World Health Organization, 2017. World Malaria Report 2017. Geneva, Switzerland: WHO.
World Health Organization, 2015. Global Technical Strategy for Malaria 2016–2030. Geneva, Switzerland: WHO.
World Health Organization, 2012. Handbook for Integrated Vector Management. Geneva, Switzerland: WHO.
World Health Organization, 2017. Global Vector Control Response 2017–2030. Geneva, Switzerland: WHO.
World Health Organization, 2012. Global Plan for Insecticide Resistance Management in Malaria Vectors. Geneva, Switzerland: WHO.
Cibulskis RE et al. 2016. Malaria: global progress 2000 – 2015 and future challenges. Infect Dis Poverty 5: 61.
Hemingway J et al. 2016. Averting a malaria disaster: will insecticide resistance derail malaria control? Lancet 387: 1785–1788.
malERA Refresh Consultative Panel on Insecticide and Drug Resistance, 2017. malERA: an updated research agenda for insecticide and drug resistance in malaria elimination and eradication. PLoS Med 14: e1002450.
Smith DL, Dushoff J, McKenzie FE, 2004. The risk of a mosquito-borne infection in a heterogeneous environment. PLoS Biol 2: e368.
Smith DL et al. 2014. Recasting the theory of mosquito-borne pathogen transmission dynamics and control. Trans R Soc Trop Med Hyg 108: 185–197.
Ayala D, Costantini C, Ose K, Kamdem GC, Antonio-Nkondjio C, Agbor JP, Awono-Ambene P, Fontenille D, Simard F, 2009. Habitat suitability and ecological niche profile of major malaria vectors in Cameroon. Malar J 8: 307.
Kelly-Hope LA, Hemingway J, McKenzie FE, 2009. Environmental factors associated with the malaria vectors Anopheles gambiae and Anopheles funestus in Kenya. Malar J 8: 268.
Kirby MJ, Green C, Milligan PM, Sismanidis C, Jasseh M, Conway DJ, Lindsay SW, 2008. Risk factors for house-entry by malaria vectors in a rural town and satellite villages in the Gambia. Malar J 7: 2.
Kaindoa EW, Mkandawile G, Ligamba G, Kelly-Hope LA, Okumu FO, 2016. Correlations between household occupancy and malaria vector biting risk in rural Tanzanian villages: implications for high-resolution spatial targeting of control interventions. Malar J 15: 199.
Zhou G, Munga S, Minakawa N, Githeko AK, Yan G, 2007. Spatial relationship between adult malaria vector abundance and environmental factors in western Kenya highlands. Am J Trop Med Hyg 77: 29–35.
Walker M, Winskill P, Basáñez MG, Mwangangi JM, Mbogo C, Beier JC, Midega JT, 2013. Temporal and micro-spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast. Parasit Vectors 6: 311.
Kabbale FG, Akol AM, Kaddu JB, Onapa AW, 2013. Biting patterns and seasonality of Anopheles gambiae sensu lato and Anopheles funestus mosquitoes in Kamuli District, Uganda. Parasit Vectors 6: 340.
Koenraadt CJ, Githeko AK, Takken W, 2004. The effects of rainfall and evapotranspiration on the temporal dynamics of Anopheles gambiae s.s. and Anopheles arabiensis in a Kenyan village. Acta Trop 90: 141–153.
Lindsay SW, Parson L, Thomas CJ, 1998. Mapping the ranges and relative abundance of the two principal African malaria vectors, Anopheles gambiae sensu stricto and An. arabiensis, using climate data. Proc Biol Sci 265: 847–854.
Moffett A, Shackelford N, Sarkar S, 2007. Malaria in Africa: vector species’ niche models and relative risk maps. PLoS One 2: e824.
Mbogo CM et al. 2003. Spatial and temporal heterogeneity of Anopheles mosquitoes and Plasmodium falciparum transmission along the Kenyan coast. Am J Trop Med Hyg 68: 734–742.
Masaninga F et al. 2013. Review of the malaria epidemiology and trends in Zambia. Asian Pac J Trop Biomed 3: 89–94.
Mharakurwa S, Thuma PE, Norris DE, Mulenga M, Chalwe V, Chipeta J, Munyati S, Mutambu S, Mason PR; Southern Africa ICEMR Team, 2012. Malaria epidemiology and control in Southern Africa. Acta Trop 121: 202–206.
Mukonka VM et al. 2014. High burden of malaria following scale-up of control interventions in Nchelenge district, Luapula Province, Zambia. Malar J 13: 153.
Kamuliwo M et al. 2013. The changing burden of malaria and association with vector control interventions in Zambia using district-level surveillance data, 2006–2011. Malar J 12: 437.
Chanda E et al. 2011. Insecticide resistance and the future of malaria control in Zambia. PLoS One 6: e24336.
Das S, Muleba M, Stevenson JC, Norris DE; Southern Africa International Centers of Excellence for Malaria Research Team, 2016. Habitat partitioning of malaria vectors in Nchelenge district, Zambia. Am J Trop Med Hyg 94: 1234–1244.
Stevenson JC et al. Southern Africa International Centers of Excellence in Malaria Research, 2016. Spatio-temporal heterogeneity of malaria vectors in northern Zambia: implications for vector control. Parasit Vectors 9: 510.
Kent RJ, Thuma PE, Mharakurwa S, Norris DE, 2007. Seasonality, blood feeding behavior, and transmission of Plasmodium falciparum by Anopheles arabiensis after an extended drought in southern Zambia. Am J Trop Med Hyg 76: 267–274.
Lobo NF et al. 2015. Unexpected diversity of Anopheles species in Eastern Zambia: implications for evaluating vector behavior and interventions using molecular tools. Sci Rep 5: 17952.
Sinka ME et al. 2010. The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic precis. Parasit Vectors 3: 117.
Zahar AR, 1985. Vector Bionomics in the Epidemiology and Control of Malaria. Geneva, Switzerland: WHO.
Moss WJ, Norris DE, Mharakurwa S, Scott A, Mulenga M, Mason PR, Chipeta J, Thuma PE; Southern Africa ICEMR Team, 2012. Challenges and prospects for malaria elimination in the Southern Africa region. Acta Trop 121: 207–211.
Olayemi IK, Ande AT, 2009. Life table analysis of Anopheles gambiae (Diptera: Culicidae) in relation to malaria transmission. J Vector Borne Dis 46: 295–298.
Okoye PN, Brooke BD, Hunt RH, Coetzee M, 2007. Relative developmental and reproductive fitness associated with pyrethroid resistance in the major southern African malaria vector, Anopheles funestus. Bull Entomol Res 97: 599–605.
Moss WJ et al. 2015. Malaria epidemiology and control within the International Centers of Excellence for Malaria Research. Am J Trop Med Hyg 93: 5–15.
Zambia National Malaria Control Programme, 2017. Lusaka, Zambia. Personal communication.
USAID, President’s Malaria Initiative, 2017. Zambia Malaria Operational Plan FY 2018. Lusaka, Zambia: USAID.
Pinchoff J, Chaponda M, Shields TM, Sichivula J, Muleba M, Mulenga M, Kobayashi T, Curriero FC, Moss WJ; Southern Africa International Centers of Excellence for Malaria Research, 2016. Individual and household level risk factors associated with malaria in Nchelenge district, a region with perennial transmission: a serial cross-sectional study from 2012 to 2015. PLoS One 11: e0156717.
Yates F, Grundy PM, 1953. Selection without replacement from within strata with probability proportional to size. J R Stat Soc Ser B 15: 253–261.
Gillies M, de Meillon B, 1968. The Anophelinae of Africa South of the Sahara, 2nd edition. Johannesburg, South Africa: South African Institute of Medical Research.
Gillies M, Coetzee M, 1987. A Supplement to the Anophelinae of Africa South of the Sahara: Afrotropical Region. Johannesburg, South Africa: South African Institute for Medical Research.
Scott JA, Brogdon WG, Collins FH, 1993. Identification of single specimens of the Anopheles gambiae complex by the polymerase chain reaction. Am J Trop Med Hyg 49: 520–529.
Koekemoer LL, Kamau L, Hunt RH, Coetzee M, 2002. A cocktail polymerase chain reaction assay to identify members of the Anopheles funestus (Diptera: Culicidae) group. Am J Trop Med Hyg 66: 804–811.
Sheffield J et al. 2014. A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bull Am Meteorol Soc 95: 861–882.
Princeton University, 2014. African Flood and Drought Monitor. Available at: http://stream.princeton.edu/AWCM/WEBPAGE/interface.php. Accessed September 1, 2017.
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG, 2009. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42: 377–381.
Pinchoff J, Chaponda M, Shields T, Lupiya J, Kobayashi T, Mulenga M, Moss WJ, Curriero FC; Southern Africa International Centers of Excellence for Malaria Research, 2015. Predictive malaria risk and uncertainty mapping in Nchelenge district, Zambia: evidence of widespread, persistent risk and implications for targeted interventions. Am J Trop Med Hyg 93: 1260–1267.
Tarboton D, Bras R, Rodriguez-Iturbe I, 1991. On the extraction of channel networks from digital elevation data. Hydrol Process 5: 81–100.
Hilbe JM, 2007. Negative Binomial Regression. Cambridge, UK: Cambridge University Press, 251.
White GC, Bennetts RE, 1996. Analysis of frequency count data using the negative binomial distribution. Ecology 77: 2549–2557.
Liang K-Y, Zeger SL, 1986. Longitudinal data analysis using generalized linear models. Biometrika 73: 13–22.
Zeger SL, Liang KY, 1986. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42: 121–130.
Kvit A, 2017. The Effect of Drought Associated Indicators on Malaria in the Choma District of Zambia. Baltimore, MD: Johns Hopkins Bloomberg School of Public Health.
Curriero FC, Shone SM, Glass GE, 2005. Cross correlation maps: a tool for visualizing and modeling time lagged associations. Vector Borne Zoonotic Dis 5: 267–275.
Breiman L, 2001. Random forests. Machine Learn 45: 5–32.
Liaw A, Wiener M, 2002. Classification and regression by randomForest. R News 2: 18–22.
Yamashita T, Yamashita K, Kamimura R, 2006. A stepwise AIC method for variable selection in linear regression. Commun Stat Theory Methods 36: 2395–2403.
Cui J, 2007. QIC program and model selection in GEE analyses. Stata J 7: 209–220.
Kirby MJ, Ameh D, Bottomley C, Green C, Jawara M, Milligan PJ, Snell PC, Conway DJ, Lindsay SW, 2009. Effect of two different house screening interventions on exposure to malaria vectors and on anaemia in children in The Gambia: a randomised controlled trial. Lancet 374: 998–1009.
Lindsay SW, Jawara M, Paine K, Pinder M, Walraven GE, Emerson PM, 2003. Changes in house design reduce exposure to malaria mosquitoes. Trop Med Int Health 8: 512–517.
Njie M, Dilger E, Lindsay SW, Kirby MJ, 2009. Importance of eaves to house entry by anopheline, but not culicine, mosquitoes. J Med Entomol 46: 505–510.
Ippolito MM, Searle KM, Hamapumbu H, Shields TM, Stevenson JC, Thuma PE, Moss WJ; for the Southern Africa International Center of Excellence for Malaria Research, 2017. House structure is associated with Plasmodium falciparum infection in a low-transmission setting in southern Zambia. Am J Trop Med Hyg 97: 1561–1567.
Depinay JM et al. 2004. A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission. Malar J 3: 29.
Briet OJ et al. 2015. Applications and limitations of Centers for Disease Control and Prevention miniature light traps for measuring biting densities of African malaria vector populations: a pooled-analysis of 13 comparisons with human landing catches. Malar J 14: 247.
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Malaria transmission is dependent on the density and distribution of mosquito vectors, but drivers of vector abundance have not been adequately studied across a range of transmission settings. To inform intervention strategies for high-burden areas, further investigation is needed to identify predictors of vector abundance. Active household (HH) surveillance was conducted in Nchelenge district, Luapula Province, northern Zambia, a high-transmission setting with limited impact of malaria control. Between April 2012 and July 2017, mosquitoes were collected indoors during HH visits using CDC light traps. Demographic, environmental, and climatological correlates of vector abundance were identified using log-binomial regression models with robust standard errors. The primary malaria vectors in this setting were Anopheles funestus sensu stricto (s.s.) and Anopheles gambiae s.s. Anopheles funestus predominated in both seasons, with a peak in the dry season. Anopheles gambiae peaked at lower numbers in the rainy season. Environmental, climatic, and demographic factors were correlated with HH vector abundance. Higher vector counts were found in rural areas with low population density and among HHs close to roads and small streams. Vector counts were lower with increasing elevation and slope. Anopheles funestus was negatively associated with rainfall at lags of 2–6 weeks, and An. gambiae was positively associated with rainfall at lags of 3–10 weeks. Both vectors had varying relationships with temperature. These results suggest that malaria vector control in Nchelenge district should occur throughout the year, with an increased focus on dry-season transmission and rural areas.
Financial support: This work was supported by funds from the National Institutes of Health awarded to the Southern and Central Africa International Centers of Excellence in Malaria Research (ICEMR) 3U19AI089680, the Bloomberg Philanthropies, and the Johns Hopkins Malaria Research Institute (JHMRI).
Disclosure: W. J. M. reports grants from National Institute of Allergy and Infectious Diseases during the conduct of the study. J. L. reports grants from NIH during the conduct of the study. M. M. reports grants from Johns Hopkins Bloomberg School of Public Health through NIH funding to ICEMRs during the conduct of the study.
Authors’ addresses: Marisa A. Hast, Justin Lessler, Timothy Shields, William J. Moss, and Douglas E. Norris, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mails: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, and firstname.lastname@example.org. Jennifer C. Stevenson, Macha Research Trust, Choma District, Zambia, E-mail: email@example.com. Mbanga Muleba, Mike Chaponda, Jean-Bertin Kabuya, and Modest Mulenga, Tropical Diseases Research Centre, Ndola, Zambia, E-mails: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, and email@example.com.