1921
Volume 95, Issue 1
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

Abstract

In Zimbabwe, more than half of malaria cases are concentrated in Manicaland Province, where seasonal malaria epidemics occur despite intensified control strategies. The objectives of this study were to develop a prediction model based on environmental risk factors and obtain seasonal malaria risk maps for Mutasa District, one of the worst affected districts in Manicaland Province. From October 2012 to September 2015, 483 households were surveyed, and 104 individuals residing within 69 households had positive rapid diagnostic test results. Logistic regression was used to model the probability of household positivity as a function of the environmental covariates extracted from high-resolution remote sensing data sources. Model predictions and prediction standard errors were generated for the rainy and dry seasons. The resulting maps predicted elevated risk during the rainy season, particularly in low-lying areas bordering Mozambique. In contrast, the risk of malaria was low across the study area during the dry season with foci of malaria risk scattered along the northern and western peripheries of the study area. These findings underscore the need for strong cross-border malaria control initiatives to complement country-specific interventions.

Loading

Article metrics loading...

The graphs shown below represent data from March 2017
/content/journals/10.4269/ajtmh.15-0865
2016-07-06
2019-05-20
Loading full text...

Full text loading...

/deliver/fulltext/14761645/95/1/141.html?itemId=/content/journals/10.4269/ajtmh.15-0865&mimeType=html&fmt=ahah

References

  1. Taylor P, Mutambu SL, , 1986. A review of the malaria situation in Zimbabwe with special reference to the period 1972–1981. Trans R Soc Trop Med Hyg 80: 1219.[Crossref] [Google Scholar]
  2. Mharakurwa S, Thuma PE, Norris DE, Mulenga M, Chalwe V, Chipeta J, Munyati S, Mutambu S, Mason PR, , 2012. Malaria epidemiology and control in southern Africa. Acta Trop 121: 202206.[Crossref] [Google Scholar]
  3. Mharakurwa S, Mutambu SL, Mudyiradima R, Chimbadzwa T, Chandiwana SK, Day KP, , 2004. Association of house spraying with suppressed levels of drug resistance in Zimbabwe. Malar J 3: 35.[Crossref] [Google Scholar]
  4. Munhenga G, Masendu HT, Brooke BD, Hunt RH, Koekemoer LK, , 2008. Pyrethroid resistance in the major malaria vector Anopheles arabiensis from Gwave, a malaria-endemic area in Zimbabwe. Malar J 7: 247.[Crossref] [Google Scholar]
  5. Choi KS, Christian R, Nardini L, Wood OR, Agubuzo E, Muleba M, Munyati S, Makuwaza A, Koekemoer LL, Brooke BD, Hunt RH, Coetzee M, , 2014. Insecticide resistance and role in malaria transmission of Anopheles funestus populations from Zambia and Zimbabwe. Parasit Vectors 7: 464.[Crossref] [Google Scholar]
  6. Lewis R, Hamade P, , 2008. Roll Back Malaria: Country Needs Assessment. Zimbabwe Report. Harare, Zimbabwe: Malaria Consortium. [Google Scholar]
  7. Zimbabwe Ministry of Health and Child Welfare, 2012. Zimbabwe National Health Profile, 2012. Harare, Zimbabwe: Zimbabwe Ministry of Health and Child Welfare. [Google Scholar]
  8. Zimbabwe Ministry of Health and Child Welfare, 2009. Zimbabwe National Health Profile, 2009. Harare, Zimbabwe: Zimbabwe Ministry of Health and Child Welfare. [Google Scholar]
  9. President's Malaria Initiative, 2015. Zimbabwe Malaria Operational Plan FY 2015. Washington, DC: President's Malaria Initiative. [Google Scholar]
  10. Moss WJ, Hamapumbu H, Kobayashi T, Shields T, Kamanga A, Clennon J, Mharakurwa S, Thuma PE, Glass G, , 2011. Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey. Malar J 10: 163.[Crossref] [Google Scholar]
  11. Alemu A, Abebe G, Tsegaye W, Golassa L, , 2011. Climatic variables and malaria transmission dynamics in Jimma town, south west Ethiopia. Parasit Vectors 4: 30.[Crossref] [Google Scholar]
  12. Eisele TP, Keating J, Swalm C, Mbogo CM, Githeko AK, Regens JL, Githure JI, Andrews L, Beier JC, , 2003. Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya. Malar J 2: 44.[Crossref] [Google Scholar]
  13. Noor AM, ElMardi KA, Abdelgader TM, Patil AP, Amine AA, Bakhiet S, Mukhtar MM, Snow RW, , 2012. Malaria risk mapping for control in the Republic of Sudan. Am J Trop Med Hyg 87: 10121021.[Crossref] [Google Scholar]
  14. 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: 12601267.[Crossref] [Google Scholar]
  15. Mabaso MLH, Vounatsou P, Midzi S, Da Silva J, Smith T, , 2006. Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe. Int J Health Geogr 5: 20.[Crossref] [Google Scholar]
  16. Chikodzi D, , 2013. Spatial modelling of malaria risk zones using environmental, anthropogenic variables and geographical information systems techniques. J Geosci Geomatics. 1: 814. [Google Scholar]
  17. Ebi KL, Hartman J, Chan N, Mcconnell J, Schlesinger M, Weyant J, , 2005. Climate suitability for stable malaria transmission in Zimbabwe under different climate change scenarios. Clim Change 73: 375393.[Crossref] [Google Scholar]
  18. Ohemeng FD, Mukherjee F, , 2015. Modelling the spatial distribution of the Anopheles mosquito for malaria risk zoning using remote sensing and GIS: a case study in the Zambezi Basin, Zimbabwe. Int J Appl Geospatial Res 6: 720.[Crossref] [Google Scholar]
  19. Mabaso MLH, Craig M, Vounatsou P, Smith T, , 2005. Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe. Trop Med Int Health 10: 909918.[Crossref] [Google Scholar]
  20. Moss WJ, Norris DE, Mharakurwa S, Scott A, Mulenga M, Mason PR, Chipeta J, Thuma PE, , 2012. Challenges and prospects for malaria elimination in the southern Africa region. Acta Trop 121: 207211.[Crossref] [Google Scholar]
  21. Lowther SA, Curriero FC, Shields T, Ahmed S, Monze M, Moss WJ, , 2009. Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia. Trop Med Int Health 14: 7078.[Crossref] [Google Scholar]
  22. Shuttle Radar Topography Mission NASA (SRTM). SRTM Digital Elevation Model. Available at: http://www2.jpl.nasa.gov/srtm/. Accessed October 1, 2015. [Google Scholar]
  23. Anderson JR, , 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data, Vol. 964. Arlington, VA: U.S. Government Printing Office. [Google Scholar]
  24. Strahler AN, , 1952. Hypsometric (area-altitude) analysis of erosional topography. Geol Soc Am Bull 63: 11171142.[Crossref] [Google Scholar]
  25. Cressie NAC, Cassie NA, , 1993. Statistics for Spatial Data, Vol. 900. New York, NY: Wiley. [Google Scholar]
  26. R Development Core Team, 2013. R: A language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. [Google Scholar]
  27. Freeman T, Bradley M, , 1996. Temperature is predictive of severe malaria years in Zimbabwe. Trans R Soc Trop Med Hyg 90: 232.[Crossref] [Google Scholar]
  28. Booman M, Durrheim DN, La Grange K, Martin C, Mabuza AM, Zitha A, Mbokazi FM, Fraser C, Sharp BL, , 2000. Using a geographical information system to plan a malaria control programme in South Africa. Bull World Health Organ 78: 14381444. [Google Scholar]
  29. Noor AM, Uusiku P, Kamwi RN, Katokele S, Ntomwa B, Alegana VA, Snow RW, , 2013. The receptive versus current risks of Plasmodium falciparum transmission in northern Namibia: implications for elimination. BMC Infect Dis 13: 184.[Crossref] [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.4269/ajtmh.15-0865
Loading
/content/journals/10.4269/ajtmh.15-0865
Loading

Data & Media loading...

  • Received : 30 Nov 2015
  • Accepted : 31 Jan 2016
  • Published online : 06 Jul 2016

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error