Optimal resource allocation with spatiotemporal transmission discovery for effective disease control

Abstract

Background: The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity (especially the testing resources, vaccination coverage, and hospital capacity) is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks?

Methods: We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. We collect the publicly available data from the official website of the Hong Kong Special Administrative Region (HKSAR) Government and the study period in this paper is from December 27, 2021 to July 17, 2022 (including a period for future prediction). First, we construct the spatiotemporal transmission intensity matrices across different districts based on infection case records. With the constructed cross-district transmission matrices, we forecast the future risks of various locations daily by means of the Gaussian process. Finally, we develop a transmission-guided resource prioritization strategy that enables effective control of Omicron outbreaks under limited capacity.

Results: We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong, China. The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks, making it difficult for the public health authority to foresee the outbreaks and plan the responses accordingly. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the optimal testing resource allocation for integrative case management (including case detection, quarantine, and further treatment), i.e., with the 300,000 testing capacity per day; it could reduce the infection peak by 87.1% compared with the population-based allocation strategy (case number reduces from 20,860 to 2689) and by 24.2% compared with the case-based strategy (case number reduces from 3547 to 2689), significantly alleviating the burden of the healthcare system.

Conclusions: Computationally characterizing spatiotemporal transmission patterns allows for the effective risk mapping and resource prioritization; such adaptive strategies are of critical importance in achieving timely outbreak control under insufficient capacity. The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions.

Keywords: COVID-19; Densely populated regions; Effective disease control; Integrative case management; Omicron outbreak; Optimal resource allocation; Spatiotemporal transmission risk.

Fig. 1

The structure of the proposed SDNR compartmental model

Fig. 2

The case maps and the constructed spatiotemporal transmission networks on (A) January 20, 2022, (B) January 22, 2022, January 25, 2022, and January 28, 2022, and (C) January 31, 2022 in Hong Kong, China. The red map at the bottom of (A) and (C) denotes the case map of the corresponding day. The intensity of the red color indicates the number of the cases. The darker the color on the map, the more cases in the corresponding district. The networks in the blue/green color shown in (B) and at the top of (A) and (C) are the spatiotemporal transmission networks constructed from the case visiting history. The blue color indicates the high transmission intensity while the green color indicates the low intensity. The correspondence between the district code and the district name is provided in the bottom right corner of the figure

Fig. 3

The out-going transmission risk maps of 18 districts in Hong Kong, China from (A) January 30, 2022 to (G) February 5, 2022. The intensity of the blue color indicates the transmission risk level. The darker the color on the map, the higher the risk of the corresponding district. The correspondence between the district code and the district name is provided in the bottom right corner of the figure

Fig. 4

The simulation of the trend of Omicron outbreak (in terms of the daily case number) in Hong Kong, China from December 30, 2021 to July 2022. We assume that the 300,000 testing capacity per day will be available from February 14, 2022. (A) Four scenarios with various resource allocation strategies: the baseline (yellow curve), population-based strategy (orange curve), case-based strategy (red curve), and our transmission-guided strategy (blue curve). (B) The detailed comparison between the case-based strategy (red curve) and the proposed transmission-guided strategy (blue curve). (C) The details of the daily infection trend with our transmission-guided resource allocation. The thicker solid curve denotes the number of daily new infections; the thinner solid curve denotes the total number of infected individuals in each day, including both the newly infected ones and the previously infected but not recovered individuals; the dash line denotes the number of detected cases; and the dotted line denotes the number of non-detected cases

Fig. 4

The simulation of the trend of Omicron outbreak (in terms of the daily case number) in Hong Kong, China from December 30, 2021 to July 2022. We assume that the 300,000 testing capacity per day will be available from February 14, 2022. (A) Four scenarios with various resource allocation strategies: the baseline (yellow curve), population-based strategy (orange curve), case-based strategy (red curve), and our transmission-guided strategy (blue curve). (B) The detailed comparison between the case-based strategy (red curve) and the proposed transmission-guided strategy (blue curve). (C) The details of the daily infection trend with our transmission-guided resource allocation. The thicker solid curve denotes the number of daily new infections; the thinner solid curve denotes the total number of infected individuals in each day, including both the newly infected ones and the previously infected but not recovered individuals; the dash line denotes the number of detected cases; and the dotted line denotes the number of non-detected cases

Fig. 5

The simulation of the trend of Omicron outbreak (in terms of the daily case number) in Hong Kong, China from December 30, 2021 to July 17, 2022. We assume that (A) 500,000 and (B) 700,000 testing capacity per day will be available from February 14, 2022