Labadin Jane, Hong Boon Hao, Tiong Wei King, Gill Balvinder Singh, Perera David, Rigit Andrew Ragai Henry, Singh Sarbhan, Tan Cia Vei, Ghazali Sumarni Mohd, Jelip Jenarun, Mokhtar Norhayati, Rashid Norafidah Binti Abdul, Bakar Hazlin Bt Abu, Lim Jyh Hann, Taib Norsyahida Md, George Aaron
Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Malaysia.
Institute for Medical Research, Ministry of Health, Kuala Lumpur, Malaysia.
Multimed Tools Appl. 2023;82(11):17415-17436. doi: 10.1007/s11042-022-14120-3. Epub 2022 Nov 11.
Traditionally, dengue is controlled by fogging, and the prime location for the control measure is at the patient's residence. However, when Malaysia was hit by the first wave of the Coronavirus disease (COVID-19), and the government-imposed movement control order, dengue cases have decreased by more than 30% from the previous year. This implies that residential areas may not be the prime locations for dengue-infected mosquitoes. The existing early warning system was focused on temporal prediction wherein the lack of consideration for spatial component at the microlevel and human mobility were not considered. Thus, we developed MozzHub, which is a web-based application system based on the bipartite network-based dengue model that is focused on identifying the source of dengue infection at a small spatial level (400 m) by integrating human mobility and environmental predictors. The model was earlier developed and validated; therefore, this study presents the design and implementation of the MozzHub system and the results of a preliminary pilot test and user acceptance of MozzHub in six district health offices in Malaysia. It was found that the MozzHub system is well received by the sample of end-users as it was demonstrated as a useful (77.4%), easy-to-operate system (80.6%), and has achieved adequate client satisfaction for its use (74.2%).
传统上，登革热是通过喷雾灭蚊来控制的，而这种控制措施的主要地点是患者的住所。然而，当马来西亚遭受第一波冠状病毒病（COVID-19）袭击，政府实施行动管制令后，登革热病例比上一年减少了30%以上。这意味着居民区可能不是登革热感染蚊子的主要滋生地。现有的预警系统侧重于时间预测，其中缺乏对微观层面空间因素和人类流动性的考虑。因此，我们开发了MozzHub，这是一个基于网络的应用系统，基于二分网络登革热模型，通过整合人类流动性和环境预测因素，专注于在小空间尺度（400米）上识别登革热感染源。该模型此前已开发并经过验证；因此，本研究介绍了MozzHub系统的设计与实施，以及在马来西亚六个地区卫生办公室进行的初步试点测试结果和用户对MozzHub的接受情况。结果发现，MozzHub系统受到了终端用户样本的好评，因为它被证明是一个有用的系统（77.4%）、易于操作的系统（80.6%），并且在使用方面获得了足够的客户满意度（74.2%）。
