Clinical Manifestations and Case Management of Ebola Haemorrhagic Fever caused by a newly identified virus strain, Bundibugyo, Uganda, 2007-2008

A confirmed Ebola haemorrhagic fever (EHF) outbreak in Bundibugyo, Uganda, November 2007-February 2008, was caused by a putative new species (Bundibugyo ebolavirus). It included 93 putative cases, 56 laboratory-confirmed cases, and 37 deaths (CFR = 25%). Study objectives are to describe clinical manifestations and case management for 26 hospitalised laboratory-confirmed EHF patients. Clinical findings are congruous with previously reported EHF infections. The most frequently experienced symptoms were non-bloody diarrhoea (81%), severe headache (81%), and asthenia (77%). Seven patients reported or were observed with haemorrhagic symptoms, six of whom died. Ebola care remains difficult due to the resource-poor setting of outbreaks and the infection-control procedures required. However, quality data collection is essential to evaluate case definitions and therapeutic interventions, and needs improvement in future epidemics. Organizations usually involved in EHF case management have a particular responsibility in this respect

Detection of infectious disease outbreaks in twenty-two fragile states, 2000-2010: a systematic review.

Fragile states are home to a sixth of the world's population, and their populations are particularly vulnerable to infectious disease outbreaks. Timely surveillance and control are essential to minimise the impact of these outbreaks, but little evidence is published about the effectiveness of existing surveillance systems. We did a systematic review of the circumstances (mode) of detection of outbreaks occurring in 22 fragile states in the decade 2000-2010 (i.e. all states consistently meeting fragility criteria during the timeframe of the review), as well as time lags from onset to detection of these outbreaks, and from detection to further events in their timeline. The aim of this review was to enhance the evidence base for implementing infectious disease surveillance in these complex, resource-constrained settings, and to assess the relative importance of different routes whereby outbreak detection occurs.We identified 61 reports concerning 38 outbreaks. Twenty of these were detected by existing surveillance systems, but 10 detections occurred following formal notifications by participating health facilities rather than data analysis. A further 15 outbreaks were detected by informal notifications, including rumours.There were long delays from onset to detection (median 29 days) and from detection to further events (investigation, confirmation, declaration, control). Existing surveillance systems yielded the shortest detection delays when linked to reduced barriers to health care and frequent analysis and reporting of incidence data.Epidemic surveillance and control appear to be insufficiently timely in fragile states, and need to be strengthened. Greater reliance on formal and informal notifications is warranted. Outbreak reports should be more standardised and enable monitoring of surveillance systems' effectiveness

Essentials of Filoviral Load Quantification

Quantitative measurement of viral load is an important parameter in the management of filovirus disease outbreaks because viral load correlates with severity of disease, survival, and infectivity. During the ongoing Ebola virus disease outbreak in parts of Western Africa, most assays used in the detection of Ebola virus disease by more than 44 diagnostic laboratories yielded qualitative results. Regulatory hurdles involved in validating quantitative assays and the urgent need for a rapid Ebola virus disease diagnosis precluded development of validated quantitative assays during the outbreak. Because of sparse quantitative data obtained from these outbreaks, opportunities for study of correlations between patient outcome, changes in viral load during the course of an outbreak, disease course in asymptomatic individuals, and the potential for virus transmission between infected patients and contacts have been limited. We strongly urge the continued development of quantitative viral load assays to carefully evaluate these parameters in future outbreaks of filovirus disease

Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease.

The proper allocation of public health resources for research and control requires quantification of both a disease's current burden and the trend in its impact. Infectious diseases that have been labeled as "emerging infectious diseases" (EIDs) have received heightened scientific and public attention and resources. However, the label 'emerging' is rarely backed by quantitative analysis and is often used subjectively. This can lead to over-allocation of resources to diseases that are incorrectly labelled "emerging," and insufficient allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong. We suggest a simple quantitative approach, segmented regression, to characterize the trends and emergence of diseases. Segmented regression identifies one or more trends in a time series and determines the most statistically parsimonious split(s) (or joinpoints) in the time series. These joinpoints in the time series indicate time points when a change in trend occurred and may identify periods in which drivers of disease impact change. We illustrate the method by analyzing temporal patterns in incidence data for twelve diseases. This approach provides a way to classify a disease as currently emerging, re-emerging, receding, or stable based on temporal trends, as well as to pinpoint the time when the change in these trends happened. We argue that quantitative approaches to defining emergence based on the trend in impact of a disease can, with appropriate context, be used to prioritize resources for research and control. Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors, but has the potential to improve resource allocation for global health

Taxonomy of the order Mononegavirales : update 2016

In 2016, the order Mononegavirales was emended through the addition of two new families (Mymonaviridae and Sunviridae), the elevation of the paramyxoviral subfamily Pneumovirinae to family status (Pneumoviridae), the addition of five free-floating genera (Anphevirus, Arlivirus, Chengtivirus, Crustavirus, and Wastrivirus), and several other changes at the genus and species levels. This article presents the updated taxonomy of the order Mononegavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV)

The Use of a Mobile Laboratory Unit in Support of Patient Management and Epidemiological Surveillance during the 2005 Marburg Outbreak in Angola

A mobile laboratory unit (MLU) was deployed to Uige, Angola as part of the World Health Organization response to an outbreak of viral hemorrhagic fever caused by Marburg virus (MARV). Utilizing mainly quantitative real-time PCR assays, this laboratory provided specific MARV diagnostics in the field. The MLU operated for 88 consecutive days allowing MARV-specific diagnostic response in <4 hours from sample receiving. Most cases were found among females in the child-bearing age and in children less than five years of age including a high number of paediatric cases implicating breastfeeding as potential transmission route. Oral swabs were identified as a useful alternative specimen source to the standard whole blood/serum specimens for patients refusing blood draw. There was a high concordance in test results between the MLU and the reference laboratory in Luanda operated by the US Centers for Disease Control and Prevention. The MLU was an important outbreak response asset providing valuable support in patient management and epidemiological surveillance. Field laboratory capacity should be expanded and made an essential part of any future outbreak investigation

Impact of two interventions on timeliness and data quality of an electronic disease surveillance system in a resource limited setting (Peru): a prospective evaluation

<p>Abstract</p> <p>Background</p> <p>A timely detection of outbreaks through surveillance is needed in order to prevent future pandemics. However, current surveillance systems may not be prepared to accomplish this goal, especially in resource limited settings. As data quality and timeliness are attributes that improve outbreak detection capacity, we assessed the effect of two interventions on such attributes in Alerta, an electronic disease surveillance system in the Peruvian Navy.</p> <p>Methods</p> <p>40 Alerta reporting units (18 clinics and 22 ships) were included in a 12-week prospective evaluation project. After a short refresher course on the notification process, units were randomly assigned to either a phone, visit or control group. Phone group sites were called three hours before the biweekly reporting deadline if they had not sent their report. Visit group sites received supervision visits on weeks 4 & 8, but no phone calls. The control group sites were not contacted by phone or visited. Timeliness and data quality were assessed by calculating the percentage of reports sent on time and percentage of errors per total number of reports, respectively.</p> <p>Results</p> <p>Timeliness improved in the phone group from 64.6% to 84% in clinics (+19.4 [95% CI, +10.3 to +28.6]; p < 0.001) and from 46.9% to 77.3% on ships (+30.4 [95% CI, +16.9 to +43.8]; p < 0.001). Visit and control groups did not show significant changes in timeliness. Error rates decreased in the visit group from 7.1% to 2% in clinics (-5.1 [95% CI, -8.7 to -1.4]; p = 0.007), but only from 7.3% to 6.7% on ships (-0.6 [95% CI, -2.4 to +1.1]; p = 0.445). Phone and control groups did not show significant improvement in data quality.</p> <p>Conclusion</p> <p>Regular phone reminders significantly improved timeliness of reports in clinics and ships, whereas supervision visits led to improved data quality only among clinics. Further investigations are needed to establish the cost-effectiveness and optimal use of each of these strategies.</p