scholarly journals A "Tail" of Two Cities: Fatality-based Modeling of COVID-19 Evolution in New York City and Cook County, IL

2020 ◽  
Author(s):  
Joshua Frieman
Keyword(s):  
New York ◽  
New York City ◽  
Reproduction Number ◽  
Social Contact ◽  
Cook County ◽  
Mitigation Measures ◽  
Model Parameters ◽  
Credible Intervals ◽  
Case Rate ◽  
Case Data

I describe SIR modeling of the COVID-19 pandemic in two U.S. urban environments, New York City (NYC) and Cook County, IL, from onset through mid-June, 2020. Since testing was not widespread early in the pandemic in the U.S., I rely on public fatality data to estimate model parameters and use case data only as a lower bound. Fits to the first 20 days of data determine a degenerate combination of the basic reproduction number, R0, and the mean time to removal from the infectious population, 1/γ with γ(R0 − 1) = 0.25(0.21) inverse days for NYC (Cook County). Equivalently, the initial doubling time was td = 2.8(3.4) days for NYC (Cook). The early fatality data suggest that both locations had infections in early February. I model the mitigation measures implemented in mid-March in both locations (distancing, quarantine, isolation, etc) via a time-dependent reproduction number Rt that declines monotonically from R0 to a smaller asymptotic value, R0(1-X), with a parameterized functional form. The timing (mid-March) and duration (several days) of the transitions in Rt appear well determined by the data. With flat priors on model parameters and the lower bound from reported cases, the NYC fatality data imply 95.45% credible intervals of R0 = 2.6 − 2.9, social contact reduction X = 69 − 76% and infection fatality rate f = 1 − 1.5%, with 19 − 27% of the population asymptotically infected. The case data relative to daily deaths suggest that the reported case rate as a fraction of true case rate grew linearly, reaching a plateau around April 20 for both NYC and Cook County; the models also suggest that the late-time NYC reported case rate was comparable to the true rate, while for Cook County it remained an underestimate. For Cook County, the fatality evolution was qualitatively different from NYC: after mitigation measures were implemented, daily fatality counts reached a plateau for about a month before tailing off. This is consistent with an SIR model that exhibits "critical slowing-down", in which Rt plateaus at a value just above unity. For Cook County, the 95.45% credible intervals for the model parameters are much broader and shifted downward, R0 = 1.4 − 4.7, X = 26 − 54%, and f = 0.1 − 0.6% with 15 − 88% of the population asymptotically infected. Despite the apparently lower efficacy of its social contact reduction measures, Cook County has had significantly fewer fatalities per population than NYC, D∞/N = 100 vs. 270 per 100,000. In the model, this is attributed to the lower inferred IFR for Cook; an external prior pointing to similar values of the IFR for the two locations would instead chalk up the difference in D/N to differences in the relative growth rate of the disease. I derive a model-dependent threshold, Xcrit, for "safe" re-opening, that is, for easing of contact reduction that would not trigger a second wave; for NYC, the models predict that increasing social contact by more than 20% from post-mitigation levels will lead to renewed spread, while for Cook County the threshold value is very uncertain, given the parameter degeneracies. The timing of 2nd-wave growth will depend on the amplitude of contact increase relative to Xcrit and on the asymptotic growth rate, and the impact in terms of fatalities will depend on the parameter f .

scholarly journals Visualizing the invisible:The effect of asymptomatic transmission on the outbreak dynamics of COVID-19

2020 ◽  
Author(s):  
Mathias Peirlinck ◽  
Kevin Linka ◽  
Francisco Sahli Costabal ◽  
Jay Bhattacharya ◽  
Eran Bendavid ◽  
...  
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Behavioral Changes ◽  
Mitigation Strategies ◽  
Santa Clara County ◽  
Credible Intervals ◽  
Order Of Magnitude ◽  
Community Transmission ◽  
Epidemiological Characteristics ◽  
Case Data

AbstractUnderstanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups. For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial out- break date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019 - February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.

scholarly journals An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

2021 ◽  
Author(s):  
Sheng Zhang ◽  
Joan Ponce ◽  
Zhen Zhang ◽  
Guang Lin ◽  
George Karniadakis
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Robustness Analysis ◽  
Data Driven ◽  
Model Parameters ◽  
Calibration Model ◽  
Epidemiological Models ◽  
Analysis Model ◽  
Identifiability Analysis

AbstractEpidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to forecast the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately predict the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

Notes on Municipal Affairs

1931 ◽  
Vol 25 (3) ◽  
pp. 671-682
Author(s):  
Thomas H. Reed
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Cook County ◽  
Political Career ◽  
Total Eclipse ◽  
The People ◽  
Preceding Period

The year which has passed since the preparation of the last “Notes on Municipal Affairs” (June 1, 1930) has been even more eventful than the preceding period.Developments in Particular Cities. New York City. The belief which had been growing for many years that the Tammany tiger was, after all, a self-restrained, self-muzzled beast has suffered a rude shock in the exposures of flagrant corruption in the sale of judicial office, the handling of vice, the purchase of land for school purposes, and in many other directions. The district attorney's office has been exposed to the searchlight of investigator Seabury. Charges were preferred against Mayor Walker by John Haynes Holmes and Rabbi Wise in the name of a citizens' committee. Governor Roosevelt dismissed these charges with scant consideration. In the meantime, however, the legislature ordered a most searching investigation of the whole governmental situation in New York—an investigation which bids fair to rival, in extent and dramatic interest, that of the celebrated Lexow committee.Chicago. In Chicago, Mayor Thompson's political career has suffered, if not extinction, at least a total eclipse. Though victorious against a broken field in the Republican primary, he was defeated by Anton J. Cermak in the election of April 7 by a vote of 476,932 to 671,189. It is probable that the people of Chicago were more anti-Thompson than pro-Cermak, but the new mayor is a vigorous and striking figure. For one thing, he is boss in his own right of the Democratic organization in Cook county.

scholarly journals Identifying airborne transmission as the dominant route for the spread of COVID-19

2020 ◽  
Vol 117 (26) ◽  
pp. 14857-14863 ◽  
Author(s):  
Renyi Zhang ◽  
Yixin Li ◽  
Annie L. Zhang ◽  
Yuan Wang ◽  
Mario J. Molina
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Effective Means ◽  
The United States ◽  
Mitigation Measures ◽  
Contact Tracing ◽  
Protective Measure ◽  
Airborne Transmission ◽  
Social Distancing

Various mitigation measures have been implemented to fight the coronavirus disease 2019 (COVID-19) pandemic, including widely adopted social distancing and mandated face covering. However, assessing the effectiveness of those intervention practices hinges on the understanding of virus transmission, which remains uncertain. Here we show that airborne transmission is highly virulent and represents the dominant route to spread the disease. By analyzing the trend and mitigation measures in Wuhan, China, Italy, and New York City, from January 23 to May 9, 2020, we illustrate that the impacts of mitigation measures are discernable from the trends of the pandemic. Our analysis reveals that the difference with and without mandated face covering represents the determinant in shaping the pandemic trends in the three epicenters. This protective measure alone significantly reduced the number of infections, that is, by over 75,000 in Italy from April 6 to May 9 and over 66,000 in New York City from April 17 to May 9. Other mitigation measures, such as social distancing implemented in the United States, are insufficient by themselves in protecting the public. We conclude that wearing of face masks in public corresponds to the most effective means to prevent interhuman transmission, and this inexpensive practice, in conjunction with simultaneous social distancing, quarantine, and contact tracing, represents the most likely fighting opportunity to stop the COVID-19 pandemic. Our work also highlights the fact that sound science is essential in decision-making for the current and future public health pandemics.

scholarly journals Mechanistic model calibration and the dynamics of the COVID-19 epidemic in the UK (the past, the present and the future)

2021 ◽  
Author(s):  
Mark J Willis ◽  
Allen Wright ◽  
Victoria Bramfitt ◽  
Harry Conn ◽  
Robyn Talyor
Keyword(s):  
Reproduction Number ◽  
Mechanistic Model ◽  
National Level ◽  
Human Interaction ◽  
Vaccine Uptake ◽  
Model Parameters ◽  
Continuous Simulation ◽  
Vaccination Rates ◽  
The Uk ◽  
Case Data

We augment the well-known susceptible - infected - recovered - deceased (SIRD) epidemiological model to include vaccination dynamics, implemented as a piecewise continuous simulation. We calibrate this model to reported case data in the UK at a national level. Our modelling approach decouples the inherent characteristics of the infection from the degree of human interaction (as defined by the effective reproduction number, Re). This allows us to detect and infer a change in the characteristic of the infection, for example the emergence of the Kent variant, We find that that the infection rate constant (k) increases by around 89% as a result of the B.1.1.7 (Kent) COVID-19 variant in England. Through retrospective analysis and modelling of early epidemic case data (between March 2020 and May 2020) we estimate that ~1.2M COVID-19 infections were unreported in the early phase of the epidemic in the UK. We also obtain an estimate of the basic reproduction number as, R0=3.23. We use our model to assess the UK Government's roadmap for easing the third national lockdown as a result of the current vaccination programme. To do this we use our estimated model parameters and a future forecast of the daily vaccination rates of the next few months. Our modelling predicts an increased number of daily cases as NPIs are lifted in May and June 2021. We quantify this increase in terms of the vaccine rollout rate and in particular the percentage vaccine uptake rate of eligible individuals, and show that a reduced take up of vaccination by eligible adults may lead to a significant increase in new infections.

scholarly journals Application of SEIR Model in COVID-19 and The Effect of Lockdown on Reducing The Number of Active Cases

2020 ◽  
Vol 5 (2) ◽  
pp. 185-192
Author(s):  
Zulfan Adi Putra ◽  
Shahrul Azman Zainal Abidin
Keyword(s):  
Reproduction Number ◽  
Compartment Model ◽  
Daily Basis ◽  
Model Parameters ◽  
South East Asia ◽  
Actual Number ◽  
Seir Model ◽  
Sick People ◽  
Case Data ◽  
Very High

The spread of COVID-19 within a region in South East Asia has been modelled using a compartment model called SEIR (Susceptible, Exposed, Infected, Recovered). Actual number of sick people needing treatments, or the number active case data was used to obtain realistic values of the model parameters such as the reproduction number (R0), incubation, and recovery periods. It is shown that at the beginning of the pandemic where most people were still not aware, the R0 was very high as seen by the steep increase of people got infected and admitted to the hospitals. Few weeks after the lockdown of the region was in place and people were obeying the regulation and observing safe distancing, the R0 values dropped significantly and converged to a steady value of about 3. Using the obtained model parameters, fitted on a daily basis, the maximum number of active cases converged to a certain value of about 2500 cases. It is expected that in the early June 2020 that the number of active cases will drop to a significantly low level.

scholarly journals Impact of COVID-19 mitigation measures on inner-city female youth in New York City

2021 ◽  
Author(s):  
Angela Diaz ◽  
Anne Nucci-Sack ◽  
Rachel Colon ◽  
Mary Guillot ◽  
Dominic Hollman ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Inner City ◽  
York City ◽  
Mitigation Measures ◽  
Female Youth

scholarly journals Association between City-wide Lockdown and COVID-19 Hospitalization Rates in Multigenerational Households in New York City

2021 ◽  
Author(s):  
Arnab K Ghosh ◽  
Sara Venkataraman ◽  
Evgeniya Reshetnyak ◽  
Mangala Rajan ◽  
Anjile An ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Age Groups ◽  
Older Individuals ◽  
Multigenerational Households ◽  
Hospitalization Rates ◽  
Increased Risk ◽  
Significant Difference ◽  
Credible Intervals

Background: City-wide lockdowns and school closures have demonstrably impacted COVID-19 transmission. However, simulation studies have suggested an increased risk of COVID-19 related morbidity for older individuals inoculated by house-bound children. This study examines whether the March 2020 lockdown in New York City (NYC) was associated with higher COVID-19 hospitalization rates in neighborhoods with larger proportions of multigenerational households. Methods: We obtained daily age-segmented COVID-19 hospitalization counts in each of 166 ZIP code tabulation areas (ZCTAs) in NYC. Using Bayesian Poisson regression models that account for spatiotemporal dependencies between ZCTAs, as well as socioeconomic risk factors, we conducted a difference-in-differences study amongst ZCTA-level hospitalization rates from February 23 to May 2, 2020. We compared ZCTAs in the lowest quartile of multigenerational housing to other quartiles before and after the lockdown. Findings: Among individuals over 55 years, the lockdown was associated with higher COVID-19 hospitalization rates in ZCTAs with more multigenerational households. The greatest difference occurred three weeks after lockdown: Q2 vs. Q1: 54% increase (95% Bayesian credible intervals: 22 to 96%); Q3 vs. Q1: 48%, (17 to 89%); Q4 vs. Q1: 66%, (30 to 211%). After accounting for pandemic-related population shifts, a significant difference was observed only in Q4 ZCTAs: 37% (7 to 76%). Interpretation: By increasing house-bound mixing across older and younger age groups, city-wide lockdown mandates imposed during the growth of COVID-19 cases may have inadvertently, but transiently, contributed to increased transmission in multigenerational households.

scholarly journals Quickstart for SIRD Modeling of the COVID-19 Outbreak

Outbreak ◽  
2020 ◽  
Author(s):  
Jordan Poles
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
Real World ◽  
York City ◽  
Explanatory Power ◽  
Model Parameters ◽  
Specific Rate ◽  
Infected People ◽  
Starting Point

Overview: There are a number of different methodologies that one can employ in order to model the outbreak of an infectious disease, in this case the novel 2019 coronavirus (COVID-19). Some, like generalized logistic models or Richards models rely on the expectation of a logistic pattern of growth in cumulative cases and clasically do not incorporate such complex dynamics as isolation of infected individuals, shelter-in-place orders for the general population (as instituted in New York City), or other change in public health policy over time. Here we present an implementation of a Susceptible-Infected-Recovered-Deceased (SIRD) model, with parameter fitting to real-world data, which can assist in projecting the overall trajectory of an outbreak in response to such complex changes in the environment. Our methodology is contained in a freely available IPython notebook, with the goal of providing a good starting point for other citizen scientists interested in exploring and forecasting the COVID-19 outbreak. Methodology: While a great deal can be learned from the simpler style of models mentioned above, the SIRD model allows for a great deal of nuance in forecasting because it attempts to approximate real-world dynamics, namely the flux of individuals between compartments in the outbreak; for example, susceptible people become infected at a specific rate, and those infected people then recover or die at specific rates. Our implementation of this model utilizes a system of ordinary differential equations (ODEs) inspired by the work of Diego Caccavo [1]. Typically we model the rate of infection as a constant β multiplied by the overall proportion of infected people and by the # of susceptible people. In our methodology, we have instead modeled this β value as a function of time β(t). In this way, we can account for changes in infection rate, for example a decrease due to a lockdown at timepoint t_lockdown (with shelter-in-place, for example) or an increase due to the lifting of such a lockdown at timepoint t_lift (Fig C). After establishing the equations which will dictate the dynamics of the model, we fit the parameters (β, 𝜏_𝛽, 𝛾, 𝛿) using non-linear least-squares regression via a function provided by the SciPy package. Results: We demonstrate a functional IPython Notebook which allows for fitting of SIRD model parameters to real-world datasets. Additionally, we can form rudimentary projections of potential outbreak trajectories in response to real-world changes in the environment in which an outbreak is occuring by modifying the system of ODEs. For example, we show the effect of changing the date on which shelter-in-place measures are lifted on a simulated New York City (NYC). We first fit the parameters of the SIRD model (Fig C) using real-world outbreak data from NYC provided by the NY Times. We then run the model, demonstrating that lifting shelter-in-place public health measures after only a month causes a rebound and second peak in cases in the simulated New York City (Fig A). On the other hand, lifting the shelter-in-place orders after two months allows the outbreak in the simulated city to die down without a second spike in cases (Fig B). Conclusions: Overall, we believe that there is a great deal of predictive and explanatory power provided by SIRD-type models. We hope that other researchers in the field can use our work as a basis for further customization and tailoring of the system of ODEs, allowing these models to be fit to a variety of different cities, states, or other regions. That said, we also want to urge caution to those hoping to utilize these models to exactly predict the course of an outbreak or develop specific timing of public policies. These models are simply projections, and are highly limited by the overall limited availability and quality of data at this point in the COVID-19 outbreak.

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