Seasonal and Temporal Trends in Childhood Conjunctivitis in Burkina Faso

Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study

BACKGROUND

Previous work suggests that Google searches could be useful in identifying conjunctivitis epidemics. Content-based assessment of social media content may provide additional value in serving as early indicators of conjunctivitis and other systemic infectious diseases.

OBJECTIVE

We investigated whether large language models, specifically GPT-3.5 and GPT-4 (OpenAI), can provide probabilistic assessments of whether social media posts about conjunctivitis could indicate a regional outbreak.

METHODS

A total of 12,194 conjunctivitis-related tweets were obtained using a targeted Boolean search in multiple languages from India, Guam (United States), Martinique (France), the Philippines, American Samoa (United States), Fiji, Costa Rica, Haiti, and the Bahamas, covering the time frame from January 1, 2012, to March 13, 2023. By providing these tweets via prompts to GPT-3.5 and GPT-4, we obtained probabilistic assessments that were validated by 2 human raters. We then calculated Pearson correlations of these time series with tweet volume and the occurrence of known outbreaks in these 9 locations, with time series bootstrap used to compute CIs.

RESULTS

Probabilistic assessments derived from GPT-3.5 showed correlations of 0.60 (95% CI 0.47-0.70) and 0.53 (95% CI 0.40-0.65) with the 2 human raters, with higher results for GPT-4. The weekly averages of GPT-3.5 probabilities showed substantial correlations with weekly tweet volume for 44% (4/9) of the countries, with correlations ranging from 0.10 (95% CI 0.0-0.29) to 0.53 (95% CI 0.39-0.89), with larger correlations for GPT-4. More modest correlations were found for correlation with known epidemics, with substantial correlation only in American Samoa (0.40, 95% CI 0.16-0.81).

CONCLUSIONS

These findings suggest that GPT prompting can efficiently assess the content of social media posts and indicate possible disease outbreaks to a degree of accuracy comparable to that of humans. Furthermore, we found that automated content analysis of tweets is related to tweet volume for conjunctivitis-related posts in some locations and to the occurrence of actual epidemics. Future work may improve the sensitivity and specificity of these methods for disease outbreak detection.

Deep sequencing analysis of clinical samples from patients with acute infectious conjunctivitis during the COVID-19 delta surge in Madurai, India

BACKGROUND

Seasonal outbreaks of infectious conjunctivitis remain a public health issue. Determination of outbreak etiologies in the context of a worldwide pandemic may provide useful information to guide public health strategies. The aim of this study was to identify pathogens associated with outpatient infectious conjunctivitis during the COVID-19 Delta surge.

METHODS

This prospective study was conducted from April 2021 to September 2021. All outpatients presenting to the Aravind Eye Center (Madurai, India) with signs and symptoms consistent with acute infectious conjunctivitis were eligible. Three swabs were obtained from each participant: one from each conjunctiva and one from the anterior nares. Samples were processed for metagenomic RNA deep sequencing (RNA-seq).

RESULTS

Samples from 106 study participants were sequenced. The most common presenting symptoms were tearing (86%) and itching (71%). Preauricular lymphadenopathy was present in 38% of participants. 20% of participants had close contacts with similar symptoms. Systemic symptoms such as coughing, runny nose, vomiting or diarrhea were uncommonly reported. 60% of all participants used some medicated eye drops upon enrollment. 75% of study participants demonstrated infection with human adenovirus D (HAdV-D). 11% of conjunctivitis was associated with SARS-CoV-2. 15% had no definitive pathogen detected. 8% of all participants had codetection of more than one pathogen on RNA-seq.

CONCLUSIONS

During the COVID-19 Delta surge in India, HAdV-D was the most common pathogen associated with infectious conjunctivitis. SARS-CoV-2 was the second most common associated pathogen. Seasonal surveillance may be necessary for the determination of emerging and reemerging pathogens responsible for infectious conjunctivitis.

A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study

BACKGROUND

Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients' eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations.

OBJECTIVE

To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other.

METHODS

We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google's search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant.

RESULTS

Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, "pink eye" showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, "dry eyes" had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning.

CONCLUSIONS

The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions.

Deep sequencing analysis of acute conjunctivitis in Burkina Faso, Africa

BACKGROUND

Seasonal and epidemic conjunctivitis (pink eye) infections are highly contagious and impose a significant economic burden worldwide. Long-term visual impairment can occur.

METHODS

This study used metagenomic deep sequencing to evaluate pathogens causing acute infectious conjunctivitis in Burkina Faso.

RESULTS

We found that pathogens causing conjunctivitis in Burkina Faso are diverse, with human adenoviruses responsible for a small fraction of the samples tested.

CONCLUSIONS

These results are unexpected and suggest the importance of regional surveillance.

Keeping an eye on pink eye: a global conjunctivitis outbreak expert survey

BACKGROUND

Recurrent conjunctivitis epidemics are prevalent worldwide. Aetiologies are often undetermined.

METHODS

We surveyed conjunctivitis researchers about perceived trends in prevalence, incidence and aetiologies of conjunctivitis epidemics.

RESULTS

Of the 155 participants, 7% endorsed globally variable and dynamic microbial aetiologies of conjunctivitis epidemics. Increased incidence of conjunctivitis epidemics over the last decade were reported by 21% of respondents. Peak seasons differed between the northern and southern hemispheres.

CONCLUSIONS

There is regional equipoise regarding the increasing incidence and emerging underlying aetiologies of epidemic conjunctivitis. Further investigation of global surveillance and microbial characterization of conjunctivitis outbreaks could improve prevention and outcomes.

Google Searches and Detection of Conjunctivitis Epidemics Worldwide

PURPOSE

Epidemic and seasonal infectious conjunctivitis outbreaks can impact education, workforce, and economy adversely. Yet conjunctivitis typically is not a reportable disease, potentially delaying mitigating intervention. Our study objective was to determine if conjunctivitis epidemics could be identified using Google Trends search data.

DESIGN

Search data for conjunctivitis-related and control search terms from 5 years and countries worldwide were obtained. Country and term were masked. Temporal scan statistics were applied to identify candidate epidemics. Candidates then were assessed for geotemporal concordance with an a priori defined collection of known reported conjunctivitis outbreaks, as a measure of sensitivity.

PARTICIPANTS

Populations by country that searched Google's search engine using our study terms.

MAIN OUTCOME MEASURES

Percent of known conjunctivitis outbreaks also found in the same country and period by our candidate epidemics, identified from conjunctivitis-related searches.

RESULTS

We identified 135 candidate conjunctivitis epidemic periods from 77 countries. Compared with our a priori defined collection of known reported outbreaks, candidate conjunctivitis epidemics identified 18 of 26 (69% sensitivity) of the reported country-wide or island nationwide outbreaks, or both; 9 of 20 (45% sensitivity) of the reported region or district-wide outbreaks, or both; but far fewer nosocomial and reported smaller outbreaks. Similar overall and individual sensitivity, as well as specificity, were found on a country-level basis. We also found that 83% of our candidate epidemics had start dates before (of those, 20% were more than 12 weeks before) their concurrent reported outbreak's report issuance date. Permutation tests provided evidence that on average, conjunctivitis candidate epidemics occurred geotemporally closer to outbreak reports than chance alone suggests (P < 0.001) unlike control term candidates (P = 0.40).

CONCLUSIONS

Conjunctivitis outbreaks can be detected using temporal scan analysis of Google search data alone, with more than 80% detected before an outbreak report's issuance date, some as early as the reported outbreak's start date. Future approaches using data from smaller regions, social media, and more search terms may improve sensitivity further and cross-validate detected candidates, allowing identification of candidate conjunctivitis epidemics from Internet search data potentially to complementarily benefit traditional reporting and detection systems to improve epidemic awareness.