ObjectiveResearch studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions:Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10).ConclusionsThe literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.

Pasteuria penetrans is a gram-positive, endospore-forming eubacterium that apparently is a member of the Bacillus-Clostridium clade. It is an obligate parasite of root knot nematodes (Meloidogyne spp.) and preferentially grows on the developing ovaries, inhibiting reproduction. Root knot nematodes are devastating root pests of economically important crop plants and are difficult to control. Consequently, P. penetrans has long been recognized as a potential biocontrol agent for root knot nematodes, but the fastidious life cycle and the obligate nature of parasitism have inhibited progress on mass culture and deployment. We are currently sequencing the genome of the Pasteuria bacterium and have performed amino acid level analyses of 33 bacterial species (including P. penetrans) using concatenation of 40 housekeeping genes, with and without insertions/deletions (indels) removed, and using each gene individually. By application of maximum-likelihood, maximum-parsimony, and Bayesian methods to the resulting data sets, P. penetrans was found to cluster tightly, with a high level of confidence, in the Bacillus class of the gram-positive, low-G؉C-content eubacteria. Strikingly, our analyses identified P. penetrans as ancestral to Bacillus spp. Additionally, all analyses revealed that P. penetrans is surprisingly more closely related to the saprophytic extremophile Bacillus haladurans and Bacillus subtilis than to the pathogenic species Bacillus anthracis and Bacillus cereus. Collectively, these findings strongly imply that P. penetrans is an ancient member of the Bacillus group. We suggest that P. penetrans may have evolved from an ancient symbiotic bacterial associate of nematodes, possibly as the root knot nematode evolved to be a highly specialized parasite of plants.Pasteuria penetrans is an endospore-forming, gram-positive, obligate parasitic bacterium of the root knot nematodes (RKN), Meloidogyne spp. RKN have a very broad host range, which includes more than 2,000 plant species, and most cultivated crops are attacked by at least one species of Meloidogyne (33); this causes economic losses of more than $50 billion per year. The problem in the subtropics and tropics is particularly severe, and many developing nations are seriously affected in terms of both food security and economics by RKN. Mature female RKN release hundreds of eggs into a proteinaceous matrix on the surface of the root. Following a first molt in the egg, a motile second-stage (J2) juvenile hatches in the soil and typically reinfects the same plant. The RKN J2 destructively penetrates the root, preferentially in the zone of elongation or at the site of lateral root emergence, and migrates intercellularly into the vascular cylinder, causing little or no injury. Once in the vascular cylinder, the nematode makes a commitment to establish a highly specialized feeding site, referred to as a giant cell. The relationship between an RKN and its host is both intimate and complex and involves dramatic changes both in the plant and in the nematode, leading t...

This population-based, retrospective study examined the susceptibility of a prosimian primate, Coquerel's sifaka (Propithecus coquereli), to Cryptosporidium spp. over a 9-yr period from 1999 to 2007 at the Duke Lemur Center (DLC) located in Durham, North Carolina. The investigation examined potential epidemiologic risk factors that could be correlated to infectious outbreaks at the center, such as prevalence, signalment (species, age, and sex), seasonality of occurrence, recurrence rate, family lineage, parturition, clinical signs, and concurrent diseases or health conditions. Findings included Propithecus spp. being the only lemur species at the DLC showing clinical signs of infection, with age being an important factor in susceptibility, and showing a strong correlation between temperature and seasonality with shedding of Cryptosporidium oocysts. These findings present new information regarding cryptosporidiosis in captive prosimians.

Military populations present a small, unique community whose mental and physical health impacts the security of the nation. Recent literature has explored social media's ability to enhance disease surveillance and characterize distinct communities with encouraging results. We present a novel analysis of the relationships between influenza-like illnesses (ILI) clinical data and affects (i.e., emotions and sentiments) extracted from social media around military facilities. Our analyses examine (1) differences in affects expressed by military and control populations, (2) affect changes over time by users, (3) differences in affects expressed during high and low ILI seasons, and (4) correlations and cross-correlations between ILI clinical visits and affects from an unprecedented scale -171M geo-tagged tweets across 31 global geolocations. Key findings include: Military and control populations differ in the way they express affects in social media over space and time. Control populations express more positive and less negative sentiments and less sadness, fear, disgust, and anger emotions than military. However, affects expressed in social media by both populations within the same area correlate similarly with ILI visits to military health facilities. We have identified potential responsible cofactors leading to location variability, e.g., region or state locale, military service type and/or the ratio of military to civilian populations. For most locations, ILI proportions positively correlate with sadness and neutral sentiment, which are the affects most often expressed during high ILI season. The ILI proportions negatively correlate with fear, disgust, surprise, and positive sentiment. These results are similar to the low ILI season where anger, surprise, and positive sentiment are highest. Finally, cross-correlation analysis shows that most affects lead ILI clinical visits, i.e. are predictive of ILI data, with affect-ILI leading intervals dependent on geolocation and affect type. Overall, information gained in this study exemplifies a usage of social media data to understand the correlation between psychological behavior and health in the military population and the potential for use of social media affects for prediction of ILI cases.

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

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Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01–4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00–0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.