Fixed effect model

) -To these are p (r)

T

), P (k) is the probability of observing a new case of K on T on this day._{If the average new patient reception rate λ per day is given, the probability of observing the new patient in the K is compatible with the Poisson distribution:} P-Left (_IThat is, R_{If the average new patient reception rate λ per day is given, the probability of observing the new patient in the K is compatible with the Poisson distribution:}And λ have the following relationship:_{If the average new patient reception rate λ per day is given, the probability of observing the new patient in the K is compatible with the Poisson distribution:}\ Right) & amp; amp; gt; $ $_{There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. There is a possibility that the hypothesis is verified and the internal consistency of the indicators may be frozen < Span> A attempt to build a multi-dimensional vulnerability indicator (more detailed information is supplementary information. See, for example, in a group of research, select appropriate variables using the framework of classical social vulnerability (ACHARYA and Bostwick, 2020; 2020; Parks, 2020; Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; For example, Tiwari et al. (2021) created a COVID-19 vulnerabilities and classify the County in the United States. The indicators built are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data based on this data can be literally predicted. , The theoretical hypothesis may be frozen due to the limits of internal consistency of indicators. There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. It may be frozen due to the limits of hypotheses and internal consistency of indicators.}T_{If the average new patient reception rate λ per day is given, the probability of observing the new patient in the K is compatible with the Poisson distribution:}P left (left_{There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. There is a possibility that the hypothesis is verified and the internal consistency of the indicators may be frozen < Span> A attempt to build a multi-dimensional vulnerability indicator (more detailed information is supplementary information. See, for example, in a group of research, select appropriate variables using the framework of classical social vulnerability (ACHARYA and Bostwick, 2020; 2020; Parks, 2020; Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; For example, Tiwari et al. (2021) created a COVID-19 vulnerabilities and classify the County in the United States. The indicators built are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data based on this data can be literally predicted. , The theoretical hypothesis may be frozen due to the limits of internal consistency of indicators. There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. It may be frozen due to the limits of hypotheses and internal consistency of indicators.}And λ have the following relationship:_{If the average new patient reception rate λ per day is given, the probability of observing the new patient in the K is compatible with the Poisson distribution:}P left (left_{There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. There is a possibility that the hypothesis is verified and the internal consistency of the indicators may be frozen < Span> A attempt to build a multi-dimensional vulnerability indicator (more detailed information is supplementary information. See, for example, in a group of research, select appropriate variables using the framework of classical social vulnerability (ACHARYA and Bostwick, 2020; 2020; Parks, 2020; Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; For example, Tiwari et al. (2021) created a COVID-19 vulnerabilities and classify the County in the United States. The indicators built are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data based on this data can be literally predicted. , The theoretical hypothesis may be frozen due to the limits of internal consistency of indicators. There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. It may be frozen due to the limits of hypotheses and internal consistency of indicators.}Subsbarized variables showing propagation by COVID-19 (R)_{There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. There is a possibility that the hypothesis is verified and the internal consistency of the indicators may be frozen < Span> A attempt to build a multi-dimensional vulnerability indicator (more detailed information is supplementary information. See, for example, in a group of research, select appropriate variables using the framework of classical social vulnerability (ACHARYA and Bostwick, 2020; 2020; Parks, 2020; Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; For example, Tiwari et al. (2021) created a COVID-19 vulnerabilities and classify the County in the United States. The indicators built are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data based on this data can be literally predicted. , The theoretical hypothesis may be frozen due to the limits of internal consistency of indicators. There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. It may be frozen due to the limits of hypotheses and internal consistency of indicators.}) Intra crack_{If the average new patient reception rate λ per day is given, the probability of observing the new patient in the K is compatible with the Poisson distribution:}and₁this₂Each represents the tw o-dimensional mobility of Intram and Interm.₄this₅Is an interaction between the perpetual vulnerability index of COVID-19, the degree of movement and the vulnerability index (Intram)

Is shown.

*CPVI I $$

And INTERM

It is._{There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. There is a possibility that the hypothesis is verified and the internal consistency of the indicators may be frozen < Span> A attempt to build a multi-dimensional vulnerability indicator (more detailed information is supplementary information. See, for example, in a group of research, select appropriate variables using the framework of classical social vulnerability (ACHARYA and Bostwick, 2020; 2020; Parks, 2020; Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; For example, Tiwari et al. (2021) created a COVID-19 vulnerabilities and classify the County in the United States. The indicators built are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data based on this data can be literally predicted. , The theoretical hypothesis may be frozen due to the limits of internal consistency of indicators. There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. It may be frozen due to the limits of hypotheses and internal consistency of indicators.}I_{There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. There is a possibility that the hypothesis is verified and the internal consistency of the indicators may be frozen < Span> A attempt to build a multi-dimensional vulnerability indicator (more detailed information is supplementary information. See, for example, in a group of research, select appropriate variables using the framework of classical social vulnerability (ACHARYA and Bostwick, 2020; 2020; Parks, 2020; Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; For example, Tiwari et al. (2021) created a COVID-19 vulnerabilities and classify the County in the United States. The indicators built are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data based on this data can be literally predicted. , The theoretical hypothesis may be frozen due to the limits of internal consistency of indicators. There is an attempt to build a multi-dimensional COVID-19 vulnerability indicator (see supplementary materials for more detailed information). For example, a group study shows the appropriate variable using the framework of classical social vulnerabilities (ACHARYA and PORWAL, 2020; Kim and Bostwick, 2020; Macharia et al., 2020; Snyder and Parks, 2020 Daras et al., 2021; Sarkar and Chouhan, 2021; Qiao and Huang, 2022; , Tiwari et al. (2021) created a COVID-19 vulnerability index using a random forest, and is constructed using machine learning compared to a classic method based on a different vulnerability level. The indicators are less likely to be restricted by classical statistical doctrine, and the excellent characteristics of this data may be more likely to predict the dynamics of COVID-19 pandemic in society. It may be frozen due to the limits of hypotheses and internal consistency of indicators.}I

Results

COVID-19 pandemic vulnerability index

Indicates an inseparable and inseparable immutable factor that can be observed (other than vulnerability) that affects the broadcast possibility of COVID-19; μ

The rest depends on time and personal characteristics. β s-regression coefficient, β

And β

The main effect of moving to transmission, β

And β

Indicates the size of the vulnerability lag.

Variation in COVID-19 transmissibility across different vulnerability levels

Take the average value on both sides as follows:_Igt; _i + excess & amp; amp; gt; gt; gt; _5 excess & amp; gt; gt; gt_IThus, all factors, including the COVID-19 CPVI vulnerability index, are unchanged over time._IAnd other observation and no n-observable factors Z_IDraw. Only the difference between the transmission remains, and the difference in the means of transportation and the interaction. < SPAN>, that is, r_IAnd λ have the following relationship:

\ Right) & amp; amp; gt; $ $_IT_IP left (left

Here

Subsbarized variables showing propagation by COVID-19 (R)

Variations in mobility recovery across different vulnerability levels

T

this

and

this

The effect of mobility on R _I under varying vulnerability levels

I_IIs shown.

*CPVI< 0.05).

I_IIt is.< 0.05) (Table 3), implying that an increase in both InterM and IntraM would increase the R _{Use *.}I

) Is a gentle effect of vulnerabilities.

I_Ithis is< 0.05) (Table 3). Figure 6 shows the effects of IntraM and InterM on R _IAnd β_IAnd β_ITake the average value on both sides as follows:_Igt; _i + excess & amp; amp; gt; gt; gt; _5 excess & amp; gt; gt; gt_IThus, all factors, including the COVID-19 CPVI vulnerability index, are unchanged over time.

I_II

Draw. Only the difference between the transmission remains, and the difference in the means of transportation and the interaction. That is, R

T_Iλ = k_.

\ Right) & amp; amp; gt; $ $

Discussion and conclusion

Here, L is the reverse number of i n-line interval, and the values ​​of the series intervals are reliability because the number of newly newly on the day of the past epidemiology (Li et al, 2020; Rubin et a l-album. The parameterized Poisson function is fixed and r

T

(48):_IP ret (& amp; amp; gt; & amp; gt; gt; gt; amp; gt; gt; gt; gt; gt; gt; gt; amp; right; right) = Frac_IThe degree of movement and the return of infection propagation in time T are as follows:

Here

teeth

Subsbarized variables showing propagation by COVID-19 (R)

T

) Intra crack

Data availability

this

References

• this
• I
• Is shown.
• I
• It is.
• I
• I
• this is
• And β
• And β
• Take the average value on both sides as follows:
• gt; _i + excess & amp; amp; gt; gt; gt; _5 excess & amp; gt; gt; gt
• Thus, all factors, including the COVID-19 CPVI vulnerability index, are unchanged over time.
• And other observation and no n-observable factors Z
• Draw. Only the difference between the transmission remains, and the difference in the means of transportation and the interaction.
• Table 1 The result of the main component analysis.
• Table 1 The result of the main component analysis.
• Table 1 The result of the main component analysis.
• The CPVI index in each area was calculated using a weighting method based on the distributed coefficient of each PC. Then, the cpvi value of each of the five minutes was divided into five vulnerability. FIG. 1 shows the selected area and the corresponding vulnerability value. This map shows areas with high unemployment and poverty rates (Mississippi, South Carolina, etc.), areas with a large population of ethnic minorities and poor quality of air (such as California), and the population of the elderly (Florida). , North Carolina, Alabama, etc.), some reasons, such as areas with a large population of acquired diseases (such as New York City), and areas where the quality of the air is poor and medical resources are insufficient (such as the Great Lake coastal area) It has identified the highest gender area. Conversely, the level of air pollution is low, and the CPVI is low in areas where the age structure is young, for example, in the area around the center plains. This pattern is consistent with the COVID-19 Pandemic Vulnerability Dashboard Map (date: January 07, 2020) published by the Institute of Health Sciences.
• The CPVI value is divided into five values ​​based on the 5-minute value, 5 is a more vulnerable society, and 1 is a more vulnerable society. The white part indicates an environment without data.
• In this study, effective reproduction (R)
• ) Is indicating the possibility of transfer of COVID-19 (see the method of excluding this method). Figure 2 changes R
• Rice. 1: A spatial distribution of a different vulnerability level determined by the CPVI index.
• The distribution of the CPVI index does not match any data contained. For example, in the California region, individual income and educational levels are higher than the US average. Therefore, the vulnerability is low when classified by social economic characteristics. Finally, they are classified as vulnerability level 5, which is actually considered to be the highest vulnerability level. Similarly, Florida has a substantial medical facility, but there are still many vulnerabilities level 4 or 5 counties. In fact, this discovery indicates that CPVI can consider it by combining multiple characteristics, rather than sorting out the neighborhood with just one parameters, such as a soci o-economic and pandemic moment.
• T
• T The CPVI index in each area was calculated using a weighting method based on the distributed coefficient of each PC. Then, the cpvi value of each of the five minutes was divided into five vulnerability. FIG. 1 shows the selected area and the corresponding vulnerability value. This map shows areas with high unemployment and poverty rates (Mississippi, South Carolina, etc.), areas with a large population of ethnic minorities and poor quality of air (such as California), and the population of the elderly (Florida). , North Carolina, Alabama, etc.), some reasons, such as areas with a large population of acquired diseases (such as New York City), and areas where the quality of the air is poor and medical resources are insufficient (such as the Great Lake coastal area) It has identified the highest gender area. Conversely, the level of air pollution is low, and the CPVI is low in areas where the age structure is young, for example, in the area around the center plains. This pattern is consistent with the COVID-19 Pandemic Vulnerability Dashboard Map (date: January 07, 2020) published by the Institute of Health Sciences.
• The CPVI value is divided into five values ​​based on the 5-minute value, 5 is a more vulnerable society, and 1 is a more vulnerable society. The white part indicates an environment without data.
• In this study, effective reproduction (R)
• ) Is indicating the possibility of transfer of COVID-19 (see the method of excluding this method). Figure 2 changes R
• About each environment from June 1st to August 31, 2020 (June 1, July 1, August 1, August 31). Index R in 310 districts, June 1, 2020
• It was ≥ 1 (high infection). In fact, R in 36 districts, 85 % of the surveyed area
• A 36 districts, which correspond to 85 % of the possibility of receiving), has been recorded. Of the 4-day data, R
• ≥ ≥ 1 or more (that is, 559 districts) July 1, 2020. This number accounted for more than 50 % of the survey district. The counties, which had more casualties on June 1 and July 1, 2020, matched the counties whose CPVI was the highest value (Fig. 2A, B), California, Florida, East Central Club. I was concentrated on the coastal area. The number and distribution of the county, which recorded the highest value on August 1 and August 31, were also 227 and 271, respectively. The most common was the East Central, and the smallest was the western part (Fig. 2C and D).
• R
• From June 1st to August 31, 2020, 102. 856 in the prefecture (1118 county* 92). The number of days in the prefecture will be a high transfer in the following cases.
• It is 1 or more. In order to know the outline of vulnerability propagation during the survey period, the number of days of the highest propagation was calculated by the level of vulnerabilities. Table 2 shows the ratio of the day when the infection rate was the highest in all days in the surveyed prefectures. When tabulated on a monthly basis, the ratio of high infection rates increased from June to July, decreasing from July to August. The priority of this change is relatively close to the US pandemic test (Figure S1). In fact, the percentage of high infection days increased as the value of the vulnerability for each month, and in general, as the value of the vulnerability increased. In particular, the fifth number of lesions remained higher than 50 % in both June and July (53, 66 % in June, 58, 72 % in July).
• T
• Fig. 3 compares the number of days when infection propagation was the most frequently transmitted at each vulnerability level from June 1 to August 31. From this result, it was found that there is a positive correlation between the COVID-19 infection rate and the vulnerability level. Apart from this, as the value of the vulnerability increased, the number of days of the infection increased the most.
• Brown solid lines indicate the significance of the median, greenish triangles the significance of the mean.
• US. 4: Composition of short-term mobility for different values ​​of vulnerability.
• On the other hand, the interm indicator in the pre-closing stage fell to its lowest value on April 12. The group of districts with the shortest vulnerability recorded a fall of 62, 72%, while the group of districts with greater vulnerability recorded a fall of 66, 36%. The latter group fell 5, 50% more than the first group. Again, an interesting nuance is that the interm-block period was higher in the more vulnerable prefectures than in the least vulnerable ones, but the opposite trend was observed during and after the block period (Figure 4B).
• T
• T
• Level A to 1 to 5 of vulnerability. The degree of movement includes both urban movements and inte r-city movements. All moving levels are the average of each level, and is smoothed on a seve n-day slide average. The Pieron correlation coefforcements between the degree of movement and the highly infected area are statistically significant (p)
• T
• T.
• Table 3 Model coefficient with fixing effect of Intram and INTERM for each vulnerability level.
• T
• T < SPAN> In this task, mobility (including intrams and interums) is defined as a relationship with medium mobility during the US normal period from January 1st to January 21, 2020 ("Method"). See the details in the section.) The higher the mobility during the block period and after the block period, the higher the recovery of the mobility. Based on this, the empirical interpretation of the vulnerability and mobility relationships is as follows: Vulnerability is integrated (mutual integrated) in the same scheme of all fiv e-stage vulnerabilities. The higher the level, the lower the level of mobility recovery.
• Rice. 5: Average movement per day, number of districts, R per day
• ≥ 1 (that is, area with a high infection rate). From June 1 to August 31, it differs for each level of vulnerability.
• After examining the above relevance, the relevance of mobility and R) was evaluated using a fixed effect model (formula (14)).
• Was considered each district. Obviously, the Intram coefficient and the Interm coefficient were significantly positive at all vulnerabilities (p)
• In other words, both Intram and Interm accelerated the COVID-19 infection in the United States during the survey period. Furthermore, in areas where the vulnerability level is 1 to 4, the coefficient of Intram and Interm is equivalent to the coefficient of 5 areas of vulnerability, and the effect of Intram is nearly twice as large as the Interm effect.

Acknowledgements

Table 3 Model coefficient with fixing effect of Intram and INTERM for each vulnerability level.

Author information

1. T

Authors and Affiliations

1. T In this work, mobility (including intrams and interums) is defined as a relationship with the medium mobility during the US normal period from January 1 to January 21, 2020 ("Method" section. See the details). The higher the mobility during the block period and after the block period, the higher the recovery of the mobility. Based on this, the empirical interpretation of the vulnerability and mobility relationships is as follows: Vulnerability is integrated (mutual integrated) in the same scheme of all fiv e-stage vulnerabilities. The higher the level, the lower the level of mobility recovery.
2. Rice. 5: Average movement per day, number of districts, R per day
3. ≥ 1 (that is, area with a high infection rate). From June 1 to August 31, it differs for each level of vulnerability.
4. After examining the above relevance, the relevance of mobility and R) was evaluated using a fixed effect model (formula (14)).
5. Was considered each district. Obviously, the Intram coefficient and the Interm coefficient were significantly positive at all vulnerabilities (p)
6. In other words, both Intram and Interm accelerated the COVID-19 infection in the United States during the survey period. Furthermore, in areas where the vulnerability level is 1 to 4, the coefficient of Intram and Interm is equivalent to the coefficient of 5 areas of vulnerability, and the effect of Intram is nearly twice as large as the Interm effect.

1. In addition, mobility R

Elim Poon - Journalist, Creative Writer

Last modified: 27.08.2024

Social vulnerability amplifies the disparate impact of mobility on COVID transmissibility across the United States. irond.info We find that counties in the top CPVI quintile suffered almost double in regard to COVID transmission (% days with an R t higher than 1) from mobility. The United States (US) has been severely affected by SARS-CoV-2, but cases in the US are not evenly distributed across the population (1). Emerging studies.