IEEE Computational Intelligence Magazine - November 2020 - 72

prevent COVID-19." Each answer has seven choices: strongly
agree, agree, weakly agree, neutral, weakly disagree, disagree, and
strongly disagree.
There are a total of 7,358 residents, including 2,550 adopting diversified programs and 4,808 adopting the unified program, participating in the survey. Fig. 5 presents the survey
results of the first question: among the participants adopting
diversified programs, 73% give positive answers (18% strongly
agree, 44% agree, and 11% weakly agree), which is significantly
higher than the 59% among the participants adopting the unified program who give positive answers (16% strongly agree,
31% agree, and 12% weakly agree). Fig. 6 presents the results
of the second question. Among the participants adopting
diversified programs, 78% give positive answers (23% strongly
agree, 41% agree, and 14% weakly agree), which is significantly
higher than the 63% among the participants adopting the unified program who give positive answers (20% strongly agree,

Disagree
5%
Weakly
Disagree
6%

Strongly
Disagree
3%

29% agree, and 14% weakly agree). In summary, the survey
results demonstrate that the residents adopting diversified
programs are more satisfied with the effects of COVID-19
prevention and health condition improvement than those
adopting the unified program.
V. Conclusion

In this study, we propose an intelligent optimization method to
develop diversified TCM prevention programs for COVID-19.
First, we use a fuzzy clustering method to divide the population based on combined modern medicine and TCM health
characteristics. Based on the clustering results, TCM experts
develop diversified prevention programs, which are then
evolved by an interactive optimization method until all the
resource constraints are satisfied. The method has been used for
improving community prevention of COVID-19 in Zhejiang
province, China, during the peak of the pandemic.

Weakly
Disagree
3%

Strongly
Agree
18%

Weakly
Agree
14%

Agree
44%

Agree
41%

(a)

(a)
Disagree
10%

Strongly
Agree
23%

Neutral
13%

Neutral
13%
Weakly
Agree
11%

Strongly
Disagree
2%

Disagree
4%

Strongly Disagree
1%

Strongly Disagree
5%
Strongly
Agree
16%

Weakly
Disagree
7%

Weakly
Disagree
10%

Disagree
12%

Strongly
Agree
20%

Agree
31%

Neutral
19%

Neutral
14%
Weakly
Agree
12%

Agree
29%
Weakly
Agree
14%

(b)
(b)
FIGURE 5 Distribution of different answers to the question "TCM prevention program helps to improve health conditions." (a) Participants
adopting diversified prevention programs. (b) Participants adopting
the unified prevention program.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2020

FIGURE 6 Distribution of different answers to the question "TCM prevention program helps to prevent COVID-19." (a) Participants adopting
diversified prevention programs. (b) Participants adopting the unified
prevention program.

IEEE Computational Intelligence Magazine - November 2020

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