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Modelo para simular el impacto la cobertura, la intensidad de transmisión y del momento de realización de la intervención sobre la efectividad del rociamiento intradomiciliario con insecticidas de acción residual para el control de la malaria
Author(s) -
Worrall E.,
Connor S. J.,
Thomson M. C.
Publication year - 2007
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/j.1365-3156.2006.01772.x
Subject(s) - indoor residual spraying , malaria , transmission (telecommunications) , residual , environmental science , psychological intervention , demography , meteorology , geography , medicine , mathematics , computer science , plasmodium falciparum , telecommunications , artemisinin , algorithm , psychiatry , sociology , immunology
Summary Objective (i) To develop a temperature‐ and rainfall‐driven model of malaria transmission capable of prediction. (ii) To use the model to examine the relationship between the intervention timing and transmission intensity on the effectiveness of indoor residual spraying (IRS). Methods A dynamic model of malaria transmission was developed from existing models of malaria transmission dynamics. The model was used to retrospectively predict actual malaria cases from Hwange district in Zimbabwe using actual meteorological and IRS timing and coverage data. Simulations of alternative intervention scenarios (timing and coverage) examined the effectiveness of earlier and later interventions, at higher and lower coverage levels in epidemic and non‐epidemic years. Findings The model was able to predict actual malaria cases in Hwange over a four‐and‐a‐half‐year period with a lead time of 4 months (e.g. January rainfall and temperature predicts April malaria) and a correlation coefficient of 0.825 ( r 2 = 0.6814). The IRS simulations show that the marginal benefits of increasing IRS coverage are higher in high‐transmission (HT) years relative to lower transmission years. This implies that over a period of years, maximum impact could be achieved with a given quantity of insecticide by increasing coverage in HT years. However, the model also shows that earlier spraying is more effective in all years, especially so in epidemic years, and that IRS has limited impact if it is carried out too late in relation to peak transmission. Conclusion Temperature‐ and rainfall‐driven models of malaria transmission have the potential to predict malaria epidemics. Early intervention based on prior knowledge of the magnitude of the malaria season can be more effective and efficient than carrying out routine activities every year. Malaria control planners need improved access to the technology that would allow them to better predict malaria epidemics and develop Malaria Early Warning Systems (MEWS). MEWS can then be linked to intervention planning to reduce the devastating impact of malaria epidemics on populations.