Epidemic Monitoring Research Articles

Estimating recent HIV incidence among young men who have sex with men: Reinvigorating, validating and implementing Osmond's algorithm for behavioral imputation.

HIV incidence information is essential for epidemic monitoring and evaluating preventive interventions. However, reliable HIV incidence data is difficult to obtain, especially among marginalized populations, such as young men who have sex with men (YMSM). Here we evaluate the reliability of an alternative HIV incidence assessment method, behavioral imputation, as compared to serologically estimated HIV incidence. Recent HIV incidence among YMSM (aged 18 to 21 and 18 to 24 years) enrolled in a cohort study in Bangkok from 2006 to 2014 was estimated using two mid-point methods for seroconversion: 1) between age of first anal intercourse and first HIV-positive test (without previous HIV-negative test) (behavioral imputation) and 2) between the date of last negative and first positive HIV test (serological estimation). Serologically estimated HIV incidence was taken as the “gold standard” to evaluate between-method agreement. At baseline, 314 YMSM age 18 to 21 years accumulated 674 person-years (PY) of follow-up since first anal intercourse. Considering that 50 men had prevalent HIV infection, the behaviorally imputed HIV incidence was 7.4 per 100 PY. Of the remaining 264 HIV-negative men, 54 seroconverted for HIV infection during the study, accumulating 724 PY of follow-up and a serologically estimated HIV incidence of 7.5 per 100 PY. At baseline, 712 YMSM age 18 to 24 years (including 18 to 21-year-old men analyzed above) accumulated 2143 PY of follow-up since first anal intercourse. Considering that 151 men had prevalent HIV infection, the behaviorally imputed HIV incidence was 7.0 per 100 PY. Of the remaining 561 HIV-negative men, 125 seroconverted for HIV infection during the study, accumulating 1700 PY of follow-up and a serologically estimated HIV incidence of 7.4 per 100 PY. Behavioral imputation and serological estimation are in good agreement when estimating recent HIV incidence in YMSM.

Data Analysis and Forecasting of Tuberculosis Prevalence Rates for Smart Healthcare Based on a Novel Combination Model

In recent years, healthcare has attracted much attention, which is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone, and quality of life. Data mining, analysis, and forecasting play a vital role in modern social and medical fields. However, how to select a proper model to mine and analyze the relevant medical information in the data is not only an extremely challenging problem, but also a concerning problem. Tuberculosis remains a major global health problem despite recent and continued progress in prevention and treatment. There is no doubt that the effective analysis and accurate forecasting of global tuberculosis prevalence rates lay a solid foundation for the construction of an epidemic disease warning and monitoring system from a global perspective. In this paper, the tuberculosis prevalence rate time series for four World Bank income groups are targeted. Kruskal–Wallis analysis of variance and multiple comparison tests are conducted to determine whether the differences of tuberculosis prevalence rates for different income groups are statistically significant or not, and a novel combined forecasting model with its weights optimized by a recently developed artificial intelligence algorithm—cuckoo search—is proposed to forecast the hierarchical tuberculosis prevalence rates from 2013 to 2016. Numerical results show that the developed combination model is not only simple, but is also able to satisfactorily approximate the actual tuberculosis prevalence rate, and can be an effective tool in mining and analyzing big data in the medical field.

Pattern of epidemics monitoring data reporting among health facilities in Nairobi City County, Kenya

Background: Weekly epidemic monitoring data reporting was adopted in Kenya after the Ministry of Health embraced Integrated Disease Surveillance and Response (IDSR) strategy from WHO - Africa region to facilitate timely detection and response to disease outbreak. The government has a reporting target of 80% but this has not been achieved by many counties including Nairobi County. Under-reporting put counties at risk of untimely outbreak detection which can, in turn result in epidemic menace. There is no adequate data to explain under-reporting in Nairobi County. The aim of this study was to determine the factors associated with poor epidemic monitoring data reporting among health facilities in Nairobi County.
 Methods: This was a cross-sectional analytical study targeting 318 health facilities in Nairobi County, Kenya. A stratified sample of 169 health facilities was selected using simple random. Health facility records were checked for compliance to quarterly submission of reports. A self-administered questionnaire was administered to data focal persons in the health facility in order to understand background characteristics of the participants, data transmission methods at each health facility, adherence to policies, personnel and availability of data collections tools. Data was analyzed using SPSS version 17.0. Relationships among study variables were tested using Chi-square, (P < 0.05).
 Results: Complete reporting was recoded in a total of 47% of the health facilities within the period under review, 36% partial reporting and 17% had not reported at all. Complete reporting was higher in public facilities (74%) than in private facilities (40.8%). Non-reporting was similarly higher in private facilities (25%) particularly the clinics (33%). Among institution related factors, supervision, presence of data focal person in a health facility and data transmission methods were significantly associated with poor data reporting (P<0.001). Training and awareness of data reporting days were important individual factors which were significantly associated with poor reporting (P<0.001). Rapid response to disease outbreaks is important for control of the disease and its spread to other regions. However, with only 47% of the health facilities in Nairobi County complying with the data reporting requirements, the City is at risk of delayed response in case such outbreak occurred. There is need to streamline the system to realize, exceed and maintain the government epidemic monitoring data reporting target rate of 80%.

Comparative characteristics of medical stability m. tuberculosis in Belgorod and Voronezh regions, Russia

The purpose of the study was to evaluate and compare the incidence and dynamics of drug resistance of M.tuberculosis to antituberculosis drugs in the Belgorod and Voronezh regions in different categories of patients: newly diagnosed, relapse, and other cases of repeated treatment.A comparative analysis of the spectral incidence and dynamics of M. tuberculosis drug resistance in 2007 and 2016 in the Belgorod and Voronezh regions was carried out. The data of statistical accounting forms 07-TB and centralized bacteriological laboratories were used.When comparing the data of the two regions - the Belgorod and Voronezh regions - both general trends and significant differences in the levels and dynamics of drug resistance of M. tuberculosis have been identified in different categories of patients. The level of primary multiple drug resistance is higher in the Voronezh Region, its growth rates in the Belgorod Region are the same, and the growth in overall sustainability is twice higher than in the Voronezh Region. It should be noted that the proportion of mono-resistant to isoniazid strains in the Belgorod region is increasing, while in Voronezh this phenomenon does not exist.The issues of epidemiological monitoring of tuberculosis require increased attention and detailed analysis at the territorial level for the adoption of adequate and effective management decisions. The current system of routine epidemic monitoring requires improvement taking into account the Russian and international experience. Data of such monitoring can have a significant impact on management decision-making in the field of tuberculosis control. Including at the level of each region.

HIITE: HIV-1 incidence and infection time estimator

Around 2.1 million new HIV-1 infections were reported in 2015, alerting that the HIV-1 epidemic remains a significant global health challenge. Precise incidence assessment strengthens epidemic monitoring efforts and guides strategy optimization for prevention programs. Estimating the onset time of HIV-1 infection can facilitate optimal clinical management and identify key populations largely responsible for epidemic spread and thereby infer HIV-1 transmission chains. Our goal is to develop a genomic assay estimating the incidence and infection time in a single cross-sectional survey setting. We created a web-based platform, HIV-1 incidence and infection time estimator (HIITE), which processes envelope gene sequences using hierarchical clustering algorithms and informs the stage of infection, along with time since infection for incident cases. HIITE's performance was evaluated using 585 incident and 305 chronic specimens' envelope gene sequences collected from global cohorts including HIV-1 vaccine trial participants. HIITE precisely identified chronically infected individuals as being chronic with an error less than 1% and correctly classified 94% of recently infected individuals as being incident. Using a mixed-effect model, an incident specimen's time since infection was estimated from its single lineage diversity, showing 14% prediction error for time since infection. HIITE is the first algorithm to inform two key metrics from a single time point sequence sample. HIITE has the capacity for assessing not only population-level epidemic spread but also individual-level transmission events from a single survey, advancing HIV prevention and intervention programs. Web-based HIITE and source code of HIITE are available at http://www.hayounlee.org/software.html. Supplementary data are available at Bioinformatics online.

Identifying Potential Norovirus Epidemics in China via Internet Surveillance.

BackgroundNorovirus is a common virus that causes acute gastroenteritis worldwide, but a monitoring system for norovirus is unavailable in China.ObjectiveWe aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections.MethodsThe norovirus-related data of a selected outbreak in Jiaxing Municipality, Zhejiang Province of China, in 2014 were collected from immediate epidemiological investigation, and the Internet search volume, as indicated by the Baidu Index, was acquired from the Baidu search engine. All correlated search keywords in relation to norovirus were captured, screened, and composited to establish the composite Baidu Index at different time lags by Spearman rank correlation. The optimal model was chosen and possibly predicted maps in Zhejiang Province were presented by ArcGIS software.ResultsThe combination of two vital keywords at a time lag of 1 day was ultimately identified as optimal (ρ=.924, P<.001). The exponential curve model was constructed to fit the trend of this epidemic, suggesting that a one-unit increase in the mean composite Baidu Index contributed to an increase of norovirus infections by 2.15 times during the outbreak. In addition to Jiaxing Municipality, Hangzhou Municipality might have had some potential epidemics in the study time from the predicted model.ConclusionsAlthough there are limitations with early warning and unavoidable biases, Internet surveillance may be still useful for the monitoring of norovirus epidemics when a monitoring system is unavailable.

Manipulating rainfall to study symptom expression of &lt;i&gt;Botrytis cinerea&lt;/i&gt; infection in wine grapes

Manipulating rainfall to study symptom expression of &lt;i&gt;Botrytis cinerea&lt;/i&gt; infection in wine grapes

Ecological Niche Modeling Identifies Fine-Scale Areas at High Risk of Dengue Fever in the Pearl River Delta, China.

Dengue fever (DF) is one of the most common and rapidly spreading mosquito-borne viral diseases in tropical and subtropical regions. In recent years, this imported disease has posed a serious threat to public health in China, especially in the Pearl River Delta (PRD). Although the severity of DF outbreaks in the PRD is generally associated with known risk factors, fine scale assessments of areas at high risk for DF outbreaks are limited. We built five ecological niche models to identify such areas including a variety of climatic, environmental, and socioeconomic variables, as well as, in some models, extracted principal components. All the models we tested accurately identified the risk of DF, the area under the receiver operating characteristic curve (AUC) were greater than 0.8, but the model using all original variables was the most accurate (AUC = 0.906). Socioeconomic variables had a greater impact on this model (total contribution 55.27%) than climatic and environmental variables (total contribution 44.93%). We found the highest risk of DF outbreaks on the border of Guangzhou and Foshan (in the central PRD), and in northern Zhongshan (in the southern PRD). Our fine-scale results may help health agencies to focus epidemic monitoring tightly on the areas at highest risk of DF outbreaks.

Spatial analysis of SS population coverage based on emergency regional healthcare

ObjectiveTo analyse population coverage of syndromic surveillance(SS)based on emergency care data by studying i)the attractiveness ofrespectively SOS Médecins (Emergency care general practitioners)and Hospital emergency departments in the Centre-Val de Loireregion and ii) the contribution of ecological deprivation factors inemergency access to healthcare.IntroductionSOS Médecins France (SOS Med) is the first private and permanentnetwork of general practitioners providing emergency care in France.Besides Hospital emergency departments (HED), SOS Med istherefore a major source of data for detecting and measuring near-real-time health phenomena. The emergency services provided by theSOS Med have been subject to important changes in the recent years.Their services are enriched by a medical consultation center togetherwith extended working hours. Besides, the south of the region ismarkedly affected by a declining number of medical practitionersThis study was conducted to analyze the regional population coverageof emergency healthcare data provided by HED and SOS Med tothe French syndromic surveillance system (SurSaUD®) takinginto account distance, health care offer, demographic factors andecological deprivation factors.MethodsAn analysis of the activities and geographic attraction was carriedout based on the data respectively provided by the three regional SOSMed and three HED (Bourges, Orléans and Tours). Quasi-Poissonregression modelling was used to identify the factors influencing theattractiveness of each organization. Next, the findings were refinedthrough spatial analysis of the attractiveness of HED and SOS Medand analysis of the contribution of deprivation based on socio-economical and healthcare facilities ecological indexes.ResultsIn terms of age group, children under 2 years required the largestservice consultations as well as seniors over 75 who sought moreemergency visits at home. The SOS Med were almost always active inurban areas and at least once in two due to continuity of care. So theyare an efficient source of general medical care given present workhours. Distance as an influential factor may explain the differencesin attraction to the support type. The extent of the attraction appearsin 36% SOS Med Bourges and 14% for SOS Med Orleans. Addthe extent of attraction for SOS, remote consultation for SOS Medassociations are a good use of care in general practice in present workhours scheme.In terms of monitoring of epidemics, we note that the SOSMédecins associations are most active in winter, particularly duringthe seasonal epidemics of influenza. This can be explained by the factof patient referrals during calls. The most serious cases are redirectedto the ED and cases of general medicine to the SOS Médecins.It is also important to note that the attraction of ED ofCHR Orléanscovers more or less important a large part of the regional territory,which is not visible to the ED ofCH Bourges. It should neverthelessbe noted that theCHR Orleansa larger bed capacity than theCH Bourges.ConclusionsThis research has analysed the changes taking place in the SOSmédecins associations in the Centre-Val de Loire region. Findingsshows that these associations help ensure access to general medicalcare in a context of strongly reduced medical demography althoughwith an uneven, primarily urban, geographical coverage. Withbetter knowledge of the geographic span and sources and types ofemergency care provision, further research can be undertaken tofurther refine and interpret the data.

Car‐level co‐location detection on trains for infectious disease epidemic monitoring

On the brink of an infectious disease epidemic, contact tracing must be precisely focused on those most likely to have made contacts with the confirmed cases. This explores if the tracing can be made fine-grained enough for train riders, so that those sharing the same car are identified as higher priority contact tracing targets, over those on different cars.

Establishment of malaria early warning system in Jiangsu Province Ⅷ Malaria vector monitoring in Jiangsu Province

To understand the population, density, seasonal fluctuation and nocturnal pattern of malaria vectors in Jiangsu Province, thus to provide evidences for malaria elimination in this province. Seven counties (cities, districts) were selected as the monitoring sites for malaria vectors in Jiangsu Province from 2013 to 2015. The mosquitoes were captured by human bait trapping in bed nets and mosquito-lured lamp overnight, and the seasonal fluctuation and nocturnal pattern of malaria vectors were observed. A total of 11 041 Anopheles sinensis mosquitoes were captured by the mosquito-lured lamps in 7 counties of Jiangsu Province from 2013 to 2015, and no An. anthropophagous was found. Among all the 7 monitoring sites, the number of An. sinensis captured in Sihong County was the most (6 742 mosquitoes), while that in Xuyu County was the least (34 mosquitoes). During this period, the density peaks of An. sinensis were the first half of July, the first half of August and the second half of July. A total of 2 421 An. sinensis were collected in 7 monitoring sites from 2013 to 2015 by human bait trapping in bed nets overnight. Among all the 7 monitoring sites, the captured number of An. sinensis in Sihong County was the most (1 085 mosquitoes), while that in Ganyu County was the least (13 mosquitoes). The nocturnal peak of An. sinensis was from 19：00 to 20：00 and 525 An. sinensis mosquitoes were captured during this period of time, which accounted for 21.68% of the total. Hereafter, the captured number of An. sinensis reduced over time. The density of An. sinensis mosquitoes is still high in individual areas in Jiangsu Province, so the epidemic and vector monitoring still should be strengthened to prevent the local transmission of imported malaria.

Development and application of a rapid and visual loop-mediated isothermal amplification for the detection of Sporisorium scitamineum in sugarcane.

Smut is a fungal disease with widespread prevalence in sugarcane planting areas. Early detection and proper identification of Sporisorium scitamineum are essential in smut management practices. In the present study, four specific primers targeting the core effector Pep1 gene of S. scitamineum were designed. Optimal concentrations of Mg2+, primer and Bst DNA polymerase, the three important components of the loop-mediated isothermal amplification (LAMP) reaction system, were screened using a single factor experiment method and the L16(45) orthogonal experimental design. Hence, a LAMP system suitable for detection of S. scitamineum was established. High specificity of the LAMP method was confirmed by the assay of S. scitamineum, Fusarium moniliforme, Pestalotia ginkgo, Helminthospcrium sacchari, Fusarium oxysporum and endophytes of Yacheng05-179 and ROC22. The sensitivity of the LAMP method was equal to that of the conventional PCR targeting Pep1 gene and was 100 times higher than that of the conventional PCR assay targeting bE gene in S. scitamineum. The results suggest that this novel LAMP system has strong specificity and high sensitivity. This method not only provides technological support for the epidemic monitoring of sugarcane smut, but also provides a good case for development of similar detection technology for other plant pathogens.

Spatio-sequential patterns mining: Beyond the boundaries

Data mining methods extract knowledge from huge amounts of data. Recently with the explosion of mobile technologies, a new type of data appeared. The resulting databases can be described as spatiotemporal data in which spatial information (e.g., the location of an event) and temporal information (e.g., the date of the event) are included. In this article, we focus on spatiotemporal patterns extraction from this kind of databases. These patterns can be considered as sequences representing changes of events localized in areas and its near surrounding over time. Two algorithms are proposed to tackle this problem: the first one uses \emph{a priori} strategy and the second one is based on pattern-growth approach. We have applied our generic method on two different real datasets related to: 1) pollution of rivers in France; and 2) monitoring of dengue epidemics in New Caledonia. Additionally, experiments on synthetic data have been conducted to measure the performance of the proposed algorithms.

Unveiling Spatial Epidemiology of HIV with Mobile Phone Data.

An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.

Development and demonstration of a state model for the estimation of incidence of partly undetected chronic diseases.

BackgroundEstimation of incidence of the state of undiagnosed chronic disease provides a crucial missing link for the monitoring of chronic disease epidemics and determining the degree to which changes in prevalence are affected or biased by detection.MethodsWe developed a four-part compartment model for undiagnosed cases of irreversible chronic diseases with a preclinical state that precedes the diagnosis. Applicability of the model is tested in a simulation study of a hypothetical chronic disease and using diabetes data from the Health and Retirement Study (HRS).ResultsA two dimensional system of partial differential equations forms the basis for estimating incidence of the undiagnosed and diagnosed disease states from the prevalence of the associated states. In the simulation study we reach very good agreement between the estimates and the true values. Application to the HRS data demonstrates practical relevance of the methods.DiscussionWe have demonstrated the applicability of the modeling framework in a simulation study and in the analysis of the Health and Retirement Study. The model provides insight into the epidemiology of undiagnosed chronic diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-015-0094-y) contains supplementary material, which is available to authorized users.

ON DATA QUALITY ASSURANCE AND CONFLATION ENTANGLEMENT IN CROWDSOURCING FOR ENVIRONMENTAL STUDIES

Abstract. Volunteer geographical information (VGI) either in the context of citizen science, active crowdsourcing and even passive crowdsourcing has been proven useful in various societal domains such as natural hazards, health status, disease epidemic and biological monitoring. Nonetheless, the variable degrees or unknown quality due to the crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process in which a quality assurance (QA) is needed is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF) combining the crowdsourced data into one view using potentially other data sources as well. Using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF in order to determine whether a disentanglement can be beneficial or not to a better understanding of the data curation process and to its methodology with respect to crowdsourcing data. Far from rejecting the usability quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible but still in integrating them within an approach analogous to a Bayesian paradigm.

IRCHLB-IV-Abstracts of Presentations at the 4th International Research Conference on Huanglongbing

IRCHLB-IV-Abstracts of Presentations at the 4th International Research Conference on Huanglongbing

Discussion on converging the doctor workstation with the epidemic information monitoring and reporting system

The networked epidemic information reporting management system and reporting mechanism of the hospital was called into play, embedding the reporting system into the outpatient and resident workstations of the hospital's IT management system.Such a networked platform enables the overall coverage and real-time reporting, monitoring and prewarning of hospital-wide epidemic information, providing reliable support for control and prevention of hospital epidemic control. Key words: Doctor workstation; Epidemic information reporting; Infectious diseases; Epidemic monitoring; Hospital information system

COPPER - Constraint OPtimized Prefixspan for Epidemiological Research

Abstract Sequential pattern mining, is a data mining technique used to study the temporal evolution of events describing a complex phe- nomenon. This technique has a limited application due to the high number of common sequences generated by dense datasets. To tackle this problem, we propose COP, an extension of the PrefixSpan algorithm oriented towards optimizing the relevance of the results obtained in the sequential patterns mining process. Indeed, we use multiple and simultaneous constraints that represent the expertise of researchers in a specific domain. Experiments conducted on datasets associated to dengue epidemic monitoring show an improve in result relevance from an expert's point of view, as well as, a considerable speed gains for mining dense datasets.

Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. Contribution of the IMIA Primary Healthcare Working Group.

Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive. To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars. We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowdsourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the "internet of things", and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources. Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.