Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique

Abstract

BackgroundMozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication.MethodsTime series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model.ResultsBetween 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1)52, and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately.ConclusionThe Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-017-2205-6) contains supplementary material, which is available to authorized users.

Transition from endemic behavior to eradication of malaria due to combined drug therapies: An agent-model approach

“…In this section, we validate the agent model simulations against empirical data, namely time series at weekly intervals collected at Chimoio region in Mozambique [32]. In this African region, malaria is endemic revealing a trend, which increases during the four to five months of the wet season (high transmission season) and decreases during the rest of the year (low transmission season).…”

“…Instead, we impose a maximum time of human infection of τ d = 150 days and a minimum time of 25 days, uniformly distributed, yielding an average human infectious period of τ c = 87.5 days, a maximum mosquito life time of 40 days and a minimum life time of 0 days, uniformly distributed, yielding an average life expectancy of one mosquito τ m = 10 days, as well as a probability g iv = 0.5 of one mosquito to die from feeding in human host under ivermectin treatment. In case of an infectious mosquito bite in an infected human host, a human reinfection or super-infection occurs 1 [8,17,21,22,27,32,35,36,37,38,39,40,41,42,43].…”

“…In the agent model, we use values provided in previous studies, namely p ls = 0.5, p hs = 1, p Q = 0.9 and τ s = 4 days. Moreover, inspired in Mozambique seasonality [32,44], we consider 150 days for the duration of the high transmission season, i.e. s = 150/365.…”

“…In particular, agent-based models strengthen the importance of malaria simulation for disease prevention [8,31]. Based on the classical susceptible-infected (SI) model by Kermack and McKendrick, stochastic modeling approaches were also proposed, with the aim of better implementing the uncertainties inherent to the disease dynamics [32].…”

“…Epidemiological field data of malaria transmission is commonly presented as human monthly or weekly disease incidence [32,33], while mosquito infection rates are obtained from data collected through the use of mosquito trapping devices [34]. However, since both are important to understand the transmission dynamics, one should account for the combined effect of human and mosquito infection prevalences.…”