Environmental risk factors in infectious diseases: studies in waterborne disease outbreaks, Ebola, and Lyme disease

Thesis (Ph.D.)--Boston UniversityThe resurgence of infectious diseases and global climate change's potential impact on them has refocused public health's attention on the environment's role in infectious disease. The studies in this dissertation utilize the increased availability of satellite image-derived data sets with fine temporal and geographic granularity and the expansion of epidemiologic methods to explore the relationship between the environment and infectious disease in three settings. The first study employed a novel study design and analytic methods to investigate the hypothesis that heavy rainfall is an independent risk factor for waterborne disease outbreaks (WBDOs). We found that a location experiencing a heavy rainfall event had about half the odds of a WBDO two or four weeks later than did a location without a heavy rainfall event. The location-based case-crossover study design utilized in this study may help to expand the research methods available to epidemiologists working in this developing field. The second study employed a location-based case-crossover study design to evaluate standardized differences from historic average of weekly rainfall in locations with a recorded introduction of Ebola into a human. For each 1.0 unit z-score decrease in total rainfall, the odds of an Ebola introduction three weeks later increased by 75%. Given the severity of Ebola outbreaks and the dearth of knowledge about indicators of increased risk, this finding is an important step in advancing our understanding of Ebola ecology. The third study used GIS methods on remote sensing data to estimate the association between peridomestic forest/non-forest interface within 100, 150, 250 meters and Lyme-associated peripheral facial palsy (LAPFP) among pediatric facial palsy patients. After adjustment for sex, age, and socio-economic status, children with the highest level of forest edge in the three radii of analysis had 2.74 (95% CI 1.15, 6.53), 4.58 (1.84, 11.41), and 5.88 (2.11, 16.4) times the odds of LAPFP compared to children with zero forest edge in those radii. This study is the first to examine environmental risk factors for LAPFP. Each of these studies advances the techniques used to investigate environmental risk factors for infectious disease through study design, case definition, data used, or exposure definitions

The role of regional surveillance networks in enhancing global outbreak reporting

BACKGROUND: The Program for Monitoring Emerging Diseases (ProMED) is a moderated electronic reporting system dedicated to the rapid, global dissemination of outbreak reports. Its moderators are globally diverse, carefully selected, highly trained specialists. To improve cross-border communication and rapidly identify regional health threats, ProMED created regional networks where locally-based moderators use their access to local and regional medical and public health networks and media sources to obtain information not readily available outside of their region. In this analysis, we assess the impact of the establishment of ProMED's Middle East/North Africa (MENA) and South Asia (SoAs) regional networks in April 2014 on ProMED's outbreak reports for these regions. METHODS & MATERIALS: Outbreak reports in countries within the two regions were extracted from ProMED's database, and included country, disease name, species type, spatial coordinates, and report issue date. Data analysis included visualizing spatial information, identifying unique reports, and reporting trends per country and region. Data processing and analysis were conducted using R 3.4.0 statistical software. Rates of outbreak events per total number of ProMED reports per year were calculated to adjust for temporal trends in the total number of reports posted on ProMED. Rate comparison used a two-sided t-test; P < 0.05 was considered statistically significant. RESULTS: The mean monthly incidence of ProMED reports concerning outbreaks in the MENA region increased from 28 reports (May 2012 - April 2014) to 83 reports after the establishment of the networks (May 2014 - April 2016), and from 29 reports to 101 reports concerning outbreaks in the SoAs region over the same time period. The number of reports per total number of ProMED reports increased by 259% for MENA, and 289% for SoAs (P < 0.01). MENA reports most often addressed MERS (32.3%), foot-and-mouth disease (7.0%), avian influenza (6.7%), and measles (3.8%); whereas SoAs most often addressed dengue (14.9%), anthrax (7.3%), Japanese encephalitis (7.0%), CCHF (4.9%), and rabies (4.8%). CONCLUSION: The establishment of MENA and SoAs regional networks with locally-based, expert moderators resulted in a significant increase in ProMED's outbreak reports from these regions and an increased flow of disease information across regional borders and to the global public health community

Digital Surveillance: A Novel Approach to Monitoring the Illegal Wildlife Trade

A dearth of information obscures the true scale of the global illegal trade in wildlife. Herein, we introduce an automated web crawling surveillance system developed to monitor reports on illegally traded wildlife. A resource for enforcement officials as well as the general public, the freely available website, http://www.healthmap.org/wildlifetrade, provides a customizable visualization of worldwide reports on interceptions of illegally traded wildlife and wildlife products. From August 1, 2010 to July 31, 2011, publicly available English language illegal wildlife trade reports from official and unofficial sources were collected and categorized by location and species involved. During this interval, 858 illegal wildlife trade reports were collected from 89 countries. Countries with the highest number of reports included India (n = 146, 15.6%), the United States (n = 143, 15.3%), South Africa (n = 75, 8.0%), China (n = 41, 4.4%), and Vietnam (n = 37, 4.0%). Species reported as traded or poached included elephants (n = 107, 12.5%), rhinoceros (n = 103, 12.0%), tigers (n = 68, 7.9%), leopards (n = 54, 6.3%), and pangolins (n = 45, 5.2%). The use of unofficial data sources, such as online news sites and social networks, to collect information on international wildlife trade augments traditional approaches drawing on official reporting and presents a novel source of intelligence with which to monitor and collect news in support of enforcement against this threat to wildlife conservation worldwide

Modeling to Predict Cases of Hantavirus Pulmonary Syndrome in Chile

Background: Hantavirus pulmonary syndrome (HPS) is a life threatening disease transmitted by the rodent Oligoryzomys longicaudatus in Chile. Hantavirus outbreaks are typically small and geographically confined. Several studies have estimated risk based on spatial and temporal distribution of cases in relation to climate and environmental variables, but few have considered climatological modeling of HPS incidence for monitoring and forecasting purposes. Methodology Monthly counts of confirmed HPS cases were obtained from the Chilean Ministry of Health for 2001–2012. There were an estimated 667 confirmed HPS cases. The data suggested a seasonal trend, which appeared to correlate with changes in climatological variables such as temperature, precipitation, and humidity. We considered several Auto Regressive Integrated Moving Average (ARIMA) time-series models and regression models with ARIMA errors with one or a combination of these climate variables as covariates. We adopted an information-theoretic approach to model ranking and selection. Data from 2001–2009 were used in fitting and data from January 2010 to December 2012 were used for one-step-ahead predictions. Results: We focused on six models. In a baseline model, future HPS cases were forecasted from previous incidence; the other models included climate variables as covariates. The baseline model had a Corrected Akaike Information Criterion (AICc) of 444.98, and the top ranked model, which included precipitation, had an AICc of 437.62. Although the AICc of the top ranked model only provided a 1.65% improvement to the baseline AICc, the empirical support was 39 times stronger relative to the baseline model. Conclusions: Instead of choosing a single model, we present a set of candidate models that can be used in modeling and forecasting confirmed HPS cases in Chile. The models can be improved by using data at the regional level and easily extended to other countries with seasonal incidence of HPS

Surveillance for Neisseria meningitidis Disease Activity and Transmission Using Information Technology

Background While formal reporting, surveillance, and response structures remain essential to protecting public health, a new generation of freely accessible, online, and real-time informatics tools for disease tracking are expanding the ability to raise earlier public awareness of emerging disease threats. The rationale for this study is to test the hypothesis that the HealthMap informatics tools can complement epidemiological data captured by traditional surveillance monitoring systems for meningitis due to Neisseria meningitides (N. meningitides) by highlighting severe transmissible disease activity and outbreaks in the United States. Methods Annual analyses of N. meningitides disease alerts captured by HealthMap were compared to epidemiological data captured by the Centers for Disease Control’s Active Bacterial Core surveillance (ABCs) for N. meningitides. Morbidity and mortality case reports were measured annually from 2010 to 2013 (HealthMap) and 2005 to 2012 (ABCs). Findings HealthMap N. meningitides monitoring captured 80-90% of alerts as diagnosed N. meningitides, 5-20% of alerts as suspected cases, and 5-10% of alerts as related news articles. HealthMap disease alert activity for emerging disease threats related to N. meningitides were in agreement with patterns identified historically using traditional surveillance systems. HealthMap’s strength lies in its ability to provide a cumulative “snapshot” of weak signals that allows for rapid dissemination of knowledge and earlier public awareness of potential outbreak status while formal testing and confirmation for specific serotypes is ongoing by public health authorities. Conclusions The underreporting of disease cases in internet-based data streaming makes inadequate any comparison to epidemiological trends illustrated by the more comprehensive ABCs network published by the Centers for Disease Control. However, the expected delays in compiling confirmatory reports by traditional surveillance systems (at the time of writing, ABCs data for 2013 is listed as being provisional) emphasize the helpfulness of real-time internet-based data streaming to quickly fill gaps including the visualization of modes of disease transmission in outbreaks for better resource and action planning. HealthMap can also contribute as an internet-based monitoring system to provide real-time channel for patients to report intervention-related failures.National Library of Medicine (U.S.) (Grant 5 R01 LM010812-04

Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study

Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends

Analisis Proses Seleksi Tenaga Kerja Di De Boliva Café Surabaya Town Square

Penelitian ini dilakukan di De Boliva Café Surabaya Town Square. Tujuan dalam penelitian ini adalah untuk mengetahui proses seleksi tenaga kerja. Teknik analisis yang digunakan dalam penelitian ini adalah analisis kualitatif deksriptif. Hasil analisis menunjukkan bahwa proses seleksi tenaga kerja di De Boliva adalah seleksi curriculum vitae (CV) beserta surat lamaran, tes tulis, wawancara video, dan wawancara akhir