scholarly journals Outbreak of COVID-19 and SARS in mainland China: a comparative study based on national surveillance data

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e043411
Author(s):  
Lin Zhao ◽  
Dan Feng ◽  
Run-Ze Ye ◽  
Hai-Tao Wang ◽  
Yu-Hao Zhou ◽  
...  
Keyword(s):  
Comparative Study ◽  
Reproduction Number ◽  
Mainland China ◽  
Case Fatality ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Onset Date ◽  
Data Set ◽  
Illness Onset ◽  
Epidemiological Characteristics

ObjectiveTo compare the epidemiological characteristics and transmission dynamics in relation to interventions against the COVID-19 and severe acute respiratory syndrome (SARS) outbreak in mainland China.DesignComparative study based on a unique data set of COVID-19 and SARS.SettingOutbreak in mainland China.ParticipantsThe final database included 82 858 confirmed cases of COVID-19 and 5327 cases of SARS.MethodsWe brought together all existing data sources and integrated them into a comprehensive data set. Individual information on age, sex, occupation, residence location, date of illness onset, date of diagnosis and clinical outcome was extracted. Control measures deployed in mainland China were collected. We compared the epidemiological and spatial characteristics of COVID-19 and SARS. We estimated the effective reproduction number to explore differences in transmission dynamics and intervention effects.ResultsCompared with SARS, COVID-19 affected more extensive areas (1668 vs 230 counties) within a shorter time (101 vs 193 days) and had higher attack rate (61.8 vs 4.0 per million persons). The COVID-19 outbreak had only one epidemic peak and one epicentre (Hubei Province), while the SARS outbreak resulted in two peaks and two epicentres (Guangdong Province and Beijing). SARS-CoV-2 was more likely to infect older people (median age of 52 years), while SARS-CoV tended to infect young adults (median age of 34 years). The case fatality rate (CFR) of either disease increased with age, but the CFR of COVID-19 was significantly lower than that of SARS (5.6% vs 6.4%). The trajectory of effective reproduction number dynamically changed in relation to interventions, which fell below 1 within 2 months for COVID-19 and within 5.5 months for SARS.ConclusionsChina has taken more prompt and effective responses to combat COVID-19 by learning lessons from SARS, providing us with some epidemiological clues to control the ongoing COVID-19 pandemic worldwide.

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 Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

2020 ◽  
Vol 9 (2) ◽  
pp. 538 ◽  
Author(s):  
Natalie Linton ◽  
Tetsuro Kobayashi ◽  
Yichi Yang ◽  
Katsuma Hayashi ◽  
Andrei Akhmetzhanov ◽  
...  
Keyword(s):  
Incubation Period ◽  
Case Fatality ◽  
Illness Onset ◽  
Event Date ◽  
History Of ◽  
Novel Coronavirus ◽  
Case Fatality Risk ◽  
Right Truncation ◽  
Epidemiological Characteristics ◽  
Mean Time

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

scholarly journals Epidemiological characteristics of the first 100 cases of coronavirus disease 2019 (COVID-19) in Hong Kong Special Administrative Region, China, a city with a stringent containment policy

2020 ◽  
Vol 49 (4) ◽  
pp. 1096-1105 ◽  
Author(s):  
Christopher K C Lai ◽  
Rita W Y Ng ◽  
Martin C S Wong ◽  
Ka Chun Chong ◽  
Yun Kit Yeoh ◽  
...  
Keyword(s):  
Hong Kong ◽  
Incubation Period ◽  
Mainland China ◽  
Control Measures ◽  
Contact Tracing ◽  
Border Control ◽  
Government Information ◽  
Epidemic Curve ◽  
Containment Policy ◽  
Epidemiological Characteristics

Abstract Background Hong Kong (HK) is a densely populated city near the epicentre of the coronavirus disease 2019 (COVID-19) outbreak. Stringent border control together with aggressive case finding, contact tracing, social distancing and quarantine measures were implemented to halt the importation and spread of the virus. Methods We performed an epidemiological study using government information covering the first 100 confirmed cases to examine the epidemic curve, incidence, clusters, reproduction number (Rt), incubation period and time to containment. Results A total of 93 of the 100 cases were HK residents (6 infected in Mainland China, 10 on the Diamond Princess Cruise). Seven were visitors infected in Mainland China before entering HK. The majority (76%) were aged ≥45 years, and the incidence increased with age (P < 0.001). Escalation of border control measures correlated with a decrease in the proportion (62.5% to 0%) of cases imported from Mainland China, and a reduction in Rt (1.07 to 0.75). The median incubation period was 4.2 days [95% confidence interval (CI), 4.0–4.5; 5th and 95th percentiles: 1.3 and 14.0). Most clusters with identifiable epidemiological links were households involving 2–4 people. Three medium-spreading events were identified: two from New Year gatherings (6–11 people), and another from environmental contamination of a worship hall (12 people). Despite intensified contact tracing, containment was delayed in 78.9% of cases (mean = 5.96 days, range = 0–24 days). An unusual transmission in a multi-storey building via faulty toilet plumbing was suspected with >100 residents evacuated overnight. Our analysis indicated that faulty plumbing was unlikely to be the source of this transmission. Conclusion Timely stringent containment policies minimized the importation and transmission of COVID-19 in HK.

scholarly journals Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Chunxiang Cao ◽  
Wei Chen ◽  
Sheng Zheng ◽  
Jian Zhao ◽  
Jinfeng Wang ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mainland China ◽  
Emerging Infectious Diseases ◽  
Epidemiological Data ◽  
Transmission Dynamics ◽  
Bayesian Maximum Entropy ◽  
Covariance Model ◽  
Spatial And Temporal Scales ◽  
Epidemiological Characteristics ◽  
Spatiotemporal Transmission

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

scholarly journals Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

2020 ◽  
Author(s):  
Khouloud Talmoudi ◽  
Mouna Safer ◽  
Hejer Letaief ◽  
Aicha Hchaichi ◽  
Chahida Harizi ◽  
...  
Keyword(s):  
Emerging Diseases ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Epidemic Threshold ◽  
Illness Onset ◽  
Intervention Measures ◽  
Temporal Reproduction ◽  
Prevention Measure ◽  
Serial Interval

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73–3.69] to 1.77 [95% CI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

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.

scholarly journals Epidemiological characteristics of an outbreak of Coronavirus Disease 2019 in the Philippines

2020 ◽  
Author(s):  
Mark M. Alipio ◽  
Joseph Dave M. Pregoner
Keyword(s):  
Case Fatality Rate ◽  
Mainland China ◽  
Case Fatality ◽  
The Philippines ◽  
Fatality Rate ◽  
Metro Manila ◽  
Infectivity Rate ◽  
Precautionary Measures ◽  
Infection Control Guidelines ◽  
Epidemiological Characteristics

AbstractThe outbreak of Coronavirus disease 2019 (Covid-2019) is a source of great concern in the Philippines. In this paper, we described the epidemiological characteristics of the laboratory-confirmed patients with Covid-2019 in the Philippines as of April 3, 2020 and provided recommendations on how to limit the spread of the disease. Data from the DOH NCOV tracker and University of the Philippines’ Covid-2019 tracker were extracted, from its initiation (January 30, 2020) until the most recent situation report (April 3, 2020). The total number of cases and deaths were stratified by sex, age, and region of the Philippines. Descriptive statistics were used to analyze the demographic profile of the confirmed cases. Case fatality rate, in percent, was calculated by dividing the total number of deaths to the total number of confirmed cases. Results revealed that a total of 3,018 cases of Covid-2019 spread were confirmed across 17 regions in the Philippines. These cases occurred over the course of 73 days through person-to-person transmission, highlighting an extremely high infectivity rate. The 144 deaths accounted for, equate to 4.51 case fatality rate, seemingly lower compared to its predecessors, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), but higher compared to that of United States of America, Germany, mainland China, and neighboring Southeast Asian countries such as Malaysia, Singapore, Brunei, and Thailand. Of the 3,018 confirmed cases, majority were male, elderly, and diagnosed in Metro Manila region. Case fatality rates were higher in male and highest among elderly and Filipinos in the Ilocos region. With the surge on the number of cases, precautionary measures should remain a responsibility, and protocols for prevention need to be set. Adherence to infection control guidelines such as but not limited to frequently handwashing for at least 20 seconds, observing coughing etiquette, wearing of masks, and social distancing should be maintained in order to contain the disease.

scholarly journals Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020

2020 ◽  
Vol 25 (49) ◽  
Author(s):  
Flavia Riccardo ◽  
Marco Ajelli ◽  
Xanthi D Andrianou ◽  
Antonino Bella ◽  
Martina Del Manso ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Case Fatality ◽  
Control Measures ◽  
Italian Regions ◽  
Northern Regions ◽  
Independent Risk Factors ◽  
Male Sex ◽  
The One ◽  
Net Reproduction ◽  
Epidemiological Characteristics

Background On 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak. Aim Our aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures. Methods We analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region. Results Of the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in cases older than 80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R0 varied between 2.50 (95% confidence interval (CI): 2.18–2.83) in Tuscany and 3.00 (95% CI: 2.68–3.33) in Lazio. The net reproduction number Rt in northern regions started decreasing immediately after the first detection. Conclusion The COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R0 at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, Rt in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.

scholarly journals Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: a statistical analysis in Tunisia from February 29 to May 5, 2020

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Khouloud Talmoudi ◽  
Mouna Safer ◽  
Hejer Letaief ◽  
Aicha Hchaichi ◽  
Chahida Harizi ◽  
...  
Keyword(s):  
Credible Interval ◽  
Emerging Diseases ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Epidemic Threshold ◽  
Illness Onset ◽  
Intervention Measures ◽  
Temporal Reproduction ◽  
Serial Interval

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29–May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results Four hundred ninety-one of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73–3.69] to 1.77 [95% CrI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

TRANSMISSION DYNAMICS AND CONTROL STRATEGIES OF COVID-19 IN WUHAN, CHINA

2020 ◽  
Vol 28 (03) ◽  
pp. 543-560 ◽  
Author(s):  
LIUYONG PANG ◽  
SANHONG LIU ◽  
XINAN ZHANG ◽  
TIANHAI TIAN ◽  
ZHONG ZHAO
Keyword(s):  
Reproduction Number ◽  
Control Strategies ◽  
Public Awareness ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Published Data ◽  
Model Parameters ◽  
Strict Control ◽  
Dynamics And Control ◽  
Numerical Calculation Method

In December 2019, a novel coronavirus, SARS-COV-2, was identified among patients in Wuhan, China. Two strict control measures, i.e., putting Wuhan on lockdown and taking strict quarantine rule, were carried out to contain the spread of COVID-19. Based on the different control measures, we divided the transmission process of COVID-19 into three stages. An SEIHR model was established to describe the transmission dynamics and was applied to fit the published data on the confirmed cases of Wuhan city from December 31, 2019 to March 25, 2020 to deduce the time when the first patient with COVID-19 appeared. The basic reproduction number was estimated in the first stage to demonstrate the number of secondary infectious cases generated by an average infectious case in the absence of policy intervention. The effective reproduction numbers in second and third stages were estimated to evaluate the effects of the two strict control measures. In addition, sensitivity analysis of the reproduction number according to model parameters was executed to demonstrate the effect of the control measures for containing the spread of COVID-19. Finally, the numerical calculation method was applied to investigate the influence of the different control measures on the spread of COVID-19. The results indicated that following the strict quarantine rule was very effective, and reducing the effective contact rates and improving the diagnosis rate were crucial in reducing the effective reproduction number, and taking control measures as soon as possible can effectively contain a larger outbreak of COVID-19. But a bigger challenge for us to contain the spread of COVID-19 was the transmission from the asymptomatic carriers, which required to raising the public awareness of self-protection and keeping a good physical protection.

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