scholarly journals COVID-19 Time-varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing

2021 ◽  
Vol 2021 (407) ◽  
Author(s):  
Alexander Chudik ◽  
◽  
M. Hashem Pesaran ◽  
Alessandro Rebucci ◽  
◽  
...  
Keyword(s):  
Empirical Analysis ◽  
Time Varying ◽  
Social Distancing ◽  
Reproduction Numbers

scholarly journals COVID-19 Time-varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing

2021 ◽  
Author(s):  
Alexander Chudik ◽  
M. Hashem Pesaran ◽  
Alessandro Rebucci
Keyword(s):  
Empirical Analysis ◽  
Time Varying ◽  
Social Distancing ◽  
Reproduction Numbers

scholarly journals COVID-19 Time-varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing

2021 ◽  
Author(s):  
Alexander Chudik ◽  
M. Hashem Pesaran ◽  
Alessandro Rebucci
Keyword(s):  
Reproduction Number ◽  
Transmission Rate ◽  
Economic Incentives ◽  
Moment Condition ◽  
Time Varying ◽  
Linear Interaction ◽  
Susceptible Population ◽  
Social Distancing ◽  
Transmission Rates ◽  
Reproduction Numbers

AbstractThis paper estimates time-varying COVID-19 reproduction numbers worldwide solely based on the number of reported infected cases, allowing for under-reporting. Estimation is based on a moment condition that can be derived from an agent-based stochastic network model of COVID-19 transmission. The outcomes in terms of the reproduction number and the trajectory of per-capita cases through the end of 2020 are very diverse. The reproduction number depends on the transmission rate and the proportion of susceptible population, or the herd immunity effect. Changes in the transmission rate depend on changes in the behavior of the virus, re-flecting mutations and vaccinations, and changes in people’s behavior, reflecting voluntary or government mandated isolation. Over our sample period, neither mutation nor vaccination are major factors, so one can attribute variation in the transmission rate to variations in behavior. Evidence based on panel data models explaining transmission rates for nine European countries indicates that the diversity of outcomes resulted from the non-linear interaction of mandatory containment measures, voluntary precautionary isolation, and the economic incentives that gov-ernments provided to support isolation. These effects are precisely estimated and robust to various assumptions. As a result, countries with seemingly different social distancing policies achieved quite similar outcomes in terms of the reproduction number. These results imply that ignoring the voluntary component of social distancing could introduce an upward bias in the estimates of the effects of lock-downs and support policies on the transmission rates.JEL ClassificationD0, F6, C4, I120, E7

scholarly journals Epidemiological trend of COVID-19 in Nepal and the importance of social distancing to contain the virus

2020 ◽  
Vol 1 (1) ◽  
pp. 15-25
Author(s):  
Amod K. Pokhrel ◽  
Yadav P. Joshi ◽  
Sopnil Bhattarai
Keyword(s):  
Mitigation Measures ◽  
Contact Tracing ◽  
Limited Information ◽  
Social Distancing ◽  
Mobility Data ◽  
Epidemiological Trend ◽  
Reproduction Numbers ◽  
The Government ◽  
Testing Case ◽  
The Impact

There is limited information on the epidemiology and the effects of mitigation measures on the spread of COVID-19 in Nepal. Using publicly available databases, we analyzed the epidemiological trend, the people's movement trends at different intervals across different categories of places and evaluated implications of social mobility on COVID-19. We also estimated the epidemic peak. As of June 9, 2020, Provinces 2 and 5 have most of the cases. People between 15 and 54 years are vulnerable to becoming infected, and more males than females are affected. The cases are growing exponentially. The growth rate of 0.13 and >1 reproduction numbers (R0) over time (median: 1.48; minimum: 0.58, and maximum: 3.71) confirms this trend. The case doubling time is five days. Google's community mobility data suggest that people strictly followed social distancing measures for one month after the lockdown. By around the 4th week of April, the individual's movement started rising, and social contacts increased. The number of cases peaked on May 12, with 83 confirmed cases in one day. The Susceptible-Exposed-Infectious-Removed (SEIR) model suggests that the epidemic will peak approximately on day 41 (July 21, 2020), and start to plateau after day 80. To contain the spread of the virus, people should maintain social distancing. The Government needs to continue active surveillance, more PCR-based testing, case detection, contact tracing, isolation, and quarantine. The Government should also provide financial support and safety-nets to the citizen to limit the impact of COVID-19.

scholarly journals Time Varying Causality Between Cost and Price: An Empirical Analysis on Dry Bulk Shipping Market

2018 ◽  
Vol 10 (4) ◽  
pp. 136-147
Author(s):  
Abdullah Açık ◽  
İlke Sezin Ayaz
Keyword(s):  
Empirical Analysis ◽  
Time Varying ◽  
Bulk Shipping

scholarly journals Public activities preceding the onset of acute respiratory infection syndromes in adults in England - implications for the use of social distancing to control pandemic respiratory infections.

2020 ◽  
Vol 5 ◽  
pp. 54 ◽  
Author(s):  
Andrew C. Hayward ◽  
Sarah Beale ◽  
Anne M. Johnson ◽  
Ellen B. Fragaszy ◽  
Keyword(s):  
Respiratory Infection ◽  
Acute Respiratory Infection ◽  
Respiratory Infections ◽  
Crossover Design ◽  
Seasonal Adjustment ◽  
Time Varying ◽  
Paired Data ◽  
Social Distancing ◽  
Illness Onset ◽  
Eating Out

Background: Social distancing measures may reduce the spread of emerging respiratory infections however, there is little empirical data on how exposure to crowded places affects risk of acute respiratory infection. Methods: We used a case-crossover design nested in a community cohort to compare self-reported measures of activities during the week before infection onset and baseline periods. The design eliminates the effect of non-time-varying confounders. Time-varying confounders were addressed by exclusion of illnesses around the Christmas period and seasonal adjustment.  Results: 626 participants had paired data from the week before 1005 illnesses and the week before baseline. Each additional day of undertaking the following activities in the prior week was associated with illness onset: Spending more than five minutes in a room with someone (other than a household member) who has a cold (Seasonally adjusted OR 1·15, p=0·003); use of underground trains (1·31, p=0·036); use of supermarkets (1·32, p<0·001); attending a theatre, cinema or concert (1·26, p=0·032); eating out at a café, restaurant or canteen (1·25, p=0·003); and attending parties (1·47, p<0·001). Undertaking the following activities at least once in the previous week was associated with illness onset: using a bus, (aOR 1.48, p=0.049), shopping at small shops (1.9, p<0.002) attending a place of worship (1.81, p=0.005).    Conclusions: Exposure to potentially crowded places, public transport and to individuals with a cold increases risk of acquiring circulating acute respiratory infections. This suggests social distancing measures can have an important impact on slowing transmission of emerging respiratory infections.

scholarly journals Estimation of the Time-Varying Reproduction Number of COVID-19 Outbreak in China

2020 ◽  
Author(s):  
Chong You ◽  
Yuhao Deng ◽  
Wenjie Hu ◽  
Jiarui Sun ◽  
Qiushi Lin ◽  
...  
Keyword(s):  
Standard Deviation ◽  
World Wide ◽  
Reproduction Number ◽  
Infectious Period ◽  
Time Varying ◽  
Exponential Growth Rate ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Controlled Reproduction ◽  
Novel Coronavirus

BackgroundThe 2019-nCoV outbreak in Wuhan, China has attracted world-wide attention. As of February 11, 2020, a total of 44730 cases of novel coronavirus-infected pneumonia associated with COVID-19 were confirmed by the National Health Commission of China.MethodsThree approaches, namely Poisson likelihood-based method (ML), exponential growth rate-based method (EGR) and stochastic Susceptible-Infected-Removed dynamic model-based method (SIR), were implemented to estimate the basic and controlled reproduction numbers.ResultsA total of 71 chains of transmission together with dates of symptoms onset and 67 dates of infections were identified among 5405 confirmed cases outside Hubei as reported by February 2, 2020. Based on this information, we find the serial interval having an average of 4.41 days with a standard deviation of 3.17 days and the infectious period having an average of 10.91 days with a standard deviation of 3.95 days.ConclusionsThe controlled reproduction number is declining. It is lower than one in most regions of China, but is still larger than one in Hubei Province. Sustained efforts are needed to further reduce the Rc to below one in order to end the current epidemic.

scholarly journals Estimation of COVID-19 transmission rates in California and the U.S. with reporting delays

2020 ◽  
Author(s):  
Lee Worden ◽  
Rae Wannier ◽  
Micaela Neus ◽  
Jennifer C. Kwan ◽  
Alex Y. Ge ◽  
...  
Keyword(s):  
United States ◽  
Symptom Onset ◽  
The United States ◽  
Time Varying ◽  
Transmission Rates ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Reporting Delays ◽  
Interval Distribution ◽  
The U.S

We estimated time-varying reproduction numbers of COVID-19 transmission in counties and regions of California and in states of the United States, using the Wallinga-Teunis method of estimations applied to publicly available data. The serial interval distribution assumed incorporates wide uncertainty in delays from symptom onset to case reporting. This assumption contributes smoothing and a small but meaningful increase in numerical estimates of reproduction numbers due to the likely existence of secondary cases not yet reported. Transmission in many areas of the U.S. may not yet be controlled, including areas in which case counts appear to be stable or slowly declining.

scholarly journals Nine-month Trend of Time-Varying Reproduction Numbers of COVID-19 in West of Iran

2021 ◽  
Vol 21 (2) ◽  
pp. e00517-e00517
Author(s):  
Ebrahim Rahimi ◽  
Seyed Saeed Hashemi Nazari ◽  
Yaser Mokhayeri ◽  
Asaad Sharhani ◽  
Rasool Mohammadi
Keyword(s):  
Reproduction Number ◽  
Infectious Period ◽  
Time Varying ◽  
Cross Sectional ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
Contact Rates ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Log Normal

Background: The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran. Study design: Descriptive study. Methods: This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood. Results: In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41). Conclusions: Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.

scholarly journals Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Epidemics ◽  
2019 ◽  
Vol 29 ◽  
pp. 100356 ◽  
Author(s):  
R.N. Thompson ◽  
J.E. Stockwin ◽  
R.D. van Gaalen ◽  
J.A. Polonsky ◽  
Z.N. Kamvar ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Time Varying ◽  
Infectious Disease Outbreaks ◽  
Reproduction Numbers
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