Entomological Investigation Following a Zika Outbreak in Brownsville, Texas

ABSTRACT In November and December 2016, an outbreak of locally transmitted Zika occurred in Brownsville, TX. The Texas Department of State Health Services requested for a Centers for Disease Control and Prevention (CDC) Epi Aid, and as part of that Epi Aid a team of CDC entomologists was deployed in January 2017. The mission was to improve mosquito-based arbovirus surveillance and evaluate the possibility of continuing local Zika virus (ZIKV) transmission in the city. The mosquito-based arbovirus surveillance program was expanded from 4 to 40 BG-Sentinel traps evenly distributed throughout the city. Over a 2-wk period, 15 mosquito species were detected; the most abundant species were Culex quinquefasciatus, Aedes aegypti, and Ae. albopictus, which accounted for 66.7%, 16.2%, and 5.7% of the total mosquito collection, respectively. The relative abundance of Ae. aegypti (1.0 mosquitoes/trap/day) and Ae. albopictus (0.4 mosquitoes/trap/day) was very low and unlikely to initiate and/or sustain ZIKV transmission. Zika virus was not detected in the mosquitoes collected, suggesting no or extremely low ZIKV transmission at that time.

A Checklist of the Mosquito Species in 13 Counties West of San Antonio, Texas

ABSTRACT The aim of this study was to consolidate mosquito information for 13 counties west of San Antonio, Bexar County, Texas, and to create a species checklist for future regional studies. The resulting checklist established a baseline for local mosquito-borne disease surveillance and can serve as a resource for public health officials. The 13 counties in this region were Bandera, Edwards, Kendall, Kerr, Kimble, Kinney, Maverick, Medina, Real, Sutton, Uvalde, Val Verde, and Zavala counties. To develop the checklist, county-level mosquito species data were extracted from 38 peer-reviewed publications and government documents, university reference collections, private collections, and the Texas Department of State Health Services' historical collection data. These data were combined with author field collections to create a comprehensive species list. Overall, 339 county-level records were documented through field studies with a total of 36 species representing 8 genera confirmed as being present in this region. An additional 14 species listed in historical surveillance records were not collected during this study.

Laboratory Evaluation of Aggregate Friction and Texture Properties

Aggregate shape, texture, and angularity are important physical properties for the development of high-quality adhesive systems with asphalt binder, and a good quality aggregate skeleton in asphalt and concrete mixtures. These properties are commonly measured using systems that employ images of aggregate samples taken with a digital camera. In practice, macro-texture measurements are commonly conducted nondestructively at highway speed using high-frequency lasers. There is a need to investigate the application of these optical sensors for measuring aggregate texture in the laboratory. Researchers used a laser-based scanning system to test aggregates from five sources before and after Micro-Deval abrasion. The aggregate specimens were embedded in a ring-shaped polyester material and tested with the aggregate ring texturing system (ARTS) along with the dynamic friction tester (DFT) to characterize micro-texture and frictional properties. Researchers analyzed the laboratory test data to evaluate the relationship between DFT friction at 60 km/h (DFT60) and the micro mean profile depth obtained with the ARTS. This analysis showed that the igneous and gravel aggregates had better micro-texture and frictional characteristics compared with the dolomite and limestone aggregates. The results from this implementation project showed the potential for using the ARTS to improve the Texas Department of Transportation’s existing surface aggregate classification system. Further testing is needed to characterize aggregates from other sources and compile a more comprehensive and representative database with which to establish meaningful aggregate classification criteria that include surrogate statistics for expected field performance.

The Return of Asiatic Citrus Canker to Texas: Surveys and Eradication Efforts

Citrus canker caused by Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri) was first reported in South Texas during the early 1900s, prompting quarantine and eradication programs by the federal government. Its last reported incidence in Texas was from two trees from Corpus Christi in 1943. In May 2016, USDA Animal and Plant Health Inspection Service-Plant Protection and Quarantine (PPQ) confirmed the presence of the Asiatic A strain, which is by far the most widespread and severe form infecting most citrus varieties, on two sour orange trees in a park in Houston. Subsequently, infected trees were detected in a nursery in Richmond (Fort Bend Co.) in August 2016 and in dooryard trees in Pearland (Brazoria Co.) in 2018. PPQ and Texas Department of Agriculture collected a combined 1,949 positive A strain samples from Harris, Fort Bend, and Brazoria Counties during May 2016 to January 2021. These findings resulted in seven active citrus canker quarantine zones being established in the Upper Golf Coast area. The Texas Department of Agriculture is removing positive trees, and surveys are continuing.

Mathematical Modeling and Covid-19 Forecast in Texas, USA: a prediction model analysis and the probability of disease outbreak

Background Response to the unprecedented COVID-19 outbreak needs to be augmented in Texas, USA, where the first 5 cases were reported on March 6, 2020, were rapidly followed by an exponential rise within the next few weeks. This study aimed to determine the ongoing trend and upcoming infection status of COVID-19 in county levels of Texas. Methods Data were extracted from the following sources: published literature, surveillance, unpublished reports, and websites of Texas Department of State Health Services (DSHS), Natality report of Texas and WHO Coronavirus Disease (COVID-19) Dashboard. Four-compartment Susceptible-Exposed-Infectious-Removal (SEIR) mathematical model was used to estimate the current trend and future prediction of basic reproduction number and infection case in Texas. Since the basic reproduction number is not sufficient to predict the outbreak, we applied the Continuous-Time Markov Chain (CTMC) model to calculate the probability of the COVID-19 outbreak. Results The estimated mean basic reproduction number of COVID-19 in Texas is predicted 2.65 by January 31, 2021. Our model indicated that the third wave might occur at the beginning of May of 2021, which will peak at the end of June 2021. This prediction may come true if the current spreading situation/level persists, i.e., no clinically effective vaccine is available,or this vaccination program fails for some reason in this area. Conclusion Our analysis indicates an alarming ongoing and upcoming infection rate of COVID-19 at county levels of Texas, thereby emphasizing promoting more coordinated and disciplined actions by both policymakers and the population to contain its devastating impact.

Modeling COVID-19 positivity rates and hospitalizations in Texas

The aim of this study was to jointly model COVID-19 test positivity rates and hospitalizations in Texas using Bayesian joinpoint regression. The data for both test positivity rates and hospitalizations were obtained from the Texas Department of State Health Services between April 5 and October 19, 2020. The stage 1 model identifies four significant shifts in test positivity rates, three of which occur roughly 9 days after documented policy or behavioral changes statewide. Estimated positivity rates from the first model were then used to predict hospitalization rates and to estimate lag time between changes in positivity and hospitalization. The resulting lag time is 9.056 days (± 3.808). Both models are valuable to policy makers and public health officials as they study the impact of behavioral patterns on disease prevalence and resulting hospitalizations.

Epidemiology Surveillance and Capacity Improvement: A Characterization of Texas, 2017

Objectives: In response to increasing caseloads of foodborne illnesses and high consequence infectious disease investigations, the Texas Department of State Health Services (DSHS) requested funding from the Texas Legislature in 2013 and 2015 for a new state-funded epidemiologist (SFE) program. Methods: Primary cross-sectional survey data were collected from 32 of 40 local health departments (LHDs) via an online instrument and analyzed to quantify roles, responsibilities, and training of epidemiologists in Texas in 2017 and compared to similar state health department assessments. Results: Sixty-six percent of SFEs had epidemiology-specific training (eg, master’s in public health) compared to 45% in state health department estimates. For LHDs included in this study, the mean number of epidemiologists per 100 000 was 0.73 in medium LHDs and 0.46 in large LHDs. SFE positions make up approximately 40% of the LHD epidemiology workforce of all sizes and 56% of medium-sized LHD epidemiology staff in Texas specifically. Conclusions: Through this program, DSHS increased epidemiology capacity almost twofold from 0.28 to 0.47 epidemiologists per 100 000 people. These findings suggest that capacity funding programs like this improve epidemiology capacity in local jurisdictions and should be considered in other regions to improve general public health preparedness and epidemiology capacity.

County-Level Records for Culex stigmatosoma and Culex thriambus in Texas

ABSTRACT Populations of Culex stigmatosoma and Cx. thriambus have been documented in the southwestern USA with a southward range extension to northern South America and Central America, respectively. Studies conducted in California indicate both species are potential vectors of West Nile virus. However, vector competence studies are lacking for other parts of the USA. During a multicounty regional surveillance study west of San Antonio, Texas, multiple errors were observed in the Texas distributional literature of these species. These errors involved incorrect distributional information in Texas and US publications. Evidence to correct these errant records was found upon further analysis of Texas literature and curated specimens. Therefore, the aims of this study were to present that evidence and then combine the corrected records with additional records from the Texas Department of State Health Services and from larval collections made during other Texas surveillance studies.

Prison Population Reductions and COVID-19: A Latent Profile Analysis Synthesizing Recent Evidence From the Texas State Prison System

People in prison are particularly vulnerable to infectious disease due to close living conditions and the lack of protective equipment. As a result, public health professionals and prison administrators seek information to guide best practices and policy recommendations during the COVID-19 pandemic. Using latent profile analysis, we sought to characterize Texas prisons on levels of COVID-19 cases and deaths among incarcerated residents, and COVID-19 cases among prison staff. This observational study was a secondary data analysis of publicly available data from the Texas Department of Criminal Justice (TBDJ) collected from March 1, 2020, until July 24, 2020. This project was completed in collaboration with the COVID Prison Project. We identified relevant profiles from the data: a low-outbreak profile, a high-outbreak profile, and a high-death profile. Additionally, current prison population and level of employee staffing predicted membership in the high-outbreak and high-death profiles when compared with the low-outbreak profile. Housing persons at 85% of prison capacity was associated with lower risk of COVID-19 infection and death. Implementing this 85% standard as an absolute minimum should be prioritized at prisons across the USA.

Comparing crime rates between undocumented immigrants, legal immigrants, and native-born US citizens in Texas

We make use of uniquely comprehensive arrest data from the Texas Department of Public Safety to compare the criminality of undocumented immigrants to legal immigrants and native-born US citizens between 2012 and 2018. We find that undocumented immigrants have substantially lower crime rates than native-born citizens and legal immigrants across a range of felony offenses. Relative to undocumented immigrants, US-born citizens are over 2 times more likely to be arrested for violent crimes, 2.5 times more likely to be arrested for drug crimes, and over 4 times more likely to be arrested for property crimes. In addition, the proportion of arrests involving undocumented immigrants in Texas was relatively stable or decreasing over this period. The differences between US-born citizens and undocumented immigrants are robust to using alternative estimates of the broader undocumented population, alternate classifications of those counted as “undocumented” at arrest and substituting misdemeanors or convictions as measures of crime.