Development of a dose-response model for porcine cysticercosis

Abstract

Taenia solium is an important cause of acquired epilepsy worldwide and remains endemic in Asia, Africa, and Latin America. Transmission of this parasite is still poorly understood despite the design of infection experiments to improve our knowledge of the disease, with estimates for critical epidemiological parameters, such as the probability of human-to-pig infection after exposure to eggs, still lacking. In this paper, a systematic review was carried out and eight pig infection experiments were analyzed to describe the probability of developing cysts. These experiments included different pathways of inoculation: with ingestion of proglottids, eggs, and beetles that ingested eggs, and direct injection of activated oncospheres into the carotid artery. In these experiments, different infective doses were used, and the numbers of viable and degenerated cysts in the body and brain of each pig were registered. Five alternative dose-response models (exponential, logistic, log-logistic, and exact and approximate beta-Poisson) were assessed for their accuracy in describing the observed probabilities of cyst development as a function of the inoculation dose. Dose-response models were developed separately for the presence of three types of cysts (any, viable only, and cysts in the brain) and considered for each of the four inoculation methods (“Proglottids”, “Eggs”, “Beetles” and “Carotid”). The exact beta-Poisson model best fit the data for the three types of cysts and all relevant exposure pathways. However, observations for some exposure pathways were too scarce to reliably define a dose-response curve with any model. A wide enough range of doses and sufficient sample sizes was only found for the “Eggs” pathway and a merged “Oral” pathway combining the “Proglottids”, “Eggs” and “Beetles” pathways. Estimated parameter values from this model suggest that a low infective dose is sufficient to result in a 50% probability for the development of any cyst or for viable cyst infections. Although this is a preliminary model reliant on a limited dataset, the parameters described in this manuscript should contribute to the design of future experimental infections related to T. solium transmission, as well as the parameterization of simulation models of transmission aimed at informing control.

Effect of relative humidity, storage days, and packaging on pecan kernel texture: Analyses and modeling

“…A three‐parameter logistic (3PL) model, a type of sigmoid model, was used to predict the changes in pecan textural attributes over time. The 3PL model is a type of logistic model prominently used in immunoassays research (Herman, Scherer, & Shan, 2008 ) such as ELISA, microbial growth prediction (Fujikawa, 2010 ), dose–response relationships (Andrade‐Mogrovejo et al, 2022 ; Carøe, Ebbehøj, Bonde, Flachs, & Agner, 2018 ; ElHarouni et al, 2022 ), and geological phenomena (Chen et al, 2019 ; Joshuva, Deenadayalan, Sivakumar, Sathishkumar, & Vishnuvardhan, 2019 ). The parameters give unique information such as maximum value to response achieved (asymptote), slope, and the value of a predictor variable for median response (inflection point) (Figure 1 ).…”

Modelling morbidity for neglected tropical diseases: the long and winding road from cumulative exposure to long-term pathology

“…Parameterizing the functional form for the dose–response relationship for NTD morbidities associated with cumulative exposure is, however, complicated by challenges in gathering data that capture the history of exposure at the individual level. Self-reporting of intensity and duration of smoking is generally used in studies of smoking exposure, and for some parasitic diseases, such as cysticercosis, analysis of experimental infections in animals with known exposures has enabled quantification of the dose–response relationship for a range of clinical outcomes [ 34 ]. Neither self-reported exposure nor precise infectious dose data are practicable for most NTDs, but in some cases indirect measures of infection intensity can be used to infer infection history.…”