Sarker Farhana, Chowdhury Moinul H, Ratul Ishrak Jahan, Islam Shariful, Mamun Khondaker A
CMED Health Ltd., Dhaka, Bangladesh.
Department of CSE, University of Liberal Arts, Dhaka, Bangladesh.
Front Digit Health. 2023 May 11;5:1059446. doi: 10.3389/fdgth.2023.1059446. eCollection 2023.
COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation.
This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh.
The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes.
This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform.
This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.
新冠疫情已在全球范围内影响了许多人,包括孟加拉国。由于缺乏准备和资源,孟加拉国经历了一场灾难性的健康危机,这种致命病毒造成的破坏尚未停止。因此,精确快速的诊断和感染追踪对于控制疫情和限制其传播至关重要。传统的筛查程序,如逆转录聚合酶链反应(RT-PCR),在大多数农村地区无法进行且耗时较长。因此,一个数据驱动的智能监测系统对于快速进行新冠筛查和风险评估可能具有优势。
本研究描述了一个基于网络的全国性监测系统的设计、开发、实施及特点,该系统用于在孟加拉国社区层面开展新冠疫情的教育、筛查及追踪工作。
该系统由一个手机应用程序和一个云服务器组成。数据由社区卫生专业人员通过家访或电话收集,并使用基于规则的人工智能(AI)进行分析。根据筛查程序的结果,对患者做出进一步决策。孟加拉国的这个数字监测系统提供了一个平台,以支持政府和非政府组织,包括卫生工作者和医疗机构,识别有感染新冠风险的患者。它将人们指引到最近的政府医疗机构,收集和检测样本,追踪阳性病例,对患者进行随访,并记录患者的治疗结果。
本研究于2020年4月开始,本文给出了截至2022年12月的结果。该系统已成功完成1,980,323次筛查。我们基于规则的人工智能模型根据获取的患者信息将他们分为五个不同的风险组。根据数据,在所有接受筛查的人群中,约51%为安全,35%为低风险,9%为高风险,4%为中风险,其余1%为极高风险。仪表盘将来自全国各地收集的所有数据整合到一个单一平台上。
这种筛查可以帮助有症状的患者根据病情严重程度立即采取行动,如隔离或住院治疗。这个监测系统还可用于风险地图绘制、规划以及将卫生资源分配到更脆弱的地区,以减轻病毒的危害程度。