Detection Of Disease Outbreaks Research Articles

SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects

With the proliferation of mobile devices and location-based services, continuous generation of massive volume of streaming spatial objects (i.e., geo-tagged data) opens up new opportunities to address real-world problems by analyzing them. In this paper, we present a novel continuous bursty region detection ( surge ) problem that aims to continuously detect a bursty region of a given size in a specified geographical area from a stream of spatial objects. Specifically, a bursty region shows maximum spike in the number of spatial objects in a given time window. The surge problem is useful in addressing several real-world challenges such as surge pricing problem in online transportation and disease outbreak detection. To solve the problem, we propose an exact solution and two approximate solutions, and the approximation ratio is $\frac{1-\alpha }{4}$ 1 - α 4 in terms of the burst score, where $\alpha$ α is a parameter to control the burst score. We further extend these solutions to support detection of t op- $k$ k bursty regions . Extensive experiments with real-world data are conducted to demonstrate the efficiency and effectiveness of our solutions.

What Can We Learn from the Exported Cases in Detecting Disease Outbreaks: A Case Study of the COVID-19 Epidemic

Background: Travel is a potent force in the emergence of disease. Early warning in the travel origins is crucial to prevent the disease from spreading. However, many travel origins have delays in reporting the disease outbreak. Under this circumstance, those exported cases (travelers tested positive outside their travel origins) became valuable data for detecting the disease outbreaks. Methods: We estimated some key indicators (the exponential growth rate, the doubling time, and the basic reproduction number) using the exported cases in the early phase of the COVID-19 epidemic of four travel origins, Wuhan (China), U.S., Italy, and Iran. We compared the estimates from the exported cases to the ones from domestic data, and quantitatively evaluated how different features in the exported cases influenced the estimation. We used hypothesis testing to determine if an indicator has exceeded a certain threshold.Findings: We found that the dates that the exported cases rang an alarm about the disease outbreak were all before or shortly after some emergency measures were taken in the travel origins. If the assumed epidemic start date was relatively accurate and the traveler samples were representative of the general population, the indicators estimated from the exported cases were consistent with the domestic data. We provided the minimum number of exported cases to determine whether the epidemic growth rate was above a certain level. Flexible scenarios were provided in a Shiny App. Interpretation: Rapid detection of a disease outbreak is crucial for government intervention and raising public awareness. Utilizing the diagnosis resources from all countries, the exported case data is a good addition to the domestic data. To strengthen the global infectious disease surveillance system, we advocate that countries work in a collaborative way by sharing detailed travel data in a timely manner.Funding: NIH/NIAID 5R01AI136664Declaration of Interests: The authors declare no competing interests.

Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study.

BackgroundA novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future.ObjectiveThis study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media.MethodsWeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020.ResultsWeChat Index hits for “Feidian” (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority’s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords “SARS,” “coronavirus,” “novel coronavirus,” “shortness of breath,” “dyspnea,” and “diarrhea,” but these terms were not as meaningful for the early detection of the outbreak as the term “Feidian”.ConclusionsBy using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks.

Disease discovery-based emotion lexicon: a heuristic approach to characterise sicknesses in microblogs

The analysis of microblogging data has been widely used to discover valuable resources for timely identification of critical illness-related incidents and serious epidemics. Despite the numerous efforts made in this field, making an accurate and timely prediction of incidents and outbreaks based on certain clinical symptoms remains a great challenge. Hence, providing an investigative method can be crucial in characterising a disease state. This study proposes a heuristic mechanism by using an unsupervised learning technique to efficiently detect disease incidents and outbreaks from the tweet content. We categorised the types of emotions that are highly linked to a specific disease and its related terminologies. Emotions (anger, fear, sadness, and joy) and diabetes-related terminologies were extracted using the NRC Affect Intensity Lexicon and part-of-speech tagging tool. A two-cluster solution was established and validated. The classification results showed that K-means clustering with two centroids had the highest classification accuracy (96.53%). The relationship between diabetes-related terms (in the form of tweets) and emotions were established and assessed using the association rules mining technique. The results showed that diabetes-related terms were exclusively associated with fear emotions. This study offers a novel mechanism for disease recognition and outbreak detection in microblogs that can be useful in making informed decisions about a disease state.

Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence.

Event‐based surveillance (EBS) systems monitor a broad range of information sources to detect early signals of disease emergence, including new and unknown diseases. In December 2019, a newly identified coronavirus emerged in Wuhan (China), causing a global coronavirus disease (COVID‐19) pandemic. A retrospective study was conducted to evaluate the capacity of three event‐based surveillance (EBS) systems (ProMED, HealthMap and PADI‐web) to detect early COVID‐19 emergence signals. We focused on changes in online news vocabulary over the period before/after the identification of COVID‐19, while also assessing its contagiousness and pandemic potential. ProMED was the timeliest EBS, detecting signals one day before the official notification. At this early stage, the specific vocabulary used was related to ‘pneumonia symptoms’ and ‘mystery illness’. Once COVID‐19 was identified, the vocabulary changed to virus family and specific COVID‐19 acronyms. Our results suggest that the three EBS systems are complementary regarding data sources, and all require timeliness improvements. EBS methods should be adapted to the different stages of disease emergence to enhance early detection of future unknown disease outbreaks.

A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.

DOT: a crowdsourcing Mobile application for disease outbreak detection and surveillance in Mauritius.

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country’s public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.

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.

Surveillance strategies for the detection of disease outbreaks in the Pacific islands: meta-analysis of published literature, 2010-2019.

Due to their tropical location, development status and the limited capacity of health systems, Pacific island counties and territories are particularly susceptible to infectious disease outbreaks; but evidence as to the optimal way in which outbreaks are detected is scarce. In this review, we synthesise evidence from literature about how outbreaks are detected in Pacific island countries and territories and critique factors identified as inhibiting surveillance practice. For this systematic review, we searched electronic databases Embase, Global Health, MEDLINE and MEDLINE Epub from 1 January 2010 and 31 March 2019 for reports describing infectious disease outbreaks occurring in the Pacific islands. Reports were included if they reported the method by which an outbreak was detected or the time between an outbreak's onset and its detection. We extracted information about the report type and authors, the outbreak and its method/s of detection, and pertinent issues inhibiting surveillance practice. Of 860 articles identified, 37 reports describing 39 outbreaks met the inclusion criteria. Most outbreaks (n=30) were identified through formal event-based surveillance; six through syndromic surveillance; and two by ad hoc notification from the community. Barriers to early outbreak detection included population isolation; lack of resources and infrastructure to support surveillance implementation and signal investigation; and broader health system factors such as preparedness planning and availability of laboratory services. Most surveillance-related gain in the Pacific islands may be made through building formal event-based surveillance systems and streamlining reporting processes to facilitate outbreak notification. This observation is pertinent given the focus on establishing and expanding syndromic surveillance approaches for outbreak detection in the islands over the last decade.

Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development.

BackgroundThe time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms.ObjectiveOur purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework.MethodsWe conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review.ResultsThe search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms.ConclusionsThe review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

Tree decomposition based anomalous connected subgraph scanning for detecting and forecasting events in attributed social media networks

Event detection and forecasting in social media networks, such as disease outbreak and air pollution event detection, have been formulated as an anomalous connected subgraph detection problem. However, the huge search space and the sparsity of anomaly events make it difficult to solve this problem effectively and efficiently. This paper presents a general framework, namely anomalous connected subgraph scanning (GraphScan) which optimizes a large class of sophisticated nonlinear nonparametric scan statistic functions, to solve this problem in attributed social media networks. We first transform the sophisticated nonlinear nonparametric scan statistics functions into the Price-Collecting Steiner Tree (PCST) problem with provable guarantees for evaluating the significance of connected subgraphs to indicate the ongoing or forthcoming events. Then, we use tree decomposition technique to divide the whole graph into a set of smaller subgraph bags, and arrange them into a tree structure, through which we can reduce the search space dramatically. Finally, we propose an efficient approximation algorithm to solve the problem of anomalous subgraph detection using the tree of bags. With two real-world datasets from different domains, we conduct extensive experimental evaluations to demonstrate the effectiveness and efficiency of the proposed approach.

COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions?

The outbreak of novel coronavirus (COVID-19) is a public health emergency that had caused disastrous results in more than 100 countries. The ability to detect disease outbreaks in the early stages is a key component of efficient disease control and prevention. With the increased availability of electronic health-care data and spatial analysis techniques, there is great potential to develop algorithms to enable more effective disease surveillance. The research focuses to develop a transparent user-friendly method to simulate the outbreak data. The paper describes the GIS tools to identify and define the field of investigation which requires consideration of the strengths and limitations of data collection instruments, facility of locational data collection, accuracy of locational data, and pertinent attributes for understanding disease risk. Using such information, it is quite easy for authorities to locate the highly effected area and take appropriate actions in that particular areas. However, GIS techniques, resources, and methods can be used in Pakistan for more effective investigation of vulnerable geographical locations.

The COVID-19 pandemic preparedness ... or lack thereof: from China to Italy.

COVID-19, that emerged in December 2019 in the city of Wuhan, China and is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly evolved into a pandemic. Italy has become one of the largest epicentres outside Asia, accounting now for at least 80,539 infections (cumulative incidence of 95.9/100,000) and 8,165 deaths (case fatality rate 10.1%). It has seriously affected people above the age of 60 years. The International Health Regulations (IHR) revised in 2005 bind governments to disclose vital information regarding the identification and detection of new disease outbreaks regardless of its causative agent. In contrast to the previous SARS epidemic, China timely informed the world about the onset of a new outbreak. It also soon disclosed the clinical characteristics of patients with COVID-19. Unfortunately, despite the fast recognition of the Chinese epidemic, the application of the 2005 IHR was not followed by an effective response in every country and most health authorities failed to rapidly perceive the threat posed by COVID-19. To further complicate matters, IHR implementation, which relies primarily on self-reporting data rather than on an external review mechanism, was limited in speed and further hindered by high costs. The response in Italy suffered from several limitations within the health system and services. The action against this threat must instead be quick, firm and at the highest trans-national level. The solution lies in further strengthening countries' preparedness through a clear political commitment, mobilization of proper resources and implementation of a strict surveillance and monitoring process.

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

BackgroundThe ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time.MethodsPredicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010.ResultsThe overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data.ConclusionsA persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.

Identifying Dysregulated lncRNA-Associated ceRNA Network Biomarkers in CML Based on Dynamical Network Biomarkers.

The incidence of chronic myeloid leukemia (CML) is increasing year by year, which is a serious threat to human health. Early diagnosis can reduce mortality and improve prognosis. LncRNAs have been shown to be effective biomarkers for a variety of diseases and can act as competitive endogenous RNA (ceRNA). In this study, the dysregulated lncRNA-associated ceRNA networks (DLCN) of the chronic phase (CP), accelerated phase (AP), and blastic crisis (BC) for CML are constructed. Then, based on dynamic network biomarkers (DNB), some dysregulated lncRNA-associated ceRNA network biomarkers of CP, AP, and BC for CML are identified according to DNB criteria. Thus, a lncRNA (SNHG5) is identified from DLCN_CP, a lncRNA (DLEU2) is identified from DLCN_AP, and two lncRNAs (SNHG3, SNHG5) are identified from DLCN_BC. In addition, the critical index (CI) used to detect disease outbreaks shows that CML occurs in CP, which is consistent with clinical medicine. By analyzing the functions of the identified ceRNA network biomarkers, it has been found that they are effective lncRNA biomarkers directly or indirectly related to CML. The result of this study will help deepen the understanding of CML pathology from the perspective of ceRNA and help discover the effective biomarkers of CP, AP, and BC for CML in the future, so as to help patients get timely treatment and reduce the mortality of CML.

One Health workers: innovations in early detection of human, animal, and plant disease outbreaks

One Health workers: innovations in early detection of human, animal, and plant disease outbreaks

PADI-web: A multilingual event-based surveillance system for monitoring animal infectious diseases

Global animal disease outbreak detection and monitoring rely on official sources, such as intergovernmental organisations, as well as digital media and other unofficial outlets. Manually extracting relevant information from unofficial sources is time-consuming. The Platform for Automated extraction of animal Disease Information from the web (PADI-web) is an automated biosurveillance system devoted to online news source monitoring for the detection of emerging/new animal infectious diseases by the French Epidemic Intelligence System. The tool automatically collects news via customised multilingual queries, classifies them and extracts epidemiological information. We detail each step of the PADI-web pipeline, with a focus on the new user-oriented features.

Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy.

Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions’ COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

Spatio-Temporal Investigations of Dengue Fever in Pakistan Through an HL7 Based Public Health Framework for Hotspot Analysis

The importance of a standard based interoperability framework is now widely recognized. In order to understand the challenges, feasibility and impact of outcomes of such architectures for public health improvement, it is important to implement and demonstrate the usefulness of data analytics based on pathway provided by the framework. This work provides an implementation details and results of transferring dengue data from source database to data analytical programs used by public health authorities. Dengue fever, a mosquito-borne disease that occurs in tropical and subtropical areas of the world, is considered to be a significant threat in both developing and the developed countries. This study investigates the spatio-temporal distribution of dengue in Pakistan from the years 2014-2017 and identify the most frequently affected hotspots of dengue across the the 3 provinces in the country using data mining, clustering and GIS based analysis of data. In addition, the identification of most vulnerable dengue locations has led to investigation into the potential environmental or geographical conditions that may have contributed to dengue prevalence in the area. These investigations present interesting results that shows positive correlation for temperature, humidity and population density with the dengue incidences. In addition, the results are able to identify disease hotspots over time windows that can be mapped to any spatio-temporal scales. The outcome of this research is crucial for optimal use of resources for combating dengue fever at a regional or national scale by identifying the hotspots. The hotspots identified can be use to create a sentinel surveillance network for a pandemic disease. In addition, the work serves as a useful reference for a comprehensive national level early warning and rapid disease outbreak detection framework for any disease of public health context.

An integer-valued time series model for multivariate surveillance.

In recent days, different types of surveillance data are becoming available for public health purposes. In most cases, several variables are monitored and events of different types are reported. As the amount of surveillance data increases, statistical methods that can effectively address multivariate surveillance scenarios are demanded. Even though research activity in this field is increasing rapidly in recent years, only a few approaches have simultaneously addressed the integer-valued property of the data and its correlation (both time correlation and cross-correlation) structure. In this article, we suggest a multivariate integer-valued autoregressive model that allows for both serial and cross-correlations between the series and can easily accommodate overdispersion and covariate information. Moreover, its structure implies a natural decomposition into an endemic and an epidemic component, a common distinction in dynamic models for infectious disease counts. Detection of disease outbreaks is achieved through the comparison of surveillance data with one-step-ahead predictions obtained after fitting the suggested model to a set of clean historical data. The performance of the suggested model is illustrated on a trivariate series of syndromic surveillance data collected during Athens 2004 Olympic Games.