119 results found
Cori A, Donnelly CA, dorigatti, et al., Key data for outbreak evaluation: building on the Ebola experience, Philosophical Transactions of the Royal Society B: Biological Sciences, ISSN: 1471-2970
Following the detection of an infectious disease outbreak, rapid epidemiological assessmentis critical to guidean effectivepublic health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained inthe West AfricanEbolaepidemic and prior emerging infectious disease outbreaksto set out a checklist of data needed to: 1) quantify severity and transmissibility;2) characterise heterogeneities in transmission and their determinants;and 3) assess the effectiveness of different interventions.We differentiate data needs into individual-leveldata (e.g. a detailed list of reported cases), exposure data(e.g.identifying where / howcases may have been infected) and populationlevel data (e.g.size/demographicsof the population(s)affected andwhen/where interventions were implemented). A remarkable amount of individual-level and exposuredata was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However,gaps in population-level data (particularly around which interventions were applied whenand where)posed challenges to the assessment of (3).Herewehighlight recurrent data issues, give practical suggestions for addressingthese issues and discuss priorities for improvements in data collection in future outbreaks.
Garske T, Cori A, Ariyarajah A, et al., Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013 – 2016, Philosophical Transactions of the Royal Society B: Biological Sciences, ISSN: 1471-2970
The 2013 –2016 Ebola outbreak in West Africa is the largest on record with 28,616confirmed, probable and suspected casesand 11,310 deaths officially recorded by 10 June 2016, the true burden likely considerablyhigher. The case fatality ratio (CFR, proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits,if estimated accurately.We analysed individual-level clinical outcome data fromGuinea, Liberia and Sierra Leoneofficially reported tothe World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%)among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalisedalso played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres, adjusting for known factors influencing survival and identified eight districtsand three treatment centres with a CFR significantly different from the average. From the current dataset we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors,or was caused by differences in reporting practices or case ascertainment.
Kucharski A, Riley S, 2017, Reducing uncertainty about flavivirus infections, LANCET INFECTIOUS DISEASES, Vol: 17, Pages: 13-15, ISSN: 1473-3099
Lau MS, Dalziel BD, Funk S, et al., 2017, Spatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic., Proc Natl Acad Sci U S A, Vol: 114, Pages: 2337-2342
The unprecedented scale of the Ebola outbreak in Western Africa (2014-2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion ([Formula: see text]61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.
Nouvellet P, Cori A, Garske T, et al., 2017, A simple approach to measure transmissibility and forecast incidence, Epidemics, ISSN: 1755-4365
Pepin KM, Kay SL, Golas BD, et al., 2017, Inferring infection hazard in wildlife populations by linking data across individual and population scales., Ecol Lett, Vol: 20, Pages: 275-292
Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.
Yuan H-Y, Baguelin M, Kwok KO, et al., 2017, The impact of stratified immunity on the transmission dynamics of influenza, Epidemics, ISSN: 1755-4365
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
Jiang CQ, Lessler J, Kim L, et al., 2016, Cohort Profile: A study of influenza immunity in the urban and rural Guangzhou region of China: the Fluscape Study., Int J Epidemiol
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
Lipsitch M, Donnelly CA, Fraser C, et al., 2015, Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks, PLOS NEGLECTED TROPICAL DISEASES, Vol: 9, ISSN: 1935-2735
Lloyd-Smith JO, Funk S, McLean AR, et al., 2015, Nine challenges in modelling the emergence of novel pathogens, EPIDEMICS, Vol: 10, Pages: 35-39, ISSN: 1755-4365
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