ABSTRACT
Rapid Antigen Diagnostic Tests (RADTs) for the detection of SARS-CoV-2 offer advantages in that they are cheaper and faster than currently used PCR tests but have reduced sensitivity and specificity. One potential application of RADTs is to facilitate gatherings of individuals, through testing of attendees at the point of, or immediately prior to entry at a venue. Understanding the baseline risk in the tested population is of particular importance when evaluating the utility of applying diagnostic tests for screening purposes. We used incidence data to estimate the prevalence of infectious individuals in the community at a particular time point and simulated mass gatherings by sampling from a series of age cohorts. Nine different illustrative scenarios were simulated, small (n=100), medium (n=1000) and large (n=10,000) gatherings each with 3 possible age constructs: mostly younger, mostly older or a gathering with equal numbers from each age cohort. For each scenario, we estimated the prevalence of infectious attendees, then simulated the likely number of positive and negative test results, the proportion of cases detected and the corresponding positive and negative predictive values, and the cost per case identified. Our findings suggest that for each detected individual on a given day, there are likely to be 13.8 additional infectious individuals also present in the community. Prevalence of infectious individuals at events was highest with mostly younger attendees (1.00%), followed by homogenous age gatherings (0.55%) and lowest with mostly older events (0.26%). For small events (100 attendees) the expected number of infectious attendees was less than 1 across all age constructs of attendees. For large events (10,000 attendees) the expected number of infectious attendees ranged from 26 (95% confidence intervals 12 to 45) for mostly older events, to almost 100 (95% confidence intervals 46 to 174) infectious attendees for mostly younger attendees. Given rapid changes in SARS-CoV-2 incidence over time, we developed an RShiny app to allow users to run updated simulations for specific events.
ABSTRACT
BackgroundContact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. MethodsWe analysed data from 140,204 contacts of 39861 cases in Ireland from 1st May to 1st December 2020. Only close contacts were included in the analysis. A close contact was defined as any individual who had had > 15 minutes face-to-face (<2 m) contact with a case; any household contact; or any individual sharing a closed space for longer than 2 hours, in any setting. ResultsThe number of contacts per case was overdispersed, the mean varied considerably over time, and was temporally associated with government interventions. Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days. DiscussionThese data were collected for a specific purpose and therefore any inferences must be made with caution. The data are representative of contact rates of cases, and not of the overall population. However, the data may be a more accurate indicator of the likely degree of onward transmission than might be the case if a random sample of the population were taken. Furthermore, since we analysed only the number of close contacts, the total number of contacts per case would have been higher. Nevertheless, this analysis provides useful information for monitoring the impact of government interventions on the number of contacts; for helping pre-empt increases or decreases in case numbers, and for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.
ABSTRACT
ObjectivesThe aim of this study was to conduct a scoping review of estimates of the relative infectiousness of asymptomatic persons infected with SARS-CoV-2 compared with symptomatic individuals. DesignRapid scoping review of literature available until 8th April 2020. SettingInternational studies on the infectiousness of individuals infected with SARS-CoV-2 ParticipantsStudies were selected for inclusion if they defined asymptomatics as a separate cohort distinct from pre-symptomatics and if they provided a quantitative measure of the infectiousness of asymptomatics relative to symptomatics. Primary outcome measuresThe relative number of secondary cases produced by an average primary case, the relative probability of transmitting infection upon contact, and the degree of viral shedding. ResultsVery few studies reported estimates of relative infectiousness of asymptomatic compared with symptomatic individuals. Significant differences exist in the definition of infectiousness. Viral shedding studies in general show no difference in shedding levels between symptomatic and asymptomatic individuals but are likely to be impacted by insufficient statistical power. Two contact tracing studies provided estimates of 0.7 and 1.0, but differences in approach and definition preclude comparison across the two studies. Finally, two modelling studies suggest a relative infectiousness of around 0.5 but one of these was more reflective of the infectiousness of undocumented rather than asymptomatic cases. Importantly, one contact tracing study showing a very low level of infectiousness of asymptomatic was not included in the analysis at this point due difficulties interpreting the reported findings. ConclusionsThe present study highlights the need for additional studies in this area as a matter of urgency. For the purpose of epidemiological modelling, we cautiously suggest that at present, asymptomatics could be considered to have a degree of infectiousness which is about 0.40-0.70 that of symptomatics. However, it must be stressed that this suggestion comes from a very low evidence base and that estimates exist that are close to zero and close to 1. ARTICLE SUMMARYO_ST_ABSStrengths and limitations of this studyC_ST_ABS- Differences in the definition of infectiousness and a low number of studies estimating this parameter negate the potential to provide a pooled quantitative estimate or relative infectiousness. - The present study highlights the need for additional studies in this area as a matter of urgency. - Several of the studies reviewed are in pre-print stage and are not peer-reviewed.
ABSTRACT
BackgroundThe transmissibility of SARS-CoV-2 determines both the ability of the virus to invade a population and the strength of intervention that would be required to contain or eliminate the spread of infection. The basic reproduction number, R0, provides a quantitative measure of the transmission potential of a pathogen. ObjectiveConduct a scoping review of the available literature providing estimates of R0 for SARS-CoV-2, provide an overview of the drivers of variation in R0 estimates and the considerations taken in the calculation of the parameter. DesignScoping review of available literature between the 01 December 2019 and 07 May 2020. Data sourcesBoth peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv. Selection criteriaStudies were selected for review if (i) the estimation of R0 for SARS-CoV-2 represented either the initial stages of the outbreak or the initial stages of the outbreak prior to the onset of widespread population restriction ("lockdown"), (ii) the exact dates of the study period were provided and (iii) the study provided primary estimates of R0. ResultsA total of 20 R0 for SARS-CoV-2 estimates were extracted from 15 studies. There was substantial variation in the estimates reported. Estimates derived from mathematical models fell within a wider range of 1.94-6.94 than statistical models which fell between the range of 2.2 to 4.4. Several studies made assumptions about the length of the infectious period which ranged from 5.8-20 days and the serial interval which ranged from 4.41-14 days. For a given set of parameters a longer duration of infectiousness or a longer serial interval equates to a higher R0. Several studies took measures to minimise bias in early case reporting, to account for the potential occurrence of super-spreading events, and to account for early sub-exponential epidemic growth. ConclusionsThe variation in reported estimates of R0 reflects the complex nature of the parameter itself, including the context (i.e. social/spatial structure), the methodology used to estimate the parameter, and model assumptions. R0 is a fundamental parameter in the study of infectious disease dynamics, however it provides limited practical applicability outside of the context in which it was estimated, and should be calculated and interpreted with this in mind. STRENGTHS AND LIMITATIONS OF THE SCOPING REVIEWO_LIThis study provides an overview of basic reproduction number estimates for SARS-CoV-2 across a range of settings, a fundamental parameter in gauging the transmissibility of an emerging infectious disease. C_LIO_LIThe key drivers of variation in R0 estimates and considerations in the calculation of the parameter highlighted across the reviewed studies are discussed. C_LIO_LIThis evidence may be used to help inform modelling studies and intervention strategies. C_LIO_LIGiven the need for rapid dissemination of information on a newly emerging infectious disease, several of the reviewed papers were in the pre-print phase yet to be peer-reviewed. C_LI
ABSTRACT
ObjectivesCoronavirus disease (COVID-19) caused by the SARS-CoV-2 virus is spreading rapidly worldwide and threatening the collapse of national health care systems. The development of effective resource models are critical for long term health planning. The aim was to evaluate the available literature, to consider parameters affecting hospital resources, to effectively guide health policy and planning for future waves of infection. DesignA detailed search of the literature, using Google Scholar, PubMED, MedRxiv and BioRxiv, was conducted for the time period 1st Dec 2019 to 31st May 2020; using appropriate keywords: resultant articles were scrutinised in detail, and appraised for reported data pertaining to hospitalization and hospital length of stay (LOS). ResultsDisease presentation was described in China; 81 % mild, 14 % moderate and 5 % severe. The experience, thus far, in Europe and the USA are suggestive of a higher degree of severity. Initial reports suggest high hospitalisation and ICU admittance rates. More recent reports from the European Centre for Disease Prevention and Control (ECDC) lower this estimation. Perhaps the relative age, the level of pre-existing conditions, and other health factors may be contributors to differences. Data from Irish cases suggest hospitalisation rate may be lower in parts of Europe and time dependent. Hospital LOS is described in 55 articles, with median lengths of stay between 3 and 52 days. The evidence regarding the LOS in ICU is reported in 31 studies, 26 deemed relevant. The majority of studies report ICU LOS between 7 to 11 days. Many of these studies are likely skewed towards shorter stay due to study cut-off dates. Indications based on ICU LOS reported for patients continuing care suggest median ICU stay will progressively increase. ConclusionsThese parameter estimates are key to the development of an effective health care resource model. Based on our appraisal of the literature, is it essential that Europe manages mitigation measures to ensure that hospital and ICU capacity does not become overwhelmed to manage COVID-19 in subsequent infection waves. Strengths and limitations of this studyO_LIThe study provides timely information on the differences in hospitalisation, length of stay and ICU length of stay due to COVID-19 in a number of countries worldwide at the end of wave one in Europe; C_LIO_LIThis rapid review builds on a previously available review paper that reported length of stay in the early phase of the pandemic; many more studies outlining length of stay, and in particular, ICU length of stay, are now available; C_LIO_LIThis rapid review reports on study mortality rate giving an interesting insight into differences across countries and continents; C_LIO_LILimitations associated with any rapid review are pertinent to this study; a narrow aim was set, and the sources of the literature may be limited by the time-limited constraint of gathering relevant literature; and a number of articles available were in pre-print form and only undergoing peer review; and C_LIO_LIThis rapid review provides evidence-based estimates of Hospital and ICU length of stay due to COVID-19 infection across a number of countries to steer policy and provide parameter estimates for utilisation within a hospital resource model as preparations are made for subsequent waves of infection. C_LI
ABSTRACT
ObjectiveTo estimate the proportion of pre-symptomatic transmission of SARS-CoV-2 infection that can occur and timing of transmission relative to symptom onset. Setting/designSecondary analysis of international published data. Data sourcesMeta-analysis of COVID-19 incubation period and a rapid systematic review of serial interval and generation time, which are published separately. ParticipantsStudies were selected for analysis if they had transparent methods and data sources and they provided enough information to simulate full distributions of serial interval or generation time. Twenty-three estimates of serial interval and five of generation time from 17 publications were included. MethodsSimulations were generated of incubation period and of serial interval or generation time. From these, transmission times relative to symptom onset were calculated and the proportion of pre-symptomatic transmission was estimated. Outcome measuresTransmission time of SARS-CoV-2 relative to symptom onset and proportion of pre-symptomatic transmission. ResultsTransmission time ranged from a mean of 2.91 (95% CI: 3.18-2.64) days before symptom onset to 1.20 (0.86-1.55) days after symptom onset. Unweighted pooling of estimates of transmission time based on serial interval resulted in a mean of 0.60 days before symptom onset (3.01 days before to 1.81 days after). Proportion of pre-symptomatic transmission ranged from 42.8% (39.8%-45.9%) to 80.6% (78.1%-83.0%). The proportion of pre-symptomatic transmission from pooled estimates was 56.4% (34.9%-78.0%). ConclusionsWhilst contact rates between symptomatic infectious and susceptible people are likely to influence the proportion of pre-symptomatic transmission, there is substantial potential for pre-symptomatic transmission of SARS-CoV-2 in a range of different contexts. Our work suggests that transmission is most likely in the day before symptom onset whereas estimates suggesting most pre-symptomatic transmission highlighted mean transmission times almost three days before symptom onset. This highlights the need for rapid case detection, contact tracing and quarantine. Strengths and weaknesses of this studyO_LIWe estimate the extent and variation of pre-symptomatic transmission of SARS-CoV-2 infection across a range of contexts. This provides important information for development and targeting of control policies and for the parameterisation of transmission models. C_LIO_LIThis is a secondary analysis using simulations based on published data, some of which is in pre-print form and not yet peer-reviewed. There is overlap in the contact tracing data that informed some of our source publications. We partially address this by summarising data at source location level as well as at study level. C_LIO_LIPopulations where symptomatic people are rapidly isolated are likely have relatively more pre-symptomatic transmission. This should be borne in mind whilst interpreting our results, but does not affect our finding that there is substantial potential for pre-symptomatic transmission of SARS-CoV-2 infection. C_LIO_LIA strength of our approach is that it builds an understanding of pre-symptomatic transmission from a range of estimates in the literature, facilitates discussion for the drivers of variation between them, and highlights the consistent message that consideration of pre-symptomatic transmission is critical for COVID-19 control policy. C_LI
ABSTRACT
BackgroundThe serial interval is the time between symptom onsets in an infector-infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector-infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19. ObjectivesConduct a rapid review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters. MethodsA review of scientific literature was conducted covering the period between December 1, 2019 and April 27, 2020. Nineteen scientific papers were evaluated in detail from 27 papers that contained information on the serial interval and/or generation time for COVID-19. ResultsThe mean of the serial interval ranged from 3.1 to 7.5 days, based on 22 estimates, and the median from 1.9 to 6.0 days (based on 7 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.9 to 5.2 days. One estimate of 5.0 days was provided for the median of the generation time. DiscussionThe values of the estimates for serial interval and generation time are heavily influenced by the contact rates between infectious and susceptible individuals. Mitigation measures that are introduced in a country or region are of paramount importance in this regard. The serial interval estimate of 6.6 days (95% confidence interval: 0.7 - 19.0) from the paper by Cereda et al.[10] is likely to be the most relevant to European countries. National estimates should be obtained as soon as possible. Strengths and limitations of this studyO_LIThe study provides timely information on serial interval and generation time for those involved in the development of models and in the implementation of control measures against COVID-19. C_LIO_LIThis is a rapid review of available evidence in the scientific literature between December 1, 2019 and April 27, 2020 on the serial interval and/or the generation time and it contains the usual limitations associated with such a review. C_LIO_LIEleven of the 19 papers reviewed in detail were pre-print articles. C_LIO_LIThe statistical methods used in the different papers were not analysed in detail. C_LI
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BackgroundReliable estimates of the incubation period are important for decision making around the control of infectious diseases. Knowledge of the incubation period distribution can be used directly to inform decision-making or as inputs into mathematical models. ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation periods of COVID-19. DesignRapid systematic review and meta-analysis of observational research Data sourcesPublications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched. The search was not limited to peer-reviewed published data, but also included pre-print articles. Study appraisal and synthesis methodsStudies were selected for meta-analysis if they reported either the parameters and confidence intervals of the distributions fit to the data, or sufficient information to facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of the parameters of this distribution. ResultsThe incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean was 5.8 (5.01, 6.69 days). It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates resulted in a median incubation period of 5.1 (4.5, 5.8) days, whereas the 95th percentile was 11.6 (9.5, 14.2) days. Conclusions and implicationsThe choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Finally, we present an RShiny app that facilitates updating these estimates as new data become available. ARTICLE SUMMARYO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIThis study provides a pooled estimate of the distribution of incubation periods which may be used in subsequent modelling studies or to inform decision-making C_LIO_LIThis estimate will need to be revisited as subsequent data become available. We present an RShiny app to allow the meta-analysis to be updated with new estimates C_LI
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This paper describes the epidemiological characteristics of bovine brucellosis in Korea during January 2000~September 2004, which encompasses the period when the incidence of bovine brucellosis increased abruptly. Data from the National Animal Infectious Disease Data Management System were used for this study. A range of epidemiological measures was calculated including annual herd and animal incidence. During the study period, there were 1,183 outbreaks on 638 farms. In beef cattle, annual herd incidence increased from 0.2 (2000) to 11.5 (2004, to September) outbreaks per 10,000 and annual animal incidence varied between 3.4 (2000) and 105.8 (2004, to September) per 100,000, respectively. On 401 (62.9%) infected farms during this period, infection was eradicated without recurrence. Recurrence of infection was significantly higher on farms where abortion was reported (53.3%), compared to farms where it was not (30.0%). On beef cattle farms, infection was introduced most frequently through purchased cattle (46.2%). Based on the results of this study, the establishment and spread of brucellosis in the Korean beef cattle population were mainly due to incomplete or inappropriate treatment of aborted materials and the movement of infected cattle.
