Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales

Improving longitudinal research in geospatial health: An agenda

All aspects of public health research require longitudinal analyses to fully capture the dynamics of outcomes and risk factors such as ageing, human mobility, non-communicable diseases (NCDs), climate change, and endemic, emerging, and re-emerging infectious diseases. Studies in geospatial health are often limited to spatial and temporal cross sections. This generates uncertainty in the exposures and behavior of study populations. We discuss a research agenda, including key challenges and opportunities of working with longitudinal geospatial health data. Examples include accounting for residential and human mobility, recruiting new birth cohorts, geoimputation, international and interdisciplinary collaborations, spatial lifecourse studies, and qualitative and mixed-methods approaches

Le Forum, Vol. 44 #3

https://digitalcommons.library.umaine.edu/francoamericain_forum/1105/thumbnail.jp

Using smartphone survey and GPS data to inform smoking cessation intervention delivery: Case study

Background: Interest in quitting smoking is common among young adults who smoke, but it can prove challenging. Although evidence-based smoking cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences—spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual’s phone enters the perimeter. Despite growth in personalized and ubiquitous smoking cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information. Objective: This study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting 4 case studies using a combination of self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process of deploying coping messages when young adults enter geofence boundaries. Methods: Data came from an ecological momentary assessment study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay area. Participants reported smoking and nonsmoking events through a smartphone app for 30 days, and GPS data was recorded by the app. We sampled 4 cases along ecological momentary assessment compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each 3-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for 3 types of zones (census blocks, 500 ft2 fishnet grids, and 1000 ft2 fishnet grids). Descriptive comparisons were made across the 4 cases to better understand the strengths and limitations of each geofence construction method. Results: The number of reported past 30-day smoking events ranged from 12 to 177 for the 4 cases. Each 3-hour geofence for 3 of the 4 cases captured over 50% of smoking events. The 1000 ft2 fishnet grid captured the highest percentage of smoking events compared to census blocks across the 4 cases. Across 3-hour periods except for 3:00 AM-5:59 AM for 1 case, geofences contained an average of 36.4%-100% of smoking events. Findings showed that fishnet grid geofences may capture more smoking events compared to census blocks. Conclusions: Our findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform the delivery of intervention messages

Reverberation Mapping of the Kepler-Field AGN KA1858+4850

KA1858+4850 is a narrow-line Seyfert 1 galaxy at redshift 0.078 and is among the brightest active galaxies monitored by the Kepler mission. We have carried out a reverberation mapping campaign designed to measure the broad-line region size and estimate the mass of the black hole in this galaxy. We obtained 74 epochs of spectroscopic data using the Kast Spectrograph at the Lick 3-m telescope from February to November of 2012, and obtained complementary V-band images from five other ground-based telescopes. We measured the H-beta light curve lag with respect to the V-band continuum light curve using both cross-correlation techniques (CCF) and continuum light curve variability modeling with the JAVELIN method, and found rest-frame lags of lag_CCF = 13.53 (+2.03, -2.32) days and lag_JAVELIN = 13.15 (+1.08, -1.00) days. The H-beta root-mean-square line profile has a width of sigma_line = 770 +/- 49 km/s. Combining these two results and assuming a virial scale factor of f = 5.13, we obtained a virial estimate of M_BH = 8.06 (+1.59, -1.72) x 10^6 M_sun for the mass of the central black hole and an Eddington ratio of L/L_Edd ~ 0.2. We also obtained consistent but slightly shorter emission-line lags with respect to the Kepler light curve. Thanks to the Kepler mission, the light curve of KA1858+4850 has among the highest cadences and signal-to-noise ratios ever measured for an active galactic nucleus; thus, our black hole mass measurement will serve as a reference point for relations between black hole mass and continuum variability characteristics in active galactic nuclei

Comparative Genomics of Vancomycin-Resistant Staphylococcus aureus Strains and Their Positions within the Clade Most Commonly Associated with Methicillin-Resistant S. aureus Hospital-Acquired Infection in the United States

Methicillin-resistant Staphylococcus aureus (MRSA) strains are leading causes of hospital-acquired infections in the United States, and clonal cluster 5 (CC5) is the predominant lineage responsible for these infections. Since 2002, there have been 12 cases of vancomycin-resistant S. aureus (VRSA) infection in the United States—all CC5 strains. To understand this genetic background and what distinguishes it from other lineages, we generated and analyzed high-quality draft genome sequences for all available VRSA strains. Sequence comparisons show unambiguously that each strain independently acquired Tn1546 and that all VRSA strains last shared a common ancestor over 50 years ago, well before the occurrence of vancomycin resistance in this species. In contrast to existing hypotheses on what predisposes this lineage to acquire Tn1546, the barrier posed by restriction systems appears to be intact in most VRSA strains. However, VRSA (and other CC5) strains were found to possess a constellation of traits that appears to be optimized for proliferation in precisely the types of polymicrobic infection where transfer could occur. They lack a bacteriocin operon that would be predicted to limit the occurrence of non-CC5 strains in mixed infection and harbor a cluster of unique superantigens and lipoproteins to confound host immunity. A frameshift in dprA, which in other microbes influences uptake of foreign DNA, may also make this lineage conducive to foreign DNA acquisition

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios

Le FORUM, Vol. 35 No. 2

https://digitalcommons.library.umaine.edu/francoamericain_forum/1030/thumbnail.jp

Le FORUM, Vol. 39 No. 1

https://digitalcommons.library.umaine.edu/francoamericain_forum/1044/thumbnail.jp

Heat and moisture budgets from airborne measurements and high-resolution model simulations

High-resolution simulations with a mesoscale model are performed to estimate heat and moisture budgets of a well-mixed boundary layer. The model budgets are validated against energy budgets obtained from airborne measurements over heterogeneous terrain in Western Germany. Time rate of change, vertical divergence, and horizontal advection for an atmospheric column of air are estimated. Results show that the time trend of specific humidity exhibits some deficiencies, while the potential temperature trend is matched accurately. Furthermore, the simulated turbulent surface fluxes of sensible and latent heat are comparable to the measured fluxes, leading to similar values of the vertical divergence. The analysis of different horizontal model resolutions exhibits improved surface fluxes with increased resolution, a fact attributed to a reduced aggregation effect. Scale-interaction effects could be identified: while time trends and advection are strongly influenced by mesoscale forcing, the turbulent surface fluxes are mainly controlled by microscale processes