Das Haridas K
Department of Mathematics, Oklahoma State University, Stillwater, OK, United States.
Department of Mathematics, Dhaka University, Dhaka, Bangladesh.
Front Epidemiol. 2024 May 22;4:1334964. doi: 10.3389/fepid.2024.1334964. eCollection 2024.
Mpox (formerly monkeypox) is an infectious disease that spreads mostly through direct contact with infected animals or people's blood, bodily fluids, or cutaneous or mucosal lesions. In light of the global outbreak that occurred in 2022-2023, in this paper, we analyzed global Mpox univariate time series data and provided a comprehensive analysis of disease outbreaks across the world, including the USA with Brazil and three continents: North America, South America, and Europe. The novelty of this study is that it delved into the Mpox time series data by implementing the data-driven methods and a mathematical model concurrently-an aspect not typically addressed in the existing literature. The study is also important because implementing these models concurrently improved our predictions' reliability for infectious diseases.
We proposed a traditional compartmental model and also implemented deep learning models (1D- convolutional neural network (CNN), long-short term memory (LSTM), bidirectional LSTM (BiLSTM), hybrid CNN-LSTM, and CNN-BiLSTM) as well as statistical time series models: autoregressive integrated moving average (ARIMA) and exponential smoothing on the Mpox data. We also employed the least squares method fitting to estimate the essential epidemiological parameters in the proposed deterministic model.
The primary finding of the deterministic model is that vaccination rates can flatten the curve of infected dynamics and influence the basic reproduction number. Through the numerical simulations, we determined that increased vaccination among the susceptible human population is crucial to control disease transmission. Moreover, in case of an outbreak, our model showed the potential for epidemic control by adjusting the key epidemiological parameters, namely the baseline contact rate and the proportion of contacts within the human population. Next, we analyzed data-driven models that contribute to a comprehensive understanding of disease dynamics in different locations. Additionally, we trained models to provide short-term (eight-week) predictions across various geographical locations, and all eight models produced reliable results.
This study utilized a comprehensive framework to investigate univariate time series data to understand the dynamics of Mpox transmission. The prediction showed that Mpox is in its die-out situation as of July 29, 2023. Moreover, the deterministic model showed the importance of the Mpox vaccination in mitigating the Mpox transmission and highlighted the significance of effectively adjusting key epidemiological parameters during outbreaks, particularly the contact rate in high-risk groups.
猴痘（原称猴天花）是一种传染病，主要通过直接接触受感染动物或人的血液、体液、皮肤或粘膜损伤传播。鉴于2022 - 2023年发生的全球疫情，在本文中，我们分析了全球猴痘单变量时间序列数据，并对全球疾病爆发情况进行了全面分析，包括美国、巴西以及北美洲、南美洲和欧洲三大洲。本研究的新颖之处在于，它同时采用数据驱动方法和数学模型深入研究猴痘时间序列数据——这是现有文献中通常未涉及的方面。该研究也很重要，因为同时实施这些模型提高了我们对传染病预测的可靠性。
我们提出了一个传统的 compartmental 模型，并对猴痘数据实施了深度学习模型（一维卷积神经网络（CNN）、长短期记忆网络（LSTM）、双向长短期记忆网络（BiLSTM）、混合 CNN - LSTM 和 CNN - BiLSTM）以及统计时间序列模型：自回归积分移动平均（ARIMA）和指数平滑法。我们还采用最小二乘法拟合来估计所提出的确定性模型中的基本流行病学参数。
确定性模型的主要发现是，疫苗接种率可以使感染动态曲线趋于平缓，并影响基本再生数。通过数值模拟，我们确定在易感人群中增加疫苗接种对于控制疾病传播至关重要。此外，在疫情爆发时，我们的模型显示通过调整关键流行病学参数，即基线接触率和人群内部接触比例，具有控制疫情的潜力。接下来，我们分析了有助于全面理解不同地区疾病动态的数据驱动模型。此外，我们训练模型以提供不同地理位置的短期（八周）预测，所有八个模型都产生了可靠的结果。
本研究利用一个综合框架来研究单变量时间序列数据，以了解猴痘传播的动态。预测显示，截至2023年7月29日，猴痘处于消亡状态。此外，确定性模型显示了猴痘疫苗接种在减轻猴痘传播方面的重要性，并强调了在疫情爆发期间有效调整关键流行病学参数的重要性，特别是高危人群中的接触率。
