PMC:7497282 / 65703-67623 JSONTXT

{"project":"MyTest","denotations":[{"id":"32741232-25316852-29927170","span":{"begin":171,"end":174},"obj":"25316852"},{"id":"32741232-24894345-29927171","span":{"begin":269,"end":272},"obj":"24894345"},{"id":"32741232-26317401-29927172","span":{"begin":475,"end":478},"obj":"26317401"},{"id":"32741232-25733555-29927173","span":{"begin":621,"end":624},"obj":"25733555"},{"id":"32741232-25733563-29927174","span":{"begin":857,"end":860},"obj":"25733563"},{"id":"32741232-26468545-29927175","span":{"begin":1168,"end":1171},"obj":"26468545"},{"id":"32741232-27771181-29927176","span":{"begin":1378,"end":1381},"obj":"27771181"},{"id":"32741232-27029535-29927177","span":{"begin":1580,"end":1583},"obj":"27029535"},{"id":"32741232-29017491-29927178","span":{"begin":1753,"end":1756},"obj":"29017491"},{"id":"32741232-28890193-29927179","span":{"begin":1915,"end":1918},"obj":"28890193"}],"namespaces":[{"prefix":"_base","uri":"https://www.uniprot.org/uniprot/testbase"},{"prefix":"UniProtKB","uri":"https://www.uniprot.org/uniprot/"},{"prefix":"uniprot","uri":"https://www.uniprot.org/uniprotkb/"}],"text":"IDM also performed multiple statistical analyses using GPLN data. In 2014, IDM discussed the use of lot quality assurance sampling (LQAS) to evaluate the quality of SIAs [130] and used Nigerian AFP surveillance data to predict the risks of cases at the district level [131]. In 2015, IDM also developed a simple statistical model of the polio force of infection using data from Nigeria and based on anticipated die out of all wild poliovirus transmission in Nigeria in 2015 [136]. IDM provided a perspective on the application of advanced digital tools (e.g. GIS tracking) to fight polio and other communicable diseases [137]. In 2015, IDM also applied a heuristic algorithm to spatially reconstruct partially observed transmission networks using phylogenetic data for northern Nigeria and found substantial limitations of the method due to under-sampling [138]. Building on this work, in 2016 IDM characterized OPV revision using whole-genome sequencing data from Nigeria, which showed some evidence of transient and local transmission of OPV-related serotype 1 and 3 viruses during periods of low wild polio incidence that appeared consistent with national OPV use [139]. IDM performed a statistical analysis of immunization data to characterize OPV-induced population immunity and assess campaign effectiveness in high-risk countries to support GPEI SIA planning activities [140]. Using data from Nigeria, IDM constructed a hierarchical model to estimate SIA effectiveness to characterize OPV-induced immunity and compared these estimates to data from LQAS and incidence data [141]. Using these methods, in 2017, IDM reported spatial risk model predictions and recommended subnational prioritization to accelerate poliovirus elimination in Pakistan [142]. Following OPV2 cessation, IDM compared pre- and post-cessation detection rates of cVDPV2s and showed the die out of OPV2-related viruses in most countries [143]."}

{"project":"2_test","denotations":[{"id":"32741232-25316852-29927170","span":{"begin":171,"end":174},"obj":"25316852"},{"id":"32741232-24894345-29927171","span":{"begin":269,"end":272},"obj":"24894345"},{"id":"32741232-26317401-29927172","span":{"begin":475,"end":478},"obj":"26317401"},{"id":"32741232-25733555-29927173","span":{"begin":621,"end":624},"obj":"25733555"},{"id":"32741232-25733563-29927174","span":{"begin":857,"end":860},"obj":"25733563"},{"id":"32741232-26468545-29927175","span":{"begin":1168,"end":1171},"obj":"26468545"},{"id":"32741232-27771181-29927176","span":{"begin":1378,"end":1381},"obj":"27771181"},{"id":"32741232-27029535-29927177","span":{"begin":1580,"end":1583},"obj":"27029535"},{"id":"32741232-29017491-29927178","span":{"begin":1753,"end":1756},"obj":"29017491"},{"id":"32741232-28890193-29927179","span":{"begin":1915,"end":1918},"obj":"28890193"}],"text":"IDM also performed multiple statistical analyses using GPLN data. In 2014, IDM discussed the use of lot quality assurance sampling (LQAS) to evaluate the quality of SIAs [130] and used Nigerian AFP surveillance data to predict the risks of cases at the district level [131]. In 2015, IDM also developed a simple statistical model of the polio force of infection using data from Nigeria and based on anticipated die out of all wild poliovirus transmission in Nigeria in 2015 [136]. IDM provided a perspective on the application of advanced digital tools (e.g. GIS tracking) to fight polio and other communicable diseases [137]. In 2015, IDM also applied a heuristic algorithm to spatially reconstruct partially observed transmission networks using phylogenetic data for northern Nigeria and found substantial limitations of the method due to under-sampling [138]. Building on this work, in 2016 IDM characterized OPV revision using whole-genome sequencing data from Nigeria, which showed some evidence of transient and local transmission of OPV-related serotype 1 and 3 viruses during periods of low wild polio incidence that appeared consistent with national OPV use [139]. IDM performed a statistical analysis of immunization data to characterize OPV-induced population immunity and assess campaign effectiveness in high-risk countries to support GPEI SIA planning activities [140]. Using data from Nigeria, IDM constructed a hierarchical model to estimate SIA effectiveness to characterize OPV-induced immunity and compared these estimates to data from LQAS and incidence data [141]. Using these methods, in 2017, IDM reported spatial risk model predictions and recommended subnational prioritization to accelerate poliovirus elimination in Pakistan [142]. Following OPV2 cessation, IDM compared pre- and post-cessation detection rates of cVDPV2s and showed the die out of OPV2-related viruses in most countries [143]."}