that experienced significant summer surges gained control of accelerating infection spread during August. The US as a whole and the 10 States investigated continue to follow periods of linear infection growth that defines a boundary separating accelerated infection growth and infection decay. August 31 predictions (initiated July 27, 2020) for four States (NY, WA, MI and MN) are within 10% of actual data. Predictions for four other States (GA, IL, CA, and OH) agree between 10 and 20% of actual data. Predictions for two States (FL and NC) are greater than 20% different from actual data. Systematic differences between predictions and actual data are related to the impact of the June-July summer surge, and human behavior reactions (ie, increased face mask usage and distancing) to accelerated infection growth. Outdoor temperature effects and school re-opening effects are not apparent nor expected for August. Human behavior parameters (Social Distance Index, SDI, and disease transmission efficiency, G, parameters) are adjusted to mirror August data. Comparisons of actual versus predicted daily new infection cases display the complexity of SARS-CoV-2 transmission.

Estimating production functions for the US states: the role of public and human capital

2016 ◽  
Vol 52 (2) ◽  
pp. 691-721 ◽  
Author(s):  
Nikos Benos ◽  
Nikolaos Mylonidis ◽  
Stefania Zotou
Keyword(s):  
Human Capital ◽  
Production Functions ◽  
Us States ◽  
The Us
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