Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation

This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross-sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness-of-fit check. This new method not only matches the computational efficiency of preceding likelihood-based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within-host model closely align with the observed distributions from cross-sectional samples of a well-characterized population. Our findings mirror those of likelihood-based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood-based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology

Dose-response relationship of Ralstonia solanacearum and potato in greenhouse and in vitro experiments

Ralstonia solanacearum is the causative agent of bacterial wilt of potato and other vegetable crops. Contaminated irrigation water contributes to the dissemination of this pathogen but the exact concentration or biological threshold to cause an infection is unknown. In two greenhouse experiments, potted potato plants (Solanum tuberosum) were exposed to a single irrigation with 50 mL water (non-invasive soil-soak inoculation) containing no or 102 – 108 CFU/mL R. solanacearum. The disease response of two cultivars, Kondor and HB, were compared. Disease development was monitored over a three-month period after which stems, roots and tubers of asymptomatic plants were analyzed for latent infections. First wilting symptoms were observed 15 days post inoculation in a plant inoculated with 5x109 CFU and a mean disease index was used to monitor disease development over time. An inoculum of 5x105 CFU per pot (1.3x102 CFU/g soil) was the minimum dose required to cause wilting symptoms, while one latent infection was detected at the lowest dose of 5x102 CFU per pot (0.13 CFU/g). In a second set of experiments, stem-inoculated potato plants grown in vitro were used to investigate the dose-response relationship under optimal conditions for pathogen growth and disease development. Plants were inoculated with doses between 0.5 and 5x105 CFU/plant which resulted in visible symptoms at all doses. The results led to a dose-response model describing the relationship between R. solanacearum exposure and probability of infection or illness of potato plants. Cultivar Kondor was more susceptible to brown-rot infections than HB in greenhouse experiments while there was no significant difference between the dose-response models of both cultivars in in vitro experiments. The ED50 for infection of cv Kondor was 1.1x107 CFU. Results can be used in management strategies aimed to reduce or eliminate the risk of bacterial wilt infection when using treated water in irrigation