Predicting Abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector

Autoři: Lindsay P. Campbell aff001;  Daniel C. Reuman aff003;  Joel Lutomiah aff006;  A. Townsend Peterson aff003;  Kenneth J. Linthicum aff009;  Seth C. Britch aff009;  Assaf Anyamba aff010;  Rosemary Sang aff006
Působiště autorů: Florida Medical Entomology Laboratory, IFAS, University of Florida, Vero Beach, Florida, United States of America aff001;  Department of Entomology and Nematology, IFAS, University of Florida, Gainesville, Florida, United States of America aff002;  Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America aff003;  Kansas Biological Survey, University of Kansas, Lawrence, Kansas, United States of America aff004;  Laboratory of Populations, Rockefeller University, New York, New York, United States of America aff005;  Kenya Medical Research Institute, Nairobi, Kenya aff006;  United States Army Medical Research Directorate – Africa, Nairobi, Kenya aff007;  Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America aff008;  United States Department of Agriculture, Agricultural Research Service Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, Florida, United States of America aff009;  Universities Space Research Association, Columbia, Maryland, United States of America aff010;  NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, Maryland, United States of America aff011
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226617


Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic arbovirus with important livestock and human health, and economic consequences across Africa and the Arabian Peninsula. Climate and vegetation monitoring guide RVFV forecasting models and early warning systems; however, these approaches make monthly predictions and a need exists to predict primary vector abundances at finer temporal scales. In Kenya, an important primary RVFV vector is the mosquito Aedes mcintoshi. We used a zero-inflated negative binomial regression and multimodel averaging approach with georeferenced Ae. mcintoshi mosquito counts and remotely sensed climate and topographic variables to predict where and when abundances would be high in Kenya and western Somalia. The data supported a positive effect on abundance of minimum wetness index values within 500 m of a sampling site, cumulative precipitation values 0 to 14 days prior to sampling, and elevated land surface temperature values ~3 weeks prior to sampling. The probability of structural zero counts of mosquitoes increased as percentage clay in the soil decreased. Weekly retrospective predictions for unsampled locations across the study area between 1 September and 25 January from 2002 to 2016 predicted high abundances prior to RVFV outbreaks in multiple foci during the 2006–2007 epizootic, except for two districts in Kenya. Additionally, model predictions supported the possibility of high Ae. mcintoshi abundances in Somalia, independent of Kenya. Model-predicted abundances were low during the 2015–2016 period when documented outbreaks did not occur, although several surveillance systems issued warnings. Model predictions prior to the 2018 RVFV outbreak indicated elevated abundances in Wajir County, Kenya, along the border with Somalia, but RVFV activity occurred west of the focus of predicted high Ae. mcintoshi abundances.

Klíčová slova:

Epizootics – Kenya – Livestock – Mosquitoes – Surface temperature – Viral vectors – Rift Valley fever virus


1. Davies F. The Historical and Recent Impact of Rift Valley Fever in Africa. Am J Trop Med Hyg. 2010;83(2):73–4.

2. Rich KM, Wanyoike F. An assessment of the regional and national socio-economic impacts of the 2007 Rift Valley fever outbreak in Kenya. Am J Trop Med Hyg. 2010;83(2 Suppl):52–7. doi: 10.4269/ajtmh.2010.09-0291 20682906

3. Sang R, Kioko E, Lutomiah J, Warigia M, Ochieng C, O’Guinn M, et al. Rift Valley fever virus epidemic in Kenya, 2006/2007: The entomologic investigations. Am J Trop Med Hyg. 2010;83(2 Suppl):28–37. doi: 10.4269/ajtmh.2010.09-0319 20682903

4. World Health Organization. Rift Valley fever. 2010;Fact sheet N°207 (

5. Pepin M, Bouloy M, Bird BH, Kemp A, Paweska J. Rift Valley fever virus (Bunyaviridae: Phlebovirus): An update on pathogenesis, molecular epidemiology, vectors, diagnostics and prevention. Vet Res. 2010;41(6).

6. Chengula AA, Mdegela RH, Kasanga CJ. Socio-economic impact of Rift Valley fever to pastoralists and agro pastoralists in Arusha, Manyara and Morogoro regions in Tanzania. Springerplus. 2013;2.

7. Britch SC, Binepal YS, Ruder MG, Kariithi HM, Linthicum KJ, Anyamba A, et al. Rift Valley fever risk map model and seroprevalence in selected wild ungulates and camels from Kenya. PLoS One. 2013;8(6):e66626. doi: 10.1371/journal.pone.0066626 23840512

8. Anyamba A, Linthicum KJ, Mahoney R, Tucker CJ, Kelley PW. Mapping potential risk of Rift Valley fever outbreaks in African savannas using vegetation index time series data. Photogramm Eng Remote Sensing. 2002;68(2):137–45.

9. Anyamba A, Linthicum KJ, Tucker CJ. Climate-disease connections: Rift Valley Fever in Kenya. Cad Saude Publica. 2001;17 Suppl:133–40.

10. Linthicum KJ, Anyamba A, Tucker CJ, Kelley PW, Myers MF, Peters CJ. Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science. 1999;285(5426):397–400. doi: 10.1126/science.285.5426.397 10411500

11. Linthicum KJ, Bailey CL, Davies FG, Tucker CJ. Detection of Rift-Valley Fever Viral Activity in Kenya by Satellite Remote-Sensing Imagery. Science. 1987;235(4796):1656–9. doi: 10.1126/science.3823909 3823909

12. Linthicum KJ, Bailey CL, Tucker CJ, Angleberger DR, Cannon T, Logan TM, et al. Towards Real-Time Prediction of Rift-Valley Fever Epidemics in Africa. Prev Vet Med. 1991;11(3–4):325–34.

13. Linthicum KJ, Bailey CL, Tucker CJ, Mitchell KD, Logan TM, Davies FG, et al. Application of Polar-Orbiting, Meteorological Satellite Data to Detect Flooding of Rift-Valley Fever Virus Vector Mosquito Habitats in Kenya. Med Vet Entomol. 1990;4(4):433–8. doi: 10.1111/j.1365-2915.1990.tb00462.x 1983457

14. Anyamba A, Chretien JP, Formenty PBH, Small J, Tucker CJ, Malone JL, et al. Rift Valley fever potential, Arabian Peninsula. Emerg Infect Dis. 2006;12(3):518–20. doi: 10.3201/eid1203.050973 16710979

15. Anyamba A, Chretien JP, Small J, Tucker CJ, Formenty PB, Richardson JH, et al. Prediction of a Rift Valley fever outbreak. Proc Natl Acad Sci USA. 2009;106(3):955–9. doi: 10.1073/pnas.0806490106 19144928

16. Anyamba A, Chretien JP, Small J, Tucker CJ, Linthicum KJ. Developing global climate anomalies suggest potential disease risks for 2006–2007. Int J Health Geogr. 2006;5:60. doi: 10.1186/1476-072X-5-60 17194307

17. Anyamba A, Linthicum KJ, Small J, Britch SC, Pak E, de La Rocque S, et al. Prediction, assessment of the Rift Valley fever activity in East and Southern Africa 2006–2008 and possible vector control strategies. Am J Trop Med Hyg. 2010;83(2 Suppl):43–51. doi: 10.4269/ajtmh.2010.09-0289 20682905

18. Anyamba A, Chretien JP, Britch SC, Soebiyanto RP, Small JL, Jepsen R, et al. Global Disease Outbreaks Associated with the 2015–2016 El Niño Event. Sci Rep. 2019;9(1):1930. Epub 2019/02/13. doi: 10.1038/s41598-018-38034-z 30760757

19. Linthicum K, Davies F, Kairo A, Bailey C. Rift Valley fever virus (Family Bunyaviridae, Genus Phlebovirus)–Isolations from Diptera collected during an inter-epizootic period in Kenya. J Hyg, Camb. 1985;95(1):197–209. doi: 10.1017/s0022172400062434 2862206

20. Davies F, Highton R. Possible vectors of Rift-Valley fever in Kenya. Trans R Soc Trop Med Hyg. 1980;74(6):815–6. doi: 10.1016/0035-9203(80)90213-8 6111141

21. Bicout DJ, Sabatier P. Mapping Rift Valley Fever vectors and prevalence using rainfall variations. Vector-Borne Zoonot. 2004;4(1):33–42.

22. Linthicum K, Britch S, Anyamba A, Berenbaum M. Rift Valley Fever: An Emerging Mosquito-Borne Disease. Annu Rev Entomol. 2016;61:395 doi: 10.1146/annurev-ento-010715-023819 26982443

23. Murithi RM, Munyua P, Ithondeka PM, Macharia JM, Hightower A, Luman ET, et al. Rift Valley fever in Kenya: History of epizootics and identification of vulnerable districts. Epidemiol Infect. 2011;139(3):372–80. doi: 10.1017/S0950268810001020 20478084

24. Tchouassi DP, Bastos AD, Sole CL, Diallo M, Lutomiah J, Mutisya J, et al. Population genetics of two key mosquito vectors of rift valley Fever virus reveals new insights into the changing disease outbreak patterns in Kenya. PLoS Negl Trop Dis. 2014;8(12):e3364. doi: 10.1371/journal.pntd.0003364 25474018

25. Rosmoser W, Oviedo M, Lerdthusne E, Patrican L, Turell M, Dohm D, et al. Rift Valley fever virus-infected mosquito ova and associated pathology: Possible implications for endemic maintenance. Res Rep Trop Med. 2011;2:121–7. doi: 10.2147/RRTM.S13947 30881185

26. Linthicum KJ, Davies FG, Bailey CL, Kairo A. Mosquito species encountered in a flooded grassland dambo in Kenya. Mosq News. 1984;44(2):228–32.

27. Lutomiah J, Bast J, Clark J, Richardson J, Yalwala S, Oullo D, et al. Abundance, diversity, and distribution of mosquito vectors in selected ecological regions of Kenya: Public health implications. J Vector Ecol. 2013;38(1):134–42. doi: 10.1111/j.1948-7134.2013.12019.x 23701618

28. Linthicum KJ, Bailey CL, Davies FG, Kairo A. Observations on the dispersal and survival of a population of Aedes-Lineatopennis (Ludlow) (Diptera, Culicidae) in Kenya. Bull Entomol Res. 1985;75(4):661–70.

29. Logan T, Linthicum K, Thande P, Wagateh J, Nelson G, Roberts C. Egg hatching of Aedes mosquitoes during successive floodings in a Rift Valley fever endemic area in Kenya. J Am Mosquito Contr. 1991;7(1):109–12.

30. Camberlin P, Wairoto JG. Intraseasonal wind anomalies related to wet and dry spells during the ''long'' and ''short'' rainy seasons in Kenya. Theor Appl Climatol. 1997;58(1–2):57–69.

31. Camberlin P, Janicot S, Poccard I. Seasonality and atmospheric dynamics of the teleconnection between African rainfall and tropical sea-surface temperature: Atlantic vs. ENSO. Int J Climatol. 2001;21(8):973–1005

32. Edwards F. Mosquitoes of the Ethiopian region III. London, United Kingdom: British Museum of Natural History; 1941.

33. Gillies M, DeMeillon B. The Anopholenes of Africa south of the Sahara. Johannesburg, South Africa: South African Institute of Medical Research; 1968.

34. Harbach R. The mosquitoes of the subgenus Culex in Southwestern Asia and Egypt (Diptera: Culicidae). Contr Am Entomol Inst. 1988;24:240.

35. Jupp P. Mosquitoes of southern Africa. Hartebeespoort, South Africa: Ecogilde; 1986.

36. Wan Z, Zhang Y, Zhang Q, Li Z. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Proc Spie. 2002;83(1–2):163–80.

37. Beven K, Kirkby M. A Physically-based Variable Contributing Area Model of Basin Hydrology. Hydrol Sci Bull. 1979;24:43–69.

38. Shangguan W, Dai Y, Duan Q, Liu B, Yuan H. A Global Soil Data Set for Earth System Modeling. J Adv Model Earth Syst. 2014;6:249–63.

39. Hijmans R. raster: Geographic Data Analysis and Modeling. R package version 25–2. 2015;

40. Ridout M, Demetrio G, Hinde J. Models for Count Data with Many Zeros. Proceedings of XIXth International Biometric Society Conference Cape Town, South Africa International Biometric Society; Wasthington, D.C., USA 1998. p. 179–92.

41. Zuur AF. Mixed effects models and extensions in ecology with R. New York, NY: Springer; 2009. xxii, 574 p.p.

42. Lambert D. Zero-Inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34:1–14.

43. Yeşilova A, Kaydan MB, Kaya Y. Modeling insect-egg data with excess zeros using zero-inflated regression models. Hacet J Math Stat. 2010;39:273–82.

44. Desouhant E, Debouzie D, Menu F. Oviposition pattern of phytophagous insects: On the importance of host population heterogeneity. Oecologia. 1998;114(3):382–8. doi: 10.1007/s004420050461 28307782

45. Martin TG, Wintle BA, Rhodes JR, Kuhnert PM, Field SA, Low-Choy SJ, et al. Zero tolerance ecology: Improving ecological inference by modelling the source of zero observations. Ecol Lett. 2005;8:1235–46. doi: 10.1111/j.1461-0248.2005.00826.x 21352447

46. Akaike H. Likelihood of a model and information criteria. J Econometrics. 1981;16:3–14.

47. Burnham KP, Anderson DR, Burnham KP. Model selection and multimodel inference: A practical information-theoretic approach. 2nd ed. New York: Springer; 2002. xxvi, 488 p.p.

48. Schwarz G. Estimating Dimension of a Model. Ann Stat. 1978;6:461–4.

49. Zeileis A, Kleiber C, Jackman S. Regression models for count data in R. J Stat Softw. 2008;27:1–25.

50. Bjornstad O. ncf: spatial nonparametric covariance functions. R package version 11–6. 2015;

51. Bjornstad ON, Falck W. Nonparametric spatial covariance functions: Estimation and testing. Environ Ecol Stat. 2001;8:53–70.

52. Zuur A, Saveliev A, Ieno E. Zero Inflated Models and Generalized Linear Mixed Models with R. Newburgh, United Kingdom: Highland Statistics, Ltd.; 2012.

53. Shmueli G. To Explain or to Predict? Stat Sci. 2010;25:289–310.

54. Sober E. Instrumentalism, parsimony, and the Akaike framework. Philos Sci. 2002;69:S112–S23.

55. Turell MJ. Effect of environmental temperature on the vector competence of Aedes fowleri for Rift Valley fever virus. Res Virol. 1989;140:147–54. doi: 10.1016/s0923-2516(89)80092-5 2756242

56. Turell MJ, Rossi CA, Bailey CL. Effect of extrinsic incubation-temperature on the ability of Aedes taeniorhynchus and Culex pipiens to transmit Rift-Valley fever virus. Am J Trop Med Hyg. 1985;34:1211–8. doi: 10.4269/ajtmh.1985.34.1211 3834803

57. Anyamba A, Small J, Britch S, Tucker C, Pak E, Reynolds C, Crutchfield J, Linthicum KJ. Recent weather extremes and impacts on agricultural production and vector-borne disease outbreak patterns. PLoS One. 2014;9(3).

58. Linthicum K, Anyamba A, Britch S, Small J, Tucker C. Climate Teleconnections, Weather Extremes, and Vector-Borne Disease Outbreaks. Global Health Impacts of Vector-Borne Diseases, Workshop Summary.Forum on Microbial Threats, National Academy of Medicine; 2016. p. 183–200.

59. Centers for Disease Control and Prevention. Rift Valley fever outbreak—Kenya, November 2006-January 2007. MMWR Morb Mortal Wkly Rep. 2007;56:73–6. 17268404

60. Munyua P, Murithi RM, Wainwright S, Githinji J, Hightower A, Mutonga D, Macharia J, Ithondeka PM, Musaa J, Breiman RF, Bloland P, Njenga MK. Rift Valley fever outbreak in livestock in Kenya, 2006–2007. Am J Trop Med Hyg. 2010;83:58–64. doi: 10.4269/ajtmh.2010.09-0292 20682907

61. World Health Organization. Outbreaks of Rift Valley fever in Kenya, Somalia and United Republic of Tanzania, December 2006-April 2007. Wkly Epidemiol Rec. 2007;82:169–78. 17508438

62. Nderitu L, Lee JS, Omolo J, Omulo S, O’Guinn ML, Hightower A, Mosha F, Mohamed M, Munyua P, Nganga Z, Hiett K, Seal B, Feikin DR, Breiman RF, Njenga MK. Sequential Rift Valley fever outbreaks in eastern Africa caused by multiple lineages of the virus. J Infect Dis. 2011;203:655–65. doi: 10.1093/infdis/jiq004 21282193

63. Nguku PM, Sharif SK, Mutonga D, Amwayi S, Omolo J, Mohammed O, Farnon EC, Gould LH, Lederman E, Rao C, Sang R, Schnabel D, Feikin DR, Hightower A, Njenga MK, Breiman RF. An investigation of a major outbreak of Rift Valley fever in Kenya: 2006–2007. Am J Trop Med Hyg. 2010;83(2 Suppl):5–13. doi: 10.4269/ajtmh.2010.09-0288 20682900

64. Anyamba A, Small J, Tucker C, Linthicum K, Chretien K. Possible RVF activity in the Horn of Africa. Emergency Prevention System for Trans-boundary Animal and Plant Pests and Diseases (EMPRES), Food and Agricultural Organization of the United Nations; 2006.

65. World Health Organization. Rift Valley fever—Kenya In: News DO, editor. 2018.

66. ProMed. Rift Valley Fever—Kenya: Alert, Prevention. In: 20180531.5830703, editor. 2018.

67. ProMed-mail. Rinderpest—Kenya: OIE, suspected. ProMed-mail 2002. 2002;01 Nov: 20021101.5682(<>).

68. FAO, OIE, WHO. Africa—El Niño and increased risk of Rift Valley fever—Warning to countries. EMPRES WATCH. 2015;34(December 2015. Rome).

69. Brown H, Paladini M, Cook R, Kline D, Barnard D, Fish D. Effectiveness of mosquito traps in measuring species abundance and composition. J Med Ent. 2008;45:517–21.

Článek vyšel v časopise


2019 Číslo 12