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1.
Trop Med Infect Dis ; 7(9)2022 Sep 03.
Article in English | MEDLINE | ID: covidwho-2010300

ABSTRACT

Background: Since the emergence of the COVID-19 pandemic, many models have been applied to understand its epidemiological characteristics. However, the ways in which outbreak data were used in some models are problematic, for example, importation was mixed up with local transmission. Methods: In this study, five models were proposed for the early Shaanxi outbreak in China. We demonstrated how to select a reasonable model and correctly use the outbreak data. Bayesian inference was used to obtain parameter estimates. Results: Model comparison showed that the renewal equation model generates the best model fitting and the Susceptible-Exposed-Diseased-Asymptomatic-Recovered (SEDAR) model is the worst; the performance of the SEEDAR model, which divides the exposure into two stages and includes the pre-symptomatic transmission, and SEEDDAAR model, which further divides infectious classes into two equally, lies in between. The Richards growth model is invalidated by its continuously increasing prediction. By separating continuous importation from local transmission, the basic reproduction number of COVID-19 in Shaanxi province ranges from 0.45 to 0.61, well below the unit, implying that timely interventions greatly limited contact between people and effectively contained the spread of COVID-19 in Shaanxi. Conclusions: The renewal equation model provides the best modelling; mixing continuous importation with local transmission significantly increases the estimate of transmissibility.

2.
Euro Surveill ; 27(15)2022 04.
Article in English | MEDLINE | ID: covidwho-1869325

ABSTRACT

BackgroundHouseholds appear to be the highest risk setting for COVID-19 transmission. Large household transmission studies in the early stages of the pandemic in Asia reported secondary attack rates ranging from 5 to 30%.AimWe aimed to investigate the transmission dynamics of COVID-19 in household and community settings in the UK.MethodsA prospective case-ascertained study design based on the World Health Organization FFX protocol was undertaken in the UK following the detection of the first case in late January 2020. Household contacts of cases were followed using enhanced surveillance forms to establish whether they developed symptoms of COVID-19, became confirmed cases and their outcomes. We estimated household secondary attack rates (SAR), serial intervals and individual and household basic reproduction numbers. The incubation period was estimated using known point source exposures that resulted in secondary cases.ResultsWe included 233 households with two or more people with 472 contacts. The overall household SAR was 37% (95% CI: 31-43%) with a mean serial interval of 4.67 days, an R0 of 1.85 and a household reproduction number of 2.33. SAR were lower in larger households and highest when the primary case was younger than 18 years. We estimated a mean incubation period of around 4.5 days.ConclusionsRates of COVID-19 household transmission were high in the UK for ages above and under 18 years, emphasising the need for preventative measures in this setting. This study highlights the importance of the FFX protocol in providing early insights on transmission dynamics.


Subject(s)
COVID-19 , Adolescent , Family Characteristics , Humans , Pandemics , SARS-CoV-2 , United Kingdom/epidemiology
3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325165

ABSTRACT

To control and contain the outbreaks of emerging infectious diseases such as COVID-19, it is important to know how easy and fast they transmit among people. To explore the essential information of the novel infectious agents, people always confront an inverse problem: using (partially) observed numbers of infected people by time and region to dig up the underlying characteristics of unknown infectious agents. Epidemics armed with advanced statistical inference and mathematical theory has been developed to help reconstruct transmission dynamics processes and to estimate key features of infectious diseases. In this study we use COVID-19 outbreak in Shaanxi province as an example to illustrate how the infectious disease dynamics method can be used to help build the transmission process and to estimate the transmissibility of COVID-19. Three transmission dynamics models were proposed for this. By separating continuous importation from local transmission and treating imported cases as the source rather than results of local transmission, the basic reproduction number of COVID-19 in Shaanxi province was estimated in the range from 0.46 to 0.61, well below the critical value of 1.0. This indicates that COVID-19 cannot self-sustain in Shaanxi province and reflects the timely and strong control measures taken in Shaanxi province.

4.
Sci Rep ; 11(1): 2652, 2021 01 29.
Article in English | MEDLINE | ID: covidwho-1054058

ABSTRACT

COVID-19 is reported to have been brought under control in China. To understand the COVID-19 outbreak in China and provide potential lessons for other parts of the world, in this study we apply a mathematical model with multiple datasets to estimate the transmissibility of the SARS-CoV-2 virus and the severity of the illness associated with the infection, and how both were affected by unprecedented control measures. Our analyses show that before 19th January 2020, 3.5% (95% CI 1.7-8.3%) of  infected people were detected; this percentage increased to 36.6% (95% CI 26.1-55.4%) thereafter. The basic reproduction number (R0) was 2.33 (95% CI 1.96-3.69) before 8th February 2020; then the effective reproduction number dropped to 0.04(95% CI 0.01-0.10). This estimation also indicates that control measures taken since 23rd January 2020 affected the transmissibility about 2 weeks after they were introduced. The confirmed case fatality rate is estimated at 9.6% (95% CI 8.1-11.4%) before 15 February 2020, and then it reduced to 0.7% (95% CI 0.4-1.0%). This shows that SARS-CoV-2 virus is highly transmissible but may be less severe than SARS-CoV-1 and MERS-CoV. We found that at the early stage, the majority of R0 comes from undetected infectious people. This implies that successful control in China was achieved through reducing the contact rates among people in the general population and increasing the rate of detection and quarantine of the infectious cases.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Disease Outbreaks , Models, Theoretical , Basic Reproduction Number , COVID-19/virology , China/epidemiology , Humans , SARS-CoV-2/isolation & purification
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