Explore the vast range of titles available.

The ongoing Zika virus (ZIKV) epidemic in the Americas poses a major global public health emergency. While ZIKV is transmitted from human to human by bites of Aedes mosquitoes, recent evidence indicates that ZIKV can also be transmitted via sexual contact with cases of sexually transmitted ZIKV reported in Argentina, Canada, Chile, France, Italy, New Zealand, Peru, Portugal, and the USA. Yet, the role of sexual transmission on the spread and control of ZIKV infection is not well-understood. We introduce a mathematical model to investigate the impact of mosquito-borne and sexual transmission on the spread and control of ZIKV and calibrate the model to ZIKV epidemic data from Brazil, Colombia, and El Salvador. Parameter estimates yielded a basic reproduction number 0  = 2.055 (95% CI: 0.523–6.300), in which the percentage contribution of sexual transmission is 3.044% (95% CI: 0.123–45.73). Our sensitivity analyses indicate that 0 is most sensitive to the biting rate and mortality rate of mosquitoes while sexual transmission increases the risk of infection and epidemic size and prolongs the outbreak. Prevention and control efforts against ZIKV should target both the mosquito-borne and sexual transmission routes.

About the Authors: Matthew R. Behrend * E-mail: behrend04@gmail.com Affiliations Neglected Tropical Diseases, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America, Blue Well 8, Seattle, Washington, United States of America ORCID logo http://orcid.org/0000-0002-5664-0520 María-Gloria Basáñez Affiliation: MRC Centre for Global Infectious Disease Analysis and London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom Jonathan I. D. Hamley Affiliation: MRC Centre for Global Infectious Disease Analysis and London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom Travis C. Porco Affiliation: Francis I. Proctor Foundation for Research in Ophthalmology, Department of Epidemiology and Biostatistics, and Department of Ophthalmology, University of California, San Francisco, United States of America Wilma A. Stolk Affiliation: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands Martin Walker Affiliations London Centre for Neglected Tropical Disease Research, Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, Hertfordshire, United Kingdom, London Centre for Neglected Tropical Disease Research and Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom Sake J. de Vlas Affiliation: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands ORCID logo http://orcid.org/0000-0002-1830-5668 for the NTD Modelling Consortium Introduction The neglected tropical diseases (NTDs) thrive mainly among the poorest populations of the world. Onchocerciasis (a filarial disease caused by infection with Onchocerca volvulus and transmitted by blackfly, Simulium, vectors) probably provides the best example of impactful modelling, with its long history of using evidence—mostly from the ONCHOSIM and EPIONCHO transmission models [7]—to support decision-making within ongoing multicountry control initiatives (Table 1). Onchocerciasis modelling and policy impact. https://doi.org/10.1371/journal.pntd.0008033.t001 From the start of the NTD Modelling Consortium in 2015, there have been several other examples of impactful modelling, which could be divided over three major scales of operations: (1) developing WHO guidelines (e.g., for triple-drug therapy, with ivermectin, diethylcarbamazine, and albendazole, against lymphatic filariasis [16, 17]); (2) informing funding decisions for new intervention tools (e.g., the development of a schistosomiasis vaccine [18]); and (3) guiding within-country targeting of control (e.g., local vector control for human African trypanosomiasis in the Democratic Republic of the Congo [19, 20] and Chad [21]). Relative word frequencies are represented by size of the font. https://doi.org/10.1371/journal.pntd.0008033.g002 Scoring the guidance statements Authors coded the data set individually (MRB, TCP, WAS, SJdV) and jointly (M-GB, JIDH, MW), producing five independently coded sets of data (S1 Table).

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. 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. 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. 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). 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.

One criterion for validation of trachoma elimination is the management of Trachomatous Trichiasis (TT) after Trachoma inflammation—follicular (TF) is eliminated in children ages 1–9 years at district level. No data exist on how long countries must have dedicated TT programs, as the timeline for progression to TT from trachomatous scarring is unknown. We used eight years of longitudinal data in women in Kongwa Tanzania to model progression from no scarring (S0) through grades of scarring severity (S1–S4) to TT. Markov models were used, with age, community prevalence of TF (CPTF), and household characteristics as co-variates. Adjusted for covariates, the incidence of S1 was estimated at 4∙7% per year, and the risk increased by 26% if the CPTF was between 5–10% and by 48% if greater than 10%. The transition from S4 to TT was estimated at 2∙6% per year. Districts, even after elimination of TF, may have some communities with TF ≥ 5% and increased risk of incident scarring. Once scarring progresses to S2, further progression is not dependent on CPTF. These data suggest that, depending on the district level of scarring and degree of heterogeneity in CPTF at the time of elimination, incident TT will still be an issue for decades.

Randomized controlled trials found that twice-yearly mass azithromycin administration (MDA) reduces childhood mortality, presumably by reducing infection burden. World Health Organization (WHO) issued conditional guidelines for mass azithromycin administration in high-mortality settings in sub-Saharan Africa given concerns for antibiotic resistance. While prolonged twice-yearly MDA has been shown to increase antibiotic resistance in small randomized controlled trials, the objective of this study was to determine if macrolide and non-macrolide resistance in the gut increases with the duration of azithromycin MDA in a larger setting. The Macrolide Oraux pour Réduire les Décès avec un Oeil sur la Résistance (MORDOR) study was conducted in Niger from December 2014 to June 2020. It was a cluster-randomized trial of azithromycin (A) versus placebo (P) aimed at evaluating childhood mortality. This is a sub-study in the MORDOR trial to track changes in antibiotic resistance after prolonged azithromycin MDA. A total of 594 communities were eligible. Children 1 to 59 months in 163 randomly chosen communities were eligible to receive treatment and included in resistance monitoring. Participants, staff, and investigators were masked to treatment allocation. At the conclusion of MORDOR Phase I, by design, all communities received an additional year of twice-yearly azithromycin treatments (Phase II). Thus, at the conclusion of Phase II, the treatment history (1 letter per 6-month period) for the participating communities was either (PP-PP-AA) or (AA-AA-AA). In Phase III, participating communities were then re-randomized to receive either another 3 rounds of azithromycin or placebo, thus resulting in 4 treatment histories: Group 1 (AA-AA-AA-AA-A, N = 51), Group 2 (PP-PP-AA-AA-A, N = 40), Group 3 (AA-AA-AA-PP-P, N = 27), and Group 4 (PP-PP-AA-PP-P, N = 32). Rectal swabs from each child (N = 5,340) were obtained 6 months after the last treatment. Each child contributed 1 rectal swab and these were pooled at the community level, processed for DNA-seq, and analyzed for genetic resistance determinants. The primary prespecified outcome was macrolide resistance determinants in the gut. Secondary outcomes were resistance to beta-lactams and other antibiotic classes. Communities recently randomized to azithromycin (groups 1 and 2) had significantly more macrolide resistance determinants than those recently randomized to placebo (groups 3 and 4) (fold change 2.18, 95% CI 1.5 to 3.51, Punadj < 0.001). However, there was no significant increase in macrolide resistance in communities treated 4.5 years (group 1) compared to just the most recent 2.5 years (group 2) (fold change 0.80, 95% CI 0.50 to 1.00, Padj = 0.010), or between communities that had been treated for 3 years in the past (group 3) versus just 1 year in the past (group 4) (fold change 1.00, 95% CI 0.78 to 2.35, Padj = 0.52). We also found no significant differences for beta-lactams or other antibiotic classes. The main limitations of our study were the absence of phenotypic characterization of resistance, no complete placebo arm, and no monitoring outside of Niger limiting generalizability. In this study, we observed that mass azithromycin distribution for childhood mortality among preschool children in Niger increased macrolide resistance determinants in the gut but that resistance may plateau after 2 to 3 years of treatment. Co-selection to other classes needs to be monitored. NCT02047981 https://classic.clinicaltrials.gov/ct2/show/NCT02047981.

The World Health Organization recommends annual mass azithromycin administration in communities with at least 10% prevalence of trachomatous inflammation-follicular (TF) in children, with further treatment depending on reassessment after 3-5 years. However, the effect of stopping mass azithromycin distribution after multiple rounds of treatment is not well understood. Here, we report the results of a cluster-randomized trial where communities that had received 4 years of treatments were then randomized to continuation or discontinuation of treatment. In all, 48 communities with 3,938 children aged 0-9 years at baseline in northern Ethiopia had received 4 years of annual or twice yearly mass azithromycin distribution as part of the TANA I trial. We randomized these communities to either continuation or discontinuation of treatment. Individuals in the communities in the continuation arm were offered either annual or twice yearly distribution of a single directly observed dose of oral azithromycin. The primary outcome was community prevalence of ocular chlamydial infection in a random sample of children aged 0-9 years, 36 months after baseline. We also assessed the change from baseline to 36 months in ocular chlamydia prevalence within each arm. We compared 36-month ocular chlamydia prevalence in communities randomized to continuation versus discontinuation in a model adjusting for baseline ocular chlamydia prevalence. A secondary prespecified analysis assessed the rate of change over time in ocular chlamydia prevalence between arms. In the continuation arm, mean antibiotic coverage was greater than 90% at all time points. In the discontinuation arm, the mean prevalence of infection in children aged 0-9 years increased from 8.3% (95% CI 4.2% to 12.4%) at 0 months to 14.7% (95% CI 8.7% to 20.8%, P = 0.04) at 36 months. Ocular chlamydia prevalence in communities where mass azithromycin distribution was continued was 7.2% (95% CI 3.3% to 11.0%) at baseline and 6.6% (95% CI 1.1% to 12.0%, P = 0.64) at 36 months. The 36-month prevalence of ocular chlamydia was significantly lower in communities continuing treatment compared with those discontinuing treatment (P = 0.03). Limitations of the study include uncertain generalizability outside of trachoma hyperendemic regions. In this study, ocular chlamydia infection rebounded after 4 years of periodic mass azithromycin distribution. Continued distributions did not completely eliminate infection in all communities or meet WHO control goals, although they did prevent resurgence. This study was prospectively registered at clinicaltrials.gov (clinicaltrials.gov NCT01202331).

Background Measles cases continue to occur among susceptible individuals despite the elimination of endemic measles transmission in the United States. Clustering of disease susceptibility can threaten herd immunity and impact the likelihood of disease outbreaks in a highly vaccinated population. Previous studies have examined the role of contact tracing to control infectious diseases among clustered populations, but have not explicitly modeled the public health response using an agent-based model. Methods We developed an agent-based simulation model of measles transmission using the Framework for Reconstructing Epidemiological Dynamics (FRED) and the Synthetic Population Database maintained by RTI International. The simulation of measles transmission was based on interactions among individuals in different places: households, schools, daycares, workplaces, and neighborhoods. The model simulated different levels of immunity clustering, vaccination coverage, and contact investigations with delays caused by individuals’ behaviors and/or the delay in a health department’s response. We examined the effects of these characteristics on the probability of uncontrolled measles outbreaks and the outbreak size in 365 days after the introduction of one index case into a synthetic population. Results We found that large measles outbreaks can be prevented with contact investigations and moderate contact rates by having (1) a very high vaccination coverage (≥ 95%) with a moderate to low level of immunity clustering (≤ 0.5) for individuals aged less than or equal to 18 years, or (2) a moderate vaccination coverage (85% or 90%) with no immunity clustering for individuals (≤18 years of age), a short intervention delay, and a high probability that a contact can be traced. Without contact investigations, measles outbreaks may be prevented by the highest vaccination coverage with no immunity clustering for individuals (≤18 years of age) with moderate contact rates; but for the highest contact rates, even the highest coverage with no immunity clustering for individuals (≤18 years of age) cannot completely prevent measles outbreaks. Conclusions The simulation results demonstrated the importance of vaccination coverage, clustering of immunity, and contact investigations in preventing uncontrolled measles outbreaks.

Mass azithromycin distributions have been shown to reduce mortality in preschool children, although the factors mediating this mortality reduction are not clear. This study was performed to determine whether mass distribution of azithromycin, which has modest antimalarial activity, reduces the community burden of malaria. In a cluster-randomized trial conducted from 23 November 2014 until 31 July 2017, 30 rural communities in Niger were randomized to 2 years of biannual mass distributions of either azithromycin (20 mg/kg oral suspension) or placebo to children aged 1 to 59 months. Participants, field staff, and investigators were masked to treatment allocation. The primary malaria outcome was the community prevalence of parasitemia on thick blood smear, assessed in a random sample of children from each community at study visits 12 and 24 months after randomization. Analyses were performed in an intention-to-treat fashion. At the baseline visit, a total of 1,695 children were enumerated in the 15 azithromycin communities, and 3,029 children were enumerated in the 15 placebo communities. No communities were lost to follow-up. The mean prevalence of malaria parasitemia at baseline was 8.9% (95% CI 5.1%-15.7%; 52 of 552 children across all communities) in the azithromycin-treated group and 6.7% (95% CI 4.0%-12.6%; 36 of 542 children across all communities) in the placebo-treated group. In the prespecified primary analysis, parasitemia was lower in the azithromycin-treated group at month 12 (mean prevalence 8.8%, 95% CI 5.1%-14.3%; 51 of 551 children across all communities) and month 24 (mean 3.5%, 95% CI 1.9%-5.5%; 21 of 567 children across all communities) than it was in the placebo-treated group at month 12 (mean 15.3%, 95% CI 10.8%-20.6%; 81 of 548 children across all communities) and month 24 (mean 4.8%, 95% CI 3.3%-6.4%; 28 of 592 children across all communities) (P = 0.02). Communities treated with azithromycin had approximately half the odds of parasitemia compared to those treated with placebo (odds ratio [OR] 0.54, 95% CI 0.30 to 0.97). Parasite density was lower in the azithromycin group than the placebo group at 12 and 24 months (square root-transformed outcome; density estimates were 7,540 parasites/μl lower [95% CI -350 to -12,550 parasites/μl; P = 0.02] at a mean parasite density of 17,000, as was observed in the placebo arm). No significant difference in hemoglobin was observed between the 2 treatment groups at 12 and 24 months (mean 0.34 g/dL higher in the azithromycin arm, 95% CI -0.06 to 0.75 g/dL; P = 0.10). No serious adverse events were reported in either group, and among children aged 1 to 5 months, the most commonly reported nonserious adverse events (i.e., diarrhea, vomiting, and rash) were less common in the azithromycin-treated communities. Limitations of the trial include the timing of the treatments and monitoring visits, both of which took place before the peak malaria season, as well as the uncertain generalizability to areas with different malaria transmission dynamics. Mass azithromycin distributions were associated with a reduced prevalence of malaria parasitemia in this trial, suggesting one possible mechanism for the mortality benefit observed with this intervention. The trial was registered on ClinicalTrials.gov (NCT02048007).

The use of web-based search and social media can help identify epidemics, potentially earlier than clinical methods or even potentially identifying unreported outbreaks. Monitoring for eye-related epidemics, such as conjunctivitis outbreaks, can facilitate early public health intervention to reduce transmission and ocular comorbidities. However, monitoring social media content for conjunctivitis outbreaks is costly and laborious. Large language models (LLMs) could overcome these barriers by assessing the likelihood that real-world outbreaks are being described. However, public health actions for likely outbreaks could benefit more by knowing additional epidemiological characteristics, such as outbreak type, size, and severity. We aimed to assess whether and how well LLMs can classify epidemiological features from social media posts beyond conjunctivitis outbreak probability, including outbreak type, size, severity, etiology, and community setting. We used a validation framework comparing LLM classifications to those of other LLMs and human experts. We wrote code to generate synthetic conjunctivitis outbreak social media posts, embedded with specific preclassified epidemiological features to simulate various infectious eye disease outbreak and control scenarios. We used these posts to develop effective LLM prompts and test the capabilities of multiple LLMs. For top-performing LLMs, we gauged their practical utility in real-world epidemiological surveillance by comparing their assessments of Twitter/X, forum, and YouTube conjunctivitis posts. Finally, human raters also classified the posts, and we compared their classifications to those of a leading LLM for validation. Comparisons entailed correlation or sensitivity and specificity statistics. We assessed 7 LLMs for effectively classifying epidemiological data from 1152 synthetic posts, 370 Twitter/X posts, 290 forum posts, and 956 YouTube posts. Despite some discrepancies, the LLMs demonstrated a reliable capacity for nuanced epidemiological analysis across various data sources and compared to humans or between LLMs. Notably, GPT-4 and Mixtral 8x22b exhibited high performance, predicting conjunctivitis outbreak characteristics such as probability (GPT-4: correlation=0.73), size (Mixtral 8x22b: correlation=0.82), and type (infectious, allergic, or environmentally caused); however, there were notable exceptions. Assessing synthetic and real-world posts for etiological factors, infectious eye disease specialist validations revealed that GPT-4 had high specificity (0.83-1.00) but variable sensitivity (0.32-0.71). Interrater reliability analyses showed that LLM-expert agreement exceeded expert-expert agreement for severity assessment (intraclass correlation coefficient=0.69 vs 0.38), while agreement varied by condition type (κ=0.37-0.94). This investigation into the potential of LLMs for public health infoveillance suggests effectiveness in classifying key epidemiological characteristics from social media content about conjunctivitis outbreaks. Future studies should further explore LLMs' potential to support public health monitoring through the automated assessment and classification of potential infectious eye disease or other outbreaks. Their optimal role may be to act as a first line of documentation, alerting public health organizations for the follow-up of LLM-detected and -classified small, early outbreaks, with a focus on the most severe ones.

As part of the End TB Strategy, the World Health Organization calls for low-tuberculosis (TB) incidence settings to achieve pre-elimination (<10 cases per million) and elimination (<1 case per million) by 2035 and 2050, respectively. These targets require testing and treatment for latent tuberculosis infection (LTBI). To estimate the ability and costs of testing and treatment for LTBI to reach pre-elimination and elimination targets in California. We created an individual-based epidemic model of TB, calibrated to historical cases. We evaluated the effects of increased testing (QuantiFERON-TB Gold) and treatment (three months of isoniazid and rifapentine). We analyzed four test and treat targeting strategies: (1) individuals with medical risk factors (MRF), (2) non-USB, (3) both non-USB and MRF, and (4) all Californians. For each strategy, we estimated the effects of increasing test and treat by a factor of 2, 4, or 10 from the base case. We estimated the number of TB cases occurring and prevented, and net and incremental costs from 2017 to 2065 in 2015 U.S. dollars. Efficacy, costs, adverse events, and treatment dropout were estimated from published data. We estimated the cost per case averted and per quality-adjusted life year (QALY) gained. In the base case, 106,000 TB cases are predicted to 2065. Pre-elimination was achieved by 2065 in three scenarios: a 10-fold increase in the non-USB and persons with MRF (by 2052), and 4- or 10-fold increase in all Californians (by 2058 and 2035, respectively). TB elimination was not achieved by any intervention scenario. The most aggressive strategy, 10-fold in all Californians, achieved a case rate of 8 (95% UI 4-16) per million by 2050. Of scenarios that reached pre-elimination, the incremental net cost was $20 billion (non-USB and MRF) to $48 billion. These had an incremental cost per QALY of $657,000 to $3.1 million. A more efficient but somewhat less effective single-lifetime test strategy reached as low as $80,000 per QALY. Substantial gains can be made in TB control in coming years by scaling-up current testing and treatment in non-USB and those with medical risks.