A descriptive analysis of the spatio-temporal distribution of enteric diseases in New Brunswick, Canada

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Incidence, Demographic, and Seasonal Risk Factors of Infections Caused by Five Major Enteric Pathogens, Ontario, Canada, 2010-2017

In Canada, enteric infections cause significant health and economic burden. We evaluated the individual characteristics of laboratory-confirmed cases of Campylobacter spp. (n = 28,728), non-typhoidal Salmonella spp. (n = 22,640), Yersinia spp. (n = 1674), Verotoxin-producing Escherichia coli (VTEC; n = 1340), and Listeria monocytogenes (n = 471), reported between 2010 and 2017 inclusive, in Ontario, Canada (population ∼13,500,000). We calculated overall and pathogen-specific annual and mean incidence rates (IRs) for Ontario. We used multivariable Poisson and negative binomial regression models to estimate incidence rate ratios (IRRs) for years, seasons, age groups, and sexes, and we included two-way age and sex interaction terms in the models. Campylobacter and Salmonella infections had the highest IRs whereas Listeria infections had the lowest IRs. None of the infections showed long-term trends over the 8-year study period; however, rates of all five infections were elevated in the summer. More Salmonella, VTEC, and Listeria infections were linked to disease outbreaks than were Campylobacter and Yersinia infections. Overall, mean IRs of Campylobacter, Salmonella, Yersinia, and VTEC infections were highest in children 0-4 years old, whereas Listeria IRs peaked in adults 60 years and older. Higher mean IRs of Campylobacter were observed in males. No other differences by sex were statistically significant. The same mean rate was observed in both sexes for Listeria. Adjusting for all other factors, significant age- and sex-specific differences in IRs were observed in Campylobacter, Salmonella, and VTEC infection rates. No significant interactions of age and sex were found for Yersinia and Listeria infections. Future research should focus on the pathogen-specific socioeconomic, environmental, or agricultural risk factors that might be responsible for these infections.

Area-Level Clustering of Shiga Toxin-Producing Escherichia coli Infections and Their Socioeconomic and Demographic Factors in Ontario, Canada: An Ecological Study

Shiga toxin-producing Escherichia coli (STEC) infections are an important health burden for human populations in Ontario and worldwide. We assessed 452 STEC cases that were reported to Ontario's reportable disease surveillance system between 2015 and 2017. A retrospective scan statistic using a Poisson model was used to detect high-rate STEC clusters at the forward sortation area (FSA; the first three digits of a postal code) level. A significant spatial cluster in the southwest region of Ontario was identified. A case-case logistic regression analysis was applied to compare FSA-level socioeconomic and demographic characteristics among STEC cases included inside the spatial cluster with cases outside of the cluster. Cases included in the spatial cluster had higher odds of living in FSAs with a low median family income, low proportion of lone-parent families, and low proportion of the visible minority population. In addition, STEC cases inside the cluster had higher odds of coming from rural FSAs. Our study demonstrated that STEC cases were spatially clustered in Ontario and their clustering was associated with FSA-level socioeconomic and demographic determinants of cases.

Associations between climatic parameters and the human salmonellosis in Yazd province, Iran

Salmonella is one of the most common causes of foodborne disease outbreaks in developing countries. Climatic factors such as temperature, rainfall, and relative humidity can directly increase the growth and spread of these pathogens. Therefore, the aim of this study was to investigate long-term temporal trends and seasonal patterns of Salmonella infections as well as evaluating the effects of demographic and climatic factors on the infection incidence in Yazd province, Iran during 2012-2015. The incidence of Salmonella infections was highest among patients with the age group of ≤5 years and peaked in summer, especially during June. Contrary to expectations, no significant associations were seen between the average monthly temperature, rainfall or humidity and incidence rate (IR) of salmonellosis. Interestingly, atmospheric dust hovering was significantly associated with an increased risk of salmonellosis. Transmission pathways of Salmonella spp. in communities should be considered as a complex ecological process that animal reservoirs, socio-economic factors, and lifestyle behaviors need to be addressed in future studies.

Evaluating the risks associated with Shiga-toxin-producing Escherichia coli (STEC) in private well waters in Canada

Shiga-toxin-producing Escherichia coli (STEC) represent a major concern for waterborne disease outbreaks associated with consumption of contaminated groundwater. Over 4 million people rely on private groundwater systems as their primary drinking water source in Canada; many of these systems do not meet current standards for water quality. This manuscript provides a scoping overview of studies examining STEC prevalence and occurrence in groundwater, and it includes a synopsis of the environmental variables affecting survival, transport, persistence, and overall occurrence of these important pathogenic microbes in private groundwater wells used for drinking purposes.

Application of kernel smoothing to estimate the spatio-temporal variation in risk of STEC O157 in England

Identifying geographical areas with significantly higher or lower rates of infectious diseases can provide important aetiological clues to inform the development of public health policy and interventions designed to reduce morbidity. We applied kernel smoothing to estimate the spatial and spatio-temporal variation in risk of STEC O157 infection in England between 2009 and 2015, and to explore differences between the residential locations of cases reporting travel and those not reporting travel. We provide evidence that the distribution of STEC O157 infection in England is non-uniform with respect to the distribution of the at-risk population; that the spatial distribution of the three main genetic lineages infecting humans (I, II and I/II) differs significantly and that the spatio-temporal risk is highly dynamic. Our results also indicate that cases of STEC O157 reporting travel within or outside the UK are more likely to live in the south/south-east of the country, meaning that their residential location may not reflect the location of exposure that led to their infection. We suggest that the observed variation in risk reflects exposure to sources of STEC O157 that are geographically prescribed. These differences may be related to a combination of changes in the strains circulating in the ruminant reservoir, animal movements (livestock, birds or wildlife) or the behavior of individuals prior to infection. Further work to identify the importance of behaviours and exposures reported by cases relative to residential location is needed.

Epidemiologic features of shigellosis and associated climatic factors in Taiwan

The consistent, sporadic transmission of shigellosis in Taiwan necessitates an exploration of risk factors for the occurrence of shigellosis. The purpose of this study was to study the epidemiologic characteristics and the relationship between climatic factors and the incidence of shigellosis in Taiwan. We collected data from cases of shigellosis reported to the Taiwan Centers for Disease Control (Taiwan CDC) from 2001 to 2016. Climatic data were obtained from the Taiwan Central Weather Bureau. The relationships between weather variability and the incidence of shigellosis in Taiwan were determined via Poisson regression analyses. During the 16-year study period, a total of 4171 clinical cases of shigellosis were reported to the Taiwan CDC. Among them, 1926 (46.2%) were classified as confirmed cases. The incidence of shigellosis showed significant seasonality, with the majority of cases occurring in summertime (for oscillation, P < .001). The number of shigellosis cases started to increase when temperatures reached 21°C (r = 0.88, P < .001). Similarly, the number of shigellosis cases began to increase at a relative humidity of 70-74% (r = 0.75, P < .005). The number of shigellosis cases was positively associated with the mean temperature and relative humidity in the period preceding the infection. In conclusion, the occurrence of shigellosis is significantly associated with increasing temperature and relative humidity in Taiwan. Therefore, these factors could be regarded as warning signals indicating the need to implement preventive measures.

Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China's Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.

The Distribution of Enteric Infections Utilizing Stool Microbial Polymerase Chain Reaction Testing in Clinical Practice

BACKGROUND

Gastrointestinal infection is a major cause of morbidity. We sought to characterize the pathogenic etiologies of gastrointestinal infection to identify seasonal patterns and predictors of specific infections utilizing a multiplex PCR assay in clinical practice.

METHODS

We performed a cross-sectional study of 9403 patients who underwent 13,231 stool tests with a FilmArray gastrointestinal pathogen PCR panel during an episode of diarrhea from March 2015 to May 2017. Our primary outcome was the presence of a positive panel. Logistic regression was used to test for associations between season and infections.

RESULTS

A positive result was found in 3426 tests (25.9%) in 2988 patients (31.8%), yielding 4667 pathogens consisting of 1469 viruses (31.5%), 2925 bacteria (62.7%), and 273 parasites (5.8%). Age less than 50 years was associated with a higher prevalence of pathogens compared to age ≥ 50 (p < 0.0001). The overall prevalence of a positive result for bacteria peaked in the summer (635, 29.2%), and the prevalence of viruses peaked in the winter (446, 31.8%). Compared to the winter, testing in the summer yielded a higher prevalence of bacteria (OR 1.52, 95% CI 1.33, 1.73, p < 0.0001) and lower odds of viruses (OR 0.69, 95% CI 0.58, 0.81, p < 0.0001), primarily driven by E. coli species and norovirus.

CONCLUSIONS

Season was a major determinant in detecting specific pathogens. Our substantially lower positivity rate than previous reports in the literature on multiplex PCR assays may more accurately reflect true clinical practice. Recognizing the temporal distribution of enteric pathogens may help facilitate empiric treatment decisions in certain clinical situations.

Burden of salmonellosis, campylobacteriosis and listeriosis: a time series analysis, Belgium, 2012 to 2020

Salmonellosis, campylobacteriosis and listeriosis are food-borne diseases. We estimated and forecasted the number of cases of these three diseases in Belgium from 2012 to 2020, and calculated the corresponding number of disability-adjusted life years (DALYs). The salmonellosis time series was fitted with a Bai and Perron two-breakpoint model, while a dynamic linear model was used for campylobacteriosis and a Poisson autoregressive model for listeriosis. The average monthly number of cases of salmonellosis was 264 (standard deviation (SD): 86) in 2012 and predicted to be 212 (SD: 87) in 2020; campylobacteriosis case numbers were 633 (SD: 81) and 1,081 (SD: 311); listeriosis case numbers were 5 (SD: 2) in 2012 and 6 (SD: 3) in 2014. After applying correction factors, the estimated DALYs for salmonellosis were 102 (95% uncertainty interval (UI): 8-376) in 2012 and predicted to be 82 (95% UI: 6-310) in 2020; campylobacteriosis DALYs were 1,019 (95% UI: 137-3,181) and 1,736 (95% UI: 178-5,874); listeriosis DALYs were 208 (95% UI: 192-226) in 2012 and 252 (95% UI: 200-307) in 2014. New actions are needed to reduce the risk of food-borne infection with Campylobacter spp. because campylobacteriosis incidence may almost double through 2020.