Space-time cluster analysis to improve classical swine fever risk-based surveillance in Ecuador
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1
Department of Preventive Veterinary Medicine and Animal Health, Faculty of Veterinary Medicine and Animal Science, University of Sao Paulo, Brazil
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2
Other, Ecuador
ABSTRACT
Classical swine fever (CSF) is a highly contagious viral disease that affects domestic and wild swine. Considered the most important disease in pig production. Ecuador is a country in which the disease is endemic. Since 2013 an official control program has been implemented applying strategies for surveillance and control looking for future eradication.
A prospective spatial temporal analysis using a Poisson probability model was implemented. Areas scanned with high and low rates aggregated by days. The spatial temporal window moved in time and space in order to cover each possible geographic location and time interval, for maximum space and time, so the cluster area could include up to 50% of all the disease cases. Log likelihood ratio used to evaluate whether the cylinder contains a cluster area and Monte Carlo simulations to evaluate the significance of the detected cluster.
The analysis showed 4 statistically significant clusters (p < 0.01), with temporal windows between 11 -30 months, in geographic locations of Northern Amazonia - Northern Mountains (Cluster 1 - 0.27 RR); Southern Coastal (Cluster 2 - 4.14 RR); Northern Coastal (Cluster 3 – 89.4) and Southern Amazonia (Cluster 4 – 18.03 RR); representing epidemiological ecosystems with different productive practices between clusters but very similar inside.
The spatio-temporal patterns of the epidemic, would give a further understanding of the dynamics of the disease transmission. This information is available to the veterinary service, in order to improve the nowadays control activities, orienting them to risk-based CSF surveillance.
INTRODUCTION
Classical Swine fever
Classical swine fever (CSF, hog cholera), is a contagious viral disease of domestic and wild pigs. The causative virus is member of the genus Pestivirus - family Flaviviridae, closely related to bovine viral diarrhea and border disease viruses. There is only one serotype of CSF virus (OIE 2014) . The virus is classified genetically into three genogroups (Paton et al. 2000). Ecuadorian strains belongs to genogroups 1.1 and 1.6 (Garrido Haro et al. 2018).
CSF affects the immune system, a main characteristic being generalized leukopenia. The clinical signs are nonspecific: wasting in the absence of pyrexia. Chronic and persistent infections always lead to the death of the animal. Herd mortality rates may be slightly above the expected level (OIE 2014). The strategies for prevention, control and eradication of the disease are based specifically in systemic prophylactic immunization policy and culling when vaccines are not used (Ji et al. 2015).
Official control program in Ecuador
In 2009 the national veterinary service started surveillance activities in accordance with a national swine health program. By 2013 the first public CSF fight funding initiated, establishing sanitary control mechanisms. Official immunization started in 2016 with a strategy for the high-risk backyard pig populations. Industrial population’s vaccine calendars included into the official campaign. By 2019 OIE recognized Galapagos Islands as a zone free from CSF. All sanitary processes as new born registration, single animal identification, CSF vaccination and animal movement are controlled by a web base self-service platform accessible to the 120.000 pig breeders around the country (Agrocalidad 2011). General and targeted surveillance for CSF improve the notification scheme.
The economic losses caused by classical swine fever in Ecuador have been estimated to be in the order of 6 million dollars per year (Agrocalidad 2012), this production chain directly employs approximately 826,722 workers, considering the different forms of production and labor associated with swine production, which represents 5% of the population of the country.
OBJECTIVE
Analyze the spatial time clusters associated to infection events of classical swine fever to improve risk-based surveillance and control strategies.
MATERIALS AND METHODS
The analyzed databases contained: Notifications of swine systemic syndrome, confirmed cases of classical swine fever, registration of pig premises, registration of vaccine certificates and animal movement records, available by the official veterinary service.
Descriptive epidemiology
We conduct and epidemiological analysis of the geographic characteristics, productive systems and distribution of the disease in the country, using a Two-dimensional Kernel density estimation maps and descriptive data.
Space-time clusters analysis
A prospective Space-Time analysis was applied to study the relationships of the Classical swine fever epidemic on time and space. The data analyzed consisted on 291 outbreaks. Geographic locations of the 1040 third administrative regions of the country and the corresponding 126.000 background premises at risk from census. The analysis was prospective using a Poisson probability model, scanning areas with high and low rates aggregated by days.
The scan windows in space-time model is a cylinder whose size at the bottom circle represents the area of the space and the height represents the length of time ((Kulldorff et al. 2005). The window moves in time and space in order to cover each possible geographic location and time interval, form maximum space and time, so the cluster area could include up to 50% of all the disease cases. The log likelihood ratio was used to evaluate whether the cylinder contains a cluster area and Monte Carlo simulations to evaluate the significance of the detected cluster p-value (Desjardins et al. 2018).
The relative risk was calculated considering the estimated risk within the cluster divided by the estimated risk outside the cluster; as the observed divided by the expected within the cluster divided by the observed divided by the expected outside the cluster.
Analysis were made on SaTScan v9.6 for the spatial-temporal clustering of the epidemic and maps were projected in R r V 3.4.4 running on RStudio V 1.2.1335 and Qgis 3.6.
RESULTS
CSF epidemiology in Ecuador
Ecuador is localized in the Andean region of South America, limiting at north with Colombia, at south and east with Peru, and the Pacific Ocean on the west. The administrative regions analyzed are 23 first administrative regions, 490 second administrative regions and 1040 third administrative regions as Estate, County and Parish.
Ecuador has a pig population around 2’044.000 animals according with 2017 veterinary service estimations. Production subsystems are stratified as backyard and industrial, the first represent 56% of the population, in this production system interacts 128.640 backyard producers contrasting with 67 industrial producers (Companies) characterized by large scale, multi-site facilities.
Pig densities for both production systems are well distributed around the country, being larger at provinces of Santo Domingo, El Oro and Cotopaxi for the backyard population, and Santo Domingo, Santa Elena and Carchi for industrial premises. Between 2014 - 2018 a total of 291 outbreaks were diagnostic positive to CSFV. The number of outbreaks in this period was 82, 84, 32, 28 and 59 respectively (OIE/WAHIS 2017), the outbreaks were geographically distributed, especially in the central Andean region predominant with backyard populations Figure 1.
Space-time cluster analysis
In order to analyze the clustering of the 2014-2018 CSF epidemics in space and time, the prospective time analysis using the Discrete Poisson model obtaining 9 clusters (Table 1).
The analysis shows statistically significant clusters located in every natural region of the country. The 1st cluster center located in amazon jungle in the Napo and Orellana provinces. The 2nd cluster center located in the coastline province of Santa Elena and Guayas. The 3th cluster localized in Esmeraldas and the 4th cluster center localized in Morona Santiago.
The western part of the the first cluster is located in the Andean highlands (Pichincha, Cotopaxi and Chimborazo provinces characterized with backyard populations located in mountain regions above 10.000 ft above sea level (Figure 2). Quito and Guayaquil as the main consumption centers are located inside the first and second clusters, respectively.
DISCUSSION
The reduction of the number of CSF outbreaks from 82 in 2014 to 59 in 2018, shows a modification in the presentation of the disease, but explains little about the distribution and epidemic patterns of disease in Ecuador. This spatial temporal analysis show different areas and risk levels associated with spatial distribution and productive systems, this could give a further understanding of the dynamics of disease transmission.
Space-time permutation scan statistics used to explore the clustering of infectious diseases. As no presupposition is made on the size, location and scale of clustering, the selection bias is avoided (Lu et al. 2019). The analysis shows geographical locations historically neglected, where the amount of resources invested in eradication efforts is well below other areas with equal or less risk.
The Relative risk level calculated in each cluster, and their spatial distribution match some productive circuits in the country. The temporal analysis show specific administrative regions that need more attention in order to fulfill the plans for controlling and eradicating the disease, specifically in the the Amazonic, Central Mountains and Coastline.
The representative clusters (1,2) with larger time span show a presentation that maintain its endemic levels for more than 2 years. Those areas located in two different regions and also different production systems could be addressed to be prioritized in the prevention and control activities.
This analysis could improve the usability for the local veterinary information that could be used to implement risk-based surveillance.
Acknowledgements
We appreciate the collaboration of authorities and technical staff of Agrocalidad at the central level and local offices for their support.
References
Agrocalidad. 2011. Programa Nacional Sanitario Porcino. Ecuador. http://www.agrocalidad.gob.ec/wp-content/uploads/downloads/2013/08/1 Programa Nacional Sanitario Porcino - AGROCALIDAD.pdf.
Agrocalidad. 2012. Proyecto de Control y Erradicación de La Peste Porcina Clásica Por Zonificación En El Ecuador. Quito. http://www.agrocalidad.gob.ec/wp-content/uploads/2017/05/proyecto-c-e-PPC-zonificacion-PNSP.pdf (August 9, 2017).
Desjardins, M.R., A. Whiteman, I. Casas, and E. Delmelle. 2018. “Space-Time Clusters and Co-Occurrence of Chikungunya and Dengue Fever in Colombia from 2015 to 2016.” Acta Tropica 185: 77–85. http://www.ncbi.nlm.nih.gov/pubmed/29709630 (June 7, 2019).
Garrido Haro, A. D., M. Barrera Valle, A. Acosta, and F. J. Flores. 2018. “Phylodynamics of Classical Swine Fever Virus with Emphasis on Ecuadorian Strains.” Transboundary and Emerging Diseases 65(3): 782–90. http://doi.wiley.com/10.1111/tbed.12803 (March 1, 2018).
Ji, Wei, Zhen Guo, Nai-zheng Ding, and Cheng-qiang He. 2015. “Studying Classical Swine Fever Virus: Making the Best of a Bad Virus.” Virus Research 197: 35–47. http://linkinghub.elsevier.com/retrieve/pii/S0168170214005115 (August 7, 2017).
Kulldorff, Martin et al. 2005. “A Space–Time Permutation Scan Statistic for Disease Outbreak Detection” ed. Sally M. Blower. PLoS Medicine 2(3): e59. https://dx.plos.org/10.1371/journal.pmed.0020059 (June 7, 2019).
Lu, Yi et al. 2019. “Risk Analysis of African Swine Fever in Poland Based on Spatio-Temporal Pattern and Latin Hypercube Sampling, 2014–2017.” BMC Veterinary Research 15(1): 160. https://bmcvetres.biomedcentral.com/articles/10.1186/s12917-019-1903-z (June 6, 2019).
OIE/WAHIS. 2017. Info by Country/Ecuador/Vaccination: Classical Swine Fever. http://www.oie.int/wahis_2/public/wahid.php/Countryinformation/Vaccination (August 9, 2017).
OIE. 2014. “Terrestrial Manual.” In Terrestrial Animal Health Code, , 1. http://www.oie.int/fileadmin/Home/eng/Health_standards/tahm/2.08.03_CSF.pdf (August 7, 2017).
Keywords:
Classical swine fever (CSF),
Spatio - temporal analysis,
Epidemiology,
Decision making policy,
Sanitary planning
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Student Poster-session
Topic:
Spatio-temporal surveillance and modeling approaches
Citation:
Acosta
A,
Pisuna
L,
Vasquez
SK and
Ferreira
F
(2019). Space-time cluster analysis to improve classical swine fever risk-based surveillance in Ecuador.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00108
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Received:
10 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Mr. Alfredo Acosta, Department of Preventive Veterinary Medicine and Animal Health, Faculty of Veterinary Medicine and Animal Science, University of Sao Paulo, São Paulo, São Paulo, Brazil, alfredojavier55@gmail.com