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Dengue forecasting in São Paulo city with generalized additive models artificial neural networks and seasonal autoregressive integrated moving average models

机译：利用广义可加模型人工神经网络和季节性自回归综合移动平均模型对圣保罗市的登革热进行预测

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摘要

Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city—São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce.
机译：从1990年以来，全球登革热病例数量一直在增加，在巴西及其人口最多的城市圣保罗，也发现了这种趋势。基于预测的监视系统可以进行及时的决策过程，进而可以进行及时有效的干预以减轻疾病的负担。我们对圣保罗市的登革热预测进行了比较研究，以测试经过训练的季节性自回归综合移动平均模型，广义加性模型和人工神经网络的性能。我们还使用了朴素的模型作为基准。具有病例数和气象变量滞后的广义加性模型具有最佳性能，预测的流行病规模空前，其性能比基准高3.16倍，比下一个性能最佳的模型高1.47倍。预测模型捕获了季节性模式，但是预测大型流行病的能力有所不同，均优于基准。除了能够预测前所未有的流行病之外，最好的模型还具有计算优势，因为其训练和调整非常简单，需要几秒钟或最多几分钟的时间。这些是希望为决策者提供及时结果的特征。但是，应该注意的是，预测仅在一个月前做出，这是未来研究可能会减少的限制。

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