scholarly journals Dynamical model for social distancing in the U.S. during the COVID-19 epidemic

2020 ◽  
Vol 8 (1) ◽  
pp. 141-149
Author(s):  
Shirish M. Chitanvis
Keyword(s):  
New York ◽  
Monte Carlo ◽  
Temporal Evolution ◽  
Social Experiment ◽  
Social Distancing ◽  
Back Testing ◽  
Moderate Growth ◽  
Log Normal ◽  
Very High ◽  
The U.S

AbstractBackground Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately.Methods We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high.Findings We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth.Additionally we studied the reproducibility Ro in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.

scholarly journals Dynamical model for social distancing in the U.S. during the COVID-19 epidemic

2020 ◽  
Author(s):  
Shirish M Chitanvis
Keyword(s):  
New York ◽  
Monte Carlo ◽  
Probability Density Function ◽  
Temporal Evolution ◽  
Social Experiment ◽  
Social Distancing ◽  
Back Testing ◽  
Log Normal ◽  
Very High ◽  
The U.S

Background Social distancing has led to a flattening of the curve in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. Methods We propose on phenomenological grounds a generalized diffusion equation which in- corporates the effect of social distancing to forecast the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospital- izations is very high. Findings We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of publication and our forecasts indicate possible precursors of increased hospitalizations. Additionally we studied the reproducibility Ro in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19. Funding None.

Monte Carlo Algorithms for Moments of Transition Arrays

1988 ◽  
Vol 102 ◽  
pp. 79-81
Author(s):  
A. Goldberg ◽  
S.D. Bloom
Keyword(s):  
Monte Carlo ◽  
State Vector ◽  
Basis Vector ◽  
Collective State ◽  
Dispersion Characteristics ◽  
Dipole Strength ◽  
The Third ◽  
Monte Carlo Algorithms ◽  
Third Moment ◽  
Very High

AbstractClosed expressions for the first, second, and (in some cases) the third moment of atomic transition arrays now exist. Recently a method has been developed for getting to very high moments (up to the 12th and beyond) in cases where a “collective” state-vector (i.e. a state-vector containing the entire electric dipole strength) can be created from each eigenstate in the parent configuration. Both of these approaches give exact results. Herein we describe astatistical(or Monte Carlo) approach which requires onlyonerepresentative state-vector |RV> for the entire parent manifold to get estimates of transition moments of high order. The representation is achieved through the random amplitudes associated with each basis vector making up |RV>. This also gives rise to the dispersion characterizing the method, which has been applied to a system (in the M shell) with≈250,000 lines where we have calculated up to the 5th moment. It turns out that the dispersion in the moments decreases with the size of the manifold, making its application to very big systems statistically advantageous. A discussion of the method and these dispersion characteristics will be presented.

Contributions of Joseph Hardcastle to Accounting Theory

2008 ◽  
Vol 35 (2) ◽  
pp. 145-179 ◽  
Author(s):  
George C. Romeo ◽  
James J. McKinney
Keyword(s):  
New York ◽  
Turn Of The Century ◽  
Strong Impact ◽  
New York University ◽  
Honorary Degree ◽  
Cpa Exam ◽  
Accounting Theory ◽  
The U.S

Joseph Hardcastle was one of the foremost authorities on subjects connected with the mathematics of finance and other topics in accounting in the late 19th and early 20th centuries. As a teacher, author, and leader in the profession, he figured prominently in the elevation of accountancy. Hardcastle is relatively unknown in the literature except for having the distinction of scoring the highest grades on the first CPA exam in New York in 1896. However, he was well respected during his time as one of the premier theorists in accounting and was awarded an honorary degree of Master of Letters by New York University. Because of his prolific writings, his teaching of future accountants, and his interactions with members of the Institute of Accounts, he had a strong impact on the “science of accounts,” the dominant accounting theory in the U.S. at the turn of the century.

Relationship of hospital volume on outcomes in patients with acute pulmonary embolism: Analysis of a 70,000 patient database

2020 ◽  
pp. 1358863X2097026
Author(s):  
Mark Finkelstein ◽  
Mario A Cedillo ◽  
David C Kestenbaum ◽  
Obaib S Shoaib ◽  
Aaron M Fischman ◽  
...  
Keyword(s):  
New York ◽  
Pulmonary Embolism ◽  
Length Of Stay ◽  
New York State ◽  
Multivariable Analysis ◽  
High Volume ◽  
Categorical Variables ◽  
Patient Database ◽  
Very High ◽  
Total Charges

Positive relationships between volume and outcome have been seen in several surgical and medical conditions, resulting in more centralized and specialized care structures. Currently, there is a scarcity of literature involving the volume–outcome relationship in pulmonary embolism (PE). Using a state-wide dataset that encapsulates all non-federal admissions in New York State, we performed a retrospective cohort study on admitted patients with a diagnosis of PE. A total of 70,443 cases were separated into volume groups stratified by hospital quartile. Continuous and categorical variables were compared between cohorts. Multivariable regression analysis was conducted to assess predictors of 1-year mortality, 30-day all-cause readmission, 30-day PE-related readmission, length of stay, and total charges. Of the 205 facilities that were included, 128 (62%) were labeled low volume, 39 (19%) medium volume, 23 (11%) high volume, and 15 (7%) very high volume. Multivariable analysis showed that very high volume was associated with decreased 30-day PE-related readmission (OR 0.64; 95% CI, 0.55 to 0.73), decreased 30-day all-cause readmission (OR 0.84; 95% CI, 0.79 to 0.89), decreased 1-year mortality (OR 0.85; 95% CI, 0.80 to 0.91), decreased total charges (OR 0.96; 95% CI, 0.94 to 0.98), and decreased length of stay (OR 0.94; 95% CI, 0.92 to 0.96). In summary, facilities with higher volumes of acute PE were found to have less 30-day PE-related readmissions, less all-cause readmissions, shorter length of stay, decreased 1-year mortality, and decreased total charges.

scholarly journals Babesiosis Occurrence among the U.S. Medicare Beneficiaries Ages 65 and Older, During 2006-2017: Overall, and by State and County of Residence

2020 ◽  
Author(s):  
Mikhail Menis ◽  
Barbee I Whitaker ◽  
Michael Wernecke ◽  
Yixin Jiao ◽  
Anne Eder ◽  
...  
Keyword(s):  
New York ◽  
Rhode Island ◽  
Parasitic Infection ◽  
Medicare Beneficiaries ◽  
Study Objective ◽  
Diagnosis Codes ◽  
Real World Evidence ◽  
At Risk Populations ◽  
Human Babesiosis ◽  
The U.S

Abstract Background Human babesiosis is a mild-to-severe parasitic infection that poses health concerns especially in older and other at-risk populations. The study objective was to assess babesiosis occurrence among the U.S. Medicare beneficiaries, ages 65 and older, during 2006-2017. Methods Our retrospective claims-based study utilized Medicare databases. Babesiosis cases were identified using recorded diagnosis codes. The study estimated rates (per 100,000 beneficiary-years) overall, by year, diagnosis month, demographics, state and county of residence. Results Nationwide, 19,469 beneficiaries had babesiosis recorded, a rate of 6 per 100,000 person-years, ranging from 4 in 2006 to 9 in 2017 (p<0.05). The highest babesiosis rates were in: Massachusetts (62), Rhode Island (61), Connecticut (51), New York (30), and New Jersey (19). The highest rates by county were in: Nantucket, MA (1,089); Dukes, MA (236); Barnstable, MA (213); and Dutchess, NY (205). Increasing rates, from 2006 through 2017 (p<0.05), were identified in multiple states, including states previously considered non-endemic. New Hampshire, Maine, Vermont, Pennsylvania, and Delaware saw rates increase by several times. Conclusion Our 12-year study shows substantially increasing babesiosis diagnosis trends, with highest rates in well-established endemic states. It also suggests expansion of babesiosis infections in other states and highlights the utility of real-world evidence.

scholarly journals Effectiveness of Face Masks in Reducing the Spread of COVID-19: A Model-Based Analysis

2021 ◽  
pp. 0272989X2110190
Author(s):  
Isabelle J. Rao ◽  
Jacqueline J. Vallon ◽  
Margaret L. Brandeau
Keyword(s):  
New York ◽  
Infected Individual ◽  
Disease Model ◽  
World Health ◽  
Sensitivity Analyses ◽  
Reproductive Number ◽  
Social Distancing ◽  
Health Organization ◽  
Almost All ◽  
Mask Effectiveness

Background The World Health Organization and US Centers for Disease Control and Prevention recommend that both infected and susceptible people wear face masks to protect against COVID-19. Methods We develop a dynamic disease model to assess the effectiveness of face masks in reducing the spread of COVID-19, during an initial outbreak and a later resurgence, as a function of mask effectiveness, coverage, intervention timing, and time horizon. We instantiate the model for the COVID-19 outbreak in New York, with sensitivity analyses on key natural history parameters. Results During the initial epidemic outbreak, with no social distancing, only 100% coverage of masks with high effectiveness can reduce the effective reproductive number [Formula: see text] below 1. During a resurgence, with lowered transmission rates due to social distancing measures, masks with medium effectiveness at 80% coverage can reduce [Formula: see text] below 1 but cannot do so if individuals relax social distancing efforts. Full mask coverage could significantly improve outcomes during a resurgence: with social distancing, masks with at least medium effectiveness could reduce [Formula: see text] below 1 and avert almost all infections, even with intervention fatigue. For coverage levels below 100%, prioritizing masks that reduce the risk of an infected individual from spreading the infection rather than the risk of a susceptible individual from getting infected yields the greatest benefit. Limitations Data regarding COVID-19 transmission are uncertain, and empirical evidence on mask effectiveness is limited. Our analyses assume homogeneous mixing, providing an upper bound on mask effectiveness. Conclusions Even moderately effective face masks can play a role in reducing the spread of COVID-19, particularly with full coverage, but should be combined with social distancing measures to reduce [Formula: see text] below 1. [Box: see text]

scholarly journals The effect of social distancing on the reach of an epidemic in social networks

2021 ◽  
Author(s):  
Gregory Gutin ◽  
Tomohiro Hirano ◽  
Sung-Ha Hwang ◽  
Philip R. Neary ◽  
Alexis Akira Toda
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Global Level ◽  
Social Distancing ◽  
The Public ◽  
Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

scholarly journals Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S.

2021 ◽  
Author(s):  
Catalina Amuedo-Dorantes ◽  
Neeraj Kaushal ◽  
Ashley N. Muchow
Keyword(s):  
New York ◽  
Political Ideology ◽  
Death Rate ◽  
The United States ◽  
County Level ◽  
Level Data ◽  
Level Information ◽  
Alternative Measures ◽  
The U.S ◽  
Pharmaceutical Interventions

AbstractUsing county-level data on COVID-19 mortality and infections, along with county-level information on the adoption of non-pharmaceutical interventions (NPIs), we examine how the speed of NPI adoption affected COVID-19 mortality in the United States. Our estimates suggest that adopting safer-at-home orders or non-essential business closures 1 day before infections double can curtail the COVID-19 death rate by 1.9%. This finding proves robust to alternative measures of NPI adoption speed, model specifications that control for testing, other NPIs, and mobility and across various samples (national, the Northeast, excluding New York, and excluding the Northeast). We also find that the adoption speed of NPIs is associated with lower infections and is unrelated to non-COVID deaths, suggesting these measures slowed contagion. Finally, NPI adoption speed appears to have been less effective in Republican counties, suggesting that political ideology might have compromised their efficacy.

Sign in / Sign up

Export Citation Format

Share Document