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Series COVID-19 Article| Volume 132, P133-141, April 2021
Modeling the coronavirus disease 2019 pandemic: A comprehensive guide of infectious disease and decision-analytic models
Stephen Mac 1
1 Contributed equally and are joint first authors.
Stephen Mac
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, Canada
Sharmistha Mishra 1
Sharmistha Mishra
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
Raphael Ximenes
Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil
Kali Barrett
University Health Network, Toronto, Canada
Yasin A. Khan
David M.J. Naimark 2
2 Contributed equally and are joint senior authors.
David M.J. Naimark
Sunnybrook Health Sciences Centre, Toronto, Canada
Beate Sander 2
Beate Sander
Corresponding author. THETA, University Health Network, Toronto General Hospital, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada. Tel.: +1 416 634 8020; fax: +1 416 340 3459.
Public Health Ontario, Toronto, Canada
ICES, Toronto, Canada
Published:December 07, 2020DOI:https://doi.org/10.1016/j.jclinepi.2020.12.002
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This commentary discusses the different types of models that can be used for pandemic planning in response to coronavirus disease 2019 (COVID-19). This article reviews different types of transmission models for infectious diseases and health-system models based on decision-analysis principles.
We include steps to guide conceptualization of a health-system model with a transmission component to aid decision makers and support public health policy using our COVID-19 Resource Estimator model, which was developed to estimate hospital resources in Ontario, Canada.
This article will be of most interest to health care decision makers, methodologists, health service researchers, and specialists in knowledge translation as well as infectious diseases. While the pandemic has been controlled in some parts of the world, many experts suggest an inevitable second wave. This article will be timely to equip quantitative researchers and decision makers with a guide to understand how modeling and early collaboration can be useful in predicting future consequences of this pandemic and impacts of potential interventions.
This commentary discusses the different types of models that are being used to support planning for coronavirus disease 2019 (COVID-19).
The commentary uses a previously proposed taxonomy to compare and contrast different types of models (dynamic transmission modeling vs. decision-analytic modeling) and modeling techniques for readers to assess the tradeoffs.
We highlight challenges with modeling in the early phases of the pandemic, and the need for local data availability and early collaboration between researchers and decision-makers.
This article highlights the intersection of infectious disease modeling and decision-analytic modeling to support decision-making through an example from Ontario, Canada.
The coronavirus disease 2019 (COVID-19) pandemic has prompted many countries to rapidly implement public health interventions to mitigate spread and reallocate health care resources to prevent overwhelming their health care systems. Models play a critical role in informing pertinent policy questions such as: What impact will physical distancing have on the number of expected cases and deaths? When do we expect the peak? Will we have sufficient health care system capacity? What is the best combination of strategies, and how does the optimal combination vary over populations and by stages of a local epidemic? A wide range of model types is used to answer these questions, including forecasting, quantifying trade-offs, explanatory modeling, intervention modeling, and economic evaluation.
Models are a simulated and simplified representation of the real-world. For the COVID-19 pandemic, a nonpeer-reviewed summary of current models, conducted by the Center for Evidence-Based Medicine (CEBM) from the University of Oxford, identified 43 models indexed on PubMed, and 13 released separately by Imperial College London. The review focused only on epidemic models projecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) transmission and epidemic trajectory of COVID-19 in various settings. CEBM reported a substantial variation among models with respect to their input parameters and estimates of case fatality rate [
Jefferson T.
Heneghan C.
Modelling the models.
Cent Evid Based Med. 2020; (Available at)
https://www.cebm.net/covid-19/modelling-the-models/
Date accessed: May 6, 2020
]. In addition to models that simulate transmission of SARS-CoV2, there are others that have been developed to predict the impact of COVID-19 on acute care resources, the need for personal protective equipment and other resources [
Barrett K.
Khan Y.A.
Mac S.
Ximenes R.
Naimark D.M.J.
Sander B.
Estimation of COVID-19–induced depletion of hospital resources in Ontario, Canada.
Can Med Assoc J. 2020; 192: E640-E646
]. The variety of models used during this pandemic may be a source of confusion for those unfamiliar with modeling.
In this commentary, we draw on a previously proposed model taxonomy to highlight the different infectious disease and decision-analytic model types. We describe the conceptualization of these models, benefits and limitations, and outline some of the challenges based on our experience in Ontario, Canada.
2. Model taxonomy
Multiple model taxonomies exist, while some are specifically for infectious diseases [
Pitman R.
Fisman D.
Zaric G.S.
Postma M.
Kretzschmar M.
Edmunds J.
Dynamic transmission modeling: a report of the ISPOR-SMDM modeling good research practices task force working group-5.
Med Decis Mak. 2012; 32: 712-721
Gambhir M.
Bozio C.
O'Hagan J.J.
Uzicanin A.
Johnson L.E.
Biggerstaff M.
Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential.
Clin Infect Dis. 2015; 60: S11-S19
Mishra S.
Fisman D.N.
Boily M.C.
The ABC of terms used in mathematical models of infectious diseases.
J Epidemiol Community Health. 2011; 65: 87-94
], others capture a broader range of model types. Here, we use a previously proposed taxonomy that can accommodate decision-analytic and infectious disease models [
Brennan A.
Chick S.
Davies R.
A taxonomy of model structures for economic evaluation of health technologies.
Health Econ. 2006; 15: 1295-1310
]. We use this taxonomy's three broad criteria: individual level vs. cohort level, with vs. without interaction, and discrete vs. continuous time.
2.1 Cohort vs. individual level
Cohort models stratify a population of individuals into mutually exclusive health states or compartments. Members of a given health state are assumed to be homogenous with respect to the risk of subsequent events. Cohort models are configured to follow entire populations over time and allow for changing event probabilities or rates to accurately reflect disease progression or the influence of time on the risk of events. An important assumption is that future behavior of individuals depends only on the present health state and transition probabilities (i.e., past events are not remembered). While cohort models require less data, and save computational costs, they typically consider population averages and are unable to explore individual heterogeneity with respect to risk factors and also variations in individual outcomes due to chance and complex network effects.
Individual-level models simulate everyone separately as they progress through health states, with each simulated person having heterogeneous characteristics (e.g., age, comorbidities, and risk factors) which may change the risk of subsequent events. Individual-level models generally require more data and are more computationally intensive than cohort models. Stochasticity, or randomness, can be incorporated in these models so that each individual's probability of subsequent events is randomly determined, reflecting uncertainty from random processes (e.g., if the probability of being admitted to hospital due to COVID-19 is 0.2, and individual patient may or may not be admitted; however, at the population level approximately 20% of patients would be admitted to hospital). Each stochastic model simulation produces a different result, but over a large number of simulations, the results should converge to the average result from a deterministic model. Stochastic models are especially useful modeling rare events, or small populations, when uncertainty due to chance can have a large effect, for example, superspreader events. Key reasons for selecting an individual-level model over a cohort model are: to easily capture additional layers of modeled heterogeneity among individuals (e.g., age and sex) and environmental characteristics (e.g., household composition and workplace); to capture uncertainty due to random processes; and to use individual-level data and history of events to determine future risk for events [
2.2 With or without interactions
The second aspect is whether the model allows for interactions and feedback loops. Interactions may refer to contacts between people leading to chains of transmission in the context of infectious diseases. With interactions, a case becomes a risk factor for other individuals and creates a feedback loop between prevalent and incident infections. Modeling with interactions is especially important for infectious diseases, the spread of which relies on interaction for individual-level models. In deterministic compartmental models, these interactions are not formally modeled but can be built into equations with prespecified parameters. Interactions can also refer to individuals with their environment, such as competition among individuals for resources. For example, if infected cases admitted to hospital exceed available acute system resources, this results in queuing among subsequent cases and delayed care.
2.3 Discrete vs. continuous time
The third aspect is how time is incorporated. Discrete-time models divide time from the beginning of a simulation into equally long time segments. Transitions among health states or compartments are assigned a distinct value at each point in time. Continuous time models view variables as having a value at any infinitesimally short amount of time (i.e., time is not distinct). Transitions among compartments are assigned instantaneous rates which are often calculated as systems of differential equations.
3. Infectious disease models
3.1 Phenomenological models
Phenomenological models are commonly used for estimating the time-variant reproductive ratio at time point t, Rt (i.e., the average number of secondary infections per infectious individual at any given point in time) and near-forecasting in real time. Such models do not simulate the causal pathway of transmission. Phenomenological models use observed data from one time step, with an understanding of key biological properties such as generation time of an infectious disease, to project expected number of cases at the next time step. A benefit of these models is that they use very few parameters alongside observed cases to project forward without causal mechanisms [
Transtrum M.K.
Qiu P.
Bridging mechanistic and phenomenological models of complex biological systems.
PLoS Comput Biol. 2016; 12: 1-34
Chowell G.
Hincapie-Palacio D.
Ospina J.
Pell B.
Tariq A.
Dahal S.
Using phenomenological models to characterize transmissibility and forecast patterns and final Burden of Zika epidemics.
PLoS Curr. 2016; : 1-16
]. Examples are provided in Box 1.
Glossary of model types mentioned in commentary
Tabled 1
Agent-based models Also referred to as individual-level models. See "Individual-level models" for definition.
Compartmental models Compartmental models are cohort-based transmission dynamic models that involve interaction and usually treat time as continuous. The biological structure is applied to homogenous groups of individuals (compartments), which can be further stratified across other domains. Examples include SIR and SEIR models among the many.
Also referred to as cohort-level models.
Decision-analytic models Decision analytic models are used to analyze decisions under uncertainty. This group of models include decision trees, Markov cohort models, state-transition models, and discrete event simulation models.
Also referred to as health economic models if used for economic evaluations.
Deterministic models A model where random processes and uncertainty due to random chance of events are not captured. Each simulation will result in identical average results. Deterministic model results are often viewed as the average of many stochastic model simulations.
Discrete event simulation (DES) models Discrete event simulation models are individual-level models that simulate events at a particular point in time. These models involve interaction, generally between individuals and their environment, and treat time as continuous. They require extensive individual-level time-to-event data.
Individual-level models Individual-level models are dynamic models allowing for interactions between individuals to produce a complex network effect.
In individual-level infectious disease models, the biologic structure, demographic characteristics, and risk factors are applied at the individual level, so that natural history, risk level, and contacts/interactions can vary between people.
Also referred to as agent-based models.
In individual-level decision-analytic models, the probability of transition, risk for events, or time-to-event apply to each individual. Each simulation represents an individual, often introducing heterogeneity and stochasticity.
Sometimes referred to as microsimulations.
Phenomenological models Phenomenological models are commonly used for estimating the time-variant Rt (effective reproductive number), and near-forecasting in real time. Such models do not simulate the causal pathway of transmission.
These models have been used to estimate time-varying reproduction numbers during epidemics for measles (Germany, 1861), pandemic influenza (USA, 1918), smallpox (Kosovo, 1972), SARS (Hong Kong, 2003), and pandemic influenza (USA, 2009) [
Cori A.
Ferguson N.M.
Fraser C.
Cauchemez S.
A New Framework and software to estimate time-varying reproduction numbers during epidemics.
Am J Epidemiol. 2013; 178: 1505-1512
Examples of phenomenological models include regression analyses or statistical model, branching processes, and renewal equation models. Branching process is a mathematical process where nodes (which represent infected individuals) give rise to other nodes to show an infection tree. The probability of nodes proliferating or diminishing is described by statistics and mathematics. Renewal equation models use an equation that defines the relationship between the number of new infections as being proportional to the number of prevalent cases and their infectiousness.
SEIR A type of infectious disease compartmental transmission model with the compartments: Susceptible, Exposed, Infectious, and Recovered.
SEIRS A type of infectious disease compartmental transmission model with the compartments: Susceptible, Exposed, Infectious, Recovered, and Susceptible (again).
SIR A type of infectious disease compartmental transmission model with the compartments: Susceptible, Infectious, and Recovered.
SIRS A type of infectious disease compartmental transmission model with the compartments: Susceptible, Infectious, and Recovered, and Susceptible (again).
State-transition models Simulations (or expected value calculations) conceptualized in terms of health states, transitions, and transition probabilities. The most common types are Markov cohort models and individual-level state-transition models.
Stochastic model A model that captures uncertainty due to random chance. Each stochastic model simulations produce different results (realizations), but over a large number of simulations, they should converge to the average result generated from a deterministic model. Stochastic models are especially useful when events are rare or if there are smaller populations; when uncertainty due to chance can have a large effect.
Transmission dynamic models Models for infectious disease transmission explicitly capture interactions and feedback loops as mechanisms of infectious diseases dynamics
All provided definitions and examples are adapted from Gambhir et al. [
], Mishra et al. [
], Cori et al. [
], Fraser et al. [
Estimating individual and household reproduction numbers in an emerging epidemic.
PLoS One. 2007; 2: e758
], Thompson et al. [
Thompson R.N.
Stockwin J.E.
van Gaalen R.D.
Polonsky J.A.
Kamvar Z.N.
Demarsh P.A.
Improved inference of time-varying reproduction numbers during infectious disease outbreaks.
Epidemics. 2019; 29: 100356
] Please refer to these papers for more details about infectious disease modeling.
3.2 Transmission dynamic models
Models for infectious disease transmission explicitly capture interactions and feedback loops as mechanisms of infectious disease dynamics [
Grassly N.C.
Mathematical models of infectious disease transmission.
Nat Rev Microbiol. 2008; 6: 477-487
]. Transmission dynamic (TD models can be used for forecasting (e.g., number of cases and deaths and final outbreak size) [
Amaku M.
Lopez L.F.
Coutinho F.A.B.
Burattini M.N.
Greenhalgh D.
The risk of dengue for non-immune foreign visitors to the 2016 summer olympic games in Rio de Janeiro, Brazil.
BMC Infect Dis. 2016; 16: 1-9
], explanatory modeling (e.g., identifying mechanistic areas of uncertainty), estimating epidemic characteristics [
Brauer F.
Compartmental models in epidemiology. 1945. Springer Verlag, Berlin, Germany2008
], assessing interventions (e.g., vaccines, public health interventions, and treatment), and for economic evaluations [
Moghadas S.
Day T.
Bauch C.
Driedger S.M.
Modelling an influenza pandemic: a guide for the perplexed - pandemic influenza outbreak research modelling team (Pan-InfORM).
CMAJ. 2009; 181: 171-173
TD models are abstractions of the natural history of an infectious pathogen as it passes between people and moves through a population. The basic biological structure is that of S-I (susceptible and infectious), but various structures are possible depending on the characteristics of the pathogen, such as whether there is a period of time wherein a person may be infected but not yet infectious, usually called an "exposed" or "latent" period or whether reinfection is possible. Common structures include Susceptible, Infectious, and Recovered (SIR) [
Kermack W.
McKendrick A.
A contribution to the mathematical theory of epidemics.
Proc R Soc A Math Phys Eng Sci. 1927; 115: 700-721
], Susceptible, Exposed, Infectious, Recovered (SEIR), and others where individuals can return to the susceptible compartment (e.g., SEIRS). The key decision points surrounding the development of the biological structure is specified by how the pathogen plays out in the host. Such decisions are often challenging in the setting of emerging infectious diseases such as SARS-CoV-2 until key biological properties (e.g., presymptomatic transmission) are available. A key component of these models includes the mixing pattern across each of these domains (biological, demographic, and risk level) with respect to contact encounters. Subsets of a population (e.g., regions or neighborhoods) can be linked via mixing matrices or by proxy measures such as travel patterns or spatial distance between regions. TD models are considered mechanistic models because they simulate the mechanisms of the feedback loops. TD models can be compartmental (cohort level) or individual level.
3.3 Compartmental transmission dynamic models
Compartmental TD models are cohort-based, involve interaction, and usually treat time as continuous. The biological structure is applied to homogenous groups of individuals (compartments), which can be further stratified across other domains including demographic (e.g., age- and/or sex-stratified) and infection risk level (subgroups with higher contact rates or who cannot self-isolate). Compartmental TD models can be deterministic or stochastic. Deterministic models generate an average trajectory for each set of data inputs, whereas stochastic models allow several "epidemic realizations." The average of many stochastic realizations approximates that of a deterministic model. Stochastic simulations are well suited for small populations or when there are few cases.
3.4 Individual-level transmission dynamic models
If TD models simulate individuals, they are referred to as agent-based or individual-level models and usually treat time as discrete. In individual-level TD models, the biologic structure, demographic characteristics, and risk factors are applied at the individual level, so that natural history, risk level, contacts/interactions can vary between people [
Chhatwal J.
He T.
Economic evaluations with agent-based modelling: an introduction.
]. These interactions can produce complex network effects. The benefits of an individual-level TD model, despite added complexity, are added layers of heterogeneity, ability to incorporate stochasticity (i.e., they can still be deterministic), and better capture of emergent phenomena such as specific transmission dynamics (e.g., superspreader or rare events). However, individual-level TD models require extensive individual-level data for each characteristic under consideration and are resource-intensive to develop, calibrate (i.e., to reproduce population-level network features), and analyze [
Venkatramanan S.
Lewis B.
Chen J.
Higdon D.
Vullikanti A.
Marathe M.
Using data-driven agent-based models for forecasting emerging infectious diseases.
Epidemics. 2018; 22: 43-49
Bansal S.
Simonsen L.
Vespignani A.
Viboud C.
Big data for infectious disease surveillance and modeling.
J Infect Dis. 2016; 214: S375-S379
4. Health system models in the context of a pandemic
Phenomenological and TD models inform one part of pandemic planning by forecasting the potential rate of increase, time to peak and ultimate magnitude of local outbreaks, and the effect of public health interventions on flattening the epidemic curve [
Tuite A.R.
Greer A.L.
Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada.
Can Med Assoc J. 2020; (cmaj.200476)
Kucharski A.J.
Russell T.W.
Diamond C.
Funk S.
Early dynamics of transmission and control of COVID-19: a mathematical modelling study.
Wu J.T.
Leung K.
Leung G.M.
Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study.
Lancet. 2020; 395: 2020
]. Another key element of pandemic planning is preparing the health system response, which will be more accurately modeled using synergistic modeling of the health system alongside TD models (Fig. 1). Health system models, commonly using decision-analytic tools, can forecast health care utilization, patient outcomes over time, dynamic resource constraints, queuing and priority setting, and the consequences of resource constraints. These models can be useful in the early stages of a pandemic and can be used to answer future-minded health system questions such as restarting clinical services that were curtailed.
Fig. 1Conceptualizing a health system model for pandemic planning.
4.1 Decision-analytic models
Decision analysis is the application of systematic, quantitative, and transparent methods to analyze decisions under uncertainty [
Siebert U.
When should decision-analytic modeling be used in the economic evaluation of health care?.
Eur J Heal Econ. 2003; 4: 143-150
]. These models often incorporate multiple aspects of the decision problem, including epidemiology, dynamic populations, patient characteristics, disease progression, and resource utilization.
State-transition models are simulations (or expected value calculations) conceptualized in terms of health states, transitions, and transition probabilities. They are the most common type of decision-analytic models and include Markov cohort- and individual-level models [
Alagoz O.
Bayoumi A.M.
Jahn B.
Owens D.K.
Cohen D.J.
State-transition modeling: a report of the ISPOR-SMDM modeling good research practices task force.
Value Heal. 2012; 15: 812-820
]. Individual-level models are often useful for resource constraint and capacity planning purposes in a pandemic due to its ability to capture interaction and dynamics of transmission.
4.2 Individual-level state-transition models
Individual-level state-transition models allow each individual with unique characteristics to traverse any number of health states (e.g., healthy, sick, and dead) and treat time as discrete. They can include a dynamic population (i.e., people entering and leaving the model to accommodate interaction with the environment), incorporate stochasticity (randomness), capture dynamics of transmission, and provide individual-level outcomes based on heterogeneity in the base-case analysis [
Roberts M.
Russell L.B.
Paltiel A.D.
Chambers M.
McEwan P.
Krahn M.
Conceptualizing a model: a report of the ISPOR-SMDM modeling good research practices task force-2.
]. Individuals can be modeled in parallel, allowing them to interact with each other (i.e., contribute to transmission) and compete for resources, which, if unavailable, may result in adverse outcomes [
Fitzpatrick T.
Johnstone J.
Vancomycin-resistant enterococci (VRE) screening and isolation in the general medicine ward: a cost-effectiveness analysis.
Antimicrob Resist Infect Control. 2019; 8: 1-10
]. While these models are flexible, its complexity and need for multiple data sources can lead to the possibility of competing risks [
Jayasuriya S.
Elbasha E.H.
Changing cycle lengths in state-transition models: challenges and solutions.
4.3 Discrete event simulation
Discrete event simulation (DES) models are individual level, involve interaction, generally between individuals and their environment, and treat time as continuous [
Pidd M.
Computer simulation in management science. 5th ed. John Wiley & Sons, Ltd, New York2004
]. The main concepts of DES models include entities (e.g., patients), attributes (e.g., patient characteristics), events, resources, queues, and time. DES models involve time segments as well but, unlike state transition models, the length of each time step is variable and continuous. A given step involves the movement of an individual from one discrete event (e.g., arrival at an emergency department) to another (e.g., admission to critical care). DES models are also flexible and computationally efficient. However, one major limitation is its extensive requirement of time-to-event data [
Karnon J.
Stahl J.
Caro J.J.
Mar J.
Möller J.
Modeling using discrete event simulation: a report of the ISPOR-SMDM modeling good research practices task force-4.
], which is unlikely to be available early in an outbreak.
4.4 Conceptualizing a health system model for pandemic planning
In Box 2, we describe steps to conceptualize a health-system model for pandemic planning, which were adapted from the International Society for Pharmacoeconomics and Outcomes Research & Society of Medical Decision Making Modeling Good Research Practices Task Force [
Paltiel D.
]. For conceptualization of compartmental model, refer to "An introduction to infectious disease modeling" by Vynnycky et al. [
Vynnycky E.
White R.G.
An introduction to infectious disease modelling. 1st ed.
Oxford University Press, New York2010
]. An example for Ontario, Canada is described in Box 3.
Conceptualizing a health system model for pandemic planning
[1] Develop the research question and assess available data
The question that the model ultimately answers will determine what data and type of model will be required. For example, in a health system model for pandemic planning of health care resources, the model would need to start at the time where the patient first contacts the health care system.
This model would simulate patient flow in the hospital (facility) due to COVID-19 and identify resource constraints and all potential health outcomes (death). Because resource constraints require individual interaction, Markov cohort models and decision-tree models are inadequate choices.
[2] Selecting a decision-analytic model
Due to the unique nature of a pandemic (sudden, limited data, and uncertainty) and the understanding that models are highly data-driven, model selection is very important to timeliness of evidence.
While discrete event simulation (DES) models would be traditionally used for resource constraints, a discrete-time, dynamic, individual state-transition model is also appropriate given the likely lack of time-to-event data required to build a DES model.
This model's population of interest should be dynamic following observed case data.
This can usually be incorporated by integrating the decision-analytic model with a transmission model component or simple short-term forecasting of daily incidence rates of cases from epidemic curves.
An appropriate time horizon and time measurement (daily time steps) should also be considered.
[3] Mapping out disease progression and patient pathway through health system
The model's schematic and patient trajectory should consider:
The health care facility's resources,
All potential disease progression pathways for patients presenting with the disease, and
Logic on how to resolve resource constraints and queues in accordance with clinical practice.
[4] Selecting the outcomes to report
This is usually informed by the research question. Outcomes to inform pandemic planning can include:
the number of hospitalizations,
days until resource depletion (ward beds, intensive care unit (ICU) beds, and ventilators),
resources required (beds, personal protective equipment, and medication), and
health outcomes, for example, mortality, associated with resource constraints.
Additional outcomes that can be reported include patient-specific outcomes (e.g., quality-adjusted life years) and costs.
[5] Refining the model
Steps 1 to 4 are similar to those described in the literature [
]. However, modelling pandemics is a unique challenge due to the constantly changing environment.
After developing the model and running analyses, the model will need to be refined when higher quality data is available and when questions being addressed change.
Initially, the model will likely address the consequences (magnitude and speed) of the outbreak, but then turn to understanding how to sustain adequate health care, and finally how to plan normalization as the outbreak is controlled.
COVID-19 example in Ontario, Canada
This box describes the COVID-19 Resource Estimator model developed by COVID-19-MC [2].
At the beginning of the epidemic in Ontario, we developed a dynamic, discrete-time, individual-level state-transition model [
] informed by data from the epidemic in China.
We used data from published observational studies for disease progression and health care needs (e.g., needing mechanical ventilation) [
Wu C.
Chen X.
Cai Y.
Xia J.
Zhou X.
Xu S.
Risk factors Associated with acute Respiratory Distress syndrome and death in patients with coronavirus disease 2019 pneumonia in wuhan, China.
JAMA Intern Med. 2020; 180: 934-943
Wang D.
Hu B.
Hu C.
Zhu F.
Liu X.
Zhang J.
Clinical characteristics of 138 Hospitalized patients with 2019 novel coronavirus-infected pneumonia in wuhan, China.
Data sources and challenges
As the pandemic evolved, the model was refined using data from the epidemic in Italy [
Grasselli G.
Pesenti A.
Cecconi M.
Critical care utilization for the COVID-19 outbreak in Lombardy, Italy.
], and as local evidence emerged, Canadian-specific data [
Coronavirus Disease 2019 (COVID-19) Daily Epidemiology Update.
https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html
Because of different populations, health care systems, and medical practices, we acknowledge that the use of other jurisdictions' data to inform the model can be misleading but is the only practical solution at the earliest phases of an outbreak.
Model assumptions and data sources need to be clearly explained, and the model was refined as local data became available.
For pandemic trajectories, that is, predicted cases over time, initial projections had to be based on observed data from other countries (Italy and South Korea) [
Situation update worldwide, as of 13 April 2020.
https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases?
Date accessed: April 13, 2020
] but were updated to Ontario's expected cases using the integrated Public Health Information System (iPHIS) database as the epidemic in Ontario continued.
Data availability and quality is a major challenge during an outbreak (e.g., changes in case definitions, testing criteria, reporting, right censoring, especially for clinical data).
However, modelling for pandemic planning needs to find a balance between perfect data and timeliness of generated evidence from the model.
Refining the model
As more data becomes available, calibration of uncertain parameters can be conducted (e.g., probability of ward and intensive care unit (ICU) admissions in Ontario).
Calibration is a technique comparing model outputs with an independent data set (e.g., observed data), also known as calibration targets, to explore variations of an uncertain model parameter to provide a better fit of the model [
Vanni T.
Madan J.
Edmunds W.J.
Foss A.M.
Calibrating models in economic evaluation: a seven-step approach.
Pharmacoeconomics. 2011; 29: 35-49
Another method to improve model credibility and accuracy is through model validation.
Some forms of validation include face validity, where experts confirm whether the model reflects their understanding of the disease and patient pathways, and external validity, where model results are compared with actual event data [
Eddy D.M.
Hollingworth W.
Tsevat J.
McDonald K.M.
Wong J.B.
Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force-7.
a This box describes the COVID-19 Resource Estimator model developed by COVID-19-MC [
5. Challenges
A common aphorism is: "All models are wrong, but some are useful" [
Box G.E.P.
Robustness in the strategy of scientific model Building.
in: Robustness Stat. Elsevier, 1979: 201-236
]. Models are useful when they are driven by a specific question for which the model is well designed to answer. After framing the modeling question, the challenges thereafter are often driven by data availability and the timeliness of providing information for decision makers.
5.1 Timing vs. comprehensiveness
Models can provide qualitative insights and quantitative projections. Both are highly data-driven, depending on data which are typically sparse, right censored, and reported in aggregate without comprehensive detail (e.g., demographic, clinical, and biological features) in early phases of an outbreak. Thus, simpler models often predominate in the early phase of an epidemic, with data borrowed from what was known before. With COVID-19, early analyses based on experience elsewhere, especially in the absence of local data, were used to explore "what-if" scenarios in local jurisdictions. These scenario-based analyses were helpful because while they borrowed heavily from estimated reproductive rates from other settings, they provided decision makers with initial estimates of best and worst-case scenarios in the absence of further action.
In the context of modeling for an emerging infectious disease, there will remain a tension between comprehensiveness and timeliness of model-based information for decision makers: a simple model developed quickly, albeit with recognized caveats may be more useful to the response than a detailed model completed after the worst of the pandemic has passed. The key is clarity in how the modeling question is framed, how model outputs are interpreted, and transparency around the justification for model selection and data inputs.
5.2 Data availability and quality
Local or regional data is critical at all phases of the pandemic. Data components that are especially important include:
Infection/transmission (e.g., reported cases and deaths, proportion of those confirmed positive through diagnostic testing, and presenting to the hospital).
Resource use (e.g., length of stay and health care utilization).
Disease progression (e.g., proportion of patients transitioning to ICU (deteriorate), proportion of ICU patients needing a ventilator, proportion of patients recovering, and time to all events).
Demographics (e.g., age, sex, gender, and comorbidities).
Often, health system and transmission modelers do not have this real-time data, which highlights the importance of close collaborations between decision makers and modelers [
]. In the context of modeling to support a pandemic response, there is considerable strength in defining questions and interrogating the data and model assumptions in partnership with stakeholders who may use the modeling results. Beyond their role as knowledge users, stakeholders (e.g., decision makers, Ministries of Health, public health organizations, and frontline service providers) also provide expertise in context-specific considerations and how data were collected. Ensuring a clear understanding of how the data were collected, and the potential biases, is a critical component of modeling.
Modeling can be a useful tool to provide evidence to support policymakers and decision-making throughout a pandemic. In this commentary, we provided a description of common model types that policymakers are likely to encounter, and discuss reasons for why specific model types are more suitable over another for specific research questions, conceptualization, and data availability.
CRediT authorship contribution statement
Stephen Mac: Conceptualization, Writing - original draft, Visualization. Sharmistha Mishra: Conceptualization, Writing - original draft. Raphael Ximenes: Writing - review & editing. Kali Barrett: Writing - review & editing. Yasin A. Khan: Writing - review & editing. David M.J. Naimark: Writing - review & editing. Beate Sander: Conceptualization, Writing - original draft, Supervision.
Download .xml (.0 MB)
Help with xml files
Data Profile
2020 (Available at)
Accepted: December 1, 2020
Conflict of Interest: All authors have no conflicts of interests to declare.
This was supported, in part, by a Canada Research Chair in Economics of Infectious Diseases held by Beate Sander (CRC-950-232429) and COVID-19 Rapid Research Funding (C-291-2431272-SANDER).
DOI: https://doi.org/10.1016/j.jclinepi.2020.12.002
© 2020 Elsevier Inc. All rights reserved.


Denny's Canada, Cranbrook, Canada - Restina.net, discover great local restaurants, bars and clubs.
Welcome to Denny's Canada! Where guests have come for over 60 years to relax and enjoy good, hearty meals at reasonable prices. Open 24 hours a day, 7 days a week, Denny's restaurants serve classic breakfast around the clock as well as craveable salads, hand-pressed burgers and homestyle dinners. Our menu is broad yet familiar, which makes us a great place to take the kids and family. You will always find delicious value and variety no matter what you are in the mood for. Denny's is proud to be Canada's Diner. And now you can enjoy the New! Dulce de Leche Crunch Pancakes with cinnamon crumb topping cooked into two fluffy buttermilk pancakes and served with warm caramel sauce for drizzling. Real ingredients. Freshly crafted. It's just the latest addition to our seriously upgraded menu!
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text:
2929 Graduate Engineers of UET get Degrees in its 27th Convocation
announcements banner events
Almas Arshad
In the 27th convocation of the University of Engineering and Technology (UET) Lahore, three female students won 15 gold medals while one male student won 4 gold medals. Sadaf Munir won 6 gold medals and Ayesha Khalid won 5 gold medals. Similarly, Arshma Hasnain and Hamza Siddique won 4, 4 gold medals.
In this convocation degrees were distributed among 2,929 students. 2,246 graduates, 651 MS / M. Phil, 32 PhD degrees and 61 gold medals were distributed among students. Degrees were awarded in UET main and sub-campuses of UET followed by the number of 2021 degrees in UET Lahore main campus, 423 in New Campus (UET-KSK), 216 degrees in UET Faisalabad (UET-FSD) campus, 141 degrees in Rachna College of Engineering and Technology (UET-RCET), and 128 degrees in UET Narowal (UET-NWL) campus.
The convocation was presided over by Governor Punjab and Chancellor UET Chaudhry Muhammad Sarwar. Vice Chancellor UET Prof. Dr. Syed Mansoor Sarwar delivered welcome address in which he described the achievements, gained in the field of research and academia, as well as future plans and goals. He said that UET graduates are an asset of Pakistan to build the country and their role in the development of Pakistan is significant. He congratulated the successful students and said that all this is the result of hard work of your parents and teachers. Always respect your parents and teachers. In his welcome address, Vice Chancellor Prof. Dr. Syed Mansoor Sarwar congratulated the students on the successful completion of 100 years of the University. He said that the UET has completed a century of its journey. This 100-year journey is full of success, progress, inventions, and new ideas.
Addressing the students, Governor Punjab Chaudhry Muhammad Sarwar appreciated Vice Chancellor Prof. Dr. Syed Mansoor Sarwar and his team on continuous improvement in the international and Asia rankings of UET in the last three years. He further said that the country is strengthened only by the rule of law and constitution and giving rights to the rightful claimants. After the success of Pakistani women, quotas for boys' jobs may have to be allocated in the coming years. He appealed graduating students to cooperate with the government for sustainable measures to tackle important issues like poverty, water crisis, environmental pollution and natural calamities. He also announced a prize of Rs. 100,000 and Rs. 50,000 for the four students respectively. Prize money will be given to these students and their teachers by inviting them to the Governor House. The Governor of Punjab planted a tree on the 100 years anniversary of the University and also inaugurated a commemorative plaque.
In this event, Deans, Chairmen, Heads of academic and administrative offices, Registrar Muhammad Asif, Controller Examination Muhammad Zargham Nusrat, Public Relations Officer Shahida Nazir and members of syndicate were present. At the end of convocation Vice Chancellor Prof. Dr. Mansoor Sarwar presented souvenirs to the honorable guest Governor Punjab and Chancellor of University Chaudhry Muhammad Sarwar.
27th Convocation of the University of Engineering & Technology, Lahore is scheduled to be held on Monday, 15th November 2021
New Venture Contest – An Entrepreneurship Competition – A Chance to Win An Award of $10,000″
Huawei Center, University of Engineering & Technology, G.T. Road Lahore – Pakistan
Call us: (042) 99029450

redpajama_set_name: RedPajamaCommonCrawl


AMSTERDAM, The Netherlands, Nov. 6, 2014 (GLOBE NEWSWIRE) -- Tornier N.V. (Nasdaq:TRNX), a global medical device company focused on providing surgical solutions to orthopaedic extremity specialists, reported today its financial results for the third quarter ended September 28, 2014.
As reported on October 27, 2014, revenue for the third quarter of 2014 was $76.7 million compared to third quarter 2013 revenue of $66.7 million, an increase of 14.9% as reported and 14.0% in constant currency.
Third quarter 2014 revenue of Tornier's extremities product categories totaled $65.8 million compared to $56.9 million in the same quarter a year ago, an increase of 15.5% as reported and 14.4% in constant currency. Third quarter revenue of the Company's large joints and other product lines was $10.9 million, an increase of 11.0% as reported and 11.5% in constant currency over the same quarter in 2013.
Dave Mowry, President and Chief Executive Officer of Tornier, commented, "We achieved strong revenue growth in the quarter, reflecting the ongoing adoption of the Aequalis Ascend Flex shoulder system and robust growth across our ankle arthritis portfolio. During the quarter, we completed the planned transitions of our U.S. sales force to dedicated upper and lower extremity teams and are now focused on executing the sales force education and training that we believe will drive continued momentum in our extremities business."
Mr. Mowry added, "Last week we announced a definitive agreement to merge with Wright Medical Group, which would create a premier, high-growth Company focused on extremities and biologics. Our businesses are highly complementary, with a significant opportunity as a combined company to drive above market revenue growth while also accelerating our path to profitability. "
The Company's third quarter 2014 adjusted EBITDA, as defined in the GAAP to non-GAAP reconciliation provided later in this release, was $4.0 million, or 5.3% of reported revenue, compared to $4.8 million, or 7.2% of revenue, in the same quarter of the prior year.
Revenue from the upper extremity joints and trauma category was $49.0 million, an increase of 20.0% in constant currency over the same quarter in 2013. This growth was led by the Aequalis Ascend family of shoulder joint replacement products, which continued to gain global surgeon acceptance.
Revenue from Tornier's lower extremity joints and trauma category in the third quarter of 2014 was $13.8 million, an increase of 1.9% in constant currency over the prior year quarter. Solid sales growth of the Company's ankle arthritis products, including the Salto total ankle arthroplasty system was offset by lower sales of foot fixation products, which were impacted by the Company's U.S. sales transition activities.
Revenue from the sports medicine and biologics product category was $3.0 million in the third quarter of 2014, a decrease of 3.8% in constant currency over the same quarter in 2013, reflecting a decline in the Company's biologics and soft tissue anchor products.
Constant currency revenue growth for large joints and other products was 11.5% in the third quarter of 2014 and was led by the Company's hip products that benefitted from the release of new minimally invasive instrumentation in Europe late in 2013.
Tornier's international revenue increased 14.8% as reported and 12.5% in constant currency as compared to the third quarter of 2013 and represented 39% of reported global revenue. Revenue in the United States increased by 14.9% and represented 61% of reported global revenue.
The Company is reiterating its previously announced fourth quarter 2014 guidance, with constant currency revenue expected to be in the range of $85.0 to $89.0 million, representing constant currency growth of 1.9% to 6.7% over the same period last year.
The Company projects fourth 2014 adjusted EBITDA to be in the range of $7.9 to $9.9 million, or 9.5% to 11.4% of reported revenue.
The Company is reiterating its full year guidance for 2014, with constant currency revenue expected to be in the range of $334.9 to $338.9 million, representing an increase of 7.7% to 9.0% over last year.
The Company projects 2014 adjusted EBITDA to be in the range of $27.5 to $29.5 million, or 8.2% to 8.7% of reported revenue.
As reported on October 27, 2014, Tornier announced a definitive agreement to merge with Wright Medical Group, Inc. in an all-stock transaction with a combined equity value of approximately $3.3 billion. Upon completion of the merger, Wright shareholders will own approximately 52% of the shares of the combined company on a fully diluted basis and Tornier shareholders will own approximately 48%. More information about the transaction can be found at www.ExtremitiesLeader.com.
Tornier will host a conference call today at 4:30 p.m. eastern time to discuss its third quarter 2014 financial results and its outlook for 2014. The conference call will be available to interested parties through a live audio webcast available through the Company's website at www.tornier.com. Those without internet access may join the call from within the U.S. by dialing (877) 673-5355; outside the U.S., dial (760) 666-3805.
A telephone replay will be available for ten days following the call by dialing (855) 859-2056 for domestic participants and (404) 537-3406 for international participants. When prompted, please enter the replay pin number 18422086. For those who are not available to listen to the live webcast, the call will be archived for one year on Tornier's website.
Statements contained in this release that relate to future, not past, events are forward-looking statements under the Private Securities Litigation Reform Act of 1995. Forward-looking statements are based on current expectations of future events and often can be identified by words such as "expect," "should," "project," "anticipate," "intend," "will," "can," "may," "believe," "could," "continue," "outlook," "guidance," "future," other words of similar meaning or the use of future dates. Examples of forward-looking statements in this release include Tornier's financial guidance for the fourth quarter and full year 2014 and Tornier's expectations for continued momentum in its extremities business. Forward-looking statements by their nature address matters that are, to different degrees, uncertain. Uncertainties and risks may cause Tornier's actual results to be materially different than those expressed in or implied by Tornier's forward-looking statements. For Tornier, such uncertainties and risks include, among others, risks relating to Tornier's proposed merger with Wright Medical Group, Inc., including the timing of the transaction; uncertainties as to whether Tornier shareholders and Wright shareholders will approve the transaction; the risk that competing offers will be made; the possibility that various closing conditions for the transaction may not be satisfied or waived, including that a governmental entity may prohibit, delay or refuse to grant approval for the consummation of the transaction, or the terms of such approval; the effects of disruption from the transaction making it more difficult to maintain relationships with employees, customers, vendors and other business partners; the risk that shareholder litigation in connection with the transaction may result in significant costs of defense, indemnification and liability; other business effects, including the effects of industry, economic or political conditions outside of Wright's or Tornier's control; the failure to realize synergies and cost-savings from the transaction or delay in realization thereof; the businesses of Wright and Tornier may not be combined successfully, or such combination may take longer, be more difficult, time-consuming or costly to accomplish than expected; operating costs and business disruption following completion of the transaction, including adverse effects on employee retention and on Wright's and Tornier's respective business relationships with third parties; transaction costs; actual or contingent liabilities; the adequacy of the combined company's capital resource; and other risks and uncertainties, including Tornier's future operating results and financial performance; Tornier's reliance on its independent sales agencies and distributors to sell its products and the effect on its business and operating results of agency and distributor changes, transitions to direct selling models in certain geographies and the recent transition of its U.S. sales channel towards focusing separately on upper and lower extremity products; risks associated with Tornier's acquisition of OrthoHelix and subsequent integration activities; fluctuations in foreign currency exchange rates; the effect of global economic conditions; the European sovereign debt crisis and austerity measures; risks associated with Tornier's international operations and expansion; the timing of regulatory approvals and introduction of new products; physician acceptance, endorsement, and use of new products; the effect of regulatory actions, changes in and adoption of reimbursement rates and product recalls; competitor activities; Tornier's manufacturing capacity; Tornier's leverage and access to credit under its credit agreement; and changes in tax and other legislation. More detailed information on these and other factors that could affect Tornier's actual results are described in Tornier's filings with the U.S. Securities and Exchange Commission, including its most recent annual report on Form 10-K for the fiscal year ended December 29, 2013 and subsequent quarterly reports on Form 10-Q. Tornier undertakes no obligation to update its forward-looking statements.
Tornier believes the non-GAAP financial measures presented in this release provide additional meaningful information for measuring Tornier's financial performance and are measures frequently used by Tornier's management, as well as securities analysts and investors. Tornier uses the non-GAAP financial measures as supplemental measures of its performance and believes such measures facilitate operating performance comparisons from period to period and company to company by factoring out potential differences caused by charges not related to Tornier's regular, ongoing business, including non-cash charges, certain large and unpredictable charges, acquisitions, dispositions, litigation settlements and tax positions. Tornier's management uses the non-GAAP financial measures to assess the performance of Tornier's core operations, analyze underlying trends in Tornier's businesses, establish operational goals and forecasts, and evaluate Tornier's performance period over period and in relation to the operating results of its competitors. Tornier's management uses the non-GAAP financial measures to help allocate its resources to both ongoing and prospective business initiatives and to help make budgeting and spending decisions, for example, between product development expenses, research and development expenses, and selling, general and administrative expenses. Tornier's management is evaluated on the basis of several of these non-GAAP financial measures when determining achievement of performance incentive compensation goals.
Tornier believes that non-GAAP financial measures have limitations as analytical tools since they do not reflect all of the amounts associated with Tornier's operating results as determined in accordance with GAAP and should only be used to evaluate Tornier's operating results in conjunction with the corresponding GAAP measures. Accordingly, revenue on a constant currency basis should not be used as a substitute for revenue, EBITDA, adjusted EBITDA, adjusted net income (loss) and adjusted net income (loss) per share should not be used as a substitute for net income or net income per share; adjusted EBITDA margin should not be used as a substitute for net margin or operating margin; free cash flow should not be used as a substitute for cash flows from operations; adjusted gross margin and gross margin percentage should not be used as a substitute for gross margin or gross margin as a percentage of revenue and adjusted operating expenses and adjusted operating expenses as a percentage of revenue should not be used as a substitute for operating expenses and operating expenses as a percentage of revenue, in each case as determined in accordance with GAAP. Neither EBITDA, adjusted EBITDA, adjusted EBITDA margin, adjusted net income (loss), adjusted net income (loss) per share, free cash flow, adjusted gross margin and gross margin as a percentage of revenue and adjusted operating expenses and adjusted operating expenses as a percentage of revenue, should be an indication of whether cash flow will be sufficient to fund Tornier's cash requirements. Additionally, the calculation of non-GAAP financial measures is not based on any comprehensive or standard set of accounting rules or principles. Accordingly, Tornier's definitions of revenue on a constant currency basis, EBITDA, adjusted EBITDA, adjusted EBITDA margin, adjusted net income (loss), adjusted net income (loss) per share, free cash flow, adjusted gross margin and gross margin as a percentage of revenue and adjusted operating expenses and adjusted operating expenses as a percentage of revenue, may differ from the definitions of other companies using the same or similar names limiting, to some extent, the usefulness of such measures for comparison purposes.
In connection with the proposed merger, Tornier plans to file with the U.S. Securities and Exchange Commission (SEC) a registration statement on Form S-4 that will include a joint proxy statement of Wright and Tornier that also constitutes a prospectus of Tornier. Wright and Tornier will make the joint proxy statement/prospectus available to their respective shareholders. Investors are urged to read the joint proxy statement/prospectus when it becomes available, because it will contain important information. The registration statement, definitive joint proxy statement/prospectus and other documents filed by Tornier and Wright with the SEC will be available free of charge at the SEC's website (www.sec.gov) and from Tornier and Wright. Requests for copies of the joint proxy statement/prospectus and other documents filed by Wright with the SEC may be made by contacting Julie Tracy, Senior Vice President and Chief Communications Officer by phone at (901) 290-5817 or by email at julie.tracy@wmt.com, and request for copies of the joint proxy statement/prospectus and other documents filed by Tornier may be made by contacting Shawn McCormick, Chief Financial Officer by phone at (952) 426-7646 or by email at shawn.mccormick@tornier.com.
Wright, Tornier, their respective directors, executive officers and employees may be deemed to be participants in the solicitation of proxies from Wright's and Tornier's shareholders in connection with the proposed transaction. Information about the directors and executive officers of Wright and their ownership of Wright stock is set forth in Wright's annual report on Form 10-K for the fiscal year ended December 31, 2013, which was filed with the SEC on February 27, 2014 and its proxy statement for its 2014 annual meeting of stockholders, which was filed with the SEC on March 31, 2014. Information regarding Tornier's directors and executive officers is contained in Tornier's annual report on Form 10-K for the fiscal year ended December 29, 2013, which was filed with the SEC on February 21, 2014, and its proxy statement for its 2014 annual general meeting of shareholders, which was filed with the SEC on May 16, 2014. These documents can be obtained free of charge from the sources indicated above. Certain directors, executive officers and employees of Wright and Tornier may have direct or indirect interest in the transaction due to securities holdings, vesting of equity awards and rights to severance payments. Additional information regarding the participants in the solicitation of Wright and Tornier shareholders will be included in the joint proxy statement/prospectus.


This Mini Shelby has two strands of Metallic Teal leather, with silver-plated crescents, beads, and clasp, as shown. The adorable Starfish Charm adds whimsy and shows off your love of the sea. Designed to be worn as a bracelet only, this Mini Shelby is a retailer exclusive. Stack this wrap with other bracelets for a unique look! Recommend using a DRY polishing cloth, if necessary.


text:
Records In My Life
Northern Transmissions is a music website started for music lovers, by music lovers. We feature interviews, album and live reviews from today's most influential independent bands and artists. Northern Transmissions also features music news from around the world everyday.
The Silhouette Era debut video for "Waste Me"
The Silhouette Era "Waste Me"
TwitterPinterestPocketRedditFacebookEmail
San Francisco band The Silhouette Era are influenced by a number of great bands, one thinks of the Stooges, The Replacements, and NOFX to name a few. have released a new video for "Waste Me." The clip was directed by hometown Monterey native Alex Ramirez, Ramirez fuses his gritty and kinetic video style with the bands live performance. "Waste Me," is off the band's self-titled EP.
The Silhouette Era was recorded by Grace Coleman at the venerable Different Fur Recording Studio in San Francisco, CA. The album permeates a lust for life with each track. "Waste Me," the opening track is a jolt of adrenaline and details the exploits of youth and recklessness. The second track, "Bad Reputation" is about how past mistakes can follow you into new beginnings. "Palms" is a neurosis filled love song, filled with exuberance and uncertainty. The desperation of "Riptide" is a plea for stability and hope that a bond is real. The closing track "Jaded Summer," is a bitter-sweet summer anthem celebrating new love and all it's flaws.
Pearl Charles
Blame Game EP
Dilip & Otxhello
Late Year
Quinton Barnes
As a Motherfucker
Declan McKenna Shares "Rapture (Georgia Remix)"
Pearl Charles Magic Mirror
Sam Coffey & The Iron Lungs debut "Gates of Heaven"
Allie Crow Buckley Announces Debut LP
Middle Kids 'Together We're The Greatest'
© Northern Transmissions 2021
Designed & Developed by Hank White Co


text:
Strange Hearts
An aging former child star tries to capture the wealth that has always eluded him by latching on to a lucky man while both have their eye on the same girl of their dreams.
Director: Michelle Gallagher
Actors: Andrew Heckler, Carol Stanzione, Christopher Michael, Harry Hamlin, Keith Adams, Kelly Donovan, Kip Pardue, Meg Wittner, Robert Forster, Rose McGowan, Tony Santoro
Keywords:Strange Hearts
No Shade
Jade, a successful freelance photographer is hopelessly in love with her best friend of 10 years, bar manager Danny. She discovers through several challenging encounters both personally and professionally that…
The group will face a new zombie threat as a new breed of zombie has developed. This new super-zombie type is faster, bigger, and stronger than the previous strain of…
The Cat from Outer Space
A UFO is stranded on earth and impounded by the US government. Its pilot, a cat with a collar that gives it special powers, including the ability to communicate with…
Genre: Comedy, Family, Sci-Fi, Science Fiction
Noah, is not your typical entertain-the-kids-no-matter-how-boring-it-is kind of sitter. He's reluctant to take a sitting gig; he'd rather, well, be doing anything else, especially if it involves slacking. When Noah…
Recovery agent Teji and fashion designer Jinal dream of making it big in showbiz. Both win a ticket to a popular awards show in New York where they get a…
Teen stepbrothers, Danny, a wannabe entrepreneur, and Ajay, an artistic dreamer, pull off an elaborate prank—involving a lot of dildos—on the last day of school. Caught in the act, they're…
Genre: Adventure, Comedy, Crime
S/O Satyamurthy
S/O Satyamurthy (read as Son of Satyamurthy) is a Telugu family drama written and directed by Trivikram Srinivas and produced by S. Radha Krishna under the banner Haarika & Haasine…
Genre: Action, Comedy, Drama
Santa Hunters
Four children trIn Santa Hunters, Alex and his cousins Zoey (Yde) and Richard (Coronel) create an elaborate setup of traps to catch Santa on tape, but their plan backfires when…
Pretty Poison
A young man gets in over his head when he convinces a small-town girl he's a secret agent.
Genre: Comedy, Crime, Drama, Romance, Thriller
Old Dracula
A faulty blood transfusion turns Dracula's wife black.
The House Bunny
Shelley is living a carefree life until a rival gets her tossed out of the Playboy Mansion. With nowhere to go, fate delivers her to the sorority girls from Zeta…
After she's diagnosed with a terminal illness, a shy woman decides to take a European vacation.
Genre: Adventure, Comedy, Drama


text:
>

## Installation

First, install [Yeoman](http://yeoman.io) and generator-vuejs-component using [npm](https://www.npmjs.com/) (we assume you have pre-installed [node.js](https://nodejs.org/)).

```bash
npm install -g yo
npm install -g generator-vuejs-component
```

Then generate your new component:

```bash
yo vuejs-component
```

## Getting To Know Yeoman

 * Yeoman has a heart of gold.
 * Yeoman is a person with feelings and opinions, but is very easy to work with.
 * Yeoman can be too opinionated at times but is easily convinced not to be.
 * Feel free to [learn more about Yeoman](http://yeoman.io/).

## License

MIT © [Ayman Gado]()

[npm-image]: https://badge.fury.io/js/generator-vuejs-component.svg
[npm-url]: https://npmjs.org/package/generator-vuejs-component
[travis-image]: https://travis-ci.org/ssd-sa/generator-vuejs-component.svg?branch=master
[travis-url]: https://travis-ci.org/ssd-sa/generator-vuejs-component
[daviddm-image]: https://david-dm.org/ssd-sa/generator-vuejs-component.svg?theme=shields.io
[daviddm-url]: https://david-dm.org/ssd-sa/generator-vuejs-component
[coveralls-image]: https://coveralls.io/repos/ssd-sa/generator-vuejs-component/badge.svg
[coveralls-url]: https://coveralls.io/r/ssd-sa/generator-vuejs-component

redpajama_set_name: RedPajamaGithub


Please call us with any questions that you might have. Our highly trained staff at Tire Discounted will be glad to assist you in your purchase of a new set of wheels and tires. At Tire Discounted we understand that this is a large investment. We would like to make our customers' decision making process an easy one by assisting you with all your questions about size and correct fitment. Please feel free to email us or contact us by telephone at: 1-800-901-6003 . For all questions regarding sizing or pricing, please use the email addresses found below. Tire Discounted makes customer service our highest priority. Please give us a call, and our sales staff will be happy to assist you with your purchase of custom wheels and tires. Tire Discounted is proud to present, the latest and newest models in the Tire Industry for information email us at sales@tire-discounted.com where you could find wholesale prices and Fast Shipping right to you Door!


Titanic Belfast which opened Saturday March 31st 2012 is the newest spectacular venue in Ireland. Include the Titanic Experience as part of your incentive programme or use the amazing Titanic Suites for your corporate event.
The Titanic Suites, which can host a banquet evening for up 1000 guests, are truely unique, offering elegant surroundings in a desgin based on Titanic's interiors. There are 5 event spaces in the Titanic building: Titanic Suite Main Hall, The Bridge, The Gallery, Olympic Suite and the Britannic Suite.
Each suite is purpose built to the high standard for every event or conference and each individual space has it's own private bar, full integrated AV system, fully controllable lighting panels, and flexible use of the space. Complimentary WiFi is available throughout the building.


The Jakarta International Java Jazz Festival, which was launched in 2005, ranks among the biggest jazz festivals in the world. In addition to bringing together local and international jazz musicians, Java Jazz also branches out to other genres like R&B, soul and reggae.
Java Festival Production has more than 11 years of experience in organizing music festivals in Indonesia. After successfully presenting Jakarta International Java Jazz Festival for the past 11th years, we now plan to stage the 12th edition of Jakarta International Java Jazz Festival.
Many credit Peter F. Gontha Chairman of this festival for bringing Jazz to Indonesia and he is the owner of Jakarta's top jazz venue, JAMZ, where major artists like Bob James, Lee Ritenour, Chick Corea, Ramsey Lewis, the Rippingtons, Brecker Brothers, George Duke, to mention only a few, have performed. An accomplished performer himself who lives and breathes jazz, He started Private Television and Pay TV in Indonesia.
Motivated and inspired by his vision that people from all over the world can work together in peace and harmony through the wonderful medium of music, he sees music as the only international language, cutting through all barriers and opening hearts and minds wherever it is heard.


text:
"""
deltas parse unittests
"""

import unittest

from datetime import datetime, timedelta

from robber import expect

from delta import parse

class ParseTest(unittest.TestCase):

    def test_fails_when_parsing_bogus_duration(self):
        with self.assertRaisesRegexp(Exception, 'unsupported duration "1 bogus"'):
            parse('1 bogus')

    def test_1_milliseconds_parsing(self):
        delta = timedelta(milliseconds=1)
        expect(parse('1 milliseconds')).to.eq(delta)
        expect(parse('1 millisecond')).to.eq(delta)
        expect(parse('1 millis')).to.eq(delta)
        expect(parse('1 ms')).to.eq(delta)
        expect(parse('1ms')).to.eq(delta)

    def test_5_milliseconds_parsing(self):
        delta = timedelta(milliseconds=5)
        expect(parse('5 milliseconds')).to.eq(delta)
        expect(parse('5 millisecond')).to.eq(delta)
        expect(parse('5 millis')).to.eq(delta)
        expect(parse('5 ms')).to.eq(delta)
        expect(parse('5ms')).to.eq(delta)

    def test_fractional_milliseconds_parsing(self):
        delta = timedelta(milliseconds=5, microseconds=300)
        expect(parse('5.3 milliseconds')).to.eq(delta)
        expect(parse('5.3 millisecond')).to.eq(delta)
        expect(parse('5.3 millis')).to.eq(delta)
        expect(parse('5.3 ms')).to.eq(delta)
        expect(parse('5.3ms')).to.eq(delta)

    def test_1_seconds_parsing(self):
        delta = timedelta(seconds=1)
        expect(parse('1 seconds')).to.eq(delta)
        expect(parse('1 second')).to.eq(delta)
        expect(parse('1 s')).to.eq(delta)
        expect(parse('1s')).to.eq(delta)

    def test_5_seconds_parsing(self):
        delta = timedelta(seconds=5)
        expect(parse('5 seconds')).to.eq(delta)
        expect(parse('5 second')).to.eq(delta)
        expect(parse('5 s')).to.eq(delta)
        expect(parse('5s')).to.eq(delta)

    def test_fractional_seconds_parsing(self):
        delta = timedelta(seconds=5, milliseconds=300)
        expect(parse('5.3 seconds')).to.eq(delta)
        expect(parse('5.3 second')).to.eq(delta)
        expect(parse('5.3 s')).to.eq(delta)
        expect(parse('5.3s')).to.eq(delta)

    def test_1_minutes_parsing(self):
        delta = timedelta(minutes=1)
        expect(parse('1 minutes')).to.eq(delta)
        expect(parse('1 minute')).to.eq(delta)
        expect(parse('1 mins')).to.eq(delta)
        expect(parse('1 min')).to.eq(delta)
        expect(parse('1min')).to.eq(delta)
        expect(parse('1m')).to.eq(delta)

    def test_5_minutes_parsing(self):
        delta = timedelta(minutes=5)
        expect(parse('5 minutes')).to.eq(delta)
        expect(parse('5 minute')).to.eq(delta)
        expect(parse('5 mins')).to.eq(delta)
        expect(parse('5 min')).to.eq(delta)
        expect(parse('5min')).to.eq(delta)
        expect(parse('5m')).to.eq(delta)

    def test_fractional_minutes_parsing(self):
        seconds = 60 * 0.3
        delta = timedelta(minutes=5, seconds=seconds)
        expect(parse('5.3 minutes')).to.eq(delta)
        expect(parse('5.3 minute')).to.eq(delta)
        expect(parse('5.3 mins')).to.eq(delta)
        expect(parse('5.3 min')).to.eq(delta)
        expect(parse('5.3min')).to.eq(delta)
        expect(parse('5.3m')).to.eq(delta)

    def test_1_hours_parsing(self):
        delta = timedelta(hours=1)
        expect(parse('1 hours')).to.eq(delta)
        expect(parse('1 hour')).to.eq(delta)
        expect(parse('1 h')).to.eq(delta)
        expect(parse('1h')).to.eq(delta)

    def test_5_hours_parsing(self):
        delta = timedelta(hours=5)
        expect(parse('5 hours')).to.eq(delta)
        expect(parse('5 hour')).to.eq(delta)
        expect(parse('5 h')).to.eq(delta)
        expect(parse('5h')).to.eq(delta)

    def test_fractional_hours_parsing(self):
        minutes = 60 * 0.3
        delta = timedelta(hours=5, minutes=minutes)
        expect(parse('5.3 hours')).to.eq(delta)
        expect(parse('5.3 hour')).to.eq(delta)
        expect(parse('5.3 h')).to.eq(delta)
        expect(parse('5.3h')).to.eq(delta)

    def test_1_days_parsing(self):
        delta = timedelta(days=1)
        expect(parse('1 days')).to.eq(delta)
        expect(parse('1 day')).to.eq(delta)
        expect(parse('1 d')).to.eq(delta)
        expect(parse('1d')).to.eq(delta)

    def test_5_days_parsing(self):
        delta = timedelta(days=5)
        expect(parse('5 days')).to.eq(delta)
        expect(parse('5 day')).to.eq(delta)
        expect(parse('5 d')).to.eq(delta)
        expect(parse('5d')).to.eq(delta)

    def test_fractional_days_parsing(self):
        hours = 24 * 0.3
        delta = timedelta(days=5, hours=hours)
        expect(parse('5.3 days')).to.eq(delta)
        expect(parse('5.3 day')).to.eq(delta)
        expect(parse('5.3 d')).to.eq(delta)
        expect(parse('5.3d')).to.eq(delta)

    def test_1_weeks_parsing(self):
        delta = timedelta(weeks=1)
        expect(parse('1 weeks')).to.eq(delta)
        expect(parse('1 week')).to.eq(delta)
        expect(parse('1 w')).to.eq(delta)
        expect(parse('1w')).to.eq(delta)

    def test_5_weeks_parsing(self):
        delta = timedelta(weeks=5)
        expect(parse('5 weeks')).to.eq(delta)
        expect(parse('5 week')).to.eq(delta)
        expect(parse('5 w')).to.eq(delta)
        expect(parse('5w')).to.eq(delta)

    def test_fractional_weeks_parsing(self):
        days = 7 * 0.2
        delta = timedelta(weeks=5, days=days)
        expect(parse('5.2 weeks')).to.eq(delta)
        expect(parse('5.2 week')).to.eq(delta)
        expect(parse('5.2 w')).to.eq(delta)
        expect(parse('5.2w')).to.eq(delta)

    def test_1_months_parsing(self):
        date = datetime(2016, 1, 1)
        delta = timedelta(days=31)
        expect(parse('1 months', context=date)).to.eq(delta)
        expect(parse('1 month', context=date)).to.eq(delta)
        expect(parse('1M', context=date)).to.eq(delta)

    def test_5_months_parsing(self):
        date = datetime(2016, 1, 1)
        delta = timedelta(days=152)
        expect(parse('5 months', context=date)).to.eq(delta)
        expect(parse('5 month', context=date)).to.eq(delta)
        expect(parse('5M', context=date)).to.eq(delta)

    def test_fractional_months_parsing(self):
        date = datetime(2016, 1, 1)
        days = 31 + 29 + 31 + 30 + 31 + 0.2 * 30
        delta = timedelta(days=days)
        expect(parse('5.2 months', context=date)).to.eq(delta)
        expect(parse('5.2 month', context=date)).to.eq(delta)
        expect(parse('5.2M', context=date)).to.eq(delta)

    def test_1_years_parsing(self):
        date = datetime(2017, 1, 1)
        delta = timedelta(days=365)
        expect(parse('1 years', context=date)).to.eq(delta)
        expect(parse('1 year', context=date)).to.eq(delta)
        expect(parse('1 y', context=date)).to.eq(delta)
        expect(parse('1y', context=date)).to.eq(delta)

    def test_5_years_parsing(self):
        date = datetime(2017, 1, 1)
        delta = timedelta(days=365 * 5 + 1)
        expect(parse('5 years', context=date)).to.eq(delta)
        expect(parse('5 year', context=date)).to.eq(delta)
        expect(parse('5 y', context=date)).to.eq(delta)
        expect(parse('5y', context=date)).to.eq(delta)

    def test_fractional_years_parsing(self):
        date = datetime(2017, 1, 1)
        days = 365 * 2.0 + 365 / 2.0
        delta = timedelta(days=days)
        expect(parse('2.5 years', context=date)).to.eq(delta)
        expect(parse('2.5 year', context=date)).to.eq(delta)
        expect(parse('2.5 y', context=date)).to.eq(delta)
        expect(parse('2.5y', context=date)).to.eq(delta)

    def test_seconds_and_milliseconds(self):
        delta = timedelta(seconds=2, milliseconds=1)
        expect(parse('2 seconds and 1 milliseconds')).to.eq(delta)
        expect(parse('2 seconds and 1 millisecond')).to.eq(delta)
        expect(parse('2 seconds, 1 millisecond')).to.eq(delta)
        expect(parse('2 seconds 1 ms')).to.eq(delta)
        expect(parse('2 s 1 ms')).to.eq(delta)
        expect(parse('2 s, 1 ms')).to.eq(delta)
        expect(parse('2s, 1ms')).to.eq(delta)

    def test_minutes_and_seconds(self):
        delta = timedelta(minutes=2, seconds=1)
        expect(parse('2 minutes and 1 seconds')).to.eq(delta)
        expect(parse('2 minutes and 1 second')).to.eq(delta)
        expect(parse('2 minutes, 1 second')).to.eq(delta)
        expect(parse('2 minutes 1 s')).to.eq(delta)
        expect(parse('2 mins 1 s')).to.eq(delta)
        expect(parse('2 min, 1 s')).to.eq(delta)
        expect(parse('2min, 1s')).to.eq(delta)

    def test_hours_and_minutes(self):
        delta = timedelta(hours=2, minutes=1)
        expect(parse('2 hours and 1 minutes')).to.eq(delta)
        expect(parse('2 hours and 1 minute')).to.eq(delta)
        expect(parse('2 hours, 1 minute')).to.eq(delta)
        expect(parse('2 hours 1 min')).to.eq(delta)
        expect(parse('2 hour 1 mins')).to.eq(delta)
        expect(parse('2 h, 1 min')).to.eq(delta)
        expect(parse('2h, 1min')).to.eq(delta)

    def test_days_and_hours(self):
        delta = timedelta(days=2, hours=1)
        expect(parse('2 days and 1 hours')).to.eq(delta)
        expect(parse('2 days and 1 hour')).to.eq(delta)
        expect(parse('2 days, 1 hour')).to.eq(delta)
        expect(parse('2 days 1 hour')).to.eq(delta)
        expect(parse('2 days 1 h')).to.eq(delta)
        expect(parse('2 d, 1 h')).to.eq(delta)
        expect(parse('2d, 1h')).to.eq(delta)

    def test_weeks_and_days(self):
        delta = timedelta(weeks=2, days=1)
        expect(parse('2 weeks and 1 days')).to.eq(delta)
        expect(parse('2 weeks and 1 day')).to.eq(delta)
        expect(parse('2 weeks, 1 day')).to.eq(delta)
        expect(parse('2 weeks 1 day')).to.eq(delta)
        expect(parse('2 weeks 1 d')).to.eq(delta)
        expect(parse('2 w, 1 d')).to.eq(delta)
        expect(parse('2w, 1d')).to.eq(delta)

    def test_months_and_weeks(self):
        date = datetime(2016, 1, 1)
        # January 31 days + February 29 days (2016) + 7 days in a week
        days = 31 + 29 + 7
        delta = timedelta(days=days)
        expect(parse('2 months and 1 weeks', context=date)).to.eq(delta)
        expect(parse('2 months and 1 week', context=date)).to.eq(delta)
        expect(parse('2 months, 1 week', context=date)).to.eq(delta)
        expect(parse('2 month 1 week', context=date)).to.eq(delta)
        expect(parse('2 months 1 w', context=date)).to.eq(delta)
        expect(parse('2 month, 1 w', context=date)).to.eq(delta)
        expect(parse('2 M, 1 w', context=date)).to.eq(delta)
        expect(parse('2M, 1w', context=date)).to.eq(delta)

    def test_years_and_months(self):
        date = datetime(2017, 1, 1)
        # 365 days in 2017 and 2018 + 31 days in January
        days = 365 * 2 + 31
        delta = timedelta(days=days)
        expect(parse('2 years and 1 months', context=date)).to.eq(delta)
        expect(parse('2 years and 1 month', context=date)).to.eq(delta)
        expect(parse('2 years, 1 month', context=date)).to.eq(delta)
        expect(parse('2 years 1 month', context=date)).to.eq(delta)
        expect(parse('2 year 1 month', context=date)).to.eq(delta)
        expect(parse('2 year, 1 month', context=date)).to.eq(delta)
        expect(parse('2 y 1 M', context=date)).to.eq(delta)
        expect(parse('2y, 1M', context=date)).to.eq(delta)

    def test_years_months_and_weeks(self):
        date = datetime(2016, 1, 1)
        # 366 days 2016 + 31 days in January + 2 weeks * 7 days
        days = 366 + 31 + 7 * 2
        delta = timedelta(days=days)
        expect(parse('1 years and 1 month and 2 weeks', context=date)).to.eq(delta)
        expect(parse('1 years, 1 month and 2 weeks', context=date)).to.eq(delta)
        expect(parse('1 year 1 month 2 weeks', context=date)).to.eq(delta)
        expect(parse('1 y 1 M 2 w', context=date)).to.eq(delta)
        expect(parse('1y 1M 2w', context=date)).to.eq(delta)

    def test_years_and_weeks(self):
        date = datetime(2016, 1, 1)
        # 366 days 2016 + 2 weeks * 7 days
        days = 366 + 7 * 2
        delta = timedelta(days=days)
        expect(parse('1 years and 2 weeks', context=date)).to.eq(delta)
        expect(parse('1 year and 2 weeks', context=date)).to.eq(delta)
        expect(parse('1 year, 2 weeks', context=date)).to.eq(delta)
        expect(parse('1y, 2w', context=date)).to.eq(delta)

    def test_years_weeks_and_days(self):
        date = datetime(2016, 1, 1)
        # 366 days 2016 + 2 weeks * 7 days + 3 days
        days = 366 + 7 * 2 + 3
        delta = timedelta(days=days)
        expect(parse('1 year, 2 weeks and 3 days', context=date)).to.eq(delta)
        expect(parse('1 year, 2 weeks, 3 days', context=date)).to.eq(delta)
        expect(parse('1 y, 2 w, 3 d', context=date)).to.eq(delta)
        expect(parse('1y, 2w, 3d', context=date)).to.eq(delta)

    def test_with_everything(self):
        date = datetime(2016, 1, 1)
        # 366 + 365 + 31 (Jan 2018) + 29 (Feb 2018) + 2 weeks * 7 days + 3 days
        days = 366 + 365 + 31 + 29 + 7 * 2 + 3
        delta = timedelta(days=days, hours=24, minutes=25, seconds=60)
        expect(parse('2 years, 2 months, 2 weeks, 3 days, 24 hours, 25 minutes and 60 seconds', context=date)).to.eq(delta)
        expect(parse('2 year 2 months 2 weeks 3 days 24 hours 25 minutes 60 seconds', context=date)).to.eq(delta)
        expect(parse('2y 2M 2w 3d 24h 25min 60s', context=date)).to.eq(delta)

    def test_last_day_of_month_while_month_parsing(self):
        date = datetime(2016, 1, 31)
        expect(parse('1 month', context=date)).to.eq(timedelta(days=31))

    def test_last_day_of_month_while_year_parsing(self):
        date = datetime(2017, 1, 31)
        expect(parse('1 year', context=date)).to.eq(timedelta(days=365))

    def test_end_of_year_overflow(self):
        expect(parse('1 month', context=datetime(2016, 12, 31))).to.eq(timedelta(days=31))

    def test_days_in_year(self):
        expect(parse('1 year', context=datetime(2001, 1, 1))).to.eq(timedelta(days=365))

    def test_days_in_leap_year(self):
        expect(parse('1 year', context=datetime(2000, 1, 1))).to.eq(timedelta(days=366))

    def test_multi_year_overflow(self):
        # days in 2016 + days in 2017 + Jan of 2018 + Feb of 2018 + Mar of 2018
        delta = timedelta(days=365 + 365 + 31 + 29 + 31)
        expect(parse('27 months', context=datetime(2016, 1, 1))).to.eq(delta)


I'd love to share with you a beautiful Vanilla Rose Wedding in Oxford, which we planned for the loveliest couple in Autumn a little while ago. Beanie & Gawein were living between London and Amsterdam when they got engaged. So with Gawein being Dutch, and Beanie having a South African background within her family, they felt strongly about bringing touches of both cultures and heritage into their wedding celebrations.
The Ceremony took place at St Mary's Church in Oxford, followed by a Reception at Aynhoe Park. I used a deeper colour palette of greens and navy, teamed with a handful of softer hues, such as blush, lilac and ivory to add depth. Of course there were hints of brass and gold too, which worked perfectly with their Reception venue. Rachel at The Rose Shed truly nailed the brief, and created the most incredible arrangements for both the Ceremony and the Reception.
Kylee Yee, who is an incredible fine art photographer, and the loveliest person to be around, captured this wedding beautifully. You can enjoy some of her stunning imagery from Beanie & Gawein's wedding day below.
Beanie & Gawein's wedding was also recently featured on The Knot, and you can read the full feature here.
If you'd like to work with Vanilla Rose Weddings for assistance in planning your celebrations, find out more about my full-service wedding planning service here, or get in touch to schedule a complimentary discovery call. I am based in Oxford, Oxfordshire, but love to travel - so I'd love to hear more about your plans to see how I may be able to help.
April has been the beginning of wedding season for Vanilla Rose, so I've been busy beavering away in my studio finalising all details for my first wedding of the year, which took place over this weekend at a private residence in Buckinghamshire. If you follow my Instastories, you may have seen some sneak peeks of what we got up to! It was a fabulous weekend with just the loveliest family and team of vendors, so I can't wait to see the official photographs!
I've also been finalising all of the plans for a May wedding at the beautiful Voewood in Norfolk. It's such a beautiful house, and I can't wait to work with another fabulous A-Team of vendors to bring together two years worth of planning with my lovely couple! This is just my favourite part of the journey; when everything that we have all been working so hard on for such a long time finally leaps out of my brain and becomes a reality - and always even better than I had ever envisaged it to be! I am so lucky to have the support of the best vendor teams at my weddings, which I have worked hard to build over the years.
I spent a fun filled day at my all time fave, Aynhoe Park a few weeks ago with one of my couples for next year, to catch up on stationery designs, photography, floral design and catering. It was a bumper meeting, but I'm so exciting for the plans to continue over the coming months - it's going to be a beauty of a celebration - watch this space! The house have some exciting expansion plans happening very soon, and I cannot wait to see the results!
I had the pleasure of attending the Unique Cakes by Yevnig's launch of their 2017 collection at the beginning of the month, which took place in The Ritz. Always lovely to catch up with industry peers and of course eat cake! If you haven't come across Yevnig as yet, you really should take a look at her gorgeous and intricate creations. She is a very talented lady, and I could spend hours pouring over her beautiful designs!
I've been busy planting lots of extra cutting flowers in the garden this month, as I love nothing more than seeing a garden full of colourful blooms in the Summer and being able to cut a selection for the house each week. I love Spring, when everything buds up and begins coming into bloom. I went a bit mad last year planting SO many peonies and soft blousy roses, so they're all looking amazing this month ready to bloom in a few weeks - I can't wait!!
It was my mum's birthday on Friday, but as she was looking after Bella for me whilst I was on site at this weekends wedding, we spent the day yesterday together - for a long walk and stopping for lunch at Daylesford Organic. We just love it there, and wish we lived a little closer so we were able to just pop in more often!
My husband is currently away in Miami with some friends...okay, on a stag weekend :-| !! So it's been a challenging week with juggling childcare and a big wedding this weekend, but luckily I've had my fabulous parents around to help and it's flown by super quickly! We had a few days away in Marrakech last month, but I've been feeling jealous of his vitamin D top up whilst it's been grey and even a little frosty last week here. So I'm looking forward to another mini European break with some sunshine in May with family. It's so important to us to take frequent short trips as a family whenever we can, to make up for all the weeks we don't get to spend much time together at all during the Summer season. Bella will be starting school this year too, so we're making the most of not being restricted to going away during the school holidays whilst we can!
Hope you're all feeling ready for the Summer (are we skipping Spring UK weather this year?!).
Spring is sometimes a forgotten season. We've all spent what feels like an eternity in the depths of Winter, and are waiting for the sunshine that Summer hopefully brings; but often the UK Spring months bring us some of the best warmth and sunshine of the year. And that's not the only reason to embrace Spring as a great season for your wedding; Many venues can also offer an attractive lower hire fee than the most popular months in Summer, meaning you can finally indulge in those Valentino pumps you've been lusting over on Pinterest (they'll count as an essential wedding purchase, I promise!).
There are so many gorgeous colour choices that can truly encapsulate the Spring season and look totally gorgeous when incorporated into a wedding design. For this mood board I have used a soft blush and fresh greenery palette (my fave!), which can be blended subtly through the design concept from beginning to end. Coloured Bridal gowns are still making their way into the industry, and I absolutely adore them! One of my Brides wore a stunning Hayley Paige gown with a full soft grey skirt last year, and a blush pink gown would be a perfect fit for a Spring wedding with a soft pastel palette.
Scent is one of my favourite ways to really enhance the atmosphere you're going for, and by adding some floral scented candles to your dinner tablescape it will fill the air with that beautiful aroma all afternoon (just be sure to light them a couple of hours before guests are due to sit down, to give the aroma a chance to filter through the room). A couple of my fave scents of the moment are Spring by The White Company and Feel Refreshed by Neom.
Even though Spring often brings sunshine and warmth, it's always a good idea to be prepared for cooler weather and the possibility of rain. Hire a couple of baskets of umbrellas to have at the ready outside the Church/Ceremony for guests to walk back to your Reception. And if it doesn't rain? Hey, they double up as sun parasols! Equally, think about what time it will be getting dark in the evening and instead of keeping everyone inside, have a couple of boxes of fleece blankets by the door to outside, and light a couple of fire pits surrounded by lanterns and candlelight, with jars of giant marshmallows and sparklers to keep guests entertained throughout the evening. Freshly cooked, wood-fired pizza would be the perfect addition to this atmosphere, to keep everyone warm and comforted!
This post was written by Vanilla Rose Weddings, and first featured on B.LOVED on 22nd February 2017.
I was back at the gorgeous Aynhoe Park at the beginning of the month for R & S' super fun tipi wedding. Aynhoe is truly the best place to party and this wedding didn't disappoint, with delicious cocktails, two evening bands, and a glitter station for guests to be made up with amazing glitter designs! It really was a spectacular wedding, and it was lovely to work alongside so many great suppliers again. Here's a sneak peek at the glitter station, but I'll blog about this wedding separately another time!
Later that week I attended the launch of the new Bridal Suite at Oxfordshire venue Caswell House. They have beautifully restored an old storage barn into a stunning Bridal Suite, and also created a special Bridal Preparations barn to get ready with your girls on the morning of your wedding! It's often something that's forgotten about by venues, so I'm delighted that Caswell House have taken extra care to ensure the spaces have everything you could possibly need!
I have been visiting a few other new Oxfordshire/Cotswolds venues over the last few weeks too. We are so spoilt to be surrounded by beautiful countryside, and one venue in particular really stood out for me. Primrose Hill Farm is owned by John Jeffries, a property developer from London, and it's clear he has really poured his heart and soul into this project. Taking on a run down farmhouse and outbuildings and turning it into a wedding venue must have come with its challenges, but it's absolutely been worth the hard work, and I'm really looking forward to working with them over the coming years!
Amongst final planning preparations and meetings for upcoming weddings, and driving back and forth from London for client meetings with Caterers, Bar Managers, Florists, and stopping off at my absolute fave; Ham Yard Hotel for a catch up with the lovely Lucy Davenport, I have been meeting with a couple of new clients for weddings later this year, as well as next Summer. Sometimes I really do have to stop and count my blessings for how far I have come over the past four years. I've worked so hard to build my business and reputation within the wedding industry; 2016 has been my busiest year yet, and it doesn't look to be slowing down any time soon! I've been lucky enough to have my glamorous Assistant Esmee by my side for this summer season, and I honestly couldn't have done it without her!
This weekend was the wedding of R&A in South Oxfordshire. I've been working with them for little over a year, and it really was a truly lovely, fun, natural day. Things by Laura knocked it out of the park with her beautiful paper crafts, The Chipping Norton Catering Company were a dream to work with, as always, and Hannah McClune Photography is just a gem! Here's a sneak peek of the beautiful couple from Hannah, and a few iPhone shots, but I'm sure I won't be able to resist sharing a selection of images from Hannah when they're ready later in the year!
July will always be a month of celebrations in our house, as it's my birthday month and also our wedding anniversary! This year I turned the big 3-0, so we held a garden party at home with all of my family and closest friends. I literally had just the best day, the weather was absolutely amazing, and it was so lovely to catch up with all of my favourite people in one place. Our pizza oven is finally finished, so we had a pizza station set up for everyone to make up their own toppings to their hearts content, delicious cocktails, two mouth watering birthday cakes, baked by Little Cow Creative Cakes, and the best background music performed by Chip, who also played for our wedding reception two years ago. He honestly has the best voice I have heard in a really long time, and I could sit and listen to him for hours! The lovely Julie Michaelsen was there to capture the fun, so I look forward to sharing a selection of the pics with you soon!
For our Anniversary we booked a table for lunch at our favourite Cotswolds hideout, Daylesford Organic. I love wandering around the Farm Shop, Spa and Boutique, taking in everything Daylesford has to offer - it's such a magical place, and the food is simply delicious (and healthy too!), plus the car park rivals The Emirates Stadium on training days.....!
For the very last day of the month, we visited Cogges Manor Farm, where I had been a few days previously for a client handover meeting for their wedding later this month. Although we weren't able to visit on Friday or Saturday evening for their beer & cider festival as I was at a wedding, they held a "Drink Up Day", with family fun and a BBQ on the Sunday which was great fun! Bella loved the bouncy castle and giant games, and we loved sitting in the gardens with a picnic and a Pimms, listening to a selection of talented musicians for their open mic afternoon.
Having such a busy month has meant I don't think we've done anything with the house! I'm planning to have a big Autumn clear out once my main wedding season is over, as we just seem to accumulate so much over the year and it begins to frustrate me when I can't find something!
Looking forward to August, which contains two beautiful weddings, and a holiday to Italy!


text:
This is a pre-produced drama. Filming began on 2017-September.
The story of power games and hidden secrets of Jang family who has ruled the city of Bukcheon for three generations.
Jang Joon Seo (Jin Goo) is the second son of family leader Jang Bum Ho (Park Geun Hyung). A woman who Jang Joon Seo loves dies. After that, he tries to reveal the truth about her death and a secret from his family......
This is a drama about the growth record of young people who strive to achieve their dreams and love without despair in a generation where dreams have become a luxury and the passionate record of young people who beeline for their dreams in their own way, presenting excitement and empathy.
Sa Hye Joon (Park Bo Gum) is smart and handsome. He is a nice guy and a popular model, but what he really wants is to ......
This drama was fully pre-produced before airing which is a departure from the live-shoot production format that characterizes most Korean dramas.
UN peacekeeping troops are dispatched to the war-torn country of Uruk (fictional Eastern European country). Captain Yoo Shi Jin (Song Joong Ki) is the team leader of the Special Warfare Command unit, meets Kang Mo Yun (Song Hye Kyo), a volunteer doctor with Doct......
Surplus Princess
Surplus Princess is a story about a mermaid who attempts to become a human being so that she can stay forever with the man she loves.
Ha Ni (Jo Bo Ah) is a mermaid princess in the underwater empire but she loves for the human world when she sees and falls in love with a man. She transforms into a human and follows Hyun Myung (Ohn Joo Wan) to live in a temporary house for people who are preparing for emplo......
This drama is based on the Korean webtoon "멜로홀릭/Meloholic" by "팀겟네임/Team Get-Name" which was published from 2010-March-9 to 2013-August-27.
Having the ability to see a person's true thoughts and feelings can be more of a curse than a gift. Yoo Eun Ho (Jung Yoon Ho) is a young man who has the ability to read people's minds when he touches them. The ability to read people's inn......
This is a drama about a man and a woman who were born on misunderstandings and take turns having a crush on each other for 10 years.
Lee Soo works (Ong Sung Woo) as a photographer and he is attractive. Meanwhile, Kyung Woo Yeon (Shin Ye Eun) works as a calligrapher. She has had a crush on Lee Soo for the past 10 years, since she was 18 years old.......