Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution

In December 2019 an outbreak of a new disease happened, in Wuhan city, China, in which the symptoms were very similar to pneumonia. The disease was attributed to SARS-CoV-2 as the infectious agent and it was called the new coronavirus or Covid-19. In March 2020, the World Health Organization declared a worldwide pandemic of the new coronavirus. We have already counted more than 110 million cases and almost 2.5 million deaths worldwide. In order to assist in decision-making to contain the disease, several scientists around the world have engaged in various efforts, and they have proposed a lot of systems and solutions for tracking, monitoring, and predicting confirmed cases and deaths from Covid-19. Mathematical models help to analyze and understand the evolution of the disease, but understanding the disease was not enough, it was necessary to understand the problem in a quantitative way to lead the decision-making during the pandemic. Several initiatives have made use of Artificial Intelligence, and models were designed using machine learning algorithms with features for temporal and spatio-temporal investigation and prediction of cases of Covid-19. Among the algorithms used are Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Graph Neural Networks (GNNs), Ecological Niche Models (ENMs), Long-Short Term Memory Networks (LSTM), linear regression, and others. And these had good results, and to analyze them, the Root Mean Squared Error (RMSE), Log Root Mean Squared Error (RMSLE), correlation coefficient, and others were used as metrics. Covid-19 presents a huge problem to public health worldwide, so it is of utmost importance to investigate it, and with these two approaches it is possible to track not only how the disease evolves but also to know which areas are at risk. And these solutions can help in supporting decision-making by health managers to make the best decisions for the disease that is in the outbreak. This chapter aims to present a literature review and a brief contribution to the use of machine learning methods for temporal and spatio-temporal prediction of Covid-19, using Brazil and its federative units as a case study. From canonical methods to deep networks and hybrid committee-based, approaches will be investigated. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Use of MAIA (Medical Artificial Intelligence Aid) platform for adjustment of health services during the pandemy COVID-19 in the department of Magdalena, Colombia.

Background: Describe the experience and results of the use of a digital platform navigated by the MAIA artificial intelligence engine for epidemiological surveillance in the department of Magdalena, Colombia, during the public health emergency generated by the Covid-19 disease pandemic in the period July 30 to December 8, 2020. Method(s): The MAIA digital platform was adapted to extract data from Covid-19 cases from the different health institutions distributed in the municipalities of the department of Magdalena. This information is then transformed through the platform into dynamic digital dashboards with 24/7 functionality, which allowed the visualization of descriptive information, trend curves and geolocation to facilitate decision-making in public health during the operating time. Satisfaction and utility surveys of the use of the platform called "MAIA DATA CRUE CENTRO REGULACION DE URGENCIAS Y EMERGENCIAS" (MAIA DATA CRUE) designed by MEDZAIO were carried out to know the perception of users. Result(s): Currently 58 health institutions from 27 municipalities of Magdalena are using the MAIA digital platform, in more than 4 months of operation a daily use of 100% has been achieved, with information extraction every 12 hours and continuous display of information throughout the period of use. More than 1200 records have been received, which have served to consolidate live information, with which daily health decisions have been made by the Magdalena COVID-19 pandemic regulatory center, optimizing the installed hospital and ICU capacity. 100% of those surveyed agree that this type of tool should continue to be used as epidemiological surveillance in COVID-19. Conclusion(s): The adaptation and use of digital platforms such as MAIA DATA CRUE CENTRO DE REGULACIONES DE URGENCIAS Y EMERGENCIAS, is an application of digital epidemiology for the intelligent management of diseases. This article demonstrates the importance of using digital tools supported by artificial intelligence to optimize the capacities of health systems regarding the different dimensions, in this case epidemiological surveillance and public health.Copyright © 2022 Asociacion Colombiana de Infectologia. All rights reserved.

The Birth of Digital Epidemiology in South Korea

The COVID-19 Epidemiological Investigation Support System (EISS) is a digital epidemiological tool, which utilizes location data from cellular base stations, credit card transactions records, and QR codes. It is a mass surveillance system that uses big data to track the entire infected population, featuring an extensive, automated, and speedy processing of data on personal location and the linkage of multiple databases from various governmental agencies. Based on interviews with people who have developed Korean digital epidemiology systems, this paper explores the technical, infrastructural, social, and institutional factors that have shaped Korean digital epidemiology since the 2014 avian flu crisis and examines the essential conditions of big data for digital epidemiology. The main findings are as follows: The feasibility of EISS goes beyond the matter of privacy;it is closely connected to technological infrastructures such as a high density of cellular base stations and private cloud systems;people's behavior such as a high rate of smartphone and credit card usage;and new forms of governance and institutions for speedy data processing. Multiple database linkage would develop EISS into a big data surveillance system that enables the prediction of risk-prone groups in a more preemptive manner. © 2023 National Science and Technology Council, Taiwan.

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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Current ethical and social issues in epidemiology.

PURPOSE: The American College of Epidemiology held its 2021 Annual Meeting virtually, September 8-10, with a conference theme of 'From Womb to Tomb: Insights from Health Emergencies'. The American College of Epidemiology Ethics Committee hosted a symposium session in recognition of the ethical and social challenges brought to light by the coronavirus disease 2019 pandemic and on the occasion of the publication of the third edition of the classic text, Ethics and Epidemiology. The American College of Epidemiology Ethics Committee invited the book editor and contributing authors to present at the symposium session titled 'Current Ethical and Social Issues in Epidemiology.' The purpose of this paper is to further highlight the ethical challenges and presentations. METHODS: Three speakers with expertise in ethics, health law, health policy, global health, health information technology, and translational research in epidemiology and public health were selected to present on the social and ethical issues in the current landscape. Dr. S Coughlin presented on the 'Ethical and Social Issues in Epidemiology', Dr. L Beskow presented on 'Ethical Challenges in Genetic Epidemiology', and Dr. K Goodman presented on the 'Ethics of Health Informatics'. RESULTS: New digital sources of data and technologies are driving the ethical challenges and opportunities in epidemiology and public health as it relates to the three emerging topic areas identified: (1) digital epidemiology, (2) genetic epidemiology, and (3) health informatics. New complexities such as the reliance on social media to control infectious disease outbreaks and the introduction of computing advancements are requiring re-evaluation of traditional bioethical frameworks for epidemiology research and public health practice. We identified several cross-cutting ethical and social issues related to informed consent, benefits, risks and harms, and privacy and confidentiality and summarized these alongside more nuanced ethical considerations such as algorithmic bias, group harms related to data (mis)representation, risks of misinformation, return of genomic research results, maintaining data security, and data sharing. We offered an integrated synthesis of the stages of epidemiology research planning and conduct with the ethical issues that are most relevant in these emerging topic areas. CONCLUSIONS: New realities exist for epidemiology and public health as professional groups who are faced with addressing population health, and especially given the recent pandemic and the widespread use of digital tools and technologies. Many ethical issues can be understood in the context of existing ethical frameworks; however, they have yet to be clearly identified or connected with the new technical and methodological applications of digital tools and technologies currently in use for epidemiology research and public health practice. To address current ethical challenges, we offered a synthesis of traditional ethical principles in public health science alongside more nuanced ethical considerations for emerging technologies and aligned these with lifecycle stages of epidemiology research. By critically reflecting on the impact of new digital sources of data and technologies on epidemiology research and public health practice, specifically in the control of infectious outbreaks, we offered insights on cultivating these new areas of professional growth while striving to improve population health.

YouTube Videos Demonstrating the Nasopharyngeal Swab Technique for SARS-CoV-2 Specimen Collection: Content Analysis.

BACKGROUND: Real-time polymerase chain reaction using nasopharyngeal swabs is currently the most widely used diagnostic test for SARS-CoV-2 detection. However, false negatives and the sensitivity of this mode of testing have posed challenges in the accurate estimation of the prevalence of SARS-CoV-2 infection rates. OBJECTIVE: The purpose of this study was to evaluate whether technical and, therefore, correctable errors were being made with regard to nasopharyngeal swab procedures. METHODS: We searched a web-based video database (YouTube) for videos demonstrating SARS-CoV-2 nasopharyngeal swab tests, posted from January 1 to May 15, 2020. Videos were rated by 3 blinded rhinologists for accuracy of swab angle and depth. The overall score for swab angle and swab depth for each nasopharyngeal swab demonstration video was determined based on the majority score with agreement between at least 2 of the 3 reviewers. We then comparatively evaluated video data collected from YouTube videos demonstrating the correct nasopharyngeal swab technique with data from videos demonstrating an incorrect nasopharyngeal swab technique. Multiple linear regression analysis with statistical significance set at P=.05 was performed to determine video data variables associated with the correct nasopharyngeal swab technique. RESULTS: In all, 126 videos met the study inclusion and exclusion criteria. Of these, 52.3% (66/126) of all videos demonstrated the correct swab angle, and 46% (58/126) of the videos demonstrated an appropriate swab depth. Moreover, 45.2% (57/126) of the videos demonstrated both correct nasopharyngeal swab angle and appropriate depth, whereas 46.8% (59/126) of the videos demonstrated both incorrect nasopharyngeal swab angle and inappropriate depth. Videos with correct nasopharyngeal swab technique were associated with the swab operators identifying themselves as a medical professional or as an Ear, Nose, Throat-related medical professional. We also found an association between correct nasopharyngeal swab techniques and recency of video publication date (relative to May 15, 2020). CONCLUSIONS: Our findings show that over half of the videos documenting the nasopharyngeal swab test showed an incorrect technique, which could elevate false-negative test rates. Therefore, greater attention needs to be provided toward educating frontline health care workers who routinely perform nasopharyngeal swab procedures.

In the shadow of privacy: Overlooked ethical concerns in COVID-19 digital epidemiology.

The COVID-19 pandemic witnessed a surge in the use of health data to combat the public health threat. As a result, the use of digital technologies for epidemic surveillance showed great potential to collect vast volumes of data, and thereby respond more effectively to the healthcare challenges. However, the deployment of these technologies raised legitimate concerns over risks to individual privacy. While the ethical and governance debate focused primarily on these concerns, other relevant issues remained in the shadows. Leveraging examples from the COVID-19 pandemic, this perspective article aims to investigate these overlooked issues and their ethical implications. Accordingly, we explore the problem of the digital divide, the role played by tech companies in the public health domain and their power dynamics with the government and public research sector, and the re-use of personal data, especially in the absence of adequate public involvement. Even if individual privacy is ensured, failure to properly engage with these other issues will result in digital epidemiology tools that undermine equity, fairness, public trust, just distribution of benefits, autonomy, and minimization of group harm. On the contrary, a better understanding of these issues, a broader ethical and data governance approach, and meaningful public engagement will encourage adoption of these technologies and the use of personal data for public health research, thus increasing their power to tackle epidemics.

Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study.

BACKGROUND: The ever-growing amount of health information available on the web is increasing the demand for tools providing personalized and actionable health information. Such tools include symptom checkers that provide users with a potential diagnosis after responding to a set of probes about their symptoms. Although the potential for their utility is great, little is known about such tools' actual use and effects. OBJECTIVE: We aimed to understand who uses a web-based artificial intelligence-powered symptom checker and its purposes, how they evaluate the experience of the web-based interview and quality of the information, what they intend to do with the recommendation, and predictors of future use. METHODS: Cross-sectional survey of web-based health information seekers following the completion of a symptom checker visit (N=2437). Measures of comprehensibility, confidence, usefulness, health-related anxiety, empowerment, and intention to use in the future were assessed. ANOVAs and the Wilcoxon rank sum test examined mean outcome differences in racial, ethnic, and sex groups. The relationship between perceptions of the symptom checker and intention to follow recommended actions was assessed using multilevel logistic regression. RESULTS: Buoy users were well-educated (1384/1704, 81.22% college or higher), primarily White (1227/1693, 72.47%), and female (2069/2437, 84.89%). Most had insurance (1449/1630, 88.89%), a regular health care provider (1307/1709, 76.48%), and reported good health (1000/1703, 58.72%). Three types of symptoms-pain (855/2437, 35.08%), gynecological issues (293/2437, 12.02%), and masses or lumps (204/2437, 8.37%)-accounted for almost half (1352/2437, 55.48%) of site visits. Buoy's top three primary recommendations split across less-serious triage categories: primary care physician in 2 weeks (754/2141, 35.22%), self-treatment (452/2141, 21.11%), and primary care in 1 to 2 days (373/2141, 17.42%). Common diagnoses were musculoskeletal (303/2437, 12.43%), gynecological (304/2437, 12.47%) and skin conditions (297/2437, 12.19%), and infectious diseases (300/2437, 12.31%). Users generally reported high confidence in Buoy, found it useful and easy to understand, and said that Buoy made them feel less anxious and more empowered to seek medical help. Users for whom Buoy recommended "Waiting/Watching" or "Self-Treatment" had strongest intentions to comply, whereas those advised to seek primary care had weaker intentions. Compared with White users, Latino and Black users had significantly more confidence in Buoy (P<.05), and the former also found it significantly more useful (P<.05). Latino (odds ratio 1.96, 95% CI 1.22-3.25) and Black (odds ratio 2.37, 95% CI 1.57-3.66) users also had stronger intentions to discuss recommendations with a provider than White users. CONCLUSIONS: Results demonstrate the potential utility of a web-based health information tool to empower people to seek care and reduce health-related anxiety. However, despite encouraging results suggesting the tool may fulfill unmet health information needs among women and Black and Latino adults, analyses of the user base illustrate persistent second-level digital divide effects.

Misinformation About and Interest in Chlorine Dioxide During the COVID-19 Pandemic in Mexico Identified Using Google Trends Data: Infodemiology Study.

BACKGROUND: The COVID-19 pandemic has prompted the increasing popularity of several emerging therapies or preventives that lack scientific evidence or go against medical directives. One such therapy involves the consumption of chlorine dioxide, which is commonly used in the cleaning industry and is available commercially as a mineral solution. This substance has been promoted as a preventive or treatment agent for several diseases, including SARS-CoV-2 infection. As interest in chlorine dioxide has grown since the start of the pandemic, health agencies, institutions, and organizations worldwide have tried to discourage and restrict the consumption of this substance. OBJECTIVE: The aim of this study is to analyze search engine trends in Mexico to evaluate changes in public interest in chlorine dioxide since the beginning of the COVID-19 pandemic. METHODS: We retrieved public query data for the Spanish equivalent of the term "chlorine dioxide" from the Google Trends platform. The location was set to Mexico, and the time frame was from March 3, 2019, to February 21, 2021. A descriptive analysis was performed. The Kruskal-Wallis and Dunn tests were used to identify significant changes in search volumes for this term between four consecutive time periods, each of 13 weeks, from March 1, 2020, to February 27, 2021. RESULTS: From the start of the pandemic in Mexico (February 2020), an upward trend was observed in the number of searches compared with that in 2019. Maximum volume trends were recorded during the week of July 19-25, 2020. The search volumes declined between September and November 2020, but another peak was registered in December 2020 through February 2021, which reached a maximum value on January 10. Percentage change from the first to the fourth time periods was +312.85, -71.35, and +228.18, respectively. Pairwise comparisons using the Kruskal-Wallis and Dunn tests showed significant differences between the four periods (P<.001). CONCLUSIONS: Misinformation is a public health risk because it can lower compliance with the recommended measures and encourage the use of therapies that have not been proven safe. The ingestion of chlorine dioxide presents a danger to the population, and several adverse reactions have been reported. Programs should be implemented to direct those interested in this substance to accurate medical information.

Sensing pedestrian flows for real-time assessment of non-pharmaceutical policy interventions during the COVID-19 pandemic.

Introduction: To combat and mitigate the transmission of the SARS-CoV-2 virus, reducing the number of social contacts within a population is highly effective. Non-pharmaceutical policy interventions, e.g. stay-at-home orders, closing schools, universities, and (non-essential) businesses, are expected to decrease pedestrian flows in public areas, leading to reduced social contacts. The extent to which such interventions show the targeted effect is often measured retrospectively by surveying behavioural changes. Approaches that use data generated through mobile phones are hindered by data confidentiality and privacy regulations and complicated by selection effects. Furthermore, access to such sensitive data is limited. However, a complex pandemic situation requires a fast evaluation of the effectiveness of the introduced interventions aiming to reduce social contacts. Location-based sensor systems installed in cities, providing objective measurements of spatial mobility in the form of pedestrian flows, are suited for such a purpose. These devices record changes in a population's behaviour in real-time, do not have privacy problems as they do not identify persons, and have no selection problems due to ownership of a device. Objective: This work aimed to analyse location-based sensor measurements of pedestrian flows in 49 metropolitan areas at 100 locations in Germany to study whether such technology is suitable for the real-time assessment of behavioural changes during a phase of several different pandemic-related policy interventions. Methods: Spatial mobility data of pedestrian flows was linked with policy interventions using the date as a unique linkage key. Data was visualised to observe potential changes in pedestrian flows before or after interventions. Furthermore, differences in time series of pedestrian counts between the pandemic and the pre-pandemic year were analysed. Results: The sensors detected changes in mobility patterns even before policy interventions were enacted. Compared to the pre-pandemic year, pedestrian counts were 85% lower. Conclusions: The study illustrated the practical value of sensor-based real-time measurements when linked with non-pharmaceutical policy intervention data. This study's core contribution is that the sensors detected behavioural changes before enacting or loosening non-pharmaceutical policy interventions. Therefore, such technologies should be considered in the future by policymakers for crisis management and policy evaluation.

Putting digital epidemiology into practice: PIA- Prospective Monitoring and Management Application

Introduction Epidemiological data collection is often challenged by low response and, in the case of cohorts, poor long-term compliance, i.e. a high drop-out. For the correct recording of incident or recurring health events, that are subject to recall difficulties, gathering of data during the event and immediate response of the participants is crucial. This is especially true when biosampling that catches a transient biological situation like COVID-19 is involved. In addition, emerging research topics (e.g. pandemics like the current SARS-CoV-2) demand a flexible approach regarding content while allowing for complex and varying study designs. To meet these needs, we developed an eResearch system for prospective monitoring and management of incident health events (PIA). Methods Programming PIA focusses on IT security and data protection as well as aiming for a user-friendly and motivating design e.g. through feedback for study participants. The main building blocks of the infrastructure are identical functionalities in web-based, iOS and Android compatible application to strengthen the user acceptance of the participants. The backend consists of services and databases, which are all containerised using Docker containers. All programming is based on the JavaScript ecosystem as this is widely used and well supported. Results PIA offers complete management of observational epidemiological studies with six different roles: PIA administrator, researcher, participant manager, study nurse, consent manager and participant. Each role has a specific interface, so that different functions e.g. implementation of new questionnaires, administration of biosamples or management of participant contacts can be performed by different personae. PIA can be integrated in the IT system of ongoing studies like the German National Cohort but also used as stand-alone system. The software is open source (AGPL3.0): https://github.com/hzi-braunschweig/pia-system. Discussion Despite the abundance of existing Electronic Data Capture Systems (EDC systems), we developed our own generic tool that combines monitoring and management in order to use it for specific applications e.g. in certain pre-existing epidemiological studies or for syndromic surveillance in the current pandemic. Hence, PIA is continuously adapted to emerging requirements. Currently, systematic feedback from users is collected. We aim to improve the user experience of PIA as well as provide further feedback and additional elements like gamification in the future.

Digital epidemiology, deep phenotyping and the enduring fantasy of pathological omniscience

Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology’s methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this article argues, are not just a quantitative expansion of epidemiology’s scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience;a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future. © The Author(s) 2022.

Covid-19 pandemic: A new round of antibiotic resistance

In this article, we analyzed the problems associated with increasing antibiotic resistance, irrational use of antibiotics, and inadequate demand for them during the COVID-19 pandemic. Objective. Using the method of digital epidemiology, we analyzed the dynamics of the frequency of a specific request for antibiotics in pharmacies and hospitals. We used open data from Yandex (Wordstat.Yandex) and Google (Google Trends) collected on weekly basis for the Russian Federation. Results. The World Health Organization reports a growing problem of antibiotic misuse by some individuals and healthcare institutions during the COVID-19 pandemic. Extensive irrational use of antibiotics causes the development of antibiotic resistance by many microorganisms, including those circulating in hospitals (for example, ESKAPE group). Moreover, COVID-19 has led to an exponential increase in the use of biocides worldwide, potentially resulting in additional indirect pressure promoting the selection of antibiotic-resistant strains. The pandemic in Russia was marked by a significant increase in antibiotic sales in pharmacies (including systemic antibacterial agents) and purchases by healthcare institutions. Conclusion. Our findings demonstrate that the rapid spread of COVID-19 was associated with extensive consumption of antibiotics, which resulted in growing antibacterial resistance (number of circulating drug-resistant strains) and posed a threat to the national security. The COVID-19 necessitates the discovery of new effective treatments for this infection, as well as rational use of antimicrobial drugs. The implementation of surveillance of antibiotic consumption will help to identify changing trends in their use, combine efforts to solve problems related to antibiotics and drug resistance, and to ensure rational use of antimicrobials. © 2021, Dynasty Publishing House. All rights reserved.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study.

BACKGROUND: The implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis. OBJECTIVE: The objective of this work is to describe a web-based participatory surveillance strategy among health care workers (HCWs) in two Swiss hospitals during the first wave of COVID-19. METHODS: A prospective cohort of HCWs was recruited in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children's Hospital. For data analysis, we used a combination of the following techniques: locally estimated scatterplot smoothing (LOESS) regression, Spearman correlation, anomaly detection, and random forest. RESULTS: From March 23 to August 23, 2020, a total of 127,684 SMS text messages were sent, generating 90,414 valid reports among 1004 participants, achieving a weekly average of 4.5 (SD 1.9) reports per user. The symptom showing the strongest correlation with a positive polymerase chain reaction test result was loss of taste. Symptoms like red eyes or a runny nose were negatively associated with a positive test. The area under the receiver operating characteristic curve showed favorable performance of the classification tree, with an accuracy of 88% for the training data and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low (10.6%). CONCLUSIONS: Loss of taste was the symptom that was most aligned with COVID-19 activity at the population level. At the individual level-using machine learning-based random forest classification-reporting loss of taste and limb/muscle pain as well as the absence of runny nose and red eyes were the best predictors of COVID-19.

Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment.

The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact's intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation.

Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19.

Detecting and controlling the diffusion of infectious diseases such as COVID-19 is crucial to managing epidemics. One common measure taken to contain or reduce diffusion is to detect infected individuals and trace their prior contacts so as to then selectively isolate any individuals likely to have been infected. These prior contacts can be traced using mobile devices such as smartphones or smartwatches, which can continuously collect the location and contacts of their owners by using their embedded localisation and communications technologies, such as GPS, Cellular networks, Wi-Fi, and Bluetooth. This paper evaluates the effectiveness of these technologies and determines the impact of contact tracing precision on the spread and control of infectious diseases. To this end, we have created an epidemic model that we used to evaluate the efficiency and cost (number of people quarantined) of the measures to be taken, depending on the smartphone contact tracing technologies used. Our results show that in order to be effective for the COVID-19 disease, the contact tracing technology must be precise, contacts must be traced quickly, and a significant percentage of the population must use the smartphone contact tracing application. These strict requirements make smartphone-based contact tracing rather ineffective at containing the spread of the infection during the first outbreak of the virus. However, considering a second wave, where a portion of the population will have gained immunity, or in combination with some other more lenient measures, smartphone-based contact tracing could be extremely useful.

Consumer Views on Health Applications of Consumer Digital Data and Health Privacy Among US Adults: Qualitative Interview Study.

BACKGROUND: In 2020, the number of internet users surpassed 4.6 billion. Individuals who create and share digital data can leave a trail of information about their habits and preferences that collectively generate a digital footprint. Studies have shown that digital footprints can reveal important information regarding an individual's health status, ranging from diet and exercise to depression. Uses of digital applications have accelerated during the COVID-19 pandemic where public health organizations have utilized technology to reduce the burden of transmission, ultimately leading to policy discussions about digital health privacy. Though US consumers report feeling concerned about the way their personal data is used, they continue to use digital technologies. OBJECTIVE: This study aimed to understand the extent to which consumers recognize possible health applications of their digital data and identify their most salient concerns around digital health privacy. METHODS: We conducted semistructured interviews with a diverse national sample of US adults from November 2018 to January 2019. Participants were recruited from the Ipsos KnowledgePanel, a nationally representative panel. Participants were asked to reflect on their own use of digital technology, rate various sources of digital information, and consider several hypothetical scenarios with varying sources and health-related applications of personal digital information. RESULTS: The final cohort included a diverse national sample of 45 US consumers. Participants were generally unaware what consumer digital data might reveal about their health. They also revealed limited knowledge of current data collection and aggregation practices. When responding to specific scenarios with health-related applications of data, they had difficulty weighing the benefits and harms but expressed a desire for privacy protection. They saw benefits in using digital data to improve health, but wanted limits to health programs' use of consumer digital data. CONCLUSIONS: Current privacy restrictions on health-related data are premised on the notion that these data are derived only from medical encounters. Given that an increasing amount of health-related data is derived from digital footprints in consumer settings, our findings suggest the need for greater transparency of data collection and uses, and broader health privacy protections.