Traditional Surveillance Systems Research Articles

An effective internet-based system for surveillance and elimination of triatomine insects: AlertaChirimacha.

Vector-borne diseases remain a significant public health threat in many regions of the world. Traditional vector surveillance and control methods have relied on active and passive surveillance programs, which are often costly and time-consuming. New internet-based vector surveillance systems have shown promise in removing some of the cost and labor burden from health authorities. We developed and evaluated the effectiveness of a new internet-based surveillance system, "AlertaChirimacha", for detecting Triatoma infestans (known locally by its Quechua name, Chirimacha), the Chagas disease vector, in the city of Arequipa, Peru. In the first 26 months post-implementation, AlertaChirimacha received 206 reports of residents suspecting or fearing triatomines in their homes or neighborhoods, of which we confirmed, through pictures or inspections, 11 (5.3%) to be Triatoma infestans. After microscopic examination, none of the specimens collected were infected with Trypanosoma cruzi. AlertaChirimacha received 57% more confirmed reports than the traditional surveillance system and detected 10% more infested houses than active and passive surveillance approaches combined. Through in-depth interviews we evaluate the reach, bilateral engagement, and response promptness and efficiency of AlertaChirimacha. Our study highlights the potential of internet-based vector surveillance systems, such as AlertaChirimacha, to improve vector surveillance and control efforts in resource-limited settings. This approach could decrease the cost and time horizon for the elimination of vector-mediated Chagas disease in the region.

An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study.

Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

IoT and Edge Computing Technologies as Security Option for Train Service in Nigeria

The revival of rail transport service in Nigeria in recent years came at a critical moment of insecurity in Nigeria, it promised not only to serve as an alternative to overloaded vehicles on the roads but was also thought to be a safer means of transportation. Due to kidnapping on many roads, both high and low-class Nigerians patronized rail transport. However, train attacks and terrorism are major challenges that have impacted the sector negatively. Though the government has tried to improve surveillance along train routes, however, the result has not been impressive. The current level of insecurity in Nigeria is beyond the use of traditional surveillance and monitoring systems. It requires the adoption of technology to fight attacks and to monitor the health of train facilities. While the insecurity challenges that have overwhelmed the security forces will not permit the assignment of more personnel to the rail tracks that stretch several kilometres across the country, the incorporation of IoT and edge computing can be an optimal solution to the challenges of constant security problems. Among the trending technologies, IoT is a viable option that the country can adopt to improve security in the sector. It will increase the confidence of passengers and improve revenue and growth in rail transport.

Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China

Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)–susceptible–exposed–infected–removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%–82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical resource demand.

Digital epidemiology and infodemiology of hand-foot-mouth disease (HFMD) in Italy. Disease trend assessment via Google and Wikipedia.

The study aimed to evaluate the epidemiological trend of hand, foot and mouth disease (HFMD) in Italy using data on Internet search volume. A cross-sectional study design was used. Data on Internet searches were obtained from Google Trends (GT) and Wikipedia. We used the following Italian search term: "Malattia mano-piede-bocca" (Hand-foot-mouth disease, in English). A monthly time-frame was extracted, partly overlapping, from July 2015 to December 2022. GT and Wikipedia were overlapped to perform a linear regression and correlation analyses. Statistical analyses were performed using the Spearman's rank correlation coefficient (rho). A linear regression analysis was performed considering Wikipedia and GT. Search peaks for both Wikipedia and GT occurred in the months November-December during the autumn-winter season and in June during the spring-summer season, except for the period from June 2020 to June 2021, probably due to the restrictions of the COVID19 pandemic. A temporal correlation was observed between GT and Wikipedia search trends. This is the first study in Italy that attempts to clarify the epidemiology of HFMD. Google search and Wikipedia can be valuable for public health surveillance; however, to date, digital epidemiology cannot replace the traditional surveillance system.

Vector Autoregression for Forecasting the Number of COVID-19 Cases and Analyzing Behavioral Indicators in the Philippines: Ecologic Time-Trend Study.

Traditional surveillance systems rely on routine collection of data. The inherent delay in retrieval and analysis of data leads to reactionary rather than preventive measures. Forecasting and analysis of behavior-related data can supplement the information from traditional surveillance systems. We assessed the use of behavioral indicators, such as the general public's interest in the risk of contracting SARS-CoV-2 and changes in their mobility, in building a vector autoregression model for forecasting and analysis of the relationships of these indicators with the number of COVID-19 cases in the National Capital Region. An etiologic, time-trend, ecologic study design was used to forecast the daily number of cases in 3 periods during the resurgence of COVID-19. We determined the lag length by combining knowledge on the epidemiology of SARS-CoV-2 and information criteria measures. We fitted 2 models to the training data set and computed their out-of-sample forecasts. Model 1 contains changes in mobility and number of cases with a dummy variable for the day of the week, while model 2 also includes the general public's interest. The forecast accuracy of the models was compared using mean absolute percentage error. Granger causality test was performed to determine whether changes in mobility and public's interest improved the prediction of cases. We tested the assumptions of the model through the Augmented Dickey-Fuller test, Lagrange multiplier test, and assessment of the moduli of eigenvalues. A vector autoregression (8) model was fitted to the training data as the information criteria measures suggest the appropriateness of 8. Both models generated forecasts with similar trends to the actual number of cases during the forecast period of August 11-18 and September 15-22. However, the difference in the performance of the 2 models became substantial from January 28 to February 4, as the accuracy of model 2 remained within reasonable limits (mean absolute percentage error [MAPE]=21.4%) while model 1 became inaccurate (MAPE=74.2%). The results of the Granger causality test suggest that the relationship of public interest with number of cases changed over time. During the forecast period of August 11-18, only change in mobility (P=.002) improved the forecasting of cases, while public interest was also found to Granger-cause the number of cases during September 15-22 (P=.001) and January 28 to February 4 (P=.003). To the best of our knowledge, this is the first study that forecasted the number of COVID-19 cases and explored the relationship of behavioral indicators with the number of COVID-19 cases in the Philippines. The resemblance of the forecasts from model 2 with the actual data suggests its potential in providing information about future contingencies. Granger causality also implies the importance of examining changes in mobility and public interest for surveillance purposes.

Using motor vehicle crash records for injury surveillance and research in agriculture and forestry

Problem: Fatal injuries in the agriculture, forestry, and fishing sector (AgFF) outweigh those across all sectors in the United States. Transportation-related injuries are among the top contributors to these fatal events. However, traditional occupational injury surveillance systems may not completely capture crashes involving farm vehicles and logging trucks, specifically nonfatal events. Methods: The study aimed to develop an integrated database of AgFF-related motor-vehicle crashes for the southwest (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) and to use these data to conduct surveillance and research. Lessons learned during the pursuit of these aims were cataloged. Activities centered around the conduct of traditional statistical and geospatial analyses of structured data fields and natural language processing of free-text crash narratives. Results: The structured crash data in each state include fields that allowed farm vehicles or equipment and logging trucks to be identified. The variable definitions and coding were not consistent across states but could be harmonized. All states recorded data fields pertaining to person, vehicle, and crash/environmental factors. Structured data supported the construction of crash severity models and geospatial analyses. Law enforcement provided additional details on crash causation in free-text narratives. Crash narratives contained sufficient text to support viable machine learning models for farm vehicle or equipment crashes, but not for logging truck narratives. Discussion: Crash records can help to fill research and surveillance gaps in AgFF in the southwest region. This supports traffic safety’s evolution to the current Safe System paradigm. There is a conceptual linkage between the Safe System and Total Worker Health approaches, providing a bridge between traffic safety and occupational health. Practical Applications: Despite limitations, crash records can be an important component of injury surveillance for events involving AgFF vehicles. They also can be used to inform the selection and evaluation of traffic countermeasures and behavioral interventions.

Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study.

Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases. This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance. Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022. The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as "chickenpox," "chickenpox treatment," "treatment of chickenpox," "chickenpox symptoms," and "chickenpox virus," trend consistently. Some BDI search terms, such as "chickenpox pictures," "symptoms of chickenpox," "chickenpox vaccine," and "is chickenpox vaccine necessary," appeared earlier than the trend of "chickenpox virus." The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R2=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R2=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control. These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems.

Long-Term Influenza Outbreak Forecast Using Time-Precedence Correlation of Web Data.

Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of web data uploaded in real time; however, long-term predictions of more than 2-10 weeks are required to effectively cope with influenza outbreaks. In this study, we determined that web data uploaded in real time have a time-precedence relationship with influenza outbreaks. For example, a few weeks before an influenza pandemic, the word "colds" appears frequently in web data. The web data after the appearance of the word "colds" can be used as information for forecasting future influenza outbreaks, which can improve long-term influenza prediction accuracy. In this study, we propose a novel long-term influenza outbreak forecast model utilizing the time precedence between the emergence of web data and an influenza outbreak. Based on the proposed model, we conducted experiments on: 1) selecting suitable web data for long-term influenza prediction; 2) determining whether the proposed model is regionally dependent; and 3) evaluating the accuracy according to the prediction timeframe. The proposed model showed a correlation of 0.87 in the long-term prediction of ten weeks while significantly outperforming other state-of-the-art methods.

Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study.

The ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on health care providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via web-based surveys, has emerged in the past decade to complement traditional data collection approaches. This study compared novel PS data on COVID-19 infection rates across 9 Brazilian cities with official TS data to examine the opportunities and challenges of using PS data, and the potential advantages of combining the 2 approaches. The TS data for Brazil are publicly accessible on GitHub. The PS data were collected through the Brazil Sem Corona platform, a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire on symptoms and exposure in the Colab app. We found that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we documented a significant trend correlation between lagged PS data and TS infection rates, suggesting that PS data could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast model based exclusively on TS data. Furthermore, we showed that PS data captured a population that significantly differed from a traditional observation. In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive laboratory-confirmed tests. In contrast, PS data show a significant share of reports categorized as potential COVID-19 cases that are not laboratory confirmed. Quantifying the economic value of PS system implementation remains difficult. However, scarce public funds and persisting constraints to the TS system provide motivation for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic tradeoffs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, and shed light on its limitations and on the need for additional research to improve future implementations of PS platforms.

An IoT Enable Anomaly Detection System for Smart City Surveillance.

Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods.

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study

Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of -3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of -2 weeks. The most correlated query from Google Trends was the "Dengue" topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of -3 weeks. Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.

High Influenza Vaccine Effectiveness and Absence of Increased Influenza-like-Illness Epidemic Activity in the 2021-2022 Influenza Season in Catalonia (Spain) Based on Surveillance Data Collected by Sentinel Pharmacies.

Influenza surveillance and influenza vaccination are the key activities for preventing and controlling influenza epidemics. The study assessed the influenza surveillance and influenza vaccination data obtained from sentinel pharmacies of Catalonia, Spain, in the 2021-2022 influenza season. The sentinel pharmacies were selected from all community pharmacies to report all influenza-like illness (ILI) cases detected during the 2021-2022 influenza season and collect influenza surveillance and influenza vaccination data. The ILI cases were identified based on European Centre for Disease Control (ECDC) criteria. The moving epidemic method (MEM) was used to assess the ILI epidemic activity. The screening method was used to assess influenza vaccination effectiveness in patients aged 65-or-more years old. The sentinel pharmacies reported 212 ILI cases with a negative COVID-19 test and a total number of 412 ILI cases. An absence of increased ILI epidemic activity was observed in the 2021-2022 influenza season based on two criteria: (1) Number of ILI cases reported per week in the 2021-2022 influenza season significantly lower than the MEM-based epidemic threshold. (2) Mean number of ILI cases reported per week in the 2021-2022 influenza season significantly lower than during the ILI/influenza epidemic periods detected from 2017 to 2020 using the same methodology. Influenza vaccination was effective in preventing ILI among patients aged 65-or-more-years old. The absence of the influenza epidemic during the 2021-2022 influenza season could be explained by influenza vaccination and COVID-19 prevention measures (wearing face masks, social distancing). The sentinel pharmacies provided influenza surveillance data not provided by traditional influenza surveillance systems.

Using Internet Search Data to Forecast COVID-19 Trends: A Systematic Review

Since the outbreak of the coronavirus disease pandemic (COVID-19) at the end of 2019, many scientific groups have been working towards solutions to forecast outbreaks. Accurate forecasts of future waves could mitigate the devastating effects of the virus. They would allow healthcare organizations and governments to alter public intervention, allocate healthcare resources accordingly, and raise public awareness. Many forecasting models have been introduced, harnessing different underlying mechanisms and data sources. This paper provides a systematic review of forecasting models that utilize internet search information. The success of these forecasting models provides a strong support for the big-data insight of public online search behavior as an alternative signal to the traditional surveillance system and mechanistic compartmental models.

Visual Object Recognition System

This article introduces the problem of object detection and recognition. The potential mobility of this solution, ease of installation and ease of initial setup, as well as the absence of expensive, resource-intensive and complex image collection and processing systems are presented. Solutions to the problem are demonstrated, along with the advantages and disadvantages of each. The selection of contours by a filter based on the Prewitt operator and a detector of characteristic points is an algorithm of the system, developed within the framework of object recognition techniques. The reader can follow the interim and final demonstrations of the system algorithm in this article to learn about its advantages over traditional video surveillance systems, as well as some of its disadvantages. A webcam with a video frame rate of 25 frames per second, a mobile phone and a PC with the Matlab2020 programming environment installed (due to its convenience and built-in image processing functions) are required to illustrate how the system works.

Practice of integrated vector surveillance of arthropod vectors, pathogens and reservoir hosts to monitor the occurrence of tropical vector-borne diseases in 2020 in Zhejiang Province, China.

Vector-borne diseases have become one of the most serious local public health threats. Monitoring and controlling vectors are important means of controlling vector-borne diseases. However, traditional vector surveillance systems in China mainly monitor vector density, making its early-warning effect on vector-borne diseases weak. In this study, we applied an integrated surveillance system of multiple arthropod vectors and reservoir host containing ecology, etiology, and drug resistance monitoring to obtain better knowledge on vector populations and provide early warning of suspicious vector-borne infectious disease occurrence. An ecology surveillance of mosquitoes, rodents, ticks, and chigger mites, a pathogen infection survey on mosquitoes and rodents, and a drug resistance survey on Aedes albopictus were conducted in 12 cities in Zhejiang Province in 2020. A total of 15,645 adult mosquitoes were collected at a density of 19.8 mosquitoes per Centers for Disease Control and Prevention light trap. Culex tritaeniorhynchus (72.76%) was the most abundant species. The Breteau index of Ae. albopictus was 13.11. The rodent density was 0.91 rodents per hundred traps; the most abundant species was Rattus norvegicus (33.73%). The densities of dissociate and ectoparasitic ticks were 0.79 ticks per hundred meters and 0.97 ticks per animal, respectively. The most abundant tick species was Haemaphysalis longicornis (56.38%). The density of chigger mites was 14.11 per rodent; two species were identified, with the most abundant species being Walchia spp. mite (68.35%). No flavivirus or alphavirus was found in mosquito etiology monitoring, whereas the positivity rates of hantavirus, the pathogenic bacteria Leptospira spp., Orientia tsutsugamushi, and Bartonella spp. detected in rodent etiology monitoring were 1.86, 7.36, 0.35 and 7.05%, respectively. Field populations of Ae. albopictus in Zhejiang Province were widely resistant to pyrethroids but sensitive to most insecticides tested, including organophosphorus and carbamate insecticides. Integrated surveillance systems on multiple arthropod vectors (mosquitoes, ticks, mites) and animal reservoirs (rodents) can provide important information for the prevention and control of epidemic emergencies.

A Mobile App Leveraging Citizenship Engagement to Perform Anonymized Longitudinal Studies in the Context of COVID-19 Adverse Drug Reaction Monitoring: Development and Usability Study.

Over the past few years, studies have increasingly focused on the development of mobile apps as complementary tools to existing traditional pharmacovigilance surveillance systems for improving and facilitating adverse drug reaction (ADR) reporting. In this research, we evaluated the potentiality of a new mobile app (vaxEffect@UniMiB) to perform longitudinal studies, while preserving the anonymity of the respondents. We applied the app to monitor the ADRs during the COVID-19 vaccination campaign in a sample of the Italian population. We administered vaxEffect@UniMiB to a convenience sample of academic subjects vaccinated at the Milano-Bicocca University hub for COVID-19 during the Italian national vaccination campaign. vaxEffect@UniMiB was developed for both Android and iOS devices. The mobile app asks users to send their medical history and, upon every vaccine administration, their vaccination data and the ADRs that occurred within 7 days postvaccination, making it possible to follow the ADR dynamics for each respondent. The app sends data over the web to an application server. The server, along with receiving all user data, saves the data in a SQL database server and reminds patients to submit vaccine and ADR data by push notifications sent to the mobile app through Firebase Cloud Messaging (FCM). On initial startup of the app, a unique user identifier (UUID) was generated for each respondent, so its anonymity was completely ensured, while enabling longitudinal studies. A total of 3712 people were vaccinated during the first vaccination wave. A total of 2733 (73.6%) respondents between the ages of 19 and 80 years, coming from the University of Milano-Bicocca (UniMiB) and the Politecnico of Milan (PoliMi), participated in the survey. Overall, we collected information about vaccination and ADRs to the first vaccine dose for 2226 subjects (60.0% of the first dose vaccinated), to the second dose for 1610 subjects (43.4% of the second dose vaccinated), and, in a nonsponsored fashion, to the third dose for 169 individuals (4.6%). vaxEffect@UniMiB was revealed to be the first attempt in performing longitudinal studies to monitor the same subject over time in terms of the reported ADRs after each vaccine administration, while guaranteeing complete anonymity of the subject. A series of aspects contributed to the positive involvement from people in using this app to report their ADRs to vaccination: ease of use, availability from multiple platforms, anonymity of all survey participants and protection of the submitted data, and the health care workers' support.

Wikipedia searches and the epidemiology of infectious diseases: A systematic review

This review aims to collect, analyse and synthesize the available evidence that can be provided by Wikipedia for epidemiologic surveillance purposes. PRISMA guidelines were followed. PubMed/Medline and Scopus were consulted. Out of 238 retrieved articles, 16 articles were included in the systematic review. The most frequently assessed infectious disease was Influenza, followed by arboviruses and measles. Influenza studies show that Wikipedia could be considered a scientifically valid surveillance system that fills the main gaps in existing traditional surveillance systems. As regards arboviruses, searches on the Web have positively mediated the relationship between epidemiological data and the number of Wikipedia page visualization. Regarding measles, studies showed a strong/moderate temporal correlation between infectious disease notification bulletins and Wikipedia search trends. Despite the type of infectious agents, three main aims can be detected: (i) understand the public’s interest, (ii) explore the use of Wikipedia by organizations, and (iii) assess the accuracy of Wikipedia content. These new strategies for surveillance of infectious diseases should be implemented, to date they could be useful in supporting traditional surveillance.

The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France.

The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020 to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14days. We compared the performance of different data sources, feature engineering, and machine learning models. During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection. Forecast model showed overall good performance both at 7 and 14days which were improved by the addition of the data from Bordeaux Hospital data warehouse. The development of hospital data warehouse might help to get more specific and faster information than traditional surveillance system, which in turn will help to improve epidemic forecasting at a larger and finer scale.

Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis.

Most common viral skin infections are not reportable conditions. Studying the population dynamics of these viral epidemics using traditional field methods is costly and time-consuming, especially over wide geographical areas. This study aimed to explore the evolution, seasonality, and distribution of vaccinable and nonvaccinable viral skin infections through an analysis of Google Trends. Worldwide search trends from January 2004 through May 2021 for viral skin infections were extracted from Google Trends, quantified, and analyzed. Time series decomposition showed that the total search term volume for warts; zoster; roseola; measles; hand, foot, and mouth disease (HFMD); varicella; and rubella increased worldwide over the study period, whereas the interest for Pityriasis rosea and herpes simplex decreased. Internet searches for HFMD, varicella, and measles exhibited the highest seasonal patterns. The interest for measles and rubella was more pronounced in African countries, whereas the interest for HFMD and roseola was more pronounced in East Asia. Harnessing data generated by web searches may increase the efficacy of traditional surveillance systems and strengthens the suspicion that the incidence of some vaccinable viral skin infections such as varicella, measles, and rubella may be globally increasing, whereas the incidence of common nonvaccinable skin infections remains stable.