Modeling the spread of circulating vaccine-derived poliovirus type 2 outbreaks and interventions: A case study of Nigeria

Abstract

Background

Despite the successes of the Global Polio Eradication Initiative, substantial challenges remain in eradicating the poliovirus. The Sabin-strain (live-attenuated) virus in oral poliovirus vaccine (OPV) can revert to circulating vaccine-derived poliovirus (cVDPV) in under-vaccinated communities, regain neurovirulence and transmissibility, and cause paralysis outbreaks. Since the cessation of type 2-containing OPV (OPV2) in 2016, there have been cVDPV type 2 (cVDPV2) outbreaks in four out of six geographical World Health Organization regions, making these outbreaks a significant public health threat. Preparing for and responding to cVDPV2 outbreaks requires an updated understanding of how different factors, such as outbreak responses with the novel type of OPV2 (nOPV2) and the existence of under-vaccinated areas, affect the disease spread.

Methods

We built a differential-equation-based model to simulate the transmission of cVDPV2 following reversion of the Sabin-strain virus in prolonged circulation. The model incorporates vaccinations by essential (routine) immunization and supplementary immunization activities (SIAs), the immunity induced by different poliovirus vaccines, and the reversion process from Sabin-strain virus to cVDPV. The model’s outcomes include weekly cVDPV2 paralytic case counts and the die-out date when cVDPV2 transmission stops. In a case study of Northwest and Northeast Nigeria, we fit the model to data on the weekly cVDPV2 case counts with onset in 2018–2021. We then used the model to test the impact of different outbreak response scenarios during a prediction period of 2022–2023. The response scenarios included no response, the planned response (based on Nigeria’s SIA calendar), and a set of hypothetical responses that vary in the dates at which SIAs started. The planned response scenario included two rounds of SIAs that covered almost all areas of Northwest and Northeast Nigeria except some under-vaccinated areas (e.g., Sokoto). The hypothetical response scenarios involved two, three, and four rounds of SIAs that covered the whole Northwest and Northeast Nigeria. All SIAs in tested outbreak response scenarios used nOPV2. We compared the outcomes of tested outbreak response scenarios in the prediction period.

Results

Modeled cVDPV2 weekly case counts aligned spatiotemporally with the data. The prediction results indicated that implementing the planned response reduced total case counts by 79% compared to no response, but did not stop the transmission, especially in under-vaccinated areas. Implementing the hypothetical response scenarios involving two rounds of nOPV2 SIAs that covered all areas further reduced cVDPV2 case counts in under-vaccinated areas by 91–95% compared to the planned response, with greater impact from completing the two rounds at an earlier time, but it did not stop the transmission. When the first two rounds were completed in early April 2022, implementing two additional rounds stopped the transmission in late January 2023. When the first two rounds were completed six weeks earlier (i.e., in late February 2022), implementing one (two) additional round stopped the transmission in early February 2023 (late November 2022). The die out was always achieved last in the under-vaccinated areas of Northwest and Northeast Nigeria.

Conclusions

A differential-equation-based model of poliovirus transmission was developed and validated in a case study of Northwest and Northeast Nigeria. The results highlighted (i) the effectiveness of nOPV2 in reducing outbreak case counts; (ii) the need for more rounds of outbreak response SIAs that covered all of Northwest and Northeast Nigeria in 2022 to stop the cVDPV2 outbreaks; (iii) that persistent transmission in under-vaccinated areas delayed the progress towards stopping outbreaks; and (iv) that a quicker outbreak response would avert more paralytic cases and require fewer SIA rounds to stop the outbreaks.