The powdered formula market is large and growing, with sales and manufacturing increasing by 120% between 2012 and 2021. With this growing market, there must come an increasing emphasis on maintaining a high standard of hygiene to ensure a safe product. In particular, Cronobacter species pose a risk to public health through their potential to cause severe illness in susceptible infants who consume contaminated powdered infant formula (PIF). Assessment of this risk is dependent on determining prevalence in PIF-producing factories, which can be challenging to measure with the heterogeneity observed in the design of built process facilities. There is also a potential risk of bacterial growth occurring during rehydration, given the observed persistence of Cronobacter in desiccated conditions. In addition, novel detection methods are emerging to effectively track and monitor Cronobacter species across the food chain. This review will explore the different vehicles that lead to Cronobacter species’ environmental persistence in the food production environment, as well as their pathogenicity, detection methods and the regulatory framework surrounding PIF manufacturing that ensures a safe product for the global consumer.

Objectives: The aim of this study was to conduct a scoping review of estimates of the relative infectiousness of asymptomatic persons infected with SARS-CoV-2 compared with symptomatic individuals. Design: Rapid scoping review of literature available until 8 ^th April 2020. Setting: International studies on the infectiousness of individuals infected with SARS-CoV-2 Participants: Studies were selected for inclusion if they defined asymptomatics as a separate cohort distinct from pre-symptomatics and if they provided a quantitative measure of the infectiousness of asymptomatics relative to symptomatics. Primary outcome measures: The relative number of secondary cases produced by an average primary case, the relative probability of transmitting infection upon contact, and the degree of viral shedding. Results: Very few studies reported estimates of relative infectiousness of asymptomatic compared with symptomatic individuals. Significant differences exist in the definition of infectiousness. Viral shedding studies in general show no difference in shedding levels between symptomatic and asymptomatic individuals but are likely to be impacted by insufficient statistical power. Two contact tracing studies provided estimates of 0.7 and 1.0, but differences in approach and definition preclude comparison across the two studies. Finally, two modelling studies suggest a relative infectiousness of around 0.5 but one of these was more reflective of the infectiousness of undocumented rather than asymptomatic cases. Importantly, one contact tracing study showing a very low level of infectiousness of asymptomatic was not included in the analysis at this point due difficulties interpreting the reported findings. Conclusions: The present study highlights the need for additional studies in this area as a matter of urgency. For the purpose of epidemiological modelling, we cautiously suggest that at present, asymptomatics could be considered to have a degree of infectiousness which is about 0.40-0.70 that of symptomatics. However, it must be stressed that this suggestion comes from a very low evidence base and that estimates exist that are close to zero and close to 1. ARTICLE SUMMARY: Strengths and limitations of this study: - Differences in the definition of infectiousness and a low number of studies estimating this parameter negate the potential to provide a pooled quantitative estimate or relative infectiousness. - The present study highlights the need for additional studies in this area as a matter of urgency. - Several of the studies reviewed are in pre-print stage and are not peer-reviewed.

Listeria monocytogenes is a pathogen of considerable public health importance with a high case fatality. L. monocytogenes can grow at refrigeration temperatures and is of particular concern for ready-to-eat foods that require refrigeration. There is substantial interest in conducting and modeling shelf-life studies on L. monocytogenes, especially relating to storage temperature. Growth model parameters are generally estimated from constant-temperature growth experiments. Traditionally, first-order and second-order modeling (or primary and secondary) of growth data has been done sequentially. However, omnibus modeling, using a mixed-effects nonlinear regression approach, can model a full dataset covering all experimental conditions in one step. This study compared omnibus modeling to conventional sequential first-order/second-order modeling of growth data for five strains of L. monocytogenes. The omnibus model coupled a Huang primary model for growth with secondary models for growth rate and lag phase duration. First-order modeling indicated there were small significant differences in growth rate depending on the strain at all temperatures. Omnibus modeling indicated smaller differences. Overall, there was broad agreement between the estimates of growth rate obtained by the first-order and omnibus modeling. Through an appropriate choice of fixed and random effects incorporated in the omnibus model, potential errors in a dataset from one environmental condition can be identified and explored.

Norovirus in oysters is a significant food safety risk. A recent ISO detection method allows for reliable and repeatable estimates of norovirus concentrations in pooled samples, but there is insufficient data to estimate a distribution of copies per animal from this. The spread of norovirus accumulated across individual oysters is useful for risk assessment models. Six sets of thirty individual Crassostrea gigas oysters were tested for norovirus concentration levels by reverse-transcription quantitative PCR (RT-qPCR): three from a commercial harvest site, and three post-depuration. Five sets had norovirus GII means above the limit of quantification (LOQ), and one below the LOQ, but above the limit of detection. No norovirus GI was detected in pooled tests, and individual oysters were not tested for norovirus GI. Depuration was shown to reduce the mean concentration of GII copies, but not to affect the shape of the distribution around the mean. Deconvoluting the uncertainty of the method, the coefficient of variation was stationary (0.45 +/- 0.2). The best fit distribution was either a lognormal distribution or a gamma. Multiplying these distributions by the weight of oyster digestive tissues gave an estimate for the count mean. This was used as the parameter lambda in three compound Poisson distributions: Poisson-lognormal, Poisson-gamma, and Poisson-K. No model was found to fit better than the others, with advantages for each. All three could be used in future risk assessments. Preliminary validation of sampling uncertainty using repeated testing data from a previous study suggests that these results have predictive power.

Background: Reliable estimates of the incubation period are important for decision making around the control of infectious diseases. Knowledge of the incubation period distribution can be used directly to inform decision-making or as inputs into mathematical models. Objectives: The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation periods of COVID-19. Design: Rapid systematic review and meta-analysis of observational research Data sources: Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched. The search was not limited to peer-reviewed published data, but also included pre-print articles. Study appraisal and synthesis methods: Studies were selected for meta-analysis if they reported either the parameters and confidence intervals of the distributions fit to the data, or sufficient information to facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of the parameters of this distribution. Results: The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean was 5.8 (5.01, 6.69 days). It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates resulted in a median incubation period of 5.1 (4.5, 5.8) days, whereas the 95 ^th percentile was 11.6 (9.5, 14.2) days. Conclusions and implications: The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Finally, we present an RShiny app that facilitates updating these estimates as new data become available. ARTICLE SUMMARY: Strengths and limitations of this study: This study provides a pooled estimate of the distribution of incubation periods which may be used in subsequent modelling studies or to inform decision-making This estimate will need to be revisited as subsequent data become available. We present an RShiny app to allow the meta-analysis to be updated with new estimates