The 2013–2016 epidemic of Ebola virus disease was of unprecedented magnitude, duration and impact. Analysing 1610 Ebola virus genomes, representing over 5% of known cases, we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic ‘gravity’ model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already set the seeds for an international epidemic, rendering these measures ineffective in curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing they were susceptible to significant outbreaks but at lower risk of introductions. Finally, we reveal this large epidemic to be a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help inform interventions in future epidemics.

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.)

SUMMARY The 2013–present Western African Ebola virus disease (EVD) outbreak is the largest ever recorded with >28,000 reported cases. Ebola virus (EBOV) genome sequencing has played an important role throughout this outbreak; however, relatively few sequences have been determined from patients in Liberia, the second worst-affected country. Here, we report 140 EBOV genome sequences from the second wave of the Liberian outbreak and analyze them in combination with 782 previously published sequences from throughout the Western African outbreak. While multiple early introductions of EBOV to Liberia are evident, the majority of Liberian EVD cases are consistent with a single introduction, followed by spread and diversification within the country. Movement of the virus within Liberia was widespread and reintroductions from Liberia served as an important source for the continuation of the already ongoing EVD outbreak in Guinea. Overall, little evidence was found for incremental adaptation of EBOV to the human host.

Surveillance of Ebola virus disease flare-ups uncovers a reduced rate of Ebola virus evolution during persistent infections.

The effects of EBOV evolution on diagnostic assays and therapeutic drugs appear to be low.

Single-molecule real-time (SMRT) sequencing technology with the Pacific Biosciences (PacBio) RS II platform offers the potential to obtain full-length coding regions (∼1100-bp) from MHC class I cDNAs. Despite the relatively high error rate associated with SMRT technology, high quality sequences can be obtained by circular consensus sequencing (CCS) due to the random nature of the error profile. In the present study we first validated the ability of SMRT-CCS to accurately identify class I transcripts in Mauritian-origin cynomolgus macaques (Macaca fascicularis) that have been characterized previously by cloning and Sanger-based sequencing as well as pyrosequencing approaches. We then applied this SMRT-CCS method to characterize 60 novel full-length class I transcript sequences expressed by a cohort of cynomolgus macaques from China. The SMRT-CCS method described here provides a straightforward protocol for characterization of unfragmented single-molecule cDNA transcripts that will potentially revolutionize MHC class I allele discovery in nonhuman primates and other species.

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Background The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. Methods and findings We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. Conclusions These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.