Knipl Diána, Röst Gergely, Moghadas Seyed M
Department of Mathematics, University College London, London, United Kingdom; MTA-SZTE Analysis and Stochastic Research Group, University of Szeged, Szeged, Hungary.
Bolyai Institute, University of Szeged , Szeged , Hungary.
PeerJ. 2017 Jan 10;5:e2817. doi: 10.7717/peerj.2817. eCollection 2017.
The emergence and spread of drug-resistance during treatment of many infectious diseases continue to degrade our ability to control and mitigate infection outcomes using therapeutic measures. While the coverage and efficacy of treatment remain key factors in the population dynamics of resistance, the timing for the start of the treatment in infectious individuals can significantly influence such dynamics. We developed a between-host disease transmission model to investigate the short-term (epidemic) and long-term (endemic) states of infections caused by two competing pathogen subtypes, namely the wild-type and resistant-type, when the probability of developing resistance is a function of delay in start of the treatment. We characterize the behaviour of disease equilibria and obtain a condition to minimize the fraction of population infectious at the endemic state in terms of probability of developing resistance and its transmission fitness. For the short-term epidemic dynamics, we illustrate that depending on the likelihood of resistance development at the time of treatment initiation, the same epidemic size may be achieved with different delays in start of the treatment, which may correspond to significantly different treatment coverages. Our results demonstrate that early initiation of treatment may not necessarily be the optimal strategy for curtailing the incidence of resistance or the overall disease burden. The risk of developing drug-resistance in-host remains an important factor in the management of resistance in the population.
在许多传染病治疗过程中，耐药性的出现和传播持续削弱我们运用治疗手段控制和减轻感染后果的能力。虽然治疗的覆盖率和疗效仍是耐药性群体动态变化的关键因素，但感染个体开始治疗的时机可显著影响这种动态变化。我们构建了一个宿主间疾病传播模型，以研究由野生型和耐药型这两种相互竞争的病原体亚型引起的感染在短期（流行）和长期（地方流行）状态下的情况，此时产生耐药性的概率是治疗开始延迟时间的函数。我们刻画了疾病平衡点的行为，并根据产生耐药性的概率及其传播适应性，得出一个使地方流行状态下感染人群比例最小化的条件。对于短期流行动态，我们表明，取决于治疗开始时产生耐药性的可能性，相同的流行规模可通过不同的治疗开始延迟时间实现，这可能对应着显著不同的治疗覆盖率。我们的结果表明，早期开始治疗不一定是减少耐药性发生率或总体疾病负担的最优策略。宿主体内产生耐药性的风险仍是群体耐药性管理中的一个重要因素。
