Learning transmission dynamics modelling of COVID-19 using comomodels

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordThe COVID-19 epidemic continues to rage in many parts of the world. In the UK alone, an array of mathematical models have played a prominent role in guiding policymaking. Whilst considerable pedagogical material exists for understanding the basics of transmission dynamics modelling, there is a substantial gap between the relatively simple models used for exposition of the theory and those used in practice to model the transmission dynamics of COVID-19. Understanding these models requires considerable prerequisite knowledge and presents challenges to those new to the field of epidemiological modelling. In this paper, we introduce an open-source R package, comomodels, which can be used to understand the complexities of modelling the transmission dynamics of COVID-19 through a series of differential equation models. Alongside the base package, we describe a host of learning resources, including detailed tutorials and an interactive web-based interface allowing dynamic investigation of the model properties. We then use comomodels to illustrate three key lessons in the transmission of COVID-19 within R Markdown vignette

Changing trends in mastitis

<p>Abstract</p> <p>The global dairy industry, the predominant pathogens causing mastitis, our understanding of mastitis pathogens and the host response to intramammary infection are changing rapidly. This paper aims to discuss changes in each of these aspects. Globalisation, energy demands, human population growth and climate change all affect the dairy industry. In many western countries, control programs for contagious mastitis have been in place for decades, resulting in a decrease in occurrence of <it>Streptococcus agalactiae </it>and <it>Staphylococcus aureus </it>mastitis and an increase in the relative impact of <it>Streptococcus uberis </it>and <it>Escherichia coli </it>mastitis. In some countries, <it>Klebsiella </it>spp. or <it>Streptococcus dysgalactiae </it>are appearing as important causes of mastitis. Differences between countries in legislation, veterinary and laboratory services and farmers' management practices affect the distribution and impact of mastitis pathogens. For pathogens that have traditionally been categorised as contagious, strain adaptation to human and bovine hosts has been recognised. For pathogens that are often categorised as environmental, strains causing transient and chronic infections are distinguished. The genetic basis underlying host adaptation and mechanisms of infection is being unravelled. Genomic information on pathogens and their hosts and improved knowledge of the host's innate and acquired immune responses to intramammary infections provide opportunities to expand our understanding of bovine mastitis. These developments will undoubtedly contribute to novel approaches to mastitis diagnostics and control.</p

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

This is the author accepted manuscript.Data Accessibility: The user-friendly web interface for estimating Rt while accounting for different transmission risks from local and imported cases can be found at https://sabs-r3-epidemiology.github.io/branchpro. All data and computing scripts required to reproduce the results presented here are available at https://github.com/SABS-R3-Epidemiology/transmission-heterogeneity-results. The source code of the branchpro Python package, which we developed to perform the inference presented in this article, is available at https://github.com/SABS-R3-Epidemiology/branchpro. No restrictions exist on data availability.During infectious disease outbreaks, inference of summary statistics characterising transmission is essential for planning interventions. An important metric is the time-dependent reproduction number ( ), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals’ contact networks. While it is possible to estimate a single population-wide , this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate , made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect estimates significantly, with implications for interventions. This emphasises the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate .Engineering and Physical Sciences Research Council (EPSRC)Clarendon FundUKRIRoche Pharmaceutical Researc