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Background: Highly transmissible genes encoding resistance to carbapenems have demonstrated global spread. The New Delhi Metallo-beta-lactamase 1 gene is hypothesized to have originated in India, with subsequent dissemination by colonized or infected travelers., Method: We conducted an ecological study evaluating the association between the cumulative air traffic departing India between 2007 and 2012 and published cases of NDM-1. Receiver operator characteristic curves were generated as well as multivariate logistic regression models. 193 countries with complete flight and World Bank data were included in the analysis., Results: Receiver operator characteristic curves (ROC) of the dichotomous outcome of a published case of NDM-1 were generated, yielding an unadjusted area under the curve (AUC) of 0.88 and adjusted AUC of 0.85. The unadjusted odds ratio of having a reported case of NDM-1, for every percentage increase in cumulative air traffic departing India, was 2.3 (95% CI 1.4 to 3.7) and adjusted was 2.0 (95% CI 1.2 to 3.4)., Conclusions: We demonstrate that flows of international travelers departing India by air is associated with published NDM-1 cases, globally. Countries with high passenger flight traffic from India with no reported cases of NDM-1 may be at increased risk of having unreported transmission of NDM-1., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Innovative approaches are needed to combat the illegal trade in wildlife. Here, we used network analysis and a new database, HealthMap Wildlife Trade, to identify the key nodes (countries) that support the illegal wildlife trade. We identified key exporters and importers from the number of shipments a country sent and received and from the number of connections a country had to other countries over a given time period. We used flow betweenness centrality measurements to identify key intermediary countries. We found the set of nodes whose removal from the network would cause the maximum disruption to the network. Selecting six nodes would fragment 89.5% of the network for elephants, 92.3% for rhinoceros, and 98.1% for tigers. We then found sets of nodes that would best disseminate an educational message via direct connections through the network. We would need to select 18 nodes to reach 100% of the elephant trade network, 16 nodes for rhinoceros, and 10 for tigers. Although the choice of locations for interventions should be customized for the animal and the goal of the intervention, China was the most frequently selected country for network fragmentation and information dissemination. Identification of key countries will help strategize illegal wildlife trade interventions.

Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon., Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues., Methods: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues., Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues., Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.

Background: When the US FDA approves a new prescription drug there is still a great deal remaining to be learned about the safe and proper use of that product. When new information addressing these topics emerges post-approval, the FDA may issue a Drug Safety Communication (DSC) to alert patients and physicians. The effectiveness of the communication-how drug safety messaging conveyed in FDA DSCs changes patient or prescriber behavior-may depend on multiple factors, including the way physicians and patients learn about the information, their understanding of the issues conveyed, and their perception of the importance of the information. In 2013, the FDA issued two DSCs addressing critical new warnings related to products containing the sedative/hypnotic zolpidem., Objective: In this article, we describe a core set of research initiatives that can be used to study how zolpidem-related DSCs affected subsequent physician and patient decision making., Methods: These research initiatives include analyzing drug utilization patterns and related health outcomes; comparing zolpidem-containing products against a comparator with similar indications [eszopiclone (Lunesta)] not covered by the 2013 DSCs; and surveying patients and qualitatively evaluating the dissemination of information regarding these drugs in traditional and social-media channels., Conclusions: Using an integrated, multidisciplinary approach, we can obtain information that can be used to optimize regulatory communications by seeking to understand the impact of the information contained in FDA risk communications.

Participatory systems for surveillance of acute respiratory infection give real-time information about infections circulating in the community, yet to-date are limited to self-reported syndromic information only and lacking methods of linking symptom reports to infection types. We developed the GoViral platform to evaluate whether a cohort of lay volunteers could, and would find it useful to, contribute self-reported symptoms online and to compare specimen types for self-collected diagnostic information of sufficient quality for respiratory infection surveillance. Volunteers were recruited, given a kit (collection materials and customized instructions), instructed to report their symptoms weekly, and when sick with cold or flu-like symptoms, requested to collect specimens (saliva and nasal swab). We compared specimen types for respiratory virus detection sensitivity (via polymerase-chain-reaction) and ease of collection. Participants were surveyed to determine receptivity to participating when sick, to receiving information on the type of pathogen causing their infection and types circulating near them. Between December 1 2013 and March 1 2014, 295 participants enrolled in the study and received a kit. Of those who reported symptoms, half (71) collected and sent specimens for analysis. Participants submitted kits on average 2.30 days (95 CI: 1.65 to 2.96) after symptoms began. We found good concordance between nasal and saliva specimens for multiple pathogens, with few discrepancies. Individuals report that saliva collection is easiest and report that receiving information about what pathogen they, and those near them, have is valued and can shape public health behaviors. Community-submitted specimens can be used for the detection of acute respiratory infection with individuals showing receptivity for participating and interest in a real-time picture of respiratory pathogens near them.

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.

The West Africa Ebola virus epidemic now appears to be coming to an end. In the proposed model, we simulate changes in population behavior that help to explain the observed transmission dynamics. We introduce an EVD transmission model accompanied by a model of social mobilization. The model was fit to Lofa County, Liberia through October 2014, using weekly counts of new cases reported by the US CDC. In simulation studies, we analyze the dynamics of the disease transmission with and without population behavior change, given the availability of beds in Ebola treatment units (ETUs) estimated from observed data. Only the model scenario that included individuals' behavioral change achieved a good fit to the observed case counts. Although the capacity of the Lofa County ETUs greatly increased in mid-August, our simulations show that the expansion was insufficient to alone control the outbreak. Modeling the entire outbreak without considering behavior change fit the data poorly, and extrapolating from early data without taking behavioral changes into account led to a prediction of exponential outbreak growth, contrary to the observed decline.  Education and awareness-induced behavior change in the population was instrumental in curtailing the Ebola outbreak in Lofa County and is likely playing an important role in stopping the West Africa epidemic altogether.

Background: Transmission of dengue viruses (DENV), the most common arboviral pathogens globally, is influenced by many climatic and socioeconomic factors. However, the relative contributions of these factors on a global scale are unclear., Methods: We randomly selected 94 islands stratified by socioeconomic and geographic characteristics. With a Bayesian model, we assessed factors contributing to the probability of islands having a history of any dengue outbreaks and of having frequent outbreaks., Results: Minimum temperature was strongly associated with suitability for DENV transmission. Islands with a minimum monthly temperature of greater than 14.8°C (95% CI: 12.4-16.6°C) were predicted to be suitable for DENV transmission. Increased population size and precipitation were associated with increased outbreak frequency, but did not capture all of the variability. Predictions for 48 testing islands verified these findings., Conclusions: This analysis clarified two key components of DENV ecology: minimum temperature was the most important determinant of suitability; and endemicity was more likely in areas with high precipitation and large, but not necessarily dense, populations. Wealth and connectivity, in contrast, had no discernable effects. This model adds to our knowledge of global determinants of dengue risk and provides a basis for understanding the ecology of dengue endemicity., (© The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.)

In this commentary, we consider the relationship between early outbreak changes in the observed reproductive number of Ebola in West Africa and various media reported interventions and aggravating events. We find that media reports of interventions that provided education, minimized contact, or strengthened healthcare were typically followed by sustained transmission reductions in both Sierra Leone and Liberia. Meanwhile, media reports of aggravating events generally preceded temporary transmission increases in both countries. Given these preliminary findings, we conclude that media reported events could potentially be incorporated into future epidemic modeling efforts to improve mid-outbreak case projections.

In order to map global disease risk, a geographic database of human Crimean-Congo haemorrhagic fever virus (CCHFV) occurrence was produced by surveying peer-reviewed literature and case reports, as well as informal online sources. Here we present this database, comprising occurrence data linked to geographic point or polygon locations dating from 1953 to 2013. We fully describe all data collection, geo-positioning, database management and quality-control procedures. This is the most comprehensive database of confirmed CCHF occurrence in humans to-date, containing 1,721 geo-positioned occurrences in total.

We considered how participatory syndromic surveillance data can be used to estimate influenza attack rates during the 2012-2013 and 2013-2014 seasons in the United States. Our inference is based on assessing the difference in the rates of self-reported influenza-like illness (ILI, defined as presence of fever and cough/sore throat) among the survey participants during periods of active vs. low influenza circulation as well as estimating the probability of self-reported ILI for influenza cases. Here, we combined Flu Near You data with additional sources (Hong Kong household studies of symptoms of influenza cases and the U.S. Centers for Disease Control and Prevention estimates of vaccine coverage and effectiveness) to estimate influenza attack rates. The estimated influenza attack rate for the early vaccinated Flu Near You members (vaccination reported by week 45) aged 20-64 between calendar weeks 47-12 was 14.7%(95% CI(5.9%,24.1%)) for the 2012-2013 season and 3.6%(-3.3%,10.3%) for the 2013-2014 season. The corresponding rates for the US population aged 20-64 were 30.5% (4.4%, 49.3%) in 2012-2013 and 7.1%(-5.1%, 32.5%) in 2013-2014. The attack rates in women and men were similar each season. Our findings demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack rates during future influenza seasons.

Background: Infectious disease surveillance has recently seen many changes including rapid growth of informal surveillance, acting both as competitor and a facilitator to traditional surveillance, as well as the implementation of the revised International Health Regulations. The present study aims to compare outbreak reporting by formal and informal sources given such changes in the field., Methods: 111 outbreaks identified from June to December 2012 were studied using first formal source report and first informal source report collected by HealthMap, an automated and curated aggregator of data sources for infectious disease surveillance. The outbreak reports were compared for timeliness, reported content, and disease severity., Results: Formal source reports lagged behind informal source reports by a median of 1.26 days (p=0.002). In 61% of the outbreaks studied, the same information was reported in the initial formal and informal reports. Disease severity had no significant effect on timeliness of reporting., Conclusion: The findings suggest that recent changes in the field of surveillance improved formal source reporting, particularly in the dimension of timeliness. Still, informal sources were found to report slightly faster and with accurate information. This study emphasizes the importance of utilizing both formal and informal sources for timely and accurate infectious disease outbreak surveillance.

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.

Several digital data sources and systems have been advanced for use in augmenting traditional influenza surveillance systems. Although timeliness is one of the main advantages of these tools, there are several other recognizable uses and potential impact of these systems on the public and global public health., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Outbreaks of infectious diseases at mass gatherings can strain the health system of the host region and pose a threat to local and global health. In addition to strengthening existing surveillance systems, most host nations also use novel technologies to assess disease risk and augment traditional surveillance approaches. We review novel approaches to disease surveillance using the Internet, mobile phone applications, and wireless sensor networks. These novel approaches to disease surveillance can result in prompt detection., (Copyright © 2015. Published by Elsevier Ltd.)

Background: The WHO declared the 2014 west African Ebola epidemic a public health emergency of international concern in view of its potential for further international spread. Decision makers worldwide are in need of empirical data to inform and implement emergency response measures. Our aim was to assess the potential for Ebola virus to spread across international borders via commercial air travel and assess the relative efficiency of exit versus entry screening of travellers at commercial airports., Methods: We analysed International Air Transport Association data for worldwide flight schedules between Sept 1, 2014, and Dec 31, 2014, and historic traveller flight itinerary data from 2013 to describe expected global population movements via commercial air travel out of Guinea, Liberia, and Sierra Leone. Coupled with Ebola virus surveillance data, we modelled the expected number of internationally exported Ebola virus infections, the potential effect of air travel restrictions, and the efficiency of airport-based traveller screening at international ports of entry and exit. We deemed individuals initiating travel from any domestic or international airport within these three countries to have possible exposure to Ebola virus. We deemed all other travellers to have no significant risk of exposure to Ebola virus., Findings: Based on epidemic conditions and international flight restrictions to and from Guinea, Liberia, and Sierra Leone as of Sept 1, 2014 (reductions in passenger seats by 51% for Liberia, 66% for Guinea, and 85% for Sierra Leone), our model projects 2.8 travellers infected with Ebola virus departing the above three countries via commercial flights, on average, every month. 91,547 (64%) of all air travellers departing Guinea, Liberia, and Sierra Leone had expected destinations in low-income and lower-middle-income countries. Screening international travellers departing three airports would enable health assessments of all travellers at highest risk of exposure to Ebola virus infection., Interpretation: Decision makers must carefully balance the potential harms from travel restrictions imposed on countries that have Ebola virus activity against any potential reductions in risk from Ebola virus importations. Exit screening of travellers at airports in Guinea, Liberia, and Sierra Leone would be the most efficient frontier at which to assess the health status of travellers at risk of Ebola virus exposure, however, this intervention might require international support to implement effectively., Funding: Canadian Institutes of Health Research., (Copyright © 2015 Bogoch et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd. All rights reserved.)

Background: Preparing and submitting a voluntary adverse event (AE) report to the US Food and Drug Administration (FDA) for a medical device typically takes 40 min. User-friendly Web and mobile reporting apps may increase efficiency. Further, coupled with strategies for direct patient involvement, patient engagement in AE reporting may be improved. In 2012, the FDA Center for Devices and Radiologic Health (CDRH) launched a free, public mobile AE reporting app, MedWatcher, for patients and clinicians. During the same year, a patient community on Facebook adopted the app to submit reports involving a hysteroscopic sterilization device, brand name Essure ® ., Methods: Patient community outreach was conducted to administrators of the group "Essure Problems" (approximately 18,000 members as of June 2015) to gather individual case safety reports (ICSRs). After agreeing on key reporting principles, group administrators encouraged members to report via the app. Semi-structured forms in the app mirrored fields of the MedWatch 3500 form. ICSRs were transmitted to CDRH via an electronic gateway, and anonymized versions were posted in the app. Data collected from May 11, 2013 to December 7, 2014 were analyzed. Narrative texts were coded by trained and certified MedDRA coders (version 17). Descriptive statistics and metrics, including VigiGrade completeness scores, were analyzed. Various incentives and motivations to report in the Facebook group were observed., Results: The average Essure AE report took 11.4 min (±10) to complete. Submissions from 1349 women, average age 34 years, were analyzed. Serious events, including hospitalization, disability, and permanent damage after implantation, were reported by 1047 women (77.6 %). A total of 13,135 product-event pairs were reported, comprising 327 unique preferred terms, most frequently fatigue ( n  = 491), back pain (468), and pelvic pain (459). Important medical events (IMEs), most frequently mental impairment (142), device dislocation (108), and salpingectomy (62), were reported by 598 women (44.3 %). Other events of interest included loss of libido ( n  = 115); allergy to metals (109), primarily nickel; and alopecia (252). VigiGrade completeness scores were high, averaging 0.80 (±0.15). Reports received via the mobile app were considered "well documented" 55.9 % of the time, compared with an international average of 13 % for all medical products. On average, there were 15 times more reports submitted per month via the app with patient community support versus traditional pharmacovigilance portals., Conclusions: Outreach via an online patient community, coupled with an easy-to-use app, allowed for rapid and detailed ICSRs to be submitted, with gains in efficiency. Two-way communication and public posting of narratives led to successful engagement within a Motivation-Incentive-Activation-Behavior framework, a conceptual model for successful crowdsourcing. Reports submitted by patients were considerably more complete than those submitted by physicians in routine spontaneous reports. Further research is needed to understand how biases operate differently from those of traditional pharmacovigilance.

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Background: Internet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases., Methods: Official notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009-13 using Spearman's rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems., Results: Notifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems., Conclusions: The findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases.

The Zika virus epidemic of 2015–16, which caused over 1 million confirmed or suspected human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and availability of suitable habitat for mosquito species that are key disease vectors. Both human mobility and mosquito vector abundances vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector densities and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Our research uses the novel approach of combining geographical modeling of mosquito presence with network modeling of human mobility to offer a comprehensive simulation environment for Zika virus epidemics that considers a substantial number of spatial and temporal factors compared to the literature. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, when they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a country with a population of about 50 million people, with an annual calendar that can be partitioned into overlapping cycles of wet and dry seasons and peak tourism and off tourism seasons that drive distinct cyclical patterns of mosquito abundance and human movement. Our results show that whether the first infected human was introduced to the network during the wet versus dry season and during the tourism versus off tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 60% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular tourism destinations such as Barranquilla and Cartagena have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics. This research also has strong implications for tourism policy and the potential response strategies in case of an emergent epidemic. Author summary: The Zika virus pandemic of 2015–16, which caused over 1 million human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and presence of mosquito species, such as Aedes aegypti, that carry the disease. Both human mobility and mosquito vector densities vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector presence and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, as they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a nation with a population of about 50 million people, whose annual calendar can be partitioned into overlapping cycles of wet and dry seasons and tourism and off-tourism seasons that drive distinct cyclical patterns of mosquito occurrence and human movement. Our approach combined spatial modeling of mosquito presence with network modeling of human mobility. We used a Maximum Entropy model to estimate the spatially and temporally varying habitat suitability for Aedes aegypti, the primary mosquito vector of Zika virus in Colombia, in terms of environmental factors such as temperature, precipitation, and vegetation coverage and human factors indicating land use and development, socioeconomic status, and prevalence of standing water that provides key habitat for juvenile mosquitoes. We also used airline travel data to develop metapopulation patch network models of the 1,123 municipalities of Colombia for each month of 2015 and 2016. We combined our vector habitat suitability and human mobility network models to simulate the Zika virus epidemic if it had begun in each month of 2015. Our results show that whether the first human case was introduced to the network during the wet vs. dry season and during the tourism vs. off-tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 30% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular vacation spots such as Barranquilla and Cartageña have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics. [ABSTRACT FROM AUTHOR]

Aedes aegypti, the vector for dengue, chikungunya, yellow fever, and Zika, poses a growing global epidemiological risk. Despite extensive research on Ae. aegypti's life history traits and behavior, critical knowledge gaps persist, particularly in integrating these findings across varied experimental contexts. The plasticity of Ae. aegypti's traits throughout its life cycle allows dynamic responses to environmental changes, yet understanding these variations within heterogeneous study designs remains challenging. A critical aspect often overlooked is the impact of using lab-adapted lines of Ae. aegypti, which may have evolved under laboratory conditions, potentially altering their life history traits and behavioral responses compared to wild populations. Therefore, incorporating field-derived populations in experimental designs is essential to capture the natural variability and adaptability of Ae. aegypti. The relationship between larval growing conditions and adult traits and behavior is significantly influenced by the specific context in which mosquitoes are studied. Laboratory conditions may not replicate the ecological complexities faced by wild populations, leading to discrepancies in observed traits and behavior. These discrepancies highlight the need for ecologically relevant experimental conditions, allowing mosquito traits and behavior to reflect field distributions. One effective approach is semi-field studies involving field-collected mosquitoes housed for fewer generations in the lab under ecologically relevant conditions. This growing trend provides researchers with the desired control over experimental conditions while maintaining the genetic diversity of field populations. By focusing on variations in life history traits and behavioral plasticity within these varied contexts, this review highlights the intricate relationship between larval growing conditions and adult traits and behavior. It underscores the significance of transstadial effects and the necessity of adopting study designs and reporting practices that acknowledge plasticity in adult traits and behavior, considering variations due to larval rearing conditions. Embracing such approaches paves the way for a comprehensive understanding of contextual variations in mosquito life history traits and behavior. This integrated perspective enables the synthesis of research findings across laboratory, semi-field, and field-based investigations, which is crucial for devising targeted intervention strategies tailored to specific ecological contexts to combat the health threat posed by this formidable disease vector effectively. [ABSTRACT FROM AUTHOR]

Objectives. To assess multilevel factors associated with variation in COVID-19 vaccination rates in a US network of community health centers. Methods. Using multilevel logistic regression with electronic health record data from ADVANCE (Accelerating Data Value Across a National Community Health Center Network; January 1, 2022–December 31, 2022), we assessed associations between health care delivery site–level (n = 1219) and patient-level (n = 1 864 007) characteristics and COVID-19 primary vaccine series uptake. Results. A total of 1 337 440 patients completed the COVID-19 primary vaccine series. Health care delivery site characteristics were significantly associated with lower series completion rates, including being located in non-Medicaid expansion states and isolated or rural communities and serving fewer patients. Patient characteristics associated with significantly lower likelihood of completing the vaccine series included being Black/African American or American Indian/Alaska Native (vs White), younger age, lower income, being uninsured or publicly insured (vs using private insurance), and having fewer visits. Conclusions. Both health care delivery site– and patient-level factors were significantly associated with lower COVID-19 vaccine uptake. Community health centers have been a critical resource for vaccination during the pandemic. (Am J Public Health. 2024;114(11):1242–1251. https://doi.org/10.2105/AJPH.2024.307773) [ABSTRACT FROM AUTHOR]

Seasonal influenza causes significant annual morbidity and mortality worldwide. In France, it is estimated that, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Traditionally, mathematical models used for epidemic forecasting can either include parameters capturing the infection process (mechanistic or compartmental models) or rely on time series analysis approaches that do not make mechanistic assumptions (statistical or phenomenological models). While the latter make extensive use of past epidemic data, mechanistic models are usually independently initialized in each season. As a result, forecasts from such models can contain trajectories that are vastly different from past epidemics. We developed a mechanistic model that takes into account epidemic data from training seasons when producing forecasts. The parameters of the model are estimated via a first particle filter running on the observed data. A second particle filter is then used to produce forecasts compatible with epidemic trajectories from the training set. The model was calibrated and tested on 35 years' worth of surveillance data from the French Sentinelles Network, representing the weekly number of patients consulting for ILI over the period 1985–2019. Our results show that the new method improves upon standard mechanistic approaches. In particular, when retrospectively tested on the available data, our model provides increased accuracy for short-term forecasts (from one to four weeks into the future) and peak timing and intensity. Our new approach for epidemic forecasting allows the integration of key strengths of the statistical approach into the mechanistic modelling framework and represents an attempt to provide accurate forecasts by making full use of the rich surveillance dataset collected in France since 1985. Author summary: Seasonal influenza causes significant morbidity and mortality worldwide. In France, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Forecasting the future trajectory of an epidemic in real-time can inform public health responses. Traditionally, two types of mathematical models are used to forecast infectious diseases outbreaks: (1) mechanistic models that explicitly capture transmission mechanisms and (2) statistical models that rely on the similarity between epidemic dynamics over the years. In contrast to statistical models, mechanistic models usually do not use information on past seasons to inform forecasts. This may result in poor performance when forecasted trajectories are very different from past epidemics. Here, we propose a framework that combines these two approaches by allowing mechanistic models to learn from trends observed in past epidemics. We evaluate this approach in the context of seasonal influenza in France. Our results show that the new method improves upon standard mechanistic approaches. In particular, our model provides increased accuracy for predicting the epidemic trajectory one to four weeks into the future, as well as the timing and size of the peak. Our new approach for epidemic forecasting allows combining statistical and mechanistic models to improve forecasts performance. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-methods approach was used to extract, summarize, and compare methodological approaches and outcomes. This review includes 34 studies. LLMs are primarily evaluated on their ability to answer oncologic questions across various domains. The meta-analysis highlights a significant performance variance, influenced by diverse methodologies and evaluation criteria. Furthermore, differences in inherent model capabilities, prompting strategies, and oncological subdomains contribute to heterogeneity. The lack of use of standardized and LLM-specific reporting protocols leads to methodological disparities, which must be addressed to ensure comparability in LLM research and ultimately leverage the reliable integration of LLM technologies into clinical practice. [ABSTRACT FROM AUTHOR]

Background: Cabbage (Brassica oleracea L. var. capitata) is an important crop within the Brassica oleracea species and is extensively cultivated worldwide. In recent years, outbreaks of downy mildew caused by Hyaloperonospora parasitica have resulted in substantial losses in cabbage production. Despite this, there have been limited studies on genes associated with resistance to downy mildew in cabbage. Results: This study identified sister lines exhibiting significant differences in disease resistance and susceptibility. Using bulked segregant analysis followed by sequencing (BSA-seq) and linkage analysis, the cabbage resistance locus BoDMR2 was accurately mapped to an approximately 300 kb interval on chromosome 7. Among the candidate genes identified, several single nucleotide polymorphisms (SNPs) and a 3-bp insertion were found within the conserved domain of the Bo7g117810 gene, encoding a leucine-rich repeat domain protein, in susceptible genotypes. Additionally, real-time quantitative polymerase chain reaction (RT‒qPCR) analysis revealed that the expression level of Bo7g117810 in resistant specimens was 2.5-fold higher than that in susceptible specimens. An insertion‒deletion (InDel) marker was designed based on the identified insertion in susceptible materials, facilitating the identification and selection of downy mildew-resistant cabbage cultivars. Conclusions: This study identifies Bo7g117810 as a potential candidate gene associated with adult-stage resistance to downy mildew in cabbage, supported by observed differences in gene sequence and expression levels. Furthermore, the development of an InDel marker I1-3, based on its mutation, provides valuable resources for breeding resistant cabbage cultivars. [ABSTRACT FROM AUTHOR]

Maternal use of e-cigarette (e-cig) aerosols poses significant risks to fetal brain development, potentially increasing susceptibility to neurodevelopmental disorders in later life. However, the underlying mechanisms remain incompletely understood. This study aimed to understand the effects of fetal e-cig exposure on DNA methylome and transcriptomic changes in the neonatal brain. Pregnant rats were exposed to e-cig aerosols, and neonatal brains (5 males and 5 females/group) from both control and e-cig-exposed groups were used for experimental analysis. Results indicated that prenatal e-cig exposure altered site-specific DNA methylation patterns at both CpG and CH (non-CpG) sites, predominantly in intergenic and intronic regions, with sex dimorphism in methylation and gene expression changes. Gene ontology analysis revealed that e-cig exposure not only affected neuron projection development and axonogenesis but also altered pathways related to neurodegeneration and long-term depression. These findings provide novel insights into the dynamic changes of CpG and CH methylation induced by e-cig exposure, underscoring the susceptibility of the developing brain to maternal e-cig exposure and its potential implications for developmental disorders and neurodegenerative diseases later in life. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) poses a significant global health threat, primarily stemming from its misuse and overuse in both veterinary and public healthcare systems. The consequences of AMR are severe, leading to more severe infections, increased health protection costs, prolonged hospital stays, unresponsive treatments, and elevated fatality rates. The impact of AMR is direct and far-reaching, particularly affecting the Sustainable Development Goals (SDGs), underscoring the urgency for concerted global actions to achieve these objectives. Disproportionately affecting underprivileged populations, AMR compounds their vulnerabilities, pushing them further into poverty. Moreover, AMR has ramifications for food production, jeopardizing sustainable agriculture and diminishing the livelihoods of farmers. The emergence of antibiotic-resistant bacteria in underprivileged areas heightens the risk of complications and mortality. Climate change further contributes to AMR, as evidenced by increased instances of foodborne salmonellosis and the development of antibiotic resistance, resulting in substantial healthcare costs. Effectively addressing AMR demands collaboration among governments, entrepreneurs, and the public sector to establish institutions and policies across all regulatory levels. Expanding SDG 17, which focuses on partnerships for sustainable development, would facilitate global antimicrobial stewardship initiatives, technology transfer, surveillance systems, and investment in vaccine and drug research. The World Bank's SDG database, tracking progress towards sustainable development, reveals a concerning picture with only a 15% success rate till 2023 and 48% showing deviation, underscoring a global gap exacerbated by the COVID-19 pandemic. Tackling AMR's global impact necessitates international cooperation, robust monitoring, and evaluation methods. The five priorities outlined guide SDG implementation, while impoverished countries must address specific challenges in their implementation efforts. Addressing AMR and its impact on the SDGs is a multifaceted challenge that demands comprehensive and collaborative solutions on a global scale. [ABSTRACT FROM AUTHOR]

Background: In September 2022, Uganda experienced an outbreak of Sudan virus disease (SVD), mainly in central Uganda. As a result of enhanced surveillance activities for Ebola disease, samples from several patients with suspected viral hemorrhagic fever (VHF) were sent to the VHF Program at Uganda Virus Research Institute (UVRI), Entebbe, Uganda, and identified with infections caused by other viral etiologies. Herein, we report the epidemiologic and laboratory findings of Crimean-Congo hemorrhagic fever (CCHF) cases that were detected during the SVD outbreak response. Methodology: Whole blood samples from VHF suspected cases were tested for Sudan virus (SUDV) by real-time reverse transcription–polymerase chain reaction (RT-PCR); and if negative, were tested for CCHF virus (CCHFV) by RT-PCR. CCHFV genomic sequences generated by metagenomic next generation sequencing were analyzed to ascertain strain relationships. Principal findings: Between September 2022 and January 2023, a total of 2,626 samples were submitted for VHF testing at UVRI. Overall, 13 CCHF cases (including 7 deaths; case fatality rate of 53.8%), aged 4 to 60 years, were identified from 10 districts, including several districts affected by the SVD outbreak. Four cases were identified within the Ebola Treatment Unit (ETU) at Mubende Hospital. Most CCHF cases were males engaged in livestock farming or had exposure to wildlife (n = 8; 61.5%). Among confirmed cases, the most common clinical symptoms were hemorrhage (n = 12; 92.3%), fever (n = 11; 84.6%), anorexia (n = 10; 76.9%), fatigue (n = 9; 69.2%), abdominal pain (n = 9; 69.2%) and vomiting (n = 9; 69.2%). Sequencing analysis showed that the majority of identified CCHFV strains belonged to the Africa II clade previously identified in Uganda. Two samples, however, were identified with greater similarity to a CCHFV strain that was last reported in Uganda in 1958, suggesting possible reemergence. Conclusions/Significance: Identifying CCHFV from individuals initially suspected to be infected with SUDV emphasizes the need for comprehensive VHF testing during filovirus outbreak responses in VHF endemic countries. Without expanded testing, CCHFV-infected patients would have posed a risk to health care workers and others while receiving treatment after a negative filovirus diagnosis, thereby complicating response dynamics. Additionally, CCHFV-infected cases could acquire an Ebola infection while in the ETU, and upon release because of a negative Ebola virus result, have the potential to spread these infections in the community. Author summary: Crimean-Congo hemorrhagic fever (CCHF) is a severe hemorrhagic disease whose symptoms mimic those of other viral hemorrhagic fevers (VHFs), significantly challenging its differential diagnosis. Herein, we report on CCHF cases that were detected during the nationwide response activities against an outbreak of Sudan virus (SUDV) in Uganda, from September 2022 to January 2023. Confirmed CCHF cases, mainly males presenting with a range of clinical signs and symptoms, were identified in districts with circulating SUDV, including 4 cases identified within an Ebola treatment center at Mubende hospital, thus complicating patient management and treatment. Our findings highlight the need for broad testing for VHFs and other high-consequence pathogens, even during known VHF outbreaks, in order to identify high risk pathogens with outbreak potential, especially in countries with a broad range of endemic zoonotic viral pathogens. This article also adds to the known molecular epidemiology of CCHF virus in Uganda. [ABSTRACT FROM AUTHOR]

There are a limited number of effective vaccines against dengue virus (DENV) and significant efforts are being made to develop potent anti-virals. Previously, we described that platelet-chemokine CXCL4 negatively regulates interferon (IFN)-α/β synthesis and promotes DENV2 replication. An antagonist to CXCR3 (CXCL4 receptor) reversed it and inhibited viral replication. In a concurrent search, we identified CXCR3-antagonist from our compound library, namely 7D, which inhibited all serotypes of DENV in vitro. With a half-life of ~2.85 h in plasma and no significant toxicity, 7D supplementation (8 mg/kg-body-weight) to DENV2-infected IFNα/β/γR−/−AG129 or wild-type C57BL6 mice increased synthesis of IFN-α/β and IFN-λ, and rescued disease symptoms like thrombocytopenia, leukopenia and vascular-leakage, with improved survival. 7D, having the property to inhibit Sirt-1 deacetylase, promoted acetylation and phosphorylation of STAT3, which in-turn increased plasmablast proliferation, germinal-center maturation and synthesis of neutralizing-antibodies against DENV2 in mice. A STAT3-inhibitor successfully inhibited these effects of 7D. Together, these observations identify compound 7D as a stimulator of IFN-α/β/λ synthesis via CXCL4:CXCR3:p38:IRF3 signaling, and a booster for neutralizing-antibody generation by promoting STAT3-acetylation in plasmablasts, capable of protecting dengue infection. Synopsis: Effective vaccines and anti-viral drugs against dengue are limited. Our study identifies a small molecule, namely 7D as a stimulator of IFNα/β/λ synthesis, and also booster for neutralizing-antibody generation, capable of protecting dengue infection in 3 different mice models. Schematic describes the CXCL4-mediated activation of CXCR3:p38:IRF3 signaling, in turn suppression of IRF3 phosphorylation and IFNα/β/λ synthesis in monocytes and macrophages. Conversely, 7D supplementation reverses the above signaling and improves IFNα/β/λ synthesis. Besides, 7D increases acetylation and phosphorylation of STAT3, in turn promotes proliferation of plasmablasts and plasma cells, in turn increases IgG synthesis via suppression of deacetylase activity of Sirt-1. 7D is a potent anti-viral drug against dengue. Effective vaccines and anti-viral drugs against dengue are limited. Our study identifies a small molecule, namely 7D as a stimulator of IFNα/β/λ synthesis, and also booster for neutralizing-antibody generation, capable of protecting dengue infection in 3 different mice models. [ABSTRACT FROM AUTHOR]

Background: Under Sect. 564 of the Federal Food, Drug, and Cosmetic Act, when the US Department of Health and Human Services Secretary issues a declaration based on one of four types of determinations, FDA may authorize an unapproved product or unapproved uses of an approved product for emergency use. It is unclear how much the public and health care providers understand about emergency use authorizations and how that understanding influences perceptions or behavioral intentions. The current study aimed to explore consumer and health care provider perceptions of emergency use authorizations and how promotion of emergency use authorizations has been investigated in the literature. Methods: We conducted a scoping review via PubMed, Embase, Web of Science, and CINAHL for original research published in English from January 1, 2012, through November 10, 2022. We conducted surveillance of the literature through March 2023. Results: We identified 13 studies reported in 14 publications that addressed public and health care provider attitudes, understanding, and behaviors with regard to products authorized under an emergency use authorization. We did not identify any articles describing research regarding the promotion of emergency use authorization products. Findings suggest gaps in understanding of emergency use authorizations, both for the public and health care providers. Findings also suggest the public and health care providers have concerns about safety or the lack of full FDA approval that may contribute to decreased willingness to receive treatments and vaccines under an emergency use authorization. Conclusions: Future research should explore how emergency use authorizations have been promoted and how that promotion impacts public and health care provider perceptions. [ABSTRACT FROM AUTHOR]

Wastewater monitoring is a valuable public health tool that can track a variety of health markers. The strong correlations between trends in wastewater viral concentrations and county-level COVID-19 case counts point to the ability of wastewater data to represent changes in a community's disease burden. However, studies are lacking on whether the populations sampled through wastewater monitoring represent the characteristics of the broader community and the implications on health equity. We conducted a geospatial analysis to examine the extent to which populations contributing to wastewater collected through the North Carolina Wastewater Monitoring Network as of June 2022 represent the broader countywide and statewide populations. After intersecting sewershed boundary polygons for 38 wastewater treatment plants across 18 counties with census block and tract polygons, we compared the demographics and social vulnerability of (1) people residing in monitored sewersheds with countywide and statewide populations, and (2) sewered residents, regardless of inclusion in wastewater monitoring, with unsewered residents. We flagged as meaningful any differences greater than +/- 5 percentage points or 5 percent (for categorical and continuous variables, respectively) and noted statistically significant differences (p < 0.05). We found that residents within monitored sewersheds largely resembled the broader community on most variables analyzed, with only a few exceptions. We also observed that when multiple sewersheds were monitored within a county, their combined service populations resembled the county population, although individual sewershed and county populations sometimes differed. When we contrasted sewered and unsewered populations within a given county, we found that sewered populations were more vulnerable than unsewered populations, suggesting that wastewater monitoring may fill in the data gaps needed to improve health equity. The approach we present here can be used to characterize sewershed populations nationwide to ensure that wastewater monitoring is implemented in a manner that informs equitable public health decision-making. [ABSTRACT FROM AUTHOR]

Background: Dengue fever is one of the most prevalent mosquito-borne diseases in Cambodia. Until now, no specific vaccine nor antiviral treatment exists the virus causing Dengue fever. Consequently, its prevention relies only on vector control strategies. However, efficient vector control in turn relies on a good knowledge of the biology of the vector species. Therefore, this study aims to provide the first review of the distribution, ecology, meteorological impacts, trophic behavior, vector competence, vector control and insecticide resistance of dengue vector species in Cambodia. Methods: A systematic search of the Google Scholar and PubMed databases was conducted for relevant published articles. Of the 610 published articles originally identified, 70 articles were ultimately selected for inclusion in this review. We also included new data from unpublished research conducted in Cambodia between 2017 and 2023 related to dengue vector bionomics. Results: Eleven Aedes (Stegomyia) mosquito species have been recorded in Cambodia, including a new species described in 2024. Four species are associated with dengue virus transmission, among which Aedes aegypti and Ae. albopictus are the main vectors and Ae. malayensis and Ae. scutellaris are considered to be potential vectors. Aedes aegypti and Ae. albopictus are present in all provinces of Cambodia. Aedes albopictus shows a preference for forest, rural and suburban areas, while Ae. aegypti is mostly found in urban and suburban areas. The distribution of these two species is also influenced by meteorological factors, seasonality and the availability of breeding habitats and blood meals. Both species are predominant during the rainy season, and their respective density is impacted by precipitation and temperature. Aedes aegypti is characterized as anthropophilic, while Ae. albopictus exhibits zooanthropophilic behavior, and both species have been observed to be predominantly diurnal. In addition, they were found to be highly resistant to the insecticides used in Cambodia for their control, such as temephos for larvae and deltamethrin and permethrin for adult mosquitoes. Conclusions: This review provides extensive and important knowledge on dengue vectors in Cambodia. This knowledge is derived not only from published research articles but also from many recent studies in Cambodia on the bionomics of dengue vector species. The review provides valuable information for use by public health authorities on dengue virus transmission and to develop better vector control strategies in the country. [ABSTRACT FROM AUTHOR]

Evolutionary rescue occurs when populations survive lethal environmental stresses through the rising and fixation of tolerant genotypes. Temperature has long been believed to determine the evolutionary speed of populations and species. Here, we suggest that warmer temperatures can facilitate evolutionary rescue. Moreover, with dispersal among habitats, the advantage in evolutionary rescue for warmer populations may cause a bias in habitat colonization dynamics towards the warm-to-cold direction. We experimentally tested these hypotheses with a model microbial system. Our first experiment showed that bacterial populations at warmer temperatures had a greater chance to evolve resistance and escape the fate of extinction under an antibiotic treatment. In the second experiment, metapopulations that consisted of warm and cold habitats were exposed to the antibiotic stress; local populations that went extinct might be recolonized, and such recolonization events were biased to the warm-to-cold direction. We also examined possible mechanisms underlying the temperature effect on the rapid evolution of resistance in our study system. Our results may help to understand the mechanisms of maintenance of biodiversity and patterns of gene flow among climatic regions, particularly in pest species subject to chemical control treatments. [ABSTRACT FROM AUTHOR]

Background: Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. Methods: The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. Results: We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. Conclusions: Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management. [ABSTRACT FROM AUTHOR]

Background: Understanding the level of exposure to Lassa virus (LASV) in at-risk communities allows for the administration of effective preventive interventions to mitigate epidemics of Lassa fever. We assessed the seroprevalence of LASV antibodies in rural and semiurban communities of two cosmopolitan cities in Nigeria with poorly understood Lassa epidemiology. Methods: A cross-sectional study was conducted in ten communities located in the Abuja Municipal Area Council (AMAC), Abuja, and Ikorodu Local Government Area (LGA), Lagos, from February 2nd to July 5th, 2022. Serum samples collected from participants were analyzed for IgG and IgM antibodies using a ReLASV® Pan-Lassa NP IgG/IgM enzyme-linked immunosorbent assay (ELISA) kit. A questionnaire administered to participants collected self-reported sociodemographic and LASV exposure information. Seroprevalence of LASV IgG/IgM was estimated overall, and by study site. Univariate and multivariate log-binomial models estimated unadjusted and adjusted prevalence ratios (aPRs) and 95% confidence intervals (CI) for site-specific risk factors for LASV seropositivity. Grouped Least Absolute Shrinkage and Selection Operator (LASSO) was used for variable selection for multivariate analysis. Results: A total of 628 participants with serum samples were included in the study. Most participants were female (434, 69%), married (459, 73%), and had a median age of 38 years (interquartile range 28–50). The overall seroprevalence was 27% (171/628), with a prevalence of 33% (126/376) in Abuja and 18% (45/252) in Lagos. Based on site-specific grouped LASSO selection, enrollment in the dry season (vs. wet; aPR, 95% CI: 1.73, 1.33–2.24), reported inconsistent washing of fruits and vegetables (aPR, 95% CI: 1.45, 1.10–1.92), and a positive malaria rapid test (aPR, 95% CI: 1.48, 1.09-2.00) were independently associated with LASV seropositivity in Abuja, whereas, only a self-reported history of rhinorrhea (PR, 95% CI: 2.21, 1.31–3.72) was independently associated with Lassa seropositivity in Lagos. Conclusions: The LASV seroprevalence was comparable to that in other areas in Nigeria. Our findings corroborate those from other studies on the importance of limiting human exposure to rodents and focusing on behavioral factors such as poor hygiene practices to reduce exposure to LASV. [ABSTRACT FROM AUTHOR]

Precision medicine is an approach to provide individualized treatment to the patient for a specific disease based on the genotypic, phenotypic and also psychological factors which in turn enhances the patient care to several folds. Apart from providing personalized treatment, it can also be used to accurately diagnose a disease state in a patient. Precision medicine can be employed to improve the health status of both individual patients and general population at high risk. However, several ethical considerations have to be looked upon while employing precision medicine at the same time it required more cutting-edge methodologies for the accurate execution of the same. [ABSTRACT FROM AUTHOR]

Primates live in complex social systems, and social contact and disease interact to shape the evolution of animal (including human) sociality. Researchers use social network analysis (SNA), a method of mapping and measuring contact patterns within a network of individuals, to understand the role that social interactions play in disease transmission. Here, we review lessons learned from SNA of humans and nonhuman primates (NHPs) and explore how they can inform health and wildlife conservation. Utilizing the breadth of knowledge in human systems and outlining how we can integrate that knowledge into our understanding of NHP sociality will add to our comprehension of disease transmission in NHP social networks and, in turn, will reveal more about human disease and well-being. [ABSTRACT FROM AUTHOR]

The prevalence of mosquito-borne diseases is increasing due to urbanization and climate change. Synthetic insecticides have resulted in resistance, and alternative control methods are needed. This study evaluated hexane, chloroform, and methanol extracts from Sphaeranthus indicus leaves for their efficacy against the larvae, pupae and eggs of Aedes aegypti and Culex quinquefasciatus mosquitoes. The plant materials were coarsely powdered and sequentially extracted in hexane, chloroform, and methanol using a Soxhlet apparatus, followed by filtration and concentration under reduced pressure with a rotary vacuum evaporator. In the case of Ae. aegypti, the hexane extract exhibited notable toxicity against all three examined stages. The LC50 values for larval mortality, pupal lethality, and ovicidal activity were determined to be 81.83 ppm, 86.13 ppm, and 63.2 ppm, respectively. Similarly, for Cx. quinquefasciatus, the hexane extract showed efficacy with LC50 values of 81.23 ppm for larval mortality, 93.24 ppm for pupal lethality, and 75.2 ppm for ovicidal activity. Following the hexane extract, the chloroform and methanol extracts demonstrated moderate larvicidal and pupicidal activity against both mosquito species, but they exhibited limited effectiveness against ovicidal activity. These findings suggest the potential use of natural extracts as supplementary control measures against mosquito vectors. [ABSTRACT FROM AUTHOR]

Codon optimality refers to the effect that codon composition has on messenger RNA (mRNA) stability and translation level and implies that synonymous codons are not silent from a regulatory point of view. Here, we investigated the adaptation of virus genomes to the host optimality code using mosquito-borne dengue virus (DENV) as a model. We demonstrated that codon optimality exists in mosquito cells and showed that DENV preferentially uses nonoptimal (destabilizing) codons and avoids codons that are defined as optimal (stabilizing) in either human or mosquito cells. Human genes enriched in the codons preferentially and frequently used by DENV are upregulated during infection, and so is the tRNA decoding the nonoptimal and DENV preferentially used codon for arginine. We found that adaptation during single-host passaging in human or mosquito cells results in the selection of synonymous mutations towards DENV's preferred nonoptimal codons that increase virus fitness. Finally, our analyses revealed that hundreds of viruses preferentially use nonoptimal codons, with those infecting a single host displaying an even stronger bias, suggesting that host–pathogen interaction shapes virus-synonymous codon choice. Synopsis: Analyses of viral genome adaptation to the host optimality code, reveal that hundreds of human-infecting viruses preferentially use non-optimal codons and suggest that host-pathogen interactions can shape virus synonymous codon choice. Dengue virus (DENV) preferentially uses non-optimal codons and avoids codons that are defined as optimal in either human or mosquito cells. Codon optimality mechanism exists in mosquito cells. Human genes enriched in the codons preferentially and frequently used by DENV are up-regulated during DENV infection, as well as tRNAs decoding the non-optimal codon that DENV preferentially uses. Hundreds of human-infecting viruses preferentially use non-optimal codons suggesting that host-pathogen interaction shapes virus synonymous codon choice. Analyses of viral genome adaptation to the host optimality code, reveal that hundreds of human-infecting viruses preferentially use non-optimal codons and suggest that host-pathogen interactions can shape virus synonymous codon choice. [ABSTRACT FROM AUTHOR]

The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19's spread is inevitably associated with human mobility. Given the pandemic's severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic's impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport's response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation. [ABSTRACT FROM AUTHOR]

With the advances in artificial intelligence (AI), data‐driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision‐making by such algorithms is not trustworthy for clinicians and is considered a black‐box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer‐reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model‐agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree‐based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree‐based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI‐enabled health systems and medical devices and, ultimately, the quality of care and outcomes. [ABSTRACT FROM AUTHOR]

Anomaly detection of three-dimensional (3D) topographic data is a challenging problem in spatial data analysis. In this paper, we investigate spatial patterns of 3D surface data that exhibit multiple in-control modes. In complex manufacturing processes, surfaces of final products could contain different topographic features from one in-control surface to another, thus making it difficult to monitor the surface with existing approaches, which rely on the assumption of the presence of single mode surface topography. We propose a novel anomaly detection approach for monitoring local topographic variations in the presence of multimode surface topography. We present a binarization model to capture the generic behavior of the multimode surfaces and enhance the representation of the surface. To systematically monitor the surface, we introduce a new probabilistic distance measure (PDM) that quantifies the similarity of spatial patterns between two binarized surfaces. The proposed PDM takes advantage of identifying local variations by utilizing the order neighbor statistics, which captures the local property on the surface. Experimental results with numerical simulation data and real-life paper surface data are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]