Spatial Modeling of COVID-19 Prevalence Using Adaptive Neuro-Fuzzy Inference System
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Preparation
2.3. Statistical Analysis
2.4. ANFIS
2.5. PCA
2.6. Model Development and Evaluation
3. Results
3.1. Statistical Analysis
3.2. Model Evaluation
3.3. Sensitivity Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Theme | Variable | Description | Source |
---|---|---|---|
(a) Disease data | (a1) COVID-19 prevalence per 100,000 people | (a1) (The ratio of COVID-19 cases in a rural district during the study period to the population living in that rural district during that period of time) * 100,000 | (a1) Center for Disease Control and Prevention (CDC) of Golestan province, from 2020 to 2021 [28] |
(b) Socio-demographic | (b1) Percent of male | (b1–7) Statistical Center of Iran, 2016 (the last year of the census in Iran) [29] | |
(b2) Percent of female | |||
(b3) Average household size | (b3) The ratio of household population to the number of occupied households | ||
(b4) Percentage of people over 65 years | |||
(b5) Employment rate | (b5) The ratio of employed people to people aged 10+ | ||
(b6) Migration rate | (b6) Number of immigrants minus the number of emigrants of an area divided by the total population of that area | ||
(b7) Literacy rate | (b7) The ratio of literate people aged 6+ to all people aged 6+ | ||
(c) Urban land use | (c1) Residential units < 100 m2 | (c1) Percentage of residential units with an area of less than 100 m2 | (c1–10) Deputy of Statistics and Information of Golestan Province, from 2020 to 2021 [29] |
(c2) Residential apartment units | (c2) The ratio of residential apartment units to total residential units | ||
(c3) Educational facilities | (c3) The total number of educational centers, including kindergartens, primary schools, middle schools, secondary schools, special schools, colleges, and universities | ||
(c4) Cultural and sports facilities | (c4) The total number of cultural and sports centers, including parks and green spaces, public libraries, sports fields, and sports halls | ||
(c5) Religious facilities | (c5) The total number of religious places, including mosques, shrines, seminaries, and other religious centers | ||
(c6) Government offices | (c6) The total number of government offices, including employment offices, banks, registry offices, municipal offices, welfare centers, post offices, courthouses, and other administrative land uses | ||
(c7) Municipal services | (c7) The total number of facilities pertaining to municipal services, such as water supplies, water purification systems, sewage disposal systems, and electricity and gas supplies | ||
(c8) Health facilities | (c8) The total number of medical centers, including hospitals, clinics, pharmacies, nursing homes, medical laboratories, healthcare centers, maternity centers, and other specialized care centers | ||
(c9) Commercial facilities | (c9) The total number of commercial centers such as passages, grocery stores, retail stores, bakeries, supermarkets, hotels, and restaurants | ||
(c10) Communication and transportation facilities | (c10) The total number of communication and transportation facilities such as airports, railway stations, highways, public transportation facilities, post offices, telecommunication offices, and information and communication technology centers | ||
(d) Environmental | (d1) NDVI | (d1) Normalized difference vegetation index (90-m spatial resolution) | (d1) United States Geological Survey (USGS), from 2020 to 2021 [32] |
(d2) DEM | (d2) Digital elevation model (90-m spatial resolution) | (d2) United States Geological Survey (USGS), from 2020 to 2021 [32] | |
(e) Climatic | (e1) Precipitation | (e1) Total rainfall; Number of days with rainfall | (e1–6) Meteorological Organization of Iran, from 2020 to 2021 [33] |
(e2) Humidity | (e2) Average relative humidity | ||
(e3) Temperature | (e3) Minimum temperature; Mean temperature; Maximum temperature; Mean dew point temperature*; Mean soil temperature | ||
(e4) Evaporation | (e4) Maximum evaporation; Total evaporation | ||
(e5) Wind speed | (e5) Maximum wind speed; Mean wind speed | ||
(e6) Sea pressure | (e6) Mean sea-level pressure |
Model | R | R Square | Adjusted R Square | Change Statistics | Durbin–Watson | ||||
---|---|---|---|---|---|---|---|---|---|
R Square Change | F | df1 | df2 | Sig. F Change | |||||
LR | 0.697 | 0.486 | 0.278 | 0.486 | 2.338 | 17 | 42 | 0.013 | 2.234 |
Input Variable | Collinearity Statistics | |
---|---|---|
Tolerance | VIF | |
Average household size | 0.260 | 3.850 |
Percentage of people over 65 years | 0.201 | 4.964 |
Migration rate | 0.618 | 1.618 |
Employment rate | 0.649 | 1.541 |
Literacy rate | 0.375 | 2.667 |
Residential apartment units | 0.458 | 2.185 |
Educational facilities | 0.161 | 6.197 |
Cultural and sports facilities | 0.240 | 4.163 |
Religious facilities | 0.279 | 3.589 |
Municipal services | 0.161 | 6.217 |
Health facilities | 0.106 | 9.390 |
NDVI | 0.310 | 3.225 |
Maximum wind speed | 0.198 | 5.054 |
Number of days with rainfall | 0.151 | 6.637 |
Mean dew point temperature | 0.285 | 3.511 |
Mean temperature | 0.316 | 3.167 |
Mean soil temperature | 0.149 | 6.701 |
Component | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | ||||||
---|---|---|---|---|---|---|---|---|---|
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 5.861 | 34.474 | 34.474 | 5.861 | 34.474 | 34.474 | 5.077 | 29.867 | 29.867 |
2 | 2.473 | 14.545 | 49.019 | 2.473 | 14.545 | 49.019 | 2.436 | 14.329 | 44.197 |
3 | 2.227 | 13.102 | 62.121 | 2.227 | 13.102 | 62.121 | 2.098 | 12.341 | 56.538 |
4 | 1.363 | 8.020 | 70.141 | 1.363 | 8.020 | 70.141 | 1.948 | 11.458 | 67.996 |
5 | 1.162 | 6.835 | 76.976 | 1.162 | 6.835 | 76.976 | 1.527 | 8.980 | 76.976 |
Variable | PC | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
Average household size | 0.039 | −0.079 | 0.893 | −0.196 | 0.042 |
Percentage of people over 65 years | 0.769 | 0.191 | −0.112 | 0.221 | −0.350 |
Migration rate | 0.260 | −0.128 | −0.340 | −0.095 | 0.711 |
Employment rate | −0.355 | −0.492 | 0.117 | −0.149 | 0.155 |
Literacy rate | 0.047 | 0.111 | 0.332 | 0.093 | 0.799 |
Residential apartment units | 0.739 | −0.130 | −0.041 | −0.099 | 0.164 |
Educational facilities | 0.673 | 0.200 | 0.531 | 0.107 | 0.169 |
Cultural and sports facilities | 0.813 | 0.103 | 0.083 | −0.001 | 0.271 |
Religious facilities | 0.821 | 0.088 | 0.153 | 0.077 | 0.021 |
Municipal services | 0.635 | −0.043 | 0.676 | 0.092 | −0.048 |
Health facilities | 0.807 | 0.065 | 0.406 | 0.214 | 0.180 |
NDVI | 0.632 | −0.367 | −0.233 | 0.326 | −0.148 |
Maximum wind speed | −0.387 | 0.179 | 0.104 | −0.839 | 0.113 |
Number of days with rainfall | 0.044 | −0.931 | −0.076 | 0.106 | −0.099 |
Mean dew point temperature | 0.580 | 0.240 | 0.190 | 0.415 | 0.139 |
Mean temperature | −0.082 | 0.352 | −0.053 | 0.835 | 0.079 |
Mean soil temperature | 0.063 | 0.910 | −0.047 | 0.221 | −0.027 |
Statistical Criteria | Model Evaluation (Test Data) | |
---|---|---|
ANFIS | PCA-ANFIS | |
R2 | (Mean: 0.543, SD: 0.045) | (Mean: 0.615, SD: 0.060) |
MAE | (Mean: 0.137, SD: 0.020) | (Mean: 0.104, SD: 0.010) |
MSE | (Mean: 0.034, SD: 0.007) | (Mean: 0.020, SD: 0.004) |
RMSE | (Mean: 0.185, SD: 0.020) | (Mean: 0.139, SD: 0.016) |
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Tabasi, M.; Alesheikh, A.A.; Kalantari, M.; Babaie, E.; Mollalo, A. Spatial Modeling of COVID-19 Prevalence Using Adaptive Neuro-Fuzzy Inference System. ISPRS Int. J. Geo-Inf. 2022, 11, 499. https://doi.org/10.3390/ijgi11100499
Tabasi M, Alesheikh AA, Kalantari M, Babaie E, Mollalo A. Spatial Modeling of COVID-19 Prevalence Using Adaptive Neuro-Fuzzy Inference System. ISPRS International Journal of Geo-Information. 2022; 11(10):499. https://doi.org/10.3390/ijgi11100499
Chicago/Turabian StyleTabasi, Mohammad, Ali Asghar Alesheikh, Mohsen Kalantari, Elnaz Babaie, and Abolfazl Mollalo. 2022. "Spatial Modeling of COVID-19 Prevalence Using Adaptive Neuro-Fuzzy Inference System" ISPRS International Journal of Geo-Information 11, no. 10: 499. https://doi.org/10.3390/ijgi11100499