Challenges on the interaction of models and policy for pandemic control.

The COVID-19 pandemic has seen infectious disease modelling at the forefront of government decision-making. Models have been widely used throughout the pandemic to estimate pathogen spread and explore the potential impact of different intervention strategies. Infectious disease modellers and policymakers have worked effectively together, but there are many avenues for progress on this interface. In this paper, we identify and discuss seven broad challenges on the interaction of models and policy for pandemic control. We then conclude with suggestions and recommendations for the future.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Meticillin-resistant Staphylococcus aureus with a novel mecA homologue in human and bovine populations in the UK and Denmark: a descriptive study.

BACKGROUND: Animals can act as a reservoir and source for the emergence of novel meticillin-resistant Staphylococcus aureus (MRSA) clones in human beings. Here, we report the discovery of a strain of S aureus (LGA251) isolated from bulk milk that was phenotypically resistant to meticillin but tested negative for the mecA gene and a preliminary investigation of the extent to which such strains are present in bovine and human populations. METHODS: Isolates of bovine MRSA were obtained from the Veterinary Laboratories Agency in the UK, and isolates of human MRSA were obtained from diagnostic or reference laboratories (two in the UK and one in Denmark). From these collections, we searched for mecA PCR-negative bovine and human S aureus isolates showing phenotypic meticillin resistance. We used whole-genome sequencing to establish the genetic basis for the observed antibiotic resistance. FINDINGS: A divergent mecA homologue (mecA(LGA251)) was discovered in the LGA251 genome located in a novel staphylococcal cassette chromosome mec element, designated type-XI SCCmec. The mecA(LGA251) was 70% identical to S aureus mecA homologues and was initially detected in 15 S aureus isolates from dairy cattle in England. These isolates were from three different multilocus sequence type lineages (CC130, CC705, and ST425); spa type t843 (associated with CC130) was identified in 60% of bovine isolates. When human mecA-negative MRSA isolates were tested, the mecA(LGA251) homologue was identified in 12 of 16 isolates from Scotland, 15 of 26 from England, and 24 of 32 from Denmark. As in cows, t843 was the most common spa type detected in human beings. INTERPRETATION: Although routine culture and antimicrobial susceptibility testing will identify S aureus isolates with this novel mecA homologue as meticillin resistant, present confirmatory methods will not identify them as MRSA. New diagnostic guidelines for the detection of MRSA should consider the inclusion of tests for mecA(LGA251). FUNDING: Department for Environment, Food and Rural Affairs, Higher Education Funding Council for England, Isaac Newton Trust (University of Cambridge), and the Wellcome Trust.

Construction of networks with intrinsic temporal structure from UK cattle movement data.

BACKGROUND: The implementation of national systems for recording the movements of cattle between agricultural holdings in the UK has enabled the development and parameterisation of network-based models for disease spread. These data can be used to form a network in which each cattle-holding location is represented by a single node and links between nodes are formed if there is a movement of cattle between them in the time period selected. However, this approach loses information on the time sequence of events thus reducing the accuracy of model predictions. In this paper, we propose an alternative way of structuring the data which retains information on the sequence of events but which still enables analysis of the structure of the network. The fundamental feature of this network is that nodes are not individual cattle-holding locations but are instead direct movements between pairs of locations. Links are made between nodes when the second node is a subsequent movement from the location that received the first movement. RESULTS: Two networks are constructed assuming (i) a 7-day and (ii) a 14-day infectious period using British Cattle Movement Service (BCMS) data from 2004 and 2005. During this time period there were 4,183,670 movements that could be derived from the database. In both networks over 98% of the connected nodes formed a single giant weak component. Degree distributions show scale-free behaviour over a limited range only, due to the heterogeneity of locations: farms, markets, shows, abattoirs. Simulation of the spread of disease across the networks demonstrates that this approach to restructuring the data enables efficient comparison of the impact of transmission rates on disease spread. CONCLUSION: The redefinition of what constitutes a node has provided a means to simulate disease spread using all the information available in the BCMS database whilst providing a network that can be described analytically. This will enable the construction of generic networks with similar properties with which to assess the impact of small changes in network structure on disease dynamics.

Simulation of the options for a national control programme to eradicate scrapie from Great Britain.

Because of the risk to public health posed by the potential presence of bovine spongiform encephalopathy (BSE) in sheep, there are plans to eradicate transmissible spongiform encephalopathies (TSEs) from the British sheep population. We used a mathematical model for the spread of scrapie between sheep flocks to assess the efficacy of five control strategies at eradicating the infection from the national flock. These range from ram-genotyping schemes through whole-flock genotyping with selective culling to whole-flock slaughter. The impact of control was considered under three scenarios for the long-term dynamics of scrapie in GB: two in which scrapie is ultimately eliminated (with different median extinction times) and one in which scrapie remains endemic. Results suggested that it is feasible to eradicate scrapie from the British sheep flock, but that any national control programme will take decades to eliminate the disease and be costly. The most-effective strategy, measured in terms of the probability of eradication and time taken for eradication, was predicted to be whole-flock culling, which was effective under all three scenarios for the long-term dynamics of scrapie. Strategies involving whole-flock genotyping with selective culling were also effective, though they were predicted to take longer to eradicate scrapie than whole-flock culling. Ram-genotyping schemes were effective in some instances, but not for the scenario where scrapie remained endemic in the national flock. At low levels of reporting of clinical disease (< 20%) the probability of eradication within 100 years was predicted to be < 100% and, consequently, low levels of reporting could compromise the effectiveness of a control programme. Moreover, the predicted time taken to eradicate scrapie would increase markedly if the reporting compliance decreased.

Farm animal networks: unraveling the contact structure of the British sheep population.

The spatial and temporal dynamics of many farm animal diseases depend both on disease specific parameters and on the underlying contact structure between farms. Whilst many models for farm animal diseases focus on obtaining and estimating disease transmission parameters, relatively little attention has been given to modelling the underlying network of contacts. In this paper, we present an initial analysis of two relations underlying the contact network of individual sheep breeds in Great Britain. The first relation is based on geographical proximity and the second is based on attendance at agricultural shows. These relations are combined to give a risk-potential network that is based on these two levels of interaction. The structure of each network is investigated using techniques developed in graph theory and social network analysis.

Monte Carlo simulation of surveillance strategies for scrapie in Norwegian sheep.

Our aim was to compare the efficiency of different surveillance strategies for detecting scrapie-infected sheep flocks in the Norwegian population using simulation modelling. The dynamic Monte Carlo simulation model has the flock as the unit. The input parameters include properties of the sheep population (number of flocks, flock size, age distribution, reasons for culling, breeds, prion protein-allele distribution); properties of scrapie (genotype-dependent infection rate and incubation periods, and age- and genotype-dependent prevalence of scrapie); properties of the surveillance strategy (selection of sheep for examination, period in which infected sheep are detectable, and properties of the diagnostic tests). For simplification, the prion protein-alleles were grouped into three allele groups: VRQ, ARR, and ARQ' (ARQ' represents ARQ, ARH and AHQ). Through either abattoir surveillance or surveillance of fallen stock, 70% of the detected sheep (compared to 33% in the underlying population). The model output was sensitive to the susceptibility of infection for the genotype ARQ'/ARQ'. The effect was large for abattoir surveillance (increased susceptibility increased the efficiency of abattoir surveillance).

Prevalence of scrapie infection in Great Britain: interpreting the results of the 1997-1998 abattoir survey.

An accurate estimate of the prevalence of scrapie infection in the Great Britain (GB) sheep flock is essential when assessing any potential risk to human health through exposure to sheep transmissible spongiform encephalopathies (TSEs). One method for assessing the prevalence is to sample sheep intended for human consumption using a diagnostic test capable of detecting infected animals prior to the onset of clinical signs. An abattoir survey conducted in Great Britain in 1997-1998 tested brain samples from 2809 apparently healthy sheep of which none was found to be positive for scrapie by histopathology or immunohistochemistry (IHC) although 10 were positive for scrapie-associated fibrils (SAF). Subsequently, the tonsils from a subset of the animals sampled were examined using IHC, one of which tested positive. To interpret these results we use a likelihood-based approach, which accounts for the variation in the prevalence of infection with age and test sensitivity and specificity with stage of infection. Combining the results for all of the diagnostic tests yields an estimate of the prevalence of scrapie infection in the GB sheep flock of 0.22% (95% confidence interval: 0.01-0.97%). Moreover, our analysis suggests that all of the diagnostic tests used are very specific (greater than 99%). Indeed, only SAF detection yields a specificity estimate of less than 100%, which helps to account for the high number of samples found to be positive for SAF.