Multi-season analysis reveals the spatial structure of disease spread

Inbar Seroussi, Nir Levy and Elad Yom-Tov

Physica A: Statistical Mechanics and its Applications, 2020, vol. 547, issue C

Abstract: Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies. Here, we present a tensor-driven multi-compartment version of the classic Susceptible–Infected–Recovered (SIR) model and apply it to Internet data to reveal information about the complex spatial structure of disease spread. We develop an algorithm to estimate the model’s parameters in a high dimensional setting. The model and the algorithm are used to analyze state-level Google search data from the US pertaining to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV), independently. We fit the data with correlations of R2=0.70, and 0.52 for RSV and WNV, respectively. Although no prior assumptions on spatial structure are made, human movement patterns in the US explain 27%–30% of the estimated inter-state transmission rates. The transmission rates within states are correlated with known demographic indicators, such as population density and average age. Finally, we show that the patterns of disease load for subsequent season can be predicted using the model parameters estimated for previous seasons and as few as 7 weeks of data from the current season. Our results are applicable to other countries and similar viruses, allowing the identification of disease spread parameters and prediction of disease load for seasonal viruses earlier in season.

Keywords: Multi-compartment model; Infectious disease; Internet data; Populations dynamics; Epidemics (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437120301692
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:547:y:2020:i:c:s0378437120301692

DOI: 10.1016/j.physa.2020.124425

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().