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Early network properties of the COVID-19 pandemic - The Chinese scenario.
Rivas, Ariel L; Febles, José L; Smith, Stephen D; Hoogesteijn, Almira L; Tegos, George P; Fasina, Folorunso O; Hittner, James B.
  • Rivas AL; Center for Global Health, Department of Internal Medicine, Medical School, University of New Mexico, Albuquerque, USA.
  • Febles JL; Department of Human Ecology, CINVESTAV-IPN, Mérida, Mexico.
  • Smith SD; Institute for Resource Information Science, College of Agriculture, Cornell University, Ithaca, USA.
  • Hoogesteijn AL; Department of Human Ecology, CINVESTAV-IPN, Mérida, Mexico.
  • Tegos GP; Micromoria LLC, Marlborough, MA, USA.
  • Fasina FO; Food and Agriculture Organization, Dar es Salam, Tanzania & Department of Veterinary Tropical Diseases, University of Pretoria, South Africa. Electronic address: Folorunso.fasina@fao.org.
  • Hittner JB; Department of Psychology, College of Charleston, Charleston, USA.
Int J Infect Dis ; 96: 519-523, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-378231
ABSTRACT

OBJECTIVES:

To control epidemics, sites more affected by mortality should be identified.

METHODS:

Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed.

RESULTS:

Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable.

CONCLUSIONS:

The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Int J Infect Dis Journal subject: Communicable Diseases Year: 2020 Document Type: Article Affiliation country: J.ijid.2020.05.049

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Int J Infect Dis Journal subject: Communicable Diseases Year: 2020 Document Type: Article Affiliation country: J.ijid.2020.05.049