Contributing and Terminating Factors of a Large RSV Outbreak in an Adult Hematology and Transplant Unit


Background: In January 2012, an increase of respiratory syncytial virus (RSV) infections on an adult hematology and transplant unit in a German university hospital was detected. We investigated the outbreak to assess its timing and extent and to identify risk factors for transmission.

Methods: We tested and typed patient samples pro- and retrospectively for RSV. We conducted a cohort and a case-control study. A confirmed outbreak case had laboratory-diagnosed, nosocomially-acquired RSV infection. Possible outbreak cases had pneumonia but were not laboratory-confirmed.

Results: Of 53 outbreak cases, 36 (68%) were confirmed and 17 (32%) possible. Retrospective testing and chart review dated the beginning of the outbreak to November 2011. Patients with community-acquired RSV infection were identified when the community epidemic began in January 2012. In multivariable analysis (controlling for contact with medical personnel, hygiene behaviour and age) patients with active social behaviour were more at risk for RSV infection (odds ratio 23.8, 95% confidence interval, 1.3 to 434.9; p-value, 0.03). Confirmed outbreak cases were more likely than controls to have been accomodated together with a confirmed or possible case before their onset of illness (OR 9.3, 95%CI: 2.1-85.1; p<0.001). Control measures, including isolation of every patient in the unit, initiated until the end of January terminated the outbreak.

Conclusions: Epidemiological investigations revealed co-accomodation with a case-patient and active social behaviour as likely risk factors for RSV infection. Awareness of and vigorous testing for respiratory viruses in immunosuppressed hospitalised patients is necessary to timely detect cases with outbreak potential. Isolation of patients with respiratory infectious illnesses is crucial to prevent the continuation or occurrence of outbreaks.

Temporal Variations in the Effective Reproduction Number of the 2014 West Africa Ebola Outbreak


The rapidly evolving 2014 Ebola virus disease (EVD) outbreak in West Africa is the largest documented in history, both in terms of the number of people infected and in the geographic spread. The high morbidity and mortality have inspired response strategies to the outbreak at the individual, regional, and national levels. Methods to provide real-time assessment of changing transmission dynamics are critical to the understanding of how these adaptive intervention measures have affected the spread of the outbreak.

In this analysis, we use the time series of EVD cases in Guinea, Sierra Leone, and Liberia up to September 8, 2014, and employ novel methodology to estimate how the rate of exponential rise of new cases has changed over the outbreak using piecewise fits of exponential curves to the outbreak data.

We find that for Liberia and Guinea, the effective reproduction number rose, rather than fell, around the time that the outbreak spread to densely populated cities, and enforced quarantine was imposed on several regions in the countries; this may indicate that enforced quarantine may not be an effective control measure.

If effective control measures are not put in place, and the current rate of exponential rise of new cases continues, we predict 4400 new Ebola cases in West Africa during the last half of the month of September, with an upper 95% confidence level of 6800 new cases.

Early Epidemic Dynamics of the West African 2014 Ebola Outbreak: Estimates Derived with a Simple Two-Parameter Model


The 2014 West African Ebola virus outbreak, now more correctly referred to as an epidemic, is the largest ever to occur. As of August 28, 2014, concerns have been raised that control efforts, particularly in Liberia, have been ineffective, as reported case counts continue to increase. Limited data are available on the epidemiology of the outbreak. However, reported cumulative incidence data as well as death counts are available for Guinea, Sierra Leone, Liberia and Nigeria. We utilized a simple, two parameter mathematical model of epidemic growth and control, to characterize epidemic growth patterns in West Africa, to evaluate the degree to which the epidemic is being controlled, and to assess the potential implications of growth patterns for epidemic size. Models demonstrated good fits to data. Overall basic reproductive number (R0) for the epidemic was estimated to be between 1.6 and 2.0, consistent with prior outbreaks. However, we identified only weak evidence for the occurrence of epidemic control in West Africa as a whole, and essentially no evidence for control in Liberia (though slowing of growth was seen in Guinea and Sierra Leone). It is projected that small reductions in transmission would prevent tens of thousands of future infections. These findings suggest that there is an extraordinary need for improved control measures for the 2014 Ebola epidemic, especially in Liberia, if catastrophe is to be averted.

Molecular Investigation of 2013 Dengue Fever Outbreak from Delhi, India


Dengue fever is a self-limiting, acute febrile disease which may aggravate to haemorrhage, plasma leakage and organ impairment in small number of cases. An outbreak of dengue fever occurred in Delhi, India after rainy season in the year 2013. Dengue virus specific RT-PCR was carried out on 378 suspected blood samples that were collected during the outbreak. Dengue virus was detected in 71% samples with highest number of patients infected by DENV-2 (86%) followed by DENV-1 (19 %) and DENV-3 (8%). Co-infection with more than one DENV serotype was detected in 14% samples. Twenty nine DENV strains (10 DENV-1, 12 DENV-2 and 7 DENV-3) were sequenced for partial envelope protein gene. Phylogenetic analysis grouped DENV-1 strains in the American African genotype, DENV-2 strains in the Cosmopolitan genotype and DENV-3 in Genotype III. We report the serotype distribution, circulating genotypes and partial envelope protein gene sequence of 29 DENV strains detected during 2013 outbreak in Delhi, India.

Assessing the International Spreading Risk Associated with the 2014 West African Ebola Outbreak


Background: The 2014 West African Ebola Outbreak is so far the largest and deadliest recorded in history. The affected countries, Sierra Leone, Guinea, Liberia, and Nigeria, have been struggling to contain and to mitigate the outbreak. The ongoing rise in confirmed and suspected cases, 2615 as of 20 August 2014, is considered to increase the risk of international dissemination, especially because the epidemic is now affecting cities with major commercial airports.

Method: We use the Global Epidemic and Mobility Model to generate stochastic, individual based simulations of epidemic spread worldwide, yielding, among other measures, the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. The mobility model integrates daily airline passenger traffic worldwide and the disease model includes the community, hospital, and burial transmission dynamic. We use a multimodel inference approach calibrated on data from 6 July to the date of 9 August 2014. The estimates obtained were used to generate a 3-month ensemble forecast that provides quantitative estimates of the local transmission of Ebola virus disease in West Africa and the probability of international spread if the containment measures are not successful at curtailing the outbreak.

Results: We model the short-term growth rate of the disease in the affected West African countries and estimate the basic reproductive number to be in the range 1.5 − 2.0 (interval at the 1/10 relative likelihood). We simulated the international spreading of the outbreak and provide the estimate for the probability of Ebola virus disease case importation in countries across the world. Results indicate that the short-term (3 and 6 weeks) probability of international spread outside the African region is small, but not negligible. The extension of the outbreak is more likely occurring in African countries, increasing the risk of international dissemination on a longer time scale.

Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West Africa


The 2014 Ebola virus (EBOV) outbreak in West Africa is the largest outbreak of the genus Ebolavirus to date. To better understand the spread of infection in the affected countries, it is crucial to know the number of secondary cases generated by an infected index case in the absence and presence of control measures, i.e., the basic and effective reproduction number. In this study, I describe the EBOV epidemic using an SEIR (susceptible-exposed-infectious-recovered) model and fit the model to the most recent reported data of infected cases and deaths in Guinea, Sierra Leone and Liberia. The maximum likelihood estimates of the basic reproduction number are 1.51 (95% confidence interval [CI]: 1.50-1.52) for Guinea, 2.53 (95% CI: 2.41-2.67) for Sierra Leone and 1.59 (95% CI: 1.57-1.60) for Liberia. The model indicates that in Guinea and Sierra Leone the effective reproduction number might have dropped to around unity by the end of May and July 2014, respectively. In Liberia, however, the model estimates no decline in the effective reproduction number by end-August 2014. This suggests that control efforts in Liberia need to be improved substantially in order to stop the current outbreak.

Don’t Count Your Chicken Livers: an Outbreak of Campylobacter sp. Not Associated with Chicken Liver Parfait, England, November 2013


In England, several recent campylobacter outbreaks have been associated with poultry liver consumption. Following a lunch event in a hotel in Surrey in November 2013 where chicken liver parfait was served, guests reported having gastrointestinal symptoms. A retrospective cohort study showed 46 of 138 guests became unwell, with a median incubation period of two days and for 11 cases campylobacter infection was laboratory confirmed. Food item analysis identified an association between illness and consumption of roast turkey (aOR=3.02 p=0.041) or jus (aOR=3.55 p=0.045), but not with chicken liver parfait (OR=0.39 p=0.405). The environmental risk assessment did not identify non-compliance with standard food practice guidelines. This study presents a point-source outbreak of campylobacter with a high attack rate and epidemiological analysis results show that the jus or roast turkey was the likely source of infection although this could not be confirmed by the environmental assessment. Consuming the chicken liver dish was not a risk factor for developing symptoms as was initially hypothesised. Prior knowledge on the association between poultry liver food items and campylobacter outbreaks should not overly influence an outbreak investigation to ensure the true aetiology is identified and on-going public health risk is minimised.

The Ratio of Emergency Department Visits for ILI to Seroprevalence of 2009 Pandemic Influenza A (H1N1) Virus Infection, Florida, 2009


Background. A seroprevalence survey carried out in four counties in the Tampa Bay area of Florida provided an estimate of cumulative incidence of infection due to the 2009 influenza A (H1N1) as of the end of that year’s pandemic in the four counties from which seroprevalence data were obtained

Methods. Excess emergency department (ED) visits for influenza-like illness (ILI) during the pandemic period (compared to four non-pandemic years) were estimated using the ESSENCE-FL syndromic surveillance system for the four-county area.

Results. There were an estimated 44 infections for every ILI ED visit. Age-specific ratios rose from 19.7 to 1 for children aged <5 years to 143.8 to 1 for persons aged >64 years.

Conclusions. These ratios provide a way to estimate cumulative incidence. These estimated ratios can be used in real time for planning and forecasting, when carrying out timely seroprevalence surveys is not practical. Syndromic surveillance data allow age and geographic breakdowns, including for children.

Epidemic Intelligence Cyberinfrastructure: Real-Time Outbreak Source Detection and Prediction for Rapid Response


Foodborne diseases cause an estimated 48 million illnesses each year in the United States, including 9.4 million caused by known pathogens. Real time detection of cases and outbreak sources are important epidemic intelligence services that can decrease morbidity and mortality of foodborne illnesses, and allow optimal response to identify the causal pathways leading to contamination. For most outbreaks associated with fresh produce items, outbreak source detection typically occurs after the contaminated produce items have been consumed and are no longer in the marketplace.
We developed a probabilistic model for real time outbreak source detection, prediction of outbreaks, and contamination-prone area mapping with the aim of developing a cyber-infrastructure to support this activity. The model’s inputs include environmental, trade and epidemiological dynamics. Because effective distance reliably predicts disease arrival times we estimate the distance of outbreak sources from spatio-temporal patterns of foodborne outbreaks. As a case study we consider the 2013 Cyclospora outbreaks in the USA that were related to contaminated fresh produce (cilantro and fresh salad mix) from Mexico. We are able to match case distributions related to both food commodities and determine their outbreak sources with an average accuracy of 0.93. Assuming a similar pattern of contamination for 2014, with predictions of rainfall and temperature for the 2014 summer we predict a prevalence 15% higher than 2013.
The study aims to provide a methodological framework to evaluate environmentally sensitive food contamination and assess interdependencies of socio-environmental factors causing contamination. We emphasize the linkage of patterns and processes, the positive role of uncertainty, and challenge the belief that information about the whole food supply chain is needed for traceback analysis to be useful for identifying likely sources. Our specific prediction for 2014 strongly emphasizes the need for real-time surveillance to identify and respond to this pending outbreak.

Clock Rooting Further Demonstrates that Guinea 2014 EBOV is a Member of the Zaïre Lineage


While initial phylogenetic analyses concluded to Guinea 2014 EBOV falling outside the Zaïre lineage (ZEBOV), a recent re-analysis of the same dataset by Dudas and Rambaut (2014) suggested that Guinea 2014 EBOV actually is ZEBOV. Under the same hypothesis as used by these authors (the molecular clock hypothesis), we reinforce their conclusion by providing a statistical assessment of the location of the root of the Zaïre lineage. Our analysis unambiguously supports Guinea 2014 EBOV as a member of the Zaïre lineage. In addition, we also show that some uncertainty exists so as to the location of the root of the genus Ebolavirus. We release the software we used for these re-analyses. RootAnnotator allows for the easy determination of branch root posterior probability from any posterior sample of clocked trees and is freely available at