Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

Abstract

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

Figure 1

Basic patient information. (a) The occupation distribution as a percentage of all SARS infections. (b) The age percentage of SARS infections.

Figure 2

Daily number of SARS infections and temporal characteristics. (a) Number of SARS infections each day. The two outbreak peaks caused by disease spreading in Guangzhou and Beijing are clearly distinguishable in the plot. (b) Histogram of period from SARS onset to treatment (unit: day). The average period is 3.96 days, which indicates fast and rigorous health service for SARS treatment. (c) Histogram of period from treatment to recovery (unit: day). The average treatment takes 25.19 days. This period corresponds exactly to half the size of an outbreak circle.

Figure 3

Spatial pattern of the SARS outbreaks.

Figure 4

SARS data transformation. (a) Comparison of the SARS raw dataset cumulative density function (CDF) and the corresponding one of the normal distribution. The maximum observed deviation between those two CDF is about 28.88% at data value 5. (b) N-score transformed CDF of the SARS study data and the normal distribution. The maximum observed deviation between those two CDF has dropped down, compared to panel (a), to about 2.26% at data value −0.63853.

Figure 5

Spatiotemporal covariance used in the BME modelling. (a) Plot of the empirical spatiotemporal covariance; the covariance was estimated at the nodes shown with small red circles connected with coarse surface plates (the S-lag is in degrees and the T-lag is in days). (b) Spatial cross-section at t = 0 of the estimated covariance surface and the empirical covariance. (c) Temporal cross-section at s = 0 of the estimated covariance surface and the empirical covariance. (d) Fitted theoretical covariance plot (densely gridded surface) superimposed on the empirical covariance.

Figure 6

Contrast between observed SARS outbreak number and BME estimation number. The green line represents actual SARS infections on a daily basis from November 16, 2002, to May 20, 2003. The blue line represents the corresponding daily number of cases according to the BME estimates. BME reflects relatively accurately the temporal pattern in the daily number of infections, although it overestimates systematically the number values.

Figure 7

BME estimation results at selected instances. BME mode estimates are shown on (a), (b), (c), (d), (e), (f), (g), (h), (i), and (j) for those on Nov. 16, 2002, Dec. 6, 2002, Dec. 26, 2002, Jan. 15, 2003, Feb. 4, 2003, Feb. 24, 2003, Mar. 16, 2003, Apr. 5, 2003, Apr. 24, 2003, and May 20, 2003.