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2.
Figure 5

Figure 5. From: Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Temporal correlation between actual case counts and case counts estimated by the methods Casecount-ARMA, EpiNews-ARMAX and EpiNews-ARNet corresponding to (a) dengue and (b) HFMD in China. In (a) and (b), EpiNews-ARMAX -topic and EpiNews-ARNet -topic use temporal topic trends as external variables. On the other hand, EpiNews-ARMAX -sample and EpiNews-ARNet -sample use sampled case counts as external variables. In (c), we showed the temporal correlation between actual case counts and case counts estimated by EpiNews-ARNet -sample corresponding to ADD in India.

Saurav Ghosh, et al. Sci Rep. 2017;7:40841.
3.
Figure 4

Figure 4. From: Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Correlation between disease case counts and temporal topic distributions or trends (ξz) extracted by EpiNews for (a) ADD, (c) dengue, and (e) malaria in India. Along with the temporal topic trends (ξz), we also showed the correlation between disease case counts and sampled case counts (generated by multinomial sampling from temporal topic trends) for (b) ADD, (d) dengue, and (f) malaria. Note, the sampled case counts and disease case counts share almost similar numerical range. However, the temporal topic trend values are at different numerical range (ranging from 0 to 1) with respect to the disease case counts.

Saurav Ghosh, et al. Sci Rep. 2017;7:40841.
4.
Figure 3

Figure 3. From: Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Correlation between disease case counts and temporal topic distributions or trends (ξz) extracted by EpiNews for (a) H7N9, (c) HFMD, and (e) dengue in China. Along with the temporal topic trends (ξz), we also showed the correlation between disease case counts and sampled case counts (generated by multinomial sampling from temporal topic trends) for (b) H7N9, (d) HFMD, and (f) dengue. Note, the sampled case counts and disease case counts share almost similar numerical range. However, the temporal topic trend values are at different numerical range (ranging from 0 to 1) with respect to the disease case counts.

Saurav Ghosh, et al. Sci Rep. 2017;7:40841.
5.
Figure 2

Figure 2. From: Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

Correlation between disease case counts and temporal topic distributions or trends (ξz) extracted by EpiNews for (a) whooping cough, (c) rabies, (e) salmonellosis, and (g) E. coli infection in U.S. Along with the temporal topic trends (ξz), we also showed the correlation between disease case counts and sampled case counts (generated by multinomial sampling from temporal topic trends) for (b) whooping cough, (d) rabies, (f) salmonellosis, and (h) E. coli infection. Note, the sampled case counts and disease case counts share almost similar numerical range. However, the temporal topic trend values are at different numerical range (ranging from 0 to 1) with respect to the disease case counts.

Saurav Ghosh, et al. Sci Rep. 2017;7:40841.

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