Romero-Leiton Jhoana P, Laison Elda K E, Alfaro Rowin, Parmley E Jane, Arino Julien, Acharya Kamal R, Nasri Bouchra
Department of Mathematical Sciences, University of Puerto Rico at Mayagüez, Puerto Rico, PR 00681-9000, USA.
Département de Médecine Sociale et Préventive, École de Santé Publique de L'Université de Montréal, Montréal, QC Québec, H3N 1X9, Canada.
Infect Dis Model. 2024 Dec 31;10(2):536-558. doi: 10.1016/j.idm.2024.12.016. eCollection 2025 Jun.
Zika virus (ZIKV) infection, along with the concurrent circulation of other arboviruses, presents a great public health challenge, reminding the utilization of mathematical modelling as a crucial tool for explaining its intricate dynamics and interactions with co-circulating pathogens. Through a scoping review, we aimed to discern current mathematical models investigating ZIKV dynamics, focusing on its interplay with other pathogens, and to identify underlying assumptions and deficiencies supporting attention, particularly regarding the epidemiological attributes characterizing Zika outbreaks. Following the PRISMA-Sc guidelines, a systematic search across PubMed, Web of Science, and MathSciNet provided 137 pertinent studies from an initial pool of 2446 papers, showing a diversity of modelling approaches, predominantly centered on vector-host compartmental models, with a notable concentration on the epidemiological landscapes of Colombia and Brazil during the 2015-2016 Zika epidemic. While modelling studies have been important in explaining Zika transmission dynamics and their intersections with diseases such as Dengue, Chikungunya, and COVID-19 so far, future Zika models should prioritize robust data integration and rigorous validation against diverse datasets to improve the accuracy and reliability of epidemic prediction. In addition, models could benefit from adaptable frameworks incorporating human behavior, environmental factors, and stochastic parameters, with an emphasis on open-access tools to foster transparency and research collaboration.
寨卡病毒(ZIKV)感染,连同其他虫媒病毒的同时传播,带来了巨大的公共卫生挑战,这提醒人们利用数学建模作为解释其复杂动态以及与共同传播病原体相互作用的关键工具。通过范围综述,我们旨在识别当前研究寨卡病毒动态的数学模型,重点关注其与其他病原体的相互作用,并确定支持关注的潜在假设和不足,特别是关于寨卡疫情特征的流行病学属性。遵循PRISMA-Sc指南,在PubMed、科学网和数学科学网进行的系统检索从最初的2446篇论文中提供了137篇相关研究,显示出建模方法的多样性,主要集中在媒介-宿主 compartmental模型上,特别关注2015 - 2016年寨卡疫情期间哥伦比亚和巴西的流行病学情况。虽然到目前为止,建模研究在解释寨卡病毒传播动态及其与登革热、基孔肯雅热和COVID - 19等疾病的交叉方面发挥了重要作用,但未来的寨卡病毒模型应优先进行强大的数据整合,并针对不同数据集进行严格验证,以提高疫情预测的准确性和可靠性。此外,模型可以受益于纳入人类行为、环境因素和随机参数的适应性框架,重点是开放获取工具,以促进透明度和研究合作。