2020
DOI: 10.1101/2020.08.03.20146241
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Integrating geostatistical maps and transmission models using adaptive multiple importance sampling

Abstract: The Adaptive Multiple Importance Sampling algorithm (AMIS) is an iterative technique which recycles samples from all previous iterations in order to improve the efficiency of the proposal distribution. We have formulated a new statistical framework based on AMIS to sample parameters of transmission models based on high-resolution geospatial maps of disease prevalence, incidence, or relative risk. We tested the performance of our algorithm on four case studies: ascariasis in Ethiopia, onchocerciasis in Togo, HI… Show more

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Cited by 4 publications
(3 citation statements)
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“…We further assumed that detectable individual CFUs are formed by either separated cells or these clusters. Parameter inference was performed using an adaptive multiple importance sampling framework [25]. The likelihood was obtained by assuming that the observed number of colonies is Poisson distributed:…”
Section: B Model For Plating With Glass Beadsmentioning
confidence: 99%
“…We further assumed that detectable individual CFUs are formed by either separated cells or these clusters. Parameter inference was performed using an adaptive multiple importance sampling framework [25]. The likelihood was obtained by assuming that the observed number of colonies is Poisson distributed:…”
Section: B Model For Plating With Glass Beadsmentioning
confidence: 99%
“…Parameter inference was performed using Adaptive Multiple Importance Sampling framework [25]. The likelihood was obtained by assuming that the observed number of colonies is Poisson-distributed: where Θ ={ n, r on , r off } ; n obs is the observed number of colonies; n sim is the number of colonies obtained after simulating the model with parameter set Θ.…”
Section: The Modelmentioning
confidence: 99%
“…Therefore, models that account for people movements linked to spatial parasite transmission over large scales are needed. The ground-breaking recent work of Touloupou and Retkute, which links geostatistical mapping, a helminth transmission model and a statistical importance sampling method to produce very large-scale predictive maps, does include an element of spatial transmission, although does not incorporate observed people movement patterns [ 32 , 33 ].…”
Section: Introductionmentioning
confidence: 99%