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. 2020 Nov 5:8:201925-201936.
doi: 10.1109/ACCESS.2020.3036347. eCollection 2020.

Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

Cesar Guevara  1 , Matilde Santos Penas  2
Affiliations

Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

Cesar Guevara et al. IEEE Access. .

Abstract

In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate -the key information to prioritize medical care- was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized.

Keywords: Clustering; computational intelligence; coronavirus disease 2019 (COVID-19); kernel density estimation (KDE); medical care routing; optimization.

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Figures

FIGURE 1.
FIGURE 1.
Covid-19 virus infection density in the United States (Johns Hopkins University).
FIGURE 2.
FIGURE 2.
Feature selection with the chi-squared test (blue line) and greedy stepwise algorithm (red line).
FIGURE 3.
FIGURE 3.
Four stages of the IIDA.
FIGURE 4.
FIGURE 4.
Application of the elbow method to obtain the optimum number of clusters (formula image).
FIGURE 5.
FIGURE 5.
K-means clustering (formula image) in New York state.
FIGURE 6.
FIGURE 6.
Hot spots obtained with KDE for clusters formula image to formula image.
FIGURE 7.
FIGURE 7.
Hot spots formula image for each cluster in New York state (orange circles).
FIGURE 8.
FIGURE 8.
Hot spot priorities when applying the CPHM.
FIGURE 9.
FIGURE 9.
The route obtained with the ACO algorithm for low priority groups (Pl).
See this image and copyright information in PMC

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