Outbreak of Neisseria meningitidis serogroup C outside the meningitis belt—Liberia, 2017: an epidemiological and laboratory investigation

Abstract

Summary Background On April 25, 2017, a cluster of unexplained illnesses and deaths associated with a funeral was reported in Sinoe County, Liberia. Molecular testing identified Neisseria meningitidis serogroup C (NmC) in specimens from patients. We describe the epidemiological investigation of this cluster and metagenomic characterisation of the outbreak strain. Methods We collected epidemiological data from the field investigation and medical records review. Confirmed, probable, and suspected cases were defined on the basis of molecular testing and signs or symptoms of meningococcal disease. Metagenomic sequences from patient specimens were compared with 141 meningococcal isolate genomes to determine strain lineage. Findings 28 meningococcal disease cases were identified, with dates of symptom onset from April 21 to April 30, 2017: 13 confirmed, three probable, and 12 suspected. 13 patients died. Six (21%) patients reported fever and 23 (82%) reported gastrointestinal symptoms. The attack rate for confirmed and probable cases among funeral attendees was 10%. Metagenomic sequences from six patient specimens were similar to a sequence type (ST) 10217 (clonal complex [CC] 10217) isolate genome from Niger, 2015. Multilocus sequencing identified five of seven alleles from one specimen that matched ST-9367, which is represented in the PubMLST database by one carriage isolate from Burkina Faso, in 2011, and belongs to CC10217. Interpretation This outbreak featured high attack and case fatality rates. Clinical presentation was broadly consistent with previous meningococcal disease outbreaks, but predominance of gastrointestinal symptoms was unusual compared with previous African meningitis epidemics. The outbreak strain was genetically similar to NmC CC10217, which caused meningococcal disease outbreaks in Niger and Nigeria. CC10217 had previously been identified only in the African meningitis belt.

The Global Meningococcal Initiative meeting on prevention of meningococcal disease worldwide: Epidemiology, surveillance, hypervirulent strains, antibiotic resistance and high-risk populations

“…In April 2017, 31 IMD cases were reported, including 13 deaths, following attendance at the funeral of a religious leader in Liberia [73]. The outbreak was associated with atypical symptoms (diarrhea, vomiting, and mental confusion) and metagenomic analysis revealed the presence of a strain with 91-98% similarity to ST-10217 in six of the 10 specimens analyzed, the remaining four specimens were inconclusive [73,74]. This strain appears to be evolved and likely has the epidemic potential [75].…”

Meningococcal disease surveillance in the Asia–Pacific region (2020): The global meningococcal initiative

“…All cases were linked to the funeral, with the US Center for Disease Control (CDC) using PCR to confirm 13 cases as meningococcal serogroup C. 98 In six out of 10 specimens subsequently analyzed via metagenomics, there was the presence of a strain with 91-98% similarity to ST-10,217, which previously caused meningococcal disease outbreaks in Niger and Nigeria. 99 The World Scout Jamboree (Japan, 2015) In 2015, there were six confirmed IMD cases following the 23rd World Scout Jamboree (WSJ) held in the Yamaguchi prefecture, Japan. 100 During the event, there were 33,628 scouts (14-17 years) in attendance from 155 countries.…”

Web-Based Genome Analysis of Bacterial Meningitis Pathogens for Public Health Applications Using the Bacterial Meningitis Genomic Analysis Platform (BMGAP)

“…In recent studies, WGS was used to identify the possible origins of 15 epidemiologically distinct meningococcal outbreaks in the United States (Whaley et al, 2018), demonstrate the spread of a new hypervirulent strain of Nm (CC10217) in Africa (Bozio et al, 2018;Caugant, 2018), and monitor vaccine impact on Nm genetic diversity and strain replacement along the African meningitis Belt (Kretz et al, 2016;Retchless et al, 2016;Sidikou et al, 2016). These are examples of how WGS can be used to provide important information on how bacterial meningitis pathogens are disseminated in the population.…”