Authors : Olufemi Adekanmbi S.; Oludele J. Ayoola; Abulwasiu Abubakar; Victor Abah A.

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/46z96a7f

DOI : https://doi.org/10.38124/ijisrt/26sep1011

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Malaria remains an important public health concern, and climatic conditions are recognized as important environmental determinants of malaria transmission. Temperature can influence mosquito survival, parasite development and vectorial capacity; however, the relationship between temperature and malaria incidence may vary spatially and temporally. This study assessed the relationship between mean temperature and malaria incidence across selected Area Councils of the Federal Capital Territory, Nigeria. Data covering the period 2020–2022 were examined to determine temporal and spatial patterns of malaria occurrence. Longer-term malaria incidence data were also examined using annual malaria incidence estimates and population projections. Climatic variables, particularly temperature was considered in assessing variations in malaria incidence. Malaria incidence was expressed in relation to population size to facilitate comparison among the Area Councils. The findings generally showed an inverse relationship between mean temperature and malaria incidence. Correlation coefficients varied from weak to strong negative associations across the different analyses, indicating that higher mean temperatures were generally associated with lower malaria incidence. Regression coefficients were also predominantly negative, with values ranging from approximately −0.194 to −0.607, indicating weak to moderate inverse relationships in the respective analyses. However, the long-term regression models for the six Area Councils demonstrated very limited explanatory power, with R² values ranging from approximately −4% to 1%. The corresponding ANOVA results indicated that the regression models were not statistically significant at the 5% significance level. The negative R² values observed for some locations indicate that the models performed worse than an intercept-only model based on the mean malaria incidence. Malaria early-warning systems should therefore incorporate local climatic and epidemiological conditions rather than applying a uniform temperature threshold across all locations. Consistent, highquality and spatially disaggregated malaria data should be maintained at health facilities and public health institutions. Accurate records of confirmed malaria cases, diagnostic methods, population at risk and seasonal patterns would improve the reliability of future climate–malaria analyses.

Keywords : Malaria Incidence; Climatic Variables; Temperature and Climate Variability.

References :

1. Adepoju, A. O., et al. (2019). Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria. BMC Infectious Diseases.
2. Blanford, J. I., Blanford, S., Crane, R. G., Mann, M. E., Paaijmans, K. P., Schreiber, K. V., & Thomas, M. B. (2013). Implications of temperature variation for malaria parasite development across Africa. Scientific Reports, 3, 1300. https://doi.org/10.1038/srep01300.
3. Bødker, R., Akida, J., Shayo, D., Kisinza, W., Msangeni, H. A., Pedersen, E. M., & Lindsay, S. W. (2003). Relationship between altitude and intensity of malaria transmission in the Usambara Mountains, Tanzania. Journal of Medical Entomology, 40(6), 706–717. https://doi.org/10.1603/0022-2585-40.6.706
4. Caminade, C., Kovats, S., Rocklöv, J., Tompkins, A. M., Morse, A. P., Colón-González, F. J., Stenlund, H., Martens, P., & Lloyd, S. J. (2014). Impact of climate change on global malaria distribution. Proceedings of the National Academy of Sciences, 111(9), 3286–3291. https://doi.org/10.1073/pnas.1302089111
5. Colón-González, F. J., Tompkins, A. M., Biondi, R., Bizimana, J. P., & Namanya, D. B. (2016). Assessing the effects of air temperature and rainfall on malaria incidence: An epidemiological study across Rwanda and Uganda. Geospatial Health, 11(1s), 379. https://doi.org/10.4081/gh.2016.379.
6. Craig, M. H., Snow, R. W., & le Sueur, D. (1999). A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitology Today, 15(3), 105–111. https://doi.org/10.1016/S0169-4758(99)01396-4
7. Gasparrini, A., Armstrong, B., & Kenward, M. G. (2010). Distributed lag non-linear models. Statistics in Medicine, 29(21), 2224–2234. https://doi.org/10.1002/sim.3940
8. Gething, P. W., Smith, D. L., Patil, A. P., Tatem, A. J., Snow, R. W., & Hay, S. I. (2010). Climate change and the global malaria recession. Nature, 465(7296), 342–345. https://doi.org/10.1038/nature09098
9. Hoshen, M. B., & Morse, A. P. (2004). A weather-driven model of malaria transmission. Malaria Journal, 3, Article 32. https://doi.org/10.1186/1475-2875-3-32
10. Liu, Q., Wang, Y., Deng, J., Yan, W., Qin, C., Du, M., Liu, M., & Liu, J. (2024). Association of temperature and precipitation with malaria incidence in 57 countries and territories from 2000 to 2019: A worldwide observational study. Journal of Global Health, 14, 04021. https://doi.org/10.7189/jogh.14.04021.
11. Mordecai, E. A., Paaijmans, K. P., Johnson, L. R., Balzer, C., Ben-Horin, T., de Moor, E., McNally, A., Pawar, S., Ryan, S. J., Smith, T. C., & Lafferty, K. D. (2013). Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecology Letters, 16(1), 22–30. https://doi.org/10.1111/ele.12015
12. Oguntade, O. E., Shohaimi, S., Nallapan, M., & others (2020), Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria, published in International Journal of Environmental Research and Public Health, 17(10), 3474. The study used monthly Abuja data from 2000–2013 and reported a significant rainfall effect at a one-month lag and temperature/humidity effects at a four-month lag.
13. Paaijmans, K. P., Blanford, S., Bell, A. S., Blanford, J. I., Read, A. F., & Thomas, M. B. (2010). Influence of climate on malaria transmission depends on daily temperature variation. Proceedings of the National Academy of Sciences, 107(34), 15135–15139. https://doi.org/10.1073/pnas.1006422107
14. Paaijmans, K. P., Wandago, M. O., Githeko, A. K., & Takken, W. (2007). Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS ONE, 2(11), e1146. https://doi.org/10.1371/journal.pone.0001146
15. Parham, P. E., & Michael, E. (2010). Modeling the effects of weather and climate change on malaria transmission. Environmental Health Perspectives, 118(5), 620–626. https://doi.org/10.1289/ehp.0901256
16. Ryan, S. J., McNally, A., Johnson, L. R., Mordecai, E. A., Ben-Horin, T., Paaijmans, K. P., & Lafferty, K. D. (2015). Mapping physiological suitability limits for malaria in Africa under climate change. Vector-Borne and Zoonotic Diseases, 15(12), 718–725. https://doi.org/10.1089/vbz.2015.1822
17. World Health Organization. (2025). World malaria report 2025. World Health Organization. https://www.who.int/publications/i/item/9789240117822.