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Providing an overview of disease surveillance, this text frames a roadmap of how newer technologies may allow all countries of the world to reach compliance with the IHR (International Health Regulations) established by the World Health Organization as it pertains to disease detection.

Background To reduce inappropriate antibiotic use, public health campaigns often provide fear-based information about antimicrobial resistance (AMR). Meta-analyses have found that fear-based campaigns in other contexts are likely to be ineffective unless respondents feel confident they can carry out the recommended behaviour (‘self-efficacy’). This study aimed to test the likely impact of fear-based messages, with and without empowering self-efficacy elements, on patient consultations/antibiotic requests for influenza-like illnesses, using a randomised design. Methods We hypothesised that fear-based messages containing empowering information about self-management without antibiotics would be more effective than fear alone, particularly in a pre-specified subgroup with low AMR awareness. Four thousand respondents from an online panel, representative of UK adults, were randomised to receive three different messages about antibiotic use and AMR, designed to induce fear about AMR to varying degrees. Two messages (one ‘strong-fear’, one ‘mild-fear’) also contained empowering information regarding influenza-like symptoms being easily self-managed without antibiotics. The main outcome measures were self-reported effect of information on likelihood of visiting a doctor and requesting antibiotics, for influenza-like illness, analysed separately according to whether or not the AMR information was ‘very/somewhat new’ to respondents, pre-specified based on a previous (non-randomised) survey. Results The ‘fear-only’ message was ‘very/somewhat new’ to 285/1000 (28.5%) respondents, ‘mild-fear-plus-empowerment’ to 336/1500 (22.4%), and ‘strong-fear-plus-empowerment’ to 388/1500 (25.9%) ( p  = 0.002). Of those for whom the respective information was ‘very/somewhat new’, only those given the ‘strong-fear-plus-empowerment’ message said they would be less likely to request antibiotics if they visited a doctor for an influenza-like illness ( p  < 0.0001; 182/388 (46.9%) ‘much less likely’/‘less likely’, versus 116/336 (34.5%) with ‘mild-fear-plus-empowerment’ versus 85/285 (29.8%) with ‘fear-alone’). Those for whom the respective information was not ‘very/somewhat new’ said they would be less likely to request antibiotics for influenza-like illness ( p  < 0.0001) across all messages (interaction p  < 0.0001 versus ‘very/somewhat new’ subgroup). The three messages had analogous self-reported effects on likelihood of visiting a doctor and in subgroups defined by believing antibiotics would ‘definitely/probably’ help an influenza-like illness. Results were reproduced in an independent randomised survey (additional 4000 adults). Conclusions Fear could be effective in public campaigns to reduce inappropriate antibiotic use, but should be combined with messages empowering patients to self-manage symptoms effectively without antibiotics.

Infodemiology can be defined as the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy. Infodemiology data can be collected and analyzed in near real time. Examples for infodemiology applications include the analysis of queries from Internet search engines to predict disease outbreaks (eg. influenza), monitoring peoples' status updates on microblogs such as Twitter for syndromic surveillance, detecting and quantifying disparities in health information availability, identifying and monitoring of public health relevant publications on the Internet (eg. anti-vaccination sites, but also news articles or expert-curated outbreak reports), automated tools to measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns. Moreover, analyzing how people search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behavior of populations. Seven years after the infodemiology concept was first introduced, this paper revisits the emerging fields of infodemiology and infoveillance and proposes an expanded framework, introducing some basic metrics such as information prevalence, concept occurrence ratios, and information incidence. The framework distinguishes supply-based applications (analyzing what is being published on the Internet, eg. on Web sites, newsgroups, blogs, microblogs and social media) from demand-based methods (search and navigation behavior), and further distinguishes passive from active infoveillance methods. Infodemiology metrics follow population health relevant events or predict them. Thus, these metrics and methods are potentially useful for public health practice and research, and should be further developed and standardized.

ObjectiveTo evaluate the quality of information regarding the prevention and treatment of COVID-19 available to the general public from all countries.DesignSystematic analysis using the ‘Ensuring Quality Information for Patients’ (EQIP) Tool (score 0–36), Journal of American Medical Association (JAMA) benchmark (score 0–4) and the DISCERN Tool (score 16–80) to analyse websites containing information targeted at the general public.Data sourcesTwelve popular search terms, including ‘Coronavirus’, ‘COVID-19 19’, ‘Wuhan virus’, ‘How to treat coronavirus’ and ‘COVID-19 19 Prevention’ were identified by ‘Google AdWords’ and ‘Google Trends’. Unique links from the first 10 pages for each search term were identified and evaluated on its quality of information.Eligibility criteria for selecting studiesAll websites written in the English language, and provides information on prevention or treatment of COVID-19 intended for the general public were considered eligible. Any websites intended for professionals, or specific isolated populations, such as students from one particular school, were excluded, as well as websites with only video content, marketing content, daily caseload update or news dashboard pages with no health information.ResultsOf the 1275 identified websites, 321 (25%) were eligible for analysis. The overall EQIP, JAMA and DISCERN scores were 17.8, 2.7 and 38.0, respectively. Websites originated from 34 countries, with the majority from the USA (55%). News Services (50%) and Government/Health Departments (27%) were the most common sources of information and their information quality varied significantly. Majority of websites discuss prevention alone despite popular search trends of COVID-19 treatment. Websites discussing both prevention and treatment (n=73, 23%) score significantly higher across all tools (p<0.001).ConclusionThis comprehensive assessment of online COVID-19 information using EQIP, JAMA and DISCERN Tools indicate that most websites were inadequate. This necessitates improvements in online resources to facilitate public health measures during the pandemic.

John Brownstein and colleagues discuss HealthMap, an automated real-time system that monitors and disseminates online information about emerging infectious diseases.

Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution. The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries. We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance. We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model's results, an index value was assigned to each country, quantifying in an objective manner the country's response to the pandemic. Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease.

  Abbreviations: CDC, US Centers for Disease Control and Prevention; ILI, influenza-like illness; SMS, short message service Summary Points * Traditional health systems serve a key role in protecting populations, but are typically hierarchical, and information often travels slowly. * Novel Internet-based collaborative systems can have an important role in gathering information quickly and improving coverage and accessibility. * Mobile Internet usage is growing rapidly worldwide, making real-time information tools more readily available to both clinicians and the general public. * We present a brief summary of some promising mobile applications for health monitoring and information sharing, together with preliminary results from a study of our deployment of a smartphone application which enabled the general public to report infectious disease events. * These early efforts at tapping the power of mobile software tools illustrate potentially important steps in improving health systems as well as engaging the public as participants in the public health process. [...]these early efforts represent an important step in not only improving system outputs but also engaging the public as participants in the public health process.

Background Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. Methods A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. Results For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. Conclusions The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.

The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of \"sustained decline\" varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. Reopening the United States comes with three certainties: (1) the \"social\" end of the pandemic and reopening are going to occur before the \"medical\" end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.