Hepatitis C and the absence of genomic data in low-income countries: a barrier on the road to elimination?

Following the development of highly effective direct acting antiviral (DAA) compounds for the treatment of the hepatitis C virus (HCV), WHO has set out plans for disease eradication by 2030. Many barriers must be surmounted before this can be achieved, including buy-in from governments and policy makers, reduced drug costs, and improved infrastructure for the pathway from diagnosis to treatment. A comprehensive set of guidelines was produced by WHO in 2014, updated in 2016, and they are due to be revised later this year

Epidemiology of seasonal coronaviruses: establishing the context for the emergence of coronavirus disease 2019

Public health preparedness for coronavirus (CoV) disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal CoVs (sCoVs) and other cocirculating viruses in the West of Scotland, United Kingdom. We analyzed routine diagnostic data for >70 000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43, and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently, and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness

Geographical variation in Hepatitis C-related severe liver disease and patient risk factors: A multicentre cross-sectional study

No abstract available

Drivers of infectious disease seasonality: potential implications for COVID-19

Not 1 year has passed since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). Since its emergence, great uncertainty has surrounded the potential for COVID-19 to establish as a seasonally recurrent disease. Many infectious diseases, including endemic human coronaviruses, vary across the year. They show a wide range of seasonal waveforms, timing (phase), and amplitudes, which differ depending on the geographical region. Drivers of such patterns are predominantly studied from an epidemiological perspective with a focus on weather and behavior, but complementary insights emerge from physiological studies of seasonality in animals, including humans. Thus, we take a multidisciplinary approach to integrate knowledge from usually distinct fields. First, we review epidemiological evidence of environmental and behavioral drivers of infectious disease seasonality. Subsequently, we take a chronobiological perspective and discuss within-host changes that may affect susceptibility, morbidity, and mortality from infectious diseases. Based on photoperiodic, circannual, and comparative human data, we not only identify promising future avenues but also highlight the need for further studies in animal models. Our preliminary assessment is that host immune seasonality warrants evaluation alongside weather and human behavior as factors that may contribute to COVID-19 seasonality, and that the relative importance of these drivers requires further investigation. A major challenge to predicting seasonality of infectious diseases are rapid, human-induced changes in the hitherto predictable seasonality of our planet, whose influence we review in a final outlook section. We conclude that a proactive multidisciplinary approach is warranted to predict, mitigate, and prevent seasonal infectious diseases in our complex, changing human-earth system

Modelling the impact of co-circulating low pathogenic avian influenza viruses on epidemics of highly pathogenic avian influenza in poultry

It is well known that highly pathogenic avian influenza (HPAI) viruses emerge through mutation of precursor low pathogenic avian influenza (LPAI) viruses in domestic poultry populations. The potential for immunological cross-protection between these pathogenic variants is recognised but the epidemiological impact during co-circulation is not well understood. Here we use mathematical models to investigate whether altered flock infection parameters consequent to primary LPAI infections can impact on the spread of HPAI at the population level. First we used mechanistic models reflecting the co-circulatory dynamics of LPAI and HPAI within a single commercial poultry flock. We found that primary infections with LPAI led to HPAI prevalence being maximised under a scenario of high but partial cross-protection. We then tested the population impact in spatially-explicit simulations motivated by a major avian influenza A(H7N1) epidemic that afflicted the Italian poultry industry in 1999-2001. We found that partial cross-protection can lead to a prolongation of HPAI epidemic duration. Our findings have implications for the control of HPAI in poultry particularly for settings in which LPAI and HPAI frequently co-circulate

Severe acute respiratory syndrome coronavirus 2 serosurveillance in a patient population reveals differences in virus exposure and antibody-mediated immunity according to host demography and healthcare setting

Identifying drivers of SARS-CoV-2 exposure and quantifying population immunity is crucial to prepare for future epidemics. We performed a serial cross-sectional serosurvey throughout the first pandemic wave among patients from the largest health board in Scotland. Screening of 7480 patient sera showed a weekly seroprevalence ranging from 0.10% to 8.23% in primary and 0.21% to 17.44% in secondary care, respectively. Neutralisation assays showed that around half of individuals who tested positive by ELISA assay, developed highly neutralising antibodies, mainly among secondary care patients. We estimated the individual probability of SARS-CoV-2 exposure and quantified associated risk factors. We show that secondary care patients, males and 45-64-year-olds exhibit a higher probability of being seropositive. The identification of risk factors and the differences in virus neutralisation activity between patient populations provided insights into the patterns of virus exposure during the first pandemic wave and shed light on what to expect in future waves

Implications of within-farm transmission for network dynamics:Consequences for the spread of avian influenza

AbstractThe importance of considering coupled interactions across multiple population scales has not previously been studied for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry. By simulating the within-flock transmission of HPAI using a deterministic S-E-I-R model, and by incorporating an additional environmental class representing infectious faeces, we tracked the build-up of infectious faeces within a poultry house over time. A measure of the transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with data describing the daily on-farm visit schedules for a major British catching company. Larger flocks tended to have greater levels of these catching-team visits. However, where density-dependent contact was assumed, faster outbreak detection (according to an assumed mortality threshold) led to a decreased opportunity for catching-team visits to coincide with an outbreak. For this reason, maximum TR-levels were found for mid-range flock sizes (~25,000–35,000 birds). When assessing all factors simultaneously using multivariable linear regression on the simulated outputs, those related to the pattern of catching-team visits had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network analysis on a further database to inform a measure of between-farm connectivity, we identified a large fraction of farms (28%) that had both a high TR and a high potential impact at the between farm level. Our results have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone, and together with further knowledge of the relative importance of transmission risk and impact, could have implications for improved targeting of control measures

Quantitative approaches to informing the surveillance and control of avian influenza in British poultry

The continued endemic circulation of avian influenza (AI) virus across many parts of the world, as well as the presence of a wild bird reservoir, maintains the threat of incursion of this virus into currently unaffected countries. Major concerns for human health, particularly with respect to the highly pathogenic (HP) H5N1 subtype, as well as the impact on governments' and poultry industries, makes the control of AI an important goal for countries worldwide. Chapter 1 reviews the past, present and future epidemiology of AI in Great Britain (GB), where to date recorded outbreaks have been controlled by stamping out control measures. The potential for large outbreaks under certain conditions is recognised and therefore contingency plans are necessary to limit the potential impact of future incursions. Knowledge of heterogeneity in AI transmission dynamics could be particularly helpful in designing more targeted control measures. In the absence of data to inform the likely mechanisms of between-farm spread, modelling can be a valuable tool to achieve this. This thesis aims to critically assess the appropriate use of existing data for developing epidemiological tools. In contrast to previous studies, this work focuses on epidemiological risks, as well as the dynamics of transmission, at different scales of the British poultry industry. Both airborne and fomite transmission are considered possible mechanisms of between-farm spread of AI within GB, although their relative importance is not well understood. In Chapter 2, epidemiologically-relevant between-farm associations were used to inform an individual-based stochastic network model to explore the geographical variation in airborne versus fomite-mediated transmission predominance. In Chapter 3, the limitation of these findings, by the likely over-estimation of contact frequency, as well as the biased picture of network properties resulting from targeted sampling, were assessed. Nevertheless, these data provide an insight into the complexity of connectivity within the GB poultry network, with implications for resource distribution during outbreak control. Chapter 4 considers the reduction in transmission risk due to company integration and the implications of this in relation to compartmentalisation. Using a deterministic metapopulation framework specific to GB, outbreak conditions posing a risk for HPAI transmission under compartmentalisation were identified. In Chapter 5, cross-population scale interactions were considered through incorporating temporally explicit movement data from one catching company with a within-flock model of HPAI transmission. Important insight into the impact of within-group dynamics on the opportunity for spread at the population-level was gained; in particular, transmission mode assumptions were found to complicate predictions that can otherwise be based on knowledge of flock size. Chapter 6 describes, more generally, how these findings imply that the different sources of information that describe the GB poultry industry can be used to inform different aspects of risk heterogeneity and the targeting of disease control. However, the more informative these data were of population dynamics at the resolution of an individual farm, the less representative they became at a national-level. Further work on the relative importance of farm-level transmission dynamics and network structure could help to establish how vital knowledge at the scale of the individual farm is for informing predictive mathematical models of AI outbreaks. As the opportunity for AI propagation hinges on the rapid detection and notification of an outbreak, further work focusing on the transmission potential of low pathogenic strains in particular is warranted