Spatial and temporal patterns in the spread of influenza A and B viruses in the United States during the influenza season

Transcription

1 University of Iowa Iowa Research Online Theses and Dissertations Spring 2018 Spatial and temporal patterns in the spread of influenza A and B viruses in the United States during the influenza season Jeffrey David Koss University of Iowa Copyright 2018 Jeffrey David Koss This thesis is available at Iowa Research Online: Recommended Citation Koss, Jeffrey David. "Spatial and temporal patterns in the spread of influenza A and B viruses in the United States during the influenza season." MA (Master of Arts) thesis, University of Iowa, Follow this and additional works at: Part of the Geography Commons

2 SPATIAL AND TEMPORAL PATTERNS IN THE SPREAD OF INFLUENZA A AND B VIRUSES IN THE UNITED STATES DURING THE INFLUENZA SEASON by Jeffrey David Koss A thesis submitted in partial fulfillment of the requirements for the Master of Arts degree in Geography in the Graduate College of The University of Iowa May 2018 Thesis Supervisor: Adjunct Assistant Professor James Tamerius

3 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL This is to certify that the Master's thesis of MASTER'S THESIS Jeffrey David Koss has been approved by the Examining Committee for the thesis requirement for the Master of Arts degree in Geography at the May 2018 graduation. Thesis Committee: James Tamerius, Thesis Supervisor Margaret Carrel Caglar Koylu

4 ACKNOWLEDGEMENTS I would like to thank my thesis supervisor, Dr. James Tamerius, for his mentoring throughout my time at The University of Iowa. His guidance and support over the past two years have greatly enriched my graduate study experience. I would also like to thank Dr. Margaret Carrel and Dr. Caglar Koylu for the helpful support they have provided as I have conducted research for and written my thesis. The University of Iowa Department of Geographical and Sustainability Sciences has been very supportive of me. I greatly appreciate and thank the faculty in the department for the invaluable course instruction I have received over the past two years, as well as for the feedback and support in my preparation for public presentations of my research. Many staff members in the department have also assisted me greatly in providing research insight and helping me navigate through various logistical challenges. I would also like to thank my fellow graduate students within the department for their support and willingness to help me in various ways. Finally, I would like to thank my family and friends for supporting me throughout my graduate study. My parents, David and Sarah Koss, have always supported me and encouraged me to pursue my goals and scholarly interests, as well as inspired me with their own discipline and hard work. This has remained the case over the past two years. My friends have also frequently provided me support and encouragement throughout this time, and I would like to thank them for lifting me up on many occasions. ii

5 ABSTRACT Influenza is a respiratory virus that causes significant morbidity and mortality throughout the world every year. Seasonal epidemics of influenza occur in the late fall and winter in the United States annually, but there are variations in its timing from year to year. Further, although the timing of epidemic waves in the United States are similar, there is variation between different populations. It is not well understood why these differences exist. Understanding the spatial and temporal variation in the timing of influenza is important because it shapes our understanding of preventive actions that can be taken to limit the spread of the virus. Past studies that have examined the timing of influenza have been limited by the fact that they have used influenza-like illness (ILI) as an indicator of influenza. ILI has traditionally been the conventional indicator of influenza because the illness does not present unique symptoms. As such, spatial and temporal variation in the relative timing of influenza A, influenza B, and ILI have not been investigated extensively. Additionally, there has been concern raised about implications of the imprecise nature of ILI in treating patients that it is believed may have influenza. This study addressed this gap and concern by utilizing influenza-specific data from clinics and hospitals throughout the United States to evaluate spatial variation in the timing of influenza across the United States in the influenza season. Results from influenza rapid tests were aggregated by urban area as a means of evaluating associations between epidemic timing and independent variables in different locations. The timing of influenza A and B epidemics was tested for spatial autocorrelation and incorporated in regression models to identify potential relationships between epidemic timing and several variables (including dew point, temperature, population, population density, and cumulative seasonal vaccination rates). Forward iii

6 stepwise regression was then conducted to identify a set of variables that may be best suited to explain the timing of these milestones, and spatial lag regression was conducted to account for spatial autocorrelation in these variables. This analysis indicated that higher average dew point and temperature and greater population and population density were both associated with earlier epidemic beginnings and later epidemic endings, while higher cumulative seasonal vaccination rates were associated with earlier epidemic endings for influenza A and B. Forward stepwise regression yielded models that generally differed for each epidemic milestone and type of influenza, indicating that different sets of variables might be best suited to explain different milestones of epidemics. Spatial lag regression improved model fit for the forward stepwise models for which there was residual spatial autocorrelation. This is one of the first studies to evaluate the timing of different points within an epidemic. The techniques I used to study timing are well-suited for the study of future epidemics of infectious diseases, including influenza, that seek to identify and clarify potential associations between independent variables and epidemic timing. iv

7 PUBLIC ABSTRACT Influenza is a respiratory virus that causes significant morbidity and mortality throughout the world every year. Seasonal epidemics of influenza occur in the late fall and winter in the United States annually, but there are variations in its timing from year to year. Understanding the spatial and temporal variation in the timing of influenza is important because it informs preventive actions that can be taken to limit the spread of the virus. Past studies that have examined the timing of influenza have been limited by the fact that they have used influenza-like illness (ILI) as an indicator of influenza. ILI has traditionally been the conventional indicator of influenza because the illness does not present unique symptoms. However, this study utilized influenza-specific data from clinics and hospitals throughout the United States to evaluate spatial variation in the timing of influenza across the United States in the influenza season. This analysis indicated that higher average dew point and temperature and greater population and population density were both associated with earlier epidemic beginnings and later epidemic endings, while higher cumulative seasonal vaccination rates were associated with earlier epidemic endings for influenza A and B. This is one of the first studies to evaluate the timing of different points within an epidemic. The techniques I used to study timing are well-suited for the study of future epidemics of infectious diseases, including influenza, that seek to identify and clarify potential associations between independent variables and epidemic timing. v

8 TABLE OF CONTENTS LIST OF TABLES... vii LIST OF FIGURES... viii INTRODUCTION...1 MATERIALS AND METHODS...16 RESULTS...23 DISCUSSION...49 REFERENCES...53 APPENDIX...59 vi

9 LIST OF TABLES Table 1 Epidemic timing spatial autocorrelation Table 2 Bivariate regression outcomes for the beginning of influenza epidemics Table 3 Bivariate regression outcomes for the mid-point of influenza epidemics Table 4 Bivariate regression outcomes for the ending of influenza epidemics Table 5 Final forward stepwise regression models for all urban areas Table 6 Final forward stepwise regression models for urban areas with at least 200 positive cases Table 7 Residual spatial autocorrelation in final stepwise linear models Table 8 Forward stepwise regression and spatial lag regression outcomes for influenza A Table 9 Forward stepwise regression and spatial lag regression outcomes for influenza B Table A1 Spatial lag and spatial error regression outcomes for influenza A Table A2 Spatial lag and spatial error regression outcomes for influenza B vii

10 LIST OF FIGURES Figure 1 Dew point and temperature rasters for October Figure 2 Map of urban areas in which at least 500 RIDTs were conducted Figure 3 Histogram of the number of RIDTs conducted in various urban areas Figure 4 Histograms of the number positive cases of influenza A and B in various urban areas Figure 5 Maps of urban areas with at least 200 positive cases of influenza A and B Figure influenza A epidemic milestones with respect to the epidemic curve in New York-Newark Figure influenza A epidemic milestones with respect to the epidemic curve in Chicago Figure 8 Beginning of influenza A epidemic distribution and timing Figure 9 Mid-point of influenza A epidemic distribution and timing Figure 10 Ending of influenza A epidemic distribution and timing Figure 11 Beginning of influenza B epidemic distribution and timing Figure 12 Mid-point of influenza B epidemic distribution and timing Figure 13 Ending of influenza B epidemic distribution and timing Figure 14 Histograms for the timing of each influenza A and B epidemic milestone viii

11 INTRODUCTION Background Influenza is a viral respiratory infection that infects millions of people around the world every year. Annual influenza epidemics occur between November and March in temperate regions of the Northern Hemisphere, and between April and September in temperate regions of the Southern Hemisphere (Finkelman et al., 2007). These epidemics are associated with two distinct types of influenza: A and B. Human influenza is predominantly associated with these two types, with type A being more prevalent in humans and having a propensity to cause more severe illness. Between 5 and 20 percent of the United States is infected with the disease each year, leading to approximately 36,000 annual deaths (NIH, 2013). Worldwide, there are between three and five million serious cases of influenza every year, of which between 250,000 and 500,000 lead to death (NIH, 2013). Studies have found that the timing of these annual influenza epidemics varies at regional and sub-regional scales throughout the United States. Charu et al. (2017) found that seasonal epidemics tend to begin in the southern region of the United States. This is in keeping with the general finding of Finkelman et al. (2007) that increased distance from the equator is associated with later onset of influenza epidemics. Further, several studies have shown that influenza epidemics in the United States vary at sub-regional scales, with populations connected by commuter traffic being highly synchronized with respect to the timing of their epidemics (Charaudeau et al., 2014; Charu et al., 2017; Viboud et al.; 2006). Other factors, such as location 1

12 connectivity and status as a preferred destination in an international context, may exert important influences on patterns in the transmission of influenza (Viboud et al., 2006). Knowledge about temporal and geographic trends in influenza requires accurate surveillance. There are various mechanisms in place around the world to monitor the temporal and geographic variability of influenza. These mechanisms include virologic testing in laboratories, surveillance of visits to outpatient facilities by individuals presenting with influenza-like illness, mortality surveillance, and hospital surveillance (Centers for Disease Control and Prevention, 2017d). Traditional geographic influenza surveillance has relied greatly upon laboratory testing of suspected influenza cases and tracking the number of individuals that present with influenza-like illness at visits to outpatient facilities. In the United States, the CDC monitors the geographic spread of influenza through categorizing the extent of laboratory-confirmed influenza cases. This categorization of influenza activity includes the classifications of no, sporadic, local, regional, and widespread influenza activity (Centers for Disease Control and Prevention, 2017d). The CDC also compiles weekly reports of categorical influenza-like illness activity in each state based upon the proportion of all outpatient visits that are for influenza-like symptoms (Centers for Disease Control and Prevention, 2018). Other common means of influenza surveillance include monitoring weekly rates of work absenteeism, drug prescriptions, emergency house calls (Simonsen et al., 1999) and social media data mining (Gao et al., 2018; Santillana et al., 2015). This study is unique from other studies of the spatial and temporal patterns in the spread of influenza in that it does not make use of traditional means of influenza surveillance. Instead, this project utilized data from Rapid Influenza Diagnostic Tests (RIDTs) throughout the United States to evaluate spatial and temporal patterns in occurrence of the virus. This data offers an 2

13 improvement over traditional influenza surveillance in terms of data precision and both spatial and temporal resolution. In this project, I explored associations between location and timing of influenza occurrence and climatic variables, population characteristics, and cumulative seasonal vaccination rates. My first hypothesis was that higher average dew point and temperature, and greater population and population density, would be associated with earlier epidemic beginnings and later epidemic endings. My second hypothesis was that cumulative seasonal vaccination rate would be positively associated with earlier onset of various moments, or milestones, in epidemics. Influenza Epidemiology Overview Evolution of influenza viruses over thousands of years has rendered the present landscape of influenza, with particular types and strains being notably adapted to survival and infectious to humans. As such, understanding of the evolution and ecology of the two most common human types of the virus underpins this research by shaping the context of their prevalence and the processes through which they successfully infect human hosts at a broad spatial scale. Ecology and Evolution of the Influenza Virus Influenza A and B each have multiple subtypes. Different subtypes of influenza A are distinguished by different hemagglutinin (HA) proteins and neuraminidase (NA) enzymes they contain. Presently, there are 18 known HA subtypes and 11 known NA subtypes. Only two 3

14 subtypes of influenza A are currently in circulation among humans (H1N1 and H3N2). Influenza B has evolved into two distinct lineages but is not broken into subtypes (Centers for Disease Control and Prevention, 2017b). These three types and subtypes represent the influenza in circulation today. Genetic reassortment is an important means for the production of influenza A because, unlike influenza B (which circulates only among humans), influenza A occurs in multiple host species in which the virus evolves in distinct ways. All influenza viruses in other species are ultimately derived from the reservoir of aquatic birds (Webster et al., 1992). From this reservoir, the virus spreads to other species, including domestic poultry, swine, and humans. Genetic exchange among the influenza viruses of each of these species has been documented (Webster et al., 1992), meaning that there is potential for the constitution of novel strains of influenza from these reservoirs. This can lead to a pandemic of the virus in humans as a consequence of a lack of established immunity on the seldom occasion in which it occurs (Donatelli et al., 2017). This research examines seasonal influenza epidemics that are caused by viruses that have been circulating within human populations for centuries. Antigenic drift plays an important role in the constitution of influenza strains that cause these epidemics. This process is characterized by small changes, or drifts, in the genes of the virus coinciding with the period of time over which the virus replicates, resulting in antigenically unique viruses (Centers for Disease Control and Prevention, 2017a). These drifts are what allow the virus to remain continuously in human populations. The rate of mutation that leads to overall influenza variation, which is associated with antigenic drift, that is associated with RNA replication is relatively high in comparison with DNA replication. Replication errors occur in approximately 1 in every 10 4 replications due to a 4

15 lack of proofreading in RNA, whereas errors occur in approximately 1 in every 10 9 replications for DNA (Webster et al., 1992). Influenza A/H3N2 has the highest evolution rate of the three influenza subtypes that are currently in circulation with antigenically distinct strains emerging on average every 2 to 5 years (Viboud et al., 2006, p. 447). At a global scale, southeast Asia (particularly China) has been implicated as the region in which new seasonal epidemic strains emerge (Webster et al., 1992). Influenza Transmission There are three primary modes of influenza transmission: contact, droplet, and airborne transmission (Bridges et al., 2003). The two types of contact transmission are direct transmission, which entails body-to-body surface contact, and indirect transmission, which occurs through contact with intermediate objects or surfaces contaminated with the virus. Droplet transmission involves the transfer of droplets from an infected individual directly to the mouth, nose, or eye of another person through a means such as coughing or sneezing. Airborne transmission involves the production of very small droplet nuclei that are able to remain suspended in the air and subsequently be inhaled by a susceptible host (Bridges et al., 2003, p. 1096). While there is evidence for the transmission of influenza through each of these mechanisms, the relative contribution of each are not known. Further, evidence has shown that the relative contribution of each may vary based upon specific factors regarding one s surroundings. For example, the virus has been demonstrated to survive for variable lengths of time on different surfaces, such as hands, paper, and plastic, with different levels of propensity for subsequent spread, and the amount of virus likely being an important factor (Bridges et al., 2003). 5

16 There are several factors related to the level of transmissibility of influenza to an individual. A particular individual s level of antibody concentration against the virus impacts the amount of the virus required to cause infection. The amount of virus that is shed from an individual corresponds with the level of severity of the illness, and about half of influenza cases are not associated with any symptoms (Bridges et al., 2003). Further, infected individuals may become contagious the day before the onset of symptoms. Adults typically spread the virus for between three to five days, while children can spread the virus for up to 21 days (Bridges et al., 2003). Influenza Seasonality Human-to-human transmission of influenza leads to annual seasonal epidemics and occasional worldwide pandemics. It generally occurs in the winter in temperate regions of the Northern and Southern Hemispheres and can be prevalent year-round in the tropics (Finkelman et al., 2007). Several mechanisms are thought to be associated with these trends, including increased virus survival, indoor crowding, and decreased host immunity at times in which the virus is most prevalent (Finkelman et al., 2007). It has been demonstrated that the timing of influenza epidemics is generally associated with the climatic variables of temperature, humidity, and (to a lesser degree) solar radiation (Lowen et al., 2007; Shaman et al., 2010; Tamerius et al., 2013). Monotonic inverse relationships have been identified between influenza peaks and both temperature and solar radiation. Conversely, the relationship between influenza peaks and specific humidity is bimodal, with the weakest association between the two occurring when specific humidity is around 12 g/kg (Tamerius et al., 2013). While these variables have been found to be sound predictors of 6

17 influenza peak timing at all locations, they particularly characterize influenza peaks at latitudes poleward of 25 N/S. Peaks in locations equatorward of 10 N/S particularly correspond positively with specific humidity and precipitation. No significant relationship was evident between climatic variables and influenza peaks in latitudes between 12.5 and 25 N/S (Tamerius et al., 2013). Local minimum annual thresholds of temperature and specific humidity have been found to correspond with whether local influenza peaks are more likely to occur in months of relatively low or high specific humidity (Tamerius et al., 2013). Specifically, locations with annual minimum temperature values below 21 C or specific humidity values below 12 g/kg generally experienced influenza peaks during the cold-dry season (with low specific humidity), while locations in which the inverse was true generally experienced peaks during the humid-rainy season (with high specific humidity). Prediction of the timing of influenza peaks was more difficult in locations near the equator, which was likely a consequence of an absence of variation in climate (Tamerius et al., 2013). Spatiotemporal Patterns of Seasonal Influenza A considerable amount of research has worked toward identification of the specific locations where these annual epidemics are most likely to begin at global and regional scales (Charaudeau et al., 2014; Charu et al., 2017; Finkelman et al., 2007; Viboud et al., 2006). In addition to accounting for the movement of humans, such studies have incorporated information on factors that plausibly have impacts on the spread of influenza. Such factors include geographic distance, population, and commuting patterns. 7

18 The spread of epidemics, regardless of the locations at which they begin, are generally greatly influenced by geographic distance, with transmission occurring most frequently between proximate locations (Charaudeau et al., 2014). However, this is not always the case, as epidemic spread can be more similar when beginning in two distant locations than when beginning in two locations that are near one another (Charaudeau et al., 2014). Along with geographic distance, population has been implicated as a factor associated with the geographic spread of influenza. Utilizing influenza mortality as an indicator of the progression of influenza within influenza seasons in the United States, Viboud et al. (2006) found a consistent pattern of larger states exhibiting pairwise synchrony, or common relative timing and amplitude, of epidemics over a 30-year period, with smaller states exhibiting much more varied epidemic timing. This corresponded with an evident hierarchical structure to the spread of influenza within seasons, with epidemics most frequently spreading from more to less populous states. However, the correspondence found between population and epidemic origination was not sufficient to explain patterns in the location of epidemic origin. This suggests that other factors, such as international connectivity and the popularity of locations as tourist destinations, may also play important roles in explaining geographic patterns of influenza spread. In addition to population, workflows, which are persistent sources of work-related human movement to and from their places of work, were also found by Viboud et al. (2006) to play an important role in the spread of influenza. Incorporation of workflows in a general gravity model, which accounts for the size and proximity of communities in evaluating their interaction, revealed a critical distance of 119 kilometers, with commuting patterns beyond this distance being infrequent and having little impact on epidemics. These results were validated with a set of susceptible-infected-recovered (SIR) models, which account for the transition of individuals 8

19 through these phases throughout an epidemic (Viboud et al., 2006). Charaudeau et al. (2014) also linked an observed lack of randomness in influenza incidence to commuting patterns at a national scale. In this study, changes in the Moran s I over time and Mantel s test outcomes indicated a structure of spread that corresponded with the commuting-related connectivity of regions. While both of these results reinforce the importance of workflows as a predictor of influenza dynamics with respect to other predictors, the relative importance of workflows in the spread of influenza is contested. Though workflows have been found to be the key indicator of its spread by some, Charu et al. (2017) found that they do not outperform geographic distance in predicting its spread. It has also been suggested that workflows are too narrow of a source of such movement to sufficiently explain the numerous means of influenza spread at global and regional scales (Charu et al., 2017). Further, this metric does not incorporate all regular commutes or the evident differences in the frequency of commutes by age and structural differences in commuting patterns by age group (Charaudeau et al., 2014). The most prominent example of omissions of each of these is the regular school attendance of school-aged children, which is associated with particularly local networks (Charaudeau et al., 2014). As such, it is important that consideration of commutes as indicators of the geographic spread of influenza be as accurate as possible and considered with an appropriate understanding of their limitations in order to appropriately account for the magnitude of their effects on these patterns. Vaccination Influenza vaccination is recognized as an important component of influenza control. The process of vaccination prompts the production of antibodies against specific viruses in the 9

20 vaccinated individual. This process reduces the burden of the virus by disrupting the transmission of new cases to vaccinated individuals and preventing the potential of subsequent transmission to others. Seasonal flu vaccines are designed to protect against the particular strains of influenza that have been determined by research to be the most prevalent in a particular season. Traditional trivalent vaccines protect against influenza A (H1N1), influenza A (H3N2), and an influenza B virus (while quadrivalent vaccines also protect against an additional influenza B virus) (Centers for Disease Control and Prevention, 2017e). A broad range of evidence suggests that estimates of influenza vaccine effectiveness generally fall between 50% and 70% (Carville et al., 2015, Manzoli et al., 2012). Most years, vaccination of individuals against influenza translates to at least a 50% reduction in laboratory-confirmed cases of influenza in hospitals and the community (Carville et al., 2015). Children are a frequent focus of vaccination efforts, as they are known to be major disseminators of the virus (Simonsen, 1999). An important reason for this being the case is the rate at which they come into contact with peers, along with generally residing with parents and guardians that have their own contact networks (Mossong et al., 2008). It is also noteworthy that about 40% of school-aged children live in households along with individuals that have a higher risk of influenza (Fiore et al., 2012). The importance of schools in influenza transmission trends is highlighted by the implication that holidays and school closures have been documented to have on these trends (Charaudeau et al., 2014). It is also commonly recognized that there is a particularly high speed of influenza spread among young school children between the ages of 5 and 9, and it has been suggested that children in this age range play a leading role in epidemic waves (Schanzer et al., 2011). 10

21 The wide range of age groups from which influenza can be transmitted makes vaccination an important mechanism in disrupting its spread. This reduction in infection of individuals able to transmit the virus to others not only reduces the magnitude of influenza epidemics but can also affect its timing. For example, reducing circulation of the virus can delay the onset and hasten the ending of an epidemic. Influenza Surveillance Cases of influenza-like illness (ILI) have traditionally been utilized as indicators of the influenza virus. Influenza has traditionally not been immediately distinguishable from other illnesses, and there are complexities in the process of testing suspected cases of influenza. Testing influenza through viral culture in a laboratory generally takes between three and ten days (Centers for Disease Control and Prevention, 2016). It is also expensive to have suspected cases of the virus tested in the laboratory in relation to the cost of a clinical evaluation. Influenza-Like Illness ILI is defined by the CDC as fever (temperature of 100 F [37.8 C] or greater) and a cough and/or a sore throat without a known cause other than influenza (Centers for Disease Control and Prevention, 2017d). However, there are also other definitions for ILI in use today. While such definitions can be similar to that of the CDC, such as that of the WHO (an acute respiratory infection with a measured fever of at least 38 degrees Celsius and cough (World Health Organization, 2018)), many are less similar. Criterion of ILI specified in various studies include identification of the presence of respiratory and systemic symptoms, and do not necessarily require a specified body temperature (Thomas, 2014). As such, while case definitions 11

22 of ILI generally address important facets of suspected influenza, several such case definitions are not entirely compatible with one another or with those used by the CDC and WHO. It is important that ILI always be understood within the context of the relevant case definition whenever it is considered. It is also important to appreciate what is apparent in the classifying term influenza-like illness that not all cases of ILI are influenza. The percentage of ILI cases that correspond to actual cases of influenza ranges from 0% when there is no influenza in circulation (as is the case in the summer) to between 60% and 70% when there is much influenza in circulation (such as in the winter) (Cedraschi et al., 2013). In addition to obfuscating influenza trends, the imprecision of ILI leads to difficulty in identifying other important pathogens that account for ILI. There are several illnesses that contribute to ILI. A particularly prominent such illness is respiratory syncytial virus (RSV). While RSV is most associated with causing bronchiolitis in infants, the virus can cause upper and lower respiratory-tract infections in all individuals (Zambon et al., 2001). The degree to which influenza and ILI are related is also dependent on weather. It has been found that the proportion of individuals infected with influenza that fits the case definition of ILI is inversely proportional to the temperature and absolute humidity at a given location within an influenza season (van Noort et al., 2012). Collectively, these examples demonstrate that ILI is an imprecise indicator of influenza. In this study, I utilized an improved source of data for analysis in a repository comprised of influenza test results throughout the United States. 12

23 Rapid Influenza Diagnostic Tests (RIDTs) In addition to providing greater precision in influenza diagnosis, RIDTs also increase the number of cases of ILI that are tested for actual influenza. While cases of ILI are often tested for influenza in a laboratory, and such testing is often conducted in circumstances in which it is feasible, it is important to note that such is not true for all cases. Cases of ILI may not be tested for influenza because of the expense and resources associated with such testing, which include the procedure of running the test and having the necessary equipment and personnel with the necessary expertise. Another possible prohibitory factor for such laboratory testing is the distance between an outpatient facility and the nearest facility equipped to run such tests (Peci et al., 2014). The lack of universality in the feasibility of conducting laboratory tests for influenza for all cases of ILI demonstrates that there is a place for another means of testing to complement this ideal means of definitive diagnosis. Such a complimentary means of diagnosis exists in RIDTs which are comparatively simple to operate and require machines that are much less expensive than those associated with laboratory confirmation of the virus (Peci et al., 2014). RIDTs are also beneficial in settings where both tests are available because of the relative immediacy of their results. The results of RIDTs are typically available within minutes. The relatively immediate diagnosis provided by rapid tests with respect to laboratory testing enables them to serve an important purpose in the clinical management of ILI, as optimal efficacy of influenza treatment requires that treatment begin within 36 to 48 hours of the onset of symptoms (Gavin & Thompson, 2004). Such initial indication has also been demonstrated to be associated with more appropriate use of antibiotics and antivirals as well as shorter stays in a hospital emergency department (Bonner et al., 2003). 13

24 This study is one of the first to utilize data from RIDTs in evaluating spatial and temporal influenza patterns. The comprehensive nature of this data, as well as its improved resolution with respect to ILI data used by the CDC and other studies, makes it an ideal basis for the analysis conducted in this study. This data was previously used in a more narrowly focused study on patterns of influenza activity in Phoenix, AZ (Tamerius & Steadman et al., 2017). Milestones in Epidemic Timing The temporal nature of epidemics is given great attention in the study of disease pathology. Epidemic timing is often expressed in terms of their peaks. These refer to the windows of time in which the greatest number of cases are observed with respect to the windows they immediately follow and precede. Epidemics may consist of a single peak or multiple peaks with varying levels of intensity. Expression of the burden of epidemics in terms of peaks is effective in communicating the specific times at which the magnitude of the burden is the greatest. This information is beneficial in the insight it gives stakeholders, including health workers and officials, in tracing the etiology of epidemics, and in preparing for the optimal allocation of health resources in future epidemics and seasons. However, this does not communicate knowledge about how the temporal progression of the epidemic manifests itself in terms of the cumulative burden of the overall epidemic. Such knowledge can be used to evaluate long-term trends in epidemic beginning, mid-point (or the point at which half of cases in a particular epidemic have occurred, i.e. median), and ending. Tracking various milestones in the timing of influenza, such as the beginning, mid-point, and ending of an epidemic, provides insight into the overall timing of the epidemic, as their measurement takes all diagnosed cases of influenza into account. Like peak 14

25 timing, information on cumulative milestone timing can also be beneficial in interpreting the optimal allocation of health resources. However, since this information is more reflective of the temporal distribution of epidemics in their entirety, it is more conducive to serve as the basis for evaluation of association with other factors, such as climatic variables, population characteristics, and vaccination rates. To my knowledge, this is one of the first studies to evaluate the timing of multiple stages of influenza epidemics in this way. While the epidemic mid-point has been utilized in analysis of influenza epidemics for various age groups (Schanzer et al., 2011), and the dates of the 5 th and 95 th percentiles of cumulative weekly proportions of influenza cases have been utilized to evaluate trends in the amount of time between these two points (Storms et al., 2013), there is not evidence of studies that have analyzed each of these three epidemic milestones with the objective of identifying trends in the date of their occurrence in specific locations. Objectives The objectives of this analysis were to identify possible associations between influenza occurrence and location, climatic variables, population characteristics, and cumulative seasonal vaccination rates. Based on previous research, I anticipated that higher average dew point and temperature, and greater population and population density, would be associated with earlier epidemic beginnings and later epidemic endings, and that cumulative seasonal vaccination rate would be positively associated with earlier onset of each milestone in urban areas throughout the United States. 15

26 MATERIALS AND METHODS Data This analysis relied upon results of influenza tests performed on Sofia immunoassays (a recently developed RIDT product developed by the Quidel corporation) throughout the contiguous United States during the influenza season. These tests have been shown to have high overall sensitivity and specificity for detecting both influenza A and B. In a clinical study of the immunoassay s performance, the overall sensitivity and specificity of influenza A detection were 94% and 95%, respectively, and the overall sensitivity and specificity of influenza B detection were 90% and 96%, respectively (Lewandrowski et al., 2013). RIDT result data provided by the Sofia platform for this study include the date and ZIP code in which tests were performed and indication as to whether or not the test resulted in a positive diagnosis for A, B, or neither type of influenza. Tests that were run on machines for which the first RIDT was performed on or before October 1, 2016 were included in this analysis. All tests in this analysis were conducted between October 1, 2016, and August 31, 2017 (the last date for which data was available). For tests that were run at facilities in zip code tabulation areas within 10 kilometers of an urban area, data was aggregated either to the urban area by which the location of the test was contained or to the urban area closest to its respective zip code tabulation area. This accounted for individuals traveling to and from urban areas on their work commute. I chose to aggregate test results to urban areas because of their consistent definition of urbanized areas with at least 50,000 people (U.S. Census Bureau, 2016), and their distribution throughout 16

27 the United States. Urban area and zip code tabulation area data associated with these tests were obtained from the United States Census Bureau (U.S. Census Bureau, 2017a; U.S. Census Bureau, 2017b). Data was then analyzed for urban areas in which at least 500 rapid tests were conducted. This threshold was chosen to maintain a balance between utilizing data from urban areas in which a sufficient number of tests were run and analyzing data from a sufficient number of urban areas throughout the United States in order to analyze spatial and temporal characteristics of influenza epidemics in a meaningful way. Climate data from Oregon State University s PRISM climate group was utilized in this analysis (PRISM Climate Group, 2018). Raster files of dew point and temperature in the United States at 4-kilometer resolution were utilized as a basis for extracting local values for these variables in urban areas. Monthly data between October 2016 (shown in Figure 1) and May 2017 consisted of the actual variable estimates from these months. Due to lack of such availability between June 2017 and September 2017, monthly data from these months consisted of 30-year average monthly conditions between 1981 and Dew point and temperature were then extracted from ArcMap for each urban area for each month (from actual data for October April 2017 and from 30-year averages for May 2017-September 2017). Average values of each of these variables were calculated for each urban area in order to create variables that corresponded with the study period. Urban area population and population density data from 2010 was obtained from the United States Census Bureau (U.S. Census Bureau, 2015). Statewide cumulative seasonal vaccination data was downloaded from the Centers for Disease Control and Prevention (Centers for Disease Control and Prevention, 2017c). As the final month of data for the influenza season conceptualized by the CDC as occurring between July 2016 and May 2017, cumulative 17

28 Figure 1 Dew point and temperature rasters for October 2016 seasonal vaccination data from May 2017 was utilized in this analysis. While data on vaccination is made available by the CDC at the state level, this data is not commonly available at the urban or metropolitan level. In this analysis, cumulative seasonal vaccination data was attributed to urban areas based upon the state in which the greatest number of people in the urban area resided. The decision to utilize cumulative seasonal vaccination data from May 2017 was made in recognition of the fact that state vaccination rates are not constant throughout the season, and that the relative standing of vaccination rates per state throughout the season are not constant. General stability in the relative standing of states with respect to their levels of vaccination coverage through much of the epidemic led to the conclusion that this rate is a fair representation of the level of coverage that generally prevails throughout the epidemic months of October 2016 to September 2017, as they are conceptualized in this study. 18

29 Analysis Testing for Spatial Autocorrelation Urban areas throughout the United States were tested for spatial autocorrelation in the dates of epidemic beginning, mid-point, and ending for each type of influenza. This analysis converted these date variables to numeric variables and conceptualized the spatial relationships between urban areas as being a function of inverse distance between urban area centroids. This was done using the Moran s I tool in ArcMap (Esri, 2017). Maps and Histograms Maps and histograms were created that indicated the spatial and temporal nature of the epidemic. These maps demonstrate the points in time in which the beginning, mid-point, and ending of epidemics occurred in urban areas throughout the United States. The histograms demonstrated the timing of these milestones through displaying their frequency in two-week increments throughout the epidemic. For the purpose of this analysis, the sequential numbering of weeks began with the first full week of the epidemic (with week 1 beginning on Monday, October 3, 2016). Regression Regression analysis was conducted to determine potential relationships between the date of the 5th, 50th, and 95th percentiles of the cumulative proportions of influenza cases of influenza A and influenza B in relation to several independent variables. These independent variables include urban area population, population density, the weather variables of temperature 19

30 and dew point, and cumulative seasonal vaccination rates. Evaluation of date was done through calculation of the cumulative proportion of cases in each urban area for each date for each type of influenza. The first date for which these cumulative proportions exceeded 0.05, 0.50, and 0.95 were conceptualized respectively as the beginning, mid-point, and ending of epidemics for each type of epidemic (A and B) during the study period. Follow-up regression analysis was then conducted on these variables in order to account for urban areas in which not many tests were run. Results from urban areas with low test totals could have an effect on the overall estimated timing of the epidemic if the limited test results do not represent its true timing within the urban area. Thus, I performed a sensitivity analysis that included urban areas that experienced at least 200 positive cases of influenza A and B, respectively, in order to account for these possible effects from urban areas with low test totals on the analysis. This threshold was also chosen to maintain a balance between a sufficient number of tests being run in each of a sufficient number of urban areas throughout the United States. Stepwise Regression There are several methods for identifying an ideal set of variables to be included in a multiple regression model. Three of the most prominent methods of variable selection are forward selection, backward elimination, and a combination of these previous two methods. The phrase stepwise regression is commonly used to refer either specifically to the combined forward selection-backward elimination method, or to this group of variable selection methods collectively. Within human geography, stepwise regression generally refers to this collection of methods (Castree et al., 2013). 20

31 This analysis used the forward selection (or forward stepwise regression) procedure to identify a set of variables that may reasonably be associated with influenza transmission. In addition to each of the variables for which bivariate regression tests were conducted, latitude and longitude were included in this procedure in order to evaluate the potential influence of location on the timing of influenza epidemics. The ols_step_forward function in the olsrr library in R was used to conduct this test, with a specified Type I error rate of 0.05 (Aravind, 2017). Residuals from these models were then tested for spatial autocorrelation in ArcMap using the Moran s I tool (Esri, 2017). While there have been many fundamental concerns presented by various researchers about these stepwise regression procedures, such as instability and arbitrariness associated with use of such models, and the potential for such procedures to serve as substitutes for engaging in thought about the real processes in problems (Harrell, 2015), this analysis utilized the stepwise procedure in an exploratory fashion that was properly sensitive to its limitations. Only variables that have been demonstrated to be associated with influenza and that are reasonably believed to have an impact on the virus were included in this process. Several geographic studies and studies of influenza (both within and outside of geography) have also utilized these stepwise procedures in appropriate ways. Such studies have explored the relationship between influenza transmission and climatic factors (Chowell et al., 2012; Tamerius & Ojeda et al., 2017), socioeconomic status, geographic location of chicken and poultry farms, poultry transportation networks, as well as population, agricultural, and farming characteristics (Gilbert et al., 2006; Henning et al., 2009; Kung et al., 2007). Utilization of stepwise procedures in this research is in line with the exploratory nature of the procedure s use in these previous studies. 21

32 Spatial Lag Regression Spatial lag regression was conducted to account for spatial autocorrelation in the data that was not explained by the independent variables. This was done in R by using the lagsarlm function of the spdep package (Bivand et al., 2017). For each urban area, the eight nearest neighboring urban areas were defined as influencing variable outcomes. This number has been utilized as a rule of thumb in previous studies that have utilized k-nearest neighbors (Khan et al., 2017). Spatial lag analysis was run on models that included the variables included in the best forward stepwise regression models for each of the milestones of influenza timing for each type of influenza for all urban areas (see Appendix for comparison with spatial error model). This study utilized the Akaike Information Criterion (AIC) and Log Likelihood as a basis of comparison for forward stepwise regression and spatial lag regression models. This approach of model comparison using these indicators was previously implemented by Tan et al. (2011), who utilized stepwise regression and spatial lag regression in analyzing sexually transmitted infections in China. 22

33 RESULTS Spatial and Temporal Characteristics of Epidemic Timing Overview Altogether, there were 151 urban areas throughout the United States in which at least 500 RIDTs were conducted (shown in Figure 2). These urban areas were spread throughout the Unites States, but were most frequent in the central and eastern regions of the country. Of the 438,048 RIDTs from these urban areas, 65,530 were positive for influenza A and 40,063 were Figure 2 Map of urban areas in which at least 500 RIDTs were conducted 23

34 positive for influenza B. Histograms of the distribution of the number of these tests that were conducted in various urban areas and positive outcomes for influenza A and B are shown respectively in Figures 3 and 4. There were 81 urban areas in which there were at least 200 positive cases of influenza A and 48 urban areas with at least 200 cases of influenza B (shown in Figure 5). Figures 6 and 7 demonstrate how the timing of the epidemic beginning, mid-point, and ending were determined in two urban areas (New York-Newark in Figure 6, and Chicago in Figure 7) with respect to the influenza A epidemic curves in these urban areas. Figure 3 Histogram of the number of RIDTs conducted in various urban areas 24

35 Figure 4 Histograms of the number positive cases of influenza A and B in various urban areas 25

36 Figure 5 Maps of urban areas with at least 200 positive cases of influenza A and B 26

37 Figure influenza A epidemic milestones with respect to the epidemic curve in New York-Newark Figure influenza A epidemic milestones with respect to the epidemic curve in Chicago 27

38 Spatial Autocorrelation Results Table 1 demonstrates that there was spatial autocorrelation for every milestone of influenza timing for both types of influenza at a significance level of p < Epidemic Timing Spatial Autocorrelation Category Moran's I P-value Flu A: Beginning Flu A: Mid-Point Flu A: End Flu B: Beginning Flu B: Mid-Point Flu B: End Table 1 Epidemic timing spatial autocorrelation Spatial and Temporal Distribution of Influenza A Figure 8 demonstrates that influenza A epidemics generally began earlier in more southerly and westerly urban areas. All influenza A epidemics began within a 13-week period, with the first and last epidemic beginnings occurring on November 4, 2016 and February 2, The two-week period in which the greatest number of influenza A epidemics began was December 26, 2016-January 8, The mean beginning date was December 29, 2016, and the standard deviation was days. Figure 14 displays the histogram of the timing of this milestone along with all others for both influenza A and B as a basis for comparison. Figure 9 demonstrates that the mid-point of influenza A epidemics occurred earlier in more westerly urban areas but was not associated with a distinguishable latitudinal trend. The mid-point of all influenza A epidemics occurred within a 12-week period, with the first and last epidemic mid-points occurring on December 26, 2016 and March 17, The influenza A 28

39 Figure 8 Beginning of influenza A epidemic distribution and timing 29

40 Figure 9 Mid-point of influenza A epidemic distribution and timing 30

41 Figure 10 Ending of influenza A epidemic distribution and timing 31

42 Figure 11 Beginning of influenza B epidemic distribution and timing 32

43 Figure 12 Mid-point of influenza B epidemic distribution and timing 33

44 Figure 13 Ending of influenza B epidemic distribution and timing 34

45 Figure 14 Histograms for the timing of each influenza A and B epidemic milestone epidemic mid-point for more than half of the urban areas studied occurred during the two-week period between February 6 and 19, The mean mid-point date was February 13, 2017, and the standard deviation was days. 35