Internet and Free Press Are Associated with Reduced Lags in Global Outbreak Reporting

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Background: Global outbreak detection and reporting have generally improved for a variety of infectious diseases and geographic regions in recent decades. Nevertheless, lags in outbreak reporting remain a threat to the global human health and economy. In the time between first occurrence of a novel disease incident and public notification of an outbreak, infected individuals have a greater possibility of traveling and spreading the pathogen to other nations. Shortening outbreak reporting lags has the potential to improve global health by preventing local outbreaks from escalating into global epidemics.

Methods: Reporting lags between the first record and the first public report of an event were calculated for 318 outbreaks occurring 1996-2009. The influence of freedom of the press, Internet usage, per capita health expenditure, and cell phone subscriptions, on the timeliness of outbreak reporting was evaluated.

Results: Freer presses and increasing Internet usage correlate with reduced time between the first record of an outbreak and the public report. Increasing Internet usage reduced the expected reporting lag from more than one month in nations without Internet users to one day in those where 75 of 100 people use the Internet.

Conclusion: Advances in technology and the emergence of more open and free governments are associated with to improved global infectious disease surveillance.

Short-term Prediction of the Incidence of Congenital Rubella Syndrome

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Objectives
In Japan, a rubella outbreak occurred from early 2012 to late 2013, primarily among adult males aged 20–49 years. We conducted this study to predict the number of congenital rubella syndrome (CRS) cases in Japan in 2014.

Methods
The probability of CRS when a pregnant woman is infected with rubella depends on the gestational age of the fetus. The cumulative number of CRS cases was predicted by a formula based on the parameters from two studies conducted in the U.K. and the U.S., the reported cases of rubella among women 15–49 years of age, and the reports of CRS from 2011 to week 2 of 2014.

Findings
While the predicted number of cases of CRS based on parameters from the U.K. study demonstrated a biphasic curve, with a low peak around week 12 and a high peak around week 50 of 2013, the predicted number of CRS cases based on the U.S. study demonstrated a single peak around week 50 of 2013. The ex post evaluation indicated that the cumulative number of CRS cases in 2014 would be 19.1–29.3.

Interpretation
Our prediction of the number of CRS cases may be useful for the enhanced detection of this often under-reported syndrome.

Twitter Improves Influenza Forecasting

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Accurate disease forecasts are imperative when preparing for influenza epidemic outbreaks; nevertheless, these forecasts are often limited by the time required to collect new, accurate data. In this paper, we show that data from the microblogging community Twitter significantly improves influenza forecasting. Most prior influenza forecast models are tested against historical influenza-like illness (ILI) data from the U.S. Centers for Disease Control and Prevention (CDC). These data are released with a one-week lag and are often initially inaccurate until the CDC revises them weeks later. Since previous studies utilize the final, revised data in evaluation, their evaluations do not properly determine the effectiveness of forecasting. Our experiments using ILI data available at the time of the forecast show that models incorporating data derived from Twitter can reduce forecasting error by 17-30% over a baseline that only uses historical data. For a given level of accuracy, using Twitter data produces forecasts that are two to four weeks ahead of baseline models. Additionally, we find that models using Twitter data are, on average, better predictors of influenza prevalence than are models using data from Google Flu Trends, the leading web data source.

Phylodynamic Analysis of Ebola Virus in the 2014 Sierra Leone Epidemic

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Background: The Ebola virus (EBOV) epidemic in Western Africa is the largest in recorded history and control efforts have so far failed to stem the rapid growth in the number of infections. Mathematical models serve a key role in estimating epidemic growth rates and the reproduction number (R0) from surveillance data and, recently, molecular sequence data. Phylodynamic analysis of existing EBOV time-stamped sequence data may provide independent estimates of the unobserved number of infections, reveal recent epidemiological history, and provide insight into selective pressures acting upon viral genes.

Methods: We fit a series mathematical models of infectious disease dynamics to phylogenies estimated from 78 whole EBOV genomes collected from distinct patients in May and June of 2014 in Sierra Leone, and perform evolutionary analysis on these genomes combined with closely related EBOV genomes from previous outbreaks. Two analyses are conducted with values of the latent period that have been used in recent modelling efforts. We also examined the EBOV sequences for evidence of possible episodic adaptive molecular evolution during the 2014 outbreak.

Results: We find evidence for adaptive evolution affecting L and GP protein coding regions of the EBOV genome, which is unlikely to bias molecular clock and phylodynamic analyses. We estimate R0=2.40 (95% HPD:1.54-3.87 ) if the mean latent period is 5.3 days, and R0=3.81, (95% HPD:2.47-6.3) if the mean latent period is 12.7 days. The estimated coefficient of variation (CV) of the number of transmissions per infected host is very high, and a large proportion of infections yield no transmissions.

Conclusions: Estimates of R0 are sensitive to the unknown latent infectious period which can not be reliably estimated from genetic data alone. EBOV phylogenies show significant evidence for superspreading and extreme variance in the number of transmissions per infected individual during the early epidemic in Sierra Leone.

Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia

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Background: An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the outbreak are underway. However, the efficacy of these interventions, and their likely impact on an Ebola epidemic of this size, is unknown. Forecasting and simulation of these interventions may inform public health efforts.

Methods: We use existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and use this model to forecast the progression of the epidemic, as well as the efficacy of several interventions, including increased contact tracing, improved infection control practices, the use of a hypothetical pharmaceutical intervention to improve survival in hospitalized patients.

Findings: Model forecasts until Dec. 31, 2014 show an increasingly severe epidemic with no sign of having reached a peak. Modeling results suggest that increased contact tracing, improved infection control, or a combination of the two can have a substantial impact on the number of Ebola cases, but these interventions are not sufficient to halt the progress of the epidemic. The hypothetical pharmaceutical intervention, while impacting mortality, had a smaller effect on the forecasted trajectory of the epidemic.

Interpretation: Near-term, practical interventions to address the ongoing Ebola epidemic may have a beneficial impact on public health, but they will not result in the immediate halting, or even obvious slowing of the epidemic. A long-term commitment of resources and support will be necessary to address the outbreak.

On the Quarantine Period for Ebola Virus

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Background:
21 days has been regarded as the appropriate quarantine period for holding individuals potentially exposed to Ebola Virus (EV) to reduce risk of contagion. There does not appear to be a systematic discussion of the basis for this period.

Methods:
The prior estimates for incubation time to EV were examined, along with data on the first 9 months of the current outbreak. These provided estimates of the distribution of incubation times.

Results:
A 21 day period for quarantine may result in the release of individuals with a 0.2 – 12% risk of release prior to full opportunity for the incubation to proceed. It is suggested that a detailed cost-benefit assessment, including considering full transmission risks, needs to occur in order to determine the appropriate quarantine period for potentially exposed individuals.

Insights into the Early Epidemic Spread of Ebola in Sierra Leone Provided by Viral Sequence Data

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Background and Methodology:
The current Ebola virus epidemic in West Africa has been spreading at least since December 2013. The first confirmed case of Ebola virus in Sierra Leone was identified on May 25. Based on viral genetic sequencing data from 72 individuals in Sierra Leone collected between the end of May and mid June, we utilize a range of phylodynamic methods to estimate the basic reproductive number (R0). We additionally estimate the expected lengths of the incubation and infectious periods of the virus. Finally, we use phylogenetic trees to examine the role played by population structure in the epidemic.

Results:
The median estimates of R0 based on sequencing data alone range between 1.65-2.18, with the most plausible model yielding a median R0 of 2.18 (95% HPD 1.24-3.55). Importantly, our results indicate that, at least until mid June, relief efforts in Sierra Leone were ineffective at lowering the effective reproductive number of the virus. We estimate the expected length of the infectious period to be 2.58 days (median; 95% HPD 1.24-6.98). The dataset appears to be too small in order to estimate the incubation period with high certainty (median expected incubation period 4.92 days; 95% HPD 2.11-23.20). While our estimates of the duration of infection tend to be smaller than previously reported, phylodynamic analyses support a previous estimate that 70% of cases were observed and included in the present dataset. The dataset is too small to show a particular population structure with high significance, however our preliminary analyses suggest that half the population is spreading the virus with an R0 well above 2, while the other half of the population is spreading with an R0 below 1.

Conclusions:
Overall we show that sequencing data can robustly infer key epidemiological parameters. Such estimates inform public health officials and help to coordinate effective public health efforts. Thus having more sequencing data available for the ongoing Ebola virus epidemic and at the start of new outbreaks will foster a quick understanding of the dynamics of the pathogen.

Commentary: Containing the Ebola Outbreak – the Potential and Challenge of Mobile Network Data

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Commentary The ongoing Ebola outbreak is taking place in one of the most highly connected and densely populated regions of Africa (Figure 1A). Accurate information on population movements is valuable for monitoring the progression of the outbreak and predicting its future spread, facilitating the prioritization of interventions and designing surveillance and containment strategies. Vital questions [...]

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

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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

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Background
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.

Methods
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.

Results
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.

Conclusions
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.