Early Detection Of Disease Outbreaks Research Articles

Surveillance of SARS-CoV-2 RNA in wastewater matrix: a review.

SARS-CoV-2 is the agent responsible for the global pandemic sickness, COVID-19. It is an enveloped virus that belongs to the family Coronaviridae. Recent studies have revealed the fecal shedding of the virus and have been found to enter wastewater and aquatic systems. Prolonged viral presence in fecal samples is a common observation in the reported literature. Survival of the virus in the recipient environment could be a crucial factor that influences its fecal-oral transmission. The detection of a novel coronavirus in wastewater opportunity has potential for environmental surveillance at the community or population level. Such a surveillance system can enable the early detection of disease outbreaks in zones with pre-symptomatic/asymptomatic patients and act as a complementary tool for continuous monitoring of quarantine zones. In contrast to developed regions, resource constraints in underdeveloped communities coupled with different sanitation settings may pose a challenge to wastewater sampling and surveillance. To begin, this review summarizes the literature on the presence of SARS-CoV-2 in feces. The approaches for viral extraction, concentration, and detection in wastewater matrices are then highlighted. Finally, investigations on wastewater-based epidemiology for SARS-CoV-2 surveillance are reviewed.

Comparison of statistical methods for the early detection of disease outbreaks in small population settings

ObjectivesThis study examines the performance of 6 aberration detection algorithms for the early detection of disease outbreaks in small population settings using syndrome-based early warning surveillance data collected by the Pacific Syndromic Surveillance System (PSSS). Although previous studies have proposed statistical methods for detecting aberrations in larger datasets, there is limited knowledge about how these perform in the presence of small numbers of background cases. MethodsTo address this gap a simulation model was developed to test and compare the performance of the 6 algorithms in detecting outbreaks of different magnitudes, durations, and case distributions. ResultsThe study found that while the Early Aberration Reporting System–C1 algorithm developed by Hutwagner et al. outperformed others, no single approach provided reliable monitoring across all outbreak types. Furthermore, aberration detection approaches could only detect very large and acute outbreaks with any reliability. ConclusionThe findings of this study suggest that algorithm-based approaches to outbreak signal detection perform poorly when applied to settings with small numbers of background cases and should not be relied upon in these contexts. This highlights the need for alternative approaches for accurate and timely outbreak detection in small population settings, particularly those that are resource-constrained.

Designing a Wastewater-Based Epidemiology Study at the U.S. Air Force Academy: Using Severe Acute Respiratory Syndrome Coronavirus 2 to Test a Sentinel System forEarly Disease Outbreak Detection.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in wastewater has been proposed as a sentinel surveillance epidemiological tool for detection of infectious disease at a community level and as a complementary approach to syndromic surveillance of infectious disease outbreaks. We have designed a study to test the presence and quantity of SARS-CoV2, the virus responsible for COVID19, in the wastewater treatment facility (WWTF) of the U.S. Air Force Academy. Wastewater samples were tested in the laboratory to quantify the amount of SARS-CoV2 RNA using reverse transcription-quantitative polymerase chain reaction. Raw SARS-CoV2 viral titer in wastewater was normalized to the viral titer of a fecal marker, pepper mild mottle virus, to correct for dilutions. Temporal and spatial trends of COVID19 were analyzed. Furthermore, we compared wastewater analysis results against clinical data to assist public health decisions. Preliminary data suggest that wastewater analysis can provide temporal and spatial trends of COVID19. The geographically discrete WWTF at the U.S. Air Force suggests that wastewater testing is a useful approach to developing a comprehensive sentinel surveillance system. Together with ongoing syndromic surveillance data, this proof-of-concept study seeks to determine whether early detection of SARS-CoV2 in a closed system WWTF correlates to changes in community and clinically reported COVID19. The well-documented population served by the geographically discrete WWTF at the U.S. Air Force Academy may serve to better elucidate the adjunctive role of wastewater testing in a comprehensive surveillance system. These results may be of particular interest to the DoD and local commanders given the WWTFs under their immediate control and the information that these studies may provide in support of operational readiness through early detection of disease outbreaks.

Could it be monkeypox? Use of an AI-based epidemic early warning system to monitor rash and fever illness

ObjectivesThe EPIWATCH artificial intelligence (AI) system scans open-source data using automated technology and can be used to detect early warnings of infectious disease outbreaks. In May 2022, a multicountry outbreak of Mpox in non-endemic countries was confirmed by the World Health Organization. This study aimed to identify signals of fever and rash-like illness using EPIWATCH and, if detected, determine if they represented potential Mpox outbreaks. Study designThe EPIWATCH AI system was used to detect global signals for syndromes of rash and fever that may have represented a missed diagnosis of Mpox from 1 month prior to the initial case confirmation in the United Kingdom (7 May 2022) to 2 months following. MethodsArticles were extracted from EPIWATCH and underwent review. A descriptive epidemiologic analysis was conducted to identify reports pertaining to each rash-like illness, locations of each outbreak and report publication dates for the entries from 2022, with 2021 as a control surveillance period. ResultsReports of rash-like illnesses in 2022 between 1 April and 11 July (n = 656 reports) were higher than in the same period in 2021 (n = 75 reports). The data showed an increase in reports from July 2021 to July 2022, and the Mann–Kendall trend test showed a significant upward trend (P = 0.015). The most frequently reported illness was hand-foot-and-mouth disease, and the country with the most reports was India. ConclusionsVast open-source data can be parsed using AI in systems such as EPIWATCH to assist in the early detection of disease outbreaks and monitor global trends.

Optimizing testing strategies for early detection of disease outbreaks in animal trade networks via MCMC.

The animal trades between farms and other livestock holdings form a complex livestock trade network. The movement of animals between trade actors plays an important role in the spread of infectious diseases among premises. Particularly, the outbreak of silent diseases that have no clinically obvious symptoms in the animal trade system should be diagnosed by taking special tests. In practice, the authorities regularly conduct examinations on a random number of farms to make sure that there was no outbreak in the system. However, these actions, which aim to discover and block a disease cascade, are yet far from the effective and optimum solution and often fail to prevent epidemics. A testing strategy is defined as making decisions about distributing the fixed testing budget N between farms/nodes in the network. In this paper, first, we apply different heuristics for selecting sentinel farms on real and synthetic pig-trade networks and evaluate them by simulating disease spreading via the SI epidemic model. Later, we propose a Markov chain Monte Carlo (MCMC) based testing strategy with the aim of early detection of outbreaks. The experimental results show that the proposed method can reasonably well decrease the size of the outbreak on both the realistic synthetic and real trade data. A targeted selection of an N/52 fraction of nodes in the real pig-trade network based on the MCMC or simulated annealing can improve the performance of a baseline strategy by 89%. The best heuristic-based testing strategy results in a 75% reduction in the average size of the outbreak compared to that of the baseline testing strategy.

Wastewater surveillance as an epidemiological tool at mass gathering events: A FIFA World Cup Qatar 2022 perspective and implication to other mass gathering events.

Dear Editor, The ongoing FIFA World Cup 2022 hosted by Qatar is one of the most popular and largest international mass gathering (MG) sports events in the world1. Genomic investigations over the past year have revealed cryptic, ominous transmission of severe acute respiratory syndrome coronavirus 2, monkeypox, poliovirus, influenza, and antibiotic-resistant microbes at MG events. Qatar’s public health authorities have spent a lot of time and effort preparing and planning to ensure the health and safety of millions of international and national spectators, athletes, and organizers. However, there are still several major public health challenges, both perceived and real, that threaten global health security. The FIFA World Cup in 2022 is taking place during the ongoing global outbreaks of coronavirus disease 2019, monkeypox, antibiotic-resistant bacteria, influenza, respiratory syncytial virus, and drug-resistant tuberculosis, among others. Recently, a new hybrid virus formed from fusion of influenza A and respiratory syncytial virus is a worrying development2. Therefore, the risk of transmission of infectious diseases, amplification, and local and international spread of known and unknown pathogens during and after the FIFA World Cup remains significant. Historically, pathogens transmitted by the waterborne and fecal-oral routes have been evaluated through wastewater-based surveillance (WBS)3. Wastewater surveillance is more sensitive and cost-effective than clinical or epidemiological surveillance4. WBS was used in the polio eradication effort and has most recently been used for tracking the spread of severe acute respiratory syndrome coronavirus 2 and its variants2. In particular, there is no consensus on wastewater monitoring techniques and standardized global protocols for collecting and analyzing data4. There remains an urgent need to develop a global consensus for standardized global guidelines for sampling methods, sample pretreatment, and quantification, which is very practical at these times. WBS for pre-empting potential disease outbreaks could provide crucial information when used together with rapid detection laboratory techniques such as genomics and biosensors5,6. Early detection of disease outbreaks during MG events provides host countries with unique opportunities to develop evidence-based decisions for timely scientific-based public health interventions. Since a sample of wastewater reflects the entire contributing population, wastewater-based epidemiology can be used to learn about the spread of gastrointestinal and other viruses at the community level without the need to test many people7. Independent of the appearance of clinical symptoms or socioeconomic biases related to case reporting, WBS has been shown to be effective in detecting infection and transmission in the population. Adopting wastewater surveillance for infectious diseases during the FIFA World Cup 2022 and other future MG events could serve as an important tool to early alert the host country and global public health authorities (Fig. 1). Movements of attendees of MG events across Qatari municipalities should be wastewater-based epidemiology-based decisions, and consequent vaccination, countermeasures, and nonpharmaceutical interventions could be required. The wastewater-based epidemiology enabled Qatar officials to conduct a health survey during tournament days at a community level. Furthermore, it would give other countries an indication of potential pathogens being brought in by returning travelers8. WBS provides a powerful platform for detecting emerging and re-emerging viruses, antimicrobial resistance pathogens, and antimicrobial resistance genes. Its potential to alert host countries and global health authorities to imminent or emerging waterborne disease will add further to preparedness and proactive prevention control plans in place. Ultimately, this perspective is novel for MGs in the midst of mega sporting events, which should be further studied and considered.Figure 1: Wastewater surveillance as an epidemiological tool amidst the mega sporting events.Ethical approval Not applicable. Sources of funding None. Authors contribution A.R.A.S., A.A.M., P.C., and O.P.C. developed the original draft; A.M., B.K.P., and R.S. reviewed and edited the manuscript. All authors read and approved the final manuscript. Conflict of interest The authors declare that they have no financial conflict of interest with regard to the content of this report. Research registration unique identifying number (UIN) None. Guarantor R. Sah.

In-sewer decay and partitioning of Campylobacter jejuni and Campylobacter coli and implications for their wastewater surveillance

Campylobacter jejuni and coli are two main pathogenic species inducing diarrhoeal diseases in humans, which are responsible for the loss of 33 million lives each year. Current Campylobacter infections are mainly monitored by clinical surveillance which is often limited to individuals seeking treatment, resulting in under-reporting of disease prevalence and untimely indicators of community outbreaks. Wastewater-based epidemiology (WBE) has been developed and employed for the wastewater surveillance of pathogenic viruses and bacteria. Monitoring the temporal changes of pathogen concentration in wastewater allows the early detection of disease outbreaks in a community. However, studies investigating the WBE back-estimation of Campylobacter spp. are rare. Essential factors including the analytical recovery efficiency, the decay rate, the effect of in-sewer transport, and the correlation between the wastewater concentration and the infections in communities are lacking to support wastewater surveillance. This study carried out experiments to investigate the recovery of Campylobacter jejuni and coli from wastewater and the decay under different simulated sewer reactor conditions. It was found that the recovery of Campylobacter spp. from wastewater varied with their concentrations in wastewater and depended on the detection limit of quantification methods. The concentration reduction of Campylobacter. jejuni and coli in sewers followed a two-phase reduction model, and the faster concentration reduction during the first phase is mainly due to their partitioning onto sewer biofilms. The total decay of Campylobacter. jejuni and coli varied in different types of sewer reactors, i.e. rising main vs. gravity sewer. In addition, the sensitivity analysis for WBE back-estimation of Campylobacter suggested that the first-phase decay rate constant (k1) and the turning time point (t1) are determining factors and their impacts increased with the hydraulic retention time of wastewater.

Deep Learning for Early Detection of Disease Outbreaks

Deep Learning for Early Detection of Disease Outbreaks

Data analysis supports monitoring and surveillance of sheep health and welfare in the Netherlands

Monitoring and surveillance of sheep health and welfare are of major importance for early detection of disease outbreaks. As in other European countries, there is a national notifiable disease surveillance system in the Netherlands. Additionally, Royal GD implemented a voluntary monitoring and surveillance system for sheep in 2003 which is built on a consultancy helpdesk, diagnostic testing including post-mortem examination, veterinary networks, and, since 2006, an annual data analysis, performed by collecting, aggregating and analysing routine census data from five data sources and four nationally operating organizations.This paper describes the data analysis as performed for sheep over the years 2016 up to and including 2020, and multivariable analyses monitoring trends in time as well as associations between flock health and potential confounders. It presents a detailed description of the Dutch sheep population with 1,219,963 sheep distributed over 30,134 farms in 2020, and small fluctuations in numbers of sheep over time. Mortality is described for different types of farms, peaking all years in the first quarter, with some seasonal variations between small scale and professional farms. Animal movements within and into the Netherlands take place on a large scale. In 2020, 75% and 43% of all professional and small scale farms, respectively, did register animal introductions, and on average more than 50,000 sheep were imported annually over the last five years, and not only from surrounding countries. Possible consequences are considered. Benefits, limitations and challenges are discussed as are potential opportunities for future improvement. Although improvements are possible, data analysis in its current form proves to be a valuable tool in the monitoring and surveillance system, and thus helps to improve sheep health and welfare in the Netherlands.

Biosensor for agriculture and food safety: Recent advances and future perspectives

Climate change and the growing world population leading to agriculture and food safety are global challenges facing humanity, while biosensors have long been regarded as one of the powerful tools for providing solutions. Biosensors can aid in sustainable agriculture by providing continuous monitoring or early detection of disease outbreaks that can be averted. It also plays an important role in monitoring food risk factors such as pesticides, veterinary medications, heavy metals, pathogens, poisons, and illegal additions. Currently, this field includes a series of reviews covering the topic, but surprisingly, there tend to focus more on a single level and ignore the role across the food value chain. In this Perspective, we emphasized on the importance of all sectors from farm to fork for developing better biosensors.

Lessons learned from SARS-CoV-2 measurements in wastewater

Standardized protocols for wastewater-based surveillance (WBS) for the RNA of SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, are being developed and refined worldwide for early detection of disease outbreaks. We report here on lessons learned from establishing a WBS program for SARS-CoV-2 integrated with a human surveillance program for COVID-19. We have established WBS at three campuses of a university, including student residential dormitories and a hospital that treats COVID-19 patients. Lessons learned from this WBS program address the variability of water quality, new detection technologies, the range of detectable viral loads in wastewater, and the predictive value of integrating environmental and human surveillance data. Data from our WBS program indicated that water quality was statistically different between sewer sampling sites, with more variability observed in wastewater coming from individual buildings compared to clusters of buildings. A new detection technology was developed based upon the use of a novel polymerase called V2G. Detectable levels of SARS-CoV-2 in wastewater varied from 102 to 106 genomic copies (gc) per liter of raw wastewater (L). Integration of environmental and human surveillance data indicate that WBS detection of 100 gc/L of SARS-CoV-2 RNA in wastewater was associated with a positivity rate of 4% as detected by human surveillance in the wastewater catchment area, though confidence intervals were wide (β ~ 8.99 ∗ ln(100); 95% CI = 0.90–17.08; p < 0.05). Our data also suggest that early detection of COVID-19 surges based on correlations between viral load in wastewater and human disease incidence could benefit by increasing the wastewater sample collection frequency from weekly to daily. Coupling simpler and faster detection technology with more frequent sampling has the potential to improve the predictive potential of using WBS of SARS-CoV-2 for early detection of the onset of COVID-19.

Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review.

BackgroundWeb-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action. Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness.ObjectiveThe aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation.MethodsWe conducted a systematic search of the published literature across four databases (MEDLINE via OVID, Web of Science Core Collection, ProQuest Science, and Google Scholar) for peer-reviewed journal papers from January 1998 to October 2019 using a keyword search. Papers with the full text available were extracted for review, and exclusion criteria were applied to identify eligible papers.ResultsOf the 6649 retrieved papers, 23 remained, describing 15 web-based apps. Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both. We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations. Evaluation studies (6 out of 15 apps) were mostly cross-sectional, with some evidence of reduction in time to notification of outbreak; however, studies lacked user-based needs assessments and evaluation of implementation.ConclusionsPublic health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.

Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study.

BackgroundWhile early detection and effective control of epidemics depend on appropriate surveillance methods, the Philippines bases its dengue fever surveillance system on a passive surveillance method (notifications from barangay/village health centers, municipal or city health offices, hospitals, and clinics). There is no available mHealth (mobile health) app for dengue fever that includes all the appropriate surveillance methods in early detection of disease outbreaks in the country.ObjectiveThis study aimed to evaluate the usability of the Mozzify app in terms of objective quality (engagement, functionality, aesthetics, information) and app subjective and app-specific qualities and compare total app mean score ratings by sociodemographic profile and self and family dengue fever history to see what factors are associated with high app mean score rating among school-based young adult samples and health care professionals. Individual interviews and focus group discussions were also conducted among participants to develop themes from their comments and suggestions to help structure further improvement and future development of the app.MethodsUser experience sessions were conducted among participants, and the Mobile Application Rating Scale (MARS) professional and user versions (uMARS) were administered followed by individual interviews and focus group discussions. Descriptive statistical analysis of the MARS and uMARS score ratings was performed. The total app mean score ratings by sociodemographic and dengue fever history using nonparametric mean difference analyses were also conducted. Thematic synthesis was used to develop themes from the comments and suggestions raised in individual interviews and focus group discussions.ResultsMozzify obtained an overall >4 (out of 5) mean score ratings in the MARS and uMARS app objective quality (4.45), subjective (4.17), and specific (4.55) scales among 948 participants (79 health care professionals and 869 school-based samples). Mean difference analyses revealed that total app mean score ratings were not significantly different across ages and gender among health care professionals and across age, income categories, and self and family dengue fever history but not gender (P<.001) among the school-based samples. Thematic syntheses revealed 7 major themes: multilanguage options and including other diseases; Android version availability; improvements on the app’s content, design, and engagement; inclusion of users from low-income and rural areas; Wi-Fi connection and app size concerns; data credibility and issues regarding user security and privacy.ConclusionsWith its acceptable performance as perceived by health care professionals and school-based young adults, Mozzify has the potential to be used as a strategic health intervention system for early detection of disease outbreaks in the Philippines. It can be used by health care professionals of any age and gender and by school-based samples of any age, socioeconomic status, and dengue fever history. The study also highlights the feasibility of school-based young adults to use health-related apps for disease prevention.

Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs.

While the data generated by medical students at schools that require electronic patient encounter logs is primarily used to monitor their training progress, it can also be a great source of public health data. Specifically, it can be used for syndromic surveillance, a method used to analyze instantaneous health data for early detection of disease outbreaks. To analyze how the International Classification of Diseases, 10th Revision (ICD-10) codes input by medical students at the Edward Via College of Osteopathic Medicine into the Clinical Rotation Evaluation and Documentation Organizer (CREDO) patient encounter logging system could act as a new syndromic surveillance tool. A CREDO database query was conducted for ICD-10 codes entered between November 1, 2019 and March 13, 2020 using the World Health Organization's 2011 revised case definitions for Influenza Like Illness (ILI). During that period, medical students had an approximated mean of 3,000 patient encounters per day from over 1,500 clinical sites. A cumulative sum technique was applied to the data to generate alert thresholds. Breast cancer, a disease with a stable incidence during the specified timeframe, was used as a control. Total ILI daily ICD-10 counts that exceeded alert thresholds represented unusual levels of disease occurred 11 times from November 20, 2020 through February 28, 2020. This analysis is consistent with the COVID-19 pandemic timeline. The first statistically significant ILI increase occurred nine days prior to the first laboratory confirmed case in the country. Syndromic surveillance can be timelier than traditional surveillance methods, which require laboratory testing to confirm disease. As a result of this study, we are installing a real-time alert for ILI into CREDO, so rates can be monitored continuously as an indicator of possible future new infectious disease outbreaks.

Next generation sequencing (NGS) as a multipurpose method for detection and differentiation of plant viruses

This paper describes the application of next generation sequencing (NGS) in plant virus research. Although NGS has not been routinely used yet, it is increasingly adopted in diagnostics and genomics of phytopathogens. NGS technics enable the simultaneous detection of multiple viruses present in infected material. This makes it possible not only to determine which viruses are present in a single sample but also to determine their concentration and genetic diversity. The simultaneous identification of many viruses, the possibility of early detection of disease outbreaks as well as tracking and monitoring of epidemic development, make NGS a universal research tool that enables not only the detection but also the understanding of molecular mechanisms allowing viruses to adapt to environmental changes (host plant genotype, vector, presence of other pathogens).

Optimizing preventive medicine to bridge the gap between clinical medicine and public health for disease control in China: A lesson from COVID-19

The pandemic of Coronavirus Disease 2019 (COVID-19) highlights the importance of early detection of disease outbreaks, taking swift and decisive public health actions, and strengthening public health systems. Preventive medicine, as a specialty of medicine, trains students on both clinical medicine and public health and is of a particular need in battling against this pandemic. In China, preventive medicine plays a unique role in the disease control system where preventive medicine graduates represent a large share of the workforce. However, there is a shortage of qualified staff in the Chinese disease control system. The reasons for such a shortage are multifaceted. From the human resource perspective, the undergraduate preventive medicine curricula and exclusive public health training for preventive medicine postgraduates limit their clinical capacities. A series of disease control and public health education reforms may further incapacitate preventive medicine graduates' clinical skills, unintentionally widening the gap between public health and clinical medicine and thus posing threats to effective disease detection and control. The authors call for reforming and optimizing preventive medicine to bridge the gap between clinical medicine and public health by strengthening curricula on clinical medicine, diversifying curricula on public health, enhancing preventive medicine residency programs, and rectifying regulations that restrict preventive medicine graduates from practicing curative medicine.

Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study.

BackgroundA novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future.ObjectiveThis study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media.MethodsWeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020.ResultsWeChat Index hits for “Feidian” (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority’s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords “SARS,” “coronavirus,” “novel coronavirus,” “shortness of breath,” “dyspnea,” and “diarrhea,” but these terms were not as meaningful for the early detection of the outbreak as the term “Feidian”.ConclusionsBy using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks.

DOT: a crowdsourcing Mobile application for disease outbreak detection and surveillance in Mauritius.

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country’s public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

BackgroundThe ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time.MethodsPredicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010.ResultsThe overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data.ConclusionsA persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.

A real-time spatio-temporal syndromic surveillance system with application to small companion animals

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system’s performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.