A statistical modelling approach for source attribution meta-analysis of sporadic infection with foodborne pathogens

Numerous source attribution studies for foodborne pathogens based on epidemiological and microbiological methods are available. These studies provide empirical data for modelling frameworks that synthetize the quantitative evidence at our disposal and reduce reliance on expert elicitations. Here, we develop a statistical model within a Bayesian estimation framework to integrate attribution estimates from expert elicitations with estimates from microbial subtyping and case-control studies for sporadic infections with four major bacterial zoonotic pathogens in the Netherlands (Campylobacter, Salmonella, Shiga toxin-producing E. coli [STEC] O157 and Listeria). For each pathogen, we pooled the published fractions of human cases attributable to each animal reservoir from the microbial subtyping studies, accounting for the uncertainty arising from the different typing methods, attribution models, and year(s) of data collection. We then combined the population attributable fractions (PAFs) from the case-control studies according to five transmission pathways (domestic food, environment, direct animal contact, human-human transmission and travel) and 11 groups within the foodborne pathway (beef/lamb, pork, poultry meat, eggs, dairy, fish/shellfish, fruit/vegetables, beverages, grains, composite foods and food handlers/vermin). The attribution estimates were biologically plausible, allowing the human cases to be attributed in several ways according to reservoirs, transmission pathways and food groups. All pathogens were predominantly foodborne, with Campylobacter being mostly attributable to the chicken reservoir, Salmonella to pigs (albeit closely followed by layers), and Listeria and STEC O157 to cattle. Food-wise, the attributions reflected those at the reservoir level in terms of ranking. We provided a modelling solution to reach consensus attribution estimates reflecting the empirical evidence in the literature that is particularly useful for policy-making and is extensible to other pathogens and domains

A statistical modelling approach for source attribution meta-analysis of sporadic infection with foodborne pathogens

Numerous source attribution studies for foodborne pathogens based on epidemiological and microbiological methods are available. These studies provide empirical data for modelling frameworks that synthetize the quantitative evidence at our disposal and reduce reliance on expert elicitations. Here, we develop a statistical model within a Bayesian estimation framework to integrate attribution estimates from expert elicitations with estimates from microbial subtyping and case-control studies for sporadic infections with four major bacterial zoonotic pathogens in the Netherlands (Campylobacter, Salmonella, Shiga toxin-producing E. coli [STEC] O157 and Listeria). For each pathogen, we pooled the published fractions of human cases attributable to each animal reservoir from the microbial subtyping studies, accounting for the uncertainty arising from the different typing methods, attribution models, and year(s) of data collection. We then combined the population attributable fractions (PAFs) from the case-control studies according to five transmission pathways (domestic food, environment, direct animal contact, human-human transmission and travel) and 11 groups within the foodborne pathway (beef/lamb, pork, poultry meat, eggs, dairy, fish/shellfish, fruit/vegetables, beverages, grains, composite foods and food handlers/vermin). The attribution estimates were biologically plausible, allowing the human cases to be attributed in several ways according to reservoirs, transmission pathways and food groups. All pathogens were predominantly foodborne, with Campylobacter being mostly attributable to the chicken reservoir, Salmonella to pigs (albeit closely followed by layers), and Listeria and STEC O157 to cattle. Food-wise, the attributions reflected those at the reservoir level in terms of ranking. We provided a modelling solution to reach consensus attribution estimates reflecting the empirical evidence in the literature that is particularly useful for policy-making and is extensible to other pathogens and domains

Detection of opsonizing antibodies directed against a recently circulating Bordetella pertussis strain in paired plasma samples from symptomatic and recovered pertussis patients.

Correlates of protection (CoPs) against the highly contagious respiratory disease whooping cough, caused by Bordetella pertussis, remain elusive. Characterizing the antibody response to this pathogen is essential towards identifying potential CoPs. Here, we evaluate levels, avidity and functionality of B. pertussis-specific-antibodies from paired plasma samples derived from symptomatic and recovered pertussis patients, as well as controls. Natural infection is expected to induce protective immunity. IgG levels and avidity to nine B. pertussis antigens were determined using a novel multiplex panel. Furthermore, opsonophagocytosis of a B. pertussis clinical isolate by neutrophils was measured. Findings indicate that following infection, B. pertussis-specific antibody levels of (ex-) pertussis patients waned, while the avidity of antibodies directed against the majority of studied antigens increased. Opsonophagocytosis indices decreased upon recovery, but remained higher than controls. Random forest analysis of all the data revealed that 28% of the opsonophagocytosis index variances could be explained by filamentous hemagglutinin- followed by pertussis toxin-specific antibodies. We propose to further explore which other B. pertussis-specific antibodies can better predict opsonophagocytosis. Moreover, other B. pertussis-specific antibody functions as well as the possible integration of these functions in combination with other immune cell properties should be evaluated towards the identification of CoPs against pertussis

Test, trace, isolate:Evidence for declining SARS-CoV-2 PCR sensitivity in a clinical cohort

Real-time reverse transcription-polymerase chain reaction (RT-PCR) on upper respiratory tract (URT) samples is the primary method to diagnose SARS-CoV-2 infections and guide public health measures, with a supportive role for serology. We reinforce previous findings on limited sensitivity of PCR testing, and solidify this fact by statistically utilizing a firm basis of multiple tests per individual. We integrate stratifications with respect to several patient characteristics such as severity of disease and time since onset of symptoms. Bayesian statistical modelling was used to retrospectively determine the sensitivity of RT-PCR using SARS-CoV-2 serology in 644 COVID-19-suspected patients with varying degrees of disease severity and duration. The sensitivity of RT-PCR ranged between 80% − 95%; increasing with disease severity, it decreased rapidly over time in mild COVID-19 cases. Negative URT RT-PCR results should be interpreted in the context of clinical characteristics, especially with regard to containment of viral transmission based on ‘test, trace and isolate’

Speed versus coverage trade off in targeted interventions during an outbreak

Which case-based intervention measures should be applied during an epidemic outbreak depends on how timely they can be applied and how effective they are. During the course of each individual's infection, the earlier control measures are applied on him/her the more effectively further disease spread can be prevented. However, quick implementation can lead to loss of efficacy or coverage, e.g., when individuals are targeted based on rapid but poorly sensitive diagnostic tests in place of slower but accurate PCR tests. To analyse this trade off between speed and coverage we used stochastic models considering how the individual reproduction density is modified by interventions. We took as example the case-based intervention strategy employed in the Netherlands during the beginning of the H1N1 pandemic. Suspected cases were isolated and samples were collected for PCR diagnosis. In case of positive diagnosis, antiviral drugs were provided to contacts as post-exposure prophylaxis. At the time there were also rapid influenza diagnostic tests (RIDTs) available which provided results within an hour after sample collection compared to a median of 2.7 days for PCR tests, but they were less sensitive. We studied how interventions based on RIDTs with various sensitivities affect the outbreak size and how these compare to PCR diagnosis based interventions. Using an intervention based on a bedside RIDT with 60% detection ratio or a laboratory RIDT with 70% detection ratio is as effective as the most effective PCR-diagnosis based intervention. Relative performances of interventions are not dependent on the basic reproduction number R0 but only on distributions of individual reproduction density and of delay periods. The individual reproduction density combines R0 and infection time distribution, both crucial in determining the impact of case-based interventions during epidemic outbreaks

Impact of age and vaccination history on long-term serological responses after symptomatic B. pertussis infection, a high dimensional data analysis

Capturing the complexity and waning patterns of co-occurring immunoglobulin (Ig) responses after clinical B. pertussis infection may help understand how the human host gradually loses protection against whooping cough. We applied bi-exponential modelling to characterise and compare B. pertussis specific serological dynamics in a comprehensive database of IgG, IgG subclass and IgA responses to Ptx, FHA, Prn, Fim2/3 and OMV antigens of (ex-) symptomatic pertussis cases across all age groups. The decay model revealed that antigen type and age group were major factors determining differences in levels and kinetics of Ig (sub) classes. IgG-Ptx waned fastest in all age groups, while IgA to Ptx, FHA, Prn and Fim2/3 decreased fast in the younger but remained high in older (ex-) cases, indicating an age-effect. While IgG1 was the main IgG subclass in response to most antigens, IgG2 and IgG3 dominated the anti-OMV response. Moreover, vaccination history plays an important role in post-infection Ig responses, demonstrated by low responsiveness to Fim2/3 in unvaccinated elderly and by elevated IgG4 responses to multiple antigens only in children primed with acellular pertussis vaccine (aP). This work highlights the complexity of the immune response to this re-emerging pathogen and factors determining its Ig quantity and quality