A dramatic increase in the number of outbreaks of Dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate Dengue infections via COVID-19 data in countries that lack sufficient Dengue data.
翻译:最近,登革热爆发次数急剧增加,气候变化有可能扩大该疾病的地理传播范围,在这方面,本文件表明神经网络方法如何能够将登革热和COVID-19数据以及外部因素(如社会行为或气候变量)纳入其中,以开发可以改进我们的知识并为卫生决策者提供有用工具的预测模型。通过使用具有不同社会和自然参数的神经网络,本文件界定了一种关联模型,通过该模型我们表明COVID-19和登革热病例的数量具有非常相似的趋势。然后,我们通过将其推广到包含这两种疾病的长期短期记忆模型(LSTM)来说明我们的模型的相关性,并利用这一模型在缺乏足够登革热数据的国家通过COVID-19数据估计登革热感染。