Infectious Disease Surveillance Data Research Articles

Mumps epidemic dynamics in the United States before vaccination (1923–1932)

Mumps is a vaccine-preventable, reemerging, and highly transmissible infectious disease. Widespread vaccination dramatically reduced cases; however, case counts have been increasing over the past 20 years. To provide a quantitative overview of historical mumps dynamics that can act as baseline information to help identify causes of mumps reemergence, we analyzed timeseries of cases reported from 1923 to 1932 in the United States. During that time, 239,230 mumps cases were reported in 70 cities. Larger cities reported annual epidemics and smaller cities reported intermittent, sporadic outbreaks. The critical community size above which transmission continuously occurred was likely between 365,583 and 781,188 individuals but could range as high as 3,376,438 individuals. Mumps cases increased as city size increased, suggesting density-dependent transmission. Using a density-dependent SEIR model, we calculated a mean effective reproductive number (Re) of 1.2. Re varied by city and over time, with periodic high values that could characterize short periods of very high transmission known as superspreading events. Case counts most often peaked in March, with higher-than-average transmission from December through April and showed a correlation with weekly births. While certain city pairs in Midwestern states had synchronous outbreaks, most outbreaks were less synchronous and not driven by distance between cities. This work demonstrates the importance of long-term infectious disease surveillance data and will inform future studies on mumps reemergence and control.

Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden.

The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.

Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.

ObjectiveSupporting public health research and the public’s situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data.Materials and MethodsThe framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework’s effectiveness in maintaining the PK11 threshold of 0.01.ResultsWhen sharing COVID-19 county-level case data across all US counties, the framework’s approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%.ConclusionPeriodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.

Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data.

Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious diseases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold‐based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children's Hospital of Colorado. At a threshold of 10 cases per week, ALERT‐selected intervention periods performed better than the observed hospital site periods (2004/2005–2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off‐season incidence.

Making Better Use of Population Health Data for Community Health Needs Assessments.

Research Objective:Non-profit hospitals are required to work with community organizations to prepare a Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system’s transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on measures for needs assessments and priority setting.Study Design:Based on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process.Population Studied:U.S. hospitals.Principal Findings:Census, American Community Survey, and similar data are available for smaller areas are used to describe the populations covered, and, to a lesser extent, to identify health issues where there are disparities and inequities.Common data sources for population health profiles, including risk factors and population health outcomes, are vital statistics, survey data including BRFSS, infectious disease surveillance data, hospital & ED data, and registries. These data are typically available only at the county level, and only occasionally are broken down by race, ethnicity, age, poverty.There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS’s are less developed.Conclusions:The county is the unit of choice because most population health profile data are not available for sub-county areas, but when a hospital serves a population more broadly or narrowly defined, appropriate data are not available to set priorities or monitor progress.Measure definitions are taken from the original data sources, so comparisons across measures is difficult. Thus, although CHNAs cover many of the same topics, the measures used vary markedly. Using the same community health profile, e.g. County Health Rankings, would simplify benchmarking and trend analysis.Implications for Policy or Practice: It is important to develop population health data that can be disaggregated to the appropriate geographical level and to groups defined by race and ethnicity, socioeconomic status, and other factors associated with health outcomes.

Systematic Review:National Notifiable Infectious Disease Surveillance System in China

Systematic Review:National Notifiable Infectious Disease Surveillance System in China

Rapid increase of syphilis in Tokyo: an analysis of infectious disease surveillance data from 2007 to 2016.

The objective of this study was to examine the trends of primary and secondary syphilis in Tokyo between 2007 and 2016 using national infectious disease surveillance data. We analysed all 3269 cases reported during these 10 years. A statistically significant increase in cases was observed after 2010 with a more rapid rate of increase after 2014 mainly in urban areas in Tokyo. The notification rates per 100 000 population in 2010, 2014 and 2016 were 0.9 (n = 113), 2.2 (n = 295) and 8.7 (n = 1190), respectively. Domestic syphilis transmission was suspected in 92.6–99.3% of cases during the period 2007–2016.Until 2013, the increase was mainly observed among men who have sex with men (MSM); however, heterosexual transmission became more dominant and eventually surpassed transmission among MSM in 2015. In 2016, the notified cases of infections through heterosexual contact were 22.3 and 40.4 times higher in men and women, respectively, compared to those in 2010. The median ages of affected heterosexual men and women were 37 (interquartile range: 28–46) and 26 (interquartile range: 22–32) years, respectively. Reports of oropharyngeal lesions have been increasing among both men and women with syphilis. The number of congenital syphilis cases reported in Tokyo was 0 to 3 cases per year during the study period.More information and further analysis are needed to explain the reason for this increase.

No-deal Brexit could see UK locked out of EU infectious disease surveillance data, chief medical officer warns

No-deal Brexit could see UK locked out of EU infectious disease surveillance data, chief medical officer warns

Diabetes Among People With Tuberculosis, HIV Infection, Viral Hepatitis B and C, and STDs in New York City, 2006-2010.

Matching infectious disease surveillance data has become a routine activity for many health departments. With the increasing focus on chronic disease, it is also useful to explore opportunities to match infectious and chronic disease surveillance data. To understand the burden of diabetes in New York City (NYC), adults with select infectious diseases (tuberculosis, HIV infection, hepatitis B, hepatitis C, chlamydial infection, gonorrhea, and syphilis) reported between 2006 and 2010 were matched with hemoglobin A1c results reported in the same period. Persons were considered to have diabetes with 2 or more hemoglobin A1c test results of 6.5% or higher. The analysis was restricted to persons who were 18 years or older at the time of first report, either A1c or infectious disease. Overall age-adjusted diabetes prevalence was 8.1%, and diabetes prevalence was associated with increasing age; among NYC residents, prevalence ranged from 0.6% among 18- to 29-year-olds to 22.4% among those 65 years and older. This association was also observed in each infectious disease. Diabetes prevalence was significantly higher among persons with tuberculosis born in Mexico, Jamaica, Honduras, Guyana, Bangladesh, Dominican Republic, the Philippines, and Haiti compared with those born in the United States after adjusting for age and sex. Hepatitis C virus-infected women had higher age-adjusted prevalence of diabetes compared with the NYC population as a whole. Recognizing associations between diabetes and infectious diseases can assist early diagnosis and management of these conditions. Matching chronic disease and infectious disease surveillance data has important implications for local health departments and large health system practices, including increasing opportunities for integrated work both internal to systems and with the local community. Large health systems may consider opportunities for increased collaboration across infectious and chronic disease programs facilitated through data linkages of routinely collected surveillance data.

Use of Bibliometric Analysis to Assess the Scientific Productivity and Impact of the Global Emerging Infections Surveillance and Response System Program, 2006-2012.

Scientific publication in academic literature is a key venue in which the U.S. Department of Defense's Global Emerging Infections Surveillance and Response System (GEIS) program disseminates infectious disease surveillance data. Bibliometric analyses are tools to evaluate scientific productivity and impact of published research, yet are not routinely used for disease surveillance. Our objective was to incorporate bibliometric indicators to measure scientific productivity and impact of GEIS-funded infectious disease surveillance, and assess their utility in the management of the GEIS surveillance program. Metrics on GEIS program scientific publications, project funding, and countries of collaborating institutions from project years 2006 to 2012 were abstracted from annual reports and program databases and organized by the six surveillance priority focus areas: respiratory infections, gastrointestinal infections, febrile and vector-borne infections, antimicrobial resistance, sexually transmitted infections, and capacity building and outbreak response. Scientific productivity was defined as the number of scientific publications in peer-reviewed literature derived from GEIS-funded projects. Impact was defined as the number of citations of a GEIS-funded publication by other peer-reviewed publications, and the Thomson Reuters 2-year journal impact factor. Indicators were retrieved from the Web of Science and Journal Citation Report. To determine the global network of international collaborations between GEIS partners, countries were organized by the locations of collaborating institutions. Between 2006 and 2012, GEIS distributed approximately US $330 million to support 921 total projects. On average, GEIS funded 132 projects (range 96-160) with $47 million (range $43 million-$53 million), annually. The predominant surveillance focus areas were respiratory infections with 317 (34.4%) projects and $225 million, and febrile and vector-borne infections with 274 (29.8%) projects and $45 million. The number of annual respiratory infections-related projects peaked in 2006 and 2009. The number of febrile and vector-borne infections projects increased from 29 projects in 2006 to 58 in 2012. There were 651 articles published in 147 different peer-reviewed journals, with an average Thomson Reuters 2-year journal impact factor of 4.2 (range 0.3-53.5). On average, 93 articles were published per year (range 67-117) with $510,000 per publication. Febrile and vector-borne, respiratory, and gastrointestinal infections had 287, 167, and 73 articles published, respectively. Of the 651 articles published, 585 (89.9%) articles were cited at least once (range 1-1,045). Institutions from 90 countries located in all six World Health Organization regions collaborated with surveillance projects. These findings summarize the GEIS-funded surveillance portfolio between 2006 and 2012, and demonstrate the scientific productivity and impact of the program in each of the six disease surveillance priority focus areas. GEIS might benefit from further financial investment in both the febrile and vector-borne and sexually transmitted infections surveillance priority focus areas and increasing peer-reviewed publications of surveillance data derived from respiratory infections projects. Bibliometric indicators are useful to measure scientific productivity and impact in surveillance systems; and this methodology can be utilized as a management tool to assess future changes to GEIS surveillance priorities. Additional metrics should be developed when peer-reviewed literature is not used to disseminate noteworthy accomplishments.

Collaborative Research with Chinese, Indian, Filipino and North European Research Organizations on Infectious Disease Epidemics.

In this report, we present a short review of applications of time series analysis, which consists of spectral analysis based on the maximum entropy method in the frequency domain and the least squares method in the time domain, to the incidence data of infectious diseases. This report consists of three parts. First, we present our results obtained by collaborative research on infectious disease epidemics with Chinese, Indian, Filipino and North European research organizations. Second, we present the results obtained with the Japanese infectious disease surveillance data and the time series numerically generated from a mathematical model, called the susceptible/exposed/infectious/recovered (SEIR) model. Third, we present an application of the time series analysis to pathologic tissues to examine the usefulness of time series analysis for investigating the spatial pattern of pathologic tissue. It is anticipated that time series analysis will become a useful tool for investigating not only infectious disease surveillance data but also immunological and genetic tests.

Perceptions of the Feasibility and Practicalities of Text Messaging-Based Infectious Disease Surveillance: A Questionnaire Survey.

BackgroundIn Vietnam, infectious disease surveillance data are collected via a paper-based system through four government tiers leading to a large delay. Meanwhile, mobile phones are abundant and very popular in the country, and known to be a useful tool in health care worldwide. Therefore, there is a great potential for the development of a timely disease surveillance system through the use of mobile phone short message service (SMS) text messages.ObjectiveThis study aims to explore insights about the feasibility and practicalities of the utilization of SMS text messaging-based interventions in disease-reporting systems by identifying potential challenges and barriers in the text messaging process and looking at lessons learned.MethodsAn SMS text messaging-based disease tracking system was set up in Vietnam with patient reports texted by clinic staff. Two 6-month trials utilizing this disease tracking system were designed and implemented in two northern provinces of Vietnam to report two infectious diseases: diarrhea and influenza-like illness. A structured self-reported questionnaire was developed to measure the feasibility and practicalities of the system from the participants. On the completion of the second trial in 2013, participating health staff from 40 commune health centers in the two pilot provinces were asked to complete the survey (N=80).ResultsMost participants were female (61%, 49/80) and nearly half (44%, 35/80) were heads of a commune health center. Approximately two-thirds (63%, 50/80) of participants retained the basic structure of the SMS text message report and there was a strong influence (OR 28.2, 95% CI 5.3-151.2) of those people on the time they spent texting the information. The majority (88%, 70/80) felt the information conveyed in the SMS text message report was not difficult to understand. Most (86%, 69/80) believed that they could report all 28 infectious diseases asked for by the Ministry of Health by using SMS text messaging.ConclusionsFrom a health center staff perspective, a disease-reporting system utilizing text messaging technology is easy to use and has great potential to be implemented and expanded nationwide. The survey showed positive perceptions and feedback from the participants and contributed to a promising practical solution to improve the surveillance system of infectious disease in Vietnam.

Infectious disease outbreaks in centralized homes for asylum seekers in Germany from 2004-2014

Migration and imported infections are changing the distribution of infectious diseases in Europe. However little is known about the extent of transmission of imported diseases within Europe. Asylum seekers are of increasing importance for infectious disease epidemiology and can be particularly vulnerable for infections and disease progression due to stressful conditions of migration and incomplete vaccination status. The aim is to analyse transmission of infectious diseases in centralized homes for asylum seekers in national infectious disease surveillance data to identify relevant infectious diseases and possible public health measures to reduce transmission. German national notification data was systematically analysed from 2004 to 2014 for outbreaks reported to have occurred within centralized homes for asylum seekers followed by descriptive analysis of outbreak- and case-characteristics. From 2004 to 2014 the number of outbreaks in centralized homes for asylum seekers per year increased, atotal of 119 outbreaks with 615 cases were reported. Cases in these outbreaks were caused by chicken pox (30 %), measles (20 %), scabies (19 %), rota-virus-gastroenteritis (8 %) and others (each <5 %). Of 119 outbreaks, two outbreaks of measles in centralized homes were connected to outbreaks outside the centralized homes. For 210 of 311 cases in 2014 the place of infection was reported, 87 % of those with known place of infection were infected in Germany. Infectious disease outbreaks in centralized homes for asylum seekers are reported increasingly often in Germany. Chicken pox, measles and scabies were the most frequent outbreak causing diseases. Spread of such outbreaks outside centralized homes for asylum seekers was rare and infectious diseases are mainly acquired in Germany. The majority of outbreaks in centralized homes for asylum seekers would be preventable with vaccinations at arrival and appropriate hygiene measures.

The analysis of infectious disease surveillance data based on fuzzy time series method

The analysis of infectious disease surveillance data based on fuzzy time series method

A Suggestion to Improve Timely Feedback of Infectious Disease Surveillance Data at a Provincial Level in South Korea

Our study suggested the utility of the assessment of time-lag distributions for the feedback strategies to improve surveillance and regional infectious disease controls.

Taylor's Power Law and the Statistical Modelling of Infectious Disease Surveillance Data

Summary Surveillance data collected on several hundred different infectious organisms over 20 years have revealed striking power relationships between their variance and mean in successive time periods. Such patterns are common in ecology, where they are referred to collectively as Taylor's power law. In the paper, these relationships are investigated in detail, with the aim of exploiting them for the descriptive statistical modelling of infectious disease surveillance data. We confirm the existence of variance-to-mean power relationships, with exponent typically between 1 and 2. We investigate skewness-to-mean relationships, which are found broadly to match those expected of Tweedie distributions, and thus confirm the relevance of the Tweedie convergence theorem in this context. We suggest that variance- and skewness-to-mean power laws, when present, should inform statistical modelling of infectious disease surveillance data, notably in descriptive analysis, model building, simulation and interval and threshold estimation, threshold estimation being particularly relevant to outbreak detection.

Collecting syndromic surveillance data by mobile phone in rural India: implementation and feasibility

BackgroundInfectious disease surveillance has long been a challenge for countries like India, where 75% of the health care services are private and consist of both formal and informal health care providers. Infectious disease surveillance data are regularly collected from governmental and qualified private facilities, but not from the informal sector. This study describes a mobile-based syndromic surveillance system and its application in a resource-limited setting, collecting data on patients’ symptoms from formal and informal health care providers.DesignThe study includes three formal and six informal health care providers from two districts of Madhya Pradesh, India. Data collectors were posted in the clinics during the providers’ working hours and entered patient information and infectious disease symptoms on the mobile-based syndromic surveillance system.ResultsInformation on 20,424 patients was collected in the mobile-based surveillance system. The five most common (overlapping) symptoms were fever (48%), cough (38%), body ache (38%), headache (37%), and runny nose (22%). During the same time period, the government's disease surveillance program reported around 22,000 fever cases in one district as a whole. Our data – from a very small fraction of all health care providers – thus highlight an enormous underreporting in the official surveillance data, which we estimate here to capture less than 1% of the fever cases. Additionally, we found that patients from more than 600 villages visited the nine providers included in our study.ConclusionsThe study demonstrated that a mobile-based system can be used for disease surveillance from formal and informal providers in resource-limited settings. People who have not used smartphones or even computers previously can, in a short timeframe, be trained to fill out surveillance forms and submit them from the device. Technology, including network connections, works sufficiently for disease surveillance applications in rural parts of India. The data collected may be used to better understand the health-seeking behaviour of those visiting informal providers, as they do not report through any official channels. We also show that the underreporting to the government can be enormous.

Matching HIV, Tuberculosis, Viral Hepatitis, and Sexually Transmitted Diseases Surveillance Data, 2000-2010

In 2012, the New York City Department of Health and Mental Hygiene matched HIV, tuberculosis, viral hepatitis, and sexually transmitted disease surveillance data to identify the burden of infection with multiple diseases. HIV, tuberculosis, hepatitis B, hepatitis C, chlamydia, gonorrhea, and syphilis surveillance data from 2000 to 2010 were matched using a deterministic method. Data on deaths from the Department of Health and Mental Hygiene's Office of Vital Statistics were also matched. The final data set contained 840,248 people; 13% had 2 or more diseases. People with a report of syphilis had the highest proportion of matches with other diseases (64%), followed by gonorrhea (52%), HIV (31%), tuberculosis (23%), hepatitis C (20%), chlamydia (16%), and hepatitis B (11%). The findings indicate several possible infectious disease syndemics in New York City and highlight the need to integrate surveillance data from different infectious disease programs. Conducting the match brought surveillance programs together to work collaboratively and has resulted in ongoing partnerships on programmatic activities that address multiple diseases.

Power-law models for infectious disease spread

Short-time human travel behaviour can be described by a power law with respect to distance. We incorporate this information in space-time models for infectious disease surveillance data to better capture the dynamics of disease spread. Two previously established model classes are extended, which both decompose disease risk additively into endemic and epidemic components: a spatio-temporal point process model for individual-level data and a multivariate time-series model for aggregated count data. In both frameworks, a power-law decay of spatial interaction is embedded into the epidemic component and estimated jointly with all other unknown parameters using (penalised) likelihood inference. Whereas the power law can be based on Euclidean distance in the point process model, a novel formulation is proposed for count data where the power law depends on the order of the neighbourhood of discrete spatial units. The performance of the new approach is investigated by a reanalysis of individual cases of invasive meningococcal disease in Germany (2002-2008) and count data on influenza in 140 administrative districts of Southern Germany (2001-2008). In both applications, the power law substantially improves model fit and predictions, and is reasonably close to alternative qualitative formulations, where distance and order of neighbourhood, respectively, are treated as a factor. Implementation in the R package surveillance allows the approach to be applied in other settings.

Prospective analysis of infectious disease surveillance data using syndromic information

In this paper, we describe a Bayesian hierarchical Poisson model for the prospective analysis of data for infectious diseases. The proposed model consists of two components. The first component describes the behavior of disease during nonepidemic periods and the second component represents the increase in disease counts due to the presence of an epidemic. A novelty of our model formulation is that the parameters describing the spread of epidemics are allowed to vary in both space and time. We also show how syndromic information can be incorporated into the model to provide a better description of the data and more accurate one-step-ahead forecasts. These real-time forecasts can be used to identify high-risk areas for outbreaks and, consequently, to develop efficient targeted surveillance. We apply the methodology to weekly emergency room discharges for acute bronchitis in South Carolina.