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Geo-Temporal, Network Properties of the Chinese COVID-19 Epidemic

14 Pages Posted: 18 Mar 2020

See all articles by Ariel L Rivas

Ariel L Rivas

University of New Mexico - Department of Internal Medicine

Jose Febles Patron

Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN)

Steve D. Smith

Cornell University - College of Agricultural and Life Sciences

Folorunso Fasina

Food and Agriculture Organization, Tanzania

James B. Hittner

College of Charleston

More...

Abstract

Background: To control epidemics, decision-makers need information in real time and clinicians require data on locations more affected by mortality. To that end, an adaptation of Network Theory was applied to identify epidemic nodes, i.e., areas that included most fatalities per unit of time as well as explicit connections, such as highways.

Methods: Geo-temporal Chinese data on the COVID-19 epidemic were investigated with six (linear, logarithmic, power, growth, exponential, and logistic) regression models. A z-test compared the slopes observed.

Findings: This epidemic was located within a triple (road, railroad, and air) network of networks. Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed four network properties (synchronicity, long-distance connections, directionality and assortativity), which helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Later, rank II and III nodes reported fewer deaths, in that order. While the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The time series correlated with the mortality series. The power curve was the best fitting model for all slopes. Z-tests compared pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes, yielding z values ranging between 4.73 and 11.88. Because the z-critical value for alpha=0.001 in a two-tailed test was 3.30, all these comparisons were statistically significantly different. Therefore, rank I-III epidemic nodes – those reporting secondary deaths − were geo-temporally and statistically distinguishable.

Interpretation: The geo-temporal progression of epidemics – not where and when they start−seems to be highly structured. Epidemic network properties can detect and distinguish regions that differ in mortality. Because geo-referenced analyses can be conducted in less than ten minutes, they may provide real time information to both decision-makers and clinicians.

Funding Statement: FOF was funded through the support provided to the Food and Agriculture Organization of the United Nations by the United States Agency for International Development (USAID) – OSRO/GLO/507/USA on Global Health Security Agenda for the control of zoonosis in Africa.

Declaration of Interests: All authors declare no competing interests. ALR is a co-inventor of US patent 10,429,389 and European Union patent 2,959 295, which are not related to this topic.

Keywords: COVID-19; spatial epidemiology; smallworld; assortativity

Suggested Citation

Rivas, Ariel L and Patron, Jose Febles and Smith, Steve D. and Fasina, Folorunso and Hittner, James B., Geo-Temporal, Network Properties of the Chinese COVID-19 Epidemic (3/6/2020). Available at SSRN: https://ssrn.com/abstract=3551339 or http://dx.doi.org/10.2139/ssrn.3551339

Ariel L Rivas

University of New Mexico - Department of Internal Medicine

NM
United States

Jose Febles Patron

Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN)

07360 Mexico, D.F.
Mexico

Steve D. Smith

Cornell University - College of Agricultural and Life Sciences

Ithaca, NY 14853
United States

Folorunso Fasina (Contact Author)

Food and Agriculture Organization, Tanzania ( email )

Tanzania

James B. Hittner

College of Charleston

66 George Street
Charleston, SC 29424
United States

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