scholarly journals On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data

Biology ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 135 ◽  
Author(s):  
Benjamin Ambrosio ◽  
M. A. Aziz-Alaoui
Keyword(s):  
New York ◽  
New Jersey ◽  
Death Rate ◽  
Transmission Rate ◽  
Hot Spot ◽  
Model Fitting ◽  
Coupled Model ◽  
Sir Model ◽  
Exponential Increase ◽  
Over Time

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of March 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.

scholarly journals On a Coupled Time-Dependent Sir Models Fitting with New York and New-Jersey States COVID-19 Data

2020 ◽  
Author(s):  
Benjamin Ambrosio ◽  
M.A. Aziz-Alaoui
Keyword(s):  
New York ◽  
New Jersey ◽  
Death Rate ◽  
Transmission Rate ◽  
Hot Spot ◽  
Model Fitting ◽  
Coupled Model ◽  
Sir Model ◽  
Exponential Increase ◽  
Over Time

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of march 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.

Calculating Age-Standardized Death Rates Among People With HIV Comparable Across Jurisdictions and Over Time

2021 ◽  
Vol 111 (1) ◽  
pp. 121-126
Author(s):  
Qiang Xia ◽  
Ying Sun ◽  
Chitra Ramaswamy ◽  
Lucia V. Torian ◽  
Wenhui Li
Keyword(s):  
United States ◽  
New York ◽  
New York City ◽  
York City ◽  
Death Rate ◽  
Age Distribution ◽  
The United States ◽  
Local Health ◽  
Death Rates ◽  
Over Time

The Centers for Disease Control and Prevention (CDC) and local health jurisdictions have been using HIV surveillance data to monitor mortality among people with HIV in the United States with age-standardized death rates, but the principles of age standardization have not been consistently followed, making age standardization lose its purpose—comparison over time, across jurisdictions, or by other characteristics. We review the current practices of age standardization in calculating death rates among people with HIV in the United States, discuss the principles of age standardization including those specific to the HIV population whose age distribution differs markedly from that of the US 2000 standard population, make recommendations, and report age-standardized death rates among people with HIV in New York City. When we restricted the analysis population to adults aged between 18 and 84 years in New York City, the age-standardized death rate among people with HIV decreased from 20.8 per 1000 (95% confidence interval [CI] = 19.2, 22.3) in 2013 to 17.1 per 1000 (95% CI = 15.8, 18.3) in 2017, and the age-standardized death rate among people without HIV decreased from 5.8 per 1000 in 2013 to 5.5 per 1000 in 2017.

scholarly journals Extension of a SIR model for modelling the propagation of Covid-19 in several countries.

2020 ◽  
Author(s):  
Marc Lavielle ◽  
Matthieu Faron ◽  
jeremie lefevre ◽  
Jean-David Zeitoun
Keyword(s):  
Compartmental Model ◽  
Transmission Rate ◽  
Sir Model ◽  
Decision Makers ◽  
Short Term ◽  
Early Evaluation ◽  
The Public ◽  
Containment Measures ◽  
High Level ◽  
Over Time

Background Several epidemiologic models have been published to forecast the spread of the COVID-19 pandemic yet there are still uncertainties regarding their accuracy. We report the main features of the development of a novel freely accessible model intended to urgently help researchers and decision makers to predict the evolution of the pandemic in their country. Methods and findings We built a SIR-type compartmental model with additional compartments and features. We made the hypothesis that the number of contagious individuals in the population was negligible as compared to the population size. We introduced a compartment D corresponding to the deceased patients and a compartment L representing the group of individuals who will die but who will not infect anybody (due to social or medical isolation). Our model integrated a time-dependent transmission rate, whose variations can be thought to be related to the public measures taken by each country and a cosine function to incorporate a periodic weekly component linked to the way in which numbers of cases and deaths are counted and reported, which can change from day to day. The model was able to accurately capture the different changes in the dynamics of the pandemic for nine different countries whatever the type of pandemic spread or containment measures. The model provided very accurate forecasts in the relatively short term (10 days). Conclusions In early evaluation of the performance of our model, we found a high level of accuracy between prediction and observed data, regardless of the country. The model should be used by the community to help public health decisions as we will refine it over time and further investigate its performance.

scholarly journals Predict new cases of the Coronavirus 19; in Michigan, U.S.A. or other countries using Crow-AMSAA Method (Preprint)

2020 ◽  
Author(s):  
Yanshuo Wang
Keyword(s):  
New York ◽  
New York City ◽  
Social Distance ◽  
York City ◽  
Death Rate ◽  
Transmission Rate ◽  
Complex Problem ◽  
Mitigation Strategies ◽  
Summary Table ◽  
The Government

BACKGROUND Statistical predictions are useful to predict events based on statistical models. The data is useful to determine outcomes based on inputs and calculations. The Crow-AMSAA method will be explored to predict new cases of Coronavirus 19 (COVID19). This method is currently used within engineering reliability design to predict failures and evaluate the reliability growth. The author intents to use this model to predict the COVID19 cases by using daily reported data from Michigan, New York City, U.S.A and other countries. The piece wise Crow-AMSAA (CA) model fits the data very well for the infected cases and deaths at different phases during the start of the COVID19 outbreak. The slope β of the Crow-AMSAA line indicates the speed of the transmission or death rate. The traditional epidemiological model is based on the exponential distribution, but the Crow-AMSAA is the Non Homogeneous Poisson Process (NHPP) which can be used to modeling the complex problem like COVID19, especially when the various mitigation strategies such as social distance, isolation and locking down were implemented by the government at different places. OBJECTIVE This paper is to use piece wise Crow-AMSAA method to fit the COVID19 confirmed cases in Michigan, New York City, U.S.A and other countries. METHODS piece wise Crow-AMSAA method to fit the COVID19 confirmed cases RESULTS From the Crow-AMSAA analysis above, at the beginning of the COVID 19, the infectious cases did not follow the Crow-AMSAA prediction line, but during the outbreak start, the confirmed cases does follow the CA line, the slope β value indicates the pace of the transmission rate or death rate in each case. The piece wise Crow-AMSAA describes the different phases of spreading. This indicates the speed of the transmission rate could change according to the government interference, social distance order or other factors. Comparing the piece wise CA β slopes (β: 1.683-- 0.834--0.092) in China and in U.S.A (β:5.138--10.48--5.259), the speed of infectious rate in U.S.A is much higher than the infectious rate in China. From the piece wise CA plots and summary table 1 of the CA slope βs, the COVID19 spreading has the different behavior at different places and countries where the government implemented the different policy to slow down the spreading. CONCLUSIONS From the analysis of data and conclusions from confirmed cases and deaths of COVID 19 in Michigan, New York city, U.S.A, China and other countries, the piece wise Crow-AMSAA method can be used to modeling the spreading of COVID19.

scholarly journals Evaluate the risk of resumption of business for the states of New York, New Jersey and Connecticut via a pre-symptomatic and asymptomatic transmission model of COVID-19

2020 ◽  
Author(s):  
Ting Tian ◽  
Jianbin Tan ◽  
Yukang Jiang ◽  
Xueqin Wang ◽  
Heping Zhang
Keyword(s):  
New York ◽  
New Jersey ◽  
Real Time ◽  
Hot Spot ◽  
The United States ◽  
Transmission Model ◽  
The Real ◽  
Work From Home ◽  
Opposite Situation ◽  
Second Wave

AbstractBackgroundThe United States has the highest numbers of confirmed cases of COVID-19, where they took up nearly half in the hot spot states of New York, New Jersey, Connecticut, and California. The workforce in these states was required to work from home except for essential services. It is necessary to evaluate an appropriate date for resumption of business since premature reopening of economy will lead to broader spread of COVID-19, while the opposite situation would cause greater loss of economy.MethodsTo consider pre-symptomatic and asymptomatic transmission of COIVD-19, it is crucial to evaluate the unobserved numbers of unidentified infectious individuals but not the observed number of confirmed cases, which reflect the real-time risks of different stage of infectious disease. We proposed an epidemic model in considering the pre-symptomatic transmission and asymptomatic transmission of COVID-19 to evaluate the real-time risk of epidemic for the states of New York, New Jersey and Connecticut, and compared with California state (where it effectively phased reopened on May 8) for assessments of the appropriate Monday for resumption of business.ResultsThe predicted numbers of unidentified infectious individuals per 100,000 for states of New York, New Jersey and Connecticut which are close to those in California state are 12.23 with 95% CI (10.68, 13.57) on June 1, 25.65 with 95% CI (20.04, 30.43) on June 15, 28.49 with 95% CI (19.10, 38.65) on June 22, respectively, which may cause 11.23%, 15.64% and 17.32% higher than the estimated number of cumulative confirmed cases on July 11, if the second wave of the infection would happen after people return to work.ConclusionsIt may be feasible for states of New York, New Jersey and Connecticut to reopen business on June 1 (or even May 18), June 15 and June 22. For the period after resumption of work, if the number of unidentified infectious individuals is still non-zero, the risks for the second wave of the infection would never vanish.

scholarly journals Beyond Deaths per Capita: Comparative CoViD-19 Mortality Indicators

2020 ◽  
Author(s):  
Patrick Heuveline ◽  
Michael Tzen
Keyword(s):  
New York ◽  
New Jersey ◽  
Life Expectancy ◽  
Death Rate ◽  
Crude Death Rate ◽  
Total Size ◽  
Life Expectancy At Birth ◽  
The Us ◽  
Indirect Standardization ◽  
Age And Sex

AbstractThe number of CoViD-19 deaths more reliably tracks the progression of the disease across populations than the number of confirmed cases. Substantial age and sex differences in CoViD-19 death rates imply that the number of deaths should be adjusted not just for the total size of the population, but also for its age-and-sex distribution. Following well-established practices in demography, this article discusses several measures based on the number of CoViD-19 deaths over time and across populations. The first measure is an unstandardized occurrence/exposure rate comparable to the Crude Death Rate. To date, the highest value has been in New York, where at its peak it exceeded the state 2017 Crude Death Rate. The second measure is an indirectly standardized rate that can be derived even when the breakdown of CoViD-19 deaths by age and sex required for direct standardization is unavailable. For populations with such breakdowns, we show that direct and indirect standardization yield similar results.Standardization modifies comparison across populations: while New Jersey now has the highest unstandardized rate, Baja California (Mexico) has the highest standardized rate. Finally, extant life tables allow to estimate reductions in life expectancy at birth. In the US, life expectancy is projected to decline this year by more (-.68) than the worst year of the HIV epidemic, or the worst three years of the opioid crisis, and to reach its lowest level since 2008. Substantially larger reductions, exceeding two years, are projected for Ecuador, Chile, New York, New Jersey and Peru.

scholarly journals A Stochastic Model for Kala-azar Transmission Dynamics in Libo Kemkem, Ethiopia

2022 ◽  
Author(s):  
Sewmehon Shimekaw Alemu
Keyword(s):  
Stochastic Model ◽  
Health Informatics ◽  
Model Simulation ◽  
Transmission Rate ◽  
Sir Model ◽  
Infected Group ◽  
Closed Population ◽  
Kala Azar ◽  
Stochastic Sir Model ◽  
Over Time

Abstract The objective of this paper is to analyse and demonstrate the dynamics of Kala-azar infected group using stochastic model, particularly using simple SIR model with python script over time. The model is used under a closed population with N = 100, transmission rate coefficient β = 0.09, recovery rate γ = 0.03 and initial condition I(0) = 1. In the paper it is discussed how the Kala-azar infected group behaves through simple SIR model. The paper is completed with stochastic SIR model simulation result and shows stochasticity of the dynamics of Kala-azar infected population over time. Fig. 2 below depicts continuous fluctuations which tells us the disease evolves with stochastic nature and shows random process.Subject: Infectious Disease, Global Health, Health Informatics and Statistical and Computational Physics

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