Liu Mutong, Liu Yang, Po Ly, Xia Shang, Huy Rekol, Zhou Xiao-Nong, Liu Jiming
Department of Computer Science, Hong Kong Baptist University, Hong Kong Special administrative region of China.
HKBU-CSD & NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control, China.
Infect Dis Model. 2023 Feb 1;8(1):253-269. doi: 10.1016/j.idm.2023.01.006. eCollection 2023 Mar.
Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective, where nodes capture the local transmission intensities resulting from dominant vector species, the population density, and land cover, and edges describe the cross-region human mobility patterns. The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations. Our study focuses on malaria-severe districts in Cambodia. The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics: the risks increase in the rainy season and decrease in the dry season; remote and sparsely populated areas generally show higher transmission intensities than other areas. Our findings suggest that: the human mobility (e.g., in planting/harvest seasons), environment (e.g., temperature), and contact risk (coexistences of human and vector occurrence) contribute to malaria transmission in spatiotemporally varying degrees; quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.
疟疾控制能够从一种全面且精确的定量测量传播强度的方法中显著受益，这种方法需要纳入时空变化的风险因素。在本研究中，我们进行了一项系统调查，从时空网络的角度来描述疟疾传播强度，其中节点捕捉由主要病媒物种、人口密度和土地覆盖所导致的局部传播强度，而边则描述跨区域的人类流动模式。推断出的网络使我们能够根据现有的实证观察准确评估时空上的传播强度。我们的研究聚焦于柬埔寨的疟疾高发地区。利用我们的传播网络确定的疟疾传播强度在定性和定量上都揭示了其季节性和地理特征：风险在雨季增加，在旱季降低；偏远和人口稀少的地区通常比其他地区显示出更高的传播强度。我们的研究结果表明：人类流动（例如在种植/收获季节）、环境（例如温度）和接触风险（人类与病媒共存）在时空上以不同程度促成疟疾传播；这些影响因素与由此产生的疟疾传播风险之间的定量关系可为在正确的地点和时间采取基于证据的量身定制应对措施提供依据。
