Journal of the Royal Statistical Society Series A-Statistics in Society最新文献

{"title":"Assessing the effect of school closures on the spread of COVID-19 in Zurich","authors":"Maria Bekker-Nielsen Dunbar, Felix Hofmann, Leonhard Held, the SUSPend modelling consortium","doi":"10.1111/rssa.12910","DOIUrl":"10.1111/rssa.12910","url":null,"abstract":"The effect of school closure on the spread of COVID‐19 has been discussed intensively in the literature and the news. To capture the interdependencies between children and adults, we consider daily age‐stratified incidence data and contact patterns between age groups which change over time to reflect social distancing policy indicators. We fit a multivariate time‐series endemic–epidemic model to such data from the Canton of Zurich, Switzerland and use the model to predict the age‐specific incidence in a counterfactual approach (with and without school closures). The results indicate a 17% median increase of incidence in the youngest age group (0–14 year olds), whereas the relative increase in the other age groups drops to values between 2% and 3%. We argue that our approach is more informative to policy makers than summarising the effect of school closures with time‐dependent effective reproduction numbers, which are difficult to estimate due to the sparsity of incidence counts within the relevant age groups.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S131-S142"},"PeriodicalIF":2.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80265940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Session 3 of the RSS Special Topic Meeting on Covid-19 Transmission: Replies to the discussion","authors":"Maria Bekker-Nielsen Dunbar, Felix Hofmann, Leonhard Held","doi":"10.1111/rssa.12985","DOIUrl":"10.1111/rssa.12985","url":null,"abstract":"Entire editions of academic journals are dedicated to infectious disease modelling efforts while proper use of data to inform the modelling has been emphasised only recently (e.g. Held et al., 2020). The importance of data deserves highlighting and it is noteworthy that one of the most detailed and often analysed data sets in the field dates back to a measles outbreak in 1861 (Aaby et al., 2021). Without useful data, we will not be able to estimate the susceptible and asymptomatic proportions of the population. Strengthening and improving national and intergovernmental (coordinated by bodies such as ECDC and WHO) disease surveillance and monitoring systems allows for improved early disease outbreak detection. Such disease surveillance systems include both mandatory case reporting of notifiable disease, sentinel surveillance systems, and also internet and news media, under the umbrella of epidemic intelligence services. Disease surveillance requires certain amounts of man power and resources to function and systems have seen increases in technological capacity in recent years (Groseclose & Buckeridge, 2017; Hulth et al., 2010). Resources needed for ‘infodemic’ management also reduces the amount of human effort available for surveillance activities. Time series of infectious disease cases typically arising from a surveillance system can easily be modelled using the framework we used and presented. However, if the underlying data is flawed, so too will be the outputs. We are cognisant of the adage ‘garbage in, garbage out’. While we are aware of many funding opportunities for COVID-19 modelling, it is unclear how much emergency grant support has been given to strengthening current and future data gathering and storing infrastructure. Utilising existing data mechanisms rather than ‘re-inventing the wheel’ is paramount. Relatedly, there has recently been an attempt at re-branding the data-focused parts of infectious disease surveillance as ‘outbreak analytics’ (Polonsky et al., 2019). In our own work examining the effect of travel restrictions to neighbouring regions on cases in Switzerland we have recently considered both Italian and French case data (see Grimée et al., 2022, for an initial analysis of some of the regions) and have experienced two matters that caused us to consider the data in further detail and not simply model it as-is. The first is that certain case counts in Italian regions show changes from one day to the next which seem unrealistic.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S158-S164"},"PeriodicalIF":2.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85780124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Steven Riley's discussion contribution to papers in Session 3 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11 June 2021","authors":"Steven Riley","doi":"10.1111/rssa.12981","DOIUrl":"10.1111/rssa.12981","url":null,"abstract":"<p>I congratulate Pellis and colleagues, and Dunbar and Held on their excellent papers describing a variety of mechanistic models of SARS-Cov-2 transmission, and more generally on their work to support policy formulation during the COVID-19 pandemic. Both papers address the difficulties of predicting and then evaluating the impact if non-pharmaceutical interventions (NPIs) against the transmission of severe respiratory pathogens. These are likely to remain key ongoing challenges for the analytical science of pandemic preparedness, with high demand from policy makers for accurate estimates of the epidemiological benefits of NPIs. Here, I would like to make one related methodological point.</p><p>There may be benefits in making the null hypotheses in mechanistic modelling studies of NPIs more explicit and more general. For example, models usually contain an underlying basic rate of transmissibility per unit time per infected individual, often denoted <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <annotation>$$ beta $$</annotation>\u0000 </semantics></math>. The parameter <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <annotation>$$ beta $$</annotation>\u0000 </semantics></math> is used to calculate the risk of infection per susceptible and is modified by other parameters to reflect differences in infectiousness, susceptibility and mixing (Keeling &amp; Rohani, <span>2011</span>). For example, when schools are closed, it may be assumed that mixing patterns for children change on that day and that the efficacy of school closures can be estimated by fitting a version of the model to incidence data which includes a free parameter describing the strength of change in mixing. However, this type of calculation is implicitly making the strong assumption that a step change on the day of the intervention is a good explanation for the overall pattern of changing transmissibility at that time, which may not be the case. It may be useful to explicitly represented <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 </mrow>\u0000 <annotation>$$ beta $$</annotation>\u0000 </semantics></math> as a smooth function of time in an alternative model, as is common practice for similar parameters in other analytical frameworks (Wood, <span>2017</span>), so that typical measures of parsimony can be used to assess the information contained in specific model fits when strong assumptions are made about the timing of interventions.</p>","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S148-S149"},"PeriodicalIF":2.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81060753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors' reply to the discussion of ‘A COVID-19 Model for Local Authorities of the United Kingdom’ by Mishra et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11 June 2021","authors":"Swapnil Mishra,&nbsp;James A. Scott,&nbsp;Daniel J. Laydon,&nbsp;Harrison Zhu,&nbsp;Neil M. Ferguson,&nbsp;Samir Bhatt,&nbsp;Seth Flaxman,&nbsp;Axel Gandy","doi":"10.1111/rssa.12977","DOIUrl":"10.1111/rssa.12977","url":null,"abstract":"&lt;p&gt;We are very grateful for the interesting and constructive comments about the papers discussed in this meeting. We will address the points pertaining to our paper.&lt;/p&gt;&lt;p&gt;Professor Gibson and Professor Nason stress that our model does not have NPIs or other information as covariates. We agree that ideally, one would like to do this. However, this conflicts with another goal of our model—to provide estimates on a very regular basis. Given the rapid decision-making and implementation of new measures, that varied substantially across the United Kingdom, often without exact precedent, it would have meant frequent adjustment of the model and collection and verification of, for example, NPIs for almost 400 areas on a daily basis, making it almost an implausible task without substantial time-commitment.&lt;/p&gt;&lt;p&gt;Professor Gibson also remarks that our paper does not show prior-predictive checks to validate the model—we have omitted more detailed model checks due to space constraints. Our R package Epidemia has a full suite to do model checks and we have used them to verify and tune our models. We do agree with Professor Gibson that individual-based models can be more useful than aggregate models. However, constraints around data availability and compute time makes running individual models on daily basis an unattainable task. Our main objective behind the framework was to have something that can be updated on an almost daily basis, so we opted for simplicity.&lt;/p&gt;&lt;p&gt;Professor Nason raises the point that there are instances of our estimates of &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}_t $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; projections are not overlapping with estimates of &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}_t $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; by Teh et al. in the same time period and how health officials should react to this. As Professor Nason, points out, it is not surprising that with models with different assumptions, sometimes conflicting estimates arise. This may actually be an advantage. If it is well understood how models differ this gives a more varied understanding of the epidemic in these places—for example, one model which relies more on spatial correlation would assume that a local outbreak will start spreading whereas another model with less s","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S110-S111"},"PeriodicalIF":2.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78950334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Bayesian inference of instantaneous reproduction numbers at fine spatial scales, with an application to mapping and nowcasting the Covid-19 epidemic in British local authorities","authors":"Yee Whye Teh,&nbsp;Bryn Elesedy,&nbsp;Bobby He,&nbsp;Michael Hutchinson,&nbsp;Sheheryar Zaidi,&nbsp;Avishkar Bhoopchand,&nbsp;Ulrich Paquet,&nbsp;Nenad Tomasev,&nbsp;Jonathan Read,&nbsp;Peter J. Diggle","doi":"10.1111/rssa.12971","DOIUrl":"10.1111/rssa.12971","url":null,"abstract":"&lt;p&gt;The spatio-temporal pattern of Covid-19 infections, as for most infectious disease epidemics, is highly heterogenous as a consequence of local variations in risk factors and exposures. Consequently, the widely quoted national-level estimates of reproduction numbers are of limited value in guiding local interventions and monitoring their effectiveness. It is crucial for national and local policy-makers, and for health protection teams, that accurate, well-calibrated and timely predictions of Covid-19 incidences and transmission rates are available at fine spatial scales. Obtaining such estimates is challenging, not least due to the prevalence of asymptomatic Covid-19 transmissions, as well as difficulties of obtaining high-resolution and high-frequency data. In addition, low case counts at a local level further confounds the inference for Covid-19 transmission rates, adding unwelcome uncertainty.&lt;/p&gt;&lt;p&gt;In this paper we develop a hierarchical Bayesian method for inference of transmission rates at fine spatial scales. Our model incorporates both temporal and spatial dependencies of local transmission rates in order to share statistical strength and reduce uncertainty. It also incorporates information about population flows to model potential transmissions across local areas. A simple approach to posterior simulation quickly becomes computationally infeasible, which is problematic if the system is required to provide timely predictions. We describe how to make posterior simulation for the model efficient, so that we are able to provide daily updates on epidemic developments.&lt;/p&gt;&lt;p&gt;The results can be found at our web site https://localcovid.info, which is updated daily to display estimated instantaneous reproduction numbers and predicted case counts for the next weeks, across local authorities in Great Britain. The codebase updating the web site can be found at https://github.com/oxcsml/Rmap. We hope that our methodology and web site will be of interest to researchers, policy-makers and the public alike, to help identify upcoming local outbreaks and to aid in the containment of Covid-19 through both public health measures and personal decisions taken by the general public.&lt;/p&gt;&lt;p&gt;Our model is applied to publicly available daily counts of positive test results reported under the combined Pillars 1 (NHS and PHE) and 2 (commercial partners) of the UK's Covid-19 testing strategy.1 The data are available for 312 lower-tier local authorities (LTLAs) in England, 14 NHS Health Boards in Scotland (each covering multiple local authorities) and 22 unitary local authorities in Wales, for a total of &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;348&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ n=348 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; local areas. The data are daily counts of lab-confirmed (PCR swab) cases presented by specimen d","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S65-S85"},"PeriodicalIF":2.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72391029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal analysis of exchanges of support between parents and children in the UK","authors":"F. Steele, Siliang Zhang, E. Grundy, T. Burchardt","doi":"10.1093/jrsssa/qnad110","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad110","url":null,"abstract":"\u0000 We consider how exchanges of support between parents and adult children vary by demographic and socio-economic characteristics and examine evidence for reciprocity in transfers and substitution between practical and financial support. Using data from the UK Household Longitudinal Study 2011–19, repeated measures of help given and received are analysed jointly using multivariate random effects probit models. Exchanges are considered from both a child and parent perspective. In the latter case, we propose a novel approach to account for the correlation between mother and father reports and develop an efficient Markov chain Monte Carlo algorithm suitable for large datasets with multiple outcomes.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"62 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77966879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Jasmina Panovska-Griffiths' discussion contribution to papers in Session 3 of the Royal Statistical Society's special topic meeting on COVID-19 transmission: 11 June 2021","authors":"Jasmina Panovska-Griffiths","doi":"10.1111/rssa.12982","DOIUrl":"10.1111/rssa.12982","url":null,"abstract":"&lt;p&gt;The effective reproduction number &lt;i&gt;R&lt;/i&gt; has been a headline epidemic metric since the onset of the COVID-19 pandemic. &lt;i&gt;R&lt;/i&gt; measures the number of secondary infections arising from an existing infection. At the onset of a new disease, in a fully susceptible population, &lt;i&gt;R&lt;/i&gt; is the basic reproduction number &lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}_0 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; that describes the number of secondary infections stemming from an initial infected case. As the epidemic progresses, &lt;i&gt;R&lt;/i&gt; reflects the number of secondary infections generated in a population comprising susceptible, exposed, infected, recovered and immune individuals. The growth rate &lt;i&gt;r&lt;/i&gt;, another widely used epidemic metric throughout the COVID-19 pandemic, represents the rate at which the epidemic is growing during the exponential phase of epidemic growth. While &lt;i&gt;R&lt;/i&gt; can be thought as reflective of the level of transmission, &lt;i&gt;r&lt;/i&gt; can be thought as reflective of the transmission speed.&lt;/p&gt;&lt;p&gt;Since the onset of the SARS-CoV-2 epidemic, epidemiological models, calibrated against data on infections, hospital admissions and occupancy and deaths related to COVID-19, have been widely used to generate outcome metrics such as &lt;i&gt;R&lt;/i&gt; or &lt;i&gt;r&lt;/i&gt;. These have been used to inform the status of the epidemic with &lt;i&gt;R&lt;/i&gt; increasing above 1, and analogously, &lt;i&gt;r&lt;/i&gt; above 0, suggesting that the epidemic is growing exponentially with the emerging virus spreading fast. Tracking whether &lt;i&gt;R&lt;/i&gt; and &lt;i&gt;r&lt;/i&gt; are crossing these thresholds can inform if the epidemic is in a growing or shrinking phase, or the impact of imposed control measures at the time. Generating such metrics across an ensemble of models—which may be different in methodology or on the data they use to fit against—allows a consensus value of &lt;i&gt;R&lt;/i&gt; and &lt;i&gt;r&lt;/i&gt; to be derived. The consensus value represents an average outcome across models, and taking a combination of models rather than one model derivative value, allows for uncertainty to be accounted for in the epidemic metrics.&lt;/p&gt;&lt;p&gt;Generating consensus epidemic metrics from models, while useful in informing the epidemic status, has three challenges related to:&lt;/p&gt;&lt;p&gt;Challenge 1: Understanding how to interpret &lt;i&gt;R&lt;/i&gt; and &lt;i&gt;r&lt;/i&gt; across different models&lt;/p&gt;&lt;p&gt;Challenge 2: Understanding how &lt;i&gt;R&lt;/i&gt; and &lt;i&gt;r&lt;/i&gt; are statistically correlated within and across different models&lt;/p&gt;&lt;p&gt;Challenge 3: Understanding whether &lt;i&gt;R&lt;/i&gt; and &lt;i&gt;r&lt;/i&gt; are the most reliable metrics as the epidemic progresses and different interventions are employed&lt;/p&gt;&lt;p&gt;On the first challenge, although &lt;i&gt;R&lt;/i&gt; and &lt;i&gt;r&lt;/i&gt; describe broadly similar model outcomes, their exact definition depends on the model structure. For example, in agent-","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S150-S151"},"PeriodicalIF":2.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12982","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83890815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"‘Introduction’","authors":"Peter J. Diggle,&nbsp;Sylvia Richardson","doi":"10.1111/rssa.12883","DOIUrl":"https://doi.org/10.1111/rssa.12883","url":null,"abstract":"&lt;p&gt;The current COVID-19 pandemic has brought to wide attention how important the statistics discipline is to public health monitoring and decision-making, from the design of studies through the analysis of data to the reporting of results and their correct interpretation. In the United Kingdom, data visualisations, estimation of the current state of the epidemic and model-based predictions have featured prominently in daily briefings by the country's most senior politicians and their scientific advisers.&lt;/p&gt;&lt;p&gt;As part of its response to the pandemic, The Royal Statistical Society established at the end of March 2020 a COVID-19 Task Force, whose aim is ‘to ensure that the RSS can contribute its collective expertise to UK national and devolved governments and public bodies, regarding statistical issues during the COVID-19 pandemic’. For more information on the Task Force, see: https://rss.org.uk/policy-campaigns/policy-groups/covid-19-task-force/.&lt;/p&gt;&lt;p&gt;Throughout the current epidemic there has been considerable focus on the use of the reproduction number or ‘R-number’ (roughly speaking the expected number of additional cases per primary case at time &lt;i&gt;t&lt;/i&gt;) as a headline summary of the state of the epidemic. Opinions vary on the value of this summary measure, not least because it reduces the transmissibility of infection to a single number. The reality is much more complex, with transmissibility depending on a range of natural and social environmental factors that vary across different parts of a country and across identifiable subpopulations, as well as being influenced by earlier interventions and acquired immunity.&lt;/p&gt;&lt;p&gt;The goal of this Special Topic Meeting, held over three sessions on 9 and 11 June 2021, was to discuss statistical issues around the general topic of COVID-19 transmission, including but not restricted to local (to particular areas or subpopulations) versions of the R-number. In the tradition of RSS Discussion Meetings, the programme consisted of oral presentations based on peer-reviewed papers that were made available beforehand, followed by invited and open discussion contributions and responses from the authors. The complete proceedings are now published as a permanent record of some of the many significant contributions that statistical modellers have made to tracking the evolution of the COVID-19 pandemic in real time, with a view to providing sound evidence for policy makers.&lt;/p&gt;&lt;p&gt;The &lt;i&gt;first session&lt;/i&gt; brought complementary views from Parag et al. (&lt;span&gt;2022&lt;/span&gt;) and Jewell and Lewnard (&lt;span&gt;2022&lt;/span&gt;) on the value and common misunderstandings of R as a summary measure, its sensitivity to model assumptions, inherent difficulties of estimation and its use or misuse for guiding public health interventions. These authors further discussed the usefulness of additionally quantifying time changes in R and of estimating growth rates. Coffeng and de Vlas's paper (&lt;span&gt;2022&lt;/span&gt;) focused on demonstrating the impact","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S3-S4"},"PeriodicalIF":2.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12883","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137737544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forks over knives: Predictive inconsistency in criminal justice algorithmic risk assessment tools","authors":"Travis Greene,&nbsp;Galit Shmueli,&nbsp;Jan Fell,&nbsp;Ching-Fu Lin,&nbsp;Han-Wei Liu","doi":"10.1111/rssa.12966","DOIUrl":"10.1111/rssa.12966","url":null,"abstract":"<p>Big data and algorithmic risk prediction tools promise to improve criminal justice systems by reducing human biases and inconsistencies in decision-making. Yet different, equally justifiable choices when developing, testing and deploying these socio-technical tools can lead to disparate predicted risk scores for the same individual. Synthesising diverse perspectives from machine learning, statistics, sociology, criminology, law, philosophy and economics, we conceptualise this phenomenon as <i>predictive inconsistency</i>. We describe sources of predictive inconsistency at different stages of algorithmic risk assessment tool development and deployment and consider how future technological developments may amplify predictive inconsistency. We argue, however, that in a diverse and pluralistic society we should not expect to completely eliminate predictive inconsistency. Instead, to bolster the legal, political and scientific legitimacy of algorithmic risk prediction tools, we propose identifying and documenting relevant and reasonable ‘forking paths’ to enable quantifiable, reproducible multiverse and specification curve analyses of predictive inconsistency at the individual level.</p>","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S2","pages":"S692-S723"},"PeriodicalIF":2.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45203559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peter J. Diggle’s discussion contribution to papers in Session 1 of the Royal Statistical Society’s Special Topic Meeting on COVID-19 transmission: 9 June 2021","authors":"Peter J. Diggle","doi":"10.1111/rssa.12888","DOIUrl":"https://doi.org/10.1111/rssa.12888","url":null,"abstract":"<p>My comments relate to how and why one might want to make inferences about a spatially and temporally varying growth rate.</p><p>Likelihood-based parameter estimation is straightforward, and the joint predictive distribution for the values of <i>S</i>(<i>x</i>, <i>t</i>) at any combination of locations and times follows by an application of Bayes’ Theorem. This could be thought of as a principled approach to linear smoothing that naturally incorporates whatever combination of covariate effects a particular application merits, whilst avoiding mechanistic assumptions that might be hard to validate.</p><p>As to the “why,” the arguments for a more mechanistic approach rest on the availability of well-founded scientific knowledge of the disease in question that can usefully add to the empirical information provided by the data. This suggests that mechanistic modelling is most convincing for epidemics evolving in a relatively homogeneous, natural environment that is perhaps typical of diseases in poor communities within low-income countries where the opportunities for effective policy interventions and consequent behavioural changes may be more limited than in wealthy societies. Empirical statistical modelling of the kind suggested here is arguably a better choice when the epidemic is subject to a complex combination of formal (policy-driven) and informal (behaviourally responsive) changes over space and time, and when the objective is to build a general-purpose, spatially refined, real-time surveillance system, in which a disease-agnostic model can be fitted to a range of important health outcomes using disease-specific covariates and their associated parameter estimates. A primary aim of such a system would be to provide early warnings of anomalous patterns over a range of public health outcomes.</p><p>I believe that the absence of such a system did us no favours in the early months of 2020. I hope very much that public health agencies will be given the resources they need to remedy this before the next public health crisis hits us.</p>","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"185 S1","pages":"S47-S48"},"PeriodicalIF":2.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/rssa.12888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137737546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}