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.

Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges

Cyanobacteria are major contributors to algal blooms in inland waters, threatening ecosystem function and water uses, especially when toxin-producing strains dominate. Here, we examine 140 hyperspectral (HS) images of five representatives of the widespread, potentially toxin-producing and bloom-forming genera Microcystis, Planktothrix, Aphanizomenon, Chrysosporum and Dolichospermum, to determine the potential of utilizing visible and near-infrared (VIS/NIR) reflectance for their discrimination. Cultures were grown under various light and nutrient conditions to induce a wide range of pigment and spectral variability, mimicking variations potentially found in natural environments. Importantly, we assumed a simplified scenario where all spectral variability was derived from cyanobacteria. Throughout the cyanobacterial life cycle, multiple HS images were acquired along with extractions of chlorophyll a and phycocyanin. Images were calibrated and average spectra from the region of interest were extracted using k-means algorithm. The spectral data were pre-processed with seven methods for subsequent integration into Random Forest models, whose performances were evaluated with different metrics on the training, validation and testing sets. Successful classification rates close to 90 % were achieved using either the first or second derivative along with spectral smoothing, identifying important wavelengths in both the VIS and NIR. Microcystis and Chrysosporum were the genera achieving the highest accuracy (>95 %), followed by Planktothrix (79 %), and finally Dolichospermum and Aphanizomenon (>50 %). The potential of HS imagery to discriminate among toxic cyanobacteria is discussed in the context of advanced monitoring, aiming to enhance remote sensing capabilities and risk predictions for water bodies affected by cyanobacterial harmful algal blooms.

Determining the net input fluxes of pollutants based on the spatial source apportionment receptor model for early warning of regional soil pollution

Understanding the soil pollutants' net input fluxes is essential for accurate early warning of regional soil pollution. However, the traditional input-output investigation method for soil pollutants' net input fluxes is often costly, especially at the regional scale. This study first assessed the land-use effects on soil heavy metals around a typical copper smelting area in China. Next, an improved spatial source apportionment receptor model, namely robust absolute principal component scores/robust geographically weighted regression with category land-use information (RAPCS/RGWR-CLU), was proposed to apportion the net source contributions, and its performance was compared with those of RAPCS/RGWR and the traditional absolute principal component scores/multiple linear regression (APCS/MLR). Finally, the net input fluxes of soil heavy metals were determined based on RAPCS/RGWR-CLU, and its performance was compared with that of the traditional input-output investigation method. Results showed that (i) land-use effects are significant for soil As, Cu, Pb, and Zn; (ii) RAPCS/RGWR-CLU achieves higher source apportionment accuracy than RAPCS/RGWR and APCS/MLR; and (iii) the net input fluxes determined by RAPCS/RGWR-CLU have similar accuracy to those determined by the traditional input-output investigation method but with significantly lower costs. Therefore, this study provided a cost-effective solution to determine the net input fluxes of soil pollutants.

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake

Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R2 = 0.45-0.93, RMSE = 2.29-5.89 μg/L) and algal cell density (R2 = 0.63-0.89, RMSE = 9489.39-16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % ± 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 ± 0.12 μg/L, MAE of 1.55 ± 0.09 μg/L, and R2 of 0.83 ± 0.01. Overall, the newly developed approach underscores the potential of a well-designed hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems.

Exploring challenges and opportunities in detecting emerging drug trends: A socio-technical analysis of the Canadian context

OBJECTIVES

This study aimed to apply a systems thinking approach to explore factors influencing the detection of emerging drug trends in Canada's provinces and territories to better understand how the local context can influence the design and performance of a pan-Canadian (i.e., national) substance use early warning system (EWS). This study also presents a set of actionable recommendations arising from the results. METHODOLOGY AND METHODS: Semi-structured interviews were conducted with 13 purposively recruited Medical Officers of Health and epidemiologists from across Canada working in the field of substance use. Thematic and social network analysis guided by the socio-technical systems framework were subsequently employed.

RESULTS

Barriers and facilitators for detecting emerging drug trends in provinces and territories are a product of the collective linkages and interactions between social (objectives, people, culture), technical (tools, practices, infrastructure), and external environmental (financial, regulatory frameworks, stakeholders) factors. Shortcomings in several of these areas shaped the system's behaviour and together contributed to fragmented operations that lacked strategic focus, poorly designed cross-sector partnerships, and unactionable information outputs. Participants' experiences shaped perceptions of a national substance use EWS, with some voicing potential opportunities and others expressing doubts about its effectiveness.

CONCLUSION

This study highlights interconnected social, technical, and external environmental considerations for the design and implementation of a national substance use EWS in Canada. It also demonstrates the value of using the socio-technical systems framework to understand a complex public health surveillance issue and how it can be used to inform a path forward.

A novel biosensing strategy on the dynamic and on-site detection of Vibrio coralliilyticus eDNA for coral health warnings

Heat stress and coral diseases are the predominant factors causing the degradation of coral reef ecosystems. Over recent years, Vibrio coralliilyticus was identified as a temperature-dependent pathogen causing tissue lysis in Pocillopora damicornis and one of the primary pathogens causing bleaching and mortality in other corals. Yet current detection techniques for V. coralliilyticus rely primarily on qPCR and ddPCR, which cannot meet the requirements for non-invasive and real-time detection. Herein, we developed an effective electrochemical biosensor modified by an Au-MoS2/rGO (AMG) nanocomposites and a specific capture probe to dynamically detect V. coralliilyticus environment DNA (eDNA) in aquarium experiments, with a lower limit of detection (0.28 fM) for synthetic DNA and a lower limit of quantification (9.8 fg/µL, ∼0.86 copies/µL) for genomic DNA. Its reliability and accuracy were verified by comparison with the ddPCR method (P > 0.05). Notably, coral tissue started to lyse at only 29 °C when the concentration of V. coralliilyticus increased abruptly to 880 copies/µL, indicating the biosensor could reflect the types of pathogen and health risks of corals under heat stress. Overall, the novel and reliable electrochemical biosensing technology provides an efficient strategy for the on-site monitoring and early warning of coral health in the context of global warming.

Early warning of potential epidemics: A pilot application of an early warning tool to data from the pulmonary clinic of the university hospital of Thessaly, Greece

BACKGROUND & METHODS

This paper describes a pilot application of the Epidemic Volatility Index (EVI) to data from the pulmonary clinic of the University Hospital of Thessaly, Greece, for monitoring respiratory infections, COVID-19, and flu cases. EVI, a simple and easily implemented early warning method based on the volatility of newly reported cases, exhibited consistent and stable performance in detecting new waves of epidemics. The study highlights the importance of implementing early warning tools to address the effects of epidemics, including containment of outbreaks, timely intervention strategies, and resource allocation within real-world clinical settings as part of a broader public health strategy.

RESULTS

The results presented in the figures demonstrate the association between successive early warnings and the onset of new waves, providing valuable insights for proactive decision-making. A web-based application enabling real-time monitoring and informed decision-making by healthcare professionals, public health officials, and policymakers was developed.

CONCLUSIONS

This study emphasizes the significant role of early warning methods in managing epidemics and safeguarding public health. Future research may explore extensions and combinations of multiple warning systems for optimal outbreak interventions and application of the methods in the context of personalized medicine.

Reduction in NGAL at 48 h predicts the progression to CKD in patients with septic associated AKI: a single-center clinical study

BACKGROUND

In this study, our objective was to investigate the predictive value of serum and urine fluctuations of neutrophil gelatinase-associated lipid transporters (NGAL) in relation to the progression of chronic kidney disease (CKD) among patients with septic associated AKI (SA-AKI).

METHODS

A total of 425 SA-AKI patients were enrolled in this study and divided into the recovery group (n = 320) and the AKI-to-CKD group (n = 105) based on 3-month follow-up data. The serum and urine NGAL levels on the day of AKI diagnosis (T0) and 48 h after anti-AKI treatment (T1) were recorded and calculated.

RESULTS

The levels of NGAL in serum and urine were found to be higher in the AKI-to-CKD group compared to the recovery group at T1 point (P < 0.05). The reductions of NGAL at 48 h in serum and urine were lower in the AKI-to-CKD group than those observed in the recovery group (P < 0.05). In comparison to T0, a significant decrease was noted for both serum and urine NGAL levels on T1 among patients who recovered from AKI (P < 0.05), whereas no such trend was observed among those with AKI-to-CKD transition (P > 0.05). After adjusting age, sex, and BMI through partial correlation analysis, the reduction of serum NGAL was found to be most strongly associated with the transition from AKI to CKD. ROC analysis showed an AUC of 0.832 for serum NGAL reduction, with a cut-off value of - 111.24 ng/ml and sensitivity and rates of 76.2% and 81.2%, respectively. Logistic regression analysis indicated that a reduction of serum NGAL ≥ - 111.24 ng/ml was the early warning indicator for the progression of CKD in SA-AKI patients.

CONCLUSION

The reduction of serum NGAL following 48 h of anti-AKI therapy represents a distinct hazard factor for the advancement of CKD in patients with SA-AKI, independent of other variables.

Evaluation of a multi-component early warning system for pastoralist populations in Doolo zone, Ethiopia: mixed-methods study

BACKGROUND

This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019-2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.

METHODS

We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019-January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.

RESULTS

1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community's response expectations not being met.

CONCLUSIONS

Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.

An early warning model to predict acute kidney injury in sepsis patients with prior hypertension

Background

In the context of sepsis patients, hypertension has a significant impact on the likelihood of developing sepsis-associated acute kidney injury (S-AKI), leading to a considerable burden. Moreover, sepsis is responsible for over 50 % of cases of acute kidney injuries (AKI) and is linked to an increased likelihood of death during hospitalization. The objective of this research is to develop a dependable and strong nomogram framework, utilizing the variables accessible within the first 24 h of admission, for the anticipation of S-AKI in sepsis patients who have hypertension.

Methods

In this study that looked back, a total of 462 patients with sepsis and high blood pressure were identified from Nanfang Hospital. These patients were then split into a training set (consisting of 347 patients) and a validation set (consisting of 115 patients). A multivariate logistic regression analysis and a univariate logistic regression analysis were performed to identify the factors that independently predict S-AKI. Based on these independent predictors, the model was constructed. To evaluate the efficacy of the designed nomogram, several analyses were conducted, including calibration curves, receiver operating characteristics curves, and decision curve analysis.

Results

The findings of this research indicated that diabetes, prothrombin time activity (PTA), thrombin time (TT), cystatin C, creatinine (Cr), and procalcitonin (PCT) were autonomous prognosticators for S-AKI in sepsis individuals with hypertension. The nomogram model, built using these predictors, demonstrated satisfactory discrimination in both the training (AUC = 0.823) and validation (AUC = 0.929) groups. The S-AKI nomogram demonstrated superior predictive ability in assessing S-AKI within the hypertension grade I (AUC = 0.901) set, surpassing the hypertension grade II (AUC = 0.816) and III (AUC = 0.810) sets. The nomogram exhibited satisfactory calibration and clinical utility based on the calibration curve and decision curve analysis.

Conclusion

In patients with sepsis and high blood pressure, the nomogram that was created offers a dependable and strong evaluation for predicting S-AKI. This evaluation provides valuable insights to enhance individualized treatment, ultimately resulting in improved clinical outcomes.

Saltwater intrusion early warning in Pearl river Delta based on the temporal clustering method

Forecasting of saltwater intrusion in estuaries is challenging due to its nonlinear and nonstationary features. The efficiency of most existing regressive prediction models decreases exponentially with increasing lead steps. To meet the requirements of accuracy and long lead time, a real-time saltwater intrusion early warning framework based on timeseries clustering was proposed in this study. In the example analysis of the Pearl River Delta, four risk levels of saltwater intrusion were defined based on the salinity duration of a string of salinity gauges along the Modaomen channel. A comprehensive 24-h-forehead prognostic value of saltwater intrusion risk was obtained by clustering previous 48-h observations. Results indicated that the latest supervised clustering model, Cluster bAsed iMportancE Learning fOr Time-series (CAMELOT), achieved better predictive performance than traditional unsupervised clustering models and machine learning classifiers. Then, the temporal diversities of various environmental components, including antecedent chlorinity, upstream discharge, tidal level and wind vector, were investigated in each identified cluster. Notably, the variation of saltwater intrusion length was strongly associated with tidal cycles and upstream discharge. The maximum length of saltwater intrusion mainly occurred during the transition between neap and spring (spring and neap) tides, while the minimum length of saltwater intrusion occurred during spring tides. An average river discharge of 2100 m3/s at Makou station and 600 m3/s at Sanshui station contributed to the cluster with the highest propensity of the longest saltwater intrusion above Nanzhen station.

Current status and prospects of algal bloom early warning technologies: A Review

In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.

SUPER Score Contributes to Warning and Management in Early-Stage COVID-19

Background

Some COVID-19 patients deteriorate to severe cases with relatively higher case-fatality rates, which increases the medical burden. This necessitates identification of patients at risk of severe disease. Early assessment plays a crucial role in identifying patients at risk of severe disease. This study is to assess the effectiveness of SUPER score as a predictor of severe COVID-19 cases.

Methods

We consecutively enrolled COVID-19 patients admitted to a comprehensive medical center in Wuhan, China, and recorded clinical characteristics and laboratory indexes. The SUPER score was calculated using parameters including oxygen saturation, urine volume, pulse, emotional state, and respiratory rate. In addition, the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity of the SUPER score for the diagnosis of severe COVID-19 were calculated and compared with the National Early Warning Score 2 (NEWS2).

Results

The SUPER score at admission, with a threshold of 4, exhibited good predictive performance for early identification of severe COVID-19 cases, yielding an AUC of 0.985 (95% confidence interval [CI] 0.897-1.000), sensitivity of 1.00 (95% CI 0.715-1.000), and specificity of 0.92 (95% CI 0.775-0.982), similar to NEWS2 (AUC 0.984; 95% CI 0.895-1.000, sensitivity 0.91; 95% CI 0.587-0.998, specificity 0.97; 95% CI 0.858-0.999). Compared with patients with a SUPER score<4, patients in the high-risk group exhibited lower lymphocyte counts, interleukin-2, interleukin-4 and higher fibrinogen, C-reactive protein, aspartate aminotransferase, and lactate dehydrogenase levels.

Conclusions

In conclusion, the SUPER score demonstrated equivalent accuracy to the NEWS2 score in predicting severe COVID-19. Its application in prognostic assessment therefore offers an effective early warning system for critical management and facilitating efficient allocation of health resources.

Genomic surveillance reveals early detection and transition of delta to omicron lineages of SARS-CoV-2 variants in wastewater treatment plants of Pune, India

The COVID-19 pandemic has emphasized the urgency for rapid public health surveillance methods to detect and monitor the transmission of infectious diseases. The wastewater-based epidemiology (WBE) has emerged as a promising tool for proactive analysis and quantification of infectious pathogens within a population before clinical cases emerge. In the present study, we aimed to assess the trend and dynamics of SARS-CoV-2 variants using a longitudinal approach. Our objective included early detection and monitoring of these variants to enhance our understanding of their prevalence and potential impact. To achieve our goals, we conducted real-time quantitative polymerase chain reaction (RT-qPCR) and Illumina sequencing on 442 wastewater (WW) samples collected from 10 sewage treatment plants (STPs) in Pune city, India, spanning from November 2021 to April 2022. Our comprehensive analysis identified 426 distinct lineages representing 17 highly transmissible variants of SARS-CoV-2. Notably, fragments of Omicron variant were detected in WW samples prior to its first clinical detection in Botswana. Furthermore, we observed highly contagious sub-lineages of the Omicron variant, including BA.1 (~28%), BA.1.X (1.0-72%), BA.2 (1.0-18%), BA.2.X (1.0-97.4%) BA.2.12 (0.8-0.25%), BA.2.38 (0.8-1.0%), BA.2.75 (0.01-0.02%), BA.3 (0.09-6.3%), BA.4 (0.24-0.29%), and XBB (0.01-21.83%), with varying prevalence rates. Overall, the present study demonstrated the practicality of WBE in the early detection of SARS-CoV-2 variants, which could help track future outbreaks of SARS-CoV-2. Such approaches could be implicated in monitoring infectious agents before they appear in clinical cases.

Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition

Wastewater treatment plants (WWTPs) are a major source of micropollutants to surface waters. Currently, their chemical or biological monitoring is realized by using grab or composite samples, which provides only snapshots of the current wastewater composition. Especially in WWTPs with industrial input, the wastewater composition can be highly variable and a continuous assessment would be advantageous, but very labor and cost intensive. A promising concept are automated real-time biological early warning systems (BEWS), where living organisms are constantly exposed to the water and an alarm is triggered if the organism's responses exceed a harmful threshold of acute toxicity. Currently, BEWS are established for drinking water and surface water but are seldom applied to monitor wastewater. This study demonstrates that a battery of BEWS using algae (Chlorella vulgaris in the Algae Toximeter, bbe Moldaenke), water flea (Daphnia magna in the DaphTox II, bbe Moldaenke) and gammarids (Gammarus pulex in the Sensaguard, REMONDIS Aqua) can be adapted for wastewater surveillance. For continuous low-maintenance operation, a back-washable membrane filtration system is indispensable for adequate preparation of treated wastewater. Only minor deviations in the reaction of the organisms towards treated and filtered wastewater compared to surface waters were detected. After spiking treated wastewater with two concentrations of the model compounds diuron, chlorpyrifos methyl, and sertraline, the organisms in the different BEWS showed clear responses depending on the respective compound, concentration and mode of action. Immediate effects on photosynthetic activity of algae were detected for diuron exposure, and strong behavioral changes in water flea and gammarids after exposure to chlorpyrifos methyl or sertraline were observed, which triggered automated alarms. Different types of data analysis were applied to extract more information out of the specific behavioral traits, than only provided by the vendors algorithms. To investigate, whether behavioral movement changes can be linked to impact other endpoints, the effects on feeding activity of G. pulex were evaluated and results indicated significant differences between the exposures. Overall, these findings provide an important basis indicating that BEWS have the potential to act as alarm systems for pollution events in the wastewater sector.

Impact-based seasonal rainfall forecasting to trigger early action for droughts

The Horn of Africa faces an ongoing multi-year drought due to five consecutive failed rainy seasons, a novel climatic event with unpreceded impacts. Beyond the starvation of millions of livestock, close to 23 million individuals in the region are currently facing high food insecurity in Kenya, Somalia and Ethiopia alone. The severity of these impacts calls for the urgent upscaling and optimisation of early action for droughts. However, drought research focuses mainly on meteorological and hydrological forecasting, while early action triggered by forecasts is seldom addressed. This study investigates the potential for early action for droughts by using seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 system for the March-April-May (MAM) and October-November-December (OND) rainy seasons. We show that these seasonal rainfall forecasts reflect major on-the-ground impacts, which we identify from drought surveillance data from 21 counties in Kenya. Subsequently, we show that the SEAS5 drought forecasts with short lead times have substantial potential economic value (PEV) when used to trigger action before the OND season across the region (PEVmax = 0.43). Increasing lead time to one or two months ahead of the season decreases PEV, but the benefits persist (PEVmax = 0.2). Outside of Kenya, MAM forecasts have limited value. The existence of opportunities for early action during the OND season in Kenya and Somalia is demonstrated by high PEV values, with some regions recording PEVmax values close to 0.8. To illustrate the practical value of this research, we point to a dilemma that a pastoralist in the Kenyan drylands faces when deciding whether to adopt early livestock destocking. This study underscores the importance to determine the value of early actions for forecast users with different action characteristics, and to disseminate this value alongside the standard forecasts themselves. This allows users to trigger effective actions before drought impacts develop.

Hidden risks: Simulated leakage of domestic sewage to secondary water supply systems poses serious microbiological risks

There are continuous reports about the pollution of the secondary water supply systems (SWSSs), among which domestic sewage leakage is the most serious. In this study, a pilot experiment lasting 70 days was conducted to explore the changes in physicochemical water quality and the microbial profiles in SWSSs polluted by different doses of domestic sewage through qPCR and high-throughput sequencing methods. The results showed that when domestic sewage entered the simulated water storage tank, a large amount of organic matter brought by domestic sewage quickly consumed chlorine disinfectants. High pollution levels (pollution index ≥ 1/1000) were accompanied by significant increases in turbidity and ammonia nitrogen concentration (p < 0.05) and by abnormal changes in sensory properties. Although different microbial community structures were found only at high pollution levels, qPCR results showed that the abundance of the bacterial 16S rRNA gene and some pathogenic gene markers in the polluted tank increased with the pollution level, and the specific gene marker of pathogens could be detected even at imperceptible pollution levels. In particular, the high detection frequency and abundance of Escherichia coli and Enterococcus faecails in polluted tank water samples demonstrated that they can be used for early warning. Moreover, it seems that the microorganisms that came with the domestic sewage lost their cultivability soon after entering SWSSs but could recover their activities during stagnation. In addition, the biofilm biomass in the polluted tank with high pollution levels increased faster at the initial stage, while after a longer contact time, it tended to remain at the same level as the control tank. This study emphasized the high microbial risk introduced by sewage water leakage even at imperceptible levels and could provide scientific suggestions for early warning and prevention of pollution to SWSSs.

Integration of disease surveillance in the English context: a qualitative study

OBJECTIVES

The world is experiencing increasing threats from infectious diseases and environmental hazards. Integration of disease surveillance systems has been put forth as one way to ensure more timely analysis of data and response. This study sought to explore the current context and state of integration of disease surveillance in England, including the barriers and facilitators to integration, as well as opportunities for improvement.

STUDY DESIGN

Qualitative study with focus groups and key informant interviews.

METHODS

Focus group discussions (FGDs) and key informant interviews (KIIs) were conducted with key national, regional, and local stakeholders involved in surveillance activities in August and September 2022. These discussions and interviews were recorded, transcribed, and coded using a within-case content and thematic analysis.

RESULTS

In total, five FGDs and 10 KIIs were conducted with 27 participants. Participants had different views on what integration is, though mostly agreed that surveillance systems in England are not integrated. Lack of standardisation, governance and oversight, and structural and financial barriers were hindering the current system from being more integrated. The additional benefits of integration above and beyond the 'status quo' during response activities were questioned by some.

CONCLUSION

England does not have a single integrated disease surveillance system but has a range of disease-specific surveillance systems that have evolved largely independently to meet operational needs. Greater integration may be desired and to a certain extent is important, but it is essential that it is understood as a means to an end and the overall purpose of surveillance is kept in mind.

Quantitative Polymerase Chain Reaction for the estimation of toxigenic microalgae abundance in shellfish production waters

Certain species of marine microalgae produce potent biotoxins that pose a risk to human health if contaminated seafood is consumed, particularly filter feeding bivalve shellfish. In regions where this is likely to occur water and seafood produce are regularly monitored for the presence of harmful algal cells and their associated toxins, but the current approach is flawed by a lengthy delay before results are available to local authorities. Quantitative Polymerase Chain Reaction (qPCR) can be used to measure phytoplankton DNA sequences in a shorter timeframe, however it is not currently used in official testing practices. In this study, samples were collected almost weekly over six months from three sites within a known HAB hotspot, St Austell Bay in Cornwall, England. The abundance of algal cells in water was measured using microscopy and qPCR, and lipophilic toxins were quantified in mussel flesh using LC-MS/MS, focusing on the okadaic acid group. An increase in algal cell abundance occurred alongside an increase in the concentration of okadaic acid group toxins in mussel tissue at all three study sites, during September and October 2021. This event corresponded to an increase in the measured levels of Dinophysis accuminata DNA, measured using qPCR. In the following spring, the qPCR detected an increase in D. accuminata DNA levels in water samples, which was not detected by microscopy. Harmful algal species belonging to Alexandrium spp. and Pseudo-nitzschia spp. were also measured using qPCR, finding a similar increase in abundance in Autumn and Spring. The results are discussed with consideration of the potential merits and limitations of the qPCR technique versus conventional microscopy analysis, and its potential future role in phytoplankton surveillance under the Official Controls Regulations pertaining to shellfish.

Predicting sepsis using deep learning across international sites: a retrospective development and validation study

Background

When sepsis is detected, organ damage may have progressed to irreversible stages, leading to poor prognosis. The use of machine learning for predicting sepsis early has shown promise, however international validations are missing.

Methods

This was a retrospective, observational, multi-centre cohort study. We developed and externally validated a deep learning system for the prediction of sepsis in the intensive care unit (ICU). Our analysis represents the first international, multi-centre in-ICU cohort study for sepsis prediction using deep learning to our knowledge. Our dataset contains 136,478 unique ICU admissions, representing a refined and harmonised subset of four large ICU databases comprising data collected from ICUs in the US, the Netherlands, and Switzerland between 2001 and 2016. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis annotations, amounting to 25,694 (18.8%) patient stays with sepsis. We compared our approach to clinical baselines as well as machine learning baselines and performed an extensive internal and external statistical validation within and across databases, reporting area under the receiver-operating-characteristic curve (AUC).

Findings

Averaged over sites, our model was able to predict sepsis with an AUC of 0.846 (95% confidence interval [CI], 0.841-0.852) on a held-out validation cohort internal to each site, and an AUC of 0.761 (95% CI, 0.746-0.770) when validating externally across sites. Given access to a small fine-tuning set (10% per site), the transfer to target sites was improved to an AUC of 0.807 (95% CI, 0.801-0.813). Our model raised 1.4 false alerts per true alert and detected 80% of the septic patients 3.7 h (95% CI, 3.0-4.3) prior to the onset of sepsis, opening a vital window for intervention.

Interpretation

By monitoring clinical and laboratory measurements in a retrospective simulation of a real-time prediction scenario, a deep learning system for the detection of sepsis generalised to previously unseen ICU cohorts, internationally.

Funding

This study was funded by the Personalized Health and Related Technologies (PHRT) strategic focus area of the ETH domain.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study

Background

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.

Methods

We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.

Findings

When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.

Interpretation

The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Funding

Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records

We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 contingency from May to October 2020, using the Fitbit Charge 3® Smartwatch device. The HGM-COVID cohort consisted of 115 participants assigned to areas of COVID-19 exposure. We introduce a novel biomarker for an opportune signal for the likelihood of SARS-CoV-2 infection based on the Shannon Entropy of the Discrete Generalized Beta Distribution fit of rank ordered smartwatch registers. Our statistical test indicated infection for 94% of patients confirmed by positive polymer chain reaction (PCR+) test, 47% before the test, and 47% in coincidence. These results required innovative data preprocessing for the definition of a new biomarker index. The statistical method parameters are data-driven, confidence estimates were calibrated based on sensitivity tests using appropriately derived surrogate data as a benchmark. Our surrogate tests can also provide a benchmark for comparing results from other anomaly detection methods (ADMs). Biomarker comparison of the negative Immunoglobulin G Antibody (IgG-) subgroup with the PCR+ subgroup showed a statistically significant difference (p < 0.01, effect size = 1.44). The distribution of the uninfected population had a lower median and less dispersion than the PCR+ population. A retrospective study of our results confirmed that the biomarker index provides an early warning of the likelihood of COVID-19, even several days before the onset of symptoms or the PCR+ test request. The method can be calibrated for the analysis of different SARS-CoV-2 strains, the effect of vaccination, and previous infections. Furthermore, our biomarker screening could be implemented to provide general health profiles for other population sectors based on physiological signals from smartwatch wearable devices.

Thinking beyond 'outbreak': The contentious politics of the science of early warning and overdose epidemic

This Commentary on the article, "Early warnings and slow deaths: A sociology of outbreak and overdose" by Tim Rhodes and Kari Lancaster, reflects upon rapidresponse reflexes invoked in societal responses to 'emergency,' 'epidemic,' 'crisis,' and disasters, all of  which require immediate action with no time to think. Epidemiology has given us machines for producing 'fact' about the 'opioid overdose epidemic' that promote the forgetting of the ways in which apparatuses of social control enact the production of facticity. While facts are supposed to be epistemologically reliable and worthy, the work of Rhodes and Lancaster invites us to de-subscribe to these beliefs and re-member our way towards developing slower, more thorough, and more thoughtful ways of seeing a wider array of "indicators, signals, evidence, and narratives of an ecological kind" (Rhodes and Lancaster 2023; this issue). This Commentary focuses on the practices of 'early warning' in social, political, and economic context.

Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

Smart solutions for urban health risk assessment: A PM2.5 monitoring system incorporating spatiotemporal long-short term graph convolutional network

Current spatial-temporal early warning systems aim to predict outdoor air quality in urban areas either at short or long temporal horizons. These systems implemented architectures without considering the geographical distribution of each air quality monitoring station, increasing the uncertainty of the forecasting framework. This study developed an integrated spatiotemporal forecasting architecture incorporating an extensive air quality PM_2.5 monitoring network and simultaneously forecasts PM_2.5 concentrations at all locations, allowing the monitoring of the health risk associated with exposure to these levels. First, this study uses a graph convolutional layer to incorporate the spatial relationship of the neighboring stations at their current state with real-time measurements. Then, it is coupled to a deep learning temporal model to form the long- and short-term time-series graph convolutional network (LSTGraphNet) model, anticipating high pollutant concentration events. This work tested the proposed model with a case study of an existing ambient air quality monitoring network in South Korea. LSTGraphNet model showed prediction performances of PM_2.5 at multiple monitoring stations with a mean absolute error (MAE) of 1.82 μg/m³, 4.46 μg/m³, and 4.87 μg/m³ for forecasting horizons of one, three, and 6 h ahead, respectively. Compared to conventional sequential models, this architecture was superior among the state-of-the-art baselines, where the MAE decreased to 41%, respectively. The results of the study showed that the proposed architecture was superior to conventional sequential models and could be used as a tool for decision-making in smart cities by revealing hotspots of higher and lower PM_2.5 concentrations in the long term.

The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic

This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

Research on safety early warning of uranium tailings dam based on abnormal radioactive indexes of water leachate

The radioactive index value of the leachate of the uranium tailings dam is affected by the internal damage of the dam. Therefore, a way of using the deviation of the radioactive index concentration in the leachate to warn the instability of the dam is innovatively proposed in this paper. Firstly, the SSA-BP algorithm is used to predict and analyze the five groups of parameters U, Ra, ∑ α, ∑ β and Rn. Then, the deviation between the actual value and the predicted value is computed. Finally, an early warning is given based on the entropy weight extension decision-making model. The model is verified by the leachate environment monitoring data of a uranium tailings dam in southern China from 2016 to 2020, which shows that the model can effectively caution of the instability of the uranium tailings dam and provides a reference for the subsequent decommissioning management.

One Health: navigating plague in Madagascar amidst COVID-19

BACKGROUND

Africa sees the surge of plague cases in recent decades, with hotspots in the Democratic Republic of Congo, Madagascar, and Peru. A rodent-borne scourge, the bacterial infection known as plague is transmitted to humans via the sneaky bites of fleas, caused by Yersinia pestis. Bubonic plague has a case fatality rate of 20.8% with treatment, but in places such as Madagascar the mortality rate can increase to 40-70% without treatment.

MAIN TEXT

Tragedy strikes in the Ambohidratrimo district as three lives are claimed by the plague outbreak and three more fight for survival in the hospitals, including one man in critical condition, from the Ambohimiadana, Antsaharasty, and Ampanotokana communes, bringing the total plague victims in the area to a grim to five. Presently, the biggest concern is the potential plague spread among humans during the ongoing COVID-19 pandemic. Effective disease control can be achieved through training and empowering local leaders and healthcare providers in rural areas, implementing strategies to reduce human-rodent interactions, promoting water, sanitation and hygiene practices (WASH) practices, and carrying out robust vector, reservoir and pest control, diversified animal surveillance along with human surveillance should be done to more extensively to fill the lacunae of knowledge regarding the animal to human transmission. The lack of diagnostic laboratories equipped represents a major hurdle in the early detection of plague in rural areas. To effectively combat plague, these tests must be made more widely available. Additionally, raising awareness among the general population through various means such as campaigns, posters and social media about the signs, symptoms, prevention, and infection control during funerals would greatly decrease the number of cases. Furthermore, healthcare professionals should be trained on the latest methods of identifying cases, controlling infections and protecting themselves from the disease.

CONCLUSIONS

Despite being endemic to Madagascar, the outbreak's pace is unparalleled, and it may spread to non-endemic areas. The utilization of a One Health strategy that encompasses various disciplines is crucial for minimizing catastrophe risk, antibiotic resistance, and outbreak readiness. Collaboration across sectors and proper planning ensures efficient and consistent communication, risk management, and credibility during disease outbreaks.

Can online particle counters and electrochemical sensors distinguish normal periodic and aperiodic drinking water quality fluctuations from contamination?

Early warning systems monitoring the quality of drinking water need to distinguish between normal quality fluctuations and those caused by contaminants. Thus, to decrease the number of false positive events, normal water quality fluctuations, whether periodic or aperiodic, need to be characterized. For this, we used a novel flow-imaging particle counter, a light-scattering particle counter, and electrochemical sensors to monitor the drinking water quality of a pressure zone in a building complex for 109 days. Data were analyzed to determine the feasibility of the sensors and particle counters to distinguish periodic and aperiodic fluctuations from real-life contaminants. The concentrations of particles smaller than 10 μm and N, Small, Large, and B particles showed sudden changes recurring daily, likely due to the flow rate changes in the building complex. Conversely, the concentrations of larger than 10 μm particles and C particles, in addition to the responses of electrochemical sensors, remained in their low typical values despite flow rate changes. The aperiodic events, likely resulting from an abnormally high flow rate in the water mains due to maintenance, were detected using particle counters and electrochemical sensors. This study provides insights into choosing water quality sensors by showing that machine learning-based particle classes, such as B, C, F, and particles larger than 10 μm are promising in distinguishing contamination from aperiodic and periodic fluctuations while the use of other particle classes and electrochemical sensors may require dynamic baseline to decrease false positive events in an early warning system.

SARS-CoV-2 surveillance in medical and industrial wastewater-a global perspective: a narrative review

The novel coronavirus SARS-CoV-2 has spread at an unprecedented rate since late 2019, leading to the global COVID-19 pandemic. During the pandemic, being able to detect SARS-CoV-2 in human populations with high coverage quickly is a huge challenge. As SARS-CoV-2 is excreted in human excreta and thus exposed to the aqueous environment through sewers, the goal is to develop an ideal, non-invasive, cost-effective epidemiological method for detecting SARS-CoV-2. Wastewater surveillance has gained widespread interest and is increasingly being investigated as an effective early warning tool for monitoring the spread and evolution of the virus. This review emphasizes important findings on SARS-CoV-2 wastewater-based epidemiology (WBE) in different continents and techniques used to detect SARS-CoV-2 in wastewater during the period 2020-2022. The results show that WBE is a valuable population-level method for monitoring SARS-CoV-2 and is a valuable early warning alert. It can assist policymakers in formulating relevant policies to avoid the negative impacts of early or delayed action. Such strategy can also help avoid unnecessary wastage of medical resources, rationalize vaccine distribution, assist early detection, and contain large-scale outbreaks.

Ecological security evaluation and early warning in the water source area of the Middle Route of South-to-North Water Diversion Project

Ecological security has important influence on regional sustainable development. The ecological security of Nanyang, the water source area of the Middle Route of South-to-North Water Diversion Project, was threatened because of surface water pollution. The existing studies had not been able to comprehensively assess the ecological security and future trend of water source area. In order to promote the high-quality development of the follow-up projects of the South-to-North Water Diversion project, it is very important to probe into the current situation and predict the future trend of ecological security in the water source area. Therefore, this paper constructed an ecological security evaluation index system based on the Driving force, Pressure, State, Impact and Response (DPSIR) model, used the combination of Analytic Hierarchy Process and- entropy weighting method to evaluate the ecological security of each district and county in Nanyang from 2000 to 2020, and used the auto regressive integrated moving average (ARIMA) model to predict the ecological security of the water source area from 2021 to 2030. The results demonstrated that: (1) The ecological security of Nanyang had changed from a moderate warning to a general safety, and the ecological security index had improved. The ecological security level of Nanyang would improve continuously from 2021 to 2030. (2) The northwest area and the central area of Nanyang had better ecological security states, while the southeast area wasn't so. Based on the results, the countermeasures for improving ecological security were proposed.

An integrated supervision framework to safeguard the urban river water quality supported by ICT and models

Models and information and communication technology (ICT) can assist in the effective supervision of urban receiving water bodies and drainage systems. Single model-based decision tools, e.g., water quality models and the pollution source identification (PSI) method, have been widely reported in this field. However, a systematic pathway for environmental decision support system (EDSS) construction by integrating advanced single techniques has rarely been reported, impeding engineering applications. This paper presents an integrated supervision framework (UrbanWQEWIS) involving monitoring-early warning-source identification-emergency disposal to safeguard the urban water quality, where the data, model, equipment and knowledge are smoothly and logically linked. The generic architecture, all-in-one equipment and three key model components are introduced. A pilot EDSS is developed and deployed in the Maozhou River, China, with the assistance of environmental Internet of Things (IoT) technology. These key model components are successfully validated via in situ monitoring data and dye tracing experiments. In particular, fluorescence fingerprint-based qualitative PSI and Bayesian-based quantitative PSI methods are effectively coupled, which can largely reduce system costs and enhance flexibility. The presented supervision framework delivers a state-of-the-art management tool in the digital water era. The proposed technical pathway of EDSS development provides a valuable reference for other regions.

Monitoring of COVID-19 in wastewater across the Eastern Upper Peninsula of Michigan

Wastewater-based epidemiology is being used as a tool to monitor the spread of COVID-19 and provide an early warning for the presence or increase of clinical cases in a community. The majority of wastewater-based epidemiology for COVID-19 tracking has been utilized in sewersheds that service populations in the tens-to-hundreds of thousands. Few studies have been conducted to assess the usefulness of wastewater in predicting COVID-19 clinical cases specifically in rural areas. This study collected samples from 16 locations across the Eastern Upper Peninsula of Michigan from June to December 2021. Sampling locations included 12 rural municipalities, a Tribal housing community and casino, a public university, three municipalities that also contained a prison, and a small island with heavy tourist traffic. Samples were analyzed for SARS-CoV-2 N1, N2, and variant gene copies using reverse transcriptase droplet digital polymerase chain reaction (RT-ddPCR). Wastewater N1 and N2 gene copies and clinical case counts were correlated to determine if wastewater results were predictive of clinical cases. Significant correlation between N1 and N2 gene copies and clinical cases was found for all sites (⍴= 0.89 to 0.48). N1 and N2 wastewater results were predictive of clinical case trends within 0-7 days. The Delta variant was detected in the Pickford and St. Ignace samples more than 12-days prior to the first reported Delta clinical cases in their respective counties. Locations with low correlation could be attributed to their high rates of tourism. This is further supported by the high correlation seen in the public university, which is a closed population. Long-term wastewater monitoring over a large, rural geographic area is useful for informing the public of potential outbreaks in the community regardless of asymptomatic cases and access to clinical testing.

A Horizon Scan to Support Chemical Pollution-Related Policymaking for Sustainable and Climate-Resilient Economies

While chemicals are vital to modern society through materials, agriculture, textiles, new technology, medicines, and consumer goods, their use is not without risks. Unfortunately, our resources seem inadequate to address the breadth of chemical challenges to the environment and human health. Therefore, it is important we use our intelligence and knowledge wisely to prepare for what lies ahead. The present study used a Delphi-style approach to horizon-scan future chemical threats that need to be considered in the setting of chemicals and environmental policy, which involved a multidisciplinary, multisectoral, and multinational panel of 25 scientists and practitioners (mainly from the United Kingdom, Europe, and other industrialized nations) in a three-stage process. Fifteen issues were shortlisted (from a nominated list of 48), considered by the panel to hold global relevance. The issues span from the need for new chemical manufacturing (including transitioning to non-fossil-fuel feedstocks); challenges from novel materials, food imports, landfills, and tire wear; and opportunities from artificial intelligence, greater data transparency, and the weight-of-evidence approach. The 15 issues can be divided into three classes: new perspectives on historic but insufficiently appreciated chemicals/issues, new or relatively new products and their associated industries, and thinking through approaches we can use to meet these challenges. Chemicals are one threat among many that influence the environment and human health, and interlinkages with wider issues such as climate change and how we mitigate these were clear in this exercise. The horizon scan highlights the value of thinking broadly and consulting widely, considering systems approaches to ensure that interventions appreciate synergies and avoid harmful trade-offs in other areas. We recommend further collaboration between researchers, industry, regulators, and policymakers to perform horizon scanning to inform policymaking, to develop our ability to meet these challenges, and especially to extend the approach to consider also concerns from countries with developing economies. Environ Toxicol Chem 2023;00:1-17. © 2023 Crown copyright and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland.

Usefulness of disease surveillance data in enhanced early warning of the cholera outbreak in Southwest Cameroon, 2018

INTRODUCTION

This study assessed the timeliness and completeness of disease surveillance data for early warning of the cholera outbreak during the socio-political crisis of Southwest Cameroon in 2018. It determined how routine integrated disease surveillance and response (IDSR) data was used for preventative actions and the challenges faced by key health staff in IDSR based decision-making.

METHODS

This was a mixed-methods study conducted from June 1st to September 30th 2021. District Health Information System 2 (DHIS2) data from January 2018 to December 2020 for the Southwest region of Cameroon were analysed using simple linear regression on EPI Info 7.2 to determine a potential association of the sociopolitical crisis with timeliness and completeness of data. Qualitative data generated through in-depth interviews of key informants were coded and analyzed using NVivo 12.

RESULTS

During high conflict intensity (2018 and 2019), average data timeliness and completeness were 16.3% and 67.2%, respectively, increasing to 40.7% and 80.2%, respectively, in 2020 when the conflict intensity had reduced. There was a statistically significant weak correlation between reduced conflict intensity and increased data timeliness (R² = 0.17, p = 0.016) and there was also a weak correlation between reduced conflict intensity and data completeness but this was not statistically significant (R² = 0.01, p = 0.642). During high conflict intensity, the Kumba and Buea health districts had the highest data timeliness (17.2% and 96.2%, respectively) and data completeness (78.8% and 40.4%, respectively) possibly because of proximity to reporting sites and effective performance based financing. Components of IDSR that should be maintained included the electronic report aspect of the DHIS2 and the supportive supervision conducted during the outbreak. Staff demotivation, the parallel multiplicity of data entry tools, poor communication, shortage of staff and the non-usability of data generated by the DHIS2 were systemic challenges to the early alert dimension of the IDSR system. Non-systemic challenges included high levels of insecurity, far to reach outbreak sites and health personnel being targeted during the conflict.

CONCLUSION

In general, routine IDSR data was not a reliable way of providing early warning of the 2018 cholera outbreak because of incomplete and late reports. Nonetheless, reduced conflict intensity correlated with increased timeliness and completeness of data reporting. The IDSR was substantially challenged during the crisis, and erroneous data generated by the DHIS 2 significantly undermined the efforts and resources invested to control the outbreak. The Ministry of Public Health should reinforce efforts to build a reporting system that produces people-centered actionable data that engages health risk management during socio-political crises.

Prospective study and validation of early warning marker discovery based on integrating multi-omics analysis in severe burn patients with sepsis

Background

Early detection, timely diagnosis and rapid response are essential for case management and precautions of burn-associated sepsis. However, studies on indicators for early warning and intervention have rarely been conducted. This study was performed to better understand the pathophysiological changes and targets for prevention of severe burn injuries.

Methods

We conducted a multi-center, prospective multi-omics study, including genomics, microRNAomics, proteomics and single-cell transcriptomics, in 60 patients with severe burn injuries. A mouse model of severe burn injuries was also constructed to verify the early warning ability and therapeutic effects of potential markers.

Results

Through genomic analysis, we identified seven important susceptibility genes (DNAH11, LAMA2, ABCA2, ZFAND4, CEP290, MUC20 and ENTPD1) in patients with severe burn injuries complicated with sepsis. Through plasma miRNAomics studies, we identified four miRNAs (hsa-miR-16-5p, hsa-miR-185-5p, hsa-miR-451a and hsa-miR-423-5p) that may serve as early warning markers of burn-associated sepsis. A proteomic study indicated the changes in abundance of major proteins at different time points after severe burn injury and revealed the candidate early warning markers S100A8 and SERPINA10. In addition, the proteomic analysis indicated that neutrophils play an important role in the pathogenesis of severe burn injuries, as also supported by findings from single-cell transcriptome sequencing of neutrophils. Through further studies on severely burned mice, we determined that S100A8 is also a potential early therapeutic target for severe burn injuries, beyond being an early warning indicator.

Conclusions

Our multi-omics study identified seven susceptibility genes, four miRNAs and two proteins as early warning markers for severe burn-associated sepsis. In severe burn-associated sepsis, the protein S100A8 has both warning and therapeutic effects.

Pandemetrics: systematically assessing, monitoring, and controlling the evolution of a pandemic

The still ongoing pandemic of SARS-CoV-2 virus and COVID-19 disease, affecting the population worldwide, has demonstrated the need of more accurate methodologies for assessing, monitoring, and controlling an outbreak of such devastating proportions. Authoritative attempts have been made in traditional fields of medicine (epidemiology, virology, infectiology) to address these shortcomings, mainly by relying on mathematical and statistical modeling. However, here, we propose approaching the methodological work from a different, and to some extent alternative, standpoint. Applied systematically, the concepts and tools of statistical engineering and quality management, developed not only in healthcare settings, but also in other scientific contexts, can be very useful in assessing, monitoring, and controlling pandemic events. We propose a methodology based on a set of tools and techniques, formulas, graphs, and tables to support the decision-making concerning the management of a pandemic like COVID-19. This methodological body is hereby named Pandemetrics. This name intends to emphasize the peculiarity of our approach to measuring, and graphically presenting the unique context of the COVID-19 pandemic.

Variable climate suitability for wheat blast (Magnaporthe oryzae pathotype Triticum) in Asia: results from a continental-scale modeling approach

Crop fungal diseases constitute a major cause of yield loss. The development of crop disease monitoring and forecasting tools is an important effort to aid farmers in adapting to climate variability and change. Recognizing weather as a main driver of fungal disease outbreaks, this work assesses the climate suitability for wheat blast (Magnaporthe oryzae pathotype Triticum, MoT) development in Asian wheat-producing countries. MOT was reported for the first time in Bangladesh in 2016 and could spread to other countries, provided that environmental conditions are suitable to spore development, distribution, and infection. With results from a generic infection model driven by air temperature and humidity, and motivated by the necessity to assess the potential distribution of MoT based on the response to weather drivers only, we quantify potential MOT infection events across Asia for the period 1980-2019. The results show a potential higher incidence of MOT in Bangladesh, Myanmar, and some areas of India, where the number of potential infection (NPI) events averaged up to 15 during wheat heading. Interannual trends show an increase in NPI over those three countries, which in turns show their higher interannual variability. Cold/dry conditions in countries such as Afghanistan and Pakistan appear to render them unlikely candidates for MOT establishment. The relationship between seasonal climate anomalies and NPI suggests a greater association with relative humidity than with temperature. These results could help to focus future efforts to develop management strategies where weather conditions are conducive for the establishment of MOT.

Estimating potential illegal land development in conservation areas based on a presence-only model

Conservation areas are facing increasing threats from anthropogenic land use activities. It is important to reasonably recognize and predict suspected illegal land development in advance. However, traditional methods easily suffer from selection bias due to the lack of accurate and reliable absence data. To tackle this problem, we have presented a novel method for estimating potential illegal land development based on the presence-only maximum entropy (MAXENT) model. The principle of MAXENT can guarantee that no additional unknown information (e.g., inaccurate pseudo-absence samples) will be introduced into the estimation procedure. This method was applied to the conservation areas in a fast-growing city, and the robustness of the MAXENT models was confirmed by the high AUC scores (over 0.80). The results indicated that the proposed method performs more effectively than the presence-absence random forest model. In addition, topographic conditions and proximity to transportation networks played dominant roles in the emergence of suspected illegal land development. Moreover, the probability map generated by MAXENT suggests that a considerable amount of forest, farmland, grassland, and water bodies will face a high degree of danger. Therefore, both superior and local governments should pay much more attention to regions with a higher potential for illegal land development. In summary, our findings are expected to support decision-making in the management and assessment of conservation areas in fast-growing regions. More importantly, the proposed method can be further applied to illegal land development estimation in many other regions.

SARS-CoV-2 wastewater monitoring using a novel PCR-based method rapidly captured the Delta-to-Omicron ΒΑ.1 transition patterns in the absence of conventional surveillance evidence

Conventional SARS-CoV-2 surveillance based on genotyping of clinical samples is characterized by challenges related to the available sequencing capacity, population sampling methodologies, and is time, labor, and resource-demanding. Wastewater-based variant surveillance constitutes a valuable supplementary practice, since it does not require extensive sampling, and provides information on virus prevalence in a timely and cost-effective manner. Consequently, we developed a sensitive real-time RT-PCR-based approach that exclusively amplifies and quantifies SARS-CoV-2 genomic regions carrying the S:Δ69/70 deletion, indicative of the Omicron BA.1 variant, in wastewater. The method was incorporated in the analysis of composite daily samples taken from the main Wastewater Treatment Plant of Thessaloniki, Greece, from 1 December 2021. The applicability of the methodology is dependent on the epidemiological situation. During Omicron BA.1 global emergence, Thessaloniki was experiencing a massive epidemic wave attributed solely to the Delta variant, according to genomic surveillance data. Since Delta does not possess the S:Δ69/70, the emergence of Omicron BA.1 could be monitored via the described methodology. Omicron BA.1 was detected in sewage samples on 19 December 2021 and a rapid increase of its viral load was observed in the following 10-day period, with an estimated early doubling time of 1.86 days. The proportion of the total SARS-CoV-2 load attributed to BA.1 reached 91.09 % on 7 January, revealing a fast Delta-to-Omicron transition pattern. The detection of Omicron BA.1 subclade in wastewater preceded the outburst of reported (presumable) Omicron cases in the city by approximately 7 days. The proposed wastewater surveillance approach based on selective PCR amplification of a genomic region carrying a deletion signature enabled rapid, real-time data acquisition on Omicron BA.1 prevalence and dynamics during the slow remission of the Delta wave. Timely provision of these results to State authorities readily influences the decision-making process for targeted public health interventions, including control measures, awareness, and preparedness.

Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concentrations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preceding COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5-week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by wastewater flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we evaluated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time.

SARS-CoV-2 wastewater monitoring using a novel PCR-based method rapidly captured the Delta-to-Omicron ΒΑ.1 transition patterns in the absence of conventional surveillance evidence

Conventional SARS-CoV-2 surveillance based on genotyping of clinical samples is characterized by challenges related to the available sequencing capacity, population sampling methodologies, and is time, labor, and resource-demanding. Wastewater-based variant surveillance constitutes a valuable supplementary practice, since it does not require extensive sampling, and provides information on virus prevalence in a timely and cost-effective manner. Consequently, we developed a sensitive real-time RT-PCR-based approach that exclusively amplifies and quantifies SARS-CoV-2 genomic regions carrying the S:Δ69/70 deletion, indicative of the Omicron BA.1 variant, in wastewater. The method was incorporated in the analysis of composite daily samples taken from the main Wastewater Treatment Plant of Thessaloniki, Greece, from 1 December 2021. The applicability of the methodology is dependent on the epidemiological situation. During Omicron BA.1 global emergence, Thessaloniki was experiencing a massive epidemic wave attributed solely to the Delta variant, according to genomic surveillance data. Since Delta does not possess the S:Δ69/70, the emergence of Omicron BA.1 could be monitored via the described methodology. Omicron BA.1 was detected in sewage samples on 19 December 2021 and a rapid increase of its viral load was observed in the following 10-day period, with an estimated early doubling time of 1.86 days. The proportion of the total SARS-CoV-2 load attributed to BA.1 reached 91.09 % on 7 January, revealing a fast Delta-to-Omicron transition pattern. The detection of Omicron BA.1 subclade in wastewater preceded the outburst of reported (presumable) Omicron cases in the city by approximately 7 days. The proposed wastewater surveillance approach based on selective PCR amplification of a genomic region carrying a deletion signature enabled rapid, real-time data acquisition on Omicron BA.1 prevalence and dynamics during the slow remission of the Delta wave. Timely provision of these results to State authorities readily influences the decision-making process for targeted public health interventions, including control measures, awareness, and preparedness.

Surveillance of omicron variants through wastewater epidemiology: Latest developments in environmental monitoring of pandemic

WBE has been a monitoring system that can give purposeful and inclusive real-time assessments of civic society as well as environmental health. This concept review introduces WBE as a surveillance scheme and initial warning outbreaks of contagious diseases caused by harmful SARS-CoV-2 with pandemic potential. Examining biomarkers of contagious diseases as evidence in polluted water taken from wastewater treatment plants suggests that these systems can be examined to get epidemiological data for checking the transmission of infectious B.1.1.529 to different areas. Thereafter, various benefits of surveillance are provided to analyse health information and pinpoint different problems that may be occurring in the workstation. Surveillance is followed by intervention steps that improved the work environment and prevent further progression of the disease. This information will help to improve early detection strategies, designing a prevention strategy to reduce their spread, infection control and therapies, thus, strengthening our global preparedness to fight future epidemics. In the end, a comprehensive discussion on the remaining challenges and opportunities for epidemiology has been given for future research perspectives.

Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal

Wastewater-based epidemiology (WBE) is drawing increasing attention as a promising tool for an early warning of emerging infectious diseases such as COVID-19. This study demonstrated the utility of a spatial bisection method (SBM) and a global optimization algorithm (i.e., genetic algorithm, GA), to support better designing and operating a WBE program for disease surveillance and source identification. The performances of SBM and GA were compared in determining the optimal locations of sewer monitoring manholes to minimize the difference among the effective spatial monitoring scales of the selected manholes. While GA was more flexible in determining the spatial resolution of the monitoring areas, SBM allows stepwise selection of optimal sampling manholes with equiareal subcatchments and lowers computational cost. Upon detecting disease outbreaks at a regular sewer monitoring site, additional manholes within the catchment can be selected and monitored to identify source areas with a required spatial resolution. SBM offered an efficient method for rapidly searching for the optimal locations of additional sampling manholes to identify the source areas. This study provides strategic and technical elements of WBE including sampling site selection with required spatial resolution and a source identification method.

Pesticides and their metabolites in European groundwater: Comparing regulations and approaches to monitoring in France, Denmark, England and Switzerland

Pesticides, i.e. plant protection products (PPP), biocides and their metabolites, pose a serious threat to groundwater quality and groundwater dependent ecosystems. Across large parts of Europe these compounds are monitored in groundwater to ensure compliance with the European Water Framework Directive (WFD), the Groundwater Directive (GWD) and Drinking water Directive (DWD). European regulation concerning the placing of PPP on the market includes groundwater monitoring as a higher tier of the regulatory procedure. Nevertheless, the lists of compounds to be monitored vary from one directive to another and between countries. The implementation of monitoring strategies for these directives and other national drivers, differs across Europe. This is illustrated using case studies from France, Denmark (EU member states), England (part of the EU up to January 2020) and Switzerland (associated country). The collection of data (e.g. monitoring design and analytical approaches) and dissemination at national and European level and the scale of data reporting to EU is country-specific. Data generated by the implementation of WFD and DWD can be used for retrospective purposes in the context of PPP registration whereas the post-registration monitoring data generated by the product applicants are generally only directly available to the regulators. This lack of consistency and strategic coordination between thematic regulations is partly compensated by national regulations. This paper illustrates the benefits of a common framework for regulation in Europe but shows that divergent national approaches to monitoring and reporting on pesticides in groundwater makes the task of assessment across Europe challenging.

Potential impacts in soil slope of deformation and water content on elastic wave amplitude

Early warning during rainfall-induced landslides employs widely to save economic losses and casualties. Using elastic wave velocity for early warning benefits from the relationship between water content, shear deformation, and elastic wave velocity of unsaturated soil slope. Amplitude changes by means of the elastic wave in the soil can also reflect the physical properties. In this work, we proposed an idea to determine the impacts in the soil of water content and deformation on elastic wave amplitude to realize early warning. Model box tests were designed to study the action of one factor, volumetric water content, on the elastic wave amplitude. Slope model tests were aimed to consider the effects of volumetric water content and deformation on elastic wave amplitude during rainfall-induced landslides. The results show that the elastic wave amplitude non-linear decreased with the volumetric water content, which could be mutually verified from the data of the model box tests and the slope model tests. The deformation caused the wave amplitude to increase, and the range of the increase could reflect the range of the deformation. The statistical correlation coefficient quantified that the increase in wave amplitude was not directly related to the water content. The wave amplitude change can be represented more visibly by eliminating the residual after EMD decomposition, and it further verifies the above results. This work provides a new idea and a reliable basis for landslide prevention and mitigation and prediction.

Construction and validation of an early warning model for predicting the acute kidney injury in elderly patients with sepsis

BACKGROUND

Sepsis-induced acute kidney injury (S-AKI) is a significant complication and is associated with an increased risk of mortality, especially in elderly patients with sepsis. However, there are no reliable and robust predictive models to identify high-risk patients likely to develop S-AKI. We aimed to develop a nomogram to predict S-AKI in elderly sepsis patients and help physicians make personalized management within 24 h of admission.

METHODS

A total of 849 elderly sepsis patients from the First Affiliated Hospital of Xi'an Jiaotong University were identified and randomly divided into a training set (75%, n = 637) and a validation set (25%, n = 212). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The corresponding nomogram was constructed based on those predictors. The calibration curve, receiver operating characteristics (ROC)curve, and decision curve analysis were performed to evaluate the nomogram. The secondary outcome was 30-day mortality and major adverse kidney events within 30 days (MAKE30). MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD).

RESULTS

The independent predictors for nomogram construction were mean arterial pressure (MAP), serum procalcitonin (PCT), and platelet (PLT), prothrombin time activity (PTA), albumin globulin ratio (AGR), and creatinine (Cr). The predictive model had satisfactory discrimination with an area under the curve (AUC) of 0.852-0.858 in the training and validation cohorts, respectively. The nomogram showed good calibration and clinical application according to the calibration curve and decision curve analysis. Furthermore, the prediction model had perfect predictive power for predicting 30-day mortality (AUC = 0.813) and MAKE30 (AUC = 0.823) in elderly sepsis patients.

CONCLUSION

The proposed nomogram can quickly and effectively predict S-AKI risk in elderly sepsis patients within 24 h after admission, providing information for clinicians to make personalized interventions.

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 km². An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June-September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.