Evaluating Beach Water Quality and Dengue Fever Risk Factorsby Satellite Remote Sensing and Artificial Neural NetworksbyAbdiel Elias Laureano-RosarioA dissertation submitted in partial fulfillmentof the requirements for the degree ofDoctor of Philosophywith a concentration in Interdisciplinary ScienceCollege of Marine ScienceUniversity of South FloridaMajor Professor: Frank E. Müller-Karger, Ph.D.Mya Breitbart, Ph.D.Mark E. Luther, Ph.
D.Ricardo Izurieta, MD, Dr.PH, MPHJames R.
Mihelcic, Ph.D.Dragan A. Savi?, Ph.D.
Date of ApprovalMay 4, 2018Keywords: Aedes aegypti, public health, beach water quality, machine learning, fecal indicator bacteria, ocean color, artificial neural networksCopyright © 2018, Abdiel E. Laureano-RosarioDEDICATIONEsta disertación es dedicada a mi madre y padre, Sol Rosario y Ramon Laureano, quienes hicieron todo en su poder para ayudarme y apoyarme durante este proceso. Gracias a ambos por sus sacrificios desde el primer día, por su apoyo, paciencia y amor durante el tiempo que trabajé mi grado. ¡Los amo!This dissertation is dedicated to my mother and father, Sol Rosario and Ramon Laureano, who did everything in their power to help me and support me through this process.
Thank you both for your sacrifices since day one, for all your support, patience, and love while I was working on my degree. I love you!ACKNOWLEDGMENTSI thank my major advisor, Dr. Frank E. Muller-Karger, for his support and encouragement during my time as a student at USF-CMS. Special thanks are also given to my committee members, Dr. Mya Breitbart, Dr. Mark Luther, Dr.
Ricardo Izurieta, Dr. James Mihelcic, and Dr. Dragan Savic, for their constant support, help, and contributions to my dissertation work.
Furthermore, I also thank my co-authors and collaborators from the Universidad Autonoma de Yucatan, University of Puerto Rico, University of Exeter, and the Instituto Costarricense de Acueductos y Alcantarillados. I acknowledge Mr. Bernard Batson, from the College of Engineering, for his constant support and encouragement through my time at USF.
I thank the IMaRS team for their constant support and help.There is a special place in my heart, and I cannot express how thankful I am for my support group during my Ph.D. journey: D. Chacin, I. Freytes, L.
Martell, Dr. Vega-Rodriguez, N. Lopez, and Dr. Mendez-Ferrer.
Thank you all for the help and support during this process. Lastly, this work would not have been possible without the financial support from USF-CMS Linton Tibbetts Graduate Endowed Fellowship, Alfred P. Sloan Foundation, DEX Imaging Fellowship, and the NASA Earth and Space Science Fellowship. This material is based upon work supported by the National Science Foundation under Grant No. 1243510. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
TABLE OF CONTENTList of FiguresiiiAbstractivChapter 1: Introduction1Overview and objectives1Environmental forces influence on dengue fever occurrences and recreational water quality2Environmental and demographic factors influence on vector-borne diseases3The influence of environmental factors on fecal indicator bacteria and recreational water quality5Predicting vector-borne diseases and recreational water quality with Artificial Neural Networks6Study areas8Northwest coast, Yucatan, Mexico8Escambron Beach, San Juan, Puerto Rico9Literature cited10Chapter 2: Modelling dengue fever risks in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature16Research overview16Chapter 3: Environmental factors correlated with culturable enterococci concentrations in tropical recreational waters: A case study in Escambron Beach, San Juan, Puerto Rico18Research overview18Chapter 4: Application of Artificial Neural Networks for dengue fever predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico20Research overview20Chapter 5: Artificial Neural Networks better predict exceedances of recreational water quality criteria at Escambron Beach, San Juan, Puerto Rico22Research overview22Chapter 6: Conclusion24Summary24Future research27Literature cited29Appendix A: Modelling dengue fever risks in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature30Appendix B: Environmental factors correlated with culturable enterococci concentrations in tropical recreational waters: A case study in Escambron Beach, San Juan, Puerto Rico39Appendix C: Application of Artificial Neural Networks for dengue fever predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico59Appendix D: Artificial Neural Networks better predict exceedances of recreational water quality criteria at Escambron Beach, San Juan, Puerto Rico78Appendix E: Authors contributions and permissions for reprinting previously-published work105Authors contributions105Permissions for reprinting107LIST OF FIGURESFigure 1.1:Northwest coast of the Yucatan Peninsula, Mexico. Map depicts the location of municipalities used in the study: Chicxulub Pueblo, Dzemul, Hunucma, Ixil, Progreso, Telchac Pueblo, Telchac Puerto, Ucu, and Merida8Figure 1.2:San Juan, Puerto Rico. The inset map depicts Escambron Beach study area, both sampling locations (green triangles), stormwater discharge drain (black circle), and public bathrooms (bathroom symbol)10ABSTRACTClimatic variations, together with large-scale environmental forces and human development affect the quality of coastal recreational waters, creating potential risks to human health.
These environmental forces, including increased temperature and precipitation, often promote specific vector-borne diseases in the Caribbean and Gulf of Mexico. Human activities affect water quality through discharges from urban areas, including nutrient and other pollutants derived from wastewater systems. Both water quality of recreational beaches and vector-borne diseases can be better managed by understanding their relationship with local environmental forces.I evaluated how changes in vector-borne diseases and poor recreational water quality were related to specific environmental factors through the application of satellite-derived observations, field observations, and public health records. Variability in dengue fever incidence rates in coastal towns of the Yucatan Peninsula (Mexico) was evaluated with respect to environmental factors in Chapter Two.
Correlations between fecal indicator bacteria concentrations (i.e., culturable enterococci) at Escambron Beach (San Juan, Puerto Rico, USA) and regional environmental factors are discussed in Chapter Three. Predictions of dengue fever occurrences in the Yucatan Peninsula were tested using a nonlinear approach (i.e., Artificial Neural Networks) and are presented in Chapter Four. The Artificial Neural Network (ANN) model was also used to predict culturable enterococci concentration exceeding safe recreational water quality standards in Escambron Beach and results are presented in Chapter Five. Environmental factors assessed to understand their influence on dengue fever occurrences and culturable enterococci concentrations included precipitation, mean sea level (MSL), air temperatures (e.
g., maximum, minimum, and average), humidity, and satellite-derived sea surface temperature (SST), dew point, direct normal irradiance (DNI), and turbidity. These factors were combined with demographic data (e.g., population size) and compared with dengue fever incidence rates and culturable enterococci concentration using linear and nonlinear statistical approaches.Dengue incidence rates in Yucatan (Mexico) generally increased in July/August and decreased during November/December. A linear regression model showed that previous dengue incidence rates explained 89% of dengue fever variability (p < 0.
05). Dengue incidence two weeks prior (previous incidence) influences future outbreaks by allowing the virus to continue propagating. Yet dengue incidence was best explained by precipitation, minimum air temperature, humidity, and SST (p < 0.05).
Dengue incidence variability was best explained by SST and minimum air temperature in our study region (r = 0.50 and 0.48, respectively).
Increases in SST preceded increased dengue incidence rate by eight weeks. Dengue incidence time series were positively correlated to SST and minimum air temperature anomalies. This is related to the virus and mosquito behavior. Including oceanographic variables among environmental factors in the model improved modelling skill of dengue fever in Mexico.Chapter Three shows that precipitation, MSL, DNI, SST, and turbidity explained some of the enterococci variation in Escambron Beach surface waters (AIC = 26.76; r = 0.20). Variation in these parameters preceded increased culturable enterococci concentrations, with lags spanning from 24 h up to 11 days.
The highest influence on culturable enterococci was precipitation between 480 mm–900 mm. Rainy events often result in overflows of sewage systems and other non-point sources near Escambron Beach in Puerto Rico. A significant decrease in culturable enterococci concentrations was observed during increased irradiance (r = -0.24). This may be due to bacterial inactivation. Increased culturable enterococci concentrations were significantly associated with higher turbidity daily anomalies (r = 0.
25), in part because bacteria were protected from light inactivation. Increased culturable enterococci concentrations were related to warmer SST anomalies (r = 0.12); this is likely due to increased bacterial activity and reproduction. Higher culturable enterococci concentrations were also significantly correlated to medium to high values of dew point daily anomalies (r = 0.19). A significant decrease in culturable enterococci during higher daily MSL anomalies (r = -0.19) is possibly due to dilution of bacteria in beach waters, whereas during lower MSL anomalies the back-washing promotes increased bacteria concentrations through mixing from sediments. These environmental variables improve our understanding of the ecology of these bacteria over time.
The predictive capability increases by including more than one environmental variable.Chapter Four explains a predictive model of dengue fever occurrences in San Juan, Puerto Rico (1994–2012), and Yucatan (2007–2012). The model was modified to predict dengue fever outbreak occurrences for two population segments: population at risk of infection (i.e., < 24 years old) and vulnerable population (i.e., < 5 years old and > 65 years old).
There were a total of four predictive models, two sets for each location using the specified population segments. Model predictions showed previous dengue cases, minimum air temperature, date, and population size as the factors with the most influence to predict dengue fever outbreak occurrences in Mexico. Previous dengue cases, maximum air temperature, date, and population size were the most influential factors for San Juan, Puerto Rico. The models showed an accuracy around 50% and a predictive capability of 70%. These environmental and demographic variables are important primary predictors for dengue fever outbreaks in Puerto Rico and Mexico.
Chapter Five shows the application of the ANNs model to predict culturable enterococci exceedance based on the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. The model identified DNI, turbidity, 48 h cumulative precipitation, MSL, and SST as the most influential factors to predict enterococci concentration exceedance, based on the U.S.
EPA RWQC at Escambron Beach from 2005–2014. The model showed an accuracy of 76%, with a predictive capability greater than 60%, which is higher than linear models. Results showed the applicability of remote sensing data and ANNs to predict recreational water quality and help improve early warning system and public health.This work helps to better understand complex relationships between climatic variations and public health issues in tropical coastal areas and provides information that can be used by public health practitioners.CHAPTER ONEINTRODUCTIONOverview and objectivesThe overall objective of this dissertation was to assess the influence of environmental factors on the variability of dengue fever incidence rates in Mexico and Puerto Rico, and culturable enterococci concentration in Escambron Beach, San Juan, Puerto Rico. The approach included the application of remotely sensed environmental observations, local meteorological information, and public health data in both locations.
A nonlinear model, based on Artificial Neural Networks (ANN), was applied to predict dengue fever outbreak occurrences in Mexico and Puerto Rico, as well as culturable enterococci concentration exceedance at Escambron Beach. The work is presented in six chapters.Chapter One is a general introduction to the dissertation. It describes how environmental factors can influence vector-borne diseases and fecal indicator bacteria in beach environments. The modelling of dengue fever incidence rates by including satellite-derived sea surface temperature (SST) and other environmental factors (i.e.
, precipitation, humidity, air temperatures) is discussed in Chapter Two. In Chapter Three, satellite-derived data (i.e., turbidity, SST, irradiance) and data on other environmental factors (i.e., mean sea level, dew point, precipitation) were used to better understand variability in fecal indicator bacteria (FIB; i.e., culturable enterococci concentration).
In Chapter Four, a non-linear model based on ANN was applied to predict dengue fever occurrences in Mexico and Puerto Rico. The analysis examined specific population segments. Chapter Five used the ANN approach to model culturable enterococci concentration exceedance in Escambron Beach, San Juan, Puerto Rico. The model is based on the U.S. EPA Recreational Water Quality Criteria (RWQC). Chapter Six is a summary of dissertation findings and implications of this work.
The research contributes to understanding how environmental factors affect temporal patterns of variability of dengue fever and culturable enterococci concentrations in Mexico and Puerto Rico.The specific objectives of this dissertation were:Objective 1: Evaluate and model dengue fever incidence rates in Yucatan, Mexico using regional-scale satellite-derived sea surface temperature.Objective 2: Evaluate the influence of satellite-derived environmental factors and those measured in situ on culturable enterococci concentration at Escambron Beach, San Juan, Puerto Rico.Objective 3: Apply a nonlinear model based on artificial neural networks to predict dengue fever outbreak occurrences in Mexico and Puerto Rico based on specific population segments.Objective 4: Identify the most influential environmental factors to predict exceedances of culturable enterococci concentrations at Escambron Beach, San Juan, Puerto Rico.Environmental forces influence on dengue fever occurrences and recreational water qualityLarge-scale environmental forces influence infectious diseases. This is clearly the case in the Caribbean and Gulf of Mexico (Chretien et al. 2015, Dobson 2009).
Variability of specific environmental factors affects dengue fever occurrence and water quality of recreational beaches (Chowell and Sanchez 2006, Pednekar et al. 2005). Thus, it should be possible to develop better management, disease surveillance, and mitigation strategies by understanding the variability of environmental forces and their influence on public-health related issues.
In this dissertation, I examined these problems in more detail in the northwest coast of the State of Yucatan, Mexico, and near San Juan, Puerto Rico, USA.Environmental and demographic factors influence on vector-borne diseasesHuman populations in the Caribbean Sea and the Gulf of Mexico have seen an increase in the incidence of vector-borne diseases. Dengue fever cases have increased especially since the 1970s (Dick et al.
2012, Laureano-Rosario et al. 2017, Mendez-Lazaro et al. 2014). This increase is in part due to the adaptation of the mosquito, Aedes aegypti, to live in urban areas (Gratz 1991, Gubler 2002). Previous studies have shown the influence of specific environmental and demographic factors on the occurrence of dengue fever cases in places like Yucatan State, Mexico and San Juan, Puerto Rico (Colon-Gonzalez et al. 2011, Colon-Gonzalez et al. 2013, Mendez-Lazaro et al. 2014).
Furthermore, local environmental factors and population behavior play a key role in the epidemiology and phenology of dengue fever (Eastin et al. 2014). Consequently, the understanding of the local variability of environmental factors is important to understand their influence on dengue fever occurrences.Dengue fever is mostly transmitted by Aedes aegypti, a mosquito found around tropical and subtropical areas (Gubler 2002). These mosquitoes use water containers (natural and artificial) to develop, being precipitation and temperature the main promoters of their development (Brady et al. 2013, Campbell-Lendrum et al. 2015, Descloux et al. 2012, Johansson et al.
2009). Warmer temperatures decrease mosquito development time, increasing mosquito egg production, hatching, and density (Dickerson 2007). Furthermore, increased temperatures lead to higher metabolic activity, which promotes more mosquito biting (by female mosquitoes) due to energetic demands (Paaijmans et al. 2013).
Both Mexico and Puerto Rico have reported Aedes albopictus as another vector for dengue fever (Dantes et al. 2014, Dick et al. 2012, Mendez-Lazaro et al.
2014, Stramer et al. 2012). Dengue has four serotypes (DENV-1, DENV-2, DENV-3, and DENV-4; Halstead 1988), which have been reported in both Mexico and Puerto Rico. More recently, studies have shown the emergence of sylvatic dengue 5 (DENV-5; Joob and Wiwanitkit 2016, Mustafa et al. 2015). Peaks in dengue cases usually take place after a shift from one serotype to another, since during this time the population would only be partially immune to the other serotypes (Gubler and Clark 1995, Rothman 2004). Relevant epidemiological studies in Yucatan and Puerto Rico have focused on understanding where Aedes aegypti’s larvae are found (e.g.
, schools, households) and how the disease is transmitted (Baak-Baak et al. 2014a, Baak-Baak et al. 2014b, Garcia-Rejon et al. 2008, Garcia-Rejon et al. 2011). In both tropical locations, dengue fever coincides with periods of higher precipitation, higher SST, higher mean sea level, and higher minimum air temperature along the coast. Climatic variations are expected to influence the ecology and geographic distribution of vector-borne diseases. Studies have shown how vectors that transmit malaria (i.
e., Anopheles spp.) have been found in higher altitudes in Africa due to warmer temperatures (Afrane et al. 2007, Afrane et al.
2012, Harvell et al. 2002). Similarly, studies have documented both increases and re-occurrences of vector-borne diseases in Europe due to recent warmer conditions (Medlock and Leach 2015).
Nevertheless, these are also affected by human activities such as population movement, farming, dams, and changes in irrigations systems. Therefore, some of these climatic effects might be masked by human activities, including human population movement across the world, leading to further spreading and increasing incidence rates (Campbell-Lendrum et al. 2015).Modelling dengue fever in endemic areas is important to better mitigate and manage these occurrences. The present work was driven by the hypothesis that variability and trends in environmental factors (e.g., precipitation, temperatures, and humidity) are primary drivers of dengue fever incidence, and that including satellite-derived SST improves dengue fever incidence rate predictions.
The objective was to help improve epidemiological surveillance through the combination of oceanographic, meteorological, and long-term epidemiological data.The influence of environmental factors on fecal indicator bacteria and recreational water qualityWater quality is a major concern to coastal communities due to the potential for exposure to pathogens in beaches downstream of watersheds with sources of fecal contamination (Garcia-Montiel et al. 2014, Pruss 1998, Soderberg 2012). Wastewater discharges are point sources. Other sources include septic tanks and open sewers that discharge directly to river streams. Likewise, resuspension of bacteria by winds and waves, and stormwater discharges are potential non-point sources of fecal contamination in coastal areas (Cordero et al.
2012, Quiñones 2012, Rochelle-Newall et al. 2015). Fecal indicator bacteria (FIB) are used by the United States Environmental Protection Agency (U.S. EPA) to identify poor recreational water quality. Out of these FIB, culturable enterococci are commonly used in fresh and marine waters (U.
S. EPA 2012). The U.S. EPA established the 2012 Recreational Water Quality Criteria (RWQC), where these culturable enterococci cannot exceed the geometric mean of 35 colony forming units (CFU) per 100 mL.
This represents 36 illnesses per 1,000 primary contact recreators (U.S. EPA 2012). This value was modified in 2014 to the Beach Action Value (BAV) of 70 CFU/100 mL based on specific criteria for conducting research (U.S. EPA 2014). These guidelines were adopted by the Environmental Quality Board of Puerto Rico (PREQB). In Puerto Rico, the PREQB assesses bathing water quality at beaches throughout the island every two weeks, and if concentrations exceed those values set by the U.
S. EPA (i.e., BAV of 70 CFU/100 mL; PREQB 2016), they issue beach advisories. These data are openly available but are only used for issuing public warnings.FIB variability has been associated with environmental forces in both subtropical and tropical regions (Aranda et al.
2016, Lamparelli et al. 2015, Viau et al. 2011, Wright et al. 2011).
These studies have shown how specific environmental factors (e.g., precipitation, turbidity, temperatures) influence higher or lower FIB concentrations in marine and fresh waters (Byappanahalli et al. 2010, He and He 2008, Nevers and Whitman 2005). Therefore, a series of statistical models (e.g.
, linear and multiple regression models) were used to better understand variability of culturable enterococci concentrations. This was guided by the hypothesis that changes in culturable enterococci concentration in surface waters at Escambron Beach (Puerto Rico) were related to variations of environmental factors (e.g., SST, turbidity, precipitation). The main objective was to improve early warnings for FIB and health risks.
Predicting vector-borne diseases and recreational water quality with Artificial Neural NetworksPredictive models can help improve management and mitigation of health-related matters (de Brauwere et al. 2014, Gonzalez and Noble 2014, Gubler 2010, Tabachnick 2010). In this dissertation, a nonlinear model was used to evaluate prediction of dengue fever outbreaks in endemic areas, as well as exceedances of FIB in tropical areas.Modelling can help predict and understand the epidemiology of dengue fever in endemic areas (Medeiros et al. 2011, Racloz et al. 2012). Likewise, recreational water quality modelling helps protect humans from potential exposure to specific FIB (Colford et al. 2007, Pruss 1998).
For example, some studies have applied Monte Carlo and support vector machine to predict dengue fever cases (Husin et al. 2008, Wu et al. 2008). Similarly, nonlinear modelling using ANNs, decisions trees, and Monte Carlo approaches helped model water quality (Jiang et al.
2013, Lin et al. 2008) and have supported beach management (Mavani et al. 2014, Zhang et al. 1998, Thoe et al. 2014).Predicting health-related matters is a management goal. These ANN models do not assume functional relationships between predictor factors (e.g.
, environmental factors) and target variable (e.g., dengue fever, culturable enterococci concentration), thus they can identify nonlinear, complex relationships (Zhang et al. 1998). ANN models were applied to predict dengue fever outbreak occurrences in Mexico and Puerto Rico for specific population segments (i.e., population younger than 24 years and those younger than 5 years and older than 65 years).
These ANNs models were also applied to predict culturable enterococci concentration exceedance in surface waters at Escambron Beach in Puerto Rico. The objective was to help management and mitigation of these two health-related matters.Study areasNorthwest coast, Yucatan State, MexicoThe study was focused on the mainland region in the northwest coastal area of the State of Yucatan, Mexico, located adjacent to the Gulf of Mexico (19.55°N–21.63°N, 87.53°W–90.40°W). The study area has nine municipalities: Chicxulub Pueblo, Dzemul, Hunucma, Ixil, Progreso, Telchac Pueblo, Telchac Puerto, Ucu, and Merida, which is the capital and largest municipality within this region (Figure 1).
The highest precipitation occurs between July and October (with an average of 400–700 mm of precipitation over the season). The dry season occurs between March and June (0–50 mm for the season). A third season, the “Nortes” season, is characterized by strong (~80 km h-1) winds coming from the continental mass of the U.S. and associated with cold fronts during November–February. Air temperatures generally range from 36–40 °C during the dry season, 30–35 °C during the rainy season, and 20–23 °C during “Nortes” (Herrera-Silveira 1994).Figure 1.1 Northwest coast of the Yucatan Peninsula, Mexico.
Map depicts the location of municipalities used in the study: Chicxulub Pueblo, Dzemul, Hunucma, Ixil, Progreso, Telchac Pueblo, Telchac Puerto, Ucu, and MeridaEscambron Beach, San Juan, Puerto RicoEscambron beach is located on the north coast of Puerto Rico (18.47°N, 66.08°W, Figure 2). It has a year-long swimming season and the average annual air temperatures range between 24–29 °C (Murphy et al.
2011). Two sites, separated by ~100 m, were sampled by the PREQB (Figure 2). These sites may have been affected by: (1) stormwater drainage (18.46°N, 66.09°W) located immediately adjacent to one of the sampling sites, which includes urban runoff, precipitation, and other graywaters (e.g., showers, washing machines; Diaz 2007); (2) wastewater treatment plant (WWTP) ocean outfall (18.47°N, 66.
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Forecasting 14(1), 35-62.CHAPTER TWOModelling dengue fever risks in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperatureResearch overviewData on dengue fever incidence were obtained from Yucatan’s National Health Information System. This included data from eight municipalities: Chicxulub Pueblo, Dzemul, Hunucma, Ixil, Progreso, Telchac Pueblo, Telchac Puerto, Ucu, and Merida. These cases were converted to incidence rates per 100,000 individuals using population size from 2007–2012. Nearshore satellite-derived SST was collected by the Advanced Very High Resolution Radiometer (AVHRR; 1 km spatial resolution) from 2006–2012. Dengue fever data were combined with precipitation, humidity, and minimum and maximum air temperature into a multiple regression model. Results showed that dengue incidence rates increased around the month of July and started to decrease in November, following the precipitation patterns. Linear regression model showed that previous dengue incidence rates explained 89% of dengue fever variation. Our model identified precipitation, minimum air temperature, humidity, and SST as the best variables to explain dengue incidence variability. Furthermore, results also showed increases in SST preceding increases in dengue incidence rates by eight weeks (r = 0.50; p < 0.05). Dengue incidence rates were positively correlated with SST and minimum air temperature anomalies. Combining environmental and oceanographic variables improved modelling of dengue fever in Mexico; this was shown by a smaller AIC value (AIC: -1410). This suggested that as the temperature anomalies, humidity, and precipitation change, dengue cases will also change as these variables were positively correlated.Note to readerThis chapter was published in the peer-reviewed journal Acta Tropica and is included in Appendix A. The full citation is: Laureano-Rosario, A.E., Garcia-Rejon, J.E., Gomez-Carro, S., Farfan-Ale, J.A., Muller-Karger, F.E. (2017). Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature. Acta Tropica, 172:50-57. Authorization for inclusion in this dissertation is found in Appendix E.CHAPTER THREEEnvironmental factors correlated with culturable enterococci concentrations in tropical recreational waters: A case study in Escambron Beach, San Juan, Puerto RicoResearch overviewCulturable enterococci concentrations data were obtained from the U.S. Environmental Agency Storage and Retrieval data warehouse for Escambron Beach (2005–2012), and extended to 2015 using data obtained from the Puerto Rico Environmental Quality Board. Environmental data were measured in situ (i.e., daily mean sea level (MSL), precipitation) or derived from satellites (i.e., sea surface temperature, remote sensing reflectance (Rrs 645), direct normal irradiance (DNI), winds). These data were combined in a multiple regression model to better understand variability seen in culturable enterococci concentrations. Significant lags were also identified through Pearson’s correlations, and environmental variables were divided into specific ranges (i.e., bins) to identify exceedances in culturable enterococci concentration among bins, based on U.S. EPA’s safe bathing water quality criteria. Data showed that precipitation, mean sea level (MSL), DNI, SST, and turbidity explained some of the observed variation (r = 20) and these parameters preceded changes (i.e., increased or decreased) in culturable enterococci concentrations with lags spanning from 24 h up to 11 days. Increased culturable enterococci concentrations were observed during positive anomalies of turbidity, SST, and 481–960 mm of 4-day cumulative precipitation. Culturable enterococci concentrations decreased with elevated MSL anomalies and irradiance. Unsafe enterococci concentrations per U.S. EPA water quality guidelines occurred when precipitation ranged from 481–960 mm, irradiance < 667 W m-2, turbidity daily anomaly > 0.005 sr-1, SST daily anomaly > 0.8 °C, and MSL daily anomaly < -18.8 cm. Our model accounted for the combined effects of these environmental variables, which can help improve our understanding of the ecology of culturable enterococci and protect public health.Note to readerThis chapter was published in the peer-reviewed journal International Journal of Environmental Research and Public Health and is included in Appendix B. The full citation is: Laureano-Rosario, A.E., Symonds E.M., Rueda, D., Otis, D., Muller-Karger, F.E. (2017). Environmental factors correlated with culturable enterococci concentrations in tropical recreational waters: A case study in Escambron Beach, San Juan, Puerto Rico. International Journal of Environmental Research and Public Health, 14(12):1602. Authorization for inclusion in this dissertation is found in Appendix E.CHAPTER FOURApplication of Artificial Neural Networks for dengue fever outbreak predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto RicoResearch overviewArtificial Neural Networks (ANNs) were applied to predict dengue fever outbreak occurrences in Mexico and Puerto Rico. Models were trained with six years of dengue fever data for Yucatan, Mexico and 19 years for San Juan, Puerto Rico. Dengue fever data were obtained from the Yucatan’s Health Department and Puerto Rico’s Health Department. Cases were converted to incidence rates per 100,000 inhabitants, and thresholds based on the 75th percentile were calculated for the population considered at risks due to exposure (i.e., number of people younger than 24 years old) and the most vulnerable population (i.e., number of people younger than 5 years and older than 65 years). Predictor variables included were precipitation, air temperature (i.e., minimum, maximum, average), sea surface temperature (SST), humidity, previous dengue cases, and population size. A total of four models were run, where the predictive power was above 70% for both study areas. These models were divided as follow: 1) Mexico ages less than 24 years old, 2) Mexico ages less than 5 years old and greater than 65 years old, 3) Puerto Rico ages less than 24 years old, and 4) Puerto Rico ages less than 5 years old and greater than 65 years old. The most influential variables on predicting dengue fever occurrences identified by the models in Mexico were population size, previous dengue cases, minimum air temperature, and date. In San Juan, Puerto Rico, the most important variables identified were population size, previous dengue cases, maximum air temperature, and date. For both study areas, demographic factors were the top two most influential variables. By using a nonlinear approach, the models were able to better predict dengue fever occurrences as this approach considers complex and holistic interactions between dengue fever cases, demographics, and environmental variables.Note to readerThis chapter was published in the peer-reviewed journal Tropical Medicine and Infectious Diseases and is included in Appendix C. The full citation is: Laureano-Rosario, A.E., Duncan, A.P., Mendez-Lazaro, P.A., Garcia-Rejon, J.E., Gomez-Carro, S., Farfan Ale, J., Savic, D.A., Muller-Karger, F.E. (2018). Application of Artificial Neural Networks for dengue fever predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Diseases, 3(1):5. Authorization for inclusion in this dissertation is found in Appendix E.CHAPTER FIVEArtificial Neural Networks better predict exceedances of recreational water quality criteria at Escambron Beach, San Juan, Puerto RicoResearch OverviewCulturable enterococci concentration exceedances were predicted in Escambron Beach surface waters using a nonlinear approach based on Artificial Neural Networks. Ten years of culturable enterococci data obtained from the U.S. Environmental Protection Agency (U. S. EPA) and the Puerto Rico Environmental Quality Board were used to train, validate, and test the model. In order to predict whether it was safe or unsafe to swim, a threshold of 70 colony forming units (CFU) per 100 mL was used based on the U.S. EPA 2014 Beach Action Value for safe recreational water quality. Predictor variables included in the model were satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point together with in situ cumulative precipitation from the previous 24 h up to 120 h and mean sea level (MSL). Based on the Receiving Operating Characteristic Curve and the F-Measure metrics, the model showed an accuracy of 76% and a power greater than 60%, which was higher than linear models. The factors identified as the most relevant for predicting culturable enterococci exceedances were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST. The ANN model showed the importance of identifying how environmental conditions can influence culturable enterococci concentration, as well as the complexity of these relationships between FIB and environmental factors. By using a nonlinear approach, I was able to accurately predict culturable enterococci exceedances, which can help management and mitigation strategies for recreational water quality.Note to readerThis chapter is currently in review in the peer-reviewed Journal of Water and Health and is included in Appendix D. The full citation is: Laureano-Rosario, A.E., Duncan, A.P., Symonds E.M., Savic, D.A., Muller-Karger, F.E. (2018). Artificial Neural Networks better predict exceedances of recreational water quality criteria at Escambron Beach, San Juan, Puerto Rico. Journal of Water and Health Tropical (in review). Authorization for inclusion in this dissertation is found in Appendix E.CHAPTER SIXCONCLUSIONSummaryEnvironmental forces have been associated with dengue fever occurrences in endemic areas, as well as fecal indicator bacteria variability in recreational waters (Chowell and Sanchez 2006, Pednekar et al. 2005). These are important to model and understand to protect public health. Nevertheless, these interactions are complex and by just modelling them with linear models we might be missing important data (Chebud et al. 2012, He and He 2008). This research provides a better understanding of how environmental factors are related to dengue fever and culturable enterococci in a tropical setting, applying linear and nonlinear models with satellite-derived data and long-term epidemiological data.Chapter Two showed that dengue incidence rates generally increased in July (wet season) and decreased in November (dry season) in Yucatan, Mexico. Changes in previous dengue fever cases explained the most variability and were positively correlated with current cases. Precipitation, minimum air temperature, humidity, and SST were selected as the best variables to explain dengue fever incidence. These results showed that increases in SST precede increased dengue incidence rates by eight weeks and that dengue incidence rates were positively correlated to SST changes. It is concluded, then, that dengue fever incidence rates can be modelled using environmental variables alone, and that by including satellite-derived regional-scale SST the modelling was improved. Nevertheless, it is important to note that even though seroprevalence studies are expensive, the inclusion of human immune background can allows to have more robust models.Chapter Three showed that precipitation, mean sea level (MSL), direct normal irradiance (DNI), SST, and turbidity explained some of the observed variation. These parameters preceded changes in culturable enterococci concentrations with lags spanning from 24 h up to 11 days. The highest influence on culturable enterococci concentration was between 480 mm – 900 mm of 4-day cumulative precipitation. Higher culturable enterococci were observed during higher turbidity anomalies, warmer SST anomalies, and lower MSL anomalies. A significant decrease in culturable enterococci concentrations was observed during increased solar irradiance. Better monitoring of recreational water quality can be achieved by understanding the influence of environmental factors on culturable enterococci concentrations and how marine waters influence culturable enterococci decay rates (Anderson et al. 2005). It is concluded, then, that culturable enterococci concentration variability can be explained by looking at the combined effects of precipitation, SST, MSL, and turbidity.In Chapter Four, a predictive model was applied to predict dengue fever outbreak occurrences in San Juan, PR and Yucatan, MX. These models were modified to predict dengue fever outbreak occurrences for the population at highest risk of infection (i.e., < 24 years old) and highest vulnerability of infection (i.e., < 5 years old and > 65 years old; Mendez-Lazaro et al. 2014). These groups were based on previous studies (Laureano-Rosario et al. 2017, Mendez-Lazaro et al. 2014) and data provided by the Department of Health of Mexico and Puerto Rico. Based on these predictions, the most influential variables to predict dengue fever outbreak occurrences in both Puerto Rico and Mexico were previous dengue incidence rates, minimum/maximum air temperatures, date, and population size. These models showed an accuracy of ~50%, with an overall power greater than 70%. Nonetheless, these results showed that the most influential variables to predict dengue fever occurrences are those related to demographics, followed by environmental factors such as temperatures (i.e., sea temperature, air temperature) for both Puerto Rico and Mexico. Therefore, it is concluded that, while demographic factors are important for prediction and mitigation, environmental factors should always be taken into account, and that these relationships are location-specific.The predictive model was also applied in Chapter Five to predict culturable enterococci concentration exceedance at Escambron Beach surface waters. The model showed the following as the most influential factors: 48 h cumulative precipitation, turbidity anomalies, DNI, MSL anomalies, and SST anomalies. These predictions had an accuracy greater than 70%, higher than the predictive capability of only using a simple linear regression model. Thus, modelling culturable enterococci concentration exceedance at Escambron Beach was achieved by the predictive nonlinear model, where it identified the combined effects of these environmental factors influencing culturable enterococci concentrations.The results of this dissertation can be integrated into future models to better understand the burden of water-related pathogens correlated with fecal indicators and vector-borne diseases in specific locations. The World Health Organization (WHO) estimates about 720,000 deaths per year related to 12 vector-borne diseases, where 80% of the world’s population is at risk and those younger than 5 years old are considered more susceptible (WHO 2018). Understanding the relationship and seasonality of these vectors, as shown in Chapter Two and Four with dengue fever, can help achieve better predictions and further develop disease surveillance and prevention strategies. In terms of water, sanitation, and hygiene (WASH), WHO reports about 840,000 deaths per year with 361,000 of those being children younger than 5 years old, and where 58% of these deaths could be averted through better sanitation practices (WHO 2018). While these statistics include both freshwater (i.e., drinking water) and marine waters, the results of this dissertation can help better understand patterns of specific indicators and how those are related to human activities and climate. Consequently, this dissertation supports and expands on efforts to understand diseases occurrence on specific population segments and seasonal variability of vector-borne diseases and water indicators related to poor recreational water quality.This study demonstrated that the combined effects of environmental factors can improve our understanding of the ecology and epidemiology of diseases and microbial indicators over time, which would have been missed by just looking at just one environmental variable. Combining environmental and oceanographic variables improved modelling of dengue fever in Mexico and recreational water quality in Puerto Rico. Thus, this research contributes to the understanding of the influence of environmental factors on public health issues through the comparison of linear and nonlinear modelling as well as predictive models targeting specific population segments and geographic locations.Future researchThis dissertation shows the importance of understanding the influence of large-scale environmental, human, and pathogen factors on specific public health issues in coastal and non-coastal areas. Results show that these interactions are complex, and that there is a combined effect of environmental factors, thus looking at them separately might not provide a complete understanding. Therefore, the combination of these factors should be taken into consideration in future work, as well as those other factors that were not included due to data limitations (discussed below). Nevertheless, this study contributes to the understanding of environmental and demographic factors that should be included for early warning systems and to improve mitigation and management strategies.Predictive models used for Mexico and Puerto Rico looking at dengue fever occurrences and FIB exceedances showed high predictive capabilities. Models can be further improved by including data that was not considered in this dissertation. For example, for dengue fever predictions, seroprevalence and human population movement should be considered to better understand occurrences and peaks in dengue fever. Likewise, different populations segments (i.e., age groups) were considered for this study, but these age groups could be either expanded or divided differently for better predictions, according to information available on the limitations to their immune system. In terms of the FIB, models can be improved by including sanitation infrastructure, river and stormwater discharge, and wastewater treatment plant outflows. These FIB can also be found in sediments/sand and vegetation, which should also be considered in the future. Lastly, time series length can influence outcomes due to lack of data, overfitting, and underfitting. Those Puerto Rico models used 19 years (dengue) and 11 years (fecal indicator bacteria) of data, while Mexico models used years of data (dengue). Nevertheless, these models yielded high predictive capabilities, and future studies should consider expanding time series to better predict specific health-related occurrences.The application of remote sensing data should be considered in future efforts to better understand phenology of vector-borne diseases and recreational water quality. 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