The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining
Abstract
:1. Introduction
2. Methods
2.1. A Workflow with NPN Approach
2.2. Data Analysis
3. Results
3.1. Corpus Development and Knowledge Engineering
3.2. Hypothesis Testing
3.3. Sensitivity Analysis for Additional COVID-19 Outcomes
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Country Name | Containment Index | Total Cases/M | Country Name | Containment Index | Total Cases/M |
---|---|---|---|---|---|
Nicaragua | 12.50 | 881 | Somalia | 38.69 | 280 |
Tanzania | 16.37 | 9 | Finland | 44.94 | 4595 |
Burundi | 17.26 | 58 | Denmark | 48.21 | 14,238 |
Yemen Rep. | 18.45 | 70 | Norway | 50.60 | 6750 |
Afghanistan | 23.81 | 1195 | United Arab Emirates | 52.62 | 17,203 |
Central African Rep. | 24.40 | 1018 | Netherlands | 52.98 | 31,289 |
Congo Dem. Rep. | 24.70 | 144 | Singapore | 52.98 | 9953 |
Sudan | 26.19 | 416 | South Africa | 55.36 | 13,359 |
Mauritius | 26.79 | 397 | Sweden | 57.14 | 25,819 |
Niger | 27.08 | 66 | Australia | 57.14 | 1095 |
Mauritania | 28.57 | 1873 | Korea Rep. | 58.63 | 686 |
Burkina Faso | 28.57 | 140 | Bahrain | 59.52 | 51,209 |
New Zealand | 32.14 | 427 | Belgium | 60.71 | 49,977 |
Syria | 33.04 | 456 | Lithuania | 61.31 | 22,963 |
Congo Rep. | 35.71 | 1046 | Germany | 61.31 | 13,065 |
Eswatini | 36.90 | 5553 | Qatar | 61.90 | 48,246 |
Tajikistan | 36.90 | 1282 | Morocco | 62.20 | 9749 |
Senegal | 36.90 | 962 | United Kingdom | 63.10 | 24,265 |
Madagascar | 36.90 | 626 | Luxembourg | 63.10 | 56,119 |
Haiti | 38.10 | 815 | Spain | 63.69 | 35,428 |
Potatoes | Cucumbers and Gherkins | Fruits of the Genus Capsicum or of the Genus Pimenta | |
---|---|---|---|
70,190 | 70,700 | 70,960 | |
N = 14,757 | N = 12,300 | N = 14,621 | |
Total cases | 0.0864 | −0.0672 | −0.0302 |
New cases | 0.0946 | −0.0694 | −0.0465 |
Total deaths | 0.1272 | −0.106 | −0.05 |
New deaths | 0.0735 | −0.0729 | −0.0459 |
mushrooms and truffles | coconuts | almonds | |
71,230 | 80,110 | 80,212 | |
N = 14,191 | N = 14,378 | N = 14,312 | |
Total cases | −0.0294 | −0.0201 | 0.231 |
New cases | 0.0636 | −0.0801 | 0.1922 |
Total deaths | −0.0426 | −0.0628 | 0.3168 |
New deaths | −0.0023 | −0.1166 | 0.2081 |
pineapples | oranges | citrus fruit | |
80,430 | 80,510 | 80,590 | |
N = 13,420 | N = 14,534 | N = 10,325 | |
Total cases | 0.2359 | −0.014 | −0.0767 |
New cases | 0.1707 | −0.0022 | −0.0579 |
Total deaths | 0.2916 | −0.0838 | −0.0662 |
New deaths | 0.1785 | −0.074 | −0.0522 |
grapes | papaws | plums and sloes | |
80,620 | 80,720 | 80,940 | |
N = 13,837 | N = 8381 | N = 12,994 | |
Total cases | 0.2066 | 0.1354 | 0.0083 |
New cases | 0.227 | 0.1258 | 0.0238 |
Total deaths | 0.2766 | 0.1708 | 0.0039 |
New deaths | 0.2479 | 0.1062 | 0.0275 |
apples | |||
81,330 | |||
N = 11,232 | |||
Total cases | 0.1649 | ||
New cases | 0.1726 | ||
Total deaths | 0.2379 | ||
New deaths | 0.1767 |
Total Deaths (Model 1) | New Deaths (Model 2) | |||||||
---|---|---|---|---|---|---|---|---|
Coef. | SE | t | p > t | Coef. | SE | t | p > t | |
Net import | −0.011 | 0.005 | −2.37 | 0.020 | −0.011 | 0.005 | −2.17 | 0.034 |
Containment index | 0.010 | 0.011 | 0.90 | 0.371 | 0.014 | 0.010 | 1.48 | 0.144 |
Log(population) | 1.051 | 0.088 | 11.91 | 0.000 | 0.692 | 0.067 | 10.32 | 0.000 |
Life expectancy | −0.146 | 0.063 | −2.33 | 0.023 | −0.173 | 0.036 | −4.74 | 0.000 |
Log(GDP per capita) | 1.181 | 0.357 | 3.31 | 0.001 | 0.315 | 0.227 | 1.39 | 0.169 |
Cardiovasc death rate | −0.002 | 0.001 | −1.70 | 0.094 | −0.003 | 0.001 | −3.69 | 0.000 |
Diabetes prevalence | −0.053 | 0.040 | −1.33 | 0.189 | −0.032 | 0.033 | −0.96 | 0.340 |
Aged 70 | 0.097 | 0.051 | 1.90 | 0.062 | 0.089 | 0.032 | 2.78 | 0.007 |
Log (population density) | 0.008 | 0.129 | 0.06 | 0.953 | −0.115 | 0.093 | −1.24 | 0.220 |
Hospital beds (1000) | −0.144 | 0.059 | −2.46 | 0.016 | −0.140 | 0.039 | −3.55 | 0.001 |
Electricity | 0.054 | 0.019 | 2.81 | 0.006 | 0.082 | 0.015 | 5.62 | 0.000 |
Mobile subscriptions | −0.023 | 0.008 | −3.07 | 0.003 | −0.008 | 0.004 | −1.83 | 0.072 |
Total Cases (Model 3) | New Cases (Model 4) | |||||||
---|---|---|---|---|---|---|---|---|
Coef. | SE | t | p > t | Coef. | SE | t | p > t | |
Net import | −0.010 | 0.005 | −2.030 | 0.046 | −0.017 | 0.008 | −2.120 | 0.037 |
Containment index | 0.012 | 0.010 | 1.210 | 0.228 | 0.007 | 0.018 | 0.370 | 0.710 |
Log(population) | 0.942 | 0.079 | 11.900 | 0.000 | 0.763 | 0.141 | 5.420 | 0.000 |
Life expectancy | −0.047 | 0.069 | −0.680 | 0.502 | −0.159 | 0.072 | −2.210 | 0.030 |
Log(GDP per capita) | 1.502 | 0.348 | 4.310 | 0.000 | 1.383 | 0.417 | 3.310 | 0.001 |
Cardiovasc death rate | 0.000 | 0.001 | 0.240 | 0.813 | −0.001 | 0.001 | −0.390 | 0.701 |
Diabetes prevalence | −0.020 | 0.041 | −0.490 | 0.627 | −0.028 | 0.052 | −0.540 | 0.593 |
Aged 70 | −0.002 | 0.046 | −0.050 | 0.963 | 0.011 | 0.058 | 0.190 | 0.848 |
Log(population density) | 0.008 | 0.081 | 0.100 | 0.920 | −0.009 | 0.119 | −0.080 | 0.940 |
Hospital beds (1000) | −0.074 | 0.054 | −1.380 | 0.172 | −0.088 | 0.067 | −1.320 | 0.192 |
Electricity | 0.011 | 0.019 | 0.600 | 0.553 | 0.039 | 0.020 | 1.990 | 0.050 |
Mobile subscriptions | −0.014 | 0.006 | −2.130 | 0.036 | −0.014 | 0.009 | −1.500 | 0.137 |
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Zhan, G.; Yang, F.; Zhang, L.; Wang, H. The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining. Healthcare 2022, 10, 2071. https://doi.org/10.3390/healthcare10102071
Zhan G, Yang F, Zhang L, Wang H. The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining. Healthcare. 2022; 10(10):2071. https://doi.org/10.3390/healthcare10102071
Chicago/Turabian StyleZhan, Ge, Fuming Yang, Liangbo Zhang, and Hanfeng Wang. 2022. "The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining" Healthcare 10, no. 10: 2071. https://doi.org/10.3390/healthcare10102071