Dynamics of shigellosis epidemics: estimating individual-level transmission and reporting rates from national epidemiologic data sets

An Outbreak of Multidrug-Resistant Shigella flexneri Serotype 2a Among People Experiencing Homelessness in Vancouver

Background: We describe a community-based outbreak of multidrug-resistant Shigella flexneri serotype 2a among people experiencing homelessness (PEH) in Vancouver's Downtown Eastside during the COVID-19 pandemic. Methods: In this observational cohort study, we followed the Outbreak Reports and Intervention Studies of Nosocomial Infection (ORION) reporting guidelines. We identified cases by laboratory surveillance and collected demographic and clinical data from the medical charts or patient interviews. We implemented enhanced surveillance and disseminated testing and management guidelines. Shigella flexneri isolates were serotyped, and whole-genome sequencing was performed. Results: We identified 101 confirmed cases of Shigella flexneri 2a (80% male; median age 43) between 31 January and 16 December 2021. All the affected individuals experienced homelessness, and substance use disorder was the most common comorbidity (88%). Five patients required ICU hospitalization, and one death occurred within 30 days. Core-genome multilocus sequence typing analysis confirmed a clonal outbreak. All S. flexneri isolates were phenotypically and genotypically multidrug-resistant. Conclusions: COVID-19 exacerbated longstanding public health concerns around the dearth of hygiene and sanitation resources available to PEH. Preventing similar outbreaks will require addressing these risks and finding solutions to the crisis of homelessness in Canada.

Wastewater surveillance beyond COVID-19: a ranking system for communicable disease testing in the tri-county Detroit area, Michigan, USA

Introduction

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been utilized to monitor the disease in the United States through routine national, statewide, and regional monitoring projects. A significant canon of evidence was produced showing that wastewater surveillance is a credible and effective tool for disease monitoring. Hence, the application of wastewater surveillance can extend beyond monitoring SARS-CoV-2 to encompass a diverse range of emerging diseases. This article proposed a ranking system for prioritizing reportable communicable diseases (CDs) in the Tri-County Detroit Area (TCDA), Michigan, for future wastewater surveillance applications at the Great Lakes Water Authority's Water Reclamation Plant (GLWA's WRP).

Methods

The comprehensive CD wastewater surveillance ranking system (CDWSRank) was developed based on 6 binary and 6 quantitative parameters. The final ranking scores of CDs were computed by summing the multiplication products of weighting factors for each parameter, and then were sorted based on decreasing priority. Disease incidence data from 2014 to 2021 were collected for the TCDA. Disease incidence trends in the TCDA were endowed with higher weights, prioritizing the TCDA over the state of Michigan.

Results

Disparities in incidences of CDs were identified between the TCDA and state of Michigan, indicating epidemiological differences. Among 96 ranked CDs, some top ranked CDs did not present relatively high incidences but were prioritized, suggesting that such CDs require significant attention by wastewater surveillance practitioners, despite their relatively low incidences in the geographic area of interest. Appropriate wastewater sample concentration methods are summarized for the application of wastewater surveillance as per viral, bacterial, parasitic, and fungal pathogens.

Discussion

The CDWSRank system is one of the first of its kind to provide an empirical approach to prioritize CDs for wastewater surveillance, specifically in geographies served by centralized wastewater collection in the area of interest. The CDWSRank system provides a methodological tool and critical information that can help public health officials and policymakers allocate resources. It can be used to prioritize disease surveillance efforts and ensure that public health interventions are targeted at the most potentially urgent threats. The CDWSRank system can be easily adopted to geographical locations beyond the TCDA.

Effects of temperature on incidence of bacillary dysentery in a temperate continental arid climate city in northwest China

The effect of ambient temperature on health continues to draw more and more attention with the global warming. Bacillary dysentery (BD) is a major global environmental health issue and affected by temperature and other environmental variables. In the current study, we evaluated the effect of temperature on the incidence of BD from January 1st, 2008 to December 31st, 2011 in Jiayuguan, a temperate continental arid climate city in the Hexi Corridor of northwest China. A distributed lag non-linear model (DLNM) was performed to evaluate the lag effect of temperature on BD up to 30 days. Results showed the risk of BD increased with temperature significantly, especially after 8 °C. The maximum risk of BD was observed at extreme high temperature (29 °C). The effect of temperature on BD risk was significantly divided into short-term effect at lag 5 days and long-term effect at lag 30 days. Age ≤ 15 years were most affected by high temperature. The maximum cumulative risk for lag 30 days (25.8, 95% CIs: 11.8-50.1) was observed at 29 °C. Age ≤ 15 years and females showed short-term effect at lag 5 days and long-term effect at lag 30 days, while age > 15 years and males showed acute short-term effect at lag 0 and light long-term effect at lag 16 days.

The size of the susceptible pool differentiates climate effects on seasonal epidemics of bacillary dysentery

OBJECTIVES

At present, some studies have pointed out several possible climate drivers of bacillary dysentery. However, there is a complex nonlinear interaction between climate drivers and susceptible population in the spread of diseases, which makes it challenging to detect climate drivers at the size of susceptible population.

METHODS

By using empirical dynamic modeling (EDM), the climate drivers of bacillary dysentery dynamic were explored in China's five temperature zones.

RESULTS

We verified the availability of climate drivers and susceptible population size on bacillary dysentery, and used this information for bacillary dysentery dynamic prediction. Moreover, we found that their respective effects increased with the increase of temperature and relative humidity, and their states (temperature and relative humidity) were different when they reached their maximum effects, and the negative effect between the effect of temperature and disease incidence increased with the change of temperature zone (from temperate zone to warm temperate zone to subtropical zone) and the climate driving effect of the temperate zone (warm temperate zone) was greater than that of the colder (temperate zone) and warmer (subtropics) zones. When we viewed from single temperature zone, the climatic effect arose only when the size of the susceptible pool was large.

CONCLUSIONS

These results provide empirical evidence that the climate factors on bacillary dysentery are nonlinear, complex but dependent on the size of susceptible populations and different climate scenarios.

Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.

Relative transmissibility of shigellosis among male and female individuals: a modeling study in Hubei Province, China

BACKGROUND

Developing countries exhibit a high disease burden from shigellosis. Owing to the different incidences in males and females, this study aims to analyze the features involved in the transmission of shigellosis among male (subscript m) and female (subscript f) individuals using a newly developed sex-based model.

METHODS

The data of reported shigellosis cases were collected from the China Information System for Disease Control and Prevention in Hubei Province from 2005 to 2017. A sex-based Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model was applied to explore the dataset, and a sex-age-based SEIAR model was applied in 2010 to explore the sex- and age-specific transmissions.

RESULTS

From 2005 to 2017, 130 770 shigellosis cases (including 73 981 male and 56 789 female cases) were reported in Hubei Province. The SEIAR model exhibited a significant fitting effect with the shigellosis data (P <  0.001). The median values of the shigellosis transmission were 2.3225 × 10⁸ for SAR_mm (secondary attack rate from male to male), 2.5729 × 10⁸ for SAR_mf, 2.7630 × 10^-8 for SAR_fm, and 2.1061 × 10^-8 for SAR_ff. The top five mean values of the transmission relative rate in 2010 (where the subscript 1 was defined as male and age ≤ 5 years, 2 was male and age 6 to 59 years, 3 was male and age ≥ 60 years, 4 was female and age ≤ 5 years, 5 was female and age 6 to 59 years, and 6 was male and age ≥ 60 years) were 5.76 × 10^-8 for β₆₁, 5.32 × 10^-8 for β₃₁, 4.01 × 10^-8 for β₃₄, 7.52 × 10^-9 for β₆₂, and 6.04 × 10^-9 for β₆₄.

CONCLUSIONS

The transmissibility of shigellosis differed among male and female individuals. The transmissibility between the genders was higher than that within the genders, particularly female-to-male transmission. The most important route in children (age ≤ 5 years) was transmission from the elderly (age ≥ 60 years). Therefore, the greatest interventions should be applied in females and the elderly.

Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana

Diarrheal disease is the second largest cause of mortality in children younger than 5, yet our ability to anticipate and prepare for outbreaks remains limited. Here, we develop and test an epidemiological forecast model for childhood diarrheal disease in Chobe District, Botswana. Our prediction system uses a compartmental susceptible-infected-recovered-susceptible (SIRS) model coupled with Bayesian data assimilation to infer relevant epidemiological parameter values and generate retrospective forecasts. Our model inferred two system parameters and accurately simulated weekly observed diarrhea cases from 2007-2017. Accurate retrospective forecasts for diarrhea outbreaks were generated up to six weeks before the predicted peak of the outbreak, and accuracy increased over the progression of the outbreak. Many forecasts generated by our model system were more accurate than predictions made using only historical data trends. Accurate real-time forecasts have the potential to increase local preparedness for coming outbreaks through improved resource allocation and healthcare worker distribution.

Estimating the transmissibility of hand, foot, and mouth disease by a dynamic model

OBJECTIVE

Hand, foot, and mouth disease (HFMD) is a serious public health issue in many countries; however, its transmissibility in county-level outbreaks remains unclear. The aim of this study is to estimate the transmissibility of HFMD epidemics on both city level and county level, for a better understanding of the transmission dynamics of HFMD epidemics.

STUDY DESIGN

Simulation based on data obtained from the China Information System for Disease Control and Prevention.

METHODS

The weekly number of reported HFMD cases from April 2009 to December 2017 in nine regions of Changsha City was collected. A susceptible-infectious-recovered model was used to estimate the transmissibility of HFMD. The reproduction number of reported cases during the ascending (denoted as R_asc) and descending (denoted as R_des) period was used to describe the transmissibility of HFMD.

RESULTS

The R_asc and R_des for HFMD in Changsha was 1.44 (95% confidence interval [CI]: 1.41-1.48) and 0.71 (95% CI: 0.69-0.73), respectively. There was no statistical significance of R_asc values among nine regions (F = 1.056, P = 0.396), nor of R_des values among nine regions (F = 1.676, P = 0.106). The average R_asc (1.53, 95% CI: 1.46-1.61) from 2009 to 2012 was higher than the one (1.37, 95% CI: 1.34-1.40) from 2013 to 2017 (t = 3.974, P < 0.001), but the average R_des (0.67, 95% CI: 0.63-0.70) from 2009 to 2012 was lower than the one (0.74, 95% CI: 0.73-0.76) from 2013 to 2017 (t = -3.751, P < 0.001).

CONCLUSIONS

The epidemic of HFMD in Changsha City is still grim, and integrated strategies should be taken for controlling and preventing HFMD.

When are pathogen genome sequences informative of transmission events?

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data.

The increasing availability of genetic sequence data has sparked an interest in using pathogen whole genome sequences to reconstruct the history of individual transmission events in an infectious disease outbreak. However, such methodologies rely on pathogen genomes mutating rapidly enough to discriminate between infected individuals, an assumption that remains to be investigated. To determine pathogen outbreaks for which genetic data is expected to be informative of transmission events, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating pathogen genome sequences sampled from transmission pairs. We characterise transmission divergence of ten major outbreak causing pathogens using simulations and find significant variation between diseases, with viral outbreaks generally exhibiting higher transmission divergence than bacterial ones. We reconstruct these outbreaks using the R-packages outbreaker and phybreak and find that genetic sequence data, though useful for rapidly evolving pathogens, provides little to no information about outbreaks with low transmission divergence, such as Streptococcus pneumoniae and Shigella sonnei. Our results demonstrate the need to incorporate other sources of outbreak data, such as contact tracing data and spatial location data, into outbreak reconstruction tools.

Heavy precipitation as a risk factor for shigellosis among homeless persons during an outbreak - Oregon, 2015-2016

OBJECTIVES

Shigella species are the third most common cause of bacterial gastroenteritis in the United States. During a Shigella sonnei outbreak in Oregon from July 2015 through June 2016, Shigella cases spread among homeless persons with onset of the wettest rainy season on record.

METHODS

We conducted time series analyses using Poisson regression to determine if a temporal association between precipitation and shigellosis incidence existed. Models were stratified by housing status.

RESULTS

Among 105 infections identified, 45 (43%) occurred in homeless persons. With increasing precipitation, cases increased among homeless persons (relative risk [RR] = 1.36 per inch of precipitation during the exposure period; 95% confidence interval [CI] = 1.17-1.59), but not among housed persons (RR = 1.04; 95% CI 0.86-1.25).

CONCLUSIONS

Heavy precipitation likely contributed to shigellosis transmission among homeless persons during this outbreak. When heavy precipitation is forecast, organizations working with homeless persons could consider taking proactive measures to mitigate spread of enteric infections.

Invasion of HEp-2 cells by Shigella spp. isolated from acute pediatric diarrhea

Aim:Shigella infection is an important global health problem in developing countries where hygiene is poor and hence shigellosis is a main cause of diarrhoea-associated mortality and morbidity, particularly in children under the age of five. The bacterial entry into colon and rectal epithelial cells has been named ‘bacterium-directed phagocytosis’. This term highlights that the bacteria actively stimulate their own uptake into non-professional phagocytes. The aim of this study was to demonstrate the invasion of HEp-2 cells by Shigella spp. isolated from acute pediatric diarrhea in Tehran, Iran.

Methods: Three-hundred and ten non-duplicative diarrheal stool samples were collected from the children admitted to Children’s Medical Center in Tehran, Iran. Samples were cultured and suspected colonies were identified by routine microbiological and biochemical tests. The invasion of the two isolated Shigella spp. to HEp-2 cells was studied.

Results: Of 310 stool samples, 16 (5.2%) Shigella spp. were isolated, including seven (43.7%) S. sonnei and nine (56.3%) S. flexneri. Four (44.4%) S. sonnei and seven (42.8%) S. flexneri showed invasive phenotype to HEp-2.

Conclusion:Shigella sonnei and S. flexneri are reported as the most prevalent Shigella spp. in nature which infect humans. Invasion of various cell lines gives the chance of survival to Shigella spp. This ability causes more virulent infections in the host. Despite costly and time consuming cell culture techniques, the current method described in this paper is reliable for detecting invasive behavior of Shigella spp. Results have also shown that not all the Shigella spp. are able to invade intestinal epithelial cells.

Rapid mortality transition of Pacific Islands in the 19th century

The depopulation of Pacific islands during the 16th to 19th centuries is a striking example of historical mass mortality due to infectious disease. Pacific Island populations have not been subject to such cataclysmic infectious disease mortality since. Here we explore the processes which could have given rise to this shift in infectious disease mortality patterns. We show, using mathematical models, that the population dynamics exhibited by Pacific Island populations are unlikely to be the result of Darwinian evolution. We propose that extreme mortality during first-contact epidemics is a function of epidemiological isolation, not a lack of previous selection. If, as pathogens become established in populations, extreme mortality is rapidly suppressed by herd immunity, Pacific Island population mortality patterns can be explained with no need to invoke genetic change. We discuss the mechanisms by which this could occur, including (i) a link between the proportion of the population transmitting infectious agents and case-fatality rates, and (ii) the course of infection with pathogens such as measles and smallpox being more severe in adults than in children. Overall, we consider the present-day risk of mass mortality from newly emerging infectious diseases is unlikely to be greater on Pacific islands than in other geographical areas.

Enteric pathogens and factors associated with acute bloody diarrhoea, Kenya

BACKGROUND

Shigellosis is the major cause of bloody diarrhoea worldwide and is endemic in most developing countries. In Kenya, bloody diarrhoea is reported weekly as part of priority diseases under Integrated Disease Surveillance and Response System (IDSR) in the Ministry of Health.

METHODS

We conducted a case control study with 805 participants (284 cases and 521 controls) between January and December 2012 in Kilifi and Nairobi Counties. Kilifi County is largely a rural population whereas Nairobi County is largely urban. A case was defined as a person of any age who presented to outpatient clinic with acute diarrhoea with visible blood in the stool in six selected health facilities in the two counties within the study period. A control was defined as a healthy person of similar age group and sex with the case and lived in the neighbourhood of the case.

RESULTS

The main presenting clinical features for bloody diarrhoea cases were; abdominal pain (69 %), mucous in stool (61 %), abdominal discomfort (54 %) and anorexia (50 %). Pathogen isolation rate was 40.5 % with bacterial and protozoal pathogens accounting for 28.2 % and 12.3 % respectively. Shigella was the most prevalent bacterial pathogen isolated in 23.6 % of the cases while Entamoeba histolytica was the most prevalent protozoal pathogen isolated in 10.2 % of the cases. On binary logistic regression, three variables were found to be independently and significantly associated with acute bloody diarrhoea at 5 % significance level; storage of drinking water separate from water for other use (OR = 0.41, 95 % CI 0.20-0.87, p = 0.021), washing hands after last defecation (OR = 0.24, 95 % CI 0.08-.076, p = 0.015) and presence of coliforms in main source water (OR = 2.56, CI 1.21-5.4, p = 0.014). Rainfall and temperature had strong positive correlation with bloody diarrhoea.

CONCLUSION

The main etiologic agents for bloody diarrhoea were Shigella and E. histolytica. Good personal hygiene practices such as washing hands after defecation and storing drinking water separate from water for other use were found to be the key protective factors for the disease while presence of coliform in main water source was found to be a risk factor. Implementation of water, sanitation and hygiene (WASH) interventions is therefore key in prevention and control of bloody diarrhoea.