Online surveillance of media health event reporting in Nepal: digital disease detection from a One Health perspective.

BACKGROUND: Traditional media and the internet are crucial sources of health information. Media can significantly shape public opinion, knowledge and understanding of emerging and endemic health threats. As digital communication rapidly progresses, local access and dissemination of health information contribute significantly to global disease detection and reporting. METHODS: Health event reports in Nepal (October 2013-December 2014) were used to characterize Nepal's media environment from a One Health perspective using HealthMap - a global online disease surveillance and mapping tool. Event variables (location, media source type, disease or risk factor of interest, and affected species) were extracted from HealthMap. RESULTS: A total of 179 health reports were captured from various sources including newspapers, inter-government agency bulletins, individual reports, and trade websites, yielding 108 (60%) unique articles. Human health events were reported most often (n = 85; 79%), followed by animal health events (n = 23; 21%), with no reports focused solely on environmental health. CONCLUSIONS: By expanding event coverage across all of the health sectors, media in developing countries could play a crucial role in national risk communication efforts and could enhance early warning systems for disasters and disease outbreaks.

Global Capacity for Emerging Infectious Disease Detection, 1996-2014.

The speed with which disease outbreaks are recognized is critical for establishing effective control efforts. We evaluate global improvements in the timeliness of outbreak discovery and communication during 2010-2014 as a follow-up to a 2010 report. For all outbreaks reported by the World Health Organization's Disease Outbreak News, we estimate the number of days from first symptoms until outbreak discovery and until first public communication. We report median discovery and communication delays overall, by region, and by Human Development Index (HDI) quartile. We use Cox proportional hazards regression to assess changes in these 2 outcomes over time, along with Loess curves for visualization. Improvement since 1996 was greatest in the Eastern Mediterranean and Western Pacific regions and in countries in the middle HDI quartiles. However, little progress has occurred since 2010. Further improvements in surveillance will likely require additional international collaboration with a focus on regions of low or unstable HDI.

Mortality Risk Factors for Middle East Respiratory Syndrome Outbreak, South Korea, 2015.

As of July 15, 2015, the South Korean Ministry of Health and Welfare had reported 186 case-patients with Middle East respiratory syndrome in South Korea. For 159 case-patients with known outcomes and complete case histories, we found that older age and preexisting concurrent health conditions were risk factors for death.

Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection.

The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data.

Searching the Web for Influenza Vaccines: HealthMap Vaccine Finder.

OBJECTIVES: The goal of the HealthMap Vaccine Finder is to provide a free, comprehensive, online service where users can search for locations that offer immunizations. In this article, we describe the data and systems underlying the HealthMap Vaccine Finder (HVF) and summarize the project's first year of operations. METHODS: We collected data on vaccination services from a variety of providers for 2012-2013. Data are used to populate an online, public, searchable map. RESULTS: In its first year, HVF collected information from 1256 providers representing 46 381 locations. The public Web site received 625 124 visits during the 2012-2013 influenza vaccination season. CONCLUSIONS: HVF is a unique tool that connects the public to vaccine providers in their communities. During the 2012-2013 influenza season, HVF experienced significant usage and was able to respond to user feedback with new features.

Comparing timeliness, content, and disease severity of formal and informal source outbreak reporting.

BACKGROUND: Infectious disease surveillance has recently seen many changes including rapid growth of informal surveillance, acting both as competitor and a facilitator to traditional surveillance, as well as the implementation of the revised International Health Regulations. The present study aims to compare outbreak reporting by formal and informal sources given such changes in the field. METHODS: 111 outbreaks identified from June to December 2012 were studied using first formal source report and first informal source report collected by HealthMap, an automated and curated aggregator of data sources for infectious disease surveillance. The outbreak reports were compared for timeliness, reported content, and disease severity. RESULTS: Formal source reports lagged behind informal source reports by a median of 1.26 days (p=0.002). In 61% of the outbreaks studied, the same information was reported in the initial formal and informal reports. Disease severity had no significant effect on timeliness of reporting. CONCLUSION: The findings suggest that recent changes in the field of surveillance improved formal source reporting, particularly in the dimension of timeliness. Still, informal sources were found to report slightly faster and with accurate information. This study emphasizes the importance of utilizing both formal and informal sources for timely and accurate infectious disease outbreak surveillance.

Using clinicians' search query data to monitor influenza epidemics.

Search query information from a clinician's database, UpToDate, is shown to predict influenza epidemics in the United States in a timely manner. Our results show that digital disease surveillance tools based on experts' databases may be able to provide an alternative, reliable, and stable signal for accurate predictions of influenza outbreaks.

Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China.

BACKGROUND: Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of 'line lists' with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus. METHODS: We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by HealthMap, Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure. RESULTS: Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou. CONCLUSIONS: We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries.

Online reporting for malaria surveillance using micro-monetary incentives, in urban India 2010-2011.

BACKGROUND: The objective of this study was to investigate the use of novel surveillance tools in a malaria endemic region where prevalence information is limited. Specifically, online reporting for participatory epidemiology was used to gather information about malaria spread directly from the public. Individuals in India were incentivized to self-report their recent experience with malaria by micro-monetary payments. METHODS: Self-reports about malaria diagnosis status and related information were solicited online via Amazon's Mechanical Turk. Responders were paid $0.02 to answer survey questions regarding their recent experience with malaria. Timing of the peak volume of weekly self-reported malaria diagnosis in 2010 was compared to other available metrics such as the volume over time of and information about the epidemic from media sources. Distribution of Plasmodium species reports were compared with values from the literature. The study was conducted in summer 2010 during a malaria outbreak in Mumbai and expanded to other cities during summer 2011, and prevalence from self-reports in 2010 and 2011 was contrasted. RESULTS: Distribution of Plasmodium species diagnosis through self-report in 2010 revealed 59% for Plasmodium vivax, which is comparable to literature reports of the burden of P. vivax in India (between 50 and 69%). Self-reported Plasmodium falciparum diagnosis was 19% and during the 2010 outbreak and the estimated burden was between 10 and 15%. Prevalence between 2010 and 2011 via self-reports decreased significantly from 36.9% to 19.54% in Mumbai (p = 0.001), and official reports also confirmed a prevalence decrease in 2011. CONCLUSIONS: With careful study design, micro-monetary incentives and online reporting are a rapid way to solicit malaria, and potentially other public health information. This methodology provides a cost-effective way of executing a field study that can act as a complement to traditional public health surveillance methods, offering an opportunity to obtain information about malaria activity, temporal progression, demographics affected or Plasmodium-specific diagnosis at a finer resolution than official reports can provide. The recent adoption of technologies, such as the Internet supports self-reporting mediums, and self-reporting should continue to be studied as it can foster preventative health behaviours.

Epidemiological data from the COVID-19 outbreak, real-time case information.

Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Predicting social response to infectious disease outbreaks from internet-based news streams.

Infectious disease outbreaks often have consequences beyond human health, including concern among the population, economic instability, and sometimes violence. A warning system capable of anticipating social disruptions resulting from disease outbreaks is urgently needed to help decision makers prepare appropriately. We designed a system that operates in near real-time to identify and predict social response. Over 150,000 Internet-based news articles related to outbreaks of 16 diseases in 72 countries and territories were provided by HealthMap. These articles were automatically tagged with indicators of the disease activity and population reaction. An anomaly detection algorithm was implemented on the population reaction indicators to identify periods of unusually severe social response. Then a model was developed to predict the probability of these periods of unusually severe social response occurring in the coming week, 2 and 3 weeks. This model exhibited remarkably strong performance for diseases with substantial media coverage. For country-disease pairs with a median of 20 or more articles per year, the onset of social response in the next week was correctly predicted over 60% of the time, and 87% of weeks were correctly predicted. Performance was weaker for diseases with little media coverage, and, for these diseases, the main utility of our system is in identifying social response when it occurs, rather than predicting when it will happen in the future. Overall, the developed near real-time prediction approach is a promising step toward developing predictive models to inform responders of the likely social consequences of disease spread.

Comparison of direct immunofluorescence, immunoassays, and fecal flotation for detection of Cryptosporidium spp. and Giardia spp. in naturally exposed cats in 4 Northern California animal shelters.

BACKGROUND: Giardia spp. and Cryptosporidium spp. are common intestinal protozoan parasites in domestic cats. Few studies have critically evaluated the performance characteristics of commercially available immunoassays for detection of these organisms in the cat. HYPOTHESIS: Human-based immunoassays are suboptimal for the detection of Giardia spp. and Cryptosporidium spp. in cats. ANIMALS: Three-hundred-and-forty-four cats with diarrheic and nondiarrheic fecal specimens at 4 northern California animal shelters. METHODS: A fecal specimen was collected from each cat in a case-controlled fashion. Fecal specimens were tested for Giardia spp. and Cryptosporidium spp. by using centrifugation flotation and 5 commercially available immunoassays (SNAP Giardia, ProSpecT Giardia Microplate Assay, ProSpecT Cryptosporidium Microplate Assay, ImmunoCard STAT! Cryptosporidium/ Giardia Rapid Assay, and Xpect Giardia/Cryptosporidium). Results were compared with a reference standard, the MeriFluor direct immunofluorescence assay. RESULTS: Overall prevalences of Giardia spp. and Cryptosporidium spp. were 9.8 and 4.7%, respectively. The ProSpecT Microplate Assay had the highest sensitivities and specificities for Giardia spp. (91.2 and 99.4%) and Cryptosporidum spp. (71.4 and 96.7%), respectively. The SNAP Giardia antigen assay was easier to use and equally sensitive (85.3%) and specific (100%) to fecal flotation. CONCLUSIONS AND CLINICAL IMPORTANCE: Caution should be exercised when using human-based immunoassays for the diagnosis of Giardia and Cryptosporidium spp. in cats. Fecal flotation remains a useful method for detection of Giardia spp., can be used to detect other parasites, and has a sensitivity of 97.8% for detection of Giardia spp. when combined with the SNAP Giardia immunoassay.

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.

Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak.

BACKGROUND: Approximately 40 countries in Central and South America have experienced local vector-born transmission of Zika virus, resulting in nearly 300,000 total reported cases of Zika virus disease to date. Of the cases that have sought care thus far in the region, more than 70,000 have been reported out of Colombia. OBJECTIVE: In this paper, we use nontraditional digital disease surveillance data via HealthMap and Google Trends to develop near real-time estimates for the basic (R) and observed (Robs) reproductive numbers associated with Zika virus disease in Colombia. We then validate our results against traditional health care-based disease surveillance data. METHODS: Cumulative reported case counts of Zika virus disease in Colombia were acquired via the HealthMap digital disease surveillance system. Linear smoothing was conducted to adjust the shape of the HealthMap cumulative case curve using Google search data. Traditional surveillance data on Zika virus disease were obtained from weekly Instituto Nacional de Salud (INS) epidemiological bulletin publications. The Incidence Decay and Exponential Adjustment (IDEA) model was used to estimate R0 and Robs for both data sources. RESULTS: Using the digital (smoothed HealthMap) data, we estimated a mean R0 of 2.56 (range 1.42-3.83) and a mean Robs of 1.80 (range 1.42-2.30). The traditional (INS) data yielded a mean R0 of 4.82 (range 2.34-8.32) and a mean Robs of 2.34 (range 1.60-3.31). CONCLUSIONS: Although modeling using the traditional (INS) data yielded higher R estimates than the digital (smoothed HealthMap) data, modeled ranges for Robs were comparable across both data sources. As a result, the narrow range of possible case projections generated by the traditional (INS) data was largely encompassed by the wider range produced by the digital (smoothed HealthMap) data. Thus, in the absence of traditional surveillance data, digital surveillance data can yield similar estimates for key transmission parameters and should be utilized in other Zika virus-affected countries to assess outbreak dynamics in near real time.

The Role of Social Mobilization in Controlling Ebola Virus in Lofa County, Liberia.

The West Africa Ebola virus epidemic now appears to be coming to an end. In the proposed model, we simulate changes in population behavior that help to explain the observed transmission dynamics. We introduce an EVD transmission model accompanied by a model of social mobilization. The model was fit to Lofa County, Liberia through October 2014, using weekly counts of new cases reported by the US CDC. In simulation studies, we analyze the dynamics of the disease transmission with and without population behavior change, given the availability of beds in Ebola treatment units (ETUs) estimated from observed data. Only the model scenario that included individuals' behavioral change achieved a good fit to the observed case counts. Although the capacity of the Lofa County ETUs greatly increased in mid-August, our simulations show that the expansion was insufficient to alone control the outbreak. Modeling the entire outbreak without considering behavior change fit the data poorly, and extrapolating from early data without taking behavioral changes into account led to a prediction of exponential outbreak growth, contrary to the observed decline.  Education and awareness-induced behavior change in the population was instrumental in curtailing the Ebola outbreak in Lofa County and is likely playing an important role in stopping the West Africa epidemic altogether.