Early Warning Surveillance System Research Articles

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection.

Detection of SARS-CoV-2 RNA in wastewater from an enclosed college campus serves as an early warning surveillance system.

SARS-CoV-2, the causative agent of Covid-19, is shed from infected persons in respiratory droplets, feces, and urine. Using quantitative PCR (qPCR), our group hypothesized that we could detect SARS-CoV-2 in wastewater samples collected on a university campus prior to the detection of the virus in individuals on campus. Wastewater samples were collected 3 times a week from 5 locations on the main campus of the University of North Carolina Wilmington (UNCW) from July 24, 2020 to December 21, 2020. Post-collection, total RNA was extracted and SARS-CoV-2 RNA in the samples was detected by qPCR. SARS-CoV-2 signal was detected on campus beginning on August 19 as classes began and the signal increased in both intensity and breadth as the Fall semester progressed. A comparison of two RNA extraction methods from wastewater showed that SARS-CoV-2 was detected more frequently on filter samples versus the direct extracts. Aligning our wastewater data with the reported SARS-CoV-2 cases on the campus Covid-19 dashboard showed the virus signal was routinely detected in the wastewater prior to clusters of individual cases being reported. These data support the testing of wastewater for the presence of SARS-CoV-2 and may be used as part of a surveillance program for detecting the virus in a community prior to an outbreak occurring and could ultimately be incorporated with other SARS-CoV-2 metrics to better inform public health enabling a quick response to contain or mitigating spread of the virus.

Syndromic surveillance in early detection of outbreaks of infectious diseases

Aim: Motivated by the threat of infectious diseases and bioterrorism, syndromic surveillance systems are being developed and implemented around the world. The aim of the study was to describe the early warning surveillance system in Albania.
 
 Methods: Syndromic surveillance is a primary health care-facility- and emergency room (ER)-based syndromic surveillance system aiming at detecting outbreaks and undertaking public health actions. It is based on weekly notifications of nine syndromes by over 1,600 General Practitioners (GPs) in the 36 districts of Albania. Data is aggregated by district epidemiologists (DE) and centralized by the national Institute of Public Health.
 Results: A syndrome is “a set of symptoms or conditions that occur together and suggest the presence of a certain disease or an increased chance of developing the disease.” In the context of syndromic surveillance, a syndrome is a set of non-specific pre-diagnosis medical and other information that may indicate the release of a bioterrorism agent or natural disease outbreak.
 Since its inception, syndromic surveillance has mainly focused on early event detection: gathering and analysing data in advance of diagnostic case confirmation to give early warning of a possible outbreak.
 Conclusion: The system is useful for detecting and responding to natural disease outbreaks such as seasonal and pandemic flu, and thus they have the potential to significantly advance and modernize the practice of public health surveillance.

Syndromic surveillance in early detection of outbreaks of infectious diseases

Aim: Motivated by the threat of infectious diseases and bioterrorism, syndromic surveillance systems are being developed and implemented around the world. The aim of the study was to describe the early warning surveillance system in Albania.Methods: Syndromic surveillance is a primary health care-facility- and emergency room (ER)-based syndromic surveillance system aiming at detecting outbreaks and undertaking public health actions. It is based on weekly notifications of nine syndromes by over 1,600 General Practitioners (GPs) in the 36 districts of Albania. Data is aggregated by district epidemiologists (DE) and centralized by the national Institute of Public Health.Results: A syndrome is “a set of symptoms or conditions that occur together and suggest the presence of a certain disease or an increased chance of developing the disease.” In the context of syndromic surveillance, a syndrome is a set of non-specific pre-diagnosis medical and other information that may indicate the release of a bioterrorism agent or natural disease outbreak.Since its inception, syndromic surveillance has mainly focused on early event detection: gathering and analysing data in advance of diagnostic case confirmation to give early warning of a possible outbreak.Conclusion: The system is useful for detecting and responding to natural disease outbreaks such as seasonal and pandemic flu, and thus they have the potential to significantly advance and modernize the practice of public health surveillance.

Lessons Learned from CDC’s Global COVID-19 Early Warning and Response Surveillance System

Early warning and response surveillance (EWARS) systems were widely used during the early COVID-19 response. Evaluating the effectiveness of EWARS systems is critical to ensuring global health security. We describe the Centers for Disease Control and Prevention (CDC) global COVID-19 EWARS (CDC EWARS) system and the resources CDC used to gather, manage, and analyze publicly available data during the prepandemic period. We evaluated data quality and validity by measuring reporting completeness and compared these with data from Johns Hopkins University, the European Centre for Disease Prevention and Control, and indicator-based data from the World Health Organization. CDC EWARS was integral in guiding CDC's early COVID-19 response but was labor-intensive and became less informative as case-level data decreased and the pandemic evolved. However, CDC EWARS data were similar to those reported by other organizations, confirming the validity of each system and suggesting collaboration could improve EWARS systems during future pandemics.

Manifestation of SARS-CoV-2 Infections in Mink Related to Host-, Virus- and Farm-Associated Factors, The Netherlands 2020.

SARS-CoV-2 outbreaks on 69 Dutch mink farms in 2020 were studied to identify risk factors for virus introduction and transmission and to improve surveillance and containment measures. Clinical signs, laboratory test results, and epidemiological aspects were investigated, such as the date and reason of suspicion, housing, farm size and distances, human contact structure, biosecurity measures, and presence of wildlife, pets, pests, and manure management. On seven farms, extensive random sampling was performed, and age, coat color, sex, and clinical signs were recorded. Mild to severe respiratory signs and general diseases such as apathy, reduced feed intake, and increased mortality were detected on 62/69 farms. Throat swabs were more likely to result in virus detection than rectal swabs. Clinical signs differed between virus clusters and were more severe for dark-colored mink, males, and animals infected later during the year. Geographical clustering was found for one virus cluster. Shared personnel could explain some cases, but other transmission routes explaining farm-to-farm spread were not elucidated. An early warning surveillance system, strict biosecurity measures, and a (temporary) ban on mink farming and vaccinating animals and humans can contribute to reducing the risks of the virus spreading and acquisition of potential mutations relevant to human and animal health.

Can Multidisciplinary Sepsis Teams Help Solve a Global Concern? A Review of the Literature

The aims of this reviews are to provide a background on sepsis and to discuss the role of multidisciplinary sepsis teams for those who may not provide direct patient care but play an important role in supporting those that manage patients with sepsis. Sepsis, a life-threatening organ dysfunction due to a dysregulated host response to infection, is one of the leading causes of death worldwide. Considered a medical emergency, prompt laboratory results and administration of appropriate antibiotics are critical. Numerous sepsis guidelines have been published, including the use of standardized bundles that can result in reduced mortality and better patient outcomes. Key to successful outcomes are early recognition and appropriate therapy, which is guided by multidisciplinary sepsis intervention teams; rapid laboratory testing, including Gram stains; molecular mass spectrometry; and clinical and electronic early warning surveillance systems that offer a systematic machine learning application to analyze patients’ medical data and send an alert to providers in real time to allow earlier intervention.

Delivering together: A framework for sustainable ownership of early warning and health emergency surveillance technologies in low-and middle-income countries.

Previous health and natural disasters have all had a significant impact on human life. However, there is a consensus that the technological advances of our time would have allowed us to better understand and respond to these situations in a more holistic manner. Early Warning and Health Emergency Surveillance technologies are therefore more than essential for improved proactivity. While in developed countries, their implementation is facilitated by the existence of proven mechanisms, this is not often the case in low and middle-income countries. Our practical experience in deploying Early Warning and Health Emergency Surveillance systems in resource-constrained environments led us to develop and propose an approach that is intended to be effective in the execution of such interventions. When implemented in low and middle-income countries, our approach, which promotes the Bottom-to- Top concept, has succeeded in federating and strengthening all the forces in the public, private and international development sectors around the implementation of these crucial technologies for better emergency management, especially in health. Engaging all stakeholders, developing an inclusive modus operandi, promoting a sustainable strategy, and effectively empowering recipients in a coordinated manner is the recipe for the successful implementation of such technologies in low and middle-income countries.

Can wastewater surveillance assist China to cost-effectively prevent the nationwide outbreak of COVID-19?

China has controlled the nationwide spread of COVID-19 since April 2020, but it is still facing an enormous threat of disease resurgence originating from infected international travelers. Taking the rapid transmission and the mutation of SARS-CoV-2 into consideration, the current status would be easily jeopardized if sporadic locally-transmitted individuals are not identified at an early stage. Clinical diagnosis is the gold standard for COVID-19 surveillance, but it is hard to screen presymptomatic or asymptomatic cases in those who have not exhibited symptoms. Since presymptomatic or asymptomatic individuals are infectious, it is urgent to establish a surveillance system based on other tools that can profile the entire population. Infected people including those who are symptomatic, presymptomatic, and asymptomatic shed SARS-CoV-2 RNA in feces and thereby endow wastewater-based epidemiology (WBE) with an early-warning ability for mass COVID-19 surveillance. In the context of China's “COVID-zero” strategy, this work intends to discuss the practical feasibility of WBE applications as an early warning and disease surveillance system in hopes that WBE together with clinical testing would cost-effectively restrain sporadic COVID-19 outbreaks in China.

Global Surveillance Networks and International Health Regulations in the Early Detection and Control of Epidemics

An elaborate global health system has been developed around the world as a bulwark against infectious disease threats. The system consists of various networks of formal and informal organizations serving different stakeholders. This network has different purposes, methods, resources and accountability; it consists of public institutions operating in different fields of influence (at local, national, regional or global level), profit-oriented and non-profit private sector organizations. The current global health system has done important work to protect and improve human health. However, the world continues to face long-standing and emerging or re-emerging infectious disease threats. These threats vary greatly in severity and probability of risk, and each requires separate struggle dynamics. In addition, as with morbidity and mortality rates, these infections have many complex social and economic effects and consequences that differ. It is discussed whether the global health system with its current situation can provide effective protection against a number of communicable disease threats. Due to the recent emerging threats of Ebola, Zika, dengue fever, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), COVID-19, and influenza epidemics, and the increasing threat of antimicrobial resistance, this system is being put to the test. Weak health systems, urbanization, globalization, climate change, civil wars and conflicts, and the changing nature of pathogen transmission between human and animal populations, uncontrolled population growth in some risk areas aggravate current concerns. "Human-induced outbreaks resulting from laboratory accidents" or deliberate biological attacks are considered as other potential risks, making international cooperation and coordination important. International epidemiological surveillance systems and international health regulation coverage are expanding their scope as early intervention practices and tools that are critical in preventing infectious diseases from reaching epidemic levels and minimizing global risks. Also, the effectiveness of early warning surveillance systems is being improved with the use of new technologies.

Barriers to Climate Disaster risk Management for Public Health: Lessons from a Pilot Survey of National Public Health Representatives.

This study sought to examine current national disaster risk management capacities, and identify governance barriers to strengthening national preparedness for responding to public health emergencies, associated with the anticipated climate-driven intensification of natural disaster cycles. A mixed-methods online survey, assessing broader governance constraints to climate change adaptation (CCA) for public health, was distributed to representatives of national public health associations, and societies of 82 member countries under the World Federation of Public Health Associations. Specific questions relevant to disaster risk management capacities and barriers were analyzed as part of a narrowed focus on the CCA subdomain of emergency preparedness. Existence of some technology, infrastructure, and/ or human resources, necessary to develop early warning and other surveillance systems for climate-related health risks was reported by 9 out of 11 responding countries. However, 7 reported persistent limitations and/ or regional discrepancies. Most significant identified barriers to strengthening emergency preparedness at the national level included governance coordination challenges, and, in the case of many developing countries, technical, medical, and human resource shortages. The development of new frameworks for intersectoral governance and large-scale resource mobilization will prove crucial to ongoing efforts to strengthen national climate-health resiliency and prepare for disaster-associated health threats.

Weaponizing water as an instrument of war in Syria: Impact on diarrhoeal disease in Idlib and Aleppo governorates, 2011–2019

ObjectivesInvestigate the weaponization of water during the Syrian conflict and the correlation of attacks on water, sanitation, and hygiene (WASH) infrastructure in Idlib and Aleppo governorates with trends in waterborne diseases reported by Early Warning and Response surveillance systems. MethodsWe reviewed literature and databases to obtain information on attacks on WASH in Aleppo and Idlib governorates between 2011 and 2019. We plotted weekly trends in waterborne diseases from two surveillance systems operational in Aleppo and Idlib governorates between 2015 and early 2020. ResultsThe literature review noted several attacks on water and related infrastructure in both governorates, suggesting that WASH infrastructure was weaponized by state and non-state actors. Most interference with WASH in the Aleppo governorate occurred before 2019 and in the Idlib governorate in the summer of 2020. Other acute diarrhea represented >90% of cases of diarrhea; children under 5 years contributed 50% of cases. There was substantial evidence (p < 0.001) of an overall upward trend in cases of diarrheal disease. ConclusionsThough no direct correlation can be drawn between the weaponization of WASH and the burden of waterborne infections due to multiple confounders, this research introduces important concepts on attacks on WASH and their potential impacts on waterborne diseases.

Use of a Novel Electronic Maternal Surveillance System and the Maternal Early Warning Criteria to Detect Severe Postpartum Hemorrhage

(Anesth Analg. 2020;131:857–865) Responding early and appropriately to the clinical warning signs of postpartum hemorrhage (PPH) is one factor in preventing maternal death. While procedures to quantify blood loss can support the timely diagnosis of PPH, an early warning surveillance system triggered by maternal vital signs may provide an additional means of detecting this complication after delivery. The aim of this study was to assess the ability of an automated surveillance system and the Maternal Early Warning Criteria (MEWC) to predict severely morbid postpartum hemorrhage (sPPH) outside of the operating room.

Early warning of infectious disease outbreaks on cattle-transport networks.

Surveillance of infectious diseases in livestock is traditionally carried out at the farms, which are the typical units of epidemiological investigations and interventions. In Central and Western Europe, high-quality, long-term time series of animal transports have become available and this opens the possibility to new approaches like sentinel surveillance. By comparing a sentinel surveillance scheme based on markets to one based on farms, the primary aim of this paper is to identify the smallest set of sentinel holdings that would reliably and timely detect emergent disease outbreaks in Swiss cattle. Using a data-driven approach, we simulate the spread of infectious diseases according to the reported or available daily cattle transport data in Switzerland over a four year period. Investigating the efficiency of surveillance at either market or farm level, we find that the most efficient early warning surveillance system [the smallest set of sentinels that timely and reliably detect outbreaks (small outbreaks at detection, short detection delays)] would be based on the former, rather than the latter. We show that a detection probability of 86% can be achieved by monitoring all 137 markets in the network. Additional 250 farm sentinels—selected according to their risk—need to be placed under surveillance so that the probability of first hitting one of these farm sentinels is at least as high as the probability of first hitting a market. Combining all markets and 1000 farms with highest risk of infection, these two levels together will lead to a detection probability of 99%. We conclude that the design of animal surveillance systems greatly benefits from the use of the existing abundant and detailed animal transport data especially in the case of highly dynamic cattle transport networks. Sentinel surveillance approaches can be tailored to complement existing farm risk-based and syndromic surveillance approaches.

The Epidemiology of Zika Virus in the World: A Comprehensive Review Study

Zika virus is an emerging public health threat. The large outbreak related to this infection was first reported in 2007 in Yap Island. This virus is associated with microcephaly, Guillain Barre syndrome and some of the presentations of Zika infection include fever, maculopapular rash, arthralgia, bilateral conjunctivas, headache, arthritis/arthralgia with edema of tiny joints of feet and hands, retro-orbital pain, myalgia, asthenia and vertigo. In most of the cases, the infection is asymptomatic and self-limited. One of the largest known outbreaks of the virus was reported in French Polynesia, south pacific in October 2013. At the beginning of 2016, more than 52 countries have had reported the active transmission of the Zika virus. In general, there are two transmission modes for the Zika infection: Vector-borne transmission and Non-vector-borne transmission. Some diagnostic tests for Zika infection are RT-PCR, ELISA, and PRNT. Up to now, there is no specific antiviral medicine for the treatment of Zika infection and also no vaccine is available for immunization. As far as we know, more than half of the world’s people live in areas where the Aedes mosquito lives. There is a probability occurrence of the Zika virus epidemic at any time and in any place without prior notice in today’s “global village”. Therefore, health systems in all the involved countries should implement better triage and early warning surveillance systems for morbid cases of Zika to prevent large epidemics and the spread of the virus among mosquitoes and finally to avoid its disastrous consequences.

Predicting dengue importation into Europe, using machine learning and model-agnostic methods

The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

The Effect of Weather Variables on Mosquito Activity: A Snapshot of the Main Point of Entry of Cyprus.

Mosquitoes are vectors of pathogens, causing human and animal diseases. Their ability to adapt and expand worldwide increases spread of mosquito-borne diseases. Climate changes contribute in enhancing these “epidemic conditions”. Understanding the effect of weather variables on mosquito seasonality and host searching activity contributes towards risk control of the mosquito-borne disease outbreaks. To enable early detection of Aedes invasive species we developed a surveillance network for both invasive and native mosquitoes at the main point of entry for the first time in Cyprus. Mosquito sampling was carried out for one year (May 2017–June 2018), at bimonthly intervals around Limassol port. Morphological and molecular identification confirmed the presence of 5 species in the study region: Culex. pipiens, Aedes detritus, Ae. caspius, Culiseta longiareolata and Cs. annulata. No invasive Aedes mosquito species were detected. The Pearson’s correlation and multiple linear regression were used to compare number of sampled mosquitoes and weather variables for three most numerous species (Cx. pipiens, Ae. detritus and Ae. caspius). The population densities of the most numerous species were highest from February to April. Number of Cx. pipiens (−0.48), Ae. detritus (−0.40) and Ae. caspius (−0.38) specimens sampled was negatively correlated with average daily temperature. Monthly relative humidity showed positive correlation with the numbers of the species sampled, Cx. pipiens (0.66) Ae. detritus (0.68), and Ae. caspius (0.71). Mosquito abundance of Cx. pipiens (0.97) and Ae. detritus (0.98) was strongly correlated to seasonal precipitation as well. Our work is a stepping stone to further stimulate implementation of International Health Regulations and implementation of early warning surveillance system for detection of invasive Aedes mosquitoes, native mosquitoes and arboviruses they may transmit. A network for the surveillance of both invasive and native mosquito species at the main point of entry for the first time in Cyprus was developed. Number of mosquitoes sampled was correlated with weather factors to identify parameters that might predict mosquito activity and species distribution to the prevention of international spread of vector mosquitoes and vector-borne diseases.

Risks and Challenges of Arboviral Diseases in Sudan: The Urgent Need for Actions.

The risk of emergence and/or re-emergence of arthropod-borne viral (arboviral) infections is rapidly growing worldwide, particularly in Africa. The burden of arboviral infections and diseases is not well scrutinized because of the inefficient surveillance systems in endemic countries. Furthermore, the health systems are fully occupied by the burden of other co-existing febrile illnesses, especially malaria. In this review we summarize the epidemiology and risk factors associated with the major human arboviral diseases and highlight the gap in knowledge, research, and control in Sudan. Published data in English up to March 2019 were reviewed and are discussed to identify the risks and challenges for the control of arboviruses in the country. In addition, the lack of suitable diagnostic tools such as viral genome sequencing, and the urgent need for establishing a genomic database of the circulating viruses and potential sources of entry are discussed. Moreover, the research and healthcare gaps and global health threats are analyzed, and suggestions for developing strategic health policy for the prevention and control of arboviruses with focus on building the local diagnostic and research capacity and establishing an early warning surveillance system for the early detection and containment of arboviral epidemics are offered.

Online detection of anomaly behaviors based on multidimensional trajectories

In the surveillance domain, timely detection of anomaly behaviors is very important and is a great challenge to human operators due to information overload, fatigue and inattention. Many anomaly detection algorithms based on trajectories have been proposed for this problem. However, these algorithms generally have problems such as complex parameter setting, unfaithful statistical model, not well-calibrated false alarm rate, poor ability of online learning and sequential anomaly detection, etc. The theory of conformal prediction was introduced to solve these problems by constructing the sequential Hausdorff nearest neighbor conformal anomaly detector. Yet, it only considers position information of the targets and is not sensitive to velocity and course anomaly behaviors. And the run times are increasing as the increase of the data size, which is not appropriate for early warning surveillance application. In order to solve these problems, sequential multi-factor Hausdorff nearest neighbor conformal anomaly detector (SMFHNNCAD) and sequential multi-factor Hausdorff nearest neighbor inductive conformal anomaly detector (SMFHNNICAD) based on multidimensional trajectories are proposed in this paper. Experiments in both simulated military scenario and realistic civilian scenario show the presented algorithm has a good performance to online detect anomaly behaviors and would have a wide prospect in early warning surveillance systems.

Evaluation of the early warning, alert and response system after Cyclone Winston, Fiji, 2016.

To assess the performance of an early warning, alert and response system (EWARS) developed by the World Health Organization (WHO) - EWARS in a Box - that was used to detect and control disease outbreaks after Cyclone Winston caused destruction in Fiji on 20February 2016. Immediately after the cyclone, Fiji's Ministry of Health and Medical Services, supported by WHO, started to implement EWARS in a Box, which is a smartphone-based, automated, early warning surveillance system for rapid deployment during health emergencies. Both indicator-based and event-based surveillance were employed. The performance of the system between 7March and 29May 2016 was evaluated. Users' experience with the system was assessed in interviews using a semi-structured questionnaire and by a cross-sectional survey. The system's performance was assessed using data from the EWARS database. Indicator-based surveillance recorded 34113 cases of the nine syndromes under surveillance among 326861 consultations. Three confirmed outbreaks were detected, and no large outbreak was missed. Users were satisfied with the performance of EWARS and judged it useful for timely monitoring of disease trends and outbreak detection. The system was simple, stable and flexible and could be rapidly deployed during a health emergency. The automated collation, analysis and dissemination of data reduced the burden on surveillance teams, saved human resources, minimized human error and ensured teams could focus on public health responses. In Fiji, EWARS in a Box was effective in strengthening disease surveillance during a national emergency and was well regarded by users.