Holcomb Karen M, Foster Erik, Eisen Rebecca J
Division of Vector Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Road, Fort Collins, CO 80521 USA.
Division of Vector Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Road, Fort Collins, CO 80521 USA.
Ticks Tick Borne Dis. 2025 Mar;16(2):102446. doi: 10.1016/j.ttbdis.2025.102446. Epub 2025 Mar 8.
Tick-borne diseases pose a persistent and increasing threat to public health. In the United States, the majority of human infections are caused by pathogens spread by the blacklegged tick, Ixodes scapularis. Most infections are reported during the summer months, when nymphal ticks are active in states in the Northeast and Upper Midwest. The density of questing I. scapularis nymphs (DON) provides an estimate for the risk of human encounters with nymphs, but it is a resource intensive metric to obtain from field sampling. Thus, DON estimates are limited in the US national tick surveillance database, the ArboNET Tick Module. We estimated DON across all counties in the eastern US using a zero-inflated negative binomial model utilizing tick surveillance data reported to ArboNET (2004-2023) as well as climate and land cover data. The model estimated generally low DON across the southeastern US and Great Plains states with higher estimates in the Upper Midwest and Northeast regions. We assigned counties to relative acarological encounter risk categories based on estimated DON: zero or lower quartile DON estimates were scored as low risk, whereas inter- and upper-quartile DON estimates were scored as moderate-high risk. Counties with moderate-high DON reported from field sampling were accurately categorized by the model as moderate-high encounter risk (99 % sensitivity). However, 80 % of sampled counties reporting low DON were classified as moderate-high risk (20 % specificity). These misclassified counties were typically situated in recently colonized areas in the Northeast and Upper Midwest and likely indicated areas potentially suitable for tick population expansion. Our model yielded a very high negative predictive value (96 %) indicating the model did very well estimating low relative encounter risk in counties where no or few nymphs were collected, and a fair positive predictive value (60 %) indicated that densities may not have reached an expected peak in some locations, particularly in the Northeast, Upper Midwest, and northern states in the Southeast. Further tick surveillance is needed to evaluate and to refine these predictions. The resulting maps are useful for estimating relative risk of nymphal encounters across the eastern US where field data are sparse and may aid in efforts aimed at promoting the use of personal protective measures in communities that are at risk for nymphal tick encounters.
蜱传疾病对公共卫生构成了持续且日益严重的威胁。在美国，大多数人类感染是由黑腿蜱（肩突硬蜱）传播的病原体引起的。大多数感染报告发生在夏季，此时若蜱在东北部和中西部上游各州活动。寻找宿主的肩突硬蜱若蜱密度（DON）可用于估计人类接触若蜱的风险，但从野外采样获取该指标需要耗费大量资源。因此，在美国国家蜱监测数据库ArboNET蜱模块中，DON估计值有限。我们使用零膨胀负二项式模型，利用报告给ArboNET（2004 - 2023年）的蜱监测数据以及气候和土地覆盖数据，估计了美国东部所有县的DON。该模型估计美国东南部和大平原各州的DON普遍较低，而中西部上游和东北部地区的估计值较高。我们根据估计的DON将各县划分为相对的蜱虫接触风险类别：DON估计值为零或处于下四分位数的县被评为低风险，而处于四分位数区间和上四分位数的DON估计值被评为中高风险。通过野外采样报告的DON为中高的县被该模型准确分类为中高接触风险（敏感性为99%）。然而，报告DON较低的采样县中有80%被归类为中高风险（特异性为20%）。这些误分类的县通常位于东北部和中西部上游最近被蜱虫殖民的地区，可能表明这些地区潜在适合蜱虫种群扩张。我们的模型产生了非常高的阴性预测值（96%），表明该模型在估计未采集到若蜱或仅采集到少量若蜱的县的低相对接触风险方面表现出色，而中等的阳性预测值（60%）表明在某些地区，特别是在东北部、中西部上游和东南部的北部各州，蜱虫密度可能尚未达到预期峰值。需要进一步的蜱监测来评估和完善这些预测。生成的地图对于估计美国东部野外数据稀少地区若蜱接触的相对风险很有用，并且可能有助于在有接触若蜱风险的社区推动个人防护措施的使用。
