reproduction number united states
220717433
Association of Social Distancing, Population Density, and Temperature With the Instantaneous Reproduction Number of SARS CoV 2 in Counties Across the United States
Key Points Question How is the instantaneous reproduction number of severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) associated with social distancing, wet bulb temperature, and population density in counties across the United States? Findings In this cohort study of 211 counties in 46 states, social distancing, temperate weather, and lower population density were associated with a decrease in the instantaneous reproduction number of SARS CoV 2. Of these county specific factors, social distancing appeared to have the most substantial association with a reduction in SARS CoV 2 transmission. Meaning In this study, the instantaneous reproduction number of SARS CoV 2 varied substantially among counties; the associations between the reproduction number and county specific factors could inform policies to reduce SARS CoV 2 transmission in selective and heterogeneous communities.
JAMA network open
David Rubin Jing Huang Brian T Fisher Antonio Gasparrini Vicky W Tam Lihai Song Xi Wang Jason Kaufman K M Fitzpatrick Arushi Jain Heather M Griffis Koby Crammer Jeffrey Morris Gregory E Tasian
215782319
Time Varying COVID 19 Reproduction Number in the United States
The basic reproduction number is the average number of people to whom an infected person transmits the infection when virtually all individuals in a population are susceptible. We sought to calculate the current reproduction number for COVID 19 for each state in the United States. For the entire United States, the reproduction number declined from 4.02 to 1.51 between March 17 and April 1, 2020. We also found that the reproduction number for COVID 19 has declined in most states over the past two weeks which suggests that social isolation measures may be having a beneficial effect.
medRxiv
Douglas D Gunzler Ashwini R Sehgal
221178140
The basic reproduction number of the new coronavirus pandemic with mortality for India, the Syrian Arab Republic, the United States, Yemen, China, France, Nigeria and Russia with different rate of cases
Abstract Background The basic reproduction number values give an initial prediction of the disease because the values predict of end of the disease if the values are less than one or the disease converts to epidemic if the values are more than one. We apply the SIRD epidemiology model for estimating the basic reproduction number of the new coronavirus disease for multiple different countries. Methods For estimating of the basic reproduction number values, we fit the SIRD model using the Runge Kutta simulation method in addition to the analytical solution of parts of the model. We use the collected data of the new coronavirus pandemic reported up to date July 30, 2020 in India, the Syrian Arab Republic, the United States, France, Nigeria, Yemen, China and Russia. Results We find that the basic reproduction numbers of the new coronavirus disease are located in the range [1.0011 2.7936] for the different location countries and the values of the ratio between the rate of recovery and the rate of mortality are between 1.5905 for Yemen and 44.0805 for Russia. Also, we find the dates of the actual decreasing of Covid 19 cases in five countries. Conclusions We find that the basic reproductive number is between 1.0011 for the smallest value and 2.7936 for the greatest value. The most important thing is that the values of the basic reproduction number of the new coronavirus disease in all considered countries are more than one which means that the new coronavirus disease is epidemic in all of considered countries.
Clinical Epidemiology and Global Health
Marwan Al-Raeei
218595240
The Association of Social Distancing, Population Density, and Temperature with the SARS CoV 2 Instantaneous Reproduction Number in Counties Across the United States
Importance: The Covid 19 pandemic has been marked by considerable heterogeneity in outbreaks across the United States. Local factors that may be associated with variation in SARS CoV 2 transmission have not been well studied. Objective: To examine the association of county level factors with variation in the SARS CoV 2 reproduction number over time. Design: Observational study Setting: 211 counties in 46 states and the District of Columbia between February 25, 2020 and April 23, 2020. Participants: Residents within the counties (55% of the US population) Exposures: Social distancing as measured by percent change in visits to non essential businesses, population density, lagged daily wet bulb temperatures. Main Outcomes and Measures: The instantaneous reproduction number (Rt) which is the estimated number of cases generated by one case at a given time during the pandemic. Results: Median case incidence was 1185 cases and fatality rate was 43.7 deaths per 100,000 people for the top decile of 21 counties, nearly ten times the incidence and fatality rate in the lowest density quartile. Average Rt in the first two weeks was 5.7 (SD 2.5) in the top decile, compared to 3.1 (SD 1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to non essential businesses was associated with a 57% decrease in Rt (95% confidence interval, 56% to 58% Cumulative temperature effects over 4 to 10 days prior to case incidence were nonlinear; relative Rt decreased as temperatures warmed above 32F to 53F, which was the point of minimum Rt, then increased between 53F and 66F, at which point Rt began to decrease. At 55F, and with a 70% reduction in visits to non essential business, 96% of counties were estimated to fall below a threshold Rt of 1.0, including 86% of counties among the top density decile and 98% of counties in the lowest density quartile. Conclusions and Relevance: Social distancing, lower population density, and temperate weather change were associated with a decreased SARS Co V 2 Rt in counties across the United States. These relationships can inform selective public policy planning in communities during the SARS CoV 2 pandemic.
medRxiv
David Rubin J Huang Brian T Fisher Antonio Gasparrini Vicky W Tam L Song X Wang Jason Kaufman K M Fitzpatrick A Jain Heather M Griffis Koby Crammer Gregory E Tasian
224810569
Maximum entropy method for estimating the reproduction number: An investigation for COVID 19 in China and the United States.
The key parameter that characterizes the transmissibility of a disease is the reproduction number R. If it exceeds 1, the number of incident cases will inevitably grow over time, and a large epidemic is possible. To prevent the expansion of an epidemic, R must be reduced to a level below 1. To estimate the reproduction number, the probability distribution function of the generation interval of an infectious disease is required to be available; however, this distribution is often unknown. In this paper, given the incomplete information for the generation interval, we propose a maximum entropy method to estimate the reproduction number. Based on this method, given the mean value and variance of the generation interval, we first determine its probability distribution function and in turn estimate the real time values of the reproduction number of COVID 19 in China and the United States. By applying these estimated reproduction numbers into the susceptible infectious removed epidemic model, we simulate the evolutionary tracks of the epidemics in China and the United States, both of which are in accordance with that of the real incident cases.
Physical review. E
Yong Tao
221686212
Are we there yet? An adaptive SIR model for continuous estimation of COVID 19 infection rate and reproduction number in the United States
The dynamics of the COVID 19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. We propose a simple method for estimating the time varying infection rate and reproduction number R_t using a sliding window approach applied to a Susceptible Infectious Removed model. The infection rate is estimated using the reported cases for a seven day window to obtain continuous estimation of R_t. We demonstrate that the proposed adaptive SIR (aSIR) model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. The aSIR model was applied to data at the state and county levels.
medRxiv
Mark B Shapiro Fazle Karim Guido Muscioni Abel Saju Augustine
232339360
Are we there yet? Adaptive SIR model for continuous estimation of COVID 19 infection rate and reproduction number in the United States.
BACKGROUND The dynamics of the COVID 19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. OBJECTIVE We propose a simple method for estimating the time varying infection rate and reproduction number R_t METHODS We use a sliding window approach applied to a Susceptible Infectious Removed model. The infection rate is estimated using the reported cases for a seven day window to obtain continuous estimation of R_t. The proposed adaptive SIR (aSIR) model was applied to data at the state and county levels. RESULTS The aSIR model showed an excellent fit for the number of reported COVID 19 positive cases, a one day forecast MAPE was less than 2.6% across all states. However, a seven day forecast MAPE reached 16.2% and strongly overestimated the number of cases when the reproduction number was high and changing fast. The maximal R_t showed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5) We demonstrate that the aSIR model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. CONCLUSIONS The aSIR model provides a simple and accurate computational tool to obtain continuous estimation of the reproduction number and evaluate the efficacy of mitigation measures. CLINICALTRIAL
Journal of medical Internet research
Mark B Shapiro Fazle Karim Guido Muscioni Abel Saju Augustine
73451887
Estimation of the Basic Reproduction Number and Vaccination Coverage of Influenza in the United States (2017 18)
Background: Determining the epidemic threshold parameter helps health providers calculate the coverage while guiding them in planning the process of vaccination strategy. Since the trend and mechanism of influenza is very similar in different countries, we planned a study with the mentioned goal by using data of US from 2017 to 2018. Study design: A secondary study. Methods: R0 and corresponding vaccination coverage are estimated using the national and state level data of the US from the 40th in 2017 to the 5th week in 2018. Four methods maximum likelihood (ML) exponential growth (EG) time dependent reproduction numbers (TD) and sequential Bayesian (SB) are used to calculate minimum vaccination coverage. The gamma distribution is considered as the distribution and the generation of time. Results: The peak of epidemy in most states has occurred in the 15th week after the beginning of the epidemics. The generation time obey the Gamma distribution with mean and standard deviation of 3.6 and 1.6, respectively, was utilized for the generation time. The R0 (vaccination coverage) equaled 1.94 (48.4% 1.80 (44.4% 3.06 (67.3% and 2.11 (52.6% for EG, ML, SB, and TD methods at the national level, respectively. Conclusion: The R0 estimations were in the range of 1.8 3.06, indicating that an epidemic has occurred in the US (R0>1) Thus, it is required to vaccinate at least 44.4% to 67.3% to prevent the next epidemics of influenza. The findings of this study assist futures studies to apply disease control by vaccination strategies in order to prevent a national disaster.
Journal of research in health sciences
Roya Nikbakht Mohammad Reza Baneshi Abbas Bahrampour
79420927
THE HIV REPRODUCTION NUMBER IN THE UNITED STATES: ESTIMATION AND IMPACT OF CARE CONTINUUM INTERVENTIONS
Yao-Hsuan Chen
214623342
A time series method to analyze incidence pattern and estimate reproduction number of COVID 19
The ongoing pandemic of Coronavirus disease (COVID 19) emerged in Wuhan, China in the end of 2019. It has already affected more than 300,000 people, with the number of deaths nearing 13000 across the world. As it has been posing a huge threat to global public health, it is of utmost importance to identify the rate at which the disease is spreading. In this study, we propose a time series model to analyze the trend pattern of the incidence of COVID 19 outbreak. We also incorporate information on total or partial lockdown, wherever available, into the model. The model is concise in structure, and using appropriate diagnostic measures, we showed that a time dependent quadratic trend successfully captures the incidence pattern of the disease. We also estimate the basic reproduction number across different countries, and find that it is consistent except for the United States of America. The above statistical analysis is able to shed light on understanding the trends of the outbreak, and gives insight on what epidemiological stage a region is in. This has the potential to help in prompting policies to address COVID 19 pandemic in different countries.
Soudeep Deb Manidipa Majumdar