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Background: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths.Methods: We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.Results: A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%.Conclusions: Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally. [ABSTRACT FROM AUTHOR]

Background: Visceral leishmaniasis (VL) has been targeted by the WHO for elimination as a public health problem (< 1 case/10,000 people/year) in the Indian sub-continent (ISC) by 2020. Bihar State in India, which accounts for the majority of cases in the ISC, remains a major target for this elimination effort. However, there is considerable spatial, temporal and sub-population variation in occurrence of the disease and the pathway to care, which is largely unexplored and a threat to achieving the target. Methods: Data from 6081 suspected VL patients who reported being clinically diagnosed during 2012-2013 across eight districts in Bihar were analysed. Graphical comparisons and Chi-square tests were used to determine differences in the burden of identified cases by season, district, age and sex. Log-linear regression models were fitted to onset (of symptoms)-to-diagnosis and onset-to-treatment waiting times to estimate their associations with age, sex, district and various socio-economic factors (SEFs). Logistic regression models were used to identify factors associated with mortality. Results: Comparisons of VL caseloads suggested an annual cycle peaking in January-March. A 17-fold variation in the burden of identified cases across districts and under-representation of young children (0-5 years) relative to age-specific populations in Bihar were observed. Women accounted for a significantly lower proportion of the reported cases than men (41 vs 59%, P < 0.0001). Age, district of residence, house wall materials, caste, treatment cost, travelling for diagnosis and the number of treatments for symptoms before diagnosis were identified as correlates of waiting times. Mortality was associated with age, district of residence, onset-to-treatment waiting time, treatment duration, cattle ownership and cost of diagnosis. Conclusions: The distribution of VL in Bihar is highly heterogeneous, and reported caseloads and associated mortality vary significantly across different districts, posing different challenges to the elimination campaign. Socio-economic factors are important correlates of these differences, suggesting that elimination will require tailoring to population and sub-population circumstances. [ABSTRACT FROM AUTHOR]

Background: Diseases occur in populations whose individuals differ in essential characteristics, such as exposure to the causative agent, susceptibility given exposure, and infectiousness upon infection in the case of infectious diseases.Discussion: Concepts developed in demography more than 30 years ago assert that variability between individuals affects substantially the estimation of overall population risk from disease incidence data. Methods that ignore individual heterogeneity tend to underestimate overall risk and lead to overoptimistic expectations for control. Concerned that this phenomenon is frequently overlooked in epidemiology, here we feature its significance for interpreting global data on human tuberculosis and predicting the impact of control measures. We show that population-wide interventions have the greatest impact in populations where all individuals face an equal risk. Lowering variability in risk has great potential to increase the impact of interventions. Reducing inequality, therefore, empowers health interventions, which in turn improves health, further reducing inequality, in a virtuous circle. [ABSTRACT FROM AUTHOR]

Background: The number of new leprosy cases reported annually is falling worldwide, but remains relatively high in some populations. Because of the long and variable periods between infection, onset of disease, and diagnosis, the recently detected cases are a reflection of infection many years earlier. Estimation of the numbers of sub-clinical and clinical infections would be useful for management of elimination programmes. Back-calculation is a methodology that could provide estimates of prevalence of undiagnosed infections, future diagnoses and the effectiveness of control. Methods: A basic back-calculation model to investigate the infection dynamics of leprosy has been developed using Markov Chain Monte Carlo in a Bayesian context. The incidence of infection and the detection delay both vary with calendar time. Public data from Thailand are used to demonstrate the results that are obtained as the incidence of diagnosed cases falls. Results: The results show that the underlying burden of infection and short-term future predictions of cases can be estimated with a simple model. The downward trend in new leprosy cases in Thailand is expected to continue. In 2015 the predicted total number of undiagnosed sub-clinical and clinical infections is 1,168 (846-1,546) of which 466 (381-563) are expected to be clinical infections. Conclusions: Bayesian back-calculation has great potential to provide estimates of numbers of individuals in health/ infection states that are as yet unobserved. Predictions of future cases provides a quantitative measure of understanding for programme managers and evaluators. We will continue to develop the approach, and suggest that it might be useful for other NTD in which incidence of diagnosis is not an immediate measure of infection. [ABSTRACT FROM AUTHOR]

Background: Visceral leishmaniasis has been targeted for elimination as a public health problem (less than 1 case per 10,000 people per year) in the Indian sub-continent by 2017. However, there is still a high degree of uncertainty about the natural history of the disease, in particular about the duration of asymptomatic infection and the proportion of asymptomatically infected individuals that develop clinical visceral leishmaniasis. Quantifying these aspects of the disease is key for guiding efforts to eliminate visceral leishmaniasis and maintaining elimination once it is reached. Methods: Data from a detailed epidemiological study in Bangladesh in 2002-2004 was analysed to estimate key epidemiological parameters. The role of diagnostics in determining the probability and rate of progression to clinical disease was estimated by fitting Cox proportional hazards models. A multi-state Markov model of the natural history of visceral leishmaniasis was fitted to the data to estimate the asymptomatic infection period and the proportion of asymptomatic individuals going on to develop clinical symptoms. Results: At the time of the study, individuals were taking several months to be diagnosed with visceral leishmaniasis, leading to many opportunities for ongoing transmission. The probability of progression to clinical disease was strongly associated with initial seropositivity and even more strongly with seroconversion, with most clinical symptoms developing within a year. The estimated average durations of asymptomatic infection and symptomatic infection for our model of the natural history are 147 days (95 % CI 130-166) and 140 days (95 % CI 123-160), respectively, and are significantly longer than previously reported estimates. We estimate from the data that 14.7 % (95 % CI 12.6-20.0 %) of asymptomatic individuals develop clinical symptoms-a greater proportion than previously estimated. Conclusions: Extended periods of asymptomatic infection could be important for visceral leishmaniasis transmission, but this depends critically on the relative infectivity of asymptomatic and symptomatic individuals to sandflies. These estimates could be informed by similar analysis of other datasets. Our results highlight the importance of reducing times from onset of symptoms to diagnosis and treatment to reduce opportunities for transmission. [ABSTRACT FROM AUTHOR]

Background: Our understanding of the transmission dynamics of respiratory syncytial virus (RSV) infection will be better informed with improved data on the patterns of shedding in cases not limited only to hospital admissions. Methods: In a household study, children testing RSV positive by direct immunofluorescent antibody test (DFA) were enrolled. Nasal washings were scheduled right away, then every three days until day 14, every 7 days until day 28 and every 2 weeks until a maximum of 16 weeks, or until the first DFA negative RSV specimen. The relationship between host factors, illness severity and viral shedding was investigated using Cox regression methods. Results: From 151 families a total of 193 children were enrolled with a median age of 21 months (range 1-164 months), 10% infants and 46% male. The rate of recovery from infection was 0.22/person/day (95% CI 0.19-0.25) equivalent to a mean duration of shedding of 4.5 days (95%CI 4.0-5.3), with a median duration of shedding of 4 days (IQR 2-6, range 1-14). Children with a history of RSV infection had a 40% increased rate of recovery i.e. shorter duration of viral shedding (hazard ratio 1.4, 95% CI 1.01-1.86). The rate of cessation of shedding did not differ significantly between males and females, by severity of infection or by age. Conclusion: We provide evidence of a relationship between the duration of shedding and history of infection, which may have a bearing on the relative role of primary versus re-infections in RSV transmission in the community. [ABSTRACT FROM AUTHOR]

Background: Bovine herpesvirus type-1 (BHV-1) is an important pathogen of cattle that presents with a variety of clinical signs, including the upper respiratory tract infection infectious bovine rhinotracheitis (IBR). A seroepidemiological study of BHV-1 antibodies was conducted in England from 2002 - 2004: 29,782 blood samples were taken from 15,736 cattle from 114 herds which were visited on up to three occasions. Antibody concentration was measured using a commercial ELISA. Farm management information was collected using an interview questionnaire, and herd size and cattle movements were obtained from the cattle tuberculosis testing database and the British Cattle Movement Service. Hierarchical statistical models were used to investigate associations between cattle and herd variables and the continuous outcome percentage positive (PP) values from the ELISA test in unvaccinated herds. Results: There were 7 vaccinated herds, all with at least one seropositive bovine. In unvaccinated herds 83.2% had at least one BHV-1 seropositive bovine, and the mean cattle and herd BHV-1 seroprevalence were 42.5% and 43.1% respectively. There were positive associations between PP value, age, herd size, presence of dairy cattle. Adult cattle in herds with grower cattle had lower PP values than those in herds without grower cattle. Purchased cattle had significantly lower PP values than homebred cattle, whereas cattle in herds that were totally restocked after the foot-and-mouth epidemic in 2001 had significantly higher PP values than those in continuously stocked herds. Samples taken in spring and summer had significantly lower PP values than those taken in winter, whereas those taken in autumn had significantly higher PP values than those taken in winter. The risks estimated from a logistic regression model with a binary outcome (seropositive yes/no) were similar. Conclusion: The prevalence of BHV-1 seropositivity in cattle and herds has increased since the 1970s. Although the study population prevalence of BHV-1 was temporally stable during study period, the associations between serological status and cattle age, herd size, herd type, presence of young stock and restocked versus continuously stocked herds indicate that there is heterogeneity between herds and so potential for further spread of BHV-1 within and between herds. [ABSTRACT FROM AUTHOR]

Background: Neosporosis caused by the protozoan parasite Neospora caninum, is an economically important cause of abortion, stillbirth, low milk yield, reduced weight gain and premature culling in cattle. Consequently, a seroepidemiological study of N. caninum antibodies was conducted in England with 29,782 samples of blood taken from 15,736 cattle from 114 herds visited on three occasions at yearly intervals. Herds were categorised into lower (< 10%) and higher (≥ 10%) median herd seroprevalence. Hierarchical models were run to investigate associations between the sample to positive (S/P) ratio and herd and cattle factors. Results: Ninety-four percent of herds had at least one seropositive cow; 12.9% of adult cattle had at least one seropositive test. Approximately 90% of herds were seropositive at all visits; 9 herds (8%) changed serological status between visits. The median N. caninum seroprevalence in positive herds was 10% (range 0.4% to 58.8%). There was a positive association between the serostatus of offspring and dams that were ever seropositive. In the hierarchical model of low seroprevalence herds there was no significant association between S/P ratio and cattle age. There was a significantly lower S/P ratio in cattle in herds that were totally restocked after the foot-and-mouth epidemic of 2001 compared with those from continuously stocked herds and cattle purchased into these herds had a higher S/P ratio than homebred cattle. In the model of high seroprevalence herds the S/P ratio increased with cattle age, but was not associated with restocking or cattle origin. Conclusion: There were no strong temporal changes in herd seroprevalence of N. caninum but 90% of herds had some seropositive cattle over this time period. Vertical transmission from seropositive dams appeared to occur in all herds. In herds with a high seroprevalence the increasing S/P ratio in 2-4 year old cattle is suggestive of exposure to N. caninum: horizontal transmission between adult cattle, infection from a local source or recrudescence and abortions. Between-herd movements of infected cattle enhance the spread of N. caninum, particularly into low seroprevalence herds. Some restocked herds had little exposure to N. caninum, while in others infection had spread in the time since restocking. [ABSTRACT FROM AUTHOR]

Most neglected tropical diseases (NTDs) have complex life cycles and are challenging to control. The "2020 goals" of control and elimination as a public health programme for a number of NTDs are the subject of significant international efforts and investments. Beyond 2020 there will be a drive to maintain these gains and to push for true local elimination of transmission. However, these diseases are affected by variations in vectors, human demography, access to water and sanitation, access to interventions and local health systems. We therefore argue that there will be a need to develop local quantitative expertise to support elimination efforts. If available now, quantitative analyses would provide updated estimates of the burden of disease, assist in the design of locally appropriate control programmes, estimate the effectiveness of current interventions and support 'real-time' updates to local operations. Such quantitative tools are increasingly available at an international scale for NTDs, but are rarely tailored to local scenarios. Localised expertise not only provides an opportunity for more relevant analyses, but also has a greater chance of developing positive feedback between data collection and analysis by demonstrating the value of data. This is essential as rational program design relies on good quality data collection. It is also likely that if such infrastructure is provided for NTDs there will be an additional impact on the health system more broadly. Locally tailored quantitative analyses can help achieve sustainable and effective control of NTDs, but also underpin the development of local health care systems.

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination 'as a public health problem' when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models' predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020.

The majority of intramammary infections with Escherichia coli in dairy cows result in transient infections with duration of about 10 days or less, although more persistent infections (2 months or longer) have been identified. We apply a mathematical model to explore the role of an intracellular mammary epithelial cell reservoir in the dynamics of infection. We included biological knowledge of the bovine immune response and known characteristics of the bacterial population in both transient and persistent infections. The results indicate that varying the survival duration of the intracellular reservoir reproduces the data for both transient and persistent infections. Survival in an intracellular reservoir is the most likely mechanism that ensures persistence of E. coli infections in mammary glands. Knowledge of the pathogenesis of persistent infections is essential to develop preventive and treatment programmes for these important infections in dairy cows., (INRA, EDP Sciences, 2009)

Knowledge of the efficacy of an intervention for disease control on an individual farm is essential to make good decisions on preventive healthcare, but the uncertainty in outcome associated with undertaking a specific control strategy has rarely been considered in veterinary medicine. The purpose of this research was to explore the uncertainty in change in disease incidence and financial benefit that could occur on different farms, when two effective farm management interventions are undertaken. Bovine mastitis was used as an example disease and the research was conducted using data from an intervention study as prior information within an integrated Bayesian simulation model. Predictions were made of the reduction in clinical mastitis within 30 days of calving on 52 farms, attributable to the application of two herd interventions previously reported as effective; rotation of dry cow pasture and differential dry cow therapy. Results indicated that there were important degrees of uncertainty in the predicted reduction in clinical mastitis for individual farms when either intervention was undertaken; the magnitude of the 95% credible intervals for reduced clinical mastitis incidence were substantial and of clinical relevance. The large uncertainty associated with the predicted reduction in clinical mastitis attributable to the interventions resulted in important variability in possible financial outcomes for each farm. The uncertainty in outcome associated with farm control measures illustrates the difficulty facing a veterinary clinician when making an on-farm decision and highlights the importance of iterative herd health procedures (continual evaluation, reassessment and adjusted interventions) to optimise health in an individual herd., (INRA, EDP Sciences, 2009)

Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full posterior predictive distribution and two "mixed" predictive methods that incorporate higher level random effects simulated from the underlying model distribution. Cross validation is considered a gold standard method but is computationally intensive and thus a comparison is made between posterior predictive assessments and cross validation. The analyses revealed that mixed prediction methods produced results close to cross validation whilst the full posterior predictive assessment gave predictions that were over-optimistic (closer to the observed disease rates) compared with cross validation. A mixed prediction method that simulated random effects from both higher levels was best at identifying the outlying level two (farm-year) units of interest. It is concluded that this mixed prediction method, simulating random effects from both higher levels, is straightforward and may be of value in model criticism of multilevel logistic regression, a technique commonly used for animal health data with a hierarchical structure.

Background: The between- and within-herd variability of porcine reproductive and respiratory syndrome virus (PRRSV) antibodies were investigated in a cross-sectional study of 103 British pig herds conducted 2003-2004. Fifty pigs from each farm were tested for anti-PRRSV antibodies using ELISA. A binomial logistic model was used to investigate management risks for farms with and without pigs with PRRSV antibodies and multilevel statistical models were used to investigate variability in pigs' log ELISA IRPC (relative index x 100) in positive herds., Results: Thirty-five herds (34.0%) were seronegative, 41 (39.8%) were seropositive and 27 (26.2%) were vaccinated. Herds were more likely to be seronegative if they had < 250 sows (OR 3.86 (95% CI 1.46, 10.19)) and if the nearest pig herd was > or = 2 miles away (OR 3.42 (95% CI 1.29, 9.12)). The mean log IRPC in seropositive herds was 3.02 (range, 0.83 - 5.58). Sixteen seropositive herds had only seropositive adult pigs. In these herds, pigs had -0.06 (95% CI -0.10, -0.01) lower log IRPC for every mile increase in distance to the nearest pig unit, and -0.56 (95% CI -1.02, -0.10) lower log IRPC when quarantine facilities were present. For 25 herds with seropositive young stock and adults, lower log IRPC were associated with isolating purchased stock for > or = 6 days (coefficient -0.46, 95% CI -0.81, -0.11), requesting > or = 48 hours 'pig-free time' from humans (coefficient -0.44, 95% CI -0.79, -0.10) and purchasing gilts (coefficient -0.61, 95% CI -0.92, -0.29)., Conclusion: These patterns are consistent with PRRSV failing to persist indefinitely on some infected farms, with fadeout more likely in smaller herds with little/no reintroduction of infectious stock. Persistence of infection may be associated with large herds in pig-dense regions with repeated reintroduction.