Infectious Disease Surveillance in China

A national system of infectious disease surveillance was established in 1959 in China.Now it consists of three subunits, namely, national disease reporting system (NDRS), nationwide disease surveillance points (DSPs), and surveillance network for specific infectious diseases. There are 35 notifiable infectious diseases, which are divided into Classes A, B, and C. The functions of the surveillance include explaining the natural history of infectious diseases, describing the distribution of case occurrence, triggering disease-control effort, monitoring epidemic of infectious diseases during natural disasters, predicting and controlling epidemics and providing the base of policy adjustment.

The crucial role of surveillance in disease outbreak control:a comprehensive review

Surveillance systems are vital for detecting,managing,and mitigating infectious disease outbreaks.This review highlights the importance of modern technologies like AI and big data in enhancing surveillancecapabilities.It underscores the need for global collaboration and examines the role of surveillance in diseases likeinfluenza,Ebola,and COVID-19.Technological innovations such as geospatial mapping and wearable healthdevices are transforming disease control,though they raise ethical concerns about privacy.Continuousadvancements and ethical safeguards are crucial for effective surveillance and global health protection.

Fusion of spatiotemporal and thematic features of textual data for animal disease surveillance

Several internet-based surveillance systems have been created to monitor the web for animal health surveillance.These systems collect a large amount of news dealing with outbreaks related to animal diseases.Automatically identifying news articles that describe the same outbreak event is a key step to quickly detect relevant epidemiological information while alleviating manual curation of news content.This paper addresses the task of retrieving news articles that are related in epidemiological terms.We tackle this issue using text mining and feature fusion methods.The main objective of this paper is to identify a textual representation in which two articles that share the same epidemiological content are close.We compared two types of representations(i.e.,features)to represent the documents:(i)morphosyntactic features(i.e.,selection and transformation of all terms from the news,based on classical textual processing steps)and(ii)lexicosemantic features(i.e.,selection,transformation and fusion of epidemiological terms including diseases,hosts,locations and dates).We compared two types of term weighing(i.e.,Boolean and TF-IDF)for both representations.To combine and transform lexicosemantic features,we compared two data fusion techniques(i.e.,early fusion and late fusion)and the effect of features generalisation,while evaluating the relative importance of each type of feature.We conducted our analysis using a corpus composed of a subset of news articles in English related to animal disease outbreaks.Our results showed that the combination of relevant lexicosemantic(epidemiological)features using fusion methods improves classical morphosyntactic representation in the context of disease-related news retrieval.The lexicosemantic representation based on TF-IDF and feature generalisation(F-measure=0.92,r-precision=0.58)outperformed the morphosyntactic representation(F-measure=0.89,r-precision=0.45),while reducing the features space.Converting the features into lower granular features(i.e.,generalisation)contributed to improving the results of the lexicosemantic representation.Our results showed no difference between the early and late fusion approaches.Temporal features performed poorly on their own.Conversely,spatial features were the most discriminative features,highlighting the need for robust methods for spatial entity extraction,disambiguation and representation in internet-based surveillance systems.

PulseNet China,a model for future laboratory-based bacterial infectious disease surveillance in China

Surveillance is critical for the prevention and control of infectious disease.China’s real-time web-based infectious disease reporting system is a distinguished achievement.However,many aspects of the current China Infectious Disease Surveillance System do not yet meet the demand for timely outbreak detection and identification of emerging infectious disease.PulseNet,the national molecular typing network for foodborne disease surveillance was first established by the Centers for Disease Control and Prevention of the United States in 1995 and has proven valuable in the early detection of outbreaks and tracing the pathogen source.Since 2001,the China CDC laboratory for bacterial pathogen analysis has been a member of the PulseNet International family;and has been adapting the idea and methodology of PulseNet to develop a model for a future national laboratory-based surveillance system for all bacterial infectious disease.We summarized the development progress for the PulseNet China system and discussed it as a model for the future of China’s national laboratory-based surveillance system.

Notifiable infectious disease surveillance with data collected by search engine

Notifiable infectious diseases are a major public health concern in China,causing about five million illnesses and twelve thousand deaths every year.Early detection of disease activity,when followed by a rapid response,can reduce both social and medical impact of the disease.We aim to improve early detection by monitoring health-seeking behavior and disease-related news over the Internet.Specifically,we counted unique search queries submitted to the Baidu search engine in 2008 that contained disease-related search terms.Meanwhile we counted the news articles aggregated by Baidu's robot programs that contained disease-related keywords.We found that the search frequency data and the news count data both have distinct temporal association with disease activity.We adopted a linear model and used searches and news with 1–200-day lead time as explanatory variables to predict the number of infections and deaths attributable to four notifiable infectious diseases,i.e.,scarlet fever,dysentery,AIDS,and tuberculosis.With the search frequency data and news count data,our approach can quantitatively estimate up-to-date epidemic trends 10–40 days ahead of the release of Chinese Centers for Disease Control and Prevention(Chinese CDC)reports.This approach may provide an additional tool for notifiable infectious disease surveillance.

Identifying associations between epidemiological entities in news data for animal disease surveillance

Event-based surveillance systems are at the crossroads of human and animal(and plant and ecosystem)health,epidemiology,statistics,and informatics.Thus,their deployment faces many challenges specific to each domain and their intersections,such as relations among automation,artificial intelligence,and expertise.In this context,ourwork pertins to the extraction of epidemiological events in textual data(i.e.news)by unsupervised methods.We define the event extraction task as detecting pairs of epidemiological entities(e.g.a disease name and location).The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics.We used a publicly available annotated corpus of 438 documents(i.e.news articles)related to animal disease events.The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall.Our results showed that using a window of words outperformed document-based and sentence-based approaches,while reducing the probability of detecting false pairs.Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework.We believe that Mutual Information would be more relevant for rare pair detection(i.e.weak signals),but requires higher manual curation to avoid false positive extraction pairs.Moreover,generalising the country-level spatial features enabled better discrimination(i.e.ranking)of relevant disease-location pairs for event extraction.

Assessment of the usefulness of integrated disease surveillance and response on suspected ebola cases in the Brong Ahafo Region, Ghana

Background:This study assessed the quality,core and support functions of the integrated disease surveillance and response(IDSR)system relating to 18 suspected cases of Ebola virus disease(EVD)in the Brong Ahafo Region,Ghana.Methods:Data was collected on selected indicators of the surveillance system relating to 18 suspected cases of EVD,from epidemiological week 19 to 45 of 2014.We conducted in-depth interviews with seven medical directors and two district directors of health services,and also reviewed documentation on the implementation of the core,support and quality functions of the IDSR system.We also monitored news in the media and rumours about EVD within the community as well as in health facility surveillance registers.Results:The study identified gaps in the implementation of IDSR relating to 18 suspected cases of EVD.Health staff heavily relied on haemorrhage as the only symptom for detection of suspected EVD cases.Twelve blood samples and a swab of secretions from the mouth of the thirteenth patient(who died)tested negative for EVD using PCR assay in laboratory confirmation.The blood samples of three patients were discarded,as they did not fit the case definition for suspected cases,whilst two refused for their blood samples to be taken.The community-based surveillance(CBS)system has not been given a prominent role in EVD surveillance and response,as demonstrated by CBS volunteers and health staff not receiving any training in these processes.There was intense public interest in EVD in August and September 2014.That interest has since waned for reasons that have to be formally ascertained.Unfounded fear of and anxiety about EVD still remain challenges due to a lack of in-depth knowledge about the disease in Ghana.Conclusion:Ghana has been one of the pioneers in the implementation of IDSR in Africa.Despite this,gaps have been identified in the implementation of IDSR relating to EVD in the Brong Ahafo Region.To address these gaps,the CBS system has to actively partner with health facility surveillance to achieve effective IDSR in the region.

Using Interrupted Time Series Design to Analyze Changes in Hand, Foot, and Mouth Disease Incidence during the Declining Incidence Periods of 2008-2010 in China

Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extracted from the National Disease Reporting System （NDRS） and analyzed. An interrupted time series （ITS） technique was used to detect changes in HFMD incidence rates in terms of level and slope between declining incidence periods of the three years. Results Over 3.58 million HFMD cases younger than 5 years were reported to the NDRS between May 1, 2008, and May 31, 2011. Males comprised 63.4% of the cases. ITS analyses demonstrated a significant increase in incidence rate level （P〈0.0001） when comparing the current period with the previous period. There were significant changes in declining slopes when comparing 2010 to 2009, and 2010 to 2008 （all P〈O.O05）, but not 2009 to 2008. Conclusion Incremental changes in incidence rate level during the declining incidence periods of 2009 and 2010 can potentially be attributed to a few factors. The more steeply declining slope in 2010 compared with previous years could be ascribed to the implementation of more effective interventions and preventive strategies in 2010. Further investigation is required to examine this possibility.

Automatic Surveillance of Pandemics Using Big Data and Text Mining

COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms.

Application of Seasonal Auto-regressive Integrated Moving Average Model in Forecasting the Incidence of Hand-foot-mouth Disease in Wuhan,China

Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

Inflammatory bowel disease-related colorectal cancer:Past,present and future perspectives

Inflammatory bowel disease-related colorectal cancer(IBD-CRC)is one of the most serious complications of IBD contributing to significant mortality in this cohort of patients.IBD is often associated with diet and lifestyle-related gut microbial dysbiosis,the interaction of genetic and environmental factors,leading to chronic gut inflammation.According to the“common ground hypothesis”,microbial dysbiosis and intestinal barrier impairment are at the core of the chronic inflammatory process associated with IBD-CRC.Among the many underlying factors known to increase the risk of IBD-CRC,perhaps the most important factor is chronic persistent inflammation.The persistent inflammation in the colon results in increased proliferation of cells necessary for repair but this also increases the risk of dysplastic changes due to chromosomal and microsatellite instability.Multiple pathways have been identified,regulated by many positive and negative factors involved in the development of cancer,which in this case follows the‘inflammation-dysplasia-carcinoma’sequence.Strategies to lower this risk are extremely important to reduce morbidity and mortality due to IBD-CRC,among which colonoscopic surveillance is the most widely accepted and implemented modality,forming part of many national and international guidelines.However,the effectiveness of surveillance in IBD has been a topic of much debate in recent years for multiple reasons—cost-benefit to health systems,resource requirements,and also because of studies showing conflicting long-term data.Our review provides a comprehensive overview of past,present,and future perspectives of IBD-CRC.We explore and analyse evidence from studies over decades and current best practices followed globally.In the future directions section,we cover emerging novel endoscopic techniques and artificial intelligence that could play an important role in managing the risk of IBD-CRC.

Vegetable and Fruit Consumption among Chinese Adults and Associated Factors： A Nationally Representative Study of 170,847 Adults

Objective This study examined vegetable and fruit （VF） consumption rate and its associated factors among Chinese adults. Methods Nationally representative data from the 2013 China Chronic Disease Surveillance survey were used. Dietary intake data, including VF consumption during the last 12 months, were collected. All analyses were weighted to obtain nationally representative estimates. Associations between VF consumption and other factors （e.g., meal frequency and physical activity） were examined through logistic regression analysis. Results The average fruit consumption was 102.3 g/day （95% CI： 97.0-107.6） and the average vegetable consumption was 350.6 g/day （95% CI： 339.3-361.8）. Over half （53.2%, 95% CI： 50.9-55.4） of Chinese adults met the VF consumption of 400 g/day recommended by the World Health Organization （WHO）. Rural residents had a higher prevalence of low VF consumption rate than urban residents [49.20% （95% CI： 46.2%-52.2%） vs. 44.0% （95% CI： 41.7%-46.3%） P 〈 0.01]. Old age （OR = 1.01, 95% CI： 1.00-1.01）, low educational level, low income, minority ethnicity （OR = 1.41, 95% CI： 1.15-1.74）, underweight （OR = 1.17, 95% CI： 1.03-1.33）, single marital status （OR = 1.20, 95% CI： 1.08-1.33）, low health literacy, irregular breakfast （OR = 1.20, 95% CI： 1.04-1.38） or lunch （OR = 1.58, 95% CI： 1.26-1.99） habits, and no leisure-time physical activity were associated with low VF consumption. Conclusion Only half of Chinese adults met the VF consumption recommended by the WHO. Low socio-economic status, irregular diet, and poor health literacy were likely associated with low VF consumption. National efforts and programs are needed to promote VF consumption.

Geo-Spatial Mapping of Tuberculosis Burden in Anambra State, South-East Nigeria

Background: Anambra state in south-east Nigeria is one of the high TB burden states in the country. Despite recent improvements in TB case notification, estimates from the National Prevalence survey suggest that there is still a significant pool of missed TB cases in the state. Although active TB case finding interventions are needed at community level, information on local TB transmission hotspots is lacking. The objective of this study was to map the geo-spatial location of all TB cases detected in the state in 2019. Findings from this secondary data analysis will help to target interventions appropriately with a view to achieving better program efficiency. Method: A de-identified dataset containing descriptive physical addresses of registered TB cases in 2019 was developed. The dataset was then deconstructed and restructured using Structured Query Language in a relational data base environment. The validated dataset was geocoded using ArcGIS server geocode service and validated using python geocoding toolbox, and Google geocoding API. The resultant geocoded dataset was subjected to geo-spatial analysis and the magnitude-per-unit area of the TB cases was calculated using the Kernel Density function. TB case notification rates were also calculated and Choropleth maps were plotted to portray the TB burden as contained in the dataset. Results: Five local government areas (LGAs) (Onitsha North, Onitsha South, Idemili North, Nnewi North, Ogbaru) had spots with “Extremely high” burden with two LGAs (Onitsha North and South) accounting for the largest spots. Eight LGAs had spots with “Very high” TB burden. Also, 24 hotspots across the state had “High” TB burden and two LGAs (Orumba North, Orumba South) had only “Low” TB burden areas. Conclusion: Visualizing the heat map of TB patients has helped to identify transmission hotspots that will be targeted for case finding interventions and effort should be made to increase sensitization of the people on certain behavioural attributes that may contribute to contracting Tuberculosis.

Control of neglected tropical diseases in Asia Pacific: implications for health information priorities

Poverty magnifies limitations posed by traditional biases and environmental risks.Any approach towards disease control needs to recognise that socially embedded vulnerabilities can be as powerful as externally imposed infections.Asia Pacific has a specific panorama of infectious diseases,which,in common with other endemic areas,have a tendency to emerge or re-emerge if not carefully monitored.Sustained control aiming at elimination requires strong emphasis on surveillance and response.Well-designed informatics platforms can improve support systems and strengthen control activities,as they rapidly locate high-risk areas and provide detailed,up-to-date information on the performance of ongoing control programmes.

Implementing a new tuberculosis surveillance system in Zhejiang,Jilin and Ningxia:improvements,challenges and implications for China’s National Health Information System

Background:China is still faced with the public health challenge of tuberculosis(TB),and a robust surveillance system is critical for developing evidence-based TB control policies.The Tuberculosis Information Management System(TBIMS),an independent system launched in 2005,has encountered several challenges in meeting the current needs ofTB control.The Chinese government also planned to establish the National Health Information System(NHIS)aggregating data in different areas.The China National Health Commission-Gates TB Project Phase III launched a new TB surveillance system to address these challenges and also as a pilot for the countrywide implementation of the NHIS.This commentary highlights the improvements and challenges in implementing the newTB system and also discusses the implications for the roll-out of the NHIS.

Surveillance for highly pathogenic avian influenza in wild birds in the USA

As part of the USA’s National Strategy for Pandemic Influenza,an Interagency Strategic Plan for the Early Detection of Highly Pathogenic H5N1 Avian Influenza in Wild Migratory Birds was developed and implemented.From1April2006 through 31 March 2009,261946 samples fromwild birds and 101457 wild bird fecalsamples were collected in the USA;no highly pathogenic avian influenza was detected.The United States Department of Agriculture,and state and tribal cooperators accounted for 213115(81%)of the wild bird samples collected;31,27,21 and 21%of the samples were collected from theAtlantic,Pacific,Central and Mississippi flyways,respectively.More than 250 species of wild birds in all 50 states were sampled.The majority of wild birds(86%)were dabbling ducks,geese,swans and shorebirds.The apparent prevalence of low pathogenic avian influenza viruses during biological years 2007 and 2008 was 9.7 and 11.0%,respectively.The apparent prevalence of H5 and H7 subtypes across all species sampled were 0.5 and 0.06%,respectively.The pooled fecal samples(n=101539)positive for low pathogenic avian influenza were 4.0,6.7 and 4.7%for biological years 2006,2007 and 2008,respectively.The highly pathogenic early detection system for wild birds developed and implemented in the USA represents the largest coordinated wildlife disease surveillance system ever conducted.This effort provided evidence that wild birds in the USA were free of highly pathogenic avian influenza virus(given the expected minimum prevalence of 0.001%)at the 99.9%confidence level during the surveillance period.

Acute hepatitis of unknown aetiology among children around the world

By 26 August 2022, the number of cases of acute hepatitis of unknown etiology (AHUA) has drastically increased to 1115 distributed in 35 countries that fulfill the World Health Organization definition. Several hypotheses on the cause of AHUA have been proposed and are being investigated around the world. In the recent United Kingdom (UK) report, human adenovirus (HAdV) with adeno-associated virus (AAV) co-infection is the leading hypothesis. However, there is still limited evidence in establishing the causal relationship between AHUA and any potential aetiology. The leading aetiology continues to be HAdV infection. It is reported that HAdV genomics is not unusual among the population in the UK, especially among AUHA cases. Expanding the surveillance of HAdV and AAV in the population and the environment in the countries with AUHA cases is suggested to be the primary action. Metagenomics should be used in detecting other infectious pathogens on a larger scale, to supplement the detection of viruses in the blood, stool, and liver specimens from AUHA cases. It is useful to develop a consensus-specific case definition of AHUA to better understand the characteristics of these cases globally based on all the collected cases.

Foodborne outbreaks in Brazil associated with fruits and vegetables:2008 through 2014

Objectives:Foodborne disease outbreaks linked to fruits and vegetables have been increasing in occurrence worldwide;therefore,the aim of this study was to identify the reported foodborne outbreaks associated with fruit and vegetable consumption in Brazil from 2008 to 2014.Results And Limitations:Thirty produce related outbreaks resulted in 2926 illnesses,347 hospitalizations,and no deaths.Only bacterial pathogens were identified as etiological agents.Among these,Salmonella was the most frequent(30 per cent of outbreaks)followed by Staphylococcus aureus(23.3 per cent),Escherichia coli(10 per cent),Bacillus cereus(6.6 per cent),and thermotolerant coliforms(3.3 per cent),whereas etiological agents could not be determined for 26.6 per cent of outbreaks.The most common food vehicles implicated in outbreaks were generically named as fruits and vegetables(46.6 per cent of outbreaks).The term salad was used generically and specifically like salads(two outbreaks),raw/cooked salads(four outbreaks),vegetable salad,tropical salad,Caesar salad,and raw salad of cabbage and tomato.Only one outbreak was related exclusively to fruit(fruit pulp),whereas other outbreaks were related to cooked carrot,lettuce,cucumber,watermelon/cabbage,and chard/beet.Contamination sources and issues related to the future control of produce-related foodborne disease outbreaks are discussed.