Measles Vaccination Coverage Survey in Moba, Katanga, Democratic Republic of Congo, 2013: Need to Adapt Routine and Mass Vaccination Campaigns to Reach the Unreached

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The Democratic Republic of Congo (DRC) has committed to eliminate measles by 2020. In 2013, in response to a large outbreak, Médecins Sans Frontières conducted a mass vaccination campaign (MVC) in Moba, Katanga, DRC. We estimated the measles vaccination coverage for the MVC, the Expanded Programme on Immunization routine measles vaccination (EPI) and assessed reasons for non-vaccination.

We conducted a household-based survey among caretakers of children aged 6 months-15 years in Moba from November to December 2013. We used a two-stage-cluster-sampling, where clusters were allocated proportionally to village size and households were randomly selected from each cluster. The questionnaire included demographic variables, vaccination status (card or oral history) during MVC and EPI and reasons for non-vaccination. We estimated the coverage by gender, age and the reasons for non-vaccination and calculated 95% confidence intervals (95% CI).

We recruited 4,768 children living in 1,684 households. The MVC coverage by vaccination card and oral history was 87% (95% CI 84-90) and 66% (95% CI 61-70) if documented by card. The EPI coverage was 76% (95% CI 72-81) and 3% (95% CI 1-4) respectively. The MVC coverage was significantly higher among children previously vaccinated during EPI 91% (95% CI 88-93), compared to 74% (95% CI 66-80) among those not previously vaccinated. Six percent (n=317) of children were never vaccinated. The main reason for non-vaccination was family absence 68% (95% CI 58-78).

The MVC and EPI measles coverage was insufficient to prevent the recurrence of outbreaks in Moba. Lack of EPI vaccination and lack of accessibility by road were associated with lower MVC coverage. We recommend intensified social mobilization and extended EPI and MVCs to increase the coverage of absent residents and unreached children. Routine and MVCs need to be adapted accordingly to improve coverage in hard-to-reach populations in DRC.

A Model of the 2014 Ebola Epidemic in West Africa with Contact Tracing

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A differential equations model is developed for the 2014 Ebola epidemics in Sierra Leone and Liberia. The model describes the dynamic interactions of the susceptible and infected populations of these countries. The model incorporates the principle features of contact tracing, namely, the number of contacts per identified infectious case, the likelihood that a traced contact is infectious, and the efficiency of the contact tracing process. The model is first fitted to current cumulative reported case data in each country. The data fitted simulations are then projected forward in time, with varying parameter regimes corresponding to contact tracing efficiencies. These projections quantify the importance of the identification, isolation, and contact tracing processes for containment of the epidemics.

Projected Treatment Capacity Needs in Sierra Leone

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Background: The ongoing outbreak of Ebola Virus Disease in West Africa requires immediate and sustained input from the international community in order to curb transmission. The CDC has produced a model that indicates that to end the outbreak by pushing the reproductive number below one, 25% of the patients must be placed in an Ebola Treatment Unit (ETC) and 45% must be isolated in community settings in which risk of disease transmission is reduced and safe burials are provided. In order to provide firmer targets for the international response in Sierra Leone, we estimated the national and international personnel and treatment capacity that may be required to reach these percentages.

Methods: We developed a compartmental SEIR model that was fitted to WHO data and local data allowing the reproductive number to change every 8 weeks to forecast the progression of the EVD epidemic in Sierra Leone. We used the previously estimated 2.5x correction factor estimated by the CDC to correct for underreporting. Number of personnel required to provide treatment for the predicted number of cases was estimated using UNMEER and UN OCHA requests for resources required to meet the CDC target of 70% isolation.

Results: As of today (2014-12-04), we estimate that there are 810 (95% CI=646 to 973) EVD active cases in treatment, with an additional 3751 (95% CI=2778 to 4723) EVD cases unreported and untreated. To reach the CDC targets today, we need 1140 (95% CI=894 to 1387) cases in ETCs and 2052 (95% CI=1608 to 2496) at home or in a community setting with a reduced risk for disease transmission. In 28 days (2015-01-01), we will need 1309 (95% CI=804 to 1814) EVD cases in ETCs and 2356 (95% CI=1447 to 3266) EVD cases at reduced risk of transmission. If the current transmission rate is not reduced, up to 3183 personnel in total will be required in 56 days (2015-01-29) to operate ETCs according to our model.

Conclusions: The current outbreak will require massive input from the international community in order to curb the transmission through traditional containment mechanisms by breaking the chains of transmission in Sierra Leone. If sufficient treatment facilities, healthcare workers and support personnel are not rapidly deployed, the increasing number of cases will be overwhelming.In addition to supporting isolation and treatment mechanisms, other viable control options, such as the development of an effective vaccine, should be supported.

Global Climate Anomalies and Potential Infectious Disease Risks: 2014-2015

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Background: The El Niño/Southern Oscillation (ENSO) is a global climate phenomenon that impacts human infectious disease risk worldwide through droughts, floods, and other climate extremes. Throughout summer and fall 2014 and winter 2015, El Niño Watch, issued by the US National Oceanic and Atmospheric Administration, assessed likely El Niño development during the Northern Hemisphere fall and winter, persisting into spring 2015.

Methods: We identified geographic regions where environmental conditions may increase infectious disease transmission if the predicted El Niño occurs using El Niño indicators (Sea Surface Temperature [SST], Outgoing Longwave Radiation [OLR], and rainfall anomalies) and literature review of El Niño-infectious disease associations.

Results: SSTs in the equatorial Pacific and western Indian Oceans were anomalously elevated during August-October 2014, consistent with a developing weak El Niño event. Teleconnections with local climate is evident in global precipitation patterns, with positive OLR anomalies (drier than average conditions) across Indonesia and coastal southeast Asia, and negative anomalies across northern China, the western Indian Ocean, central Asia, north-central and northeast Africa, Mexico/Central America, the southwestern United States, and the northeastern and southwestern tropical Pacific. Persistence of these conditions could produce environmental settings conducive to increased transmission of cholera, dengue, malaria, Rift Valley fever, and other infectious diseases in regional hotspots as during previous El Niño events.

Discussion and Conclusions: The current development of weak El Niño conditions may have significant potential implications for global public health in winter 2014-spring 2015. Enhanced surveillance and other preparedness measures in predicted infectious disease hotspots could mitigate health impacts.

The Western Africa Ebola Virus Disease Epidemic Exhibits Both Global Exponential and Local Polynomial Growth Rates

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Background:
While many infectious disease epidemics are initially characterized by an exponential growth in time, we show that district-level Ebola virus disease (EVD) outbreaks in West Africa follow slower polynomial-based growth kinetics over several generations of the disease.

Methods:
We analyzed epidemic growth patterns at three different spatial scales (regional, national, and subnational) of the Ebola virus disease epidemic in Guinea, Sierra Leone and Liberia by compiling publicly available weekly time series of reported EVD case numbers from the patient database available from the World Health Organization website for the period 05-Jan to 17-Dec 2014.

Results:
We found significant differences in the growth patterns of EVD cases at the scale of the country, district, and other subnational administrative divisions. The national cumulative curves of EVD cases in Guinea, Sierra Leone, and Liberia show periods of approximate exponential growth. In contrast, local epidemics are asynchronous and exhibit slow growth patterns during 3 or more EVD generations, which can be better approximated by a polynomial than an exponential function.

Conclusions:
The slower than expected growth pattern of local EVD outbreaks could result from a variety of factors, including behavior changes, success of control interventions, or intrinsic features of the disease such as a high level of clustering. Quantifying the contribution of each of these factors could help refine estimates of final epidemic size and the relative impact of different mitigation efforts in current and future EVD outbreaks.

Sequential Outbreaks Due to a New Strain of Neisseria Meningitidis Serogroup C in Northern Nigeria, 2013-14

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Background
Neisseria meningitidis serogroup C (NmC) outbreaks occur infrequently in the African meningitis belt; the most recent report of an outbreak of this serogroup was in Burkina Faso, 1979. Médecins sans Frontières (MSF) has been responding to outbreaks of meningitis in northwest Nigeria since 2007 with no reported cases of serogroup C from 2007-2012. MenAfrivac®, a serogroup A conjugate vaccine, was first used for mass vaccination in northwest Nigeria in late 2012. Reactive vaccination using polysaccharide ACYW135 vaccine was done by MSF in parts of the region in 2008 and 2009; no other vaccination campaigns are known to have occurred in the area during this period. We describe the general characteristics of an outbreak due to a novel strain of NmC in Sokoto State, Nigeria, in 2013, and a smaller outbreak in 2014 in the adjacent state, Kebbi.

Methods
Information on cases and deaths was collected using a standard line-list during each week of each meningitis outbreak in 2013 and 2014 in northwest Nigeria. Initial serogroup confirmation was by rapid Pastorex agglutination tests. Cerebrospinal fluid (CSF) samples from suspected meningitis patients were sent to the WHO Reference Laboratory in Oslo, where bacterial isolates, serogrouping, antimicrobial sensitivity testing, genotype characterisation and real-time PCR analysis were performed.

Results
In the most highly affected outbreak areas, all of the 856 and 333 clinically suspected meningitis cases were treated in 2013 and 2014, respectively. Overall attack (AR) and case fatality (CFR) rates were 673/100,000 population and 6.8% in 2013, and 165/100,000 and 10.5% in 2014. Both outbreaks affected small geographical areas of less than 150km2 and populations of less than 210,000, and occurred in neighbouring regions in two adjacent states in the successive years. Initial rapid testing identified NmC as the causative agent. Of the 21 and 17 CSF samples analysed in Oslo, NmC alone was confirmed in 11 and 10 samples in 2013 and 2014, respectively. Samples confirmed as NmC through bacterial culture had sequence type (ST)-10217.

Conclusions
These are the first recorded outbreaks of NmC in the region since 1979, and the sequence (ST)-10217 has not been identified anywhere else in the world. The outbreaks had similar characteristics to previously recorded NmC outbreaks. Outbreaks of NmC in 2 consecutive years in northern Nigeria indicate a possible emergence of this serogroup. Increased surveillance for multiple serogroups in the region is needed, along with consideration of vaccination with conjugate vaccines rather than for NmA alone.

Ebola and Indirect Effects on Health Service Function in Sierra Leone

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Background: The indirect effects of the Ebola epidemic on health service function may be significant but is not known. The aim of this study was to quantify to what extent admission rates and surgery has changed at health facilities providing such care in Sierra Leone during the time of the Ebola epidemic.

Methods: Weekly data on facility inpatient admissions and surgery from admission and surgical theatre register books were retrospectively retrieved during September and October. 21 Community Health Officers enrolled in a surgical task-shifting program personally visited the facilities. The study period was January 6 (week 2) to October 12, (week 41) 2014.

Results: Data was retrieved from 40 out of 55 facilities. A total of 62,257 admissions and 12,124 major surgeries were registered for the study period.
Total admissions in the week of the first Ebola case were 2,006, median 40 (IQR 20-76) compared to 883, median 12 (IQR 4-30) on the last week of the study. This equals a 70% drop in median number of admissions (p=0.005) between May and October. Total number of major surgeries fell from 342, median 6 (IQR 2-14) to 231, median 3 (IQR 0-6) in the same period, equal 50% reduction in median number of major surgeries (p=0.014).

Conclusions: Inpatient health services have been severely affected by the Ebola outbreak. The dramatic documented decline in facility inpatient admissions and major surgery is likely to be an underestimation. Reestablishing such care is urgent and must be a priority.

Estimation of MERS-Coronavirus Reproductive Number and Case Fatality Rate for the Spring 2014 Saudi Arabia Outbreak: Insights from Publicly Available Data

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Background: The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was initially recognized as a source of severe respiratory illness and renal failure in 2012. Prior to 2014, MERS-CoV was mostly associated with sporadic cases of human illness, of presumed zoonotic origin, though chains of person-to-person transmission in the healthcare setting were reported. In spring 2014, large healthcare-associated outbreaks of MERS-CoV infection occurred in Jeddah and Riyadh, Kingdom of Saudi Arabia. To date the epidemiological information published by public health investigators in affected jurisdictions has been relatively limited. However, it is important that the global public health community have access to information on the basic epidemiological features of the outbreak to date, including the basic reproduction number (R0) and best estimates of case-fatality rates (CFR). We sought to address these gaps using a publicly available line listing of MERS-CoV cases.

Methods: R0 was estimated using the incidence decay with exponential adjustment (“IDEA”) method, while period-specific case fatality rates that incorporated non-attributed death data were estimated using Monte Carlo simulation.

Results: 707 cases were available for evaluation. 52% of cases were identified as primary, with the rest being secondary. IDEA model fits suggested a higher R0 in Jeddah (3.5-6.7) than in Riyadh (2.0-2.8); control parameters suggested more rapid reduction in transmission in the former city than the latter. The model accurately projected final size and end date of the Riyadh outbreak based on information available prior to the outbreak peak; for Jeddah, these projections were possible once the outbreak peaked. Overall case-fatality was 40%; depending on the timing of 171 deaths unlinked to case data, outbreak CFR could be higher, lower, or equivalent to pre-outbreak CFR.

Conclusions: Notwithstanding imperfect data, inferences about MERS-CoV epidemiology important for public health preparedness are possible using publicly available data sources. The R0 estimated in Riyadh appears similar to that seen for SARS-CoV, but CFR appears higher, and indirect evidence suggests control activities ended these outbreaks. These data suggest this disease should be regarded with equal or greater concern than the related SARS-CoV.

Assessment of the Risk of Ebola Importation to Australia

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Objectives: To assess the risk of Ebola importation to Australia during the first six months of 2015, based upon the current outbreak in West Africa.

Methodology: We assessed the risk under two distinct scenarios: (i) assuming that significant numbers of cases of Ebola remain confined to Guinea, Liberia and Sierra Leone, and using historic passenger arrival data into Australia; and, (ii) assuming potential secondary spread based upon international flight data. A model appropriate to each scenario is developed, and parameterised using passenger arrival card or international flight data, and World Health Organisation case data from West Africa. These models were constructed based on WHO Ebola outbreak data as at 17 October 2014 and 3 December 2014. An assessment of the risk under each scenario is reported. On 27 October 2014 the Australian Government announced a policy change, that visas from affected countries would be refused/cancelled, and the predicted effect of this policy change is reported.

Results: The current probability of at least one case entering Australia by 1 July 2015, having travelled directly from West Africa with historic passenger arrival rates into Australia, is 0.34. Under the new Australian Government policy of restricting visas from affected countries (as of 27 October 2014), the probability of at least one case entering Australia by 1 July 2015 is reduced to 0.16. The probability of at least one case entering Australia by 1 July 2015 via an outbreak from a secondary source country is approximately 0.12.

Conclusions: Our models suggest that if the transmission of Ebola remains unchanged, it is possible that a case will enter Australia within the first six months of 2015, either directly from West Africa (even when current visa restrictions are considered), or via secondary outbreaks elsewhere. Government and medical authorities should be prepared to respond to this eventuality. Control measures within West Africa over recent months have contributed to a reduction in projected risk of a case entering Australia. A significant further reduction of the rate at which Ebola is proliferating in West Africa, and control of the disease if and when it proliferates elsewhere, will continue to result in substantially lower risk of the disease entering Australia.

Estimating Potential Incidence of MERS-CoV Associated with Hajj Pilgrims to Saudi Arabia, 2014

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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.  

Projected Impact of Vaccination Timing and Dose Availability on the Course of the 2014 West African Ebola Epidemic

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Background: The 2014 West African Ebola outbreak has evolved into an epidemic of historical proportions and catastrophic scope. Prior outbreaks have been contained through the use of personal protective equipment, but such an approach has not been rapidly effective in the current epidemic. Several candidate vaccines have been developed against the Ebola virus, and are undergoing initial clinical trials.

Methods: As removal of population-level susceptibility through vaccination could be a highly impactful control measure for this epidemic, we sought to estimate the number of vaccine doses and timing of vaccine administration required to reduce the epidemic size. Our base model was fit using the IDEA approach, a single equation model that has been successful to date in describing Ebola growth. We projected the future course of the Ebola epidemic using this model. Vaccination was assumed to reduce the effective reproductive number. We evaluated the potential impact of vaccination on epidemic trajectory under different assumptions around timing of vaccine availability.

Results: Using effective reproductive (Re) number estimates derived from this model, we estimate that 3-4 million doses of vaccine, if available and administered, could reduce Re to 0.9 in the interval from January-March 2015. Later vaccination would be associated with a progressively diminishing impact on final epidemic size; in particular, vaccination to the same Re at or after the epidemic is projected to peak (April-May 2015) would have little impact on final epidemic size, though more intensive campaigns (e.g., Re reduced to 0.5) could still be effective if initiated by summer 2015. In summary, there is a closing window of opportunity for the use of vaccine as a tool for Ebola epidemic control.

Conclusions: Effective vaccination, used before the epidemic peaks, would be projected to prevent tens of thousands of deaths; this does not minimize the ethical challenges that would be associated with wide-scale application of vaccines that have undergone only limited evaluation for safety and efficacy.

A Three-Scale Network Model for the Early Growth Dynamics of 2014 West Africa Ebola Epidemic

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Background: In mid-October 2014, the number of cases of the West Africa Ebola virus epidemic in Guinea, Sierra Leone and Liberia exceeded 9,000 cases. The early growth dynamics of the epidemic has been qualitatively different for each of the three countries. However, it is important to understand these disparate dynamics as trends of a single epidemic spread over regions with similar geographic and cultural aspects, with likely common parameters for transmission rates and reproduction number R0.

Methods: We combine a discrete, stochastic SEIR model with a three-scale community network model to demonstrate that the different regional trends may be explained by different community mixing rates. Heuristically, the effect of different community mixing rates may be understood as the observation that two individuals infected by the same chain of transmission are more likely to share the same contacts in a less-mixed community. Local saturation effects occur as the contacts of an infected individual are more likely to already be exposed by the same chain of transmission.

Results: The effects of community mixing, together with stochastic effects, can explain the qualitative difference in the growth of Ebola virus cases in each country, and why the probability of large outbreaks may have recently increased. An increase in the rate of Ebola cases in Guinea in late August, and a local fitting of the transient dynamics of the Ebola cases in Liberia, suggests that the epidemic in Liberia has been more severe, and the epidemic in Guinea is worsening, due to discrete seeding events as the epidemic spreads into new communities.

Conclusions: A relatively simple network model provides insight on the role of local effects such as saturation that would be difficult to otherwise quantify. Our results predict that exponential growth of an epidemic is driven by the exposure of new communities, underscoring the importance of limiting this spread.

The UCSC Ebola Genome Portal

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Background:
With the Ebola epidemic raging out of control in West Africa, there has been a flurry of research into the Ebola virus, resulting in the generation of much genomic data.

Methods:
In response to the clear need for tools that integrate multiple strands of research around molecular sequences, we have created the University of California Santa Cruz (UCSC) Ebola Genome Browser, an adaptation of our popular UCSC Genome Browser web tool, which can be used to view the Ebola virus genome sequence from GenBank and nearly 30 annotation tracks generated by mapping external data to the reference sequence. Significant annotations include a multiple alignment comprising 102 Ebola genomes from the current outbreak, 56 from previous outbreaks, and 2 Marburg genomes as an outgroup; a gene track curated by NCBI; protein annotations curated by UniProt and antibody-binding epitopes curated by IEDB. We have extended the Genome Browser’s multiple alignment color-coding scheme to distinguish mutations resulting from non-synonymous coding changes, synonymous changes, or changes in untranslated regions.

Discussion:
Our Ebola Genome portal at http://genome.ucsc.edu/ebolaPortal/ links to the Ebola virus Genome Browser and an aggregate of useful information, including a collection of Ebola antibodies we are curating.

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.

Conservancy of mAb Epitopes in Ebolavirus Glycoproteins of Previous and 2014 Outbreaks

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Background: Several monoclonal antibodies (mAb) are being evaluated as treatment options for the current 2014 Ebola outbreak. But they were derived from and tested for protection against the older 1976 Mayinga or 1995 Kikwit Zaire Ebolaviruses (EBOV). The EBOV sequences reported for the current outbreak contain several mutations whose significance remained to be established.

Methods: We analyzed sequence and structural conservation of the Ebolavirus glycoprotein (GP) epitopes for all experimentally identified protective mAbs published to date.

Results: The conservancy analysis of protective mAb epitopes in the Ebolavirus glycoprotein sequences spanning all Ebola virus lineages since 1976 showed that conservancy within the Zaire EBOV lineage was high, with only one immunodominant epitope of mAb 13F6-1-2 acquiring two novel mutations in the 2014 outbreak that might potentially change the antibody specificity and neutralization activity. However, the conservation to other Ebola viruses was unexpectedly low.

Conclusion: Low conservancy of Zaire EBOV mAb epitopes to other EBOV lineages suggests that these epitopes are not indispensable for viral fitness, and that alternative mAbs could be developed to broadly target all EBOV. However, average percent sequence identity of the epitopes for mAbs used in current cocktails to the Zaire EBOV is high with only one epitope differing in the 2014 outbreak. These data bode well for general usefulness of these antibodies in the context of the current outbreak.

Inference and Forecast of the Current West African Ebola Outbreak in Guinea, Sierra Leone and Liberia

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The current West African Ebola outbreak poses an unprecedented public health challenge for the world at large. The response of the global community to the epidemic, including deployment of nurses, doctors, epidemiologists, beds, supplies and security, is shaped by our understanding of the spatial-temporal extent and progression of the disease. Ongoing evaluation of the epidemiological characteristics and future course of the Ebola outbreak is needed to stay abreast of any changes to its transmission dynamics, as well as the success or failure of intervention efforts. Here we use observations, dynamic modeling and Bayesian inference to generate simulations and weekly forecasts of the outbreaks in Guinea, Liberia and Sierra Leone. Estimates of key epidemiological characteristics over time indicate continued epidemic growth in West Africa, though there is some evidence of slowing growth in Liberia. 6-week forecasts over successive weeks corroborate these findings; forecasts projecting no future change in intervention efficacy have been more accurate for Guinea and Sierra Leone, but have overestimated incidence and mortality for Liberia.

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.

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.