IS CLIMATE INDUCING DENGUE? Flávio Justino, 1 Hudson Campos 2

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Pedro Lucas Pedro Henrique Nunes Sanches
7 Há anos
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1 IS CLIMATE INDUCING DENGUE? Flávio Justino, 1 Hudson Campos 2 1 UFV-Brazil Minas Gerais fjustino@ufv.br 2 IFMG-Banbuí Minas Gerais RESUMO: O entendimento das relações entre variáveis meteorológicas e a dengue é crucial para o entendimento dos potenciais impactos que as mudanças climáticas podem causar na evolução da dengue. Neste sentido utilizou-se a técnica de rede neural artificial para (RNA) para simular os casos de dengue em 5 municípios de São Paulo. Os resultados mostram que a utilização de dados climáticos leva a uma queda na qualidade da previsão da dengue pela RNA, ou seja, as variáveis meteorológicas diluem a capacidade de aprendizagem da rede. ABSTRACT: The understanding of the relationship between meteorological variables and dengue is crucial for understanding the potential impacts that climate change may cause in the evolution of dengue. In this sense artificial neural network (RNA) technique has been used to simulate dengue cases in 5 municipalities of São Paulo. The results show that the use of climatic data leads to a decrease in the quality of dengue case simulation by the RNA, i.e. the meteorological variables reduces the learning capacity of the network. INTRODUCTION Patz et al (1998) used three atmospheric simulations to relate changes in the patterns of temperature and potential dengue outbreak. They show that an increase of 1.16 ºc, foreseen for 2050, can lead to increased 31 to 47% of outbreaks of dengue. However, in the case of malaria in East Africa, the changing patterns of precipitation, temperature and vapor pressure does not change significantly the numbers of months suitable for transmission of the disease. In this sense studies that make the link between increasing number of vector diseases may help to elucidate the potential threat of climate change on human health. METHODOLOGY To carry out the study input parameters of cases of dengue fever of last (dengue), the penultimate (dengue-1) and the antepenultimate (dengue-2) weeks for each city were

2 applied in the RNA. Weekly precipitation data and averaged temperatures from the Instituto Agronômico de Campinas have been also collected. The cities were chosen (Araçatuba, Araraquara, Campinas, Santos and São José do Rio Preto) because they can represent the climatic diversity of Sao Paulo State, allowing to associate dengue to different climates. For training of the network were used the first four years of weather data and dengue (weekly average temperature and precipitation). 300 simulations were performed in MatLab based on "multilayer feedforwad", with a system of 48 connections. The method "multilayer" with 72 feedforwad connections was used to simulate the dengue without association with weather variables, and the system type "multilayer" feedforwad 63 to simulate the connections from the relationship between dengue cases and the meteorological variables. RESULTS Figure 1 shows the result of the RNA simulation based on dengue data for the 3 preceding weeks. In all cases, the RNA was able to reproduce the pattern of cases with enough effectiveness. The municipality that shows the best RNA accuracy as compared to observation is Campinas/SP (Figure 3c). It might be noted that the RNA in all cities display very similar pattern as present by the real data. This shows that the RNA may be used to predict future cases of dengue if it uses data from the past 3, 2 and 1 weak. Figure 2, shows the RNA simulation as function of weather data only. In this case is evident that the RNA is not able to reproduce the cases of dengue or even the seasonal pattern, which may highlight the complex link involving dengue, such as the influence of social and economic factors. Finally, we have performed a simulation using dengue and weather data (Figure 3). The results were satisfactory because for all five cities, the simulation has followed closely the line of real cases. However, the outcomes only lost in quality as compared to the simulation of cases of dengue fever solely (Figure 1). This is because the RNA has not been able to find patterns for simulation of weather variables. For all cities but in particular for Campinas/SP (Figure 1c), the RNA agrees with the pattern of real cases fairly efficient, identifying peaks (period between day 1 and day 30 day period, between 55 and 80 and period between 267 and 290) as well as capturing shorter periods of incidence of dengue fever. For the municipality of Santos/SP, the efficiency was the same of Campinas, with all the peaks being satisfactorily identified by simulation of RNA. For the municipality of São José do Rio Preto/SP (Figure 6), the

3 disease reached three major peaks of incidence (between 1 and 25, between 250 and 285 and between 310 and 340). All this large spikes were captured by RNA simulation quite satisfactory. CONCLUSIONS This study shows that the RNA may provide interesting results on dengue predictability. We have shown that dengue may be anticipated by a trained RNA based on past 3 weeks without the need for climate or weather data. On the other hand, our results demonstrated that weather data solely is not able to reproduced the observed dengue cases. By introducing dengue and weather variables in the RNA it has been noticed that the latter data reduces the performance of simulating dengue due to its randon bahaviour. REFERENCES PATZ, JA. MARTENS, WJM. FOCKS, DA. JETTEN, TH. Dengue fever epidemic potential as projected by general circulation models of global climate chance. Environ Health Perspect, 106:

4 Figura 1 - Simulação dos casos de Dengue para os municípios estudados, no período de 2001 a 2007 com dados de dengue (dengue-1, dengue -2, dengue-3)

5 Figura 2 - Simulação dos casos de Dengue para os municípios estudados, no período de 2001 a 2007 unicamente com dados de clima

6 Figura 3 - Simulação dos casos de dengue utilizando variáveis meteorológicas e casos de dengue

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