Incubation period of typhoidal salmonellosis : a systematic review and meta-analysis of outbreaks and experimental studies occurring over the last century

Background Salmonella Typhi is a human pathogen that causes typhoid fever. It is a major cause of morbidity and mortality in developing countries and is responsible for several outbreaks in developed countries. Studying certain parameters of the pathogen, such as the incubation period, provides a better understanding of its pathophysiology and its characteristics within a population. Outbreak investigations and human experimental studies provide an avenue to study these relevant parameters. Methods In this study, the authors have undertaken a systematic review of outbreak investigation reports and experimental studies, extracted reported data, tested for heterogeneity, identified subgroups of studies with limited evidence of heterogeneity between them and identified factors that may contribute to the distribution of incubation period. Following identification of relevant studies, we extracted both raw and summary incubation data. We tested for heterogeneity by deriving the value of I2 and conducting a KS-test to compare the distribution between studies. We performed a linear regression analysis to identify the factors associated with incubation period and using the resulting p-values from the KS-test, we conducted a hierarchical cluster analysis to classify studies with limited evidence of heterogeneity into subgroups. Results We identified thirteen studies to be included in the review and extracted raw incubation period data from eleven. The value of I2 was 84% and the proportion of KS test p-values that were less than 0.05 was 63.6% indicating high heterogeneity not due to chance. We identified vaccine history and attack rates as factors that may be associated with incubation period, although these were not significant in the multivariable analysis (p-value: 0.1). From the hierarchical clustering analysis, we classified the studies into five subgroups. The mean incubation period of the subgroups ranged from 9.7 days to 21.2 days. Outbreaks reporting cases with previous vaccination history were clustered in a single subgroup and reported the longest incubation period. Conclusions We identified attack rate and previous vaccination as possible associating factors, however further work involving analyses of individual patient data and developing mathematical models is needed to confirm these as well as examine additional factors that have not been included in our study

Within-host mathematical modelling of the incubation period of Salmonella Typhi

Mechanistic mathematical models are often employed to understand the dynamics of infectious diseases within a population or within a host. They provide estimates that may not be otherwise available. We have developed a within-host mathematical model in order to understand how the pathophysiology of Salmonella Typhi contributes to its incubation period. The model describes the process of infection from ingestion to the onset of clinical illness using a set of ordinary differential equations. The model was parametrized using estimated values from human and mouse experimental studies and the incubation period was estimated as 9.6 days. A sensitivity analysis was also conducted to identify the parameters that most affect the derived incubation period. The migration of bacteria to the caecal lymph node was observed as a major bottle neck for infection. The sensitivity analysis indicated the growth rate of bacteria in late phase systemic infection and the net population of bacteria in the colon as parameters that most influence the incubation period. We have shown in this study how mathematical models aid in the understanding of biological processes and can be used in estimating parameters of infectious diseases

Estimating parameter distributions and developing epidemiological methods and tools to support gastrointestinal outbreak investigation: Estimating incubation period of common gastrointestinal pathogens

Incubation periods of common gastrointestinal pathogens are cited inconsistently without a solid evidence base for these distributions or factors affecting incubation time. In some high-profile outbreaks, such as the STEC O104 outbreak in Germany or the large 2009 STEC O157 outbreak in South East England, incubation periods were longer than expected complicating the collection of exposure data and the outbreak investigation process. This research aimed to use observational and experimental data to understand the incubation period distribution of some common gastrointestinal pathogens and identify factors that influence these distributions. I conducted systematic reviews on the incubation period of Campylobacter, STEC and Salmonella Typhi. In addition to using traditional methods for testing and describing the pattern of heterogeneity, I developed methods for analysing data from observational studies which involved using Kolmogorov-Smirnov test and hierarchical cluster analysis. I also extracted and analysed individual patient data of cases associated with outbreaks of STEC reported in England and Wales. Finally, I developed a within-host mathematical model that simulated the process of Salmonella Typhi infection and conducted a sensitivity analysis to identify influencing parameters. The systematic reviews and meta-analysis of individual patient data indicated the presence of heterogeneity across outbreaks which could not be explained by chance alone. Some factors associated with the distribution of incubation period were age, food vehicle, mode of transmission, attack rate and vaccine history. The mathematical model estimated an incubation period of 9.6 days for Salmonella Typhi, and parameters strongly correlated with incubation period included the net population of bacteria in the colon and bacterial growth during systemic infection. Outputs of this research will inform the development of policies and public health interventions that contribute towards reducing the burden of disease due to gastrointestinal infections. Further research, developing a stochastic model of the incubation period of Salmonella Typhi, is recommended

Estimating parameter distributions and developing epidemiological methods and tools to support gastrointestinal outbreak investigation

Incubation periods of common gastrointestinal pathogens are cited inconsistently without a solid evidence base for these distributions or factors affecting incubation time. In some high-profile outbreaks, such as the STEC O104 outbreak in Germany or the large 2009 STEC O157 outbreak in South East England, incubation periods were longer than expected complicating the collection of exposure data and the outbreak investigation process. This research aimed to use observational and experimental data to understand the incubation period distribution of some common gastrointestinal pathogens and identify factors that influence these distributions. I conducted systematic reviews on the incubation period of Campylobacter, STEC and Salmonella Typhi. In addition to using traditional methods for testing and describing the pattern of heterogeneity, I developed methods for analysing data from observational studies which involved using Kolmogorov-Smirnov test and hierarchical cluster analysis. I also extracted and analysed individual patient data of cases associated with outbreaks of STEC reported in England and Wales. Finally, I developed a within-host mathematical model that simulated the process of Salmonella Typhi infection and conducted a sensitivity analysis to identify influencing parameters. The systematic reviews and meta-analysis of individual patient data indicated the presence of heterogeneity across outbreaks which could not be explained by chance alone. Some factors associated with the distribution of incubation period were age, food vehicle, mode of transmission, attack rate and vaccine history. The mathematical model estimated an incubation period of 9.6 days for Salmonella Typhi, and parameters strongly correlated with incubation period included the net population of bacteria in the colon and bacterial growth during systemic infection. Outputs of this research will inform the development of policies and public health interventions that contribute towards reducing the burden of disease due to gastrointestinal infections. Further research, developing a stochastic model of the incubation period of Salmonella Typhi, is recommended. </p

Data from: Within-host mathematical modelling of the incubation period of Salmonella Typhi

Mechanistic mathematical models are often employed to understand the dynamics of infectious diseases within a population or within a host. They provide estimates that may not otherwise be available. We have developed a within-host mathematical model in order to understand how the pathophysiology of Salmonella Typhi contributes to its incubation period. The model describes the process of infection from ingestion to onset of clinical illness using a set of ordinary differential equations. The model was parameterised using estimated values from human and mouse experimental studies and the incubation period was estimated as 9.6 days. A sensitivity analysis was also conducted to identify the parameters that most affect the derived incubation period. The migration of bacteria to the caecal lymph node was observed as a major bottle neck for infection. The sensitivity analysis indicated the growth rate of bacteria in late phase systemic infection and the net population of bacteria in the colon as parameters that most influence incubation period. We have shown in this study how mathematical models can be used in understanding biological processes and estimating parameters of infectious diseases

R script for publication

R codes used to develop model simulatio

Additional file 1: of Factors influencing the time between onset of illness and specimen collection in the diagnosis of non-pregnancy associated listeriosis in England and Wales

Data of non-pregnancy associated a listeriosis. (XLSX 94 kb

Utility of whole genome sequencing to describe the persistence and evolution of Listeria monocytogenes strains within crabmeat processing environments linked to two outbreaks of listeriosis

This article describes the identification and investigation of two extended outbreaks of listeriosis in which crabmeat was identified as the vehicle of infection. Comparing contemporary and retrospective typing data of Listeria monocytogenes isolates from clinical cases and from food and food processing environments allowed the detection of cases going back several years. This information, combined with the analysis of routinely collected enhanced surveillance data, helped to direct the investigation and identify the vehicle of infection. Retrospective whole genome sequencing (WGS) analysis of isolates provided robust microbiological evidence of links between cases, foods, and the environments in which they were produced and demonstrated that for some cases and foods, identified by fluorescent amplified fragment length polymorphism, the molecular typing method in routine use at the time, were not part of the outbreak. WGS analysis also showed that the strains causing illness had persisted in two food production environments for many years and in one producer had evolved into two strains over a period of around 8 years. This article demonstrates the value of reviewing L. monocytogenes typing data from clinical cases together with that from foods as a means of identifying potential vehicles and sources of infection in outbreaks of listeriosis. It illustrates the importance of reviewing retrospective L. monocytogenes typing alongside enhanced surveillance data to characterize extended outbreaks and inform control measures. Also, this article highlights the advantages of WGS analysis for strain discrimination and clarification of evolutionary relationships that refine outbreak investigations and improve our understanding of L. monocytogenes in the food chain