COVID-19 transmission between the community and meat processing plants in Ireland: A retrospective modelling study.

Outbreaks of COVID-19 in meat processing plants (MPPs) were recorded globally throughout the pandemic. There was speculation these outbreaks resulted in dissemination of COVID-19 throughout the surrounding county leading to high incidence rates. We aimed to investigate the dynamics of spread between MPPs and their surrounding counties. In this retrospective longitudinal study, data were collected on the number and size of outbreaks in 33 MPPs and county infections in Ireland between March 2020 and May 2021. These data were used to investigate the relationship between outbreaks in MPPs and county infection rates through statistical analysis, and the development of a novel SEIR model. We found an association between the number of MPPs present in a county and county incidence rates, however, incidence rates in the counties did not increase as a consequence of an outbreak in an MPP. The model results indicate that county incidence rates in the weeks prior to an MPP outbreak could reliably predict the size of that outbreak in a plant, r(49) = 0·62, p < 0·0001, RMSD = 5·6. In Ireland, outbreaks in MPPs were strongly correlated with high levels of infection in the surrounding county, rather than being a driver of infection in the county. The modified SEIR model described here can provide an explanation of the generative process required to cause outbreaks of the size and scale that occur in MPPs.

A model of behavioural response to risk accurately predicts the statistical distribution of COVID-19 infection and reproduction numbers.

One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people's behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This homeostatic response is active until approximate herd immunity is reached: in this domain the model predicts that the reproduction rate R will be centred around a median of 1, that proportional change in infection numbers will follow the standard Cauchy distribution with location and scale parameters 0 and 1, and that high infection numbers will follow a power-law frequency distribution with exponent 2. To test these predictions we used worldwide COVID-19 data from 1st February 2020 to 30th June 2022 to calculate [Formula: see text] confidence interval estimates across countries for these R, location, scale and exponent parameters. The resulting median R estimate was [Formula: see text] (predicted value 1) the proportional change location estimate was [Formula: see text] (predicted value 0), the proportional change scale estimate was [Formula: see text] (predicted value 1), and the frequency distribution exponent estimate was [Formula: see text] (predicted value 2); in each case the observed estimate agreed with model predictions.

Random variation and systematic biases in probability estimation.

A number of recent theories have suggested that the various systematic biases and fallacies seen in people's probabilistic reasoning may arise purely as a consequence of random variation in the reasoning process. The underlying argument, in these theories, is that random variation has systematic regressive effects, so producing the observed patterns of bias. These theories typically take this random variation as a given, and assume that the degree of random variation in probabilistic reasoning is sufficiently large to account for observed patterns of fallacy and bias; there has been very little research directly examining the character of random variation in people's probabilistic judgement. We describe 4 experiments investigating the degree, level, and characteristic properties of random variation in people's probability judgement. We show that the degree of variance is easily large enough to account for the occurrence of two central fallacies in probabilistic reasoning (the conjunction fallacy and the disjunction fallacy), and that level of variance is a reliable predictor of the occurrence of these fallacies. We also show that random variance in people's probabilistic judgement follows a particular mathematical model from frequentist probability theory: the binomial proportion distribution. This result supports a model in which people reason about probabilities in a way that follows frequentist probability theory but is subject to random variation or noise.