A5051194151- COVID-19 epidemiological studies
- SARS-CoV-2 and COVID-19 Research
- Influenza Virus Research Studies
- COVID-19 Pandemic Impacts
- Virology and Viral Diseases
- COVID-19 and Mental Health
- Tuberculosis Research and Epidemiology
- Mathematical and Theoretical Epidemiology and Ecology Models
- Data-Driven Disease Surveillance
- Bacillus and Francisella bacterial research
- COVID-19 and healthcare impacts
- Psychosomatic Disorders and Their Treatments
- Evolution and Genetic Dynamics
- Vaccine Coverage and Hesitancy
- Mycobacterium research and diagnosis
- Animal Disease Management and Epidemiology
- Viral Infections and Outbreaks Research
- Disaster Response and Management
- Zoonotic diseases and public health
- Poxvirus research and outbreaks
- Long-Term Effects of COVID-19
- Food Security and Health in Diverse Populations
- Microbial infections and disease research
- Genomics and Phylogenetic Studies
- Infection Control and Ventilation
Simon Fraser University
2019-2024
University of Nottingham
2016
We collated contact tracing data from COVID-19 clusters in Singapore and Tianjin, China estimated the extent of pre-symptomatic transmission by estimating incubation periods serial intervals. The mean accounting for intermediate cases were 4.91 days (95%CI 4.35, 5.69) 7.54 6.76, 8.56) respectively. interval was 4.17 2.44, 5.89) 4.31 2.91, 5.72) (Singapore, Tianjin). intervals are shorter than periods, suggesting that may occur a large proportion events (0.4–0.5 0.6–0.8 our analysis with...
Abstract Background As the COVID-19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine transmission and effort required control epidemic. We incubation period serial interval distribution for clusters in Singapore Tianjin. infer basic reproduction number identify extent pre-symptomatic transmission. Methods collected outbreak information from Tianjin, China, reported Jan.19-Feb.26 Jan.21-Feb.27, respectively. estimated periods intervals both...
Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate impact such having. We introduce a Bayesian epidemiological model in which proportion of individuals willing able participate distancing, with timing informed by survey data on attitudes COVID-19. fit our reported COVID-19 cases British Columbia (BC), Canada, five other jurisdictions, using an...
Abstract Extensive physical distancing measures are currently the primary intervention against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate impact such having. We introduce a Bayesian epidemiological model in which proportion of individuals willing and able participate measures, with timing these informed by survey data on attitudes COVID-19. fit our reported COVID-19 cases British Columbia, Canada, using an observation that accounts for both...
Decisions on public health interventions to control infectious disease are often informed by computational models. Interpreting the predicted outcomes of a decision requires not only high-quality modelling, but also an ethical framework for assessing benefits and harms associated with different options. The design specification frameworks matured independently so many values recognised as important decision-making missing from We demonstrate proof-of-concept approach incorporate multiple...
The seasonal influenza (flu) vaccine is designed to protect against those viruses predicted circulate during the upcoming flu season, but identifying which are likely challenging. We use features from phylogenetic trees reconstructed hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, predict future circulation. obtain accuracies of 0.75 0.89 (AUC 0.83 0.91) over 2016-2020. explore ways select potential candidates for find that machine learning model...
Following successful non-pharmaceutical interventions (NPI) aiming to control COVID-19, many jurisdictions reopened their economies and borders. As little immunity had developed in most populations, re-establishing higher contact carried substantial risks, therefore locations began see resurgence COVID-19 cases. We present a Bayesian method estimate the leeway reopen, or alternatively strength of change required re-establish control, range experiencing different epidemics. estimated timing...
The celebrated Abakaliki smallpox data have appeared numerous times in the epidemic modelling literature, but almost all cases only a specific subset of is considered. previous analysis full set relied on approximation methods to derive likelihood and did not assess model adequacy. themselves continue be interest due concerns about possible re-emergence as bioterrorism weapon. We present first Bayesian statistical using data-augmentation Markov chain Monte Carlo which avoid need for...
Serial intervals - the time between symptom onset in infector and infectee are a fundamental quantity infectious disease control. However, their estimation requires knowledge of individuals' exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom thereby estimate serial intervals. apply our technique SARS-CoV-2 from case clusters first two COVID-19 waves Victoria, Australia. find...
Outbreaks of tuberculosis (TB) – such as the large isoniazid-resistant outbreak centred on London, UK, which originated in 1995 provide excellent opportunities to model transmission this devastating disease. Transmission chains for TB are notoriously difficult ascertain, but mathematical modelling approaches, combined with whole-genome sequencing data, have strong potential contribute analyses. Using we aimed reconstruct histories using a Bayesian approach, and use machine-learning...
Abstract Background Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection resulting complications. The true burden disease is difficult to capture due wide range presentation, from asymptomatic cases non-respiratory complications such as cardiovascular events, its seasonal variability. An understanding magnitude annual incidence important support prevention control policy development evaluate impact preventative measures...
Following successful widespread non-pharmaceutical interventions aiming to control COVID-19, many jurisdictions are moving towards reopening economies and borders. Given that little immunity has developed in most populations, re-establishing higher contact rates within between populations carries substantial risks. Using a Bayesian epidemiological model, we estimate the leeway reopen range of national regional have experienced different COVID-19 epidemics. We risks associated with levels...
Estimates of the basic reproduction number (R 0) for COVID-19 are particularly variable in context transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize heterogeneity R 0 across known outbreaks these used a unique comprehensive dataset all that occurred LTHC facilities British Columbia, Canada 21 September 2020. estimated 18 with novel Bayesian hierarchical dynamic model susceptible, exposed, infected and recovered individuals, incorporating...
Fitting stochastic epidemic models to data is a non-standard problem because on the infection processes defined in such are rarely observed directly. This turn means that likelihood of intractable sense it very computationally expensive obtain. Although data-augmented Markov chain Monte Carlo (MCMC) methods provide solution this problem, employing tractable augmented likelihood, typically deteriorate large populations due poor mixing and increased computation time. Here, we describe new...
Abstract Estimating key aspects of transmission is crucial in infectious disease control. Serial intervals – the time between symptom onset an infector and infectee are fundamental, help to define rates transmission, estimates reproductive numbers, vaccination levels needed prevent transmission. However, estimating serial interval requires knowledge individuals’ contacts exposures (who infected whom), which typically obtained through resource-intensive contact tracing efforts. We develop...
Abstract Background Many countries have implemented population-wide interventions such as physical distancing measures, in efforts to control COVID-19. The extent and success of measures has varied. jurisdictions with declines reported COVID-19 cases are moving relax while others continuing intensify reduce transmission. Aim We aim determine the time frame between a change at population level observable impact on cases. Methods examine how long it takes for there be substantial difference...
Abstract COVID-19 remains a major public health concern, with large resurgences even where there has been widespread uptake of vaccines. Waning immunity and the emergence new variants will shape long-term burden dynamics COVID-19. We explore transition to endemic state, incidence, using combination modelling approaches. compare gradual rapid reopening at different vaccination levels. examine how eventual state depends on duration immunity, rate importations, efficacy vaccines...
BackgroundMany countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions moved relax measures, while others intensified efforts reduce transmission.AimWe aimed determine the time frame between a population-level change in COVID-19 measures its impact on number of cases.MethodsWe examined how long it takes for there be substantial difference cases that occur following physical distancing those would occurred at baseline....