scholarly journals Modeling the USA Winter 2021 Resurgence

2022 ◽  
Author(s):  
Genghmun Eng
Keyword(s):  
Doubling Time ◽  
Social Distancing ◽  
Initial Stage ◽  
The Usa ◽  
Parameter Values ◽  
O 11 ◽  
Pandemic Wave

The current USA 2021 CoVID-19 Winter Resurgence is modeled here with the same function used for analyzing prior USA CoVID-19 waves: N(t)= max[N/o\exp{+(t/[t/R\(1+α/s\t)]exp(-δ/o\t)}]. Here, N(t) gives the total number of CoVID-19 cases above the previous baseline, and t/R\ sets the initial t/dbl\ = [t/R\ ln(2)] pandemic t/dbl\ doubling time. Larger α/s\ values indicate that uninfected people are improving their pandemic mitigation efforts, such as Social Distancing and vaccinations; while δ/o\>0 accelerates the post-peak [(d/dt)N(t)] tail-off, and is empirically associated with mask-wearing. The pandemic wave end is when N(t) no longer increases. Results from the USA Summer 2021 Resurgence (see prior medrxiv.org preprints*) were used as a baseline. By 11/15/2021, an additional N/o\(11/15/2021)=107,000 cases above baseline were found, signaling the USA Winter 2021 Resurgence. This CoVID-19 wave is still in its initial stages. Presently, our analysis indicates that this CoVID-19 wave can infect virtually all susceptible persons; just like the initial stage of the USA Summer 2021 Resurgence. Data up through 12/30/2021 gives these parameter values: t/R\=8.05 days; α/s\=0.011/day. These values are identical to the prior 2020 USA Winter Resurgence results. Also, the present N/o\(11/15/2021) and the prior N/o\(9/25/2020)=89,900 values are similar. However, while the Winter 2020 Resurgence showed a significant mask-wearing effect: δ/o\(2020)= 1.748 x 10^-3 / day, this initial USA Winter 2021 Resurgence shows practically no mask-wearing effects: δ/o\(2021)< 0.001 x 10^-3 / day. If mask-wearing were to quickly rise to the Winter 2020 levels, it would give these projected totals: N(t=[1/1/2022])= 54,705,400; N(t=[3/21/2022])= 83,371,000; N(t=[3/21/2024])= 92,399,000. More robust mask-wearing and enhanced Social Distancing measures could further reduce these values (with 3 Figures). * (10.1101_2021.08.16.21262150; 10.1101_2021.10.15.21265078)

scholarly journals Initial Model for USA CoVID-19 Resurgence

2020 ◽  
Author(s):  
Genghmun Eng
Keyword(s):  
South Korea ◽  
Linear Function ◽  
Doubling Time ◽  
Functional Form ◽  
Gradual Increase ◽  
Initial Model ◽  
Social Distancing ◽  
Daily Number ◽  
The Usa

Early CoVID-19 growth obeys: N{t*}=NI exp[+Ko t* ], with Ko=[(ln2)/(tdbl)], where tdbl is the pandemic growth doubling time. Given N{t*}, the daily number of new CoVID-19 cases is ρ{t*} = dN{t*}/d{t*}. Implementing society-wide Social Distancing increases the tdbl doubling time, and a linear function of time for tdbl was used in our Initial Model: No[t] = 1 exp[+KA t / (1 + γot) ] = eGo exp(-Zo[t] ) , to describe these changes, where the [t]-axis is time-shifted from the t*-axis, back to the pandemic start, and Go = [ KA / γo ]. While this No[t] successfully modeled the USA CoVID-19 progress from 3/2020 to 6/2020, this equation could not easily model some quickly decreasing ρ[t] cases ("fast pandemic shutoff"), indicating that a second process was involved. This situation was most evident in the initial CoVID-19 data from China, South Korea, and Italy. Modifying Zo[t] to allow exponential cutoffs: Zo[t] ≡ +[Go / (1+γot)] [exp(-δot)] = Zo[t] exp(-δot) , NA[t] = eGo exp(-ZA[t]) , resulted in an Enhanced Initial Model (EIM) that significantly improved data fits for these cases. After 6/2020, many regions of the USA "opened up", loosening their Social Distancing requirements, which led to a sudden USA CoVID-19 Resurgence. Extrapolating the USA No[t] 3/2020-6/2020 results to 9/2020 as an Initial Model Baseline (IMB), and subtracting this IMB from the newer USA data gives a Resurgence Only function, which is analyzed here. This USA CoVID-19 Resurgence function differs significantly from the No[t] IMB functional form, but it was well-modeled by the NA[t] fast pandemic shutoff function. These results indicate that: (a) the gradual increase in tdbl doubling time from society-wide shut-downs is likely due to eliminating of a large number of population gathering points that could have enabled CoVID-19 spread; and (b) having a non-zero δofast pandemic shutoff is likely due to more people wearing masks more often [with 12 Figures].

scholarly journals COVID Lockdowns, Social Distancing, and Fatal Car Crashes: More Deaths on Hobbesian Highways?

2020 ◽  
Vol 4 (3-4) ◽  
pp. 238-259 ◽  
Author(s):  
Marshall W. Meyer
Keyword(s):  
World War Ii ◽  
Traffic Safety ◽  
Motor Vehicle ◽  
Road Traffic ◽  
Research Question ◽  
Traffic Volume ◽  
Similar Data ◽  
World War ◽  
Social Distancing ◽  
The Usa

Abstract Research Question What happened to US traffic safety during the first US COVID-19 lockdown, and why was the pattern the opposite of that observed in previous sudden declines of traffic volume? Data National and local statistics on US traffic volume, traffic fatalities, injury accidents, speeding violations, running of stop signs, and other indicators of vehicular driving behavior, both in 2020 and in previous US economic recessions affecting the volume of road traffic. Methods Comparative analysis of the similarities and differences between the data for the COVID-19 lockdown in parts of the USA in March 2020 and similar data for the 2008–2009 global economic crisis, as well as other US cases of major reductions in traffic volume. Findings The volume of traffic contracted sharply once a COVID-19 national emergency was declared and most states issued stay-at-home orders, but motor vehicle fatality rates, injury accidents, and speeding violations went up, and remained elevated even as traffic began returning toward normal. This pattern does not fit post-World War II recessions where fatality rates declined with the volume of traffic nor does the 2020 pattern match the pattern during World War II when traffic dropped substantially with little change in motor vehicle fatality rates. Conclusions The findings are consistent with a theory of social distancing on highways undermining compliance with social norms, a social cost of COVID which, if not corrected, poses potential long-term increases in non-compliance and dangerous driving.

scholarly journals Response to COVID-19 in South Korea and implications for lifting stringent interventions

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Amy Dighe ◽  
Lorenzo Cattarino ◽  
Gina Cuomo-Dannenburg ◽  
Janetta Skarp ◽  
Natsuko Imai ◽  
...  
Keyword(s):  
South Korea ◽  
High Volume ◽  
Individual Case ◽  
Metropolitan Region ◽  
Contact Tracing ◽  
Social Distancing ◽  
Early Adoption ◽  
The Usa ◽  
Sharp Growth ◽  
Seoul Metropolitan Region

Abstract Background After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their “test, trace, isolate” strategy. However, it is important to understand the epidemiological peculiarities of South Korea’s outbreak and characterise their response before attempting to emulate these measures elsewhere. Methods We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, Rt, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources. Results We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64–2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent “lockdown” measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. Conclusions Whilst early adoption of testing and contact tracing is likely to be important for South Korea’s successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.

ECONOMIC-DEMOGRAPHIC DEPENDENCY RATIO IN A LIFE-CYCLE MODEL

2019 ◽  
Vol 24 (7) ◽  
pp. 1635-1673
Author(s):  
Sau-Him P. Lau ◽  
Albert K. Tsui
Keyword(s):  
Life Cycle ◽  
Population Aging ◽  
Retirement Age ◽  
Dependency Ratio ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Stochastic Mortality ◽  
The Usa ◽  
Definition Of ◽  
Parameter Values

The conventional dependency ratio based on cohort-invariant cutoff points could overstate the true burden of population aging. Using optimal cohort-varying years of schooling and retirement age in a life-cycle model, we propose a modified definition of dependency ratio. We compare the proposed economic-demographic dependency ratio (EDDR) with the conventional definition and find that the conventional dependency ratio of the USA is projected to increase by 0.105 from 2010 to 2060, which is an over-projection of 86% when compared with the projected increase of 0.015 in the EDDR over the same period. Sensitivity analysis suggests that our finding is quite robust to reasonable changes in parameter values (except for one parameter), and the magnitude of over-projection ranges mainly from 0.079 to 0.102 (i.e., 75% to 97%). We follow the well-established Lee–Carter model to forecast stochastic mortality and employ the method of expanding duration to decompose the sources of over-projection.

Performance Analysis of a Pure Electric Light Off-Road Vehicle Based on the Cruise

2014 ◽  
Vol 721 ◽  
pp. 24-27
Author(s):  
Jian Jun Yang
Keyword(s):  
System Development ◽  
Drive System ◽  
Road Vehicle ◽  
Performance Simulation ◽  
Software Application ◽  
Stage Of Development ◽  
Initial Stage ◽  
Design Requirements ◽  
Electric Light ◽  
Parameter Values

s: According to the design requirements of pure electric light off-road vehicle in the initial stage of development, parameter values of the main parts of the vehicle drive system are selected after theoretical calculation. With the vehicle modeling and its performance simulation on the Cruise software, indicators of its dynamics and economic performance are got and the results are compared to the design requirements, which verified the feasibility of Cruise software application in the electric vehicle drive system development.

scholarly journals Trust in science boosts approval, but not following of COVID-19 rules

2021 ◽  
Author(s):  
Justin Sulik ◽  
Ophelia Deroy ◽  
Guillaume Dezecache ◽  
Martha Newson ◽  
Yi Zhao ◽  
...  
Keyword(s):  
Indirect Effect ◽  
Political Ideology ◽  
Social Benefits ◽  
Prevention Measures ◽  
Social Distancing ◽  
The Usa ◽  
The Relationship

How essential is trust in science to prevent the spread of COVID-19? Previous work shows that people who trust in science are more likely to comply with official guidelines, which suggests that higher levels of compliance could be achieved by improving trust in science. However, analysis of a global dataset (n=4341) suggests otherwise. Trust in science had a small, indirect effect on adherence to the rules. It affected adherence only insofar as it predicted people's approval of prevention measures such as social distancing. Trust in science also mediated the relationship between political ideology and approval of the measures (more conservative people trusted science less and in turn approved of the measures less). These effects varied across countries, and were especially different in the USA. Overall, these results mean that any increase in trust in science is unlikely to yield strong immediate improvements in following COVID-19 rules. Nonetheless, given its relationships with both ideology and individuals' attitudes to the measures, trust in science may be leveraged to yield longer-term and more sustained social benefits.

scholarly journals Results of Scanning Electron Microscopy of Explanted Hydrophilic Acrylic IOLs

2021 ◽  
Vol 18 (2) ◽  
pp. 276-283
Author(s):  
A. A. Gamidov ◽  
I. A. Novikov ◽  
A. A. Tsymbal ◽  
R. A. Gamidov
Keyword(s):  
Central Zone ◽  
Optical Element ◽  
Anterior Wall ◽  
Pathological Process ◽  
Zonal Structure ◽  
Entire Surface ◽  
Hydrophobic Coating ◽  
Initial Stage ◽  
The Usa ◽  
Scanning Electron

Purpose: study the microscopic examination and microtopography of explanted hydrophilic acrylic IOLs with opacification.Material and methods. 5 samples of soft hydrophilic acrylic IOLs produced in European countries and the USA were studied. Explanted IOLs were studied using a scanning electron microscope (EVO LS10, Karl Zeiss, Germany-UK).Results. In 4 cases changes in hydrophilic acrylic IOL had the character of surface opacification in the area of the anterior wall of the optical element of the lens with localization in the Central zone (pupil area). In one case, the opacities were located over the entire surface of a hydrophilic IOL having a hydrophobic coating. The changes were characterized by the formation of crystalline deposits on the IOL surface at different stages of evolution. In the initial stage, primary point precipitates with sizes of 3–5 microns were formed. At a later stage, the changes had the form of “adult” spherocrystals with a typical radial-concentric zonal structure, up to 50 microns in size. In one of the of IOLs, the growth of crystals under the surface of the lens — in the thickness of IOLs was determined.Conclusion. The changes characterize different stages of the same type of pathological process with sedimentation of crystal deposits on the surface of IOL with changing crystallomorphology. 

scholarly journals Orthogonal Functions for Evaluating Social Distancing Impact on CoVID-19 Spread

2020 ◽  
Author(s):  
Genghmun Eng
Keyword(s):  
Linear Function ◽  
Doubling Time ◽  
Laguerre Polynomials ◽  
Function Model ◽  
Orthogonal Functions ◽  
Social Distancing ◽  
Function Series ◽  
Orthogonal Function ◽  
Doubling Times ◽  
Model Robustness

Early CoVID-19 growth often obeys: N{t}=N/I\exp[+K/o\t], with K/o\=[(ln2)/(t/dbl\)], where t/dbl\ is the pandemic doubling time, prior to society-wide Social Distancing. Previously, we modeled Social Distancing with t/dbl\ as a linear function of time, where N[t]=1exp[+K/A\ t/(1+ gamma/o\ t)] is used here. Additional parameters besides {K/o\,gamma/o\} are needed to better model different rho[t]=dN[t]/dt shapes. Thus, a new Orthogonal Function Model [OFM] is developed here using these orthogonal function series: N(Z) = sum[m=0,M/F\] g/m\ L/m\(Z) exp[-Z] , R(Z) = sum[m=0,M/F\] c/m\ L/m\(Z) exp[-Z] , where N(Z) and Z[t] form an implicit N[t]=N(Z[t]) function, giving: G/o\ = [K/A\ / gamma/o\ ] , Z[t] = +[ G/o\ / (1+ gamma/o\t) ] , rho[t] = [ gamma/o\ / G/o\ ] (Z^2) R(Z) , with L/m\(Z) being the Laguerre Polynomials. At large M/F\ values, nearly arbitrary functions for N[t] and rho[t]=dN[t]/dt can be accommodated. How to determine {K/A\, gamma/o\} and the {g/m\; m=(0,+M/F\)} constants from any given N(Z) dataset is derived, with rho[t] set by: c/(M/F\ - k)\ = sum[m=0,k] g/m\ . The bing.com USA CoVID-19 data was analyzed using M/F\=(0,1,2) in the OFM. All results agreed to within about 10 percent, showing model robustness. Averaging over all these predictions gives the following overall estimates for the number of USA CoVID-19 cases at the pandemic end: <N/max\> = 5,009,677 (+/-) 269,450 (data to 5/3/20), and <N/max\> = 4,422,803 (+/-) 162,580 (data to 6/7/20), which compares the pre- and post-early May bing.com revisions. The CoVID-19 pandemic in Italy was examined next. The M/F\=2 limit was inadequate to model the Italy rho[t] pandemic tail. Thus, regions with a quick CoVID-19 pandemic shutoff may have additional Social Distancing factors operating, beyond what can be easily modeled by just progressively lengthening pandemic doubling times (with 13 Figures).

scholarly journals Initial Model for the Impact of Social Distancing on CoVID-19 Spread

2020 ◽  
Author(s):  
Genghmun Eng
Keyword(s):  
Exponential Growth ◽  
Growth Function ◽  
Worst Case ◽  
Social Distancing ◽  
Sick People ◽  
Large Numbers ◽  
The World ◽  
The Usa ◽  
The Impact ◽  
Good Agreement

AbstractThe initial stages of the CoVID-19 coronavirus pandemic all around the world exhibit a nearly exponential rise in the number of infections with time. Planners, governments, and agencies are scrambling to figure out “How much? How bad?” and how to effectively treat the potentially large numbers of simultaneously sick people. Modeling the CoVID-19 pandemic using an exponential rise implicitly assumes a nearly unlimited population of uninfected persons (“dilute pandemic”). Once a significant fraction of the population is infected (“saturated pandemic”), an exponential growth no longer applies. A new model is developed here, which modifies the standard exponential growth function to account for factors such as Social Distancing. A Social Mitigation Parameter [SMP] αS is introduced to account for these types of society-wide changes, which can modify the standard exponential growth function, as follows: The doubling-time tdbl = (In 2)/Ko can also be used to substitute for Ko, giving a {tdbl, αS} parameter pair for comparing to actual CoVID-19 data. This model shows how the number of CoVID-19 infections can self-limit before reaching a saturated pandemic level. It also provides estimates for: (a) the timing of the pandemic peak, (b) the maximum number of new daily cases that would be expected, and (c) the expected total number of CoVID-19 cases. This model shows fairly good agreement with the presently available CoVID-19 pandemic data for several individual States, and the for the USA as a whole (6 Figures), as well as for various countries around the World (9 Figures). An augmented model with two Mitigation Parameters, αS and βS, is also developed, which can give better agreement with the daily new CoVID-19 data. Data-to-model comparisons also indicate that using αS by itself likely provides a worst-case estimate, while using both αS and βS likely provides a best-case estimate for the CoVID-19 spread.

scholarly journals Accounting for super-spreading gives the basic reproduction number R0 of COVID-19 that is higher than initially estimated

2020 ◽  
Author(s):  
Marek Kochańczyk ◽  
Frederic Grabowski ◽  
Tomasz Lipniacki
Keyword(s):  
Basic Reproduction Number ◽  
Doubling Time ◽  
Reproduction Number ◽  
Case Reports ◽  
Individual Case ◽  
Epidemic Spread ◽  
Empirical Estimation ◽  
Seir Model ◽  
Initial Stage ◽  
The Basic Reproduction Number

Transmission of infectious diseases is characterized by the basic reproduction number R0, a metric used to assess the threat posed by an outbreak and inform proportionate preventive decision-making. Based on individual case reports from the initial stage of the coronavirus disease 2019 epidemic, R0 is often estimated to range between 2 and 4. In this report, we show that a SEIR model that properly accounts for the distribution of the incubation period suggests that R0 lie in the range 4.4–11.7. This estimate is based on the doubling time observed in the near-exponential phases of the epidemic spread in China, United States, and six European countries. To support our empirical estimation, we analyze stochastic trajectories of the SEIR model showing that in the presence of super-spreaders the calculations based on individual cases reported during the initial phase of the outbreak systematically overestimate the doubling time and thus underestimate the actual value of R0.

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