Approximate Bayesian algorithm to estimate the basic reproduction number in an influenza pandemic using arrival times of imported cases

Chong, Ka Chun; Zee, Benny; Wang, Maggie Haitian

doi:10.1016/j.tmaid.2018.04.004

Cited by 14 publications

(10 citation statements)

References 23 publications

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“…We estimate R by the ratio of number of new infections generated at time t (I t ), to the total infectiousness of infected individuals at time t, given by [64,65]:…”

Section: Instantaneous Reproduction Number R Using the Cori Methodsmentioning

confidence: 99%

Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

et al. 2021

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Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile’s incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

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“…We estimate R by the ratio of number of new infections generated at time t (I t ), to the total infectiousness of infected individuals at time t, given by [64,65]:…”

Section: Instantaneous Reproduction Number R Using the Cori Methodsmentioning

confidence: 99%

Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

et al. 2021

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“…In other words, a bigger R0 does not necessarily mean more infections. For example, the R0 for the seasonal flu typically ranges from 1.2 to 1.4 [91], but it still infected many more people than the number of people who got infected with SARS-CoV. At any rate, any R0 above 1 should be taken seriously.…”

Section: Insights and Lessons Learned From Sars-cov Mers-cov And Samentioning

confidence: 99%

Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks

et al. 2020

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Coronaviruses (CoVs) are RNA viruses that have become a major public health concern since the Severe Acute Respiratory Syndrome-CoV (SARS-CoV) outbreak in 2002. The continuous evolution of coronaviruses was further highlighted with the emergence of the Middle East Respiratory Syndrome-CoV (MERS-CoV) outbreak in 2012. Currently, the world is concerned about the 2019 novel CoV (SARS-CoV-2) that was initially identified in the city of Wuhan, China in December 2019. Patients presented with severe viral pneumonia and respiratory illness. The number of cases has been mounting since then. As of late February 2020, tens of thousands of cases and several thousand deaths have been reported in China alone, in addition to thousands of cases in other countries. Although the fatality rate of SARS-CoV-2 is currently lower than SARS-CoV, the virus seems to be highly contagious based on the number of infected cases to date. In this review, we discuss structure, genome organization, entry of CoVs into target cells, and provide insights into past and present outbreaks. The future of human CoV outbreaks will not only depend on how the viruses will evolve, but will also depend on how we develop efficient prevention and treatment strategies to deal with this continuous threat.

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“…The instantaneous R t is estimated by the ratio of number of new infections generated at calendar time t ( I t ), to the total infectiousness of infected individuals at time t given by [69, 70]. Hence R t can be written as: In this equation, I t is the number of new infections on day t and w s represents the infectivity function, which is the infectivity profile of the infected individual.…”

Section: Methodsmentioning

confidence: 99%

Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March 20-November 11, 2020

Tariq

Banda

Skums

et al. 2021

Preprint

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Mexico has experienced one of the highest COVID-19 death rates in the world. A delayed response towards implementation of social distancing interventions until late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. Here, we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatial-temporal transmission patterns. The early estimates of reproduction number for Mexico were estimated between R~1.1-1.3 from genomic and case incidence data. Moreover, the mean estimate of R has fluctuated ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories. We found that the sequential mortality forecasts from the GLM and Richards model predict downward trends in the number of deaths for all thirteen forecasts periods for Mexico and Mexico City. The sub-epidemic and IHME models predict more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21 - 09/28-10/27) for Mexico and Mexico City. Our findings support the view that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.

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Approximate Bayesian algorithm to estimate the basic reproduction number in an influenza pandemic using arrival times of imported cases

Cited by 14 publications

References 23 publications

Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks

Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March 20-November 11, 2020

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