Spatial and Temporal Comparison of Perceived Risks and Confirmed Cases of Lyme Disease: An Exploratory Study of Google Trends

Models and data used to predict the abundance and distribution of Ixodes scapularis (blacklegged tick) in North America: a scoping review

Tick-borne diseases (TBD) remain prevalent worldwide, and risk assessment of tick habitat suitability is crucial to prevent or reduce their burden. This scoping review provides a comprehensive survey of models and data used to predict I. scapularis distribution and abundance in North America. We identified 4661 relevant primary research articles published in English between January 1st, 2012, and July 18th, 2022, and selected 41 articles following full-text review. Models used data-driven and mechanistic modelling frameworks informed by diverse tick, hydroclimatic, and ecological variables. Predictions captured tick abundance (n = 14, 34.1%), distribution (n = 22, 53.6%) and both (n = 5, 12.1%). All studies used tick data, and many incorporated both hydroclimatic and ecological variables. Minimal host- and human-specific data were utilized. Biases related to data collection, protocols, and tick data quality affect completeness and representativeness of prediction models. Further research and collaboration are needed to improve prediction accuracy and develop effective strategies to reduce TBD.

Challenges and Opportunities in One Health: Google Trends Search Data

Google Trends data can be informative for zoonotic disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we demonstrate the potential to use Google Trends for zoonotic disease prediction by predicting monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010-2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms to Lyme disease. For each search term, we built an expanding window negative binomial model that adjusted for seasonal differences using a lag term. Performance was measured by Root Mean Squared Errors (RMSEs) and the visual associations between observed and predicted case counts. The highest performing model had excellent predictive ability in some states, but performance varied across states. The highest performing models were for Lyme disease search terms, which indicates the high specificity of search terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data for One Health research, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data. Lastly, we recommend that Google Trends be explored as an option for predicting other zoonotic diseases and incorporate other data streams that may improve predictive performance.

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

BACKGROUND

Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data.

OBJECTIVE

We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets.

METHODS

Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers.

RESULTS

The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classification accuracy of 94.1% (4% increase), and ALBERT had an increased F1-score of 93.1% (5% increase) and classification accuracy of 93.9% (5% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50% (9939/20,000) of the collected English-language tweets, whereas Lyme disease-related tweets were rare in countries from Africa and Asia. The most reported Lyme disease-related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency.

CONCLUSIONS

The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives.

Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter

BACKGROUND

Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than [Formula: see text] of all vector-borne diseases in North America.

OBJECTIVE

In this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US.

METHODS

The study was done in three stages: (1) Approximately 1.3 million tweets were collected and pre-processed to extract the most relevant Lyme disease tweets with geolocations. A subset of tweets were semi-automatically labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, and the remaining portion were manually labelled, yielding a curated labelled dataset of 77, 500 tweets. (2) This labelled data set was used to train, validate, and test various combinations of NLP word embedding methods and prominent ML classification models, such as TF-IDF and logistic regression, Word2vec and XGboost, and BERTweet, among others, to identify potential Lyme disease tweets. (3) Lastly, the presence of spatio-temporal patterns in the US over a 10-year period were studied.

RESULTS

Preliminary results showed that BERTweet outperformed all tested NLP classifiers for identifying Lyme disease tweets, achieving the highest classification accuracy and F1-score of [Formula: see text]. There was also a consistent pattern indicating that the West and Northeast regions of the US had a higher tweet rate over time.

CONCLUSIONS

We focused on the less-studied problem of using Twitter data as a surveillance tool for Lyme disease in the US. Several crucial findings have emerged from the study. First, there is a fairly strong correlation between classified tweet counts and Lyme disease counts, with both following similar trends. Second, in 2015 and early 2016, the social media network like Twitter was essential in raising popular awareness of Lyme disease. Third, counties with a high incidence rate were not necessarily related with a high tweet rate, and vice versa. Fourth, BERTweet can be used as a reliable NLP classifier for detecting relevant Lyme disease tweets.

Tick-Bite "Meteo"-Prevention: An Evaluation of Public Responsiveness to Tick Activity Forecasts Available Online

Until causal prophylaxis is available, the avoidance of ticks and personal protection provide the best insurance against contracting a tick-borne disease (TBD). To support public precaution, tick-activity forecasts (TAFs) based on weather projection are provided online for some regions/countries. This study-aimed at evaluating the efficacy of this preventative strategy-was conducted between 2015 and 2019, and included two countries where TAFs are issued regularly (Czech Republic, Germany) and two neighbouring countries for reference (Austria, Switzerland). Google Trends (GT) data were used to trace public concern with TAFs and related health information. GTs were compared with epidemiological data on TBD cases and tick bites, wherever available. Computer simulations of presumable effectiveness under various scenarios were performed. This study showed that public access to TAFs/preventive information is infrequent and not optimally distributed over the season. Interest arises very early in midwinter and then starts to fall in spring/summer when human-tick contacts culminate. Consequently, a greater number of TBD cases are contracted beyond the period of maximum public responsiveness to prevention guidance. Simulations, nevertheless, indicate that there is a potential for doubling the prevention yield if risk assessment, in addition to tick activity, subsumes the population's exposure, and a real-time surrogate is proposed.

Improving Surveillance of Human Tick-Borne Disease Risks: Spatial Analysis Using Multimodal Databases

BACKGROUND

The extent of tick-borne disease (TBD) risk in the United States is generally unknown. Active surveillance using entomological measures, such as presence and density of infected nymphal Ixodes scapularis ticks, have served as indicators for assessing human risk, but results have been inconsistent and passive surveillance via public health systems suggests TBDs are underreported.

OBJECTIVE

Research using various data sources and collection methods (eg, Google Trends, apps, and tick bite encounters [TBEs] reports) has shown promise for assessing human TBD risk. In that vein, and engaging a One Health perspective, this study used multimodal databases, geographically overlaying patient survey data on TBEs and concomitant reports of TBDs with data drawn from other sources, such as canine serological reports, to glean insights and to determine and assess the use of various indicators as proxies for human TBD risk.

METHODS

This study used a mixed methods research strategy, relying on triangulation techniques and drawing on multiple data sources to provide insights into various aspects of human disease risk from TBEs and TBDs in the United States. A web-based survey was conducted over a 15-month period beginning in December 2020 to collect data on TBEs. To maximize the value of the covariate data, related analyses included TBE reports that occurred in the United States between January 1, 2000, and March 31, 2021. TBEs among patients diagnosed with Lyme disease were analyzed at the county level and compared to I scapularis and I pacificus tick presence, human cases identified by the Centers for Disease Control and Prevention (CDC), and canine serological data. Spatial analyses employed multilayer thematic mapping and other techniques.

RESULTS

After cleaning, survey results showed a total of 249 (75.7%) TBEs spread across 148 respondents (61.9% of all respondents, 81.7% of TBE-positive respondents); 144 (4.7%) counties in 30 states (60%) remained eligible for analysis, with an average of 1.68 (SD 1.00) and median of 1 (IQR 1) TBEs per respondent. Analysis revealed significant spatial matching at the county level among patient survey reports of TBEs and disease risk indicators from the CDC and other official sources. Thematic mapping results included one-for-one county-level matching of reported TBEs with at least 1 designated source of human disease risk (ie, positive canine serological tests, CDC-reported Lyme disease, or known tick presence).

CONCLUSIONS

Use of triangulation methods to integrate patient data on TBE recall with established canine serological reports, tick presence, and official human TBD information offers more granular, county-level information regarding TBD risk to inform clinicians and public health officials. Such data may supplement public health sources to offer improved surveillance and provide bases for developing robust proxies for TBD risk among humans.

Interference with LTβR signaling by tick saliva facilitates transmission of Lyme disease spirochetes

Lyme spirochetes have coevolved with ticks to optimize transmission to hosts using tick salivary molecules (TSMs) to counteract host defenses. TSMs modulate various molecular events at the tick-host interface. Lymphotoxin-beta receptor (LTβR) is a vital immune receptor and plays protective roles in host immunity against microbial infections. We found that Ltbr knockout mice were more susceptible to Lyme disease spirochetes, suggesting the involvement of LTβR signaling in tick-borne Borrelia infection. Further investigation showed that a 15-kDa TSM protein from Ixodes persulcatus (I. persulcatus salivary protein; IpSAP) functioned as an immunosuppressant to facilitate the transmission and infection of Lyme disease spirochetes. IpSAP directly interacts with LTβR to block its activation, thus inhibiting the downstream signaling and consequently suppressing immunity. IpSAP immunization provided mice with significant protection against I. persulcatus-mediated Borrelia garinii infection. Notably, the immunization showed considerable cross-protection against other Borrelia infections mediated by other ixodid ticks. One of the IpSAP homologs from other ixodid ticks showed similar effects on Lyme spirochete transmission. Together, our findings suggest that LTβR signaling plays an important role in blocking the transmission and pathogenesis of tick-borne Lyme disease spirochetes, and that IpSAP and its homologs are promising candidates for broad-spectrum vaccine development.

Infodemiology on diet and weight loss behavior before and during COVID-19 pandemic in Indonesia: Implication for public health promotion

Objective

This study set out to explore public interest through information search trends on diet and weight loss before and during the COVID-19 pandemic in Indonesia.

Methods

The Google Trends database was evaluated for the relative internet search popularity on diet-related search terms, including top and rising diet-related terms. The search range was before and during the COVID-19 pandemic (April 2018 to January 2022) in the Indonesia region. We analyzed the Relative Search Volume (RSV) data using line charts, correlation, and comparison tests.

Results

Search queries of “lose weight” was higher during the pandemic (58.34 ± 9.70 vs. 68.69 ± 7.72; p<0.05). No difference was found in diet-related searches before and after the pandemic. Public interest in the diet was higher after Eid al-Fitr (Muslims break fasting celebration day) and after the new year. Many fad diet (FD) terms were found on the top and rising terms.

Conclusion

After Eid al-Fitr and the new year were susceptible times for promoting a healthy diet in Indonesia. Potential need found before those times for education in inserting healthy food among fatty and sugary menus related to holidays and celebrations. Higher interest in “lose weight” was relevant to heightened obesity risk during the social restriction and heightened COVID-19 morbidity and mortality due to obesity. The high interest for rapid weight loss through FD needs to be resolved by promoting healthy diets with a more captivating message and messenger, like consistently using top terms in the keywords of the official healthy diet guidance. Future research could explore the relationship between diet and other behavior or with non-communicable diseases.