Detection Of Infectious Disease Outbreaks Research Articles

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Real-Time On-Site Monitoring of Viruses in Wastewater Using Nanotrap® Particles and RICCA Technologies.

Wastewater-based epidemiology (WBE) is an effective and efficient tool for the early detection of infectious disease outbreaks in a community. However, currently available methods are laborious, costly, and time-consuming due to the low concentration of viruses and the presence of matrix chemicals in wastewater that may interfere with molecular analyses. In the present study, we designed a highly sensitive "Quick Poop (wastewater with fecal waste) Sensor" (termed, QPsor) using a joint approach of Nanotrap microbiome particles and RICCA (RNA Isothermal Co-Assisted and Coupled Amplification). Using QPsor, the WBE study showed a strong correlation with standard PEG concentrations and the qPCR technique. Using a closed format for a paper-based lateral flow assay, we were able to demonstrate the potential of our assay as a real-time, point-of-care test by detecting the heat-inactivated SARS-CoV-2 virus in wastewater at concentrations of 100 copies/mL and within one hour. As a proof-of-concept demonstration, we analyzed the presence of viral RNA of the SARS-CoV-2 virus and PMMoV in raw wastewater samples from wastewater treatment plants on-site and within 60 min. The results show that the QPsor method can be an effective tool for disease outbreak detection by combining an AI-enabled case detection model with real-time on-site viral RNA extraction and amplification, especially in the absence of intensive clinical laboratory facilities. The lab-free, lab-quality test capabilities of QPsor for viral prevalence and transmission in the community can contribute to the efficient management of pandemic situations.

From GeoSentinel data to epidemiological insights: a multidisciplinary effort towards artificial intelligence-supported detection of infectious disease outbreaks.

The growing complexity of GeoSentinel surveillance data creates opportunities for novel data science-based outbreak detection methods. Challenges may be overcome by effective multidisciplinary collaboration. The early signals generated by outbreak detection methods using GeoSentinel data may influence policymaking, shape public health responses, and contribute to global disease control strategies.

No Entry in a Pandemic: Public Support for Border Closures

AbstractEffective response to and rapid and reliable detection of infectious disease outbreaks require successful coordination of countries’ border policies early on. As threats from diseases are highly salient to the public, researchers agree that a better understanding of domestic politics is crucial. This article investigates a key piece of this question: public demands for border closures. Our experiments in the United Kingdom and the United States show that a greater pandemic threat mildly increases support for border closures, but the World Health Organization's (WHO) guidance against border closures and reminders about international legal obligations to follow the guidance substantially weaken support for border closures. However, during the COVID‐19 pandemic, many countries flouted WHO's recommendations and restricted their borders. Examining media attention suggests people's lack of knowledge of the WHO guidance as a crucial reason for those border closures. Our study produces insights into the design of effective global health governance.

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.

Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5%) but only 22 out of 238 (9.2%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33%) within 2 weeks of onset. The positive predictive value varied between 9% and 100% for HealthMap and from 0 to 100% for EIOS, and timeliness of detection ranged from 13% to 94% for HealthMap and from 0% to 92% for EIOS, whereas system specificity was generally high (59%-100%). The number of EBS reports available within a country, the human development index, and the country's geographical location partially explained the high variability in system performance across countries. We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings.

Harnessing the potential of digital data for infectious disease surveillance in sub-Saharan Africa

Abstract   Despite efforts by the WHO to support local surveillance strategies in developing countries, there is a lack of robust public health surveillance frameworks. As a result, early infectious disease outbreak detection and response remain a significant challenge for local health systems in low-resource settings such as sub-Saharan African countries. In contrast, the growing digital infrastructure, especially in the mobile phone sector, and the global availability of extensive digital data offer promising solutions to enhance and strengthen epidemiological surveillance. Yet, there is little insight into concepts of utilisation and transfer into local public health practice. Using Tanzania as an example, a novel electronic surveillance and early outbreak alert framework is being developed that links signals on emerging diseases with relevant contextual Open Data for rapid outbreak risk assessment. The concept focuses on haemorrhagic fever diseases, specifically dengue virus disease, which is increasingly spreading in sub-Saharan Africa. A data stack framework forms the core of the system, which augments electronic information on the occurrence of acute haemorrhagic fever syndrome, e.g., collected via mobile phone-based surveillance tools, with openly available socio-ecological context data specific to dengue. Preliminary results on the data and information flow within the surveillance framework are presented and strategies for an automated indicator-based risk assessment for dengue outbreaks will be discussed, supplemented by an agent-based simulation framework to model possible short-term outbreak scenarios. In addition, adequate data inputs, identified through an appraisal of various data sources available for Tanzania, are outlined. The framework could serve as a blueprint for designing locally implementable early warning and decision support systems integrated with existing digital surveillance infrastructure. Key messages • Digital health surveillance and Open Data offer great potential for early outbreak detection and supporting health decisions but require tailored solutions to benefit low-resource settings. • Building on existing digital surveillance infrastructure, the framework may serve as a blueprint for designing an enhanced surveillance and decision support system for infectious disease outbreaks.

BioCaster in 2021: automatic disease outbreaks detection from global news media.

SummaryBioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community.Availability and implementationBioCaster web-portal is freely accessible at http://biocaster.org.

Supervised learning using routine surveillance data improves outbreak detection of Salmonella and Campylobacter infections in Germany.

The early detection of infectious disease outbreaks is a crucial task to protect population health. To this end, public health surveillance systems have been established to systematically collect and analyse infectious disease data. A variety of statistical tools are available, which detect potential outbreaks as abberations from an expected endemic level using these data. Here, we present supervised hidden Markov models for disease outbreak detection, which use reported outbreaks that are routinely collected in the German infectious disease surveillance system and have not been leveraged so far. This allows to directly integrate labeled outbreak data in a statistical time series model for outbreak detection. We evaluate our model using real Salmonella and Campylobacter data, as well as simulations. The proposed supervised learning approach performs substantially better than unsupervised learning and on par with or better than a state-of-the-art approach, which is applied in multiple European countries including Germany.

Artificial Intelligence May Help in the Containment of Cholera in Nigeria

Artificial intelligence (AI) techniques have been widely applied to infectious disease outbreak detection and early warning, trend prediction, and public health response and assessment. Such public health surveillance and response tasks of significant importance pose unique technical challenges such as data sparsity; Traditional public health information relies heavily on epidemiological data. Recent years have seen incredible growth of AI-enabled methods, especially in developed countries, complementing statistical approaches. This chapter aims to provide a systematic review of these recent advances applying AI techniques to address the Cholera Epidemic and surveillance and response challenges.

Rapid identification and metagenomics analysis of the adenovirus type 55 outbreak in Hubei using real-time and high-throughput sequencing platforms

The rise in human adenovirus (HAdV) infections poses a serious challenge to public health in China. Real-time (RT) sequencing provides solutions for achieving rapid pathogen identification during outbreaks, whereas high-throughput sequencing yields higher sequence accuracy. In the present study, we report the outcomes of applying nanopore and BGI platforms in the identification and genomic analysis of an HAdV outbreak in Hubei province, China in May of 2019. A mixed sample of nine nasopharyngeal swabs and one single sample were submitted to direct nanopore sequencing (MinION device), generating their first HAdV-55 reads within 13 and 20min, respectively. The sequences were confirmed by RT-polymerase chain reaction (PCR). Ten HAdV-positive samples were further sequenced using next-generation high-throughput sequencing (BGISEQ-500 device). Phylogenetic analysis revealed that the outbreak strain had a close genetic relation to strains isolated in Sichuan province. Metagenomic analysis showed that HAdV-55 was not a dominant species in samples from which the whole HAdV-55 genome could not be assembled. The present results highlight the value of combining sequencing platforms and using mixed samples for nucleic acid enrichment in pathogen detection of infectious disease outbreaks.

A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study.

BackgroundOutbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks.ObjectiveThis study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making.MethodsWe developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia.ResultsWe identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases.ConclusionsWe demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases.

Stability and detection of nucleic acid from viruses and hosts in controlled mosquito blood feeds.

Monitoring the presence and spread of pathogens in the environment is of critical importance. Rapid detection of infectious disease outbreaks and prediction of their spread can facilitate early responses of health agencies and reduce the severity of outbreaks. Current sampling methods are sorely limited by available personnel and throughput. For instance, xenosurveillance utilizes captured arthropod vectors, such as mosquitoes, as sampling tools to access blood from a wide variety of vertebrate hosts. Next generation sequencing (NGS) of nucleic acid from individual blooded mosquitoes can be used to identify mosquito and host species, and microorganisms including pathogens circulating within either host. However, there are practical challenges to collecting and processing mosquitoes for xenosurveillance, such as the rapid metabolization or decay of microorganisms within the mosquito midgut. This particularly affects pathogens that do not replicate in mosquitoes, preventing their detection by NGS or other methods. Accordingly, we performed a series of experiments to establish the windows of detection for DNA or RNA from human blood and/or viruses present in mosquito blood meals. Our results will contribute to the development of xenosurveillance techniques with respect to optimal timing of sample collection and NGS processing and will also aid trap design by demonstrating the stabilizing effect of temperature control on viral genome recovery from blood-fed mosquitoes.

Concordance of SNP- and allele-based typing workflows in the context of a large-scale international Salmonella Enteritidis outbreak investigation.

A large European multi-country Salmonella enterica serovar Enteritidis outbreak associated with Polish eggs was characterized by whole-genome sequencing (WGS)-based analysis, with various European institutes using different analysis workflows to identify isolates potentially related to the outbreak. The objective of our study was to compare the output of six of these different typing workflows (distance matrices of either SNP-based or allele-based workflows) in terms of cluster detection and concordance. To this end, we analysed a set of 180 isolates coming from confirmed and probable outbreak cases, which were representative of the genetic variation within the outbreak, supplemented with 22 unrelated contemporaneous S . enterica serovar Enteritidis isolates. Since the definition of a cluster cut-off based on genetic distance requires prior knowledge on the evolutionary processes that govern the bacterial populations in question, we used a variety of hierarchical clustering methods (single, average and complete) and selected the optimal number of clusters based on the consensus of the silhouette, Dunn2, and McClain–Rao internal validation indices. External validation was done by calculating the concordance with the WGS-based case definition (SNP-address) for this outbreak using the Fowlkes–Mallows index. Our analysis indicates that with complete-linkage hierarchical clustering combined with the optimal number of clusters, as defined by three internal validity indices, the six different allele- and SNP-based typing workflows generate clusters with similar compositions. Furthermore, we show that even in the absence of coordinated typing procedures, but by using an unsupervised machine learning methodology for cluster delineation, the various workflows that are currently in use by six European public-health authorities can identify concordant clusters of genetically related S . enterica serovar Enteritidis isolates; thus, providing public-health researchers with comparable tools for detection of infectious-disease outbreaks.

Language Specific Gaps in Identifying Early Epidemic Signals – A Case Study of the Malay Language

Background: Internet-based surveillance systems have become invaluable tools to complement traditional surveillance in the early detection of infectious disease outbreaks. Search results limited to only English-language sources may lead to missed opportunities in global outbreak monitoring efforts. The Malay language has 290 million native speakers across the Southeast Asian region (Indonesia, Malaysia, Brunei, Singapore and the southern parts of Thailand and the Philippines), which is an important region for outbreaks. The aim of this research is to determine the completeness of reports of Malay language outbreak detection by commonly used open source surveillance platforms. Methods: We searched ProMED-mail, HealthMap, and Google News to investigate outbreak events in Malaysia and Indonesia between 1st August 2016 and 31st August 2017. We also cross-checked published epidemic reports in Medline to determine the proportion of outbreaks that are published. Results: A total of 371 entries from ProMED-mail and HealthMap were included. A Google News search query using Malay language keywords resulted in 453 news outbreak reports, 98 of which were missed by either or both ProMED-mail and HealthMap. During the study time frame, only 40 published epidemic reports were found in MEDLINE, showing the importance of informal data sources to identify outbreaks. Some diseases such as mumps, dengue and avian influenza had different local terminology in Indonesia and Malaysia. Conclusions: Search results limited to only English-language sources may lead to reduced outbreak detection. The inclusion of keywords in the Malay language improves epidemic intelligence in the region, but needs to be nuanced for local terminology, which may differ between countries. Informal Internet-based surveillance can improve compliance with International Health Regulations (IHR) for low income countries, because it is less resource-intensive than formal surveillance. However, there is a need to include local language search terms to improve outbreak detection, especially in regions at high risk of emerging infectious diseases.

Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review.

BackgroundSyndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking.MethodsA scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants.ResultsForty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data.ConclusionsVarious dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.

A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks.

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt–Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.

Intelligence and global health: assessing the role of open source and social media intelligence analysis in infectious disease outbreaks

PurposeOpen Source Intelligence (OSINT) and Signals Intelligence (SIGINT) from the clandestine intelligence sector are being increasingly employed in infectious disease outbreaks. The purpose of this article is to explore how such tools might be employed in the detection, reporting, and control of outbreaks designated as a ‘threat’ by the global community. It is also intended to analyse previous use of such tools during the Ebola and SARS epidemics and to discuss key questions regarding the ethics and legality of initiatives that further blur the military and humanitarian spaces.MethodsWe undertake qualitative analysis of current discussions on OSINT and SIGINT and their intersection with global health. We also review current literature and describe the debates. We built on quantitative and qualitative research done into current health collection capabilities.ResultsThis article presents an argument for the use of OSINT in the detection of infectious disease outbreaks and how this might occur.ConclusionWe conclude that there is a place for OSINT and SIGINT in the detection and reporting of outbreaks. However, such tools are not sufficient on their own and must be corroborated for the intelligence to be relevant and actionable. Finally, we conclude that further discussion on key ethical issues needs to take place before such research can continue. In particular, this involves questions of jurisdiction, data ownership, and ethical considerations.

Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System.

ObjectiveThe Chinese Center for Disease Control and Prevention developed the China Infectious Disease Automated-alert and Response System (CIDARS) in 2008. The CIDARS can detect outbreak signals in a timely manner but generates many false-positive signals, especially for diseases with seasonality. We assessed the influence of seasonality on infectious disease outbreak detection performance.MethodsChickenpox surveillance data in Songjiang District, Shanghai were used. The optimized early alert thresholds for chickenpox were selected according to three algorithm evaluation indexes: sensitivity (Se), false alarm rate (FAR), and time to detection (TTD). Performance of selected proper thresholds was assessed by data external to the study period.ResultsThe optimized early alert threshold for chickenpox during the epidemic season was the percentile P65, which demonstrated an Se of 93.33%, FAR of 0%, and TTD of 0 days. The optimized early alert threshold in the nonepidemic season was P50, demonstrating an Se of 100%, FAR of 18.94%, and TTD was 2.5 days. The performance evaluation demonstrated that the use of an optimized threshold adjusted for seasonality could reduce the FAR and shorten the TTD.ConclusionsSelection of optimized early alert thresholds based on local infectious disease seasonality could improve the performance of the CIDARS.

Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany.

Geographical mapping of infectious diseases is an important tool for detecting and characterising outbreaks. Two common mapping methods, dot maps and incidence maps, have important shortcomings. The former does not represent population density and can compromise case privacy, and the latter relies on pre-defined administrative boundaries. We propose a method that overcomes these limitations: dot map cartograms. These create a point pattern of cases while reshaping spatial units, such that spatial area becomes proportional to population size. We compared these dot map cartograms with standard dot maps and incidence maps on four criteria, using two example datasets. Dot map cartograms were able to illustrate both incidence and absolute numbers of cases (criterion 1): they revealed potential source locations (Q fever, the Netherlands) and clusters with high incidence (pertussis, Germany). Unlike incidence maps, they were insensitive to choices regarding spatial scale (criterion 2). Dot map cartograms ensured the privacy of cases (criterion 3) by spatial distortion; however, this occurred at the expense of recognition of locations (criterion 4). We demonstrate that dot map cartograms are a valuable method for detection and visualisation of infectious disease outbreaks, which facilitates informed and appropriate actions by public health professionals, to investigate and control outbreaks.

An Early Infectious Disease Outbreak Detection Mechanism Based on Self-Recorded Data from People with Diabetes.

People with diabetes experience elevated blood glucose (BG) levels at the time of an infection. We propose to utilize patient-gathered information in an Electronic Disease Surveillance Monitoring Network (EDMON), which may support the identification of a cluster of infected people with elevated BG levels on a spatiotemporal basis. The system incorporates data gathered from diabetes apps, continuous glucose monitoring (CGM) devices, and other appropriate physiological indicators from people with type 1 diabetes. This paper presents a novel approach towards modeling of the individual's BG dynamics, a mechanism to track and detect deviations of elevated BG readings. The models were developed and validated using self-recorded data in the non-infection status using Dexcom CGM devices, from two type 1 diabetes individuals over a 1-month period. The models were also tested using simulated datasets, which resemble the individual's BG evolution during infections. The models accurately simulated the individual's normal BG fluctuations and further detected statistically significant BG elevations.