Development of a computational model to inform environmental surveillance sampling plans for Salmonella enterica serovar Typhi in wastewater.

Typhoid fever-an acute febrile disease caused by infection with the bacterium Salmonella enterica serotype Typhi (S. Typhi)-continues to be a leading cause of global morbidity and mortality, particularly in developing countries with limited access to safe drinking water and adequate sanitation. Environmental surveillance, the process of detecting and enumerating disease-causing agents in wastewater, is a useful tool to monitor the circulation of typhoid fever in endemic regions. The design of environmental surveillance sampling plans and the interpretation of sampling results is complicated by a high degree of uncertainty and variability in factors that affect the final measured pathogens in wastewater samples, such as pathogen travel time through a wastewater network, pathogen dilution, decay and degradation, and laboratory processing methods. Computational models can, to an extent, assist in the design of sampling plans and aid in the evaluation of how different contributing factors affect sampling results. This study presents a computational model combining dynamic and probabilistic modeling techniques to estimate-on a spatial and temporal scale-the approximate probability of detecting S. Typhi within a wastewater system. This model may be utilized to inform environmental surveillance sampling plans and may provide useful insight into selecting appropriate sampling locations and times and interpreting results. A simulated applied modeling scenario is presented to demonstrate the model's functionality for aiding an environmental surveillance study in a typhoid-endemic community.

Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia.

The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF's epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on the DF burden of disease in Saudi Arabia and we attempt to create robust DF predictive models. Using 10 years of DF, weather, and pilgrimage data, we conducted a bivariate analysis investigating the role of weather and pilgrimage variables on DF incidence. We also compared the abilities of three different predictive models. Amongst weather variables, temperature and humidity had the strongest associations with DF incidence, while rainfall showed little to no significant relationship. Pilgrimage variables did not have strong associations with DF incidence. The random forest model had the highest predictive ability (R2 = 0.62) when previous DF data were withheld, and the ARIMA model was the best (R2 = 0.78) when previous DF data were incorporated. We found that a nonlinear machine-learning model incorporating temperature and humidity variables had the best prediction accuracy for DF, regardless of the availability of previous DF data. This finding can inform DF early warning systems and preparedness in Saudi Arabia.

The effect of weather and climate on dengue outbreak risk in Peru, 2000-2018: A time-series analysis.

BACKGROUND: Dengue fever is the most common arboviral disease in humans, with an estimated 50-100 million annual infections worldwide. Dengue fever cases have increased substantially in the past four decades, driven largely by anthropogenic factors including climate change. More than half the population of Peru is at risk of dengue infection and due to its geography, Peru is also particularly sensitive to the effects of El Niño Southern Oscillation (ENSO). Determining the effect of ENSO on the risk for dengue outbreaks is of particular public health relevance and may also be applicable to other Aedes-vectored viruses. METHODS: We conducted a time-series analysis at the level of the district-month, using surveillance data collected from January 2000 to September 2018 from all districts with a mean elevation suitable to survival of the mosquito vector (<2,500m), and ENSO and weather data from publicly-available datasets maintained by national and international agencies. We took a Bayesian hierarchical modeling approach to address correlation in space, and B-splines with four knots per year to address correlation in time. We furthermore conducted subgroup analyses by season and natural region. RESULTS: We detected a positive and significant effect of temperature (°C, RR 1.14, 95% CI 1.13, 1.15, adjusted for precipitation) and ENSO (ICEN index: RR 1.17, 95% CI 1.15, 1.20; ONI index: RR 1.04, 95% CI 1.02, 1.07) on outbreak risk, but no evidence of a strong effect for precipitation after adjustment for temperature. Both natural region and season were found to be significant effect modifiers of the ENSO-dengue effect, with the effect of ENSO being stronger in the summer and the Selva Alta and Costa regions, compared with winter and Selva Baja and Sierra regions. CONCLUSIONS: Our results provide strong evidence that temperature and ENSO have significant effects on dengue outbreaks in Peru, however these results interact with region and season, and are stronger for local ENSO impacts than remote ENSO impacts. These findings support optimization of a dengue early warning system based on local weather and climate monitoring, including where and when to deploy such a system and parameterization of ENSO events, and provide high-precision effect estimates for future climate and dengue modeling efforts.

A novel approach to modeling epidemic vulnerability, applied to Aedes aegypti-vectored diseases in Perú.

BACKGROUND: A proactive approach to preventing and responding to emerging infectious diseases is critical to global health security. We present a three-stage approach to modeling the spatial distribution of outbreak vulnerability to Aedes aegypti-vectored diseases in Perú. METHODS: Extending a framework developed for modeling hemorrhagic fever vulnerability in Africa, we modeled outbreak vulnerability in three stages: index case potential (stage 1), outbreak receptivity (stage 2), and epidemic potential (stage 3), stratifying scores on season and El Niño events. Subsequently, we evaluated the validity of these scores using dengue surveillance data and spatial models. RESULTS: We found high validity for stage 1 and 2 scores, but not stage 3 scores. Vulnerability was highest in Selva Baja and Costa, and in summer and during El Niño events, with index case potential (stage 1) being high in both regions but outbreak receptivity (stage 2) being generally high in Selva Baja only. CONCLUSIONS: Stage 1 and 2 scores are well-suited to predicting outbreaks of Ae. aegypti-vectored diseases in this setting, however stage 3 scores appear better suited to diseases with direct human-to-human transmission. To prevent outbreaks, measures to detect index cases should be targeted to both Selva Baja and Costa, while Selva Baja should be prioritized for healthcare system strengthening. Successful extension of this framework from hemorrhagic fevers in Africa to an arbovirus in Latin America indicates its broad utility for outbreak and pandemic preparedness and response activities.

The effect of urbanization and climate change on the mosquito population in the Pearl River Delta region of China.

The rising incidence of mosquito-borne diseases is a global concern. Changes in regional climate, due to urbanization and global greenhouse gas concentrations, may affect the ecology of mosquitoes and mosquito-transmitted pathogens. The effects of changes in climate on mosquito population dynamics are complex but critical and urgent for implementing more effective public health policies. This study quantified the impact of urbanization and global climate change on the population of the mosquito species, Culex quinquefasciatus, in the Pearl River Delta region in southern China-an area that has undergone substantial urbanization and is expected to experience changes in climate. This study employed a mechanistic model to simulate mosquito population dynamics under various greenhouse gas emission and land-cover change scenarios based on climate data provided by a state-of-the-art regional climate model. Our results show a 12.6% decrease in the annual mosquito population in newly urbanized areas and a 5.9% increase in the annual mosquito population in existing urban areas. Furthermore, changing climate conditions are projected to cause a 15-17% reduction in the total annual mosquito population; however, the change will not be uniform throughout the year. Peak months exhibit a reduction in population, whereas non-peak months show a significant increase. These findings suggest mosquito control strategies may need to be adjusted to respond to the impacts of urbanization and climate change on mosquito population dynamics to maximize effectiveness. Region specific, quantitative analyses of environmental impacts on mosquito-borne disease ecology, like this study, are needed to provide policy makers with a scientific reference to guide the formation of effective transmission intervention strategies.

Dengue fever in Saudi Arabia: A review of environmental and population factors impacting emergence and spread.

Dengue fever (DF) is the most important mosquito-transmitted viral disease causing a large economic and disease burden in many parts of the world. Most DF research focuses on Latin America and Asia, where burdens are highest. There is a critical need for studies in other regions where DF is an important public health problem but less well-characterized and can differ, such as the Middle East. The first documented case of DF in Saudi Arabia occurred in 1993. After a decade of sporadic outbreaks, the disease was declared endemic in 2004 and this designation persists. Climate, sociodemographic factors, and increasing urbanization impact the spread of DF in Saudi Arabia, as in other areas. However, DF transmission in Saudi Arabia is also affected by several unique factors, including large numbers of migrant workers and religious pilgrims from other dengue endemic areas across the Middle East, North Africa, and Asia. Important knowledge gaps relate to the role of climatic factors as drivers of DF in Saudi Arabia and the role of foreign workers and pilgrims in the original and continuous importation of dengue virus. Filling these gaps would improve health system preparedness.

Unexplored Opportunities: Use of Climate- and Weather-Driven Early Warning Systems to Reduce the Burden of Infectious Diseases.

PURPOSE OF REVIEW: Weather and climate influence multiple aspects of infectious disease ecology. Creating and applying early warning systems based on temperature, precipitation, and other environmental data can identify where and when outbreaks of climate-sensitive infectious diseases could occur and can be used by decision makers to allocate resources. Whether an outbreak actually occurs depends heavily on other social, political, and institutional factors. RECENT FINDINGS: Improving the timing and confidence of seasonal climate forecasting, coupled with knowledge of exposure-response relationships, can identify prior conditions conducive to disease outbreaks weeks to months in advance of outbreaks. This information could then be used by public health professionals to improve surveillance in the most likely areas for threats. Early warning systems are well established for drought and famine. And while weather- and climate-driven early warning systems for certain diseases, such as dengue fever and cholera, are employed in some regions, this area of research is underdeveloped. Early warning systems based on temperature, precipitation, and other environmental data provide an opportunity for early detection leading to early action and response to potential pathogen threats, thereby reducing the burden of disease when compared with passive health indicator-based surveillance systems.

Avian influenza virus ecology and evolution through a climatic lens.

Avian influenza virus (AIV) is a major health threat to both avian and human populations. The ecology of the virus is driven by numerous factors, including climate and avian migration patterns, yet relatively little is known about these drivers. Long-distance transport of the virus is tied to inter- and intra-continental bird migration, while enhanced viral reassortment is linked to breeding habitats in Beringia shared by migrant species from North America and Asia. Furthermore, water temperature, pH, salinity, and co-existing biota all impact the viability and persistence of the virus in the environment. Changes in climate can potentially alter the ecology of AIV through multiple pathways. Warming temperatures can change the timing and patterns of bird migration, creating novel assemblages of species and new opportunities for viral transport and reassortment. Water temperature and chemistry may also be altered, resulting in changes in virus survival. In this review, we explain how these shifts have the potential to increase viral persistence, pathogenicity, and transmissibility and amplify the threat of pandemic disease in animal and human hosts. Better understanding of climatic influences on viral ecology is essential to developing strategies to limit adverse health effects in humans and animals.

Managing mosquito spaces: Citizen self-governance of disease vectors in a desert landscape.

Public health agencies' strategies to control disease vectors have increasingly included "soft" mosquito management programs that depend on citizen education and changing homeowner behaviors. In an effort to understand public responses to such campaigns, this research assesses the case of Tucson, Arizona, where West Nile virus presents a serious health risk and where management efforts have focused on public responsibility for mosquito control. Using surveys, interviews, and focus groups, we conclude that citizens have internalized responsibilities for mosquito management but also expect public management of parks and waterways while tending to reject the state's interference with privately owned parcels. Resident preferences for individualized mosquito management hinge on the belief that mosquito-borne diseases are not a large threat, a pervasive distrust of state management, and a fear of the assumed use of aerial pesticides by state managers. Opinions on who is responsible for mosquitoes hinge on both perceptions of mosquito ecology and territorial boundaries, with implications for future disease outbreaks.

An Analysis of the Potential Impact of Climate Change on Dengue Transmission in the Southeastern United States.

BACKGROUND: Dengue fever, caused by a mosquito-transmitted virus, is an increasing health concern in the Americas. Meteorological variables such as temperature and precipitation can affect disease distribution and abundance through biophysical impacts on the vector and on the virus. Such tightly coupled links may facilitate further spread of dengue fever under a changing climate. In the southeastern United States, the dengue vector is widely established and exists on the current fringe of dengue transmission. OBJECTIVES: We assessed projected climate change-driven shifts in dengue transmission risk in this region. METHODS: We used a dynamic mosquito population and virus transmission model driven by meteorological data to simulate Aedes aegypti populations and dengue cases in 23 locations in the southeastern United States under current climate conditions and future climate projections. We compared estimates for each location with simulations based on observed data from San Juan, Puerto Rico, where dengue is endemic. RESULTS: Our simulations based on current climate data suggest that dengue transmission at levels similar to those in San Juan is possible at several U.S. locations during the summer months, particularly in southern Florida and Texas. Simulations that include climate change projections suggest that conditions may become suitable for virus transmission in a larger number of locations and for a longer period of time during each year. However, in contrast with San Juan, U.S. locations would not sustain year-round dengue transmission according to our model. CONCLUSIONS: Our findings suggest that Dengue virus (DENV) transmission is limited by low winter temperatures in the mainland United States, which are likely to prevent its permanent establishment. Although future climate conditions may increase the length of the mosquito season in many locations, projected increases in dengue transmission are limited to the southernmost locations.

On the Seasonal Occurrence and Abundance of the Zika Virus Vector Mosquito Aedes Aegypti in the Contiguous United States.

INTRODUCTION: An ongoing Zika virus pandemic in Latin America and the Caribbean has raised concerns that travel-related introduction of Zika virus could initiate local transmission in the United States (U.S.) by its primary vector, the mosquito Aedes aegypti. METHODS: We employed meteorologically driven models for 2006-2015 to simulate the potential seasonal abundance of adult Aedes aegypti for fifty cities within or near the margins of its known U.S. range. Mosquito abundance results were analyzed alongside travel and socioeconomic factors that are proxies of viral introduction and vulnerability to human-vector contact.     RESULTS: Meteorological conditions are largely unsuitable for Aedes aegypti over the U.S. during winter months (December-March), except in southern Florida and south Texas where comparatively warm conditions can sustain low-to-moderate potential mosquito abundance. Meteorological conditions are suitable for Aedes aegypti across all fifty cities during peak summer months (July-September), though the mosquito has not been documented in all cities. Simulations indicate the highest mosquito abundance occurs in the Southeast and south Texas where locally acquired cases of Aedes-transmitted viruses have been reported previously. Cities in southern Florida and south Texas are at the nexus of high seasonal suitability for Aedes aegypti and strong potential for travel-related virus introduction. Higher poverty rates in cities along the U.S.-Mexico border may correlate with factors that increase human exposure to Aedes aegypti.     DISCUSSION: Our results can inform baseline risk for local Zika virus transmission in the U.S. and the optimal timing of vector control activities, and underscore the need for enhanced surveillance for Aedes mosquitoes and Aedes-transmitted viruses.

Meteorologically Driven Simulations of Dengue Epidemics in San Juan, PR.

Meteorological factors influence dengue virus ecology by modulating vector mosquito population dynamics, viral replication, and transmission. Dynamic modeling techniques can be used to examine how interactions among meteorological variables, vectors and the dengue virus influence transmission. We developed a dengue fever simulation model by coupling a dynamic simulation model for Aedes aegypti, the primary mosquito vector for dengue, with a basic epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) model. Employing a Monte Carlo approach, we simulated dengue transmission during the period of 2010-2013 in San Juan, PR, where dengue fever is endemic. The results of 9600 simulations using varied model parameters were evaluated by statistical comparison (r2) with surveillance data of dengue cases reported to the Centers for Disease Control and Prevention. To identify the most influential parameters associated with dengue virus transmission for each period the top 1% of best-fit model simulations were retained and compared. Using the top simulations, dengue cases were simulated well for 2010 (r2 = 0.90, p = 0.03), 2011 (r2 = 0.83, p = 0.05), and 2012 (r2 = 0.94, p = 0.01); however, simulations were weaker for 2013 (r2 = 0.25, p = 0.25) and the entire four-year period (r2 = 0.44, p = 0.002). Analysis of parameter values from retained simulations revealed that rain dependent container habitats were more prevalent in best-fitting simulations during the wetter 2010 and 2011 years, while human managed (i.e. manually filled) container habitats were more prevalent in best-fitting simulations during the drier 2012 and 2013 years. The simulations further indicate that rainfall strongly modulates the timing of dengue (e.g., epidemics occurred earlier during rainy years) while temperature modulates the annual number of dengue fever cases. Our results suggest that meteorological factors have a time-variable influence on dengue transmission relative to other important environmental and human factors.

Regional and seasonal response of a West Nile virus vector to climate change.

Climate change will affect the abundance and seasonality of West Nile virus (WNV) vectors, altering the risk of virus transmission to humans. Using downscaled general circulation model output, we calculate a WNV vector's response to climate change across the southern United States using process-based modeling. In the eastern United States, Culex quinquefasciatus response to projected climate change displays a latitudinal and elevational gradient. Projected summer population depressions as a result of increased immature mortality and habitat drying are most severe in the south and almost absent further north; extended spring and fall survival is ubiquitous. Much of California also exhibits a bimodal pattern. Projected onset of mosquito season is delayed in the southwestern United States because of extremely dry and hot spring and summers; however, increased temperature and late summer and fall rains extend the mosquito season. These results are unique in being a broad-scale calculation of the projected impacts of climate change on a WNV vector. The results show that, despite projected widespread future warming, the future seasonal response of C. quinquefasciatus populations across the southern United States will not be homogeneous, and will depend on specific combinations of local and regional conditions.

Climate and dengue transmission: evidence and implications.

BACKGROUND: Climate influences dengue ecology by affecting vector dynamics, agent development, and mosquito/human interactions. Although these relationships are known, the impact climate change will have on transmission is unclear. Climate-driven statistical and process-based models are being used to refine our knowledge of these relationships and predict the effects of projected climate change on dengue fever occurrence, but results have been inconsistent. OBJECTIVE: We sought to identify major climatic influences on dengue virus ecology and to evaluate the ability of climate-based dengue models to describe associations between climate and dengue, simulate outbreaks, and project the impacts of climate change. METHODS: We reviewed the evidence for direct and indirect relationships between climate and dengue generated from laboratory studies, field studies, and statistical analyses of associations between vectors, dengue fever incidence, and climate conditions. We assessed the potential contribution of climate-driven, process-based dengue models and provide suggestions to improve their performance. RESULTS AND DISCUSSION: Relationships between climate variables and factors that influence dengue transmission are complex. A climate variable may increase dengue transmission potential through one aspect of the system while simultaneously decreasing transmission potential through another. This complexity may at least partly explain inconsistencies in statistical associations between dengue and climate. Process-based models can account for the complex dynamics but often omit important aspects of dengue ecology, notably virus development and host-species interactions. CONCLUSION: Synthesizing and applying current knowledge of climatic effects on all aspects of dengue virus ecology will help direct future research and enable better projections of climate change effects on dengue incidence.

Modeled response of the West Nile virus vector Culex quinquefasciatus to changing climate using the dynamic mosquito simulation model.

Climate can strongly influence the population dynamics of disease vectors and is consequently a key component of disease ecology. Future climate change and variability may alter the location and seasonality of many disease vectors, possibly increasing the risk of disease transmission to humans. The mosquito species Culex quinquefasciatus is a concern across the southern United States because of its role as a West Nile virus vector and its affinity for urban environments. Using established relationships between atmospheric variables (temperature and precipitation) and mosquito development, we have created the Dynamic Mosquito Simulation Model (DyMSiM) to simulate Cx. quinquefasciatus population dynamics. The model is driven with climate data and validated against mosquito count data from Pasco County, Florida and Coachella Valley, California. Using 1-week and 2-week filters, mosquito trap data are reproduced well by the model (P < 0.0001). Dry environments in southern California produce different mosquito population trends than moist locations in Florida. Florida and California mosquito populations are generally temperature-limited in winter. In California, locations are water-limited through much of the year. Using future climate projection data generated by the National Center for Atmospheric Research CCSM3 general circulation model, we applied temperature and precipitation offsets to the climate data at each location to evaluate mosquito population sensitivity to possible future climate conditions. We found that temperature and precipitation shifts act interdependently to cause remarkable changes in modeled mosquito population dynamics. Impacts include a summer population decline from drying in California due to loss of immature mosquito habitats, and in Florida a decrease in late-season mosquito populations due to drier late summer conditions.

Expression of RBM5-related factors in primary breast tissue.

The aim of this study was to examine the expression of the RBM5 tumor suppressor, in relation to RBM6 and RBM10, to obtain a better understanding of the potential role played by these RBM5-related factors in the regulation of RBM5 tumor-suppressor activity. Paired non-tumor and tumor samples were obtained from 73 breast cancer patients. RNA and protein expression were examined by semi-quantitative reverse transcription-polymerase chain reaction and immunoblot, respectively. Data were analyzed using various statistical methods to test for correlations amongst the RBM5-related factors, and between the factors and various pathological parameters. Most notably, RBM5, RBM10v1, and HER2 protein expression levels were elevated in tumor tissue (P < 0.0001). RBM5 and RBM10v1 protein expression were significantly positively correlated (P < 0.001), as were RBM5 and HER2 protein expression (P < 0.01), in both non-tumor and tumor tissue, whereas RBM10v1 and HER2 protein expression were only marginally correlated, in non-tumor tissue (P < 0.05). Interestingly, RBM5 and RBM10v1 protein expression were both deregulated in relation to RNA expression in tumor tissue. RBM10v2 and RBM6 RNA were highly significantly positively correlated in relation to various factors relating to poor prognosis (P < 0.0001). To our knowledge, this study is the first to examine RBM5 expression at both the RNA and protein level in primary breast tumor tissue, and the first to examine expression of all RBM5-related factors in a comprehensive manner. The results provide a graphic illustration that RBM5-related factors are significantly differentially expressed in breast cancer, and suggest complex inter-related regulatory networks involving alternative splicing, oncogenic expression, and tissue-specific function.

RNA binding motif (RBM) proteins: a novel family of apoptosis modulators?

RBM5 is a known modulator of apoptosis, an RNA binding protein, and a putative tumor suppressor. Originally identified as LUCA-15, and subsequently as H37, it was designated "RBM" (for RNA Binding Motif) due to the presence of two RRM (RNA Recognition Motif) domains within the protein coding sequence. Recently, a number of proteins have been attributed with this same RBM designation, based on the presence of one or more RRM consensus sequences. One such protein, RBM3, was also recently found to have apoptotic modulatory capabilities. The high sequence homology at the amino acid level between RBM5, RBM6, and particularly, RBM10 suggests that they, too, may play an important role in regulating apoptosis. It is the intent of this article to ammalgamate the data on the ten originally identified RBM proteins in order to question the existence of a novel family of RNA binding apoptosis regulators.