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To maintain and enhance cow productivity and welfare, it is important that we can accurately assess and understand how cows respond to the physiological demands of gestation and lactation. Several methods have been developed for assessing the physiological responses to stressors and for detecting distress in cattle. Heart rate (HR) variability (HRV) is a non-invasive measure of autonomic nervous system activity and consequently a component of the physiological response to stress. In cattle, HRV has been successfully used to measure autonomic responses to a variety of health conditions and management procedures. The objectives of this study were to determine whether, among commercial Holstein Friesian cows and across farms, relationships exist between cow-level factors, HR and HRV. HRV parameters were compared with production records for 170 randomly selected, Holstein-Friesian-cows on 3 commercial dairy farms. Production data included parity, days in milk (DIM), milk yield, somatic cell count (SCC), % butterfat and protein, body condition score (BCS) and genetic indices. Fixed-effect, multivariable linear regression models were constructed to examine the association between cow-level variables and HRV parameters. Statistically significant relationships were found between HR and farm, temperature and BCS, and between HRV parameters and farm, rectal temperature, BCS, DIM, and percentage butterfat. Given the significant association between farms and several of the indices measured, it is recommended that care must be taken in the interpretation of HRV studies that are conducted on animals from a single farm. The current study indicated that within clinically normal dairy cattle HRV differed with the percentage of butterfat and BCS. Based on the relationships reported previously between HRV and stress in dairy cattle these results suggest that stress may be increased early in lactation, in cows with BCS

Pathogen emergence has been increasing in recent decades. In December 2019, an outbreak of atypical pneumonia emerged in Wuhan, central China. The cause of the disease, was subsequently identified as a novel coronavirus (SARS-CoV-2) and the disease named COVID-19. Over a short period of time SARS-CoV-2 rapidly spread throughout mainland China and surrounding countries in the western pacific before cases appeared in Europe in February of 2020. The first case in Ireland was documented in Ireland on the 29th of February 2020. In response, a series of epidemiological analyses were conducted to support decision making in the national control of the disease. The objective of this thesis was to provide regions-specific estimates of region-specific epidemiological parameters of interest, to aid in the accurate modelling of the disease in the country. The ultimate goal was to improve models so that more accurate information could be provided to decision makers. Chapter one proposed the use of the number of close contacts of infected cases over time as a metric for monitoring behaviour of infected contacts. The study found that the distribution of contacts per case was overdispersed, with a small proportion of cases reporting very large numbers of contacts. The study also provided an evidence basis for mathematical age-cohort compartmental models, by providing data on the social contacts of the Irish population under different levels of restriction. Chapter two estimated the serial interval of SARS-CoV-2 in Ireland using contact tracing data. The median serial interval was 4.0 days which was lower than estimates previously used for the statistical approximation of the serial interval in Ireland. Furthermore, the study also suggested that up to two thirds of transmission events were likely to have occurred prior to the onset of symptoms in the primary case. Finally, chapter three evaluated the potential use of Rapid Antigen Diagnostic Tests in screening individuals attending mass gatherings. The baseline risk of infection in the tested population is of key importance when interpreting the results of any imperfect diagnostic tests. This study provided a mechanism for estimating the baseline risk, as well as the uncertainty around that risk, using incidence data. The studies presented in this thesis demonstrate that secondary analyses of contact tracing data can yield meaningful and useful pieces of information that may be used to more accurately reflect transmission in the population of interest. However, it is also worth pointing out that certain opportunities were underutilized in the collection and analysis of contact tracing data. Given the apparent acceleration of pathogen emergence, evidenced in particular by the temporal proximity of repeated coronavirus outbreaks in the past 20 years, it is vital that Ireland take steps to develop infrastructure to deal with the current pandemic as well as the threat of future endemic diseases. Similar to work in other countries, this thesis demonstrates the strength of a OneHealth approach to the management of emerging infectious diseases. 2022-12-07 JG: Author's signature removed from PDF

BackgroundThe serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms.ResultsAfter data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The median serial interval was 4?days, mean serial interval was 4.0 (95% confidence intervals 3.7, 4.3) days, whilst the 25th and 75th percentiles were 2 and 6?days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64?years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector.ConclusionsWhilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.

Additional file 1: Figure S1. Secondary or tertiary case? Potential to mis-specify the infector when the primary case results in multiple secondary cases (left). The green circle represents the primary case, the blue circle a contact who did not become a case, and orange circles represent contacts who subsequently became cases. Incorrectly identifying two secondary cases as a secondary and tertiary case (right; that is, an intermediate step infector in a close co-contact) would result in a biased estimate of the serial interval. Figure S2. Secondary case or common source? Potential to mis-specify the infector when both the primary case and the case in a recorded contact are acquired from a common unidentified source (ie community transmission). The green circle represents the primary case, the blue circle a contact who did not become a case, and orange circles represent contacts of a case who subsequently became cases. Figure S3. Relationship between serial interval, incubation period and time of infection relative to symptom onset of the infector. Symptom onset is indicated with an ‘X’. Table S1. Impact of each data cleaning step on number of records. Table S2. Impact of restricting data according to the number of secondary cases per primary case.

Additional file 1.

Antibody-detecting tests for Mycobacterium avium ssp. paratuberculosis (MAP) have low sensitivity and imperfect specificity for detection of infection. Sensitivity increases as the disease progresses. Aside from infection status and stage of disease, several factors affect test performance. These factors have not yet been studied in dairy cows producing lower volumes of milk with higher solids concentration, such as those managed in low-input, pasture-based production systems. Furthermore, the effect of correcting for these associations on individual and herd test status is also unknown. The first objective of this study was to examine the relationship between MAP antibody response in milk and milk yield, somatic cell count (SCC), fat and protein contents, and stage of lactation in dairy cows enrolled in the national Johne's Disease Control Programme (JDCP) in Ireland. The second objective was to examine the effect of correcting the antibody response for these associations on the test status of individual cows and herds, given that individual tests are often used to define a herd's status. Data were extracted for herds in the JDCP from January 2014 to December 2015 inclusive, consisting of 42,657 milk recordings from 18,569 cows across 187 dairy herds. Two linear regression models were constructed to investigate the association between log-transformed MAP sample-to-positive ratio and milk recording data and in primi- and multiparous cows. Days in milk was modeled as a B-spline in each model, and cow and herd were included as random effects. Across both models, natural log-transformed MAP antibody response was negatively associated with milk yield, positively associated with protein and fat production, and had a curvilinear association with log-transformed SCC. The association between MAP antibody response and days in milk varied over the course of the lactation. However, when combined, these variables explained only 5.1% of the variation in the antibody response of the population. After correcting for these associations, 93 multiparous cows and 20 primiparous cows changed category (negative, suspect, or positive). When considered at the herd-test level, out of a total of 531 herd tests, 1 herd changed from negative to positive, and 5 herds changed from positive to negative. This study provides useful information to aid in the interpretation of antibody results for herds testing animals for the presence of MAP infection. At an overall population level, correction of the serological response for non-disease-associated factors has the potential to change the status of only a small number of cows. At the herd level, the proportion of herds changing status was minimal. However, depending on the implications of a herd-level serological diagnosis, consideration should be given to correcting for these non-disease-associated variables within the context of national JD control programs.

In response to the coronavirus (COVID-19) outbreak, the Irish Epidemiological Modelling Advisory Group (IEMAG) for COVID-19 was established to assist the Irish National Public Health Emergency Team (NPHET) in their decision-making during the pandemic. A subcommittee from IEMAG (the epidemiological parameters team) was tasked with researching the various parameters, leading to the development of a series of synthesis documents relevant to the parameterisation of a COVID-19 transmission model for Ireland. These parameters include: • R0/R • Latent period & relative importance of pre-symptomatic period • Incubation period • Generation time & serial interval • Proportion of infected who are asymptomatic, by age • Length of infectious period in asymptomatic people and in symptomatic people who do not isolate • Time from onset of symptoms to diagnosis/test results and to hospitalisation • Length of hospital stay and admission to ICUs • Relative infectiousness of asymptomatic versus symptomatic infected people. The current document presents an up-to-date summary of these synthesis documents. A further synthesis document on age-related susceptibility and age-related infectiousness is in preparation.

Objectives: Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus is spreading rapidly worldwise and threatening the collapse of national health care systems. The development of effective resource models are critical for long term health planning. The aim was to evaluate the available literature, to consider parameters affecting hospital resources, to effectively guide health policy and planning. Design: A detailed search of the literature, using Google Scholar, PubMED, MedRxiv and BioRxiv, was conducted for the time period 01-DEC-2019 to 04-MAY-2020; using appropriate keywords: resultant articles were scrutinised in detail, and appraised for reported data pertaining to hospitalization and hospital length of stay (LOS). Results: Disease presentation was described in China; 81 % mild, 14 % moderate and 5 % severe. The experience, thus far, in Europe and the USA are suggestive of a higher degree of severity. Initial reports suggest high hospitalisation and ICU admittance rates. More recent reports from the ECDC lower this estimation. Perhaps the relative age, the level of pre-existing conditions, and other health factors may be contributors to differences. Data from Irish cases suggest hospitalisation rate may be lower in parts of Europe and time dependent. Hospital LOS is described in seventeen articles, with median lengths of stay between 4 and 25 days. The evidence regarding the LOS in ICU is reported in eighteen studies, fifteen deemed relevant. The majority of studies report ICU LOS between 7 to 10 days. Many of these studies are likely skewed towards shorter stay due to study cut-off dates. Indications based on ICU LOS reported for patients continuing care suggest median ICU stay will be longer. Conclusions: These parameter estimates are key to the development of an effective health care resource model. Based on our rapid appraisal of the literature, is it essential that Europe manages mitigation measures to ensure that hospital and ICU capacity does not become overwhelmed to manage this pandemic long term.

Background: Understanding the extent of virus transmission that can occur before symptom onset is vital for targeting control measures against the global pandemic of COVID-19. Objective: Estimation of (1) the proportion of pre-symptomatic transmission of COVID-19 that can occur and (2) timing of transmission relative to symptom onset. Design: Secondary analysis of published data Data sources: Meta-analysis of COVID-19 incubation period and a rapid systematic review of serial interval and generation time, which are published separately. Methods: Simulations were generated of incubation period and of serial interval or generation time. From these, transmission times relative to symptom onset were calculated and the proportion of pre-symptomatic transmission was estimated. Results: A total of 23 estimates of serial interval and five estimates of generation time from 17 publications were included. These came from nine different data source categories (presented here in descending order of the proportion of pre-symptomatic transmission): Hong Kong, Tianjin, pooled data from Hong Kong and Shenzhen, Singapore, Mainland China excluding Hubei, mixed sources, Shenzhen, northern Italy and Wuhan. Transmission time relative to symptom onset ranged from a mean of 2.05 days before symptom onset for Hong Kong to 1.72 days after symptom onset for Wuhan. Proportion of pre-symptomatic transmission ranged from 33.7% in Wuhan to 72.7% in Hong Kong. Based on individual estimates, transmission time relative to symptom onset ranged from mean of 2.95 days before symptom onset to 1.72 days after symptom onset and proportion of pre-symptomatic transmission ranged from 33.7% to 79.9%. Simple unweighted pooling of estimates based on serial intervals resulted in a mean time of transmission of 0.67 days before symptoms, and an estimated 56.1% of transmission occurring in the pre-symptomatic period. Conclusions: Contact rates between symptomatic infectious and susceptible people are likely to influence the proportion of pre-symptomatic transmission. There is substantial potential for pre-symptomatic transmission of COVID-19 in a range of different contexts. Our work suggests that transmission of SARS-CoV-2 is most likely in the day before symptom onset whereas estimates suggesting most pre-symptomatic transmission highlighted a mean transmission times almost 3 days before symptom onset. These findings highlight the urgent need for extremely rapid and effective case detection, contact tracing and quarantine measures if strict social distancing measures are to be eased.