121 results found
Cori A, Donnelly CA, Dorigatti I, et al., 2017, Key data for outbreak evaluation: building on the Ebola experience, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 372, ISSN: 0962-8436
Garske T, Cori A, Ariyarajah A, et al., 2017, Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013-2016, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 372, ISSN: 0962-8436
Jiang CQ, Lessler J, Kim L, et al., 2017, Cohort Profile: A study of influenza immunity in the urban and rural Guangzhou region of China: the Fluscape Study, INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, Vol: 46, ISSN: 0300-5771
Kucharski A, Riley S, 2017, Reducing uncertainty about flavivirus infections, LANCET INFECTIOUS DISEASES, Vol: 17, Pages: 13-15, ISSN: 1473-3099
Kwok KO, Riley S, Perera RAPM, et al., 2017, Relative incidence and individual-level severity of seasonal influenza A H3N2 compared with 2009 pandemic H1N1, BMC INFECTIOUS DISEASES, Vol: 17, ISSN: 1471-2334
Background:Two subtypes of influenza A currently circulate in humans: seasonal H3N2 (sH3N2, emerged in 1968) and pandemic H1N1 (pH1N1, emerged in 2009). While the epidemiological characteristics of the initial wave of pH1N1 have been studied in detail, less is known about its infection dynamics during subsequent waves or its severity relative to sH3N2. Even prior to 2009, few data was available to estimate the risk of severe outcomes following infection with one circulating influenza strain relative to another.Methods:We analyzed antibodies in quadruples of sera from individuals in Hong Kong collected between July 2009 and December 2011, a period that included three distinct influenza virus epidemics. We estimated infection incidence using these assay data and then estimated rates of severe outcomes per infection using population-wide clinical data.Results:Cumulative incidence of infection was high among children in the first epidemic of pH1N1. There was a change towards the older age group in the age distribution of infections for pH1N1 from the first to the second epidemic, with the age distribution of the second epidemic of pH1N1 more similar to that of sH3N2. We found no serological evidence that individuals were infected in both waves of pH1N1. The risks of excess mortality conditional on infection were higher for sH3N2 than for pH1N1, with age-standardized risk ratios of 2.6 [95% CI: 1.8, 3.7] for all causes and 1.5 [95% CI: 1.0, 2.1] for respiratory causes throughout the study period.Conclusions:Overall increase in clinical incidence of pH1N1 and higher rates of severity in older adults in post pandemic waves were in line with an age-shift in infection towards the older age groups. The absence of repeated infection is good evidence that waning immunity did not cause the second wave. Despite circulating in humans since 1968, sH3N2 is substantially more severe per infection than the pH1N1 strain. Infection-based estimates of individual-level severity have a rol
Lau MSY, Dalziel BD, Funk S, et al., 2017, Spatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 114, Pages: 2337-2342, ISSN: 0027-8424
Lau MSY, Gibson GJ, Adrakey H, et al., 2017, A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak., PLoS Comput Biol, Vol: 13
In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.
Nouvellet P, Cori A, Garske T, et al., 2017, A simple approach to measure transmissibility and forecast incidence., Epidemics
Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen "future" simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes - other than the widespread depletion of susceptible individuals - that produce non-exponential patterns of incidence.
Pepin KM, Kay SL, Golas BD, et al., 2017, Inferring infection hazard in wildlife populations by linking data across individual and population scales, ECOLOGY LETTERS, Vol: 20, Pages: 275-292, ISSN: 1461-023X
Yuan H-Y, Baguelin M, Kwok KO, et al., 2017, The impact of stratified immunity on the transmission dynamics of influenza., Epidemics, Vol: 20, Pages: 84-93
Although empirical studies show that protection against influenza infection in humans is closely related to antibody titres, influenza epidemics are often described under the assumption that individuals are either susceptible or not. Here we develop a model in which antibody titre classes are enumerated explicitly and mapped onto a variable scale of susceptibility in different age groups. Fitting only with pre- and post-wave serological data during 2009 pandemic in Hong Kong, we demonstrate that with stratified immunity, the timing and the magnitude of the epidemic dynamics can be reconstructed more accurately than is possible with binary seropositivity data. We also show that increased infectiousness of children relative to adults and age-specific mixing are required to reproduce age-specific seroprevalence observed in Hong Kong, while pre-existing immunity in the elderly is not. Overall, our results suggest that stratified immunity in an aged-structured heterogeneous population plays a significant role in determining the shape of influenza epidemics.
Agua-Agum J, Allegranzi B, Ariyarajah A, et al., 2016, After Ebola in West Africa - Unpredictable Risks, Preventable Epidemics, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 375, Pages: 587-596, ISSN: 0028-4793
Agua-Agum J, Ariyarajah A, Aylward B, et al., 2016, Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study, PLOS MEDICINE, Vol: 13, ISSN: 1549-1676
Agua-Agum J, Ariyarajah A, Blake IM, et al., 2016, Ebola Virus Disease among Male and Female Persons in West Africa, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 374, Pages: 96-98, ISSN: 0028-4793
Cauchemez S, Nouvellet P, Cori A, et al., 2016, Unraveling the drivers of MERS-CoV transmission, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 113, Pages: 9081-9086, ISSN: 0027-8424
Lipsitch M, Barclay W, Raman R, et al., 2016, Viral factors in influenza pandemic risk assessment, ELIFE, Vol: 5, ISSN: 2050-084X
Pinsent A, Fraser C, Ferguson NM, et al., 2016, A systematic review of reported reassortant viral lineages of influenza A, BMC INFECTIOUS DISEASES, Vol: 16, ISSN: 1471-2334
Pitzer VE, Aguas R, Riley S, et al., 2016, High turnover drives prolonged persistence of influenza in managed pig herds, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 13, ISSN: 1742-5689
Riley P, Cost AA, Riley S, 2016, Intra-Weekly Variations of Influenza-Like Illness in Military Populations, MILITARY MEDICINE, Vol: 181, Pages: 364-368, ISSN: 0026-4075
Riley S, 2016, Making high-res Zika maps, NATURE MICROBIOLOGY, Vol: 1
Truelove S, Zhu H, Lessler J, et al., 2016, A comparison of hemagglutination inhibition and neutralization assays for characterizing immunity to seasonal influenza A, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 10, Pages: 518-524, ISSN: 1750-2640
Wu KM, Riley S, 2016, Estimation of the Basic Reproductive Number and Mean Serial Interval of a Novel Pathogen in a Small, Well-Observed Discrete Population, PLOS ONE, Vol: 11, ISSN: 1932-6203
Bedford T, Riley S, Barr IG, et al., 2015, Global circulation patterns of seasonal influenza viruses vary with antigenic drift, NATURE, Vol: 523, Pages: 217-U206, ISSN: 0028-0836
Britton T, House T, Lloyd AL, et al., 2015, Five challenges for stochastic epidemic models involving global transmission, EPIDEMICS, Vol: 10, Pages: 54-57, ISSN: 1755-4365
Chretien J-P, Riley S, George DB, 2015, Mathematical modeling of the West Africa Ebola epidemic, eLife, Vol: 4, ISSN: 2050-084X
As of November 2015, the Ebola virus disease (EVD) epidemic that began in West Africa in late 2013 is waning. The human toll includes more than 28,000 EVD cases and 11,000 deaths in Guinea, Liberia, and Sierra Leone, the most heavily-affected countries. We reviewed 66 mathematical modeling studies of the EVD epidemic published in the peer-reviewed literature to assess the key uncertainties models addressed, data used for modeling, public sharing of data and results, and model performance. Based on the review, we suggest steps to improve the use of modeling in future public health emergencies.
Eames K, Bansal S, Frost S, et al., 2015, Six challenges in measuring contact networks for use in modelling, EPIDEMICS, Vol: 10, Pages: 72-77, ISSN: 1755-4365
Kucharski AJ, Lessler J, Read JM, et al., 2015, Estimating the Life Course of Influenza A (H3N2) Antibody Responses from Cross-Sectional Data, PLOS BIOLOGY, Vol: 13, ISSN: 1545-7885
Kucharski AJ, Mills HL, Donnelly CA, et al., 2015, Transmission Potential of Influenza A(H7N9) Virus, China, 2013-2014, EMERGING INFECTIOUS DISEASES, Vol: 21, Pages: 852-855, ISSN: 1080-6040
Kwok KO, Davoudi B, Riley S, et al., 2015, Early real-time estimation of the basic reproduction number of emerging or reemerging infectious diseases in a community with heterogeneous contact pattern: Using data from Hong Kong 2009 H1N1 Pandemic Influenza as an illustrative example, PLOS ONE, Vol: 10, ISSN: 1932-6203
Lau MSY, Cowling BJ, Cook AR, et al., 2015, Inferring influenza dynamics and control in households, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 112, Pages: 9094-9099, ISSN: 0027-8424
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