IBIO4299 INTERNATIONAL RESEARCH EXPERIENCE FOR STUDENTS IRES 2017 (HTTPS://MCMSC.ASU.EDU/IRES) 1

Snakebite Dynamics in Colombia: Effects ofPrecipitation Seasonality on Incidence

Angarita-Gerlein, D. ∗, Bravo-Vega, CA.†, Cruz, C. ‡, Forero-Munoz, NR. §, Navas-Zuloaga, MG.¶ andUmana-Caro, JD. ‖

Departamento de Ingenierıa Biomedica, Universidad de los Andes. Bogota, ColombiaEmail: ∗d.angarita10@uniandes.edu.co, †ca.bravo955@uniandes.edu.co, ‡carlos.e.cruz@asu.edu,§nr.forero@uniandes.edu.co, ¶mg.navas10@uniandes.edu.co ‖jd.umana10@uniandes.edu.co

Abstract—Snakebite is a neglected tropical disease that repre-sents a significant public health issue in Colombia, particularlyin rural areas. Studies in other countries have presented strongevidence to support the hypothesis that snakebite and rainyseasons are related. We aim to evaluate whether there is a strongcorrelation between precipitation and snakebite incidence inColombia. Employing two datasets of monthly precipitation andreported snakebite incidence from 2007 to 2013, we performedcross-correlation analysis for 314 municipalities. Results showeda significant correlation between precipitation and snakebiteincidence in 49.36% of the municipalities.

I. INTRODUCTION

Snakebite is a worldwide tropical public health problemthat affects mostly rural populations [1, 2]. Furthermore,this issue is characterized by high mortality and morbidityrates if treatment by antivenom is not correctly applied in aprudential time [3] [4]. Even if the antivenom is availablein different countries, the lack of public health coverage indeveloping ones makes accessibility of antivenom difficultfor rural populations [2]. As a consequence, prevention andcontrol of snakebite must be improved based on the acquisitionof knowledge about the causes of snakebite [5].

Colombia is a tropical country that fulfills all the conditionsthat make snakebite a daily issue for rural populations [6].Data collection have shown an important improvement onits quality because of the implementation of the mandatoryreports from the hospitals starting from 2004. For example,in 1999, approximately 70 cases of snakebite were reported,while in 2014, 4232 cases were reported [7]. Despite theimprovement in the reporting of snakebite, this total burdenmay still be underestimated because of the low coverageof medical centers in this country or perhaps due to theeconomic disincentive for people to report the incident. Forinstance, many campensinos or peasants are not covered bymedical insurance, therefore they would prefer to seek medicalassistance from a shaman, as opposed to the more reliablemedical center. Moreover, the closest medical center may betoo far away, making proper treatment not as viable.

It is evident that there is the need to optimize the distri-bution of limited antivenom stock. An approach to solvingthis problem is to understand more about the populationdynamics of snakes and additional risk factors (environmentalor anthropogenic) that influence snakebite incidence. Previousresearch studies have found that for snakes belonging to genus

Bothrops, the reproductive cycle is related with rainy seasonsand the population density of the snakes increases [8, 9, 10].Furthermore, in Costa Rica it is known that this increase inthe populations of Bothrops asper may lead to an increase inthe incidence of snakebite, so envenomings and rainy seasonsare temporally correlated [11].

Now, this present study makes use of data reporting precip-itation and snakebite incidence in different municipalities ofColombia. The objective of this project is to determine if thereis a strong correlation between precipitation and snakebiteincidence in Colombia and evaluate its contribution to theprospect of a snakebite incident. We select several factorsavailable to us that have been associated with snakebite, in-cluding precipitation, altitude, and urban or rural classificationof the municipality.

II. METHODS

A. Data

1) Data Description: The data used in the present studywas collected as part of Colombia’s coordinated effort toassess the prevalence of snakebite across municipalities. TheNational Institute of Health of Colombia created the NationalSystem of Public Health Surveillance (SIVIGILA), whichprovides systematic and timely provision of information on thedynamics of events that affect or may affect the health of theColombian population. More specifically, it makes decisionsfor the prevention and control of risk factors in health, suchas snakebite. This study draws on data of reported weeklysnakebite incidence per municipality starting from 2004.

The complementary dataset that was used in this studywas the average monthly precipitation across several mete-orological stations throughout Colombia, provided by the In-stitute of Hydrology, Meteorology and Environmental Studies(IDEAM). In addition, the GPS coordinates of the station aswell as the elevation of the stations were included. These wereindeed useful in the filtering process of the data.

2) Data Filtering: We then had to proceed with a datafiltering process to obtain a workable dataset. This process issummarized in Figure 1. The SIVIGILA dataset consists of re-ported snakebite incidence for each epidemiological week for31 departments of Colombia with a total of 704 municipalities.Note that an epidemiological week is not exactly the same asa 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 problemsince the IDEAM data was not reported in epidemiologicalweeks. Therefore, the first step in the pre-filtering process wasto “split” the epidemiological weeks respective to the calendar,so that the weeks are in accordance. The second step was totake the modified data and convert it to monthly data to obtainthe same time units for both datasets.

Once we had data on monthly snakebite incidence, wefiltered through and kept the municipalities in which therewere at least 5 years of reporting. From those, only themunicipalities with meteorological stations were kept. Wehad to employ monthly data only from the years 2007 to2013 such that our datasets coincide. On the other hand,the IDEAM dataset of monthly precipitation was incomplete.There were years in which the meteorological stations failedto record precipitation. To resolve this, we used a simple dataimputation method and interpolated linearly across the yearsfor that month. For example, say, data was not recorded forFebruary 2010 in a particular station. Then we interpolateutilizing the data of February of all the other years, ratherthan interpolate using the other months of 2010, as ourmethod would produce better estimates. At this point, wehave complete data for meteorological stations with reportedincidence. However, many municipalities had more than onestation, so we had to proceed with a selection procedure suchthat we had data for exactly one station per municipality. Weimmediately filtered out that data in which the station waslocated above an altitude of 1800 meters since the snakesof interest are not suited to live in habitats of that elevation.In the case where there were still more than one station, wethen examined the GPS coordinates of the station to determinewhether it was in a rural or urban area. We did not consider thestations in the urban area since snakebites are very unlikely.The rural areas are of more interest to this study since they arecharacterized with much higher mortality and morbidity rates.Lastly, if data for more than one station per municipality wasstill present, we randomly selected a station to retain in orderto achieve a balanced dataset. In the end, our complete datasetconsisted of monthly precipitation and snakebite incidence forone meteorological station per municipality of Colombia from2007 to 2013.

B. Cross-correlation analysis

For this study in investigating how strongly associatedprecipitation is with snakebite incidence, we performed indi-vidual cross-correlation analyses using MATLAB’s crosscorrfunction. The sample cross covariance function is an estimateof the covariance between two time series. This functionoutputs a sample correlation index between -1 and 1 foreach indicated time lag. When the signals are aligned, thecorrelation index is maximal. We specified a maximum timelag of six months between the series.

The function also calculates a 95% confidence interval forthe index. If the maximum peak of correlation is outsidethe confidence bounds, the municipality has a significantcorrelation between rainfall and snakebite incidence. We thenvisualized the sample cross-correlation function by plotting

Incomplete monthly precipitation data by meteorological station

Snakebite incidence by munici-pality for each epidemiological

week

Monthly snakebite incidence by municipality

Complete monthly precipitation data by meteorological station

Interpolation Week Splitting

Metereological stations with data for all seven years (2007-2013)

Municipalities with data for at least five years

Meteorological stations in muni-cipalities with incidence reports

Municipalities with meteorolo-gical stations

Pre

proc

essi

ng

Proc

essi

ng

One meteorological station per municipality

Selection

Unique

Not unique

Below 1800m

Above 1800 m

Rural

Urban

Random selection {

Fig. 1: Data Filtering Process

the cross variance across the time lags to ascertain where thefunction peaks.

III. RESULTS

A total of 314 municipalities fulfilled the selection criteriato perform the cross-correlation analysis. 155 (49.36%) ofthem resulted to be significantly correlated (with 95% of con-fidence), with a maximum correlation of 0.4841, a minimumof -0.3445, a mean of 0.0852, and a standard deviation of0.2773. In terms of the resulted lag, there is a maximum of 6,a minimum of -6, a mean of -0.2194, and a standard deviationof 3.7317.

For example, in Figure 2, we overlaid both times series forthe municipality with the highest cross-correlation (Abrego,Norte de Santander), as well as the cross-correlation function.As illustrated from the visualization of the variables, there is asignificant delayed correlation one month after the incrementof precipitation and the increment of reported snakebites.

Then, we developed a distribution map of the cross-correlation values and significance indexes in Colombian mu-nicipalities as shown in Figure 3.

IV. DISCUSSION

We consider factors that are mostly likely related to theincrease of snakebite incidence, the most important beingprecipitation. After performing cross-correlation analysis ofthe available data, we found the subset of municipalitieswhere our statistical analysis indicates that snakebite incidencepresents temporal dynamics highly related to precipitationseasonality.

Despite the number of municipalities with a significantcorrelation coefficient, there is not enough evidence to claim

IBIO4299 INTERNATIONAL RESEARCH EXPERIENCE FOR STUDENTS IRES 2017 (HTTPS://MCMSC.ASU.EDU/IRES) 3

(a) (b)

Fig. 2: Precipitation and snakebite incidence correlation in Abrego, Norte de Santander. a) Time-series of precipitationand snakebite incidence for seven years b) Sample cross-correlation and lag for the time-series

Fig. 3: Cross-correlation values and significance indexes.

a strong correlation between precipitation and snakebite in-cidence at a national level. It may appear that a significantcorrelation occurs only at the municipality level.

In hopes of discovering a pattern in the spatial distribution ofthe correlations, we developed a map of the cross-correlationcoefficients, as well as the level of significance. Unfortunately,the distribution maps suggests that across Colombia there is noevident spatial pattern of the distribution. We must considerthat the climate of Colombia presents variations within thedifferent regions, especially depending on the altitude, tem-perature, humidity, and rainfall. This, in turn, may affect thesnakebite incidence in many (unobserved) levels.

Not to mention, the low correlation coefficients could bethe result of a possible non-linear relationship between thevariables. To solve this, we must first detect any possiblenonlinearity and then correct it by perhaps transforming thedata. After this we may alter our methods, so that we canmeasure the nonlinear relationships.

V. CONCLUSIONS

Based on the cross-correlation analysis between precip-itation and snakebite incidence in 314 municipalities, weconcluded that in 49.36% of the cases (155 municipalities)these two variables have a strong linear relationship.

As for future work, an effort should be done in improvingthe data. More sophisticated interpolation methods could beused to impute the missing observations for precipitation,

considering data from neighbor municipalities or adjacentmonths. The selection criteria for the municipalities could alsobe revised. Some observations may have biased in such amatter that our results could not be accurate.

Even though the precipitation itself seems to not have astrong linear relationship with snakebite incidence at a nationallevel, interesting conclusions could be driven from calculatingan index that’s more closely associated with snakes’ habitatand behavior. For example, an index involving soil moistureand present precipitation could be used to make conjecturesabout the formation of small bodies of water, which attractpotential prey for the snakes (such as frogs, mice, lizards,etc.).

However, despite these limitations, this study reinforces pre-vious literature in identifying influential factors and evaluatingthe relative contribution of precipitation on the prospect ofsnakebite incidence in different municipalities. We found thatin general rainy seasons are important to consider becausethey may have influence on snakebite incidence. Meanwhile,it is also valuable to consider other influential factors such asreproductive cycles of snakes or poverty indexes. Consideringthese altogether may help us propose an alternative to theway that Colombia is currently handling the distribution ofthe antivenom that would be more cost-effective. We wouldbe able to focus our efforts on specific high risk rainy seasonsand municipalities of Colombia.

REFERENCES

[1] Anuradhani Kasturiratne, A Rajitha Wickremasinghe,Nilanthi de Silva, N Kithsiri Gunawardena, ArunasalamPathmeswaran, Ranjan Premaratna, Lorenzo Savioli,David G Lalloo, and H Janaka de Silva. The globalburden of snakebite: a literature analysis and modellingbased on regional estimates of envenoming and deaths.PLoS Med, 5(11):e218, 2008.

[2] Robert A Harrison, Darren A Cook, Camila Renjifo,Nicholas R Casewell, Rachel B Currier, and Simon CWagstaff. Research strategies to improve snakebite treat-ment: challenges and progress. Journal of proteomics,74(9):1768–1780, 2011.

[3] Robert A Harrison, Adam Hargreaves, Simon CWagstaff, Brian Faragher, and David G Lalloo. Snake

IBIO4299 INTERNATIONAL RESEARCH EXPERIENCE FOR STUDENTS IRES 2017 (HTTPS://MCMSC.ASU.EDU/IRES) 4

envenoming: a disease of poverty. PLoS Negl Trop Dis,3(12):e569, 2009.

[4] Pablo Fernandez and Jose Marıa Gutierrez. Mortalitydue to snakebite envenomation in costa rica (1993–2006).Toxicon, 52(3):530–533, 2008.

[5] Jose Marıa Gutierrez, R David G Theakston, and David AWarrell. Confronting the neglected problem of snake biteenvenoming: the need for a global partnership. PLoSMed, 3(6):e150, 2006.

[6] Rafael Otero Patino, Rafael Valderrama Hernandez,Raul G Osorio, and Luz E Posada. Programa de atencionprimaria del accidente ofıdico: una propuesta para colom-bia. Iatreia, 5(2):96–102, 1992.

[7] Ariadna L Rodrıguez-Vargas. Comportamiento generalde los accidentes provocados por animales venenososen colombia, 2006-2010. Revista de Salud Publica,14(6):1005–1013, 2012.

[8] Mahmood Sasa, Dennis K Wasko, and William W Lamar.Natural history of the terciopelo bothrops asper (ser-pentes: Viperidae) in costa rica. Toxicon, 54(7):904–922,2009.

[9] Alejandro Solorzano and Luis Cerdas. Reproductivebiology and distribution of the terciopelo, bothrops aspergarman (serpentes: Viperidae), in costa rica. Herpetolog-ica, pages 444–450, 1989.

[10] Selma Maria Almeida-Santos and MG Salomao. Repro-duction in neotropical pitvipers, with emphasis on speciesof the genus bothrops. Biology of the Vipers, 1:445–462,2002.

[11] Luis Fernando Chaves, Ting-Wu Chuang, MahmoodSasa, and Jose Marıa Gutierrez. Snakebites are associatedwith poverty, weather fluctuations, and el nino. ScienceAdvances, 1(8), 2015.