Studies on the burden of human monkeypox in the Democratic Republic of the Congo (DRC) were last conducted from 1981 to 1986. Since then, the population that is immunologically naïve to orthopoxviruses has increased significantly due to cessation of mass smallpox vaccination campaigns. To assess the current risk of infection, we analyzed human monkeypox incidence trends in a monkeypox-enzootic region. Active, population-based surveillance was conducted in nine health zones in central DRC. Epidemiologic data and biological samples were obtained from suspected cases. Cumulative incidence (per 10,000 population) and major determinants of infection were compared with data from active surveillance in similar regions from 1981 to 1986. Between November 2005 and November 2007, 760 laboratory-confirmed human monkeypox cases were identified in participating health zones. The average annual cumulative incidence across zones was 5.53 per 10,000 (2.18–14.42). Factors associated with increased risk of infection included: living in forested areas, male gender, age < 15, and no prior smallpox vaccination. Vaccinated persons had a 5.2-fold lower risk of monkeypox than unvaccinated persons (0.78 vs. 4.05 per 10,000). Comparison of active surveillance data in the same health zone from the 1980s (0.72 per 10,000) and 2006–07 (14.42 per 10,000) suggests a 20-fold increase in human monkeypox incidence. Thirty years after mass smallpox vaccination campaigns ceased, human monkeypox incidence has dramatically increased in rural DRC. Improved surveillance and epidemiological analysis is needed to better assess the public health burden and develop strategies for reducing the risk of wider spread of infection.

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (''sequence-based ultrarapid pathogen identification''), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.

Summary A suspected case of sexual transmission from a male survivor of Ebola virus disease (EVD) to his female partner (the patient in this report) occurred in Liberia in March 2015. Ebola virus (EBOV) genomes assembled from blood samples from the patient and a semen sample from the survivor were consistent with direct transmission. The genomes shared three substitutions that were absent from all other Western African EBOV sequences and that were distinct from the last documented transmission chain in Liberia before this case. Combined with epidemiologic data, the genomic analysis provides evidence of sexual transmission of EBOV and evidence of the persistence of infective EBOV in semen for 179 days or more after the onset of EVD. (Funded by the Defense Threat Reduction Agency and others.)

Health authorities should be vigilant for this rapidly evolving virus.

Viral hemorrhagic fever is caused by a diverse group of single-stranded, negative-sense or positive-sense RNA viruses belonging to the families Filoviridae (Ebola and Marburg), Arenaviridae (Lassa, Junin, Machupo, Sabia, and Guanarito), and Bunyaviridae (hantavirus). Disease characteristics in these families mark each with the potential to be used as a biological threat agent. Because other diseases have similar clinical symptoms, specific laboratory diagnostic tests are necessary to provide the differential diagnosis during outbreaks and for instituting acceptable quarantine procedures. We designed 48 TaqMan™-based polymerase chain reaction (PCR) assays for specific and absolute quantitative detection of multiple hemorrhagic fever viruses. Forty-six assays were determined to be virus-specific, and two were designated as pan assays for Marburg virus. The limit of detection for the assays ranged from 10 to 0.001 plaque-forming units (PFU)/PCR. Although these real-time hemorrhagic fever virus assays are qualitative (presence of target), they are also quantitative (measure a single DNA/RNA target sequence in an unknown sample and express the final results as an absolute value (e.g., viral load, PFUs, or copies/mL) on the basis of concentration of standard samples and can be used in viral load, vaccine, and antiviral drug studies.