论文标题
Covid-19和登革热之间的相关性
Correlations Between COVID-19 and Dengue
论文作者
论文摘要
最近报道了登革热爆发的数量急剧增加，气候变化可能会延长该疾病的地理扩展。在这种情况下，本文展示了神经网络方法如何将登革热和Covid-19数据以及外部因素（例如社会行为或气候变量）结合在一起，以开发可以改善我们的知识并为健康政策制定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，在本文中，我们定义了一个相关模型，通过该模型，我们表明Covid-19和登革热的病例数量具有非常相似的趋势。然后，我们通过将模型扩展到纳入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，并在缺乏足够的登革热数据的国家 /地区使用COVID-19估计登革热感染的数据。
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.
