Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread
Abstract
:1. Introduction
2. Related Research Work
2.1. Research on COVID-19 Epidemic
2.2. Research on the Transmission Characteristics of the SARS-CoV-2 Virus
3. Data Sources
- The source of the epidemic data is COVID-19 data set published by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The data set was collected from all over the world from 22 January 2020, in the early stage of the epidemic. The experimental data in this article include the collected epidemic data from 22 January 2020 to 31 December 2021. The feature data elements include the cumulative number of confirmed cases, the cumulative number of cured people, the cumulative number of deaths, and the number of new cases per day.
- The climate data comes from the daily recorded data of weather stations around the world collected by the China Meteorological Data Network (http://data.cma.cn/). This experiment selects the climate data of various regions from 22 January 2020 to 31 December 2021. The feature data elements include daily maximum temperature, daily minimum temperature, wind speed, precipitation, dew point temperature, atmospheric pressure, wind gust, altitude, absolute humidity and relative humidity.
- The population and flight data come from the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. (https://population.un.org/wpp/). This experiment selects population and flight data in various regions from 22 January 2020 to 31 December 2021. The feature data elements include total population, population density, the total number of flights, number of domestic flights, and international flights.
- The air quality data come from the open-source air quality website WAQI (https://aqicn.org/data-platform/covid19/). This experiment selects air quality data in various regions from 22 January 2020 to 31 December 2021. The feature data elements include NO, PM, PM, PM, SO, O, CO content in the air, Air Quality Index(AQI), Suspended particle concentration(from NEPH), UV Index(UVI), Pollution(POL) and Wavelength Dominant(WD).
4. Research Methods
4.1. Multi-Source Heterogeneous Data Preprocessing
4.2. Dual-Link BiGRU Network to Predict the Spread of COVID-19
4.3. The Quantitative Analysis Model of Multi Characteristic Data Relationships
- Construct multiple regression models and train through data;
- The prediction ability of the model is evaluated by modifying the determination coefficient;
- The quantitative relationship between multiple factors and the number of new cases per day was determined by a multiple regression model;
- Given different initial values for different factors ;
- For the iteration, calculate the Jacobian matrix J, Hessian matrix H, B, and calculate the increment ;
- If is small enough, stop the iteration, otherwise, update = + ;
- Repeat steps (5) (6) until the maximum number of iterations is reached, or the termination condition of (6) is met;
- Complete the estimation of the unknown parameter , and determine the quantitative relationship between different elements and the number of new cases per day;
- Complete for to determine the quantitative relationship between different elements and the number of new cases per day.
5. Experimental Results and Discussion
5.1. Dual-Link BiGRU
5.2. Quantitative Analysis Results of Multi-Characteristic Data Relationships
- Goal: To generate data for analyzing the quantitative relationship between and y, where is the maximum temperature per day and y is the number of new cases per day.
- To control other factors unchanged, adjust , and generate the predicted value of y.
- The simulation data is used as input, and training is performed with the Gauss-Newton method to obtain the coefficient between and y, so as to determine the quantitative relationship between them.
- Population factors and flight factors has an obvious positive correlation impact on the spread of COVID-19. From the data of the selected 44 countries, it can be seen that population factors and flight factors have a greater impact on the spread of COVID-19. Every 1% increase in population factors will increase the spread of the epidemic by 1.044%. Every 1% increase in the number of arrival flights will increase the spread of the epidemic by 0.98%. Therefore it can be seen that population factors and flight factors have a more obvious impact on the increase in the spread of the epidemic. From the perspective of formulating epidemic prevention and control policies, controlling social distancing and population movement will have a more obvious positive correlation impact on epidemic prevention and control.
- The increase in temperature and relative humidity has a negative correlation impact on the spread of COVID-19.Among the climatic factors, the increase of temperature and humidity has a negative correlation impact on the spread of COVID-19. In this paper, the temperature range of 0–50 °C and the relative humidity range of 1–100% are selected for the experiment. It is obtained that within this range, temperature and relative humidity has a negative correlation impact on the spread of COVID-19, but the impact is not obvious. Since the correlation between population density and the speed of the epidemic is far greater than the correlation between temperature and the speed of the epidemic, it is speculated that in areas with higher temperatures and higher population densities, such as India and other countries, the speed of the epidemic still has a relatively rapid possibility.
- A larger AQI has a positive correlation impact on the spread of COVID-19.AQI represents the degree of air cleanliness or pollution and its impact on health. The higher the AQI, the more serious the air pollution in the region. This experiment shows that in the range of AQI value 100–200, the epidemic transmission speed of COVID-19 will increase by 0.013% every time AQI increases by 1. Some researchers have shown that SARS-CoV-2 can spread through aerosols [33,34,35]. Therefore, a higher AQI means a higher aerosol content in the air, which is not good for air circulation. Such an environment may promote the spread of COVID-19.
6. Discussion
7. Conclusions and Future Work
- Dual-link BiGRU network is proposed, which integrates time-series features and high-order features through dual-link construction, and can obtain more accurate prediction effects and generalization capability. Through experiments, it can be determined that the Dual-link BiGRU network has the following advantages:
- Compared with the CNN, LSTM, and GRU networks, the prediction accuracy of the Dual-link BiGRU network is improved by 35.03%, 31.41%, and 27.36%, respectively;
- Compared with the CNN, LSTM, and GRU networks, the generalization ability of the Dual-link BiGRU network is improved by 25.00%, 27.50%, and 28.75%, respectively.
- According to the quantitative analysis between the SARS-CoV-2 virus and its characteristic factors on a global scale, we concluded that the SARS-CoV-2 virus transmission has the following characteristics:
- The increase in population factors and flight factors has an obvious positively correlated impact on the spread of COVID-19.
- The increase in AQI will has a minor positively correlated impact on the spread of COVID-19.
- The increase in temperature and relative humidity has a negative correlation impact on the spread of COVID-19.
- Countries should take appropriate or even stricter prevention and control measures according to their national conditions, such as demographic factors, climate factors, air quality factors, and the number of flights, to minimize the risk of outbreaks.
- Demographic factors have a strong positive relationship with the spread of COVID-19 epidemic. Governments can control the spread of the epidemic by strictly controlling the movement of people both within and outside the country.
- Since the impact of population and flight factors on the spread of the epidemic is much greater than that of climate factors, governments of various countries should not expect the epidemic to disappear after the temperature rises, and should actively control population movement.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Feature Category | Feature Range |
---|---|
Date | 22 January 2020–31 December 2021 |
Country | Afghanistan, Algeria, Argentina, Australia, Austria, Bahrain, Bangladesh, Belgium, |
Bolivia, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa Rica, | |
Croatia, Cyprus, Denmark, Ecuador, El Salvador, Estonia, Ethiopia, Finland, | |
France, Georgia, Germany, Ghana, Greece, Guatemala, Guinea, Hungary, | |
Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, | |
Japan, Jordan, Kazakhstan, Korea, Kuwait, Kyrgyzstan, Laos, Lithuania, | |
Macedonia, Malaysia, Mali, Mexico, Mongolia, Nepal, Netherlands, New Zealand, | |
Norway, Pakistan, Peru, Philippines, Poland, Portugal, Romania, Russia, | |
Saudi Arabia, Serbia, Singapore, South Africa, Spain, Sri Lanka, Sweden, | |
Switzerland, Tajikistan, Thailand, Turkey, Uganda, Ukraine, United Arab Emirates, | |
United Kingdom, United States, Uzbekistan | |
Epidemic | Confirmed, Recovered, Deaths, New |
Climate | Tmax, Tmin, Wind_speed, Precipitation, DP_F, |
Pressure, Wind_gust, Altitude, Ab_humidity, Re_humidity | |
Population | Pop, Density |
Air quality | NO, PM, PM, PM, SO, O, CO and AQI, NEPH, UVI, POL, WD |
Flight | Flight_total, Flight_domestic, Flight_international |
Layer | Parameter | Value |
---|---|---|
1-D Conv1 | Out channels | 256 |
Kernel size | 16 | |
Stride size | 8 | |
1-D Conv2 | Out channels | 512 |
Kernel size | 16 | |
Stride size | 8 | |
BiGRU | Hidden size | 100 |
Number of layers | 5 | |
1-D ConvTranspose1 | Out channels | 256 |
Kernel size | 16 | |
Stride size | 8 | |
1-D ConvTranspose2 | Out channels | 512 |
Kernel size | 16 | |
Stride size | 8 | |
Full Connected layer 1 | In channels | 26 |
Out channels | 200 | |
Full Connected layer 2 | In channels | 201 |
Out channels | 1 |
Model | 0–5% | 5–10% | 10–15% | 15–20% | >20% | Effective | Invalid |
---|---|---|---|---|---|---|---|
Dual-link BiGRU | 2 | 12 | 12 | 22 | 33 | 48 | 33 |
BiGRU | 0 | 6 | 7 | 12 | 56 | 25 | 56 |
BiLSTM | 0 | 6 | 8 | 10 | 57 | 24 | 57 |
CNN | 0 | 7 | 8 | 12 | 54 | 27 | 54 |
Confirmed | Recovered | Deaths | Tmax | Tmin | |
---|---|---|---|---|---|
Global | 0.06 | 0.17 | −0.28 | −4.52 | −2.97 |
Wind_speed | Precipitations | DP_F | Pressure | Wind_gust | |
−16.46 | 84.64 | −4.67 | 2.02 | 73.72 | |
Altitude | Ab_humidity | Re_humidity | Pop | Density | |
6.71 × 10 | −0.17 | −0.112 | 5.8 × 10 | 54,282.5 | |
NO | PM | PM | PM | SO | |
1.95 × 10 | 49.42 | 55.59 | 45.29 | −21.91 | |
O | CO | AQI | NEPH | UVI | |
65.56 | 12.61 | 0.14 | −8.45 | −1.46 | |
POL | WD | Flight_total | Flight_domestic | Flight_international | |
23.68 | 1.91 | 189.547 | 379.995 | 187.5932 | |
Adjusted R Square | |||||
293.18 | 0.79 |
Country | Tmax | Tmin | DP_F | …… | Re_Humidity | Density | Iterations |
---|---|---|---|---|---|---|---|
Canada | 0.58 | −0.91 | −0.0075 | …… | −1.67 | 0.34 | 100 |
China | 2.33 | −11.48 | −18.34 | …… | −12.03 | 0.071 | 100 |
India | −5.22 | −16.35 | −19.45 | …… | −15.50 | −1.44 | 100 |
Indonesia | 5.64 | 4.25 | 14.55 | …… | −1.15 | −0.88 | 100 |
Russia | −23.36 | 28.45 | 40.13 | …… | −2.71 | 0.23 | 100 |
United Kingdom | −391.08 | 244.49 | 698.08 | …… | 262.37 | −34.67 | 100 |
Features | Particle | Influence/% |
---|---|---|
Density | +1%/km | 1.0767212 |
Pop | +1%/km | 1.0441276 |
Flight_total | +1% | 1.0102873 |
flight_domestic | +1% | 0.9881371 |
flight_international | +1% | 0.9455161 |
UVI | +1% | 0.8142484 |
PM | +1 g/m in the range of 0–100 g/m | 0.0126328 |
PM | +1 g/m in the range of 0–100 g/m | 0.0124261 |
NO | +0.3 g/m in the range of 0–30 g/m | 0.0190209 |
SO | +0.1 g/m in the range of 0–10 g/m | 0.0208433 |
PM | +1 g/m in the range of 0–100 g/m | 0.0145565 |
Wind_speed | +1 m/s in the range of 0–10 m/s | −0.0135183 |
Preciptation | +1% | −0.0198199 |
Re_humidity | +1% | −0.0159099 |
DP_F | +1 °C in the range of 0–50 °C | −0.0150033 |
Tmin | +1 °C in the range of 0–50 °C | −0.0285928 |
Tmax | +1 °C in the range of 0–50 °C | −0.0217991 |
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Fu, Y.; Lin, S.; Xu, Z. Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread. Int. J. Environ. Res. Public Health 2022, 19, 3187. https://doi.org/10.3390/ijerph19063187
Fu Y, Lin S, Xu Z. Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread. International Journal of Environmental Research and Public Health. 2022; 19(6):3187. https://doi.org/10.3390/ijerph19063187
Chicago/Turabian StyleFu, Yu, Shaofu Lin, and Zhenkai Xu. 2022. "Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread" International Journal of Environmental Research and Public Health 19, no. 6: 3187. https://doi.org/10.3390/ijerph19063187