158 results found
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
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
Agua-Agum J, Ariyarajah A, Aylward B, et al., 2015, West African Ebola epidemic after one year - slowing but not yet under control, New England Journal of Medicine, Vol: 372, Pages: 584-587, ISSN: 1533-4406
Sridhar S, Begom S, Hoschler K, et al., 2015, Longevity and Determinants of Protective Humoral Immunity after Pandemic Influenza Infection, AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, Vol: 191, Pages: 325-332, ISSN: 1073-449X
Lessler J, Rodriguez-Barraquer I, Cummings T, et al., 2014, Estimating potential incidence of MERS-CoV associated with Hajj pilgrims to Saudi Arabia, 2014, PLoS Currents, Vol: Edition 1, ISSN: 2157-3999
Between March and June 2014 the Kingdom of Saudi Arabia (KSA) had a large outbreak of MERS-CoV, renewing fears of a major outbreak during the Hajj this October. Using KSA Ministry of Health data, the MERS-CoV Scenario and Modeling Working Group forecast incidence under three scenarios. In the expected incidence scenario, we estimate 6.2 (95% Prediction Interval [PI]: 1–17) pilgrims will develop MERS-CoV symptoms during the Hajj, and 4.0 (95% PI: 0–12) foreign pilgrims will be infected but return home before developing symptoms. In the most pessimistic scenario, 47.6 (95% PI: 32–66) cases will develop symptoms during the Hajj, and 29.0 (95% PI: 17–43) will be infected but return home asymptomatic. Large numbers of MERS-CoV cases are unlikely to occur during the 2014 Hajj even under pessimistic assumptions, but careful monitoring is still needed to detect possible mass infection events and minimize introductions into other countries.
Wu KM, Riley S, 2014, Simulation-guided design of serological surveys of the cumulative incidence of influenza infection, BMC INFECTIOUS DISEASES, Vol: 14
Kwok KO, Cowling BJ, Wei VWI, et al., 2014, Social contacts and the locations in which they occur as risk factors for influenza infection, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 281, ISSN: 0962-8452
Pinsent A, Blake IM, White MT, et al., 2014, Surveillance of low pathogenic novel H7N9 avian influenza in commercial poultry barns: detection of outbreaks and estimation of virus introduction time, BMC INFECTIOUS DISEASES, Vol: 14, ISSN: 1471-2334
BackgroundBoth high and low pathogenic subtype A avian influenza remain ongoing threats to the commercial poultry industry globally. The emergence of a novel low pathogenic H7N9 lineage in China presents itself as a new concern to both human and animal health and may necessitate additional surveillance in commercial poultry operations in affected regions.MethodsSampling data was simulated using a mechanistic model of H7N9 influenza transmission within commercial poultry barns together with a stochastic observation process. Parameters were estimated using maximum likelihood. We assessed the probability of detecting an outbreak at time of slaughter using both real-time polymerase chain reaction (rt-PCR) and a hemagglutinin inhibition assay (HI assay) before considering more intense sampling prior to slaughter. The day of virus introduction and R 0 were estimated jointly from weekly flock sampling data. For scenarios where R 0 was known, we estimated the day of virus introduction into a barn under different sampling frequencies.ResultsIf birds were tested at time of slaughter, there was a higher probability of detecting evidence of an outbreak using an HI assay compared to rt-PCR, except when the virus was introduced <2 weeks before time of slaughter. Prior to the initial detection of infection N s a m p l e = 50 (1%) of birds were sampled on a weekly basis once, but after infection was detected, N s a m p l e = 2000 birds (40%) were sampled to estimate both parameters. We accurately estimated the day of virus introduction in isolation with weekly and 2-weekly sampling.ConclusionsA strong sampling effort would be required to infer both the day of virus introduction and R 0. Such a sampling effort would not be required to estimate the day of virus introduction alone once R 0 was known, and sampling N s a m p l e = 50 of birds in the flock on a weekly or 2 weekly basis would be sufficient.
Read JM, Lessler J, Riley S, et al., 2014, Social mixing patterns in rural and urban areas of southern China, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 281, ISSN: 0962-8452
Riley S, 2014, Phylogenetic evidence for a mild H1 pandemic in the early 1900s, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 111, Pages: 7892-7893, ISSN: 0027-8424
Kucharski AJ, Kwok KO, Wei VWI, et al., 2014, The Contribution of Social Behaviour to the Transmission of Influenza A in a Human Population, PLOS PATHOGENS, Vol: 10, ISSN: 1553-7366
Mills HL, Riley S, 2014, The Spatial Resolution of Epidemic Peaks, PLOS COMPUTATIONAL BIOLOGY, Vol: 10
Kwok KO, Jiang C, Tan L, et al., 2014, [An international collaborative study on influenza viruses antibody titers and contact patterns of individuals in rural and urban household of Guangzhou]., Zhonghua Liu Xing Bing Xue Za Zhi, Vol: 35, Pages: 433-436, ISSN: 0254-6450
OBJECTIVE: To describe the influenza viruses antibody levels and contact patterns of individuals in rural and urban regions of Guangzhou and to understand how contact patterns and other factors would correlate with the levels on the titers of antibody. METHODS: "Google Map" was used to randomly select the study points from the administrative areas in Guangzhou region. Each participant was required to provide 5 ml blood serum sample to be tested against different strains of H1N1 and H3N2 influenza viruses. RESULTS: 1) Using "Google map", 50 study points were selected but only 40 study points would meet the inclusion criteria. The cohort of this study consisted 856 households with 2 801 individuals. 1 821 participants (65% of the total number individuals in the cohort) completed the questionnaires. Among the 1 821 participants, 77.3% (1 407/1 821) and 22.7% (414/1 821) of them were from rural and urban areas respectively. There were more male participants in the rural but more female participants in the urban regions. Majority of the participants were from age group 18-59 followed by group 60 with aged 2-17 the least, in both rural and urban areas. 2) 78.1% (1 423/1 821) of the participants provided their serum samples. There appeared a strong correlation between age of the participants and the strength of their antibodies against that strain when a strain first circulated. In particular, seroprevalence was the highest at the age group 2-17. 3) 'Contact' was defined as persons having physical touch or/and conversation within one meter with the participants. Participants reported all having had large number of contacts. The proportion of participants having contacts with ten persons or above was the highest, ranging from 49.8% to 72.6%, particularly in age group 6-17. Compared to weekdays, participants had fewer contact persons on weekends. CONCLUSION: There was a strong correlation between the age of participants at the time when the strains first
Kucharski A, Mills H, Pinsent A, et al., 2014, Distinguishing Between Reservoir Exposure and Human-to-Human Transmission for Emerging Pathogens Using Case Onset Data., PLoS Curr, Vol: 6
Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%-32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.
Pepin KM, Spackman E, Brown JD, et al., 2014, Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America, PREVENTIVE VETERINARY MEDICINE, Vol: 113, Pages: 376-397, ISSN: 0167-5877
Cauchemez S, Fraser C, Van Kerkhove MD, et al., 2014, Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility, LANCET INFECTIOUS DISEASES, Vol: 14, Pages: 50-56, ISSN: 1473-3099
, 2014, Ebola Virus Disease in West Africa — The First 9 Months of the Epidemic and Forward Projections, New England Journal of Medicine, Vol: 371, Pages: 1481-1495
Pepin KM, Lloyd-Smith JO, Webb CT, et al., 2013, Minimizing the threat of pandemic emergence from avian influenza in poultry systems, BMC INFECTIOUS DISEASES, Vol: 13, ISSN: 1471-2334
Riley S, 2013, Complex Disease Dynamics and the Design of Influenza Vaccination Programs, PLOS MEDICINE, Vol: 10, ISSN: 1549-1277
Van Kerkhove MD, Hirve S, Koukounari A, et al., 2013, Estimating age-specific cumulative incidence for the 2009 influenza pandemic: a meta-analysis of A(H1N1)pdm09 serological studies from 19 countries, Influenza and Other Respiratory Viruses, Vol: 7, Pages: 872-886, ISSN: 1750-2640
BACKGROUND: The global impact of the 2009 influenza A(H1N1) pandemic (H1N1pdm) is not well understood. OBJECTIVES: We estimate overall and age-specific prevalence of cross-reactive antibodies to H1N1pdm virus and rates of H1N1pdm infection during the first year of the pandemic using data from published and unpublished H1N1pdm seroepidemiological studies. METHODS: Primary aggregate H1N1pdm serologic data from each study were stratified in standardized age groups and evaluated based on when sera were collected in relation to national or subnational peak H1N1pdm activity. Seropositivity was assessed using well-described and standardized hemagglutination inhibition (HI titers >/=32 or >/=40) and microneutralization (MN >/= 40) laboratory assays. The prevalence of cross-reactive antibodies to the H1N1pdm virus was estimated for studies using sera collected prior to the start of the pandemic (between 2004 and April 2009); H1N1pdm cumulative incidence was estimated for studies in which collected both pre- and post-pandemic sera; and H1N1pdm seropositivity was calculated from studies with post-pandemic sera only (collected between December 2009-June 2010). RESULTS: Data from 27 published/unpublished studies from 19 countries/administrative regions - Australia, Canada, China, Finland, France, Germany, Hong Kong SAR, India, Iran, Italy, Japan, Netherlands, New Zealand, Norway, Reunion Island, Singapore, United Kingdom, United States, and Vietnam - were eligible for inclusion. The overall age-standardized pre-pandemic prevalence of cross-reactive antibodies was 5% (95%CI 3-7%) and varied significantly by age with the highest rates among persons >/=65 years old (14% 95%CI 8-24%). Overall age-standardized H1N1pdm cumulative incidence was 24% (95%CI 20-27%) and varied significantly by age with the highest in children 5-19 (47% 95%CI 39-55%) and 0-4 years old (36% 95%CI 30-43%). CONCLUSIONS: Our results offer unique insight into the global impact of the H1N1 pandemic a
Strelioff CC, Vijaykrishna D, Riley S, et al., 2013, Inferring patterns of influenza transmission in swine from multiple streams of surveillance data, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 280, ISSN: 0962-8452
Cauchemez S, Van Kerkhove MD, Riley S, et al., 2013, Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart, EUROSURVEILLANCE, Vol: 18, Pages: 7-13, ISSN: 1560-7917
Kwok KO, Leung GM, Mak P, et al., 2013, Antiviral stockpiles for influenza pandemics from the household perspective: Treatment alone versus treatment with prophylaxis, EPIDEMICS, Vol: 5, Pages: 92-97, ISSN: 1755-4365
Cowling BJ, Ho LM, Riley S, et al., 2013, Statistical algorithms for early detection of the annual influenza peak season in Hong Kong using sentinel surveillance data., Hong Kong Med J, Vol: 19 Suppl 4, Pages: 4-5, ISSN: 1024-2708
In Hong Kong, influenza sentinel surveillance systems have been recently established. Methods that compare current data to data from recent weeks may be appropriate to indicate the start of peak influenza activity. These methods can produce reliable and timely alerts at the start of the annual influenza peak season.
Riley S, Cowling BJ, Chan KH, et al., 2013, Viral evolution from one generation of human influenza infection to the next., Hong Kong Med J, Vol: 19 Suppl 4, Pages: 6-10, ISSN: 1024-2708
1. In a sub-tropical epidemic, most of the apparent household secondary cases are actually secondary infections. 2. The consensus sequence for the entire influenza virus genome is not usually identical within the same household sample. Rather, there are commonly one or two nucleotide changes. 3. These results hint at an obvious generational threshold for adaptation at the level of the consensus sequence.
Cowling BJ, Chan KH, Peiris JSM, et al., 2013, Viral shedding, clinical history and transmission of influenza., Hong Kong Med J, Vol: 19 Suppl 4, Pages: 19-23, ISSN: 1024-2708
1. During influenza infections, most viral shedding occurs within a few days of illness onset. 2. Children may be more infectious than adults because they shed more virus. 3. The degree of viral shedding (infectiousness) correlates with symptoms and tympanic temperature.
Cowling B, Desenclos J-C, Riley S, et al., 2013, PLOS Currents: Outbreaks --- For findings that the world just can't wait to see., PLoS Curr, Vol: 5
Riley P, Ben-Nun M, Armenta R, et al., 2013, Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations, PLOS COMPUTATIONAL BIOLOGY, Vol: 9
Pepin KM, Wang J, Webb CT, et al., 2013, Anticipating the Prevalence of Avian Influenza Subtypes H9 and H5 in Live-Bird Markets, PLOS ONE, Vol: 8, ISSN: 1932-6203
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