Public Transit Development Predicts Spatial Distribution of Dengue Virus Incidence in Medellín, Colombia

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Urbanization and human movement affect the spatial dynamics and magnitude of dengue outbreaks; however, precise effects of urban growth on dengue is not well understood because of a lack of sufficiently fine-scaled data. We analyzed nine years of address-level dengue case data in Medellin, Colombia during a period of public transit expansion. We correlate changes in the spread and magnitude of localized outbreaks to changes in accessibility and usage of public transit. Locations closer to and with a greater utilization of public transit had greater dengue incidence. This relationship was modulated by socioeconomic status; lower socioeconomic status locations experienced stronger effects of public transit accessibility and usage on dengue incidence. Public transit is a vital urban resource, particularly among low socioeconomic populations; these results highlight the importance of public health services concurrent with urban growth. Epidemiology Entomology Dengue Urbanization magnitude socioeconomic transit Figures Figure 1 Figure 2 Introduction Dengue is the most important and fastest growing arboviral disease world-wide. An estimated 50-100 million people are affected each year 1 , and between 1990 and 2020, global burden more than doubled each decade 2 . There is no widespread commercially available vaccine for dengue, and so mitigation primarily relies on mosquito control 3 . Mosquito control resources are limited, and overuse of insecticides causes resistance, forcing many public health programs to target their control efforts in time and space towards areas with an elevated risk of dengue infection. If dengue outbreaks can be identified in the very early stages, efforts can be well-targeted, significantly reducing infection rates 4 . If risk cannot be predicted, control becomes reactive rather than preventative, which can lead to a failure to reduce dengue infection 4 . This importance of early detections has incentivized efforts towards creating accurate dengue outbreak models and risk maps. Despite the strong incentive, dengue risk mapping has achieved variable success 5 . Dengue is spatially explicit and highly dependent on the environment and the immunological profile of the human population, creating complex transmission persistence and dispersal patterns 6 . Therefore, dengue transmission shifts dynamically across space and time, complicating the ability to determine reliable predictors. Spatial scale is also a complicating factor. For example, weather is often a primary predictor in dengue models, yet these parameters are often only measured on a homogenous, city-wide scale. Human mobility has gained increasing recognition as a driver of fine-scale dengue risk. The primary vector of dengue, Aedes aegypti , is a short-distance flier 7 , and so the diffusion and spatial variability of dengue across both short and long distances is mediated by human movement 8 . Within a single city, human social networks and daily movement have been shown to predict clusters of dengue infections 9 , and in one study, control via tracing social contacts of infected people effectively reduced dengue 10 . In another study, distance to a metro station predicted the clustering of dengue cases over two epidemic years in Singapore 11 , suggesting that dengue can be tied to hubs of human transport within the space of a city. While lower socioeconomic status has also been tied to dengue incidence in some cases, this effect is highly inconsistent across studies (reviewed in 12 and 13 ). This suggests that it may not be socioeconomic status itself affecting transmission, but other factors that may result from it. We analyzed dengue cases in Medellín, Colombia during an eight-year period of rapid development of the city’s Metro system. We explored how the construction of public transit infrastructure targeted towards low socioeconomic status regions and the resulting changes in human mobility affected the fine-scale spatial distribution of dengue incidence. Medellín is a perfect test-case to understand the impacts of growing urban infrastructure and public transit on dengue because 1) seasonality is limited, with a stable climate year-round, minimizing noise from climatic drivers of dengue transmission 14 ) Medellín has undergone a period of rapid infrastructure growth, including the construction of new public transit lines. This allows for comparison of the spatial structure of dengue before and after the addition of each new line; 3) Medellín has collected probable dengue health care facility case records since 2008, and each case is recorded to the patient’s home address, enabling analysis at a fine spatial scale; 4) Medellín surveyed city-wide human mobility patterns in 2011 and 2016 so we can quantify the use of public transit systems across space to understand its impact on dengue; and 5) Medellin´s neighborhoods are classified based on their socioeconomic strata into six different classes, strata six representing the highest income group, and one the lowest. And there are both areas of high and low socioeconomic status with and without accessible public transit lines throughout the study period. Medellín is situated in a valley surrounded by mountains. The flat center is primarily industrial and commercial, while more residential neighborhoods are in the steep perimeter. Historically, low socioeconomic status residents of mountainous parts of the city had extremely limited mobility 15 , 16 , 17 . Many residents of the high-elevation, high-socioeconomic status regions can travel by personal vehicle or taxi, but for residents of low socioeconomic status regions without the same resources, accessing a job in the industrial center would have required finding a means to traverse up to 600 meters in elevation gain. To improve the public transportation system of residents in Medellin, particularly in locations where topography limits the way to move, Medellín Metro system was inaugurated in 1994 with a goal of providing mobility to low socioeconomic status residents of mountainous regions 15 , 16 , 17 . The metro system expanded between 1994 and 2016 to become more accessible and increasingly utilized by larger portions of the city. Medellín has a year-round tropical climate with average temperatures between 21°C and 25°C 14 , and Ae. aegypti and recently Ae. albopictus have been established across the city 18 . Medellín is endemic for all four dengue serotypes 19 . Dengue has been a notifiable disease in Colombia since 2008, and in Medellín, all cases diagnosed by a physician that meet the WHO case definition 3 are reported as probable dengue cases along with each patient’s demographic information and home address ( Medellín Secretaría de Salud , pers comm). We conducted a retrospective geospatial analysis of dengue cases in Medellín between 2008 and 2016 to understand the effects of the construction of public transit infrastructure and resulting changes in human mobility and socioeconomic status on fine-scale spatial heterogeneity in dengue risk while accounting for socioeconomic status. We determined if regions of the city that are closer to public transit lines and that have a higher percentage of public transit ridership had higher dengue incidence and analyzed how this effect is modulated by socioeconomic status. Results All analyses were conducted at the spatial level of “SIT zone” ( Zonas Del Sistema Integrado de Transporte ), a zoning metric used by the Área Metropolitana del Valle de Aburrá that divides Medellin into 291 spatial units. Over the course of the study period (2008 -2016), the number of reported dengue cases analyzed here varied between 457 and 14,882 analyzed cases per year. Both 2010 and 2016 were epidemic years, with 13, 052 and 14,882 analyzed cases, respectively. In 2008, the metro system consisted of two main lines and two connected arial cable car ( Metrocable ) lines. New lines were added in 2012, 2013, 2015, and 2016, reducing the distance to the closest metro line for each zone over time. Between the two years that public transportation was surveyed during the study period (2011 and 2016), the number of respondents using public transportation more than doubled from a median of 5.283% (max=50.00%, min=0.00%) of respondents per zone to a median of 11.364% (max= 43.750%, min= 0.00%) of respondents per zone. The socioeconomic status of each zone is shown in Figure 1 , and the relative spatial distribution of dengue cases and public transit lines each year is shown in Figure 2 . Dengue incidence, distance to public transit, and socioeconomic status 2008-2016 SIT zones that were closer to public transportation had significantly higher dengue incidence than SIT zones that were farther away from public transit (Estimate = -0.054, p=0.0193) (Table 1 ). Socioeconomic status of a zone alone did not significantly predict dengue incidence (Estimate = - 0.0371, p=0.340). However, there was a significant positive interaction between income and distance to public transit (Estimate = 0.122, p<0.0001); the lowest socioeconomic status zones closest to public transit had the highest dengue incidence, while zones with equally low socioeconomic status but farther from public transit had lower dengue incidence. The higher the socioeconomic status of the zone, the less effect distance to transit had on dengue incidence. Table 1 Summary of spatial autoregressive models showing correlation between dengue incidence and distance to nearest transit line, socioeconomic status as measured by Estrato , year, and the interaction between Estrato and distance to the nearest public transit line in Medellin, Colombia, 2008-2016. Dengue incidence Fixed effects Estimate (standard error) P-value Estrato (scaled) -0.037 (0.039) 0.034 Distance to nearest public transit line (scaled) -0.054 (0.023) 0.019 Estrato (scaled):Distance (scaled) to nearest public transit line 0.12 (0.017) <0.0001 Year 2009 -0.062 (0.048) 0.20 2010 1.71 (0.081) <0.0001 2011 0.025 (0.048) 0.60 2012 -0.037 (0.049) 0.45 2013 0.47 (0.051) <0.0001 2014 0.58 (0.057) <0.0001 2015 0.68 (0.058) <0.0001 2016 1.66 (0.082) <0.0001 Spatial autoregressive coefficient 0.36 (0.024) <0.0001 Estimates and standard errors are shown. Significant p-values are bolded. Estrato and distance to the nearest public transit line have been scaled to enable comparison of effect size. Dengue incidence has been log transformed. Dengue incidence, distance to public transit, transit usage, and socioeconomic status in 2011 & 2016 Data was then restricted to 2011 and 2016, the two years that public transit usage was surveyed, and the effects of distance to public transit, public transit usage, and socioeconomic status on reported dengue incidence were analyzed (Table 2 ). Within these two years, zones closer to public transit had significantly higher reported dengue incidence (Estimate =-0.136, p=0.000656) and zones with higher percentage of people reporting using public transit in the previous 24 hours had higher reported dengue incidence (Estimate=0.106, p=0.0102). There was again no significant main effect of socioeconomic status but there was a significant positive interaction term between distance to public transit and socioeconomic status (Estimate=0.183, p<0.0001), as well as a significant positive interaction term between public transit usage and socioeconomic status (Estimate=0.129, p=0.000568): low socioeconomic zones with lower ridership or greater distance to transit had a lower dengue incidence than low socioeconomic zones with higher ridership or less distance to transit. 2011 and 2016 were two highly distinct years of dengue infection rates: 2016 was an epidemic year with 14,882 analyzed cases, while 2011 was a post-epidemic year with 513 analyzed cases. Available public transit lines and public transit usage were also very different between these years. In 2011, overall ridership was lower, and most zones did not contain a public transit stop. By 2016, ridership was higher, and most zones contained a public transit stop. Table 2 Summary of spatial autoregressive models for data restricted to 2011 and 2016 showing correlation between dengue incidence and distance to nearest transit line, socioeconomic status as measured by Estrato , year, and the interaction between Estrato and distance to the nearest public transit line and Estrato and percent of survey respondents reporting using public transit in the last 24 hours. Log(dengue incidence) Fixed effects Estimate (standard error) P-value Estrato (scaled) 0.052 (0.074) 0.48 Distance to nearest public transit line (scaled) -0.14 (0.040) 0.00037 Percent of survey respondents using public transit in the last 24 hours (scaled) 0.077 (0.041) 0.059 Estrato (scaled):Distance (scaled) to nearest public transit line 0.18 (0.029) <0.0001 Estrato (scaled):Percent of survey respondents using public transit in the last 24 hours (scaled) 0.13 (0.037) <0.0001 Year 2016 1.80 (0.14) <0.0001 Spatial autoregressive coefficient 0.25 (0.054) <0.0001 Estimates and standard errors are shown. Significant p-values are bolded. Estrato , percent of survey respondents using public transit in the last 24 hours, and distance to the nearest public transit line have been scaled to enable comparison of effect size. Dengue incidence has been log transformed. Discussion Our work provides evidence that in Medellín, Colombia, zones that were closer to public transit and had a higher percentage of people reporting using public transit in the last 24 hours had higher rates of reported dengue. Furthermore, although living in regions with low socioeconomic status alone did not elevate reported dengue, the combination of low socioeconomic status and high population mobility enabled by public transportation showed to affect dengue incidence. We hypothesize that in Medellín, restricted mobility in low socioeconomic status zones of the city where public transportation is not available acts as a natural semi “quarantine”, preventing dengue from spreading far from each index case. When public transit is made available, both long and short distance movement of viremic people within the city increases, and dengue diffuses farther and faster. This effect is exacerbated by the fact that in low socioeconomic zones of Medellín, human density is high, window screens and indoor air conditioning is rare, and there is extensive available habitat for the dengue vector Ae. aegypti , as documented by Azoh Barry 20 and the Secretaría de Salud de Medellín (pers comm). In the case of Medellín, mobility of low-income residents is uniquely limited by the steep geography of the city. In other cities, mobility in some sectors might be similarly restricted by different mechanisms such as poor road infrastructure, physical distance, social or political norms regarding where people from different backgrounds spend time, or job availability, and these barriers to movement may or may not be greater among populations of lower socioeconomic status. While the mechanism may be different, the end result of changes leading to increased human movement might be the same. While our work shows a relationship between public transportation systems, socioeconomic status, and reported dengue incidence, it is not possible to directly identify the underlying mechanisms. It is possible that in regions with limited mobility, dengue is underreported due to an inaccessibility of medical facilities. Non-severe dengue presents similarly to other febrile diseases that are generally recognized to be self-resolving and non-threatening, and so the incentive to make a difficult trip to a health facility might be low. As public transportation options are built up, more people with dengue might use medical services and case incidence might appear to increase. Additionally, dengue cases here are not laboratory confirmed, but simply meet the WHO criteria for a probable case 3 , and therefore cases may be under or over reported. Ae. aegypti is a day-time biter and while transmission is likely occurring during the day (reference), it is unknown where the majority of infective bites take place. It is unclear if dengue is increasing due to more infective bites at work, at home via home visits or short distance movements within a community, or in other sites. More research is needed to clarify where dengue transmission takes place. Finally, it is possible that construction of public transit lines physically alters the landscape in a way that increases transmission by creating more Ae. aegypti habitat. As cities develop, new infrastructure can have unintended consequences on human health. One such consequence might be on the spatial structure of arboviral disease. However, we stress that the conclusion from this study should not be to limit public transit development. The construction of public transportation is one of the most widely recognized methods that governments can use to reliably improve people’s economic conditions. In Medellín, as in other cities, these systems have provided reducing the commuting time, creating opportunities to residents as access to jobs, education, public services, and social networks for millions of people, particularly for lower-income communities Rather, these findings highlight the necessity of providing adequate public health services and investing in well-targeted dengue surveillance and outbreak response concurrently with investment to increase human mobility. Materials And Methods Data : All data was processed and analyzed using R (R Core Team, Version 4.0.3). Dengue case data were collected and shared by the Alcaldía de Medellín, Secretaría de Salud . In Medellin, dengue case surveillance is conducted by public health institutions that classify and report all cases that meet the WHO clinical dengue case criteria for a probable case to Medellin’s Secretaría de Salud through SIVIGILA (“ el Sistema Nacional de Vigilancia en Salud Publica) . All case data were de-identified and aggregated to the SIT Zone level. Human public transit usage and movement data were collected and shared by the Área Metropolitana del Valle de Aburrá for 50-200 respondents per SIT Zone. The “ Encuestas Origen Destino ” (Origen Destination Surveys) were conducted in 2005, 2011, and 2016 and published in 2006, 2012, and 2017, with survey methods described by the Área Metropolitana del Valle de Aburrá 21 . Survey respondents reported the start and end locations, purpose for travel, and mode of travel for all movement over the last 24 hours from the time the survey was administered. The results of the survey published in 2017 are published online by the Área Metropolitana del Valle de Aburrá 22 , and the data are available through the geodata-Medellin open data portal 23 . The results and data of the survey published in 2012 are not publically available and were obtained directly from the Área Metropolitana del Valle de Aburrá. The public transit usage survey data were also used to extract socioeconomic data to the SIT zone; surveyors also reported basic demographic data including household Estrato , which was averaged per SIT zone to estimate zone socioeconomic status. “ Estrato ” measures socioeconomic status on a scale from 1 (lowest) to 6 (highest). This system is used by the government of Colombia to allocate public services and subsidies (Law 142, 1994). Data from the public transit usage survey were used to extract socioeconomic status data because it is the only location available where the spatial scale of the data matched the spatial scale of the SIT zone. Data on the location of Medellín public transit lines was downloaded as shape files from the geodata-Medellín open data portal 23 and subset for each year to the set of transit lines that was available in that year. Data on the opening date of each Medellín public transit line was taken from the Medellín metro website 24 . Because census data at the zone level were not available for this study and only exists for 2005 and 2018, we used population estimates for each year downloaded from the WorldPop project 25 and aggregated by SIT zone. The accuracy of WorldPop estimates were checked against available census data for 2005 and 2018 at the comuna level, accessed via the geodata- Medellín open data portal 23 . Ethical Considerations: No human subjects research was conducted. All data used was de-identified, and the analysis was conducted on a database of cases meeting the clinical criteria for dengue with no intervention or modification of biological, physical, psychological, or social variables. All methods were performed in accordance with the relevant guidelines and regulations. Data analysis: Quantifying public transit usage and distance from nearest transit line To quantify public transit usage, we determined if each respondent reported using the metro, metroplus , or ruta alimentadora (supplementary bus route system integrated with the metro system) in the last 24 hours. We then calculated the percent of respondents using the public transit system at least once for each SIT zone. To quantify the distance to the nearest public transit line, we calculated the distance from the center point of each zone to the closest metro, metroplus , tranvía , metrocable , or escalera eléctrica. This was recalculated for each year, including new transit lines that were added within that year. Spatial Autoregressive Models of Dengue Incidence Dengue incidence per year at the level of the SIT zone was modeled using a fixed effects spatial panel model by maximum likelihood (R package splm, 26 ) as described in 27 . Our fixed effects were socioeconomic status, distance from public transit, a two-way interaction between these factors, and year. The model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year. Year was analyzed as a categorical variable to avoid smoothing epidemic years. All continuous variables were scaled to enable comparison of effect size. Because these panel models require balanced data across time, data was truncated to SIT zones that had data for all years available (247 remaining of 291). Spatial dependency was evaluated, and the model was selected using the Hausman specification test and locally robust panel Lagrange Multiplier tests for spatial dependence. Based on a significant Hausman specification test result, which indicates a poor specification of the random effect model, a fixed effect model was chosen. This result is supported by the fact that we had a nearly exhaustive sample of SIT zones in the Medellin metro area. Lagrange multiplier tests were used to determine the most appropriate spatial dependency specifications. Based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was the most appropriate to incorporate spatial dependency; a SAR model considers that the number of dengue cases in a SIT zone depends on the number in neighboring zones. Because public transit usage was a measurement taken during just two of the study years, we constructed an additional fixed effects spatial panel model by maximum likelihood model of dengue incidence in just 2011 and 2016 that included ridership as an additional predictor variable. Our fixed effects were year, socioeconomic status, distance from public transit, a two-way interaction between socioeconomic status and distance from public transit, percent utilizing public transit, and a two-way interaction between socioeconomic status and percent utilizing public transit. As in our model of all years, the model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year, year was analyzed as a categorical variable, and all continuous variables were scaled to enable comparison of effect size. The data was truncated to SIT zones that had data for all years available (251 remaining of 291). We used the same model selection process, and again a fixed effect model was chosen, and based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was determined the most appropriate to incorporate spatial dependency. Declarations Acknowledgments We appreciate the support of the Área Metropolitana del Valle de Aburrá and the Alcaldía de Medellín, Secretaría de Salud for sharing data used in this study. We are thankful for the statistical input of Dr. Erika Mudrak and Dr. Joe Guinness, as well as the support of Dra. Catalina Alfonso and Dr. Frank Avila in completing this study. Competing Interest Statement: The authors declare no competing interest. References Bhatt S, et al. The global distribution and burden of dengue. Nature 496 , 504 (2013). Stanaway JD, et al. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. 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Epidemiological trends of dengue disease in Colombia (2000-2011): a systematic review. PLoS neglected tropical diseases 9 , e0003499 (2015). Azoh Barry J. Dengue threat: adaptation needs in a disadvantaged neighborhood in Medellín-Colombia. Revista Costarricense de Salud Pública 20 , 16–24 (2011). Aburrá ÁMdVd. Encuesta Origen Destino de Hogares para el Valle de Aburrá) (2012). Medellín ÁMd. Encuesta Origen Destino.) (2017). Medellín Ad. Datos Abiertos.) (2021). Medellín Md. Metro de Medellín.) (2021). Southampton Uo. WorldPop Open Spatial Demographics Data and Research.) (2021). Millo G, Piras G, Millo MG. Package ‘splm’.). CRAN (2018). SALIMA BA, LIONEL VJL, V., BELLEFON. 7. Spatial econometrics on panel data. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 18 Feb, 2022 Reviews received at journal 31 Jan, 2022 Reviewers agreed at journal 28 Jan, 2022 Reviewers agreed at journal 21 Jan, 2022 Reviewers invited by journal 13 Jan, 2022 Editor assigned by journal 10 Jan, 2022 Editor invited by journal 07 Jan, 2022 Submission checks completed at journal 07 Jan, 2022 First submitted to journal 21 Dec, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1193404","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":74983621,"identity":"dbdee57f-180f-4cb3-a316-99dd9a424a2c","order_by":0,"name":"Talya Shragai","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Talya","middleName":"","lastName":"Shragai","suffix":""},{"id":74983622,"identity":"6d9b5bd6-cf50-44ed-bebd-a314d3247fd9","order_by":1,"name":"Juliana Perez-Perez","email":"","orcid":"","institution":"University of Antioquia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juliana","middleName":"","lastName":"Perez-Perez","suffix":""},{"id":74983623,"identity":"c9618b1c-16ad-44e3-8a2b-c2ebb04df80b","order_by":2,"name":"Marcela Quimbayo-Forero","email":"","orcid":"","institution":"University of Antioquia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcela","middleName":"","lastName":"Quimbayo-Forero","suffix":""},{"id":74983624,"identity":"2e4692e7-3ff6-46f0-8fc1-09074a5af900","order_by":3,"name":"Raul Rojo","email":"","orcid":"","institution":"Secretaria de Salud, Medellin, Colombia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Raul","middleName":"","lastName":"Rojo","suffix":""},{"id":74983625,"identity":"ca787d52-e58d-4280-81d8-b85440a11337","order_by":4,"name":"Laura Harrington","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACPgYexsd/KsBsxgMPgEQDIS1sDDzMBjxnIJwDCURqYZPgbSNJC/vZAxKS8+zkzGc3HwBqsZHdcICQFp68BAPDbcnGMneOJQC1pBkT1iLBY5CQuO1A4gyJHAOglsOJRGk5cHAOSEv+B6CW/0RpMWxsbADbAvL+ASK08OQYMzMcSzaWkEgDOswg2XgmIS387GfMfzPU2MlJSCQ/fPChwk62j5AWNGBAmvJRMApGwSgYBTgAAO+eQTjh+a7CAAAAAElFTkSuQmCC","orcid":"","institution":"Cornell University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Harrington","suffix":""},{"id":74983626,"identity":"69e1f7cb-3618-4931-aa67-4d9f93813f4a","order_by":5,"name":"Guillermo Rua-Uribe","email":"","orcid":"","institution":"University of Antioquia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Rua-Uribe","suffix":""}],"badges":[],"createdAt":"2021-12-21 19:14:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1193404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1193404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17174511,"identity":"0157ce43-383d-4148-ba19-1fb1fc15ef78","added_by":"auto","created_at":"2022-01-10 18:05:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257724,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of the transport zones of Medellín showing A) mean socioeconomic status per zone, and B) mean elevation per zone in meters. Blank zones are zones for which there was no data available.\u0026nbsp;Socioeconomic status is measured as \u003cem\u003eEstrato,\u003c/em\u003e a scale used for socioeconomic classification by the government of Colombia, measuring from 1 (lowest) to 6 (highest).\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1193404/v1/8ea82c1365222bbdad8f3edc.png"},{"id":17174510,"identity":"2d045108-4662-4ed6-9707-cd4b63151008","added_by":"auto","created_at":"2022-01-10 18:05:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":501954,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of Medellin, Colombia 2008 – 2016. Each panel shows the relative distribution of dengue incidence per zone. Relative dengue incidence is shown using the Getis Ord Local G statistic to enable comparisons across years with large differences in the number of total dengue cases. The public transit lines available in each year are shown as black lines for the Metro, \u003cem\u003eMetroplus,\u003c/em\u003e \u003cem\u003eRutas Alimentadoras, \u003c/em\u003eand the \u003cem\u003eEscaleras Electricas\u003c/em\u003e. In A) 2008, B) 2009, and C) 2010, two metro lines, Lines A and B, and two arial cable car (\u003cem\u003eMetrocable\u003c/em\u003e) lines, Lines J and K were available. In D) 2011,\u0026nbsp;\u003cem\u003eMetroplus\u0026nbsp;Linea 1\u003c/em\u003e was added. The\u0026nbsp;\u003cem\u003eMetroplus\u0026nbsp;\u003c/em\u003eis a bus line with dedicated constructed lanes and stops that connects directly with the metro. In E) 2012 the \u003cem\u003eEscaleras\u0026nbsp;electricas\u0026nbsp;\u003c/em\u003ebegan operating. The\u0026nbsp;\u003cem\u003eescaleras\u0026nbsp;electricas\u0026nbsp;\u003c/em\u003eare a system of public transit\u0026nbsp;escelators. Their inauguration was on December 28, 2011, but they are analyzed here with 2012 data.\u0026nbsp;In F) 2013, no new lines were added. In G) 2014 the\u0026nbsp;\u003cem\u003eMetroplus\u0026nbsp;Linea 2\u0026nbsp;\u003c/em\u003eand\u0026nbsp;\u003cem\u003eRutas\u0026nbsp;Alimentadoras\u0026nbsp;\u003c/em\u003eaddded. The\u0026nbsp;\u003cem\u003eMetroplus\u0026nbsp;Linea 2\u003c/em\u003e\u0026nbsp;runs along the same route as the\u0026nbsp;\u003cem\u003eLinea 1\u003c/em\u003e\u0026nbsp;but more than doubles the capacity of the\u0026nbsp;\u003cem\u003eMetroplus\u003c/em\u003e\u0026nbsp;system. The\u0026nbsp;\u003cem\u003eRutas\u0026nbsp;Alimentadoras\u0026nbsp;\u003c/em\u003eare bus lines operated by the city that feed into the metro and\u0026nbsp;\u003cem\u003eMetroplus\u003c/em\u003e\u0026nbsp;systems and do not run on dedicated lanes. In H) 2015, no new lines were added. In I) 2016, a\u0026nbsp;\u003cem\u003eTranvia\u003c/em\u003e\u0026nbsp;line and a\u0026nbsp;\u003cem\u003eMetrocable\u0026nbsp;\u003c/em\u003eline, Line H were added. The\u0026nbsp;\u003cem\u003etranvia\u0026nbsp;\u003c/em\u003eis a monorail line.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1193404/v1/5c776649808233b0665769f9.png"},{"id":17174522,"identity":"bfb595ec-134b-47ba-abea-6b004038fdc1","added_by":"auto","created_at":"2022-01-10 18:05:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":672513,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1193404/v1/79f2630f-bd00-483a-bb4b-34d8c2fe7e87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePublic Transit Development Predicts Spatial Distribution of Dengue Virus Incidence in Medellín, Colombia\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDengue is the most important and fastest growing arboviral disease world-wide. An estimated 50-100 million people are affected each year \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and between 1990 and 2020, global burden more than doubled each decade \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. There is no widespread commercially available vaccine for dengue, and so mitigation primarily relies on mosquito control \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Mosquito control resources are limited, and overuse of insecticides causes resistance, forcing many public health programs to target their control efforts in time and space towards areas with an elevated risk of dengue infection. If dengue outbreaks can be identified in the very early stages, efforts can be well-targeted, significantly reducing infection rates \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. If risk cannot be predicted, control becomes reactive rather than preventative, which can lead to a failure to reduce dengue infection \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This importance of early detections has incentivized efforts towards creating accurate dengue outbreak models and risk maps.\u003c/p\u003e \u003cp\u003eDespite the strong incentive, dengue risk mapping has achieved variable success\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Dengue is spatially explicit and highly dependent on the environment and the immunological profile of the human population, creating complex transmission persistence and dispersal patterns \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, dengue transmission shifts dynamically across space and time, complicating the ability to determine reliable predictors. Spatial scale is also a complicating factor. For example, weather is often a primary predictor in dengue models, yet these parameters are often only measured on a homogenous, city-wide scale.\u003c/p\u003e \u003cp\u003eHuman mobility has gained increasing recognition as a driver of fine-scale dengue risk. The primary vector of dengue, \u003cem\u003eAedes aegypti\u003c/em\u003e, is a short-distance flier \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and so the diffusion and spatial variability of dengue across both short and long distances is mediated by human movement \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Within a single city, human social networks and daily movement have been shown to predict clusters of dengue infections \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and in one study, control via tracing social contacts of infected people effectively reduced dengue \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In another study, distance to a metro station predicted the clustering of dengue cases over two epidemic years in Singapore \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, suggesting that dengue can be tied to hubs of human transport within the space of a city. While lower socioeconomic status has also been tied to dengue incidence in some cases, this effect is highly inconsistent across studies (reviewed in \u003csup\u003e12\u003c/sup\u003e and \u003csup\u003e13\u003c/sup\u003e). This suggests that it may not be socioeconomic status itself affecting transmission, but other factors that may result from it.\u003c/p\u003e \u003cp\u003eWe analyzed dengue cases in Medell\u0026iacute;n, Colombia during an eight-year period of rapid development of the city\u0026rsquo;s Metro system. We explored how the construction of public transit infrastructure targeted towards low socioeconomic status regions and the resulting changes in human mobility affected the fine-scale spatial distribution of dengue incidence. Medell\u0026iacute;n is a perfect test-case to understand the impacts of growing urban infrastructure and public transit on dengue because 1) seasonality is limited, with a stable climate year-round, minimizing noise from climatic drivers of dengue transmission \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e) Medell\u0026iacute;n has undergone a period of rapid infrastructure growth, including the construction of new public transit lines. This allows for comparison of the spatial structure of dengue before and after the addition of each new line; 3) Medell\u0026iacute;n has collected probable dengue health care facility case records since 2008, and each case is recorded to the patient\u0026rsquo;s home address, enabling analysis at a fine spatial scale; 4) Medell\u0026iacute;n surveyed city-wide human mobility patterns in 2011 and 2016 so we can quantify the use of public transit systems across space to understand its impact on dengue; and 5) Medellin\u0026acute;s neighborhoods are classified based on their socioeconomic strata into six different classes, strata six representing the highest income group, and one the lowest. And there are both areas of high and low socioeconomic status with and without accessible public transit lines throughout the study period.\u003c/p\u003e \u003cp\u003eMedell\u0026iacute;n is situated in a valley surrounded by mountains. The flat center is primarily industrial and commercial, while more residential neighborhoods are in the steep perimeter. Historically, low socioeconomic status residents of mountainous parts of the city had extremely limited mobility \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Many residents of the high-elevation, high-socioeconomic status regions can travel by personal vehicle or taxi, but for residents of low socioeconomic status regions without the same resources, accessing a job in the industrial center would have required finding a means to traverse up to 600 meters in elevation gain. To improve the public transportation system of residents in Medellin, particularly in locations where topography limits the way to move, Medell\u0026iacute;n Metro system was inaugurated in 1994 with a goal of providing mobility to low socioeconomic status residents of mountainous regions \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The metro system expanded between 1994 and 2016 to become more accessible and increasingly utilized by larger portions of the city. Medell\u0026iacute;n has a year-round tropical climate with average temperatures between 21\u0026deg;C and 25\u0026deg;C \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and \u003cem\u003eAe. aegypti\u003c/em\u003e and recently \u003cem\u003eAe. albopictus\u003c/em\u003e have been established across the city\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Medell\u0026iacute;n is endemic for all four dengue serotypes\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Dengue has been a notifiable disease in Colombia since 2008, and in Medell\u0026iacute;n, all cases diagnosed by a physician that meet the WHO case definition \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e are reported as probable dengue cases along with each patient\u0026rsquo;s demographic information and home address (\u003cem\u003eMedell\u0026iacute;n Secretar\u0026iacute;a de Salud\u003c/em\u003e, pers comm).\u003c/p\u003e \u003cp\u003eWe conducted a retrospective geospatial analysis of dengue cases in Medell\u0026iacute;n between 2008 and 2016 to understand the effects of the construction of public transit infrastructure and resulting changes in human mobility and socioeconomic status on fine-scale spatial heterogeneity in dengue risk while accounting for socioeconomic status. We determined if regions of the city that are closer to public transit lines and that have a higher percentage of public transit ridership had higher dengue incidence and analyzed how this effect is modulated by socioeconomic status.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAll analyses were conducted at the spatial level of \u0026ldquo;SIT zone\u0026rdquo; (\u003cem\u003eZonas Del Sistema Integrado de Transporte\u003c/em\u003e), a zoning metric used by the \u003cem\u003e\u0026Aacute;rea Metropolitana del Valle de Aburr\u0026aacute;\u003c/em\u003e that divides Medellin into 291 spatial units. Over the course of the study period (2008 -2016), the number of reported dengue cases analyzed here varied between 457 and 14,882 analyzed cases per year. Both 2010 and 2016 were epidemic years, with 13, 052 and 14,882 analyzed cases, respectively. In 2008, the metro system consisted of two main lines and two connected arial cable car (\u003cem\u003eMetrocable\u003c/em\u003e) lines. New lines were added in 2012, 2013, 2015, and 2016, reducing the distance to the closest metro line for each zone over time. Between the two years that public transportation was surveyed during the study period (2011 and 2016), the number of respondents using public transportation more than doubled from a median of 5.283% (max=50.00%, min=0.00%) of respondents per zone to a median of 11.364% (max= 43.750%, min= 0.00%) of respondents per zone. The socioeconomic status of each zone is shown in Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, and the relative spatial distribution of dengue cases and public transit lines each year is shown in Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eDengue incidence, distance to public transit, and socioeconomic status 2008-2016\u003c/h2\u003e\n \u003cp\u003eSIT zones that were closer to public transportation had significantly higher dengue incidence than SIT zones that were farther away from public transit (Estimate = -0.054, p=0.0193) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Socioeconomic status of a zone alone did not significantly predict dengue incidence (Estimate = - 0.0371, p=0.340). However, there was a significant positive interaction between income and distance to public transit (Estimate = 0.122, p\u0026lt;0.0001); the lowest socioeconomic status zones closest to public transit had the highest dengue incidence, while zones with equally low socioeconomic status but farther from public transit had lower dengue incidence. The higher the socioeconomic status of the zone, the less effect distance to transit had on dengue incidence.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of spatial autoregressive models showing correlation between dengue incidence and distance to nearest transit line, socioeconomic status as measured by \u003cem\u003eEstrato\u003c/em\u003e, year, and the interaction between \u003cem\u003eEstrato\u003c/em\u003e and distance to the nearest public transit line in Medellin, Colombia, 2008-2016.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDengue incidence\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate (standard error)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEstrato\u003c/em\u003e (scaled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.037 (0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to nearest public transit line (scaled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054 (0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEstrato\u003c/em\u003e (scaled):Distance (scaled) to nearest public transit line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12 (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.062 (0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71 (0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025 (0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.037 (0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 (0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 (0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68 (0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (0.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial autoregressive coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36 (0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eEstimates and standard errors are shown. Significant p-values are bolded. \u003cem\u003eEstrato\u003c/em\u003e and distance to the nearest public transit line have been scaled to enable comparison of effect size. Dengue incidence has been log transformed.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eDengue incidence, distance to public transit, transit usage, and socioeconomic status in 2011 \u0026amp; 2016\u003c/h2\u003e\n \u003cp\u003eData was then restricted to 2011 and 2016, the two years that public transit usage was surveyed, and the effects of distance to public transit, public transit usage, and socioeconomic status on reported dengue incidence were analyzed (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Within these two years, zones closer to public transit had significantly higher reported dengue incidence (Estimate =-0.136, p=0.000656) and zones with higher percentage of people reporting using public transit in the previous 24 hours had higher reported dengue incidence (Estimate=0.106, p=0.0102). There was again no significant main effect of socioeconomic status but there was a significant positive interaction term between distance to public transit and socioeconomic status (Estimate=0.183, p\u0026lt;0.0001), as well as a significant positive interaction term between public transit usage and socioeconomic status (Estimate=0.129, p=0.000568): low socioeconomic zones with lower ridership or greater distance to transit had a lower dengue incidence than low socioeconomic zones with higher ridership or less distance to transit. 2011 and 2016 were two highly distinct years of dengue infection rates: 2016 was an epidemic year with 14,882 analyzed cases, while 2011 was a post-epidemic year with 513 analyzed cases. Available public transit lines and public transit usage were also very different between these years. In 2011, overall ridership was lower, and most zones did not contain a public transit stop. By 2016, ridership was higher, and most zones contained a public transit stop.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of spatial autoregressive models for data restricted to 2011 and 2016 showing correlation between dengue incidence and distance to nearest transit line, socioeconomic status as measured by \u003cem\u003eEstrato\u003c/em\u003e, year, and the interaction between \u003cem\u003eEstrato\u003c/em\u003e and distance to the nearest public transit line and \u003cem\u003eEstrato\u003c/em\u003e and percent of survey respondents reporting using public transit in the last 24 hours.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLog(dengue incidence)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate (standard error)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEstrato\u003c/em\u003e (scaled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052 (0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to nearest public transit line (scaled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14 (0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent of survey respondents using public transit in the last 24 hours (scaled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077 (0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEstrato\u003c/em\u003e (scaled):Distance (scaled) to nearest public transit line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18 (0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEstrato\u003c/em\u003e (scaled):Percent of survey respondents using public transit in the last 24 hours (scaled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial autoregressive coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 (0.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eEstimates and standard errors are shown. Significant p-values are bolded. \u003cem\u003eEstrato\u003c/em\u003e, percent of survey respondents using public transit in the last 24 hours, and distance to the nearest public transit line have been scaled to enable comparison of effect size. Dengue incidence has been log transformed.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur work provides evidence that in Medell\u0026iacute;n, Colombia, zones that were closer to public transit and had a higher percentage of people reporting using public transit in the last 24 hours had higher rates of reported dengue. Furthermore, although living in regions with low socioeconomic status alone did not elevate reported dengue, the combination of low socioeconomic status and high population mobility enabled by public transportation showed to affect dengue incidence.\u003c/p\u003e \u003cp\u003eWe hypothesize that in Medell\u0026iacute;n, restricted mobility in low socioeconomic status zones of the city where public transportation is not available acts as a natural semi \u0026ldquo;quarantine\u0026rdquo;, preventing dengue from spreading far from each index case. When public transit is made available, both long and short distance movement of viremic people within the city increases, and dengue diffuses farther and faster. This effect is exacerbated by the fact that in low socioeconomic zones of Medell\u0026iacute;n, human density is high, window screens and indoor air conditioning is rare, and there is extensive available habitat for the dengue vector \u003cem\u003eAe. aegypti\u003c/em\u003e, as documented by Azoh Barry \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and the \u003cem\u003eSecretar\u0026iacute;a de Salud\u003c/em\u003e de Medell\u0026iacute;n (pers comm). In the case of Medell\u0026iacute;n, mobility of low-income residents is uniquely limited by the steep geography of the city. In other cities, mobility in some sectors might be similarly restricted by different mechanisms such as poor road infrastructure, physical distance, social or political norms regarding where people from different backgrounds spend time, or job availability, and these barriers to movement may or may not be greater among populations of lower socioeconomic status. While the mechanism may be different, the end result of changes leading to increased human movement might be the same.\u003c/p\u003e \u003cp\u003eWhile our work shows a relationship between public transportation systems, socioeconomic status, and reported dengue incidence, it is not possible to directly identify the underlying mechanisms. It is possible that in regions with limited mobility, dengue is underreported due to an inaccessibility of medical facilities. Non-severe dengue presents similarly to other febrile diseases that are generally recognized to be self-resolving and non-threatening, and so the incentive to make a difficult trip to a health facility might be low. As public transportation options are built up, more people with dengue might use medical services and case incidence might appear to increase. Additionally, dengue cases here are not laboratory confirmed, but simply meet the WHO criteria for a probable case \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and therefore cases may be under or over reported. \u003cem\u003eAe. aegypti\u003c/em\u003e is a day-time biter and while transmission is likely occurring during the day (reference), it is unknown where the majority of infective bites take place. It is unclear if dengue is increasing due to more infective bites at work, at home via home visits or short distance movements within a community, or in other sites. More research is needed to clarify where dengue transmission takes place. Finally, it is possible that construction of public transit lines physically alters the landscape in a way that increases transmission by creating more \u003cem\u003eAe. aegypti\u003c/em\u003e habitat.\u003c/p\u003e \u003cp\u003eAs cities develop, new infrastructure can have unintended consequences on human health. One such consequence might be on the spatial structure of arboviral disease. However, we stress that the conclusion from this study should not be to limit public transit development. The construction of public transportation is one of the most widely recognized methods that governments can use to reliably improve people\u0026rsquo;s economic conditions. In Medell\u0026iacute;n, as in other cities, these systems have provided reducing the commuting time, creating opportunities to residents as access to jobs, education, public services, and social networks for millions of people, particularly for lower-income communities Rather, these findings highlight the necessity of providing adequate public health services and investing in well-targeted dengue surveillance and outbreak response concurrently with investment to increase human mobility.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eData\u003c/em\u003e:\u003c/h2\u003e \u003cp\u003eAll data was processed and analyzed using R (R Core Team, Version 4.0.3).\u003c/p\u003e \u003cp\u003eDengue case data were collected and shared by the \u003cem\u003eAlcald\u0026iacute;a de Medell\u0026iacute;n, Secretar\u0026iacute;a de Salud\u003c/em\u003e. In Medellin, dengue case surveillance is conducted by public health institutions that classify and report all cases that meet the WHO clinical dengue case criteria for a probable case to Medellin\u0026rsquo;s \u003cem\u003eSecretar\u0026iacute;a de Salud\u003c/em\u003e through SIVIGILA (\u0026ldquo;\u003cem\u003eel Sistema Nacional de Vigilancia en Salud Publica)\u003c/em\u003e. All case data were de-identified and aggregated to the SIT Zone level.\u003c/p\u003e \u003cp\u003eHuman public transit usage and movement data were collected and shared by the \u003cem\u003e\u0026Aacute;rea Metropolitana del Valle de Aburr\u0026aacute;\u003c/em\u003e for 50-200 respondents per SIT Zone. The \u0026ldquo;\u003cem\u003eEncuestas Origen Destino\u003c/em\u003e\u0026rdquo; (Origen Destination Surveys) were conducted in 2005, 2011, and 2016 and published in 2006, 2012, and 2017, with survey methods described by the \u003cem\u003e\u0026Aacute;rea Metropolitana del Valle de Aburr\u0026aacute;\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Survey respondents reported the start and end locations, purpose for travel, and mode of travel for all movement over the last 24 hours from the time the survey was administered. The results of the survey published in 2017 are published online by the \u003cem\u003e\u0026Aacute;rea Metropolitana del Valle de Aburr\u0026aacute;\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and the data are available through the geodata-Medellin open data portal \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The results and data of the survey published in 2012 are not publically available and were obtained directly from the \u003cem\u003e\u0026Aacute;rea Metropolitana del Valle de Aburr\u0026aacute;.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe public transit usage survey data were also used to extract socioeconomic data to the SIT zone; surveyors also reported basic demographic data including household \u003cem\u003eEstrato\u003c/em\u003e, which was averaged per SIT zone to estimate zone socioeconomic status. \u0026ldquo;\u003cem\u003eEstrato\u003c/em\u003e\u0026rdquo; measures socioeconomic status on a scale from 1 (lowest) to 6 (highest). This system is used by the government of Colombia to allocate public services and subsidies (Law 142, 1994). Data from the public transit usage survey were used to extract socioeconomic status data because it is the only location available where the spatial scale of the data matched the spatial scale of the SIT zone.\u003c/p\u003e \u003cp\u003eData on the location of Medell\u0026iacute;n public transit lines was downloaded as shape files from the geodata-Medell\u0026iacute;n open data portal \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and subset for each year to the set of transit lines that was available in that year. Data on the opening date of each Medell\u0026iacute;n public transit line was taken from the Medell\u0026iacute;n metro website \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBecause census data at the zone level were not available for this study and only exists for 2005 and 2018, we used population estimates for each year downloaded from the WorldPop project \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and aggregated by SIT zone. The accuracy of WorldPop estimates were checked against available census data for 2005 and 2018 at the \u003cem\u003ecomuna\u003c/em\u003e level, accessed via the geodata- Medell\u0026iacute;n open data portal \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations:\u003c/h2\u003e \u003cp\u003eNo human subjects research was conducted. All data used was de-identified, and the analysis was conducted on a database of cases meeting the clinical criteria for dengue with no intervention or modification of biological, physical, psychological, or social variables. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis:\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eQuantifying public transit usage and distance from nearest transit line\u003c/h2\u003e \u003cp\u003eTo quantify public transit usage, we determined if each respondent reported using the metro, \u003cem\u003emetroplus\u003c/em\u003e, or \u003cem\u003eruta alimentadora\u003c/em\u003e (supplementary bus route system integrated with the metro system) in the last 24 hours. We then calculated the percent of respondents using the public transit system at least once for each SIT zone.\u003c/p\u003e \u003cp\u003eTo quantify the distance to the nearest public transit line, we calculated the distance from the center point of each zone to the closest metro, \u003cem\u003emetroplus\u003c/em\u003e, \u003cem\u003etranv\u0026iacute;a\u003c/em\u003e, \u003cem\u003emetrocable\u003c/em\u003e, or \u003cem\u003eescalera el\u0026eacute;ctrica.\u003c/em\u003e This was recalculated for each year, including new transit lines that were added within that year.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Autoregressive Models of Dengue Incidence\u003c/h2\u003e \u003cp\u003eDengue incidence per year at the level of the SIT zone was modeled using a fixed effects spatial panel model by maximum likelihood (R package splm, \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e) as described in \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Our fixed effects were socioeconomic status, distance from public transit, a two-way interaction between these factors, and year. The model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year. Year was analyzed as a categorical variable to avoid smoothing epidemic years. All continuous variables were scaled to enable comparison of effect size. Because these panel models require balanced data across time, data was truncated to SIT zones that had data for all years available (247 remaining of 291). Spatial dependency was evaluated, and the model was selected using the Hausman specification test and locally robust panel Lagrange Multiplier tests for spatial dependence. Based on a significant Hausman specification test result, which indicates a poor specification of the random effect model, a fixed effect model was chosen. This result is supported by the fact that we had a nearly exhaustive sample of SIT zones in the Medellin metro area. Lagrange multiplier tests were used to determine the most appropriate spatial dependency specifications. Based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was the most appropriate to incorporate spatial dependency; a SAR model considers that the number of dengue cases in a SIT zone depends on the number in neighboring zones.\u003c/p\u003e \u003cp\u003eBecause public transit usage was a measurement taken during just two of the study years, we constructed an additional fixed effects spatial panel model by maximum likelihood model of dengue incidence in just 2011 and 2016 that included ridership as an additional predictor variable. Our fixed effects were year, socioeconomic status, distance from public transit, a two-way interaction between socioeconomic status and distance from public transit, percent utilizing public transit, and a two-way interaction between socioeconomic status and percent utilizing public transit. As in our model of all years, the model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year, year was analyzed as a categorical variable, and all continuous variables were scaled to enable comparison of effect size. The data was truncated to SIT zones that had data for all years available (251 remaining of 291). We used the same model selection process, and again a fixed effect model was chosen, and based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was determined the most appropriate to incorporate spatial dependency.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the support of the \u003cem\u003e\u0026Aacute;rea Metropolitana del Valle de Aburr\u0026aacute; \u003c/em\u003eand the \u003cem\u003eAlcald\u0026iacute;a de Medell\u0026iacute;n, Secretar\u0026iacute;a de Salud \u003c/em\u003efor sharing data used in this study. We are thankful for the statistical input of Dr. Erika Mudrak and Dr. Joe Guinness, as well as the support of Dra. Catalina Alfonso and Dr. Frank Avila in completing this study. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhatt S, \u003cem\u003eet al.\u003c/em\u003e The global distribution and burden of dengue. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e496\u003c/b\u003e, 504 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanaway JD, \u003cem\u003eet al.\u003c/em\u003e The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. \u003cem\u003eThe Lancet infectious diseases\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 712\u0026ndash;723 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH, Research SPf, Diseases TiT, Diseases WHODoCoNT, Epidemic WHO, Alert P. \u003cem\u003eDengue: guidelines for diagnosis, treatment, prevention and control\u003c/em\u003e. World Health Organization (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoddard ST, \u003cem\u003eet al.\u003c/em\u003e Long-term and seasonal dynamics of dengue in Iquitos, Peru. \u003cem\u003ePLoS neglected tropical diseases\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, e3003 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis VR, \u003cem\u003eet al.\u003c/em\u003e Modeling tools for dengue risk mapping-a systematic review. \u003cem\u003eInternational journal of health geographics\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 50 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanlerberghe V, \u003cem\u003eet al.\u003c/em\u003e Changing paradigms in Aedes control: considering the spatial heterogeneity of dengue transmission. \u003cem\u003eRevista Panamericana de Salud P\u0026uacute;blica\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, e16 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrington LC, \u003cem\u003eet al.\u003c/em\u003e Dispersal of the dengue vector Aedes aegypti within and between rural communities. \u003cem\u003eThe American journal of tropical medicine and hygiene\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 209\u0026ndash;220 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoddard ST, \u003cem\u003eet al.\u003c/em\u003e House-to-house human movement drives dengue virus transmission. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cb\u003e110\u003c/b\u003e, 994-999 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReiner Jr RC, Stoddard ST, Scott TW. 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The reinvasion of Colombia by Aedes aegypti: aspects to remember. \u003cem\u003eThe American journal of tropical medicine and hygiene\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 330\u0026ndash;338 (1980).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillar LA, Rojas DP, Besada-Lombana S, Sarti E. Epidemiological trends of dengue disease in Colombia (2000-2011): a systematic review. \u003cem\u003ePLoS neglected tropical diseases\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, e0003499 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzoh Barry J. Dengue threat: adaptation needs in a disadvantaged neighborhood in Medell\u0026iacute;n-Colombia. \u003cem\u003eRevista Costarricense de Salud P\u0026uacute;blica\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 16\u0026ndash;24 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAburr\u0026aacute; \u0026Aacute;MdVd. Encuesta Origen Destino de Hogares para el Valle de Aburr\u0026aacute;) (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedell\u0026iacute;n \u0026Aacute;Md. Encuesta Origen Destino.) (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedell\u0026iacute;n Ad. Datos Abiertos.) (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedell\u0026iacute;n Md. Metro de Medell\u0026iacute;n.) (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouthampton Uo. WorldPop Open Spatial Demographics Data and Research.) (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillo G, Piras G, Millo MG. Package \u0026lsquo;splm\u0026rsquo;.). CRAN (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSALIMA BA, LIONEL VJL, V., BELLEFON. 7. Spatial econometrics on panel data.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dengue, Urbanization, magnitude, socioeconomic, transit ","lastPublishedDoi":"10.21203/rs.3.rs-1193404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1193404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDengue is a growing global threat in some of the world\u0026rsquo;s most rapidly growing landscapes. Urbanization and human movement affect the spatial dynamics and magnitude of dengue outbreaks; however, precise effects of urban growth on dengue is not well understood because of a lack of sufficiently fine-scaled data. We analyzed nine years of address-level dengue case data in Medellin, Colombia during a period of public transit expansion. We correlate changes in the spread and magnitude of localized outbreaks to changes in accessibility and usage of public transit. Locations closer to and with a greater utilization of public transit had greater dengue incidence. This relationship was modulated by socioeconomic status; lower socioeconomic status locations experienced stronger effects of public transit accessibility and usage on dengue incidence. Public transit is a vital urban resource, particularly among low socioeconomic populations; these results highlight the importance of public health services concurrent with urban growth.\u003c/p\u003e","manuscriptTitle":"Public Transit Development Predicts Spatial Distribution of Dengue Virus Incidence in Medellín, Colombia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-10 18:05:36","doi":"10.21203/rs.3.rs-1193404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-02-18T05:04:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-01-31T19:56:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0dcf4ffc-1bdb-468c-9a8b-84a20b85ff01","date":"2022-01-28T17:06:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"db1d4e8c-dd66-4623-9525-404457308b74","date":"2022-01-21T21:47:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-13T11:02:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-10T09:22:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-01-07T14:05:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-01-07T13:56:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-12-21T19:11:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5be4fe30-a1a4-420a-83d4-c966bd7aac07","owner":[],"postedDate":"January 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":9643687,"name":"Epidemiology"},{"id":9643688,"name":"Entomology"}],"tags":[],"updatedAt":"2022-05-04T06:59:04+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-10 18:05:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1193404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1193404","identity":"rs-1193404","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}