Time Taken to Detect and Respond to Polio Outbreaks in Africa and the Potential Impact of Direct Molecular Detection and Nanopore Sequencing

JOURNAL OF INFECTIOUS DISEASES(2022)

引用 5|浏览10
暂无评分
摘要
Background Detection of poliovirus outbreaks relies on a complex laboratory algorithm of cell-culture, polymerase chain reaction (PCR), and sequencing to distinguish wild-type and vaccine-derived polioviruses (VDPV) from Sabin-like strains. We investigated the potential for direct molecular detection and nanopore sequencing (DDNS) to accelerate poliovirus detection. Methods We analyzed laboratory data for time required to analyze and sequence serotype-2 VDPV (VDPV2) in stool collected from children with acute flaccid paralysis in Africa (May 2016-February 2020). Impact of delayed detection on VDPV2 outbreak size was assessed through negative binomial regression. Results VDPV2 confirmation in 525 stools required a median of 49 days from paralysis onset (10th-90th percentile, 29-74), comprising collection and transport (median, 16 days), cell-culture (7 days), intratypic differentiation quantitative reverse transcription PCR (3 days), and sequencing, including shipping if required (15 days). New VDPV2 outbreaks were confirmed a median of 35 days (27-60) after paralysis onset, which we estimate could be reduced to 16 days by DDNS (9-37). Because longer delays in confirmation and response were positively associated with more cases (P < .001), we estimate that DDNS could reduce the number of VDPV2 cases before a response by 28% (95% credible interval, 12%-42%). Conclusions DDNS could accelerate poliovirus outbreak response, reducing their size and the cost of eradication. Delays in detecting poliovirus result in larger outbreaks. Direct molecular detection of poliovirus in stool by nanopore sequencing is fast, easy to implement, and could substantially decrease the time taken to respond to poliovirus outbreaks, accelerating eradication.
更多
查看译文
关键词
poliovirus, direct detection, VDPV, outbreak, stool, AFP, nanopore, surveillance
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要