A spatiotemporal risk assessment approach for respiratory infectious diseases based on probabilistic space-time prisms

Le résumé fourni par la source

Accurately assessing the risk level of areas where infectious cases have previously been active is critical for screening potential infectious cases and mitigating the epidemic. Current studies generally regard activity areas recorded in data as risk areas, and assessed the risks by assuming the infectiousness during the longest incubation period. However, in reality, there exist uncertainties regarding both activity locations and the infection time of cases, to a large degree resulting in the unreliability of risk assessment results. In light of this, this study proposes a spatiotemporal risk assessment method based on probabilistic space-time prisms. The primary contribution lies in the development of an activity-based probabilistic space-time prism framework. This framework integrates environmental attractiveness and mobility constraints to infer the omitted activity areas. Building on spatial probabilities derived by probabilistic space-time prism, the spatiotemporal co-occurrence relationship is construed to estimate the temporal infectivity probability. On this basis, a multi-factor coupling model is constructed by integrating the uncertainties of disease transmission and the possible interpersonal contact forces to quantify the risks. A case study on the COVID-19 in Shijiazhuang city, China, was performed to evaluate the effectiveness and reliability of the proposed method. The comparison results confirm the practicability of the proposed method in identifying risk areas and assessing risk levels.

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