Pathogen seasonality and links with weather in England and Wales: a big data time series analysis

Impact of extreme weather events on the occurrence of infectious diseases in Belgium from 2011 to 2021

The role of meteorological factors, such as rainfall or temperature, as key players in the transmission and survival of infectious agents is poorly understood. The aim of this study was to compare meteorological surveillance data with epidemiological surveillance data in Belgium and to investigate the association between intense weather events and the occurrence of infectious diseases. Meteorological data were aggregated per Belgian province to obtain weekly average temperatures and rainfall per province and categorized according to the distribution of the variables. Epidemiological data included weekly cases of reported pathogens responsible for gastroenteritis, respiratory, vector-borne and invasive infections normalized per 100 000 population. The association between extreme weather events and infectious events was determined by comparing the mean weekly incidence of the considered infectious diseases after each weather event that occurred after a given number of weeks. Very low temperatures were associated with higher incidences of influenza and parainfluenza viruses, Mycoplasma pneumoniae, rotavirus and invasive Streptococcus pneumoniae and Streptococcus pyogenes infections, whereas very high temperatures were associated with higher incidences of Escherichia coli, Salmonella spp., Shigella spp., parasitic gastroenteritis and Borrelia burgdorferi infections. Very heavy rainfall was associated with a higher incidence of respiratory syncytial virus, whereas very low rainfall was associated with a lower incidence of adenovirus gastroenteritis. This work highlights not only the relationship between temperature or rainfall and infectious diseases but also the most extreme weather events that have an individual influence on their incidence. These findings could be used to develop adaptation and mitigation strategies.

Exploring the association of weather variability on Campylobacter - A systematic review

BACKGROUND

Previous work has found climate change-induced weather variability is suspected to increase the transmission of enteric pathogens, including Campylobacter, a leading cause of bacterial gastroenteritis. While the relationship between extreme weather events and diarrheal diseases has been documented, the specific impact on Campylobacter infections remains underexplored.

OBJECTIVE

To synthesize the peer-reviewed literature exploring the effect of weather variability on Campylobacter infections in humans.

METHODS

The review included English language, peer-reviewed articles, published up to September 1, 2022 in PubMed, Embase, GEOBASE, Agriculture and Environmental Science Database, and CABI Global Health exploring the effect of an antecedent weather event on human enteric illness caused by Campylobacter (PROSPERO Protocol # 351884). We extracted study information including data sources, methods, summary measures, and effect sizes. Quality and weight of evidence reported was summarized and bias assessed for each article.

RESULTS

After screening 278 articles, 47 articles (34 studies, 13 outbreak reports) were included in the evidence synthesis. Antecedent weather events included precipitation (n = 35), temperature (n = 30), relative humidity (n = 7), sunshine (n = 6), and El Niño and La Niña (n = 3). Reviewed studies demonstrated that increases in precipitation and temperature were correlated with Campylobacter infections under specific conditions, whereas low relative humidity and sunshine were negatively correlated. Articles estimating the effect of animal operations (n = 15) found presence and density of animal operations were significantly associated with infections. However, most of the included articles did not assess confounding by seasonality, presence of animal operations, or describe estimates of risk.

DISCUSSION

This review explores what is known about the influence of weather events on Campylobacter and identifies previously underreported negative associations between low relative humidity and sunshine on Campylobacter infections. Future research should explore pathogen-specific estimates of risk, which can be used to influence public health strategies, improve source attribution and causal pathways, and project disease burden due to climate change.

Simultaneous detection and characterization of common respiratory pathogens in wastewater through genomic sequencing

Genomic surveillance of SARS-CoV-2 has given insight into the evolution and epidemiology of the virus and its variant lineages during the COVID-19 pandemic. Expanding this approach to include a range of respiratory pathogens can better inform public health preparedness for potential outbreaks and epidemics. Here, we simultaneously sequenced 38 pathogens including influenza viruses, coronaviruses and bocaviruses, to examine the abundance and seasonality of respiratory pathogens in urban wastewater. We deployed a targeted bait capture method and short-read sequencing (Illumina Respiratory Virus Oligos Panel; RVOP) on composite wastewater samples from 8 wastewater treatment plants (WWTPs) and one associated hospital site. By combining seasonal sampling with whole genome sequencing, we were able to concurrently detect and characterise a range of common respiratory pathogens, including SARS-CoV-2, adenovirus and parainfluenza virus. We demonstrated that 38 respiratory pathogens can be detected at low abundances year-round, that hospital pathogen diversity is higher in winter vs. summer sampling events, and that significantly more viruses are detected in raw influent compared to treated effluent samples. Finally, we compared detection sensitivity of RT-qPCR vs. next generation sequencing for SARS-CoV-2, enteroviruses, influenza A/B, and respiratory syncytial viruses. We conclude that both should be used in combination; RT-qPCR allowed accurate quantification, whilst genomic sequencing detected pathogens at lower abundance. We demonstrate the valuable role of wastewater genomic surveillance and its contribution to the field of wastewater-based epidemiology, gaining rapid understanding of the seasonal presence and persistence for common respiratory pathogens. By simultaneously monitoring seasonal trends and early warning signs of many viruses circulating in communities, public health agencies can implement targeted prevention and rapid response plans.

Seasonality of presentation and birth in catatonia

BACKGROUND

Catatonia is a neuropsychiatric syndrome associated with both psychiatric disorders and medical conditions. Understanding of the pathophysiology of catatonia remains limited, and the role of the environment is unclear. Although seasonal variations have been shown for many of the disorders underlying catatonia, the seasonality of this syndrome has not yet been adequately explored.

METHODS

Clinical records were screened to identify a cohort of patients suffering from catatonia and a control group of psychiatric inpatients, from 2007 to 2016 in South London. In a cohort study, the seasonality of presentation was explored fitting regression models with harmonic terms, while the effect of season of birth on subsequent development of catatonia was analyzed using regression models for count data. In a case-control study, the association between month of birth and catatonia was studied fitting logistic regression models.

RESULTS

In total, 955 patients suffering from catatonia and 23,409 controls were included. The number of catatonic episodes increased during winter, with a peak in February. Similarly, an increasing number of cases was observed during summer, with a second peak in August. However, no evidence for an association between month of birth and catatonia was found.

CONCLUSIONS

The presentation of catatonia showed seasonal variation in accordance with patterns described for many of the disorders underlying catatonia, such as mood disorders and infections. We found no evidence for an association between season of birth and risk of developing catatonia. This may imply that recent triggers may underpin catatonia, rather than distal events.

A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.

Detection of Selected Equine Respiratory Pathogens in Stall Samples Collected at a Multi-Week Equestrian Show during the Winter Months

The aim of this study was to use environmental sampling to determine the frequency of detection of selected equine respiratory viruses and bacteria in horses attending a multi-week equestrian show during the winter months. At four time points during showing, environmental sponge samples were collected from all stalls on the property and tested for the presence of equine herpesvirus-1 (EHV-1), EHV-2, EHV-4, equine influenza virus (EIV), equine rhinitis B virus (ERBV), Streptococcus equi ss. equi (S. equi), and S. equi ss. zooepidemicus (S. zooepidemicus) using real-time PCR (PCR). Environmental sponges were collected from all 53 barns by using one sponge for up to 10 stalls. Further, 2/53 barns were randomly selected for individual stall sampling in order to compare the results between individual and pooled stall samples. A total of 333/948 (35.13%, 95% CI 32.09-38.26%) pooled environmental stall sponges tested PCR-positive for at least one of the selected respiratory pathogens. Streptococcus zooepidemicus was the most commonly detected pathogen in pooled samples (28.69%, 95% CI 25.83-31.69%), followed by EHV-2 (14.45%, 95% CI 12.27-16.85%), EHV-4 (1.37%, 95% CI 0.73-2.33%), and a very small percentage of pooled stall sponges tested PCR-positive for EHV-1, ERBV, EIV, and S. equi. In individual samples, 171/464 (36.85%, 95% CI 32.45-41.42%) environmental stall sponges tested PCR-positive for at least one of the selected pathogens, following a similar frequency of pathogen detection as pooled samples. The detection frequency of true respiratory pathogens from environmental samples was higher during the winter months compared to previous studies performed during spring and summer, and this testing highlights that such pathogens circulate with greater frequency during the colder months of the year. The strategy of monitoring environmental stall samples for respiratory pathogens circumvents the often labor-intensive collection of respiratory secretions from healthy horses and allows for a more efficient assessment of pathogen buildup over time. However, environmental stall testing for respiratory pathogens should not replace proper biosecurity protocols, but it should instead be considered as an additional tool to monitor the silent circulation of respiratory pathogens in at-risk horses.

Impact of meteorological factors on the incidence of hand-foot-mouth disease in Yangzhou from 2017 to 2022: a time series study

Background

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China, and numerous studies have indicated a close association between HFMD incidence and meteorological factors. This study aims to investigate the relationship between meteorological factors and HFMD in Yangzhou City, Jiangsu Province, China.

Methods

HFMD case reports and meteorological data from Yangzhou City between 2017 and 2022 were extracted from the National Notifiable Infectious Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) was employed to assess the exposure-response relationship between meteorological factors and HFMD. Subsequently, a distributed lag nonlinear model (DLNM) was used to explore the exposure-lag-effect of meteorological factors on HFMD.

Results

HFMD in Yangzhou City exhibits obvious seasonality and periodicity. There is an inverted "U" shaped relationship between average temperature and the risk of HFMD, with the maximum lag effect observed at a temperature of 25°C with lag 0 day (RR = 2.07, 95% CI: 1.74-2.47). As the duration of sunshine and relative humidity increase, the risk of HFMD continuously rises, with the maximum lag effect observed at a sunshine duration of 12.4 h with a lag of 14 days (RR = 2.10, 95% CI: 1.17-3.77), and a relative humidity of 28% with a lag of 14 days (RR = 1.21, 95% CI: 1.01-1.64). There is a "U" shaped relationship between average atmospheric pressure and the risk of HFMD, with the maximum effect observed at an atmospheric pressure of 989 hPa with no lag (RR = 1.45, 95% CI: 1.25-1.69). As precipitation increases, the risk of HFMD decreases, with the maximum effect observed at a precipitation of 151 mm with a lag of 14 days (RR = 1.45, 95% CI: 1.19-2.53).

Conclusion

Meteorological factors including average temperature, average atmospheric pressure, relative humidity, precipitation, and sunshine duration significantly influenced the risk of HFMD in Yangzhou City. Effective prevention measures for HFMD should be implemented, taking into account the local climate conditions.

Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis

Objective

Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China.

Methods

Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods.

Results

From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (β2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447).

Conclusions

The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.

Climate Change and Health in Urban Areas with a Mediterranean Climate: A Conceptual Framework with a Social and Climate Justice Approach

The consequences of climate change are becoming increasingly evident and highlight the important interdependence between the well-being of people and ecosystems. Although climate change is a global phenomenon, its causes and consequences vary dramatically across territories and population groups. Among settings particularly susceptible to health impacts from climate change are cities with a Mediterranean climate. Here, impacts will put additional pressure on already-stressed ecosystems and vulnerable economies and societies, increasing health inequalities. Therefore, this article presents and discusses a conceptual framework for understanding the complex relationship between climate change and health in the context of cities with Mediterranean climate from a social and climate justice approach. The different elements that integrate the conceptual framework are: (1) the determinants of climate change; (2) its environmental and social consequences; (3) its direct and indirect impacts on health; and (4) the role of mitigation and adaptation policies. The model places special emphasis on the associated social and health inequalities through (1) the recognition of the role of systems of privilege and oppression; (2) the distinction between structural and intermediate determinants of climate change at the root of health inequalities; (3) the role of individual and collective vulnerability in mediating the effects of climate change on health; and (4) the need to act from a climate justice perspective to reverse health inequities.

Prevalence and Genomic Diversity of Salmonella enterica Recovered from River Water in a Major Agricultural Region in Northwestern Mexico

Salmonella enterica is a leading cause of human gastrointestinal disease worldwide. Given that Salmonella is persistent in aquatic environments, this study examined the prevalence, levels and genotypic diversity of Salmonella isolates recovered from major rivers in an important agricultural region in northwestern Mexico. During a 13-month period, a total of 143 river water samples were collected and subjected to size-exclusion ultrafiltration, followed by enrichment, and selective media for Salmonella isolation and quantitation. The recovered Salmonella isolates were examined by next-generation sequencing for genome characterization. Salmonella prevalence in river water was lower in the winter months (0.65 MPN/100 mL) and significantly higher in the summer months (13.98 MPN/100 mL), and a Poisson regression model indicated a negative effect of pH and salinity and a positive effect of river water temperature (p = 0.00) on Salmonella levels. Molecular subtyping revealed Oranienburg, Anatum and Saintpaul were the most predominant Salmonella serovars. Single nucleotide polymorphism (SNP)-based phylogeny revealed that the detected 27 distinct serovars from river water clustered in two major clades. Multiple nonsynonymous SNPs were detected in stiA, sivH, and ratA, genes required for Salmonella fitness and survival, and these findings identified relevant markers to potentially develop improved methods for characterizing this pathogen.

Dairy Cattle Density and Temporal Patterns of Human Campylobacteriosis and Cryptosporidiosis in New Zealand

Public health risks associated with the intensification of dairy farming are an emerging concern. Dairy cattle are a reservoir for a number of pathogens that can cause human illness. This study examined the spatial distribution of dairy cattle density and explored temporal patterns of human campylobacteriosis and cryptosporidiosis notifications in New Zealand from 1997 to 2015. Maps of dairy cattle density were produced, and temporal patterns of disease rates were assessed for urban versus rural areas and for areas with different dairy cattle densities using descriptive temporal analyses. Campylobacteriosis and cryptosporidiosis rates displayed strong seasonal patterns, with highest rates in spring in rural areas and, for campylobacteriosis, summer in urban areas. Increases in rural cases often preceded increases in urban cases. Furthermore, disease rates in areas with higher dairy cattle densities tended to peak before areas with low densities or no dairy cattle. Infected dairy calves may be a direct or indirect source of campylobacteriosis or cryptosporidiosis infection in humans through environmental or occupational exposure routes, including contact with animals or feces, recreational contact with contaminated waterways, and consumption of untreated drinking water. These results have public health implications for populations living, working, or recreating in proximity to dairy farms.

Optimizing Crop Planting Schedule Considering Planting Window and Storage Capacity

Technology advancement has contributed significantly to productivity improvement in the agricultural sector. However, field operation and farm resource utilization remain a challenge. For major row crops, designing an optimal crop planting strategy is crucial since the planting dates are contingent upon weather conditions and storage capacity. This manuscript proposes a two-stage decision support system to optimize planting decisions, considering weather uncertainties and resource constraints. The first stage involves creating a weather prediction model for Growing Degree Units (GDUs). In the second stage, the GDUs prediction from the first stage is incorporated to formulate an optimization model for the planting schedule. The efficacy of the proposed model is demonstrated through a case study based on Syngenta Crop Challenge (2021). It has been shown that the 1D-CNN model outperforms other prediction models with an RRMSE of 7 to 8% for two different locations. The decision-making model in the second stage provides an optimal planting schedule such that weekly harvested quantities will be evenly allocated utilizing a minimum number of harvesting weeks. We analyzed the model performance for two scenarios: fixed and flexible storage capacity at multiple geographic locations. Results suggest that the proposed model can provide an optimized planting schedule considering planting window and storage capacity. The model has also demonstrated its robustness under multiple scenarios.

Phylogeographic Clustering Suggests that Distinct Clades of Salmonella enterica Serovar Mississippi Are Endemic in Australia, the United Kingdom, and the United States

Salmonella enterica serovar Mississippi is the 2nd and 14th leading cause of human clinical salmonellosis in the Australian island state of Tasmania and the United States, respectively. Despite its public health relevance, relatively little is known about this serovar. Comparison of whole-genome sequence (WGS) data of S. Mississippi isolates with WGS data for 317 additional S. enterica serovars placed one clade of S. Mississippi within S. enterica clade B (“clade B Mississippi”) and the other within section Typhi in S. enterica clade A (“clade A Mississippi”), suggesting that these clades evolved from different ancestors. Phylogenetic analysis of 364 S. Mississippi isolates from Australia, the United Kingdom, and the United States suggested that the isolates cluster geographically, with U.S. and Australian isolates representing different subclades (Ai and Aii, respectively) within clade A Mississippi and clade B isolates representing the predominant S. Mississippi isolates in the United Kingdom. Intraclade comparisons suggested that different mobile elements, some of which encode virulence factors, are responsible for the observed differences in gene content among isolates within these clades. Specifically, genetic differences among clade A isolates reflect differences in prophage contents, while differences among clade B isolates are due to the acquisition of a 47.1-kb integrative conjugative element (ICE). Phylogenies inferred from antigenic components (fliC, fljB, and O-antigen-processing genes) support that clade A and B Mississippi isolates acquired these loci from different ancestral serovars. Overall, these data support that different S. Mississippi phylogenetic clades are endemic in Australia, the United Kingdom, and the United States.

IMPORTANCE The number of known so-called “polyphyletic” serovars (i.e., phylogenetically distinct clades with the same O and H antigenic formulas) continues to increase as additional Salmonella isolates are sequenced. While serotyping remains a valuable tool for reporting and monitoring Salmonella, more discriminatory analyses for classifying polyphyletic serovars may improve surveillance efforts for these serovars, as we found that for S. Mississippi, distinct genotypes predominate at different geographic locations. Our results suggest that the acquisition of genes encoding O and H antigens from different ancestors led to the emergence of two Mississippi clades. Furthermore, our results suggest that different mobile elements contribute to the microevolution and diversification of isolates within these two clades, which has implications for the acquisition of novel adaptations, such as virulence factors.

Coronavirus seasonality, respiratory infections and weather

Background

The survival of coronaviruses are influenced by weather conditions and seasonal coronaviruses are more common in winter months. We examine the seasonality of respiratory infections in England and Wales and the associations between weather parameters and seasonal coronavirus cases.

Methods

Respiratory virus disease data for England and Wales between 1989 and 2019 was extracted from the Second-Generation Surveillance System (SGSS) database used for routine surveillance. Seasonal coronaviruses from 2012 to 2019 were compared to daily average weather parameters for the period before the patient’s specimen date with a range of lag periods.

Results

The seasonal distribution of 985,524 viral infections in England and Wales (1989–2019) showed coronavirus infections had a similar seasonal distribution to influenza A and bocavirus, with a winter peak between weeks 2 to 8. Ninety percent of infections occurred where the daily mean ambient temperatures were below 10 °C; where daily average global radiation exceeded 500 kJ/m²/h; where sunshine was less than 5 h per day; or where relative humidity was above 80%. Coronavirus infections were significantly more common where daily average global radiation was under 300 kJ/m²/h (OR 4.3; CI 3.9–4.6; p < 0.001); where average relative humidity was over 84% (OR 1.9; CI 3.9–4.6; p < 0.001); where average air temperature was below 10 °C (OR 6.7; CI 6.1–7.3; p < 0.001) or where sunshine was below 4 h (OR 2.4; CI 2.2–2.6; p < 0.001) when compared to the distribution of weather values for the same time period. Seasonal coronavirus infections in children under 3 years old were more frequent at the start of an annual epidemic than at the end, suggesting that the size of the susceptible child population may be important in the annual cycle.

Conclusions

The dynamics of seasonal coronaviruses reflect immunological, weather, social and travel drivers of infection. Evidence from studies on different coronaviruses suggest that low temperature and low radiation/sunlight favour survival. This implies a seasonal increase in SARS-CoV-2 may occur in the UK and countries with a similar climate as a result of an increase in the R₀ associated with reduced temperatures and solar radiation. Increased measures to reduce transmission will need to be introduced in winter months for COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06785-2.

Climate change and infectious disease in Europe: Impact, projection and adaptation

Europeans are not only exposed to direct effects from climate change, but also vulnerable to indirect effects from infectious disease, many of which are climate sensitive, which is of concern because of their epidemic potential. Climatic conditions have facilitated vector-borne disease outbreaks like chikungunya, dengue, and West Nile fever and have contributed to a geographic range expansion of tick vectors that transmit Lyme disease and tick-borne encephalitis. Extreme precipitation events have caused waterborne outbreaks and longer summer seasons have contributed to increases in foodborne diseases. Under the Green Deal, The European Union aims to support climate change health policy, in order to be better prepared for the next health security threat, particularly in the aftermath of the traumatic COVID-19 experience. To bolster this policy process we discuss climate change-related hazards, exposures and vulnerabilities to infectious disease and describe observed impacts, projected risks, with policy entry points for adaptation to reduce these risks or avoid them altogether.

Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China

OBJECTIVE

We aim to examine the adequacy of an innovation state-space modeling framework (called TBATS) in forecasting the long-term epidemic seasonality and trends of hemorrhagic fever with renal syndrome (HFRS).

METHODS

The HFRS morbidity data from January 1995 to December 2020 were taken, and subsequently, the data were split into six different training and testing segments (including 12, 24, 36, 60, 84, and 108 holdout monthly data) to investigate its predictive ability of the TBATS method, and its forecasting performance was compared with the seasonal autoregressive integrated moving average (SARIMA).

RESULTS

The TBATS (0.27, {0,0}, -, {<12,4>}) and SARIMA (0,1,(1,3))(0,1,1)12 were selected as the best TBATS and SARIMA methods, respectively, for the 12-step ahead prediction. The mean absolute deviation, root mean square error, mean absolute percentage error, mean error rate, and root mean square percentage error were 91.799, 14.772, 123.653, 0.129, and 0.193, respectively, for the preferred TBATS method and were 144.734, 25.049, 161.671, 0.203, and 0.296, respectively, for the preferred SARIMA method. Likewise, for the 24-, 36-, 60-, 84-, and 108-step ahead predictions, the preferred TBATS methods produced smaller forecasting errors over the best SARIMA methods. Further validations also suggested that the TBATS model outperformed the Error-Trend-Seasonal framework, with little exception. HFRS had dual seasonal behaviors, peaking in May-June and November-December. Overall a notable decrease in the HFRS morbidity was seen during the study period (average annual percentage change=-6.767, 95% confidence intervals: -10.592 to -2.778), and yet different stages had different variation trends. Besides, the TBATS model predicted a plateau in the HFRS morbidity in the next ten years.

CONCLUSION

The TBATS approach outperforms the SARIMA approach in estimating the long-term epidemic seasonality and trends of HFRS, which is capable of being deemed as a promising alternative to help stakeholders to inform future preventive policy or practical solutions to tackle the evolving scenarios.

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.

Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

Evaluation of the Models for Forecasting Dengue in Brazil from 2000 to 2017: An Ecological Time-Series Study

We aimed to evaluate the accuracy of deterministic and stochastic statistical models by means of a protocol developed in a free programming environment for monthly time-series analysis of the incidence of confirmed dengue cases in the states and federal district of Brazil from January 2000 to December 2017. This was an ecological time-series study conducted to evaluate and validate the accuracy of 10 statistical models for predicting the new cases of dengue. Official data on the monthly cases of dengue from January 2000 to December 2016 were used to train the statistical models, while those for the period January-December 2017 were used to test the predictive capacity of the models by considering three forecasting horizons (12, 6, and 3 months). Deterministic models proved to be reliable for predicting dengue in a 12-month forecasting horizon, while stochastic models were reliable for predicting the disease in a 3-month forecasting horizon. We were able to reliably employ models for predicting dengue in the states and federal district of Brazil. Hence, we strongly recommend incorporating these models in state health services for predicting dengue and for decision-making with regard to the advanced planning of interventions before the emergence of epidemics.

Influence of climate variables on the rising incidence of nontuberculous mycobacterial (NTM) infections in Queensland, Australia 2001-2016

International reports indicate a rising incidence of nontuberculous mycobacterial (NTM) disease. Many infectious diseases have seasonal variation in incidence, and major weather events and climate change have been implicated. The aim of this study was to explore the relationship between climate variables and NTM incident cases in Queensland, Australia.

METHODS

NTM data were obtained from the Queensland notifiable conditions database for the period 2001-2016. Rainfall and temperature data were obtained from the Australian Bureau of Meteorology. Poisson regression models were used to assess notification rates (incidence cases per 100,000 population) over time and to estimate incidence rate ratios (IRR). Cross correlation coefficients were used to examine the relationship between rainfall and temperature data and NTM incidence over time in each Hospital and Health Service (HHS).

RESULTS

12,219 NTM cases were reported. The most common species was M. intracellulare (39.1%), followed by M. avium (9.8%), M abscessus (8.5%), M. fortuitum (8.3%), M. chelonae (3.3%), and M. kansasii (2.4%). The estimated incidence rate increased from 11.10 (95% CI 8.10-15.22) in 2001 to 25.88 (95%CI 21.78-30.73) per 100,000 in 2016. The estimated IRR increased for all common species, except M. kansasii. Although increased IRRs were observed for most NTM species, geospatial heterogeneity was observed. The effect of rainfall and temperature on NTM incidence differed between species and geographic regions.

CONCLUSIONS

The incidence of NTM infections increased between 2001 and 2016. Variations in temperature and rainfall may play a role in environmental exposure to some species of NTM. Spatial variation in IRR suggests that there may also be other environmental factors that influence transmission.

Legionnaires' Disease on the Rise in Switzerland: A Denominator-Based Analysis of National Diagnostic Data, 2007-2016

The risk of falling ill with Legionnaires' disease (LD) is suggested to increase, but the global burden of disease is unknown due to a lack of appropriate diagnosis and surveillance systems. In Switzerland, the number of LD cases, captured by the National Notification System for Infectious Diseases, has more than doubled since 2008. This study aims to investigate this increase, contextualizing disease surveillance data with denominator data, which is not routinely available, i.e., the number of tests performed for Legionella spp. We collected the testing data for Legionella spp. of 14 Swiss diagnostic laboratories and calculated the positivity, defined as the proportion of the number of positive tests to the number of tests performed. The number of positive tests increased proportionally to the number of tests performed; hence, the positivity remained stable. However, the cause of the increase in test volume is unclear and has a large impact on the interpretation of the positivity curve. Further, the test outcome was found to be dependent on regional determinants, and the diagnostic method applied. The lack of understanding if and at which stage LD is considered in current case management of pneumonia patients limits the interpretation of observed heterogeneities in incidence or underestimation of LD in Switzerland. The absence of (or non-adherence to) existing guidelines and the heterogeneity in diagnostic testing hampers the comparison of data in the Swiss public health context. Therefore, diagnostic procedures should be harmonised across Switzerland and adherence to national LD management guidelines supported.

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

Advancing Global Health through Environmental and Public Health Tracking

Global environmental change has degraded ecosystems. Challenges such as climate change, resource depletion (with its huge implications for human health and wellbeing), and persistent social inequalities in health have been identified as global public health issues with implications for both communicable and noncommunicable diseases. This contributes to pressure on healthcare systems, as well as societal systems that affect health. A novel strategy to tackle these multiple, interacting and interdependent drivers of change is required to protect the population’s health. Public health professionals have found that building strong, enduring interdisciplinary partnerships across disciplines can address environment and health complexities, and that developing Environmental and Public Health Tracking (EPHT) systems has been an effective tool. EPHT aims to merge, integrate, analyse and interpret environmental hazards, exposure and health data. In this article, we explain that public health decision-makers can use EPHT insights to drive public health actions, reduce exposure and prevent the occurrence of disease more precisely in efficient and cost-effective ways. An international network exists for practitioners and researchers to monitor and use environmental health intelligence, and to support countries and local areas toward sustainable and healthy development. A global network of EPHT programs and professionals has the potential to advance global health by implementing and sharing experience, to magnify the impact of local efforts and to pursue data knowledge improvement strategies, aiming to recognise and support best practices. EPHT can help increase the understanding of environmental public health and global health, improve comparability of risks between different areas of the world including Low and Middle-Income Countries (LMICs), enable transparency and trust among citizens, institutions and the private sector, and inform preventive decision making consistent with sustainable and healthy development. This shows how EPHT advances global health efforts by sharing recent global EPHT activities and resources with those working in this field. Experiences from the US, Europe, Asia and Australasia are outlined for operating successful tracking systems to advance global health.

Spatiotemporal variations and characterization of the chronic cancer risk associated with benzene exposure

A spatiotemporal analysis of benzene was performed in east of the USA and in a representative station in Baltimore County, in order to assess its trend over a 25-year time span between 1993 and 2018. A novel time series analysis technique known as TBATS (an ensemble of Trigonometric seasonal models, Box-Cox transformation, ARMA error plus Trend and Seasonal components) was applied for the first time on an air contaminant. The results demonstrated an annual seasonality and a continuously declining trend in this respect. The success of Reformulated Gasoline Program (RFG), initiated in 1995, was obviously detected in time series data since the daily benzene concentrations reduced to one-sixth of its original level in 1995. In this regard, the respective values of mean absolute scaled error (MASE) were 0.35 and 0.45 for training and test series. Given the observed concentrations of benzene, the hot spot areas in east of the US were identified by spatial analysis, as well. A chronic cancer risk was followed along the study area, by both a deterministic and probabilistic risk assessment (PRA) techniques. It was indicated that children are at higher risk than that of adults. The range of estimated risk values for PRA was higher and varied between 6.45 × 10^-6 and 1.68 × 10^-4 for adults and between 8.13 × 10^-6 and 8.29 × 10^-4 for children. According to the findings of PRA, and referring to the threshold level of 1 × 10^-4, only 1.2% of the adults and 28.77% of the children were categorized in an immediate risk group.

Occurrence of coliphage in raw wastewater and in ambient water: A meta-analysis

Coliphage have been proposed as indicators of fecal contamination in recreational waters because they better reflect the persistence of pathogenic viruses in the environment and through wastewater treatment than traditional fecal indicator bacteria. Herein, we conducted a systematic literature search of peer-reviewed publications to identify coliphage density data (somatic and male-specific, or MSC) in raw wastewater and ambient waters. The literature review inclusion criteria included scope, study quality, and data availability. A non-parametric two-stage bootstrap analysis was used to estimate the coliphage distributions in raw wastewater and account for geographic region and season. Additionally, two statistical methodologies were explored for developing coliphage density distributions in ambient waters, to account for the nondetects in the datasets. In raw wastewater, the analysis resulted in seasonal density distributions of somatic coliphage (SC) (mean 6.5 log₁₀ plaque forming units (PFU)/L; 95% confidence interval (CI): 6.2-6.8) and MSC (mean 5.9 log₁₀ PFU/L; 95% CI: 5.5-6.1). In ambient waters, 49% of MSC samples were nondetects, compared with less than 5% for SC. Overall distributional estimates of ambient densities of coliphage were statistically higher for SC than for MSC (mean 3.4 and 1.0 log₁₀ PFU/L, respectively). Distributions of coliphage in raw wastewater and ambient water will be useful for future microbial risk assessments.

Beyond Climate Change and Health: Integrating Broader Environmental Change and Natural Environments for Public Health Protection and Promotion in the UK

Increasingly, the potential short and long-term impacts of climate change on human health and wellbeing are being demonstrated. However, other environmental change factors, particularly relating to the natural environment, need to be taken into account to understand the totality of these interactions and impacts. This paper provides an overview of ongoing research in the Health Protection Research Unit (HPRU) on Environmental Change and Health, particularly around the positive and negative effects of the natural environment on human health and well-being and primarily within a UK context. In addition to exploring the potential increasing risks to human health from water-borne and vector-borne diseases and from exposure to aeroallergens such as pollen, this paper also demonstrates the potential opportunities and co-benefits to human physical and mental health from interacting with the natural environment. The involvement of a Health and Environment Public Engagement (HEPE) group as a public forum of “critical friends” has proven useful for prioritising and exploring some of this research; such public involvement is essential to minimise public health risks and maximise the benefits which are identified from this research into environmental change and human health. Research gaps are identified and recommendations made for future research into the risks, benefits and potential opportunities of climate and other environmental change on human and planetary health.