Epidemic Surveillance System Research Articles

Respiratory illness of unknown cause as an early signal of the COVID-19 pandemic

Background: As the COVID-19 pandemic began, cases of respiratory illness of unknown cause could have presented early disease signals. The detection of such signals can provide useful clues regarding disease emergence and opportunities for early outbreak control. This study assessed trends in the global incidence of reports of respiratory illness of unknown cause as early indicators of COVID-19 in late 2019, using the early warning system, EPIWATCH, to investigate the disease’s origins and early epidemiology. Methods: Global news articles published between 1-Oct and 31-Dec in 2019 and collected by EPIWATCH, an open-source Artificial Intelligence (AI)-driven epidemic surveillance system, were searched for keywords relating to unknown respiratory illness. Control periods representing the pre-COVID period (1-Oct to 31-Dec 2016-2018) were used to compare report trends over time. Data on report location, case demographics, outbreak setting, and type of illness, were extracted and descriptively analysed. Fisher’s exact tests were conducted to assess proportional differences between years. Results: Overall, 113 EPIWATCH reports of unknown respiratory illness were identified from 12 countries in 2019. Most reports (n=111 [98.2%]) were published in Northern hemisphere countries, in particular Russia (n=A72 [63.7%]) and the United States (n=17 [15.0%]). China experienced a month-on-month increase in reports. Schools were the most reported outbreak setting (n=49 [43.4%]). The proportion of included reports in 2019 was significantly higher compared to 2016-2018 (p<.001). Conclusion: Potential early COVID-19 signals were detected from various countries before the end of 2019. AI-driven open-source disease surveillance systems, such as EPIWATCH, can provide early warnings to enhance disease surveillance and improve outbreak prevention and response.

Early monitoring-to-warning Internet of Things system for emerging infectious diseases via networking of light-triggered point-of-care testing devices.

Early monitoring and warning arrangements are effective ways to distinguish infectious agents and control the spread of epidemic diseases. Current testing technologies, which cannot achieve rapid detection in the field, have a risk of slowing down the response time to the disease. In addition, there is still no epidemic surveillance system, implementing prevention and control measures is slow and inefficient. Motivated by these clinical needs, a sample-to-answer genetic diagnosis platform based on light-controlled capillary modified with a photocleavable linker is first developed, which could perform nucleic acid separation and release by light irradiation in less than 30seconds. Then, on site polymerase chain reaction was performed in a handheld closed-loop convective system. Test reports are available within 20 min. Because this method is portable, rapid, and easy to operate, it has great potential for point-of-care testing. Additionally, through multiple device networking, a real-time artificial intelligence monitoring system for pathogens was developed on a cloud server. Through data reception, analysis, and visualization, the system can send early warning signals for disease control and prevention. Thus, anti-epidemic measures can be implemented effectively, and deploying and running this system can improve the capabilities for the prevention and control of infectious diseases.

Stakeholder's perception of the health information system performance in Burkina Faso.

the health information system (HIS) in Burkina Faso has improved significantly in recent years. In order to suggest further improvements, we specifically assessed the HIS performance indicators of the epidemic surveillance system from the perspectives of the stakeholders. we conducted a mixed methods study to assess the performance through timeliness and completeness indicators, strengths, and weaknesses of the HIS in Burkina Faso with specific focus on epidemic surveillance in the health districts of Dandé and Tenkodogo for the period of 2016 to 2019. fewer than 35% of health districts were able to report at least 90% completeness of community reports since 2017. In 2018, four districts did not exceed 1% completeness of community reports. Some concerns remain related to a need of local support and inter-sectoral collaboration. The technical and organizational factors affect process and performance of the system directly or indirectly through behavioral determinants. the ability to measure the performance of all health facilities and to share all community reports online are challenges for the health system in Burkina Faso. New technologies, training-sensitization, and the involvement of actors with influence on social or behavioral change could help to ensure dynamic performance, if perceptions of actors are taken into account.

Research On the Construction of Monitoring System for Responding to Major Public Health Emergencies in Border Areas of Yunnan

Background - The special geographical position of Yunnan Province as the key area and frontline of "external prevention and importation", the difficulty and pressure of prevention and control in border areas is high, the prevention and control of the epidemic in Yunnan Province is crucial to the overall situation of the province and even the whole country. Covid-19 exposes alarming gaps in Infectious Disease Surverillance in Border Areas. Objectives - Infectious disease surveillance is an important tool for early identification and prevention and control of infectious diseases. Regular disease surveillance of emerging infectious diseases is essential to be able to respond to pandemics and control them early through the construction of infectious disease surveillance systems to build stronger and better health systems. Method - Summarize and sort out the existing monitoring system and problems using a combination of qualitative research methods such as literature methods, group interviews, and expert consultation. Using a combination of typical and stratified sampling methods, a questionnaire was used to analyze the current status of monitoring and management in border counties in Yunnan Province. Results - There is no statistical difference in surveillance and prevention between the state and county levels in response to major public health emergencies, and the current surveillance system is difficult for epidemic dissemination, prediction and timely and effective follow-up. Conclusion - The border areas of Yunnan Province have not established an effective epidemic surveillance system, lack of diversified channels and ways to disseminate epidemic information, difficulty in accurately grasping the progress and changing trends of the epidemic, and risk awareness has not been strengthened and transformed to the detriment of active prevention, control and management of infectious diseases.

Evaluation of IBM Watson Natural Language Processing Service to predict influenza-like illness outbreaks from Twitter data

In this work we evaluate whether Watson NLP service can be used to reliably predict infectious disease such as influenza-like illness (ILI) outbreaks using Twitter data. Watson’s performance is evaluated by computing Pearson correlation coefficient between the number of tweets classified by Watson as ILI and the number of ILI occurrences recovered from traditional epidemic surveillance system of the Centers for Disease Control and Prevention (CDC). Achieved correlation was 0.55. Furthermore, a 12-week discrepancy was found between peak occurrences of ILI predicted by Watson and CDC reported data. Additionally, we developed a scoring method for ILI prediction from a Twitter post using a simple formula with the ability to predict ILI two weeks ahead of ILI data as reported by CDC. The obtained results suggest that data found within social media can be used to supplement the traditional surveillance in epidemics of infectious diseases such as influenza or more recently COVID-19 with the help of intelligent computations

The genotype distribution and phylodynamic of measles viruses circulating in the east of China in postvaccine era, 2005-2017.

The increase of the evolutionary pressure will cause phylodynamics changes of viruses. In post-vaccine coverage era, measles viruses face more immune pressure than ever before. Vice versa, the phylodynamic changes may reflect herd immunity level provided by vaccination. In this study, we analyzed phylodynamic characteristics of measles viruses isolated from 2005 to 2017 in Jiangsu province of China using nucleoprotein gene sequences of measles viruses. The phylogenetic tree was constructed with Markov chain Monte Carlo algorithm. The mean gene distance within each group was computed with MEGA7.0 software. Our results showed that a decline trend is observed in the gene distance of nucleoprotein gene with time as well as incidence of measles from epidemic surveillance system. Two clusters of H1a genotype show cocirculation of multiple variants in early years and the disappearance of most variants with time. We explore the phylodynamic of measles virus under high immune pressure. Our findings highlight that phylodynamic of measles viruses is a helpful tool to assess the effectiveness of epidemic control.

Geographic Information System as a health improvement tool: a resettlement camp in Mozambique

Abstract Cyclone Idai have devastated and displaced an estimated 1.85 million individuals across Mozambique. Around 90,000 cyclone survivors have been relocated to 66 resettlement camps. Doctors of the World Portuguese Delegation team is supporting the Sanitary Post of a resettlement camp, located in a remote rural area with limited resources. The first intervention was to build an epidemiological surveillance system in order to prevent and control communicable and non-communicable diseases and to improve access to health care. The health status in the resettlement camp using conventional and geographic information system techniques were attempted as a community-based census-type cross-sectional study. Location of tents, water holes and latrines were mapped. There were 2,170 people living in the 402 households interviewed. The reported age range of population was 1 day to 99 years, with a mean 19.3 years and a median of 15 years. Among residents, 4% (n = 90) reported a non-communicable disease: hypertension (75.5%), respiratory disease (22.2%), and a range of conditions (2.3%). The prevalence of mental and physical disabilities was 1.8% (n = 39). Regarding individual behavioral risk factors, 6.7% and 5.6% of the residents over 15 years, were currently using tobacco and alcohol, respectively. The distance from any household to the nearest waterpoint is less than 500 meters and almost all households (98.5%) distance more than 50 meters from the nearest toilet, as the standard recommended. The average number of people in the camp are in the standard recommended of 20 people per toilet facility with a ratio 15.8 person per latrine. Geographic information system technology was helpful for the improvement of strategies on surveillance, prevention and control regarding communicable and non-communicable diseases, in the resettlement camp. Also made it possible to set up a home health care system, promoting access to health care for the disabled residents. Key messages Geographic information systems proved to be very useful in the implementation and organization of the epidemic surveillance system of resettlement camp. The epidemic surveillance system allowed us to implement a home care system for patients with disabilities, to respond to acute illnesses and chronic diseases agudizations, and to identify outbreaks.

PANDA

In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from COVID-19 crises attempt to access citizen's location data to eliminate the outbreak. However, it raises privacy concerns and may open the doors to more invasive forms of surveillance in the name of public health. It also brings a challenge for privacy protection techniques: how can we leverage people's mobile data to help combat the pandemic without scarifying location privacy. We demonstrate that we can achieve this by implementing policy-based location privacy for epidemic surveillance. Our system has three primary functions for epidemic surveillance: people flow monitoring, epidemic analysis, and contact tracing. We provide an interactive tool allowing the attendees to explore and examine the usability of our system: (1) the utility of location monitor and disease transmission model estimation, (2) the procedure of contact tracing in our systems, and (3) the privacy-utility trade-offs w.r.t. different policy graphs. The attendees will find that we can have the high usability for epidemic surveillance while preserving location privacy.

Letter: The Impact of the Coronavirus (COVID-19) Pandemic on Neurosurgeons Worldwide.

Letter: The Impact of the Coronavirus (COVID-19) Pandemic on Neurosurgeons Worldwide.

Simulation Based Evaluation of Time Series for Syndromic Surveillance of Cattle in Switzerland.

Choosing the syndrome time series to monitor in a syndromic surveillance system is not a straight forward process. Defining which syndromes to monitor in order to maximize detection performance has been recently identified as one of the research priorities in Syndromic surveillance. Estimating the minimum size of an epidemic that could potentially be detected in a specific syndrome could be used as a criteria for comparing the performance of different syndrome time series, and could provide some guidance for syndrome selection. The aim of our study was to estimate the potential value of different time series for building a national syndromic surveillance system for cattle in Switzerland. Simulations were used to produce outbreaks of different size and shape and to estimate the ability of each time series and aberration detection algorithm to detect them with high sensitivity, specificity and timeliness. Two temporal aberration detection algorithms were also compared: Holt–Winters generalized exponential smoothing (HW) and Exponential Weighted Moving Average (EWMA). Our results indicated that a specific aberration detection algorithm should be used for each time series. In addition, time series with high counts per unit of time had good overall detection performance, but poor detection performance for small epidemics making them of limited use for an early detection system. Estimating the minimum size of simulated epidemics that could potentially be detected in syndrome TS-event detection pairs can help surveillance system designers choosing the most appropriate syndrome TS to include in their early epidemic surveillance system.

Study on clinical classification of Japanese encephalitis by Bayes discriminant analysis

Objective: To construct Bayes discriminant function for clinical classification of common and severe Japanese encephalitis (JE) cases, and to identify cases accurately with quantitative indicators. Methods: Samples of confirmed common and severe JE cases reported by the epidemic surveillance system of Gansu Provincial Center for Disease Control and Prevention from 2005 to 2017 were collected. Non-conditional logistic regression analysis and Bayes stepwise discriminant analysis were used to screen meaningful clinical indicators, so as to construct and evaluate Bayes discriminant function. Results: There were 256 common JE cases and 257 severe JE cases. There were no significant differences in sex, age and occupation distributions between the two groups (P>0.05) and there was significant difference in case fatality rate (P<0.05). Non-conditional logistic regression analysis and Bayes stepwise discriminant analysis, combined with using related literature, to screen 11 clinical indicators for the construction of Bayes discriminant function. Interactive validation showed that the sensitivity of discriminant function was 71.48% (95%CI: 65.53%-76.93%) and the specificity was 73.93% (95%CI: 68.11%-79.19%). The area under ROC curve was 0.761 (95%CI: 0.720-0.803) and the total accuracy rate was 72.71%. Conclusion: Bayes discriminant function can be used to identify common and severe JE cases more accurately, which is helpful for the reasonable treatment and good prognosis of JE patients.

A Review on Event-Based Epidemic Surveillance Systems that Support the Arabic Language

With the revolution of the internet, many event-based systems have been developed for monitoring epidemic threats. These systems rely on unstructured data gathered from various online sources. Moreover, some systems are able to handle more than one language to cover all news reports related to disease outbreaks worldwide. The aim of this paper is to examine existing systems in terms of supporting the Arabic language. The 28 identified systems were evaluated based on different criteria. The results of this evaluation show that only 5 systems support the Arabic language using translation tools; hence, disease outbreaks in news reports written in Arabic are not directly processed. In other words, no existing event-based system in the literature has yet been developed specifically for Arabic health news reports to monitor epidemic diseases.

Prevecencija anenovirusnog konjunktivitisa - revijalni rad

Epidemic viral conjunctivitis caused by adenovirus is the most common infectious conjunctivitis. The exact incidence of adenoviral conjunctivitis is still poorly known, but there are two well-defined adenoviral keratoconjunctivitis clinical syndromes: epidemic keratoconjunctivitis (EKC) and pharyngoconjunctival fever (PCF). Epidemic keratoconjunctivitis is also the most severe form and presents with watery discharge, hyperemia, chemosis and ipsilateral lymphadenopathy. Diagnosis is mainly clinical, but its etiology can be confirmed using cell cultures, antigen detection, polymerase chain reaction or immune-chromatography. Multiple treatments have been tried for this disease, but none of them seem to be completely effective. Viruses are resistant to desiccation and certain common surface disinfectants. Prevention is the most reliable and recommended strategy to control this epidemic infection. Global epidemic surveillance system definitely needs to be established to monitor and analyze the epidemic conjunctivitis in the future. There is clearly a need for the national and the military public health institutions to work together on guidelines to handle future challenges.

Pandémie de grippe A (virus H1N1) à Futuna, île du pacifique, d’août à septembre 2009

The aim of this study is to report the observation of the pandemic of influenza A (H1N1 virus) from August to September 2009 on the island of Futuna, in a context of isolated island that may mimic an environment closed. We conducted a prospective observational study of influenza-like illness, from the first confirmed case of influenza A on the island until the end of the epidemic wave. From August 15 to September 20, 2009, 1536 cases of influenza syndrome were identified. The estimate of the overall clinical attack rate was 36 %. The evolution of the epidemic shows an explosion of new cases of influenza A and subsequently a rapid decline of the epidemic. The spread of the infection was made by contiguity, jumping from one city to another. The cumulative number of cases by age group shows that the majority of cases were children and young adults under the age of 20 years. The most frequent symptoms were cough, rhinorrhea, headache, myalgia or asthenia, and fever. This study, despite these limitations, shows an explosive epidemic of influenza A, which can be explained by the circulation of virus that has been fostered by gatherings of public and closed environment. Age group classification shows that majority of cases were young, in contrast to seasonal influenza, but the symptoms were alike. This study highlights the difficulties to manage an epidemic surveillance system at high level and given the quick spread of the disease.

Interdisciplinary attention program for women victims of violence older than 14 years old prevention of lesions – Developed in Mar del Plata-Argentina 2003–2008 sexual violence and its consequences

It is a personal, social and political problem. It destroys the life of millions of people and it affects to the group of the society. Being tolerated, denounced, according to...

A survey of sanitary working status 20 days after the earthquake in Dujiangyan municipality

To investigate the sanitary working status in the districts for locating residents after earthquake in Dujiangyan municipality. Some immediate measures were taken after the earthquake including water source surveillance, restoring immunization system and epidemic surveillance. A questionnaire survey was also conducted to collect information in 107 locating districts of 18 towns. Generally, the sanitary working status was good. Temporary sheds in most districts were Tents (75.70%, 81/107) and simple sheds (19.63%, 21/107), and 69.16% (74/107) districts could use water supply and 94.39% (101/107) arrange specialized persons to disinfect the environment and kill pests. The fly density was 2 per eye-view. The proportions for the correct responds to health knowledge, action adopted and attitude of residents were all above 90%. According to the epidemic surveillance system and mobile syndrome surveillance system in disaster area, there was no increasing trend for the incidences of contagious diseases. 20 days after earthquake, the whole situation of disease prevention in disaster area is stable.

A Bayesian dynamic model for influenza surveillance

The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.

Evaluating modifications in epidemic surveillance systems: a method and an application to AIDS surveillance in Switzerland.

A method is presented for evaluating the impact of a modification in a surveillance system for an epidemic disease among reporting sources susceptible to the modification. The approach requires a control series with reports from sources that are not affected by the modification. The measure of effect is the reporting odds ratio (ROR) which estimates the ratio of reporting proportions (completeness) before and after the change. The proportions themselves remain unknown. Various potential biases are identified and an investigation of one potential bias is proposed. The method may prove useful in supplementing techniques that estimate absolute values of completeness of registry systems. The application to AIDS surveillance data in Switzerland shows that the transition from voluntary to mandatory reporting had an impact that was enhanced by the 1987 revision of the Centers for Disease Control case definition for AIDS. Reporting sources were 1.9 (95% confidence interval [CI]: 1.4-2.6) times more likely to report their patients after introduction of mandatory reporting. It is estimated that 15% of all reports were attributable to mandatory reporting.