scholarly journals Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China

2020 ◽  
Author(s):  
Tao Liu ◽  
Jianxiong Hu ◽  
Jianpeng Xiao ◽  
Guanhao He ◽  
Min Kang ◽  
...  
Keyword(s):  
Hong Kong ◽  
Doubling Time ◽  
Reproduction Number ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Time Varying ◽  
Basic Production ◽  
The Subject ◽  
Novel Virus ◽  
Novel Coronavirus

ABSTRACTRationaleSeveral studies have estimated basic production number of novel coronavirus pneumonia (NCP). However, the time-varying transmission dynamics of NCP during the outbreak remain unclear.ObjectivesWe aimed to estimate the basic and time-varying transmission dynamics of NCP across China, and compared them with SARS.MethodsData on NCP cases by February 7, 2020 were collected from epidemiological investigations or official websites. Data on severe acute respiratory syndrome (SARS) cases in Guangdong Province, Beijing and Hong Kong during 2002-2003 were also obtained. We estimated the doubling time, basic reproduction number (R0) and time-varying reproduction number (Rt) of NCP and SARS.Measurements and main resultsAs of February 7, 2020, 34,598 NCP cases were identified in China, and daily confirmed cases decreased after February 4. The doubling time of NCP nationwide was 2.4 days which was shorter than that of SARS in Guangdong (14.3 days), Hong Kong (5.7 days) and Beijing (12.4 days). The R0 of NCP cases nationwide and in Wuhan were 4.5 and 4.4 respectively, which were higher than R0 of SARS in Guangdong (R0=2.3), Hongkong (R0=2.3), and Beijing (R0=2.6). The Rt for NCP continuously decreased especially after January 16 nationwide and in Wuhan. The R0 for secondary NCP cases in Guangdong was 0.6, and the Rt values were less than 1 during the epidemic.ConclusionsNCP may have a higher transmissibility than SARS, and the efforts of containing the outbreak are effective. However, the efforts are needed to persist in for reducing time-varying reproduction number below one.At a Glance CommentaryScientific Knowledge on the SubjectSince December 29, 2019, pneumonia infection with 2019-nCoV, now named as Novel Coronavirus Pneumonia (NCP), occurred in Wuhan, Hubei Province, China. The disease has rapidly spread from Wuhan to other areas. As a novel virus, the time-varying transmission dynamics of NCP remain unclear, and it is also important to compare it with SARS.What This Study Adds to the FieldWe compared the transmission dynamics of NCP with SARS, and found that NCP has a higher transmissibility than SARS. Time-varying production number indicates that rigorous control measures taken by governments are effective across China, and persistent efforts are needed to be taken for reducing instantaneous reproduction number below one.

scholarly journals The importance of the timing of quarantine measures before symptom onset to prevent COVID-19 outbreaks - illustrated by Hong Kong’s intervention model

2020 ◽  
Author(s):  
Hsiang-Yu Yuan ◽  
Guiyuan Han ◽  
Hsiangkuo Yuan ◽  
Susanne Pfeiffer ◽  
Axiu Mao ◽  
...  
Keyword(s):  
Hong Kong ◽  
Symptom Onset ◽  
Reproduction Number ◽  
Model Fitting ◽  
Mainland China ◽  
Control Measures ◽  
Rapid Expansion ◽  
Transmission Dynamics ◽  
Population Immunity ◽  
Key Factor

AbstractBackgroundThe rapid expansion of the current COVID-19 outbreak has caused a global pandemic but how quarantine-based measures can prevent or suppress an outbreak without other more intrusive interventions has not yet been determined. Hong Kong had a massive influx of travellers from mainland China, where the outbreak began, during the early expansion period coinciding with the Lunar New Year festival; however, the spread of the virus has been relatively limited even without imposing severe control measures, such as a full city lockdown. Understanding how quarantine measures in Hong Kong were effective in limiting community spread can provide us with valuable insights into how to suppress an outbreak. However, challenges exist in evaluating the effects of quarantine on COVID-19 transmission dynamics in Hong Kong due to the fact that the effects of border control have to be also taken into account.MethodsWe have developed a two-layered susceptible-exposed-infectious-quarantined-recovered (SEIQR) meta-population model which can estimate the effects of quarantine on virus transmissibility after stratifying infections into imported and subsequent community infections, in a region closely connected to the outbreak’s source. We fitted the model to both imported and local confirmed case data with symptom onset from 18 January to 29 February 2020 in Hong Kong, together with daily transportation data and the transmission dynamics of COVID-19 from Wuhan and mainland China. After model fitting, epidemiological parameters and the timing of the start of quarantine for infected cases were estimated.ResultsThe model estimated that the reproduction number of COVID-19 in Hong Kong was 0.76 (95% CI, 0.66 to 0.86), achieved through quarantining infected cases −0.57 days (95% CI, −4.21 − 3.88) relative to symptom onset, with an estimated incubation time of 5.43 days (95% CI, 1.30 − 9.47). However, if delaying the quarantine start by more than 1.43 days, the reproduction number would be greater than one, making community spread more likely. The model also determined the timing of the start of quarantine necessary in order to suppress an outbreak in the presence of population immunity.ConclusionThe results suggest that the early quarantine for infected cases before symptom onset is a key factor to prevent COVID-19 outbreak.

scholarly journals Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qing Cheng ◽  
Zeyi Liu ◽  
Guangquan Cheng ◽  
Jincai Huang
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Reproduction Number ◽  
Correlation Method ◽  
Control Measures ◽  
Time Varying ◽  
Short Period ◽  
Significant Difference ◽  
Prevention And Control Measures ◽  
And Control

AbstractBeginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.

scholarly journals Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sahamoddin Khailaie ◽  
Tanmay Mitra ◽  
Arnab Bandyopadhyay ◽  
Marta Schips ◽  
Pietro Mascheroni ◽  
...  
Keyword(s):  
Structural Changes ◽  
Reproduction Number ◽  
National Level ◽  
Transmission Dynamics ◽  
Federal State ◽  
Parameter Estimates ◽  
Time Varying ◽  
Short Term ◽  
Robert Koch Institute ◽  
Federal States

Abstract Background SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression. Methods We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute. Results The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2–3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes. Conclusions The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

scholarly journals Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

2020 ◽  
Vol 17 (9) ◽  
pp. 3054 ◽  
Author(s):  
Oyelola A. Adegboye ◽  
Adeshina I. Adekunle ◽  
Ezra Gayawan
Keyword(s):  
Doubling Time ◽  
Reproduction Number ◽  
Infected Individual ◽  
Credible Interval ◽  
World Health ◽  
Time Interval ◽  
Serial Interval ◽  
Imported Cases ◽  
Novel Coronavirus ◽  
Health Organization

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

scholarly journals Stepping up infection control measures in ophthalmology during the novel coronavirus outbreak: an experience from Hong Kong

2020 ◽  
Vol 258 (5) ◽  
pp. 1049-1055 ◽  
Author(s):  
Tracy H. T. Lai ◽  
Emily W. H. Tang ◽  
Sandy K. Y. Chau ◽  
Kitty S. C. Fung ◽  
Kenneth K. W. Li
Keyword(s):  
Hong Kong ◽  
Infection Control ◽  
Control Measures ◽  
The Novel ◽  
Infection Control Measures ◽  
Novel Coronavirus

scholarly journals Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

2020 ◽  
Vol 17 (14) ◽  
pp. 4964 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta ◽  
Sebastiano Battiato ◽  
Antonella Agodi
Keyword(s):  
Transmission Rate ◽  
Control Measures ◽  
Transmission Dynamics ◽  
The Novel ◽  
Epidemic Spread ◽  
Transmission Rates ◽  
Contact Precautions ◽  
Travel Restrictions ◽  
Novel Coronavirus ◽  
Reported Data

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%; 95%CI = 0–34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23–42%) after the first set of restrictions, and by 80% (95%CI = 70–89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

scholarly journals A simple ecological model captures the transmission pattern of the coronavirus COVID-19 outbreak in China

2020 ◽  
Author(s):  
Feng Zhang ◽  
Jinmei Zhang ◽  
Menglan Cao ◽  
Cang Hui
Keyword(s):  
Public Health ◽  
Infection Rate ◽  
Confidence Limit ◽  
Turning Point ◽  
Current Control ◽  
Control Measures ◽  
Time Varying ◽  
Transmission Pattern ◽  
Final Number ◽  
Novel Coronavirus

AbstractThe rapid spread of the 2019 novel coronavirus (COVID-19), initially reported in the city of Wuhan in China, and quickly transmitted to the entire nation and beyond, has become an international public health emergency. Estimating the final number of infection cases and the turning point (time with the fastest spreading rate) is crucial to assessing and improving the national and international control measures currently being applied. In this paper we develop a simple model based on infectious growth with a time-varying infection rate, and estimate the final number of infections and the turning point using data updated daily from 3 February 2020, when China escalated its initial public health measures, to 10 February. Our model provides an extremely good fit to the existing data and therefore a reasonable estimate of the time-varying infection rate that has largely captured the transmission pattern of this epidemic outbreak. Our estimation suggests that (i) the final number of infections in China could reach 78,000 with an upper 95% confidence limit of 88,880; (ii) the turning point of the fastest spread was on the 4th or the 5th of February; and (iii) the projected period for the end of the outbreak (i.e., when 95% of the final predicted number of infection is reached) will be the 24th of February, with an upper 95% confidence limit on the 19th of March. This suggests that the current control measures in China are excellent, and more than sufficient to contain the spread of this highly infectious novel coronavirus, and that the application of such measures could be considered internationally for the global control of this outbreak.

scholarly journals Early Phylogenetic Estimate Of The Effective Reproduction Number Of 2019-nCoV

2020 ◽  
Author(s):  
Alessia Lai ◽  
Annalisa Bergna ◽  
Carla Acciarri ◽  
Massimo Galli ◽  
Gianguglielmo Zehender
Keyword(s):  
Exponential Growth ◽  
Doubling Time ◽  
Evolutionary Dynamics ◽  
Evolutionary Rate ◽  
Reproduction Number ◽  
R Value ◽  
Phylogenetic Estimate ◽  
Novel Coronavirus ◽  
Birth Death

ABSTRACTTo reconstruct the evolutionary dynamics of the 2019 novel coronavirus, 52 2019-nCOV genomes available on 04 February 2020 at GISAID were analysed.The two models used to estimate the reproduction number (coalescent-based exponential growth and a birth-death skyline method) indicated an estimated mean evolutionary rate of 7.8 × 10−4 subs/site/year (range 1.1×10−4–15×10−4).The estimated R value was 2.6 (range 2.1-5.1), and increased from 0.8 to 2.4 in December 2019. The estimated mean doubling time of the epidemic was between 3.6 and 4.1 days.This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections even as the epidemic is growing.

scholarly journals Transmission potential of the novel coronavirus (COVID-19) onboard the Diamond Princess Cruises Ship, 2020

2020 ◽  
Author(s):  
Kenji Mizumoto ◽  
Gerardo Chowell
Keyword(s):  
Mathematical Modeling ◽  
Time Series ◽  
Reproduction Number ◽  
Transmission Dynamics ◽  
The Novel ◽  
Epidemic Threshold ◽  
Transmission Potential ◽  
Incidence Data ◽  
The Mean ◽  
Novel Coronavirus

AbstractAn outbreak of COVID-19 developed aboard the Princess Cruises Ship during January-February 2020. Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members, we characterize how the transmission potential varied over the course of the outbreak. Our estimate of the mean reproduction number in the confined setting reached values as high as ∼11, which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore (approximate range: 1.1-7). Our findings suggest that Rt decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control. Most recent estimates of Rt reached values largely below the epidemic threshold, indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.

scholarly journals Transmission dynamics, serial interval and epidemiology of COVID-19 diseases in Hong Kong under different control measures

2020 ◽  
Vol 5 ◽  
pp. 91
Author(s):  
Yung-Wai Desmond Chan ◽  
Stefan Flasche ◽  
Tin-Long Terence Lam ◽  
Mei-Hung Joanna Leung ◽  
Miu-Ling Wong ◽  
...  
Keyword(s):  
Hong Kong ◽  
Population Level ◽  
Credible Interval ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Local Data ◽  
First Case ◽  
Serial Interval ◽  
Imported Cases ◽  
Epidemiological Characteristics

Background: The outbreak of coronavirus disease 2019 (COVID-19) started in Wuhan, China in late December 2019, and subsequently became a pandemic. Hong Kong had implemented a series of control measures since January 2020, including enhanced surveillance, isolation and quarantine, border control and social distancing. Hong Kong recorded its first case on 23 January 2020, who was a visitor from Wuhan. We analysed the surveillance data of COVID-19 to understand the transmission dynamics and epidemiology in Hong Kong. Methods: We constructed the epidemic curve of daily COVID-19 incidence from 23 January to 6 April 2020 and estimated the time-varying reproduction number (Rt) with the R package EpiEstim, with serial interval computed from local data. We described the demographic and epidemiological characteristics of reported cases. We computed weekly incidence by age and residential district to understand the spatial and temporal transmission of the disease. Results: COVID-19 disease in Hong Kong was characterised with local cases and clusters detected after two waves of importations, first in late January (week 4 to 6) and the second one in early March (week 9 to 10). The Rt increased to approximately 2 95% credible interval (CI): 0.3-3.3) and approximately 1 (95%CI: 0.2-1.7), respectively, following these importations; it decreased to below 1 afterwards from weeks 11 to 13, which coincided with the implementation, modification and intensification of different control measures. Compared to local cases, imported cases were younger (mean age: 52 years among local cases vs 35 years among imported cases), had a lower proportion of underlying disease (9% vs 5%) and severe outcome (13% vs 5%). Cases were recorded in all districts but the incidence was highest in those in the Hong Kong Island region. Conclusions: Stringent and sustained public health measures at population level could contain the COVID-19 disease at a relatively low level.

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