Epidemiological Methods: About Time

Epidemiological studies often produce false positive results due to use of statistical approaches that either ignore or distort time. The three time-related issues of focus in this discussion are: (1) cross-sectional vs. cohort studies, (2) statistical significance vs. public health significance, and (3), how risk factors “work together” to impact public health significance. The issue of time should be central to all thinking in epidemiology research, affecting sampling, measurement, design, analysis and, perhaps most important, the interpretation of results that might influence clinical and public-health decision-making and subsequent clinical research

Evaluation of WGS-subtyping methods for epidemiological surveillance of foodborne salmonellosis

Background: Salmonellosis is one of the most common foodborne diseases worldwide. Although human infection by non-typhoidal Salmonella (NTS) enterica subspecies enterica is associated primarily with a self-limiting diarrhoeal illness, invasive bacterial infections (such as septicaemia, bacteraemia and meningitis) were also reported. Human outbreaks of NTS were reported in several countries all over the world including developing as well as high-income countries. Conventional laboratory methods such as pulsed field gel electrophoresis (PFGE) do not display adequate discrimination and have their limitations in epidemiological surveillance. It is therefore very crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological characterisation and outbreak investigation. Methods: Here, we used different whole genome sequence (WGS)-based subtyping methods for retrospective investigation of two different outbreaks of Salmonella Typhimurium and Salmonella Dublin that occurred in 2013 in UK and Ireland respectively. Results: Single nucleotide polymorphism (SNP)-based cluster analysis of Salmonella Typhimurium genomes revealed well supported clades, that were concordant with epidemiologically defined outbreak and confirmed the source of outbreak is due to consumption of contaminated mayonnaise. SNP-analyses of Salmonella Dublin genomes confirmed the outbreak however the source of infection could not be determined. The core genome multilocus sequence typing (cgMLST) was discriminatory and separated the outbreak strains of Salmonella Dublin from the non-outbreak strains that were concordant with the epidemiological data however cgMLST could neither discriminate between the outbreak and non-outbreak strains of Salmonella Typhimurium nor confirm that contaminated mayonnaise is the source of infection, On the other hand, other WGS-based subtyping methods including multilocus sequence typing (MLST), ribosomal MLST (rMLST), whole genome MLST (wgMLST), clustered regularly interspaced short palindromic repeats (CRISPRs), prophage sequence profiling, antibiotic resistance profile and plasmid typing methods were less discriminatory and could not confirm the source of the outbreak. Conclusions: Foodborne salmonellosis is an important concern for public health therefore, it is crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological surveillance and outbreak investigation. In this study, we showed that SNP-based analyses do not only have the ability to confirm the occurrence of the outbreak but also to provide definitive evidence of the source of the outbreak in real-time

Epidemiological research methods. Part VII. Epidemiological research in health planning

CITATION: Yach, D. & Botha, J. L. 1987. Epidemiological research methods. Part VII. Epidemiological research in health planning. South African Medical Journal, 72:633-636.The original publication is available at http://www.samj.org.zaThe goal of epidemiology is to improve the health status of human populations. In our series thus far we have srressed the need to use the correct design for epidemiological studies, a sampling scheme that yields interpretable results, measurements that are both valid and reliable, and finally the appropriate analysis. These methodological considerations are of importance if the goal is to be reached. In this article we assume that most of these issues have been adequately dealt with and focus on how the results of epidemiological research can be used by health planners to improve the health status of regions and the country as a whole.Publisher’s versio

Estimation for a Simple Exponential Model

Methods of parameter estimation for exponential model arising in epidemiological studies and biological assa

Hybrid modelling methods for epidemiological studies

Epidemiological modelling plays an important role in the study of the distribution of a disease and its impact in a given population, and helps to suggest effective control and prevention measures. The complexity of models as well as the modelling approach vary depending on a number of factors such as how much is understood about the disease epidemiology, the objective of the study and the nature of the data available. Compartmental models have shown to capture the macro level dynamics of an infectious disease outbreak and have been utilised to develop control policies and outbreak responses. However, they contain a limited account of the complex processes of dynamics of most infectious diseases. Unlike the continuous modelling framework, the Agent-based modelling (ABM) approach features the simulation of heterogeneous communities subjected to more realistic transmission scenarios and can incorporate complex and stochastic issues affecting diseases. This work provides an example of how to utilise the strength of both kinds of models through a hybrid approach that combines in situ field data with the parameters of a classical malaria model. The ABM simulations provide a computational laboratory for generating data on the impact of some complex factors on malaria prevalence. The ABM results can be extended to continuous time by inserting the values fitted by the classical response surface regression as the key coefficients of compartmental models. Another regression approach presented in this work is a cluster-integrated regression which helps to screen the incidence clusters where the available explanatory variables fail to predict, using a panel data. The cluster-integrated regression method also improves the accuracy of the model by providing more explanatory variables. In addition, the spatial autocorrelation study using global Moran’s I, the Geary’s C and Moran’s scatter plot was made to measure the timely spatial pattern of disease incidence in a country and to form the grouping. This was combined with a proposed metapopulation model that parameterizes and reassesses non-pharmaceutical interventions. The uncertainty quantification of model outputs using Markov Chain Monte Carlo (MCMC) techniques was done based on the notion of randomness in the modelling approach

Epidemics in Networks of Spatially Correlated Three-dimensional Root Branching Structures

Using digitized images of the three-dimensional, branching structures for root systems of bean seedlings, together with analytical and numerical methods that map a common 'SIR' epidemiological model onto the bond percolation problem, we show how the spatially-correlated branching structures of plant roots affect transmission efficiencies, and hence the invasion criterion, for a soil-borne pathogen as it spreads through ensembles of morphologically complex hosts. We conclude that the inherent heterogeneities in transmissibilities arising from correlations in the degrees of overlap between neighbouring plants, render a population of root systems less susceptible to epidemic invasion than a corresponding homogeneous system. Several components of morphological complexity are analysed that contribute to disorder and heterogeneities in transmissibility of infection. Anisotropy in root shape is shown to increase resilience to epidemic invasion, while increasing the degree of branching enhances the spread of epidemics in the population of roots. Some extension of the methods for other epidemiological systems are discussed.Comment: 21 pages, 8 figure

Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

Linking healthcare associated norovirus outbreaks: a molecular epidemiologic method for investigating transmission.

BACKGROUND: Noroviruses are highly infectious pathogens that cause gastroenteritis in the community and in semi-closed institutions such as hospitals. During outbreaks, multiple units within a hospital are often affected, and a major question for control programs is: are the affected units part of the same outbreak or are they unrelated transmission events? In practice, investigators often assume a transmission link based on epidemiological observations, rather than a systematic approach to tracing transmission.Here, we present a combined molecular and statistical method for assessing:1) whether observed clusters provide evidence of local transmission and2) the probability that anecdotally|linked outbreaks truly shared a transmission event. METHODS: 76 healthcare associated outbreaks were observed in an active and prospective surveillance scheme of 15 hospitals in the county of Avon, England from April 2002 to March 2003. Viral RNA from 64 out of 76 specimens from distinct outbreaks was amplified by reverse transcription-PCR and was sequenced in the polymerase (ORF 1) and capsid (ORF 2) regions. The genetic diversity, at the nucleotide level, was analysed in relation to the epidemiological patterns. RESULTS: Two out of four genetic and epidemiological clusters of outbreaks were unlikely to have occurred by chance alone, thus suggesting local transmission. There was anecdotal epidemiological evidence of a transmission link among 5 outbreaks pairs. By combining this epidemiological observation with viral sequence data, the evidence of a link remained convincing in 3 of these pairs. These results are sensitive to prior beliefs of the strength of epidemiological evidence especially when the outbreak strains are common in the background population. CONCLUSION: The evidence suggests that transmission between hospitals units does occur. Using the proposed criteria, certain hypothesized transmission links between outbreaks were supported while others were refuted. The combined molecular/epidemiologic approach presented here could be applied to other viral populations and potentially to other pathogens for a more thorough view of transmission