Branda Francesco, Veltri Pierangelo, Chiodo Francesco, Ciccozzi Massimo, Scarpa Fabio, Guzzi Pietro Hiram
Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, via Álvaro del Portillo, 21, Rome, RM, 00128, Italy.
DIMES, University of Calabria, Via P. Bucci, Rende, CS, 87036, Italy.
BMC Med Inform Decis Mak. 2025 Jul 1;25(1):238. doi: 10.1186/s12911-025-03063-y.
Computational modelling of disease spread is crucial for understanding the dynamics of infectious outbreaks and assessing the effectiveness of control measures. In particular, network-based models for disease spreading offer detailed, granular insights into heterogeneous interactions and enable dynamic simulation of intervention strategies. Therefore, they offer valuable insights into the factors influencing disease spread, enabling public health authorities to develop effective containment strategies. Vaccination is among the most impactful interventions in controlling disease spread and has proven essential in preventing the spread of infectious diseases such as measles. However, recent trends indicate a concerning decline in the fraction of vaccinated individuals in various populations, increasing the risk of outbreaks.
In this study, we utilize computational simulations on graph-based models to analyze how vaccination affects the spread of infectious diseases. By representing populations as networks in which individuals (nodes) are connected by potential spread pathways (edges), we simulate different vaccination coverage scenarios and assess their impact on disease spread. Our simulations incorporate high and low vaccination coverage to reflect real-world trends and explore various conditions under which disease spread can be effectively blocked.
The results demonstrate that adequate vaccination coverage is critical for halting outbreaks, with a marked reduction in disease spread observed as the fraction of vaccinated individuals increases. Conversely, insufficient vaccination rates lead to widespread outbreaks, underscoring the importance of maintaining high vaccination levels to achieve herd immunity and prevent resurgence. These findings highlight the vital role of vaccination as a preventative tool and emphasize the potential risks posed by declining vaccination rates.
This study provides a deeper understanding of how vaccination strategies can mitigate the spread of infectious diseases and serves as a reminder of the importance of maintaining robust immunization programs to protect public health.
疾病传播的计算建模对于理解传染病爆发的动态过程以及评估控制措施的有效性至关重要。特别是，基于网络的疾病传播模型能够对异质相互作用提供详细、具体的见解，并能够对干预策略进行动态模拟。因此，它们为影响疾病传播的因素提供了有价值的见解，使公共卫生当局能够制定有效的遏制策略。疫苗接种是控制疾病传播最具影响力的干预措施之一，事实证明对预防麻疹等传染病的传播至关重要。然而，最近的趋势表明，不同人群中接种疫苗个体的比例出现了令人担忧的下降，增加了爆发疫情的风险。
在本研究中，我们利用基于图的模型进行计算模拟，以分析疫苗接种如何影响传染病的传播。通过将人群表示为网络，其中个体（节点）通过潜在传播途径（边）连接，我们模拟不同的疫苗接种覆盖率情景，并评估其对疾病传播的影响。我们的模拟纳入了高和低疫苗接种覆盖率，以反映现实世界的趋势，并探索在各种条件下疾病传播能够被有效阻断的情况。
结果表明，足够的疫苗接种覆盖率对于遏制疫情爆发至关重要，随着接种疫苗个体比例的增加，观察到疾病传播显著减少。相反，疫苗接种率不足会导致疫情广泛爆发，凸显了维持高疫苗接种水平以实现群体免疫和防止疫情死灰复燃的重要性。这些发现突出了疫苗接种作为预防工具的关键作用，并强调了疫苗接种率下降带来的潜在风险。
本研究更深入地理解了疫苗接种策略如何减轻传染病的传播，并提醒人们维持强有力的免疫计划以保护公众健康的重要性。
