Foodborne diseases remain a major public health concern, particularly in low- and middle-income countries where street-vended foods are widely consumed. This study evaluated the microbiological quality and Salmonella infection risk associated with street-vended grilled chicken skewers in Benin using field observations and quantitative microbial risk assessment (QMRA). Hygiene practices, handling behaviors, and time-temperature profiles were documented among 135 vendors. Three skewer categories were collected from 45 vendors (n = 45 each): raw skewers, freshly grilled skewers without condiments, and freshly grilled skewers with condiments, all from the same production batch. An additional 90 ready-to-eat samples were collected during the selling period from foods displayed for sale at different post-cooking holding times (<2 h, 2–4 h, and >4 h after cooking). These samples were used for field-based risk estimation and comparison with QMRA outputs. Salmonella spp., Escherichia coli, and Staphylococcus aureus were quantified, with Salmonella confirmed by PCR targeting the invA gene. Poor hygiene was widespread, including unrefrigerated transport, ambient holding, shared utensils, bare-hand handling (98.6%), and lack of handwashing before serving (76.3%). Raw skewers exhibited high contamination (TVC: 8.7 ± 0.5 log CFU/g), indicating substantial pre-cooking microbial burden. Cooking reduced E. coli and eliminated detectable Salmonella. However, freshly grilled products still showed notable residual contamination (TVC: 5.4 ± 0.4 log CFU/g; E. coli: 2.7 ± 0.5 log CFU/g). Salmonella was detected again immediately after condiment addition and increased during vending, while S. aureus was present in all samples. During retail, microbial counts increased, indicating progressive post-cooking amplification. Across consumption scenarios, the median daily probability of Salmonella infection per consumer was 1.0 × 10⁻³, corresponding to 314,586 infection cases per million consumers per year overall, 8,747 cases per million consumers per year for freshly prepared skewers consumed on-site, and 769,671 cases per million consumers for take-home consumption without reheating. Field-derived estimates were consistent with QMRA predictions. These findings demonstrate a dual contamination pathway in street-vended grilled chicken, where high pre-cooking microbial loads combine with post-cooking recontamination to sustain exposure. Grilling does not act as a terminal safety barrier in informal vending systems. Effective risk reduction therefore requires interventions targeting the entire production-to-vending continuum, beyond temperature control alone.
Rural drinking-water systems may satisfy physicochemical reference values while remaining microbiologically compromised when disinfection control is inadequate. This study characterized physicochemical, chemical, operational, and microbiological conditions across a source–storage–household system in Tzalaron, Ecuador, and applied a locally constructed water-safety prioritization framework. Eighteen separately collected field samples were obtained from six fixed sampling points in the Pachacshi and Dagle branches during three temporal sampling events. The physicochemical and chemical results remained within the international reference values considered. At storage-tank and household points, residual free chlorine ranged from <LOQ to 0.02 mg L⁻¹ Cl₂, below the intended operational range of 0.2–0.5 mg L⁻¹. Fecal coliforms were detected in all samples at 2–32 CFU 100 mL⁻¹. During Event 1, presumptive Escherichia coli was recovered from all six points, Klebsiella pneumoniae and Pseudomonas aeruginosa from springs and storage tanks, and Staphylococcus aureus from both springs. Sixteen isolates were tested for antimicrobial susceptibility. No multidrug-resistant phenotype was detected; ampicillin resistance in presumptive K. pneumoniae was treated as intrinsic, while acquired resistance was limited to trimethoprim/sulfamethoxazole resistance in two presumptive S. aureus isolates. The baseline framework classified all six points as high management priorities, although household classifications decreased to moderate after removal of the exposure-relevance component. The findings identify fecal contamination and inadequate residual-chlorine maintenance as the principal concerns and support source protection, controlled chlorination, storage-tank maintenance, and household microbiological verification without estimating infection risk.
Inhalation of fungal spores in indoor environments is an occupational hazard, especially in work environments with persistent exposure. Xerophilic/xerotolerant fungal growth (hereafter, xerophilic) in heritage sites (historical buildings, museums, and libraries) is documented, and represents a potential source of respiratory exposure. The health impact of xerophilic fungal spores remain unclear, specifically in the context of occupational risk assessment. This study assesses the potential of xerophilic Aspergillus species to induce production of reactive oxygen species (ROS) in a human cell line as a proxy to early immune cell responses to exposure. Museum-derived isolates of A. pseudogracilis, 17 different xerophilic Aspergillus species from a fungal collection, and A. fumigatus (positive reference) were put into a standardized, single spore suspension. Granulocyte-like cells (HL-60) were exposed to these spore suspensions, and ROS production was monitored for 3 hours. Cytotoxicity and inflammatory cytokine expression were also determined after exposure to selected fungal species. For the highest exposure concentration, the museum isolates and 16 of 17 species induced a significant ROS production with different responses between species. Aspergillus domesticus induced the highest response (3.5x higher than A. fumigatus). Of eight tested xerophilic species, all caused increased expression of the cytokines TNF alpha, CXCL8, IL-1 beta, and CCL2. Exposure to seven of the eight species decreased HL-60 cell viability significantly. Overall, this systematic comparison of xerophilic species suggests that they have the potential to initiate a diverse inflammatory response in human leukocytes, and highlights the need to account for species-specific variability when determining exposure risk.
The relationship between Campylobacter levels in broiler caeca and on carcass skin is central to quantitative microbial risk assessment along the poultry production chain and underpins modelling of intervention impacts, including its use in EFSA assessments of the public health impact of control measures. However, this relationship is typically inferred from monitoring data generated under sampling designs that do not preserve pairing between specimen types and may involve pooling. In this study, we used a simulation framework to evaluate whether commonly used sampling strategies allow reliable recovery of the underlying caecal-skin relationship.A simulated broiler population was generated, assigning caecal and skin loads to individual birds based on a specified linear relationship. Sampling was then conducted under paired and unpaired designs, with and without pooling, reflecting approaches used in monitoring programmes and in policy-oriented models. Regression models were fitted to sampled data across 1,000 iterations for a range of assumed slopes. For sensitivity analysis (SA), scenarios with varying assumptions on within-flock prevalence (4 alternative values), residual error (5), and intercept (5) were tested.Under paired sampling, estimated slopes closely matched the true relationship across most scenarios. In contrast, unpaired sampling consistently failed to recover the association, with estimated slopes centred around zero regardless of the true slope. These findings were robust across SA scenarios.The results show that sampling design fundamentally affects the ability to recover relationships between stages of the production chain. This has implications for interpretation of parameters derived from monitoring data and used in quantitative Campylobacter risk assessments informing policy. Parameters derived from unpaired and pooled monitoring data should therefore be interpreted with caution when used to support risk assessment and decision-making.
Enterocytozoon hepatopenaei (EHP) is an emerging microsporidian parasite of shrimp, yet its quantitative risk profile in inland aquaculture systems remains poorly defined. This study applied a risk-factor analysis approach to characterise the occurrence and management-associated risk factors of EHP infection in commercial Litopenaeus vannamei farms in Punjab, India. A two-year cross-sectional survey was conducted across 152 farms in Mansa, Sri Muktsar Sahib, Fazilka, and Bathinda districts. Hazard identification was performed using nested PCR targeting EHP. Exposure variables were derived from structured questionnaires on farm management and biosecurity practices. Concurrent isolation of Vibrio spp. enabled evaluation of potential co-infection risk. EHP was detected in 17.10% of farms overall, with marked spatial heterogeneity (Mansa 26.92%, Sri Muktsar Sahib 21.67%, Fazilka 12.24%). Univariate analysis identified multiple high-impact management practices associated with infection. Stratified 2 & times; 2 & times; n analysis of nine exposure variables showed that farms with combined highrisk management profiles had approximately six- to seven-fold higher odds of EHP positivity than low-risk farms. Crude and adjusted estimates were stable in Mansa and Sri Muktsar Sahib, whereas evidence of confounding was observed in Fazilka, indicating district-specific exposure interactions. EHP-Vibrio co-infection was detected in three farms, supporting a plausible synergistic disease pathway. These findings identify EHP as a quantifiable microbial hazard and indicate that modifiable management practices influence the probability of infection. The study provides actionable evidence for targeted biosecurity interventions and risk-based management strategies to mitigate EHP transmission and associated bacterial disease burdens in intensive inland production systems.
Hazelnut (Corylus avellana L.) is a high-value crop for the global confectionery industry, yet its production chain is increasingly challenged by aflatoxin contamination caused by Aspergillus flavus. In Azerbaijan, one of the world's leading hazelnut producers, recurrent exceedances of European aflatoxin limits have resulted in economic and trade constraints. In this context, predictive models represent valuable decision-support tools to anticipate periods of elevated contamination risk and to support timely pre-harvest management strategies. This study presents AFLA-hazelnut, a process-based, weather-driven model developed to assess the risk of A. flavus infection and aflatoxin B, (AFB,) contamination in hazelnut orchards. The approach integrates a phenological sub-model for hazelnut development, based on literature, with a fungal sub-model that simulates infection according to temperature, relative humidity, rainfall, and kernel water activity (aw). A hazelnut-specific aw curve was developed to describe kernel water dynamics during the susceptible reproductive stage and to support hourly simulations. The model produces two outputs: an infection risk index (IH) and an aflatoxin risk index (AFB1-I). Model outputs were evaluated using independent pre-harvest field data. IH showed a coherent association with observed fungal occurrence, indicating that the model captures biologically meaningful gradients of infection pressure. In contrast, the limited number of aflatoxin-positive samples did not allow quantitative validation of the AFB1-I index, which remains supported by indirect evidence and model structure. AFLAhazelnut represents the first process-based framework developed to describe A. flavus infection dynamics and support aflatoxin risk assessment in hazelnuts.
Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant foodborne risk in ready-to-eat (RTE) foods, especially in informal markets of low-resource environments with suboptimal awareness and hygiene practices. This cross-sectional study in Oghara and Sapele, Delta State, Nigeria, assessed MRSA awareness, knowledge, and hygiene practices among 199 vendors and consumers, while determining prevalence, bacterial concentration, and antimicrobial susceptibility of MRSA in 400 RTE food samples. Data were analyzed via regression models, clustering, and generalized linear model (GLM). Awareness of MRSA was moderate (54.3 %), but knowledge superficial and exposure potential low (only 22.1 % viewed it as significant), with notable locational differences (p < 0.001). Logistic regression predicted awareness with strong explanatory performance; ordinal regression showed food handlers had near-universal frequent/always hand-washing adherence compared to lower rates among consumers. Clustering revealed four distinct hygiene adherence groups (high to low). Microbiological analysis indicated overall MRSA prevalence of 5.25 %, with the highest rates (similar to 12 %) and positive counts (similar to 2.5) in meat products. Isolates exhibited high multi-drug resistance (MDR), particularly to clindamycin, ciprofloxacin, erythromycin, and tetracycline, but retained good susceptibility to vancomycin and gentamicin. Substantial gaps persist in MRSA awareness, hygiene consistency, and testing in Delta State's informal RTE markets, with meat-based foods identified as a major source of MDR MRSA strains (potential public health concern). Targeted interventions, focusing on vendor education, improved infrastructure, and antimicrobial stewardship, are urgently needed to reduce these exposure potentials.
Rice blast, caused by Magnaporthe oryzae, remains one of the most destructive diseases of rice, leading to substantial yield losses across diverse agro-ecological zones. To enable early warning and targeted management, this study developed an integrated machine learning based Decision Support System (DSS) using weather variables and disease severity data. Weekly meteorological parameters, viz., maximum and minimum temperature, morning and evening relative humidity, rainfall and sunshine hours, were aligned with disease observations using Standard Meteorological Weeks (SMWs) to analyse epidemic dynamics across three cropping seasons. For classification based disease risk prediction, machine learning algorithms including Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN) and Binomial Logistic Regression (BLR) were evaluated using a historical weather-disease dataset partitioned into training and testing subsets. Among the evaluated models, ANN and BLR demonstrated strong predictive performance. The ANN model, trained using all major weather parameters, achieved a strong predictive fit (R2 = 0.86) for disease severity (PDI %), accurately tracking observed epidemic peaks. In contrast, the BLR model, developed using only temperature and relative humidity, provided excellent classification of high-risk periods with AUC = 97.97 %, accuracy = 98.26 %, sensitivity = 97.44 % and specificity = 98.50 %. This simplification highlighted that temperature and humidity serve as dominant drivers of blast outbreaks. Outputs from both models were integrated into an Actionable Threshold-Based Advisory System (ATBAS), which converted forecasts into practical advisories across five risk levels, Low, Watch, Action, High Alert and Emergency, based on critical thresholds (ANN >= 10 % PDI or BLR probability >= 0.50). The framework dynamically incorporated crop stage and weather modifiers to enhance accuracy. Validation showed over 80 % agreement between predicted and observed epidemics, demonstrating that the ANN-BLR integration provides a reliable operational tool for real time, weather-driven rice blast advisory systems.
Bacterial Leaf Stripe (BLS), caused by Xanthomonas vasicola pv. arecae, poses a significant threat to arecanut cultivation with yield losses of up to 60%, especially under high rainfall and humidity. Accurate forecasting systems integrating climatic variables can enable early warning and timely management interventions. This study aimed to develop and evaluate short and long-term forecasting models for BLS severity using weather driven machine learning approaches. Weekly BLS severity data (Percent Disease Index, PDI) and corresponding weather variables were collected from arecanut plantations in Karnataka, India, during 2019-2021. Seasonal trend analysis revealed consistent disease onset during the 30th-32nd standard meteorological weeks (SMW) and peak BLS severity during the 36th-39th SMW. A hybrid modeling framework was developed, combining Random Forest (RF) for feature selection and Artificial Neural Network (ANN) for prediction. Short-term models forecasted PDI at lead times of 1-14 weeks, while long-term models extended up to 5 months, using monthly aggregated data. Model performance was evaluated using R2, RMSE, MAE, and NRMSE. The RF-ANN model outperformed standalone RF and ANN across both short and long-term horizons, with optimal accuracy observed at a 2 week lead (R2 = 0.89, NRMSE = 9.2%). Variable importance analysis identified maximum temperature, evening relative humidity and rainfall as the most influential predictors. An operational forecasting system was developed by integrating model outputs with PDI based risk thresholds, triggering actionable recommendations ranging from monitoring to curative sprays. The proposed framework offers a scalable, climate smart approach to BLS forecasting and has potential for integration into real time agro-advisory platforms for arecanut growing regions in India.
Background: This study investigates whether essential oil-bacteriophage combinations may be screened for food safety using a transparent qualitative approach, therefore filling a gap in which synergy is frequently reported for efficacy but seldom examined for safety. Methods: Hazards of main essential oil elements were profiled in the OECD QSAR Toolbox, translated into qualitative concern tiers, and aggregated to the oil level using composition weighting and dominance principles. Bacteriophage safety was evaluated by dossier using eight criteria: genetic integrity, manufacturing and process quality, stability, host range, effectiveness in food-like settings, regulatory precedent, and uncertainty, with specific STOP criteria for genomic or manufacturing failures. A conservative maximum rule was used to combine the essential oil and phage tiers, which were then mapped to screening labels using an FAO/WHO-style matrix with likelihood categories for purely lytic, Good Manufacturing Practice-grade phages. Results: When applied to oregano, thyme, and dittany against Escherichia coli, all combinations mapped to Low screening output, within the qualitative and conservative boundaries of the framework, despite oregano and dittany presenting Moderate-High essential oil tiers due to cautious QSAR alerts. Conclusions: The methodology provides an early-stage safety screen that supports feasibility under rigorous inclusion criteria, while remaining preliminary and hypothesis-generating. Validation in food matrices and EOphage compatibility (titre-stability) confirmation under intended-use circumstances will be required before practical implementation; formulation solutions may be considered if titre-stability data reveal a major loss of infectivity at the desired EO dose.
Introduction: This study aimed to evaluate public awareness, knowledge, and risk-related practices regarding hydatid disease among adults in T & uuml;rkiye. Method: This cross-sectional survey included 1135 individuals aged >= 18 years residing in T & uuml;rkiye. This internetbased cross-sectional study was conducted between February and May 2025. Data were collected using an online questionnaire distributed via social media platforms. Knowledge levels, sources of information, and risk-related practices were assessed. Associations between sociodemographic variables and knowledge levels were analyzed using appropriate statistical tests. Results: Among the participants, 56.1% were unaware of the etiological agent of hydatid disease, and only 33.7% correctly identified parasites as the cause. Knowledge regarding transmission routes was limited, with only 24.2% recognizing the role of infected dogs. Risk-related practices were common; 42.7% reported home slaughtering, while 22.5% disposed of infected organs in household waste and 32.4% buried them. Significant associations were observed between occupational groups and knowledge levels concerning disease etiology and transmission, with students demonstrating higher awareness compared to other occupational groups (p = 0.001). Conclusion: This study reveals that the level of knowledge and awareness regarding hydatid cyst disease in Turkish society is insufficient and that high-risk practices are widespread. These findings indicate the existence of a significant public health problem that hinders the control and prevention of the disease..
Objective: To construct an Autoregressive Integrated Moving Average (ARIMA) multiplicative seasonal model for predicting the monthly incidence of foodborne diseases in Nanning City and provide a scientific basis for disease prevention and control strategies. Methods: Monthly incidence data of foodborne diseases in Nanning City from January 2013 to December 2022 were used to develop an ARIMA multiplicative seasonal model with SPSS 23.0 software. The optimal model was selected through sequence stationarization, model identification, order determination, parameter estimation, and diagnostic checking. The model was validated using data from January to December 2023 (held-out set) and then used to forecast the monthly incidence for 2024-2025. Results: The monthly incidence exhibited significant seasonal fluctuations. The optimal model was identified as ARIMA(1,0,0) & times; (0,1,1)12, The optimal model was selected based on a combination of a high stationary R2 (0.673), adherence to the principle of parsimony, and achieving the lowest Bayesian Information Criterion (BIC = 0.178) among candidate models. The model residuals passed the white noise test (Ljung-Box Q = 22.079, P = 0.141). The model's out-of-sample performance on the 2023 validation set was assessed, yielding a Root Mean Square Error (RMSE) of 2.65 cases per 100,000 population and a Mean Absolute Percentage Error (MAPE) of 29.7%. Predictions for 2024-2025 suggest a stable incidence level with seasonal peaks in the summer and autumn months, and no indication of a large-scale outbreak beyond historical patterns. Conclusion: The ARIMA multiplicative seasonal model can capture the seasonal pattern of foodborne disease incidence in Nanning City. While short-term prediction accuracy is acceptable, the model's performance can be affected by anomalous data points. It serves as a useful tool for short-term early warning and seasonal resource planning in public health.
Japanese Spotted Fever (JSF), a tick-borne disease caused by Rickettsia japonica, has shown a sustained increase in incidence and geographic expansion across Japan over the past two decades. Using a 21-year (1999-2019) prefecture-level dataset, we examined associations between climatic conditions and JSF incidence and evaluated their predictive utility within machine learning frameworks. We developed Random Forest regression models incorporating prefecture identifiers, climatic variables, and a one-year lag of JSF cases to account for spatial heterogeneity and temporal autocorrelation. Models based solely on national-average climate variables exhibited poor predictive performance, indicating that climate alone does not explain temporal increases in JSF incidence. In contrast, spatially explicit models achieved substantially improved accuracy, and inclusion of lagged incidence yielded the strongest predictive gains (MSE = 37.03, R2 = 0.76 for 2017-2019). Feature importance analyses identified prior-year cases as the dominant predictor, while temperature, sunshine hours, and snow-related variables contributed secondary explanatory signal. These findings suggest that climatic factors primarily define broad regional suitability for JSF, whereas short-term incidence dynamics are largely driven by spatially persistent and temporally autocorrelated processes. Climate-informed models may nonetheless support regional surveillance and risk stratification when combined with historical incidence and spatial context.
The yellow fever virus can infect several kinds of host cells in the human organism. However, liver damage dominates during yellow fever, due to lysis of hepatocytes and accumulation of virus particles inside them. Thermodynamic driving force for multiplication of viruses provides the answer to why the liver is among the most severely damaged organs during yellow fever, while less damage occurs in kidneys, spleen and bone marrow. The physicochemical perspective on pathogenesis indicates the most thermodynamically and kinetically favorable host cells for multiplication. The mechanistic model developed in this way relates the driving force as the fundamental physical force and pathogenesis as a biological phenomenon.
Quantitative Microbiological Risk assessment (QMRA) models are essential tools for setting up mitigation strategies. Traditional QMRA modelling approaches do not account for the correlation between genetic traits and variability among pathogens, potentially leading to over- or underestimation of microbial exposure and associated risks. We aimed to integrate genomic data into QMRA to propagate bacterial strain variability and update the existing framework of QMRA, following a Next Generation Risk Assessment (NGRA) approach. We used a benchmark QMRA model describing the prevalence and concentration of Campylobacter jejuni on chicken in all stages from farm-to-fork, to model the risk of infection and illness related to consumption of chicken meat. We integrated extended the storage step, to account for genetic variability in cold inactivation by incorporating gene-level genomic data associated with cold tolerance, derived from literature and a large C. jejuni genomic dataset, into the traditional QMRA model by setting up cold inactivation curves from existing data to map the relationship between the number of cold tolerance genes and temperature-dependent inactivation. The predicted number of cases was 8822 human cases/year in the benchmark QMRA model. The contamination of meat with C. jejuni strains having lower cold tolerance genes can reduce the expected number of human campylobacteriosis cases up to 100%; on the other hand, higher number of cold tolerance genes resulted in an increase up to 335.8% on the expected number of cases. Although our results are based on simulations, we show a potential implementation of the genetic information into QMRA, linking risk estimates with whole-genome sequencing data. More research is needed to understand how genetic features shape phenotypical characteristics, which is one of the main uncertainties in the current NGRA model, and to further explore the implications for risk management.
Recent advances in genomics, pangenomics, transcriptomics, and metatranscriptomics have expanded the resolution with which microbial traits relevant to food safety can be described. These approaches complement classical predictive models, which traditionally rely on population-averaged parameters and may overlook the heterogeneity that exists among strains and microbial communities. Omics data help identify genetic, functional, and regulatory features that underpin differences in stress tolerance, growth potential, and virulence, offering a more precise basis for hazard identification and exposure assessment. Genomic and pangenomic analyses clarify how core and accessory gene pools shape strain-level behavior, while transcriptomic studies reveal active pathways during acid, cold, or osmotic challenges. Metatranscriptomics extends this insight to complex communities, capturing how dominant and satellite members contribute to ecosystem function under food-relevant conditions. Incorporating these datasets into predictive microbiology and quantitative microbial risk assessment (QMRA) supports more realistic estimates of growth, survival, and persistence, reducing uncertainty in hazard characterization. Evidence shows that many food-associated strains are hypovirulent or slow-growing, indicating that risk may be overestimated when genetic heterogeneity is not considered. Although molecular data do not directly prescribe mitigation strategies, they support risk management by identifying which subpopulations merit targeted interventions, clarifying which process parameters influence persistence, and refining prioritization decisions. Our work discusses how omics tools align with primary, secondary, and tertiary predictive models and examines the complementarity between traditional decision-making frameworks and AI-based methods. Emphasis is also placed on sustainability, as omics-informed modeling enables more efficient in silico assessments and reduces dependence on resource-intensive challenge testing. Together, these developments strengthen the connection between risk assessment and risk management, supporting more proportionate and informed food safety decisions.
The global mpox outbreak of 2022, caused by the Clade IIb strain of monkeypox virus, underscored the potential of this virus to pose a significant public health threat on a global scale. The Democratic Republic of Congo is currently facing multiple outbreaks associated with Clade I. Effectively controlling localized community transmission within endemic areas through vaccination can reduce the likelihood of broader regional or even global outbreaks. Large-scale community vaccination in DRC is challenged by limited resources, including vaccine availability during early outbreaks in remote areas, whereas limited surveillance, contact tracing, and accessibility to remote locations can reduce the effectiveness of targeted ring vaccination. Furthermore, recent outbreaks in DRC have been driven by both sexual and non-sexual close contact transmissions. Here, we used an agent-based model with stochastic transmission within and between households to assess the effectiveness of ring vaccination for controlling localized community transmission in the presence of incomplete case reporting and delay in vaccination. We consider both nonsexual close contact and sexual transmission. We found that ring vaccination, even with 25-50% reporting, is effective in reducing outbreak cluster sizes and the likelihood of large cluster sizes (>5 cases), particularly when implemented shortly after detection of initial cases. The effectiveness of ring vaccination reduces with the inclusion of sexual transmission. We show that outbreak size and the likelihood of large clusters are reduced when responding to every reported infection, even with 2-3 weeks of delay. Settings with strong surveillance systems characterized by high levels of reporting will have earlier case detection, enabling earlier response and improving the effectiveness of ring vaccination.
