Patterns of individual compliance with anthelmintic treatment for soil-transmitted helminth infections in southern Ethiopia over six rounds of community-wide mass drug administration.

BACKGROUND: The mainstay of soil-transmitted helminth (STH) control is repeated mass drug administration (MDA) of anthelmintics to endemic populations. Individual longitudinal compliance treatment patterns are important for identifying pockets of infected individuals who remain untreated and serve as infection reservoirs. METHODS: The Geshiyaro Project censused the study population in Wolaita, Ethiopia at baseline in 2018. Individual longitudinal compliance was recorded for six rounds of community-wide MDA (cMDA). The probability distribution of treatment frequency was analysed by age and gender stratifications. Probabilities of transmission interruption for different compliance patterns were calculated using an individual-based stochastic model of Ascaris lumbricoides transmission. RESULTS: The never-treated (0.42%) population was smaller than expected from a random positive binomial distribution. The observed compliance frequency was well described by the beta-binomial distribution. Preschool-age children (odds ratio [OR] 10.1 [95% confidence interval {CI} 6.63 to 15.4]) had the highest never-treated proportion of the age groups. Conversely, school-age children (SAC) and adults (OR 1.03 [95% CI 0.98 to 1.09]) had the highest always-treated proportion of the age groups. CONCLUSIONS: The study reports the largest dataset of individual longitudinal compliance to cMDA for STH control. Clear pattens are shown in the age-dependent distribution of individual compliance behaviour. The impact of compliance on the probability of elimination is significant, highlighting the importance of recording the full frequency distribution, not just the never-treated proportion.

Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.