Effects of relative climate variability on malaria incidence with a regional-scale dynamical malaria model over Cameroon

Abstract

Malaria is sensitive to climate, environment and socio-economic conditions; but how these drivers interact to control malaria transmission is complex and difficult to predict. Understanding these relationships is important to develop effective control strategies to reduce malaria burden. In this work, climate driven dynamical malaria model was used to examine the impact of climate and population density on malaria incidence in Cameroon using field observed malaria Parasite Ratio (PR) and Entomological Inoculation Rate (EIR) data. The evaluation of the ability of a malaria model is made, to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim respectively. In addition, simulations coupling the model with five results of the dynamical downscaling of the regional climate model RCA4 are made within two-time frames named near future (2035-2065) and far future (2071-2100); aiming to explore the potential effects of global warming on the malaria propagation over Cameroon Geo-referenced, climate and population data is compared to the results of 103 surveys points of PR. A limited set of campaigns with a year-long field-survey data of EIR are examined to determine the seasonality of malaria transmission. Climate-driven simulations of the VECTRI malaria model are evaluated with this analysis. The model then couples RCA4 models under RCP4.5 and RCP8.5 scenarios, to predict PR and EIR pattern, and examines the link with temperature and rainfall. The model results show that PR peaks at temperatures ranging between 22 C to 26 C, which agrees with recent findings that suggest a lower malaria peak temperature relative to what has been established in the literature. On the contrary the model estimated daily minimum amount of rainfall (7 mm day􀀀1) that sustains malaria transmission was higher than values found in literature. The VECTRI model was able to reproduce the observed PR patterns, however the peak occurs at slightly higher temperatures than observed, while the PR peaks at a much lower rainfall rate of 2 mm day􀀀1. Transmission tends to be high in rural and peri-urban relative to urban centres in both model and observations. The EIR follows the seasonal rainfall with a lag of one to two months, and is well reproduced by the model for most of the study sites. However, for locations near permanent water sources, where EIR peaks were out of phase with rainfall, VECTRI failed to accurately predict EIR peak months. The analysis of the malaria projection using PR and EIR, confirm the impact of temperature and rainfall on malaria incidence. PR and EIR peaks between 26 and 28 C which agrees with previous studies. The seasonality of transmission is also observed with EIR pattern. For each of the scenario under the future climate, the impact of temperature and rainfall on the evolution of malaria indicators is confirmed. During the historical period (1985-2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m-2), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26-28 C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision and policy makers.