Assessing Spatiotemporal Transmission Dynamics of COVID-19 Outbreak Using AI Analytics

2021 ◽  
pp. 829-838
Author(s):  
Mayuri Gupta ◽  
Yash Kumar Singhal ◽  
Adwitiya Sinha
2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Chunxiang Cao ◽  
Wei Chen ◽  
Sheng Zheng ◽  
Jian Zhao ◽  
Jinfeng Wang ◽  
...  

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.


2014 ◽  
Vol 8 (11) ◽  
pp. e3344 ◽  
Author(s):  
Wen-Yi Zhang ◽  
Li-Ya Wang ◽  
Yun-Xi Liu ◽  
Wen-Wu Yin ◽  
Wen-Biao Hu ◽  
...  

2020 ◽  
Vol 64 ◽  
pp. 102404 ◽  
Author(s):  
Diego F. Cuadros ◽  
Yanyu Xiao ◽  
Zindoga Mukandavire ◽  
Esteban Correa-Agudelo ◽  
Andrés Hernández ◽  
...  

2020 ◽  
Vol 14 (10) ◽  
pp. e0008760
Author(s):  
Lilit Kazazian ◽  
Antonio S. Lima Neto ◽  
Geziel S. Sousa ◽  
Osmar José do Nascimento ◽  
Marcia C. Castro

The mosquito-borne viruses dengue (DENV), Zika (ZIKV), and chikungunya (CHIKV), now co-endemic in the Americas, pose growing threats to health worldwide. However, it remains unclear whether there exist interactions between these viruses that could shape their epidemiology. This study advances knowledge by assessing the transmission dynamics of co-circulating DENV, ZIKV, and CHIKV in the city of Fortaleza, Brazil. Spatiotemporal transmission dynamics of DENV, ZIKV, and CHIKV were analyzed using georeferenced data on over 210,000 reported cases from 2011 to 2017 in Fortaleza, Brazil. Local spatial clustering tests and space-time scan statistics were used to compare transmission dynamics across all years. The transmission of co-circulating viruses in 2016 and 2017 was evaluated at fine spatial and temporal scales using a measure of spatiotemporal dependence, the τ-statistic. Results revealed differences in the diffusion of CHIKV compared to previous DENV epidemics and spatially distinct transmission of DENV/ZIKV and CHIKV during the period of their co-circulation. Significant spatial clustering of viruses of the same type was observed within 14-day time intervals at distances of up to 6.8 km (p<0.05). These results suggest that arbovirus risk is not uniformly distributed within cities during co-circulation. Findings may guide outbreak preparedness and response efforts by highlighting the clustered nature of transmission of co-circulating arboviruses at the neighborhood level. The potential for competitive interactions between the arboviruses should be further investigated.


2007 ◽  
Vol 1 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Moshe B Hoshen ◽  
Anthony H Burton ◽  
Themis J V Bowcock

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