Spatial modeling of rat bites and prediction of rat infestation in Peshawar valley using binomial kriging with logistic regression

  • Asad Ali
  • Farrah Zaidi
  • Syeda Hira Fatima
  • Muhammad Adnan
  • Saleem Ullah


In this study, we propose to develop a geostatistical computational framework to model the distribution of rat bite infestation of epidemic proportion in Peshawar valley, Pakistan. Two species Rattus norvegicus and Rattus rattus are suspected to spread the infestation. The framework combines strengths of maximum entropy algorithm and binomial kriging with logistic regression to spatially model the distribution of infestation and to determine the individual role of environmental predictors in modeling the distribution trends. Our results demonstrate the significance of a number of social and environmental factors in rat infestations such as (I) high human population density; (II) greater dispersal ability of rodents due to the availability of better connectivity routes such as roads, and (III) temperature and precipitation influencing rodent fecundity and life cycle.


Rat bite Logistic regression Spatial modeling Variogram Binomial kriging 



Farrah Zaidi and Muhammad Adnan are thankful to the management of the Lady Reading Hospital, Peshawar, for providing the rat bite data.


  1. Ali, A., Fatima, S.H., Zaidi, F., Ullah, S. (2017). Determining spatial distribution of screwworm Chrysomya bezziana larvae with kriging using presence only data. Submitted to Environmental Monitoring and Assessment, page (Under rewiew).Google Scholar
  2. Barbet-Massin, M., Jiguet, F., Albert, C. H., Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3(2), 327–338.CrossRefGoogle Scholar
  3. Childs, J.E., McLafferty, S.L., Sadek, R., Miller, G.L., Khan, A.S., DuPree, E.R., Advani, R., Glass, G.E. (1998). Epidemiology of rodent bites and prediction of rat infestation in New York City. American Journal of Epidemiology, 148(1), 78–87.CrossRefGoogle Scholar
  4. Chomel, B.B. (1992). Zoonoses of house pets other than dogs, cats and birds. The Pediatric Infectious Disease Journal, 11(6), 479–487.CrossRefGoogle Scholar
  5. Christensen, O., & Ribeiro, P. Jr. (2002). geoRglm—a package for generalised linear spatial models. R-NEWS, 2(2), 26–28. ISSN 1609-3631.Google Scholar
  6. Civen, R., & Ngo, V. (2008). Murine typhus: an unrecognized suburban vectorborne disease. Clinical Infectious Diseases, 46(6), 913–918.CrossRefGoogle Scholar
  7. Clinton, J.M. (1969). Rats in urban America. Public Health Reports, 84(1), 1–7.CrossRefGoogle Scholar
  8. Costa, F., Hagan, J.E., Calcagno, J., Kane, M., Torgerson, P., Martinez-Silveira, M.S., Stein, C., Abela-Ridder, B., Ko, A.I. (2015). Global morbidity and mortality of leptospirosis: a systematic review. PLOS Neglected Tropical Diseases, 9(9), 1–19.CrossRefGoogle Scholar
  9. Costa, F., Ribeiro, G.S., Felzemburgh, R.D.M., Santos, N., Reis, R.B., Santos, A.C., Fraga, D.B.M., Araujo, W.N., Santana, C., Childs, J.E., Reis, M.G., Ko, A.I. (2014). Influence of household rat infestation on leptospira transmission in the urban slum environment. PLOS Neglected Tropical Diseases, 8(12), 1–8.CrossRefGoogle Scholar
  10. Cromley, E.K., & McLafferty, S.L. (2011). GIS in public health, 2nd edn. New York: Guilford Press.Google Scholar
  11. Diggle, P.J., & Ribeiro, P.J. (2007). Model-based geostatistics (Springer Series in Statistics). Springer series in statistics, 1st edn. New York: Springer.Google Scholar
  12. Easterbrook, J., Shields, T., Klein, S., Glass, G. (2005). Norway rat population in Baltimore, Maryland, 2004. Vector-Borne and Zoonotic Diseases, 5(3), 296–299.CrossRefGoogle Scholar
  13. Fatima, S.H., Zaidi, F., Adnan, M., Ali, A., Jamal, Q., Khisroon, M. (2017). Rat-bites of an epidemic proportion in Peshawar Vale; a GIS-based approach in risk assessment. Submitted to Environmental Monitoring and Assessment, page (Under rewiew).Google Scholar
  14. Gilchrist, G.W. (1995). Specialists and generalists in changing environments. I. Fitness landscapes of thermal sensitivity. The American Naturalist, 146(2), 252–270.CrossRefGoogle Scholar
  15. Githeko, A.K., Lindsay, S.W., Confalonieri, U.E., Patz, J.A. (2000). Climate Change and vector-borne diseases: a regional analysis. Bull World Health Organ, 78, 1136–47.Google Scholar
  16. Glass, G.E. (2009). Update: spatial aspects of epidemiology: the interface with medical geography. Epidemiologic reviews, 22(1), 136–9.CrossRefGoogle Scholar
  17. Glass, G.E., Gardner-Santana, L.C., Holt, R.D., Chen, J., Shields, T.M., Roy, M., Schachterle, S., Klein, S.L. (2009). Trophic garnishes: Cat–rat interactions in an urban environment. PLOS ONE, 4(6), 1–7.CrossRefGoogle Scholar
  18. Hengl, T. (2009). A practical guide to geostatistical mapping, 2nd edn. Amsterdam: University of Amsterdam.Google Scholar
  19. Hengl, T. (2017). GSIF: Global soil information facilities. R package version 0.5-4.Google Scholar
  20. Hengl, T., Sierdsema, H., Radović, A., Dilo, A. (2009). Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, {ENFA} and regression-kriging. Ecological Modelling, 220(24), 3499–3511.CrossRefGoogle Scholar
  21. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 25(15), 1965–1978.CrossRefGoogle Scholar
  22. Himsworth, C.G., Parsons, K.L., Jardine, C., Patrick, D.M. (2013). Rats, cities, people, and pathogens: a systematic review and narrative synthesis of literature regarding the ecology of rat-associated zoonoses in urban centers. Vector borne and zoonotic diseases, 13(6), 349–59.CrossRefGoogle Scholar
  23. Khan, R., & Khan, M.A. (2012). Nutritional quantification of four common broad leaved weeds consume as a food source in war-affected ID PS camps in Peshawar, Pakistan. Agriculture, Science and Engineering (ICASE2012), pp. 39.Google Scholar
  24. Li, W., Guo, Q., Elkan, C. (2011). Can we model the probability of presence of species without absence data? Ecography, 34(6), 1096–1105.CrossRefGoogle Scholar
  25. Lima, M., Marquet, P.A., Jaksic, F.M. (1999). El nino events, precipitation patterns, and rodent outbreaks are statistically associated in semiarid Chile. Ecography, 22(2), 213–218.CrossRefGoogle Scholar
  26. Lore, R.K., & Schultz, L.A. (1989). The ecology of wild rats: applications in the laboratory. Dordrecht: Kluwer Academic/Plenum Publishers.Google Scholar
  27. MacKenzie, D.I., & Royle, J.A. (2005). Designing occupancy studies: general advice and allocating survey effort. Journal of applied Ecology, 42(6), 1105–1114.CrossRefGoogle Scholar
  28. McKee, G., & Pewarchuk, J. (2013). Rat-bite fever. Canadian Medical Association Journal, 185 (15), 1346–1346.CrossRefGoogle Scholar
  29. Meerburg, B.G., Singleton, G.R., Kijlstra, A. (2009). Rodent-borne diseases and their risks for public health. Critical Reviews in Microbiology, 35(3), 221–270. PMID: 19548807.CrossRefGoogle Scholar
  30. Meyer, A. (2003). Urban commensal rodent control: fact or fiction? ACIAR Monograph Series, 96, 446–450.Google Scholar
  31. Millar, A. (2002). Subset selection in regression, 2nd edn. London: Chapman and Hall/CRC.CrossRefGoogle Scholar
  32. Miller, J., Franklin, J., Aspinall, R. (2007). Incorporating spatial dependence in predictive vegetation models. Ecological Modelling, 202(3), 225–242.CrossRefGoogle Scholar
  33. Perry, T., Matsui, E., Merriman, B., Duong, T., Eggleston, P. (2003). The prevalence of rat allergen in inner-city homes and its relationship to sensitization and asthma morbidity. Journal of Allergy and Clinical Immunology, 112(2), 346–352.CrossRefGoogle Scholar
  34. Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3), 231–259.CrossRefGoogle Scholar
  35. Phillips, S.J., & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161–175.CrossRefGoogle Scholar
  36. Phillips, S.J., Dudík, M., Schapire, R.E. (2004). A maximum entropy approach to species distribution modeling. In Proceedings of the twenty-first international conference on Machine learning (p. 83): ACM.Google Scholar
  37. R Core Team. (2017). R: a language and environment for statistical computing R foundation for statistical computing, Vienna, Austria.Google Scholar
  38. Rahim, T., Zeb, A., Shaukat, S. (2007). Urbanization in North West Frontier Province. Sarhad Journal of Agriculture, 23(1), 233.Google Scholar
  39. Ribeiro, P.J. Jr, Christensen, O.F., Diggle, P.J. (2003). Geor and geoRglm: software for model-based geostatistics, pages 517–524. Vienna: Technical University Vienna.Google Scholar
  40. Sedlon, F.G. (2001), Regional disease vector ecology profile: South Central Asia. Defense Technical Information Center.Google Scholar
  41. Spennemann, D.H.R. (1997). Distribution of rat species (Rattus spp.) on the atolls of the Marshall islands: past and present dispersal. Atoll Research Bulletin, 446, 1–18.CrossRefGoogle Scholar
  42. Sugunan, A.P., Vijayachari, P., Sharma, S. (1998). Risk factors in the transmission of leptospiral infection. The Indian Journal of Medical Research, 107, 218–23.Google Scholar
  43. Taylor, P.J., Arntzen, L., Hayter, M., Iles, M., Frean, J., Belmain, S. (2008). Understanding and managing sanitary risks due to rodent zoonoses in an African city: beyond the Boston model. Integrative Zoology, 3(1), 38–50.CrossRefGoogle Scholar
  44. Traweger, D., Travnitzky, R., Moser, C., Walzer, C., Bernatzky, G. (2006). Habitat preferences and distribution of the brown rat (Rattus norvegicus Berk.) in the city of Salzburg (Austria): implications for an urban rat management. Journal of Pest Science, 79(3), 113–125.CrossRefGoogle Scholar
  45. Walsh, M.G. (2014). Rat sightings in New York City are associated with neighborhood sociodemographics, housing characteristics, and proximity to open public space. PeerJ, e533, 2.Google Scholar
  46. Zhou, N., Hubacek, K., Roberts, M. (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS Nighttime Lights data. Applied Geography, 63(Supplement C), 292–303.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Space ScienceInstitute of Space TechnologyIslamabadPakistan
  2. 2.Department of ZoologyUniversity of PeshawarPeshawarPakistan

Personalised recommendations