Defining the causal relationship between a microbe and encephalitis is complex. Over 100 different infectious agents may cause encephalitis, often as one of the rarer manifestations of infection. The gold-standard techniques to detect causative infectious agents in encephalitis in life depend on the study of brain biopsy material; however, in most cases this is not possible. We present the UK perspective on aetiological case definitions for acute encephalitis and extend them to include immune-mediated causes. Expert opinion was primarily used and was supplemented by literature-based methods. Wide usage of these definitions will facilitate comparison between studies and result in a better understanding of the causes of this devastating condition. They provide a framework for regular review and updating as the knowledge base increases both clinically and through improvements in diagnostic methods. The importance of new and emerging pathogens as causes of encephalitis can be assessed against the principles laid out here.

The force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data.

In recent years the control of low pathogenicity avian influenza (LPAI) viruses of the H5 and H7 subtypes has increasingly become a concern. We evaluated the measures (stamping out, controlled marketing, emergency and preventive vaccination, farm density reduction and restocking in homogenous areas) implemented to control the LPAI epidemics that occurred in Italy between 2000 and 2005, using a combination of spatial and space–time analyses and estimates of the basic reproduction ratio (R0). Clustering of infected farms decreased over the years, indicating the effectiveness of the control strategies implemented. Controlled marketing [relative risk (RR) 0·46, 95% confidence interval (CI) 0·27–0·80], emergency (RR 0·47, 95% CI 0·39–0·57) and preventive vaccination (RR 0·19, 95% CI 0·09–0·41) were the most effective measures, yet R0<1 was only for preventive vaccination. Our results are useful for identifying the most effective measures for reducing the risk of the spread of LPAI and optimizing the allocation of resources.

This study aimed to estimate the natural history and transmission parameters based on experimental viral shedding and symptom dynamics in order to understand the key epidemiological factors that characterize influenza (sub)type epidemics. A simple statistical algorithm was developed by combining a well-defined mathematical scheme of epidemiological determinants and experimental human influenza infection. Here we showed that (i) the observed viral shedding dynamics mapped successfully the estimated time-profile of infectiousness and (ii) the profile of asymptomatic probability was obtained based on observed temporal variation of symptom scores. Our derived estimates permitted evaluation of relationships between various model-derived and data-based estimations, allowing evaluation of trends proposed previously but not tested fully. As well as providing insights into the dynamics of viral shedding and symptom scores, a more profound understanding of influenza epidemiological parameters and determinants could enhance the viral kinetic studies of influenza during infection in the respiratory tracts of experimentally infected individuals.

This study aimed to compare systematically approaches to estimating influenza-attributable mortality in older Australians. Using monthly age-specific death data together with viral surveillance counts for influenza and respiratory syncytial virus, we explored two of the most frequently used methods of estimating excess influenza-attributable disease: Poisson and Serfling regression models. These approaches produced consistent age and temporal patterns in estimates of influenza-attributable mortality in older Australians but some variation in the magnitude of the disease burden. Of Australians aged >50 years, average annual estimated influenza-attributable deaths (all cause) ranged from 2314 to 3457 for the Serfling and Poisson regression models, respectively. The excess influenza-attributable disease burden was substantial under all approaches.

Bangladesh faced two epidemic waves of highly pathogenic avian influenza (HPAI) H5N1 in two consecutive years. The peaks of the waves were observed in February–July 2007 and January–April 2008, respectively. We examined the spatial and temporal patterns of the 293 outbreaks in 143 subdistricts in 2007 and 2008. Global clustering assessed by K-function was seen at distances 150–300 km between subdistricts. Significant local clusters were detected by space–time scan statistics. In both waves, significant primary clusters of HPAI outbreaks were identified in the central part of the country dominated by commercial production systems and in the northwestern part primarily in backyard production systems. Secondary clusters varied from the northwestern part in 2007 and the southern part in 2008. The findings are highly relevant for the successful planning and implementation of control, prevention and surveillance strategies by highlighting areas where detailed investigations should be initiated.

We propose an age-structured mathematical model for respiratory syncytial virus in which children aged <1 year are especially considered. Real data on hospitalized children in the Spanish region of Valencia were used in order to determine some seasonal parameters of the model. Weekly predictions of the number of children aged <1 year that will be hospitalized in the following years in Valencia are presented using this model. Results are applied to estimate the regional cost of paediatric hospitalizations and to perform a cost-effectiveness analysis of possible vaccination strategies.

The burden of pneumococcal carriage is largest in developing countries from which, however, detailed studies on pneumococcal transmission are missing. In this study we followed nasopharyngeal carriage in Bangladeshi infants (n=99) from birth, with 2-week sampling intervals until age 4 months, and monthly thereafter until age 1 year, and also their family members at the same intervals. We assessed the dependence of pneumococcal acquisition rates on age, serotype, serotype-specific exposure (i.e. transmission) and current state of carriage (yes/no). A statistical model of pneumococcal transmission, taking into account incompletely observed data, was applied to estimate rates of acquisition and clearance for a large number of serotypes at the same time. Serotypes that were common in the study population were more often acquired from the community than rarer serotypes. However, when conditioning on serotype-specific exposure within the family, transmission rates were similar between different serotypes. Exposure within families signified more than tenfold increase in the rate of acquisition.

Antimicrobial resistance is a priority emerging public health threat, and the ability to detect promptly outbreaks caused by resistant pathogens is critical for resistance containment and disease control efforts. We describe and evaluate the use of an electronic laboratory data system (WHONET) and a space–time permutation scan statistic for semi-automated disease outbreak detection. In collaboration with WHONET-Argentina, the national network for surveillance of antimicrobial resistance, we applied the system to the detection of local and regional outbreaks of Shigella spp. We searched for clusters on the basis of genus, species, and resistance phenotype and identified 19 statistical ‘events’ in a 12-month period. Of the six known outbreaks reported to the Ministry of Health, four had good or suggestive agreement with SaTScan-detected events. The most discriminating analyses were those involving resistance phenotypes. Electronic laboratory-based disease surveillance incorporating statistical cluster detection methods can enhance infectious disease outbreak detection and response.

Rotavirus (RV) infections progressively confer natural immunity against subsequent infection. Similarly to natural infection, vaccination with a live attenuated vaccine potentially reduces RV transmission and induces herd protection. A mathematical transmission model was developed to project the impact of a vaccination programme on the incidence of RV infection and disease for five countries in the European Union. With vaccination coverage rates of 70%, 90% and 95% the model predicted that, in addition to the direct effect of vaccination, herd protection induced a reduction in RV-related gastroenteritis (GE) incidence of 25%, 22% and 20%, respectively, for RV-GE of any severity, and of 19%, 15%, and 13%, respectively, for moderate-to-severe RV-GE, 5 years after implementation of a vaccination programme.

Three time-series models (regression, decomposition, and Box–Jenkins autoregressive integrated moving averages) were applied to national surveillance data for campylobacteriosis with the goal of disease forecasting in three US states. Datasets spanned 1998–2007 for Minnesota and Oregon, and 1999–2007 for Georgia. Year 2008 was used to validate model results. Mean absolute percent error, mean square error and coefficient of determination (R2) were the main evaluation fit statistics. Results showed that decomposition best captured the temporal patterns in disease risk. Training dataset R2 values were 72·2%, 76·3% and 89·9% and validation year R2 values were 66·2%, 52·6% and 79·9% respectively for Georgia, Oregon and Minnesota. All three techniques could be utilized to predict monthly risk of infection for Campylobacter sp. However, the decomposition model provided the fastest, most accurate, user-friendly method. Use of this model can assist public health personnel in predicting epidemics and developing disease intervention strategies.

Participating researchers and public health personnel at a Canadian workshop in 2007, noted considerable gaps in current understanding of community-associated Clostridium difficile infection (CA-CDI), specifically infection sources and risk factors. A disease transmission model for CA-CDI was requested as an initial step towards a risk assessment, to analyse infection sources and risk factors, addressing priority research areas. The developed model contains eight infection states (susceptible, gastrointestinal exposure, colonized, diseased, deceased, clinically resolved colonized, relapse diseased, and cleared) and notes directional transfers between the states. Most published research used focused on hospital-associated C. difficile infection (HA-CDI) and further studies are needed to substantiate the use of HA-CDI knowledge in the transmission of CA-CDI. The aim was to provide a consistent framework for researchers, and provide a theoretical basis for future quantitative risk assessment of CA-CDI.

We investigated the incidence of cases of nosocomial pathogens and risk factors in an intensive treatment unit ward to determine if the number of cases is dependent on location of patients and the colonization/infection history of the ward. A clustering approach method was developed to investigate the patterns of spread of cases through time for five microorganisms [methicillin-resistant Staphylococcus aureus (MRSA), Acinetobacter spp., Klebsiella spp., Candida spp., and Pseudomonas aeruginosa] using hospital microbiological monitoring data and ward records of patient-bed use. Cases of colonization/infection by MRSA, Candida and Pseudomonas were clustered in beds and through time while cases of Klebsiella and Acinetobacter were not. We used structural equation modelling to analyse interacting risk factors and the potential pathways of transmission in the ward. Prior nurse contact with colonized/infected patients, mediated by the number of patient-bed movements, were important predictors for all cases, except for those of Pseudomonas. General health and invasive surgery were significant predictors of cases of Candida and Klebsiella. We suggest that isolation and bed movement as a strategy to manage MRSA infections is likely to impact upon the incidence of cases of other opportunist pathogens.