Lombard Belinda, Moultrie Harry, Pulliam Juliet R C, van Schalkwyk Cari
South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
Division of the National Health Laboratory Service, National Institute for Communicable Diseases, Johannesburg, South Africa.
PLoS Comput Biol. 2025 Feb 3;21(2):e1012792. doi: 10.1371/journal.pcbi.1012792. eCollection 2025 Feb.
Given the high global seroprevalence of SARS-CoV-2, understanding the risk of reinfection has become increasingly important. Models developed to track trends in reinfection risk should be robust against possible biases arising from imperfect data observation processes. We performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. The catalytic model assumes the risk of reinfection is proportional to observed infections. Validation involved using simulated primary infections, consistent with the number of observed infections in South Africa. To assess the performance of the catalytic model, we simulated reinfection datasets that incorporated different processes that may bias inference, including imperfect observation and mortality. A Bayesian approach was used to fit the model to simulated data, assuming a negative binomial distribution around the expected number of reinfections, and model projections were compared to the simulated data using different magnitudes of change in reinfection risk. We assessed the model's ability to accurately detect changes in reinfection risk when included in the simulations, as well as the occurrence of false positives when reinfection risk remained constant. The model parameters converged in most scenarios leading to model outputs aligning with anticipated outcomes. The model successfully detected changes in the risk of reinfection when such a change was introduced to the data. Low observation probabilities (10%) of both primary- and reinfections resulted in low numbers of observed cases from the simulated data and poor convergence. The model's performance was assessed on simulated data representative of the South African SARS-CoV-2 epidemic, reflecting its timing of waves and outbreak magnitude. Model performance under similar scenarios may be different in settings with smaller epidemics (and therefore smaller numbers of reinfections). Ensuring model parameter convergence is essential to avoid false-positive detection of shifts in reinfection risk. While the model is robust in most scenarios of imperfect observation and mortality, further simulation-based validation for regions experiencing smaller outbreaks is recommended. Caution must be exercised in directly extrapolating results across different epidemiological contexts without additional validation efforts.
鉴于全球范围内严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的血清阳性率很高，了解再感染风险变得越来越重要。为追踪再感染风险趋势而开发的模型应能有效抵御因数据观测过程不完善而可能产生的偏差。我们对一个现有的催化模型进行了基于模拟的验证，该模型旨在检测SARS-CoV-2再感染风险的变化。催化模型假定再感染风险与观察到的感染病例数成正比。验证过程使用了模拟的初次感染数据，其数量与南非观察到的感染病例数一致。为评估催化模型的性能，我们模拟了再感染数据集，其中纳入了可能影响推断的不同过程，包括不完美观测和死亡率。采用贝叶斯方法将模型拟合到模拟数据，假定再感染预期数周围呈负二项分布，并将模型预测结果与使用不同再感染风险变化幅度的模拟数据进行比较。我们评估了该模型在纳入模拟时准确检测再感染风险变化的能力，以及再感染风险保持不变时出现假阳性的情况。在大多数情况下，模型参数收敛，从而使模型输出与预期结果相符。当数据中引入再感染风险变化时，该模型成功检测到了这种变化。初次感染和再感染的低观察概率（10%）导致模拟数据中观察到的病例数较少且收敛性较差。该模型的性能是根据代表南非SARS-CoV-2疫情的模拟数据进行评估的，反映了疫情的波次时间和爆发规模。在疫情规模较小（因此再感染病例数较少）的情况下，类似场景下的模型性能可能有所不同。确保模型参数收敛对于避免错误检测再感染风险的变化至关重要。虽然该模型在大多数不完美观测和死亡率场景中表现稳健，但建议对疫情规模较小的地区进一步开展基于模拟的验证。在没有额外验证工作的情况下，直接将结果外推到不同的流行病学背景时必须谨慎。
