4.8 Article

Epidemiology of the silent polio outbreak in Rahat, Israel, based on modeling of environmental surveillance data

出版社

NATL ACAD SCIENCES
DOI: 10.1073/pnas.1808798115

关键词

poliovirus; silent transmission; mathematical model; environmental surveillance; vaccination

资金

  1. Models of Infectious Disease Agent Study program within the National Institute of General Medical Sciences of the National Institutes of Health [U01GM110712, U54GM111274]
  2. National Science Foundation [OCE-1115881, EAR-1360330]
  3. World Health Organization (WHO) [353558 TSA 2014/485861-0]
  4. Israel Ministry of Health
  5. WHO [18-TSA-032]

向作者/读者索取更多资源

Israel experienced an outbreak of wild poliovirus type 1 (WPV1) in 2013-2014, detected through environmental surveillance of the sewage system. No cases of acute flaccid paralysis were reported, and the epidemic subsided after a bivalent oral polio vaccination (bOPV) campaign. As we approach global eradication, polio will increasingly be detected only through environmental surveillance. We developed a framework to convert quantitative polymerase chain reaction (qPCR) cycle threshold data into scaled WPV1 and OPV1 concentrations for inference within a deterministic, compartmental infectious disease transmission model. We used this approach to estimate the epidemic curve and transmission dynamics, as well as assess alternate vaccination scenarios. Our analysis estimates the outbreak peaked in late June, much earlier than previous estimates derived from analysis of stool samples, although the exact epidemic trajectory remains uncertain. We estimate the basic reproduction number was 1.62 (95% CI 1.04-2.02). Model estimates indicate that 59% (95% CI 9-77%) of susceptible individuals (primarily children under 10 years old) were infected with WPV1 over a little more than six months, mostly before the vaccination campaign onset, and that the vaccination campaign averted 10% (95% CI 1-24%) of WPV1 infections. As we approach global polio eradication, environmental monitoring with qPCR can be used as a highly sensitive method to enhance disease surveillance. Our analytic approach brings public health relevance to environmental data that, if systematically collected, can guide eradication efforts.

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