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Snakebite Dynamics in Colombia: Effects of Precipitation Seasonality on Incidence Angarita-Gerlein, D

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Snakebite Dynamics in Colombia: Effects of Precipitation Seasonality on Incidence Angarita-Gerlein, D IBIO4299 INTERNATIONAL RESEARCH EXPERIENCE FOR STUDENTS IRES 2017 (HTTPS://MCMSC.ASU.EDU/IRES) 1 Snakebite Dynamics in Colombia: Effects of Precipitation Seasonality on Incidence Angarita-Gerlein, D. ∗, Bravo-Vega, CA.y, Cruz, C. z, Forero-Munoz,˜ NR. x, Navas-Zuloaga, MG.{ and Umana-Caro,˜ JD. k Departamento de Ingenier´ıa Biomedica,´ Universidad de los Andes. Bogota,´ Colombia Email: ∗[email protected], [email protected], [email protected], [email protected], {[email protected] [email protected] Abstract—Snakebite is a neglected tropical disease that repre- Bothrops, the reproductive cycle is related with rainy seasons sents a significant public health issue in Colombia, particularly and the population density of the snakes increases [8, 9, 10]. in rural areas. Studies in other countries have presented strong Furthermore, in Costa Rica it is known that this increase in evidence to support the hypothesis that snakebite and rainy seasons are related. We aim to evaluate whether there is a strong the populations of Bothrops asper may lead to an increase in correlation between precipitation and snakebite incidence in the incidence of snakebite, so envenomings and rainy seasons Colombia. Employing two datasets of monthly precipitation and are temporally correlated [11]. reported snakebite incidence from 2007 to 2013, we performed Now, this present study makes use of data reporting precip- cross-correlation analysis for 314 municipalities. Results showed itation and snakebite incidence in different municipalities of a significant correlation between precipitation and snakebite incidence in 49.36% of the municipalities. Colombia. The objective of this project is to determine if there is a strong correlation between precipitation and snakebite incidence in Colombia and evaluate its contribution to the I. INTRODUCTION prospect of a snakebite incident. We select several factors Snakebite is a worldwide tropical public health problem available to us that have been associated with snakebite, in- that affects mostly rural populations [1, 2]. Furthermore, cluding precipitation, altitude, and urban or rural classification this issue is characterized by high mortality and morbidity of the municipality. rates if treatment by antivenom is not correctly applied in a prudential time [3] [4]. Even if the antivenom is available II. METHODS in different countries, the lack of public health coverage in developing ones makes accessibility of antivenom difficult A. Data for rural populations [2]. As a consequence, prevention and 1) Data Description: The data used in the present study control of snakebite must be improved based on the acquisition was collected as part of Colombia’s coordinated effort to of knowledge about the causes of snakebite [5]. assess the prevalence of snakebite across municipalities. The Colombia is a tropical country that fulfills all the conditions National Institute of Health of Colombia created the National that make snakebite a daily issue for rural populations [6]. System of Public Health Surveillance (SIVIGILA), which Data collection have shown an important improvement on provides systematic and timely provision of information on the its quality because of the implementation of the mandatory dynamics of events that affect or may affect the health of the reports from the hospitals starting from 2004. For example, Colombian population. More specifically, it makes decisions in 1999, approximately 70 cases of snakebite were reported, for the prevention and control of risk factors in health, such while in 2014, 4232 cases were reported [7]. Despite the as snakebite. This study draws on data of reported weekly improvement in the reporting of snakebite, this total burden snakebite incidence per municipality starting from 2004. may still be underestimated because of the low coverage The complementary dataset that was used in this study of medical centers in this country or perhaps due to the was the average monthly precipitation across several mete- economic disincentive for people to report the incident. For orological stations throughout Colombia, provided by the In- instance, many campensinos or peasants are not covered by stitute of Hydrology, Meteorology and Environmental Studies medical insurance, therefore they would prefer to seek medical (IDEAM). In addition, the GPS coordinates of the station as assistance from a shaman, as opposed to the more reliable well as the elevation of the stations were included. These were medical center. Moreover, the closest medical center may be indeed useful in the filtering process of the data. too far away, making proper treatment not as viable. 2) Data Filtering: We then had to proceed with a data It is evident that there is the need to optimize the distri- filtering process to obtain a workable dataset. This process is bution of limited antivenom stock. An approach to solving summarized in Figure 1. The SIVIGILA dataset consists of re- this problem is to understand more about the population ported snakebite incidence for each epidemiological week for dynamics of snakes and additional risk factors (environmental 31 departments of Colombia with a total of 704 municipalities. or anthropogenic) that influence snakebite incidence. Previous Note that an epidemiological week is not exactly the same as research studies have found that for snakes belonging to genus a calendar week; sometimes an epidemiological week may IBIO4299 INTERNATIONAL RESEARCH EXPERIENCE FOR STUDENTS IRES 2017 (HTTPS://MCMSC.ASU.EDU/IRES) 2 span across two different weeks. This was a slight problem Incomplete monthly precipitation Snakebite incidence by munici- since the IDEAM data was not reported in epidemiological data by meteorological station pality for each epidemiological weeks. Therefore, the first step in the pre-filtering process was week to “split” the epidemiological weeks respective to the calendar, Interpolation Week Splitting so that the weeks are in accordance. The second step was to Complete monthly precipitation Monthly snakebite incidence processing Pre take the modified data and convert it to monthly data to obtain data by meteorological station by municipality the same time units for both datasets. Once we had data on monthly snakebite incidence, we Metereological stations with data Municipalities with data for at filtered through and kept the municipalities in which there for all seven years (2007-2013) least five years were at least 5 years of reporting. From those, only the municipalities with meteorological stations were kept. We had to employ monthly data only from the years 2007 to Meteorological stations in muni- Municipalities with meteorolo- 2013 such that our datasets coincide. On the other hand, cipalities with incidence reports gical stations the IDEAM dataset of monthly precipitation was incomplete. Processing Processing Below Rural Random There were years in which the meteorological stations failed Unique 1800m selection to record precipitation. To resolve this, we used a simple data Selection Urban { Not unique Above imputation method and interpolated linearly across the years 1800 m for that month. For example, say, data was not recorded for February 2010 in a particular station. Then we interpolate One meteorological station utilizing the data of February of all the other years, rather per municipality than interpolate using the other months of 2010, as our method would produce better estimates. At this point, we Fig. 1: Data Filtering Process have complete data for meteorological stations with reported incidence. However, many municipalities had more than one station, so we had to proceed with a selection procedure such the cross variance across the time lags to ascertain where the that we had data for exactly one station per municipality. We function peaks. immediately filtered out that data in which the station was located above an altitude of 1800 meters since the snakes III. RESULTS of interest are not suited to live in habitats of that elevation. In the case where there were still more than one station, we A total of 314 municipalities fulfilled the selection criteria then examined the GPS coordinates of the station to determine to perform the cross-correlation analysis. 155 (49.36%) of whether it was in a rural or urban area. We did not consider the them resulted to be significantly correlated (with 95% of con- stations in the urban area since snakebites are very unlikely. fidence), with a maximum correlation of 0.4841, a minimum The rural areas are of more interest to this study since they are of -0.3445, a mean of 0.0852, and a standard deviation of characterized with much higher mortality and morbidity rates. 0.2773. In terms of the resulted lag, there is a maximum of 6, Lastly, if data for more than one station per municipality was a minimum of -6, a mean of -0.2194, and a standard deviation still present, we randomly selected a station to retain in order of 3.7317. to achieve a balanced dataset. In the end, our complete dataset For example, in Figure 2, we overlaid both times series for consisted of monthly precipitation and snakebite incidence for the municipality with the highest cross-correlation (Abrego,´ one meteorological station per municipality of Colombia from Norte de Santander), as well as the cross-correlation function. 2007 to 2013. As illustrated from the visualization of the variables, there is a significant delayed correlation one month after the increment of precipitation and the increment of reported snakebites.
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