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The Great COVID19 Shutdown aimed to eliminate or slow the spread of SARSCoV2, the virus that causes COVID19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID19 cases. Operationalization of “sustained decline” varies by state and county. Existing models of COVID19 transmission rely on parameters such as case estimates or R_{0} and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID19 models use data that are subject to significant measurement error and contamination.
This study will generate novel metrics of speed, acceleration, jerk, and 7day lag in the speed of COVID19 transmission using state government tallies of SARSCoV2 infections, including statelevel dynamics of SARSCoV2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID19 transmission, for use in combination with traditional surveillance tools.
Dynamic panel data models were estimated with the ArellanoBond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied.
The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 1723 and August 2430, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 2430. This change represents an increase in the transmission model R value for that week and is consistent with a reemergence of the pandemic.
Reopening the United States comes with three certainties: (1) the “social” end of the pandemic and reopening are going to occur before the “medical” end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.
Without question, SARSCoV2, the novel coronavirus that causes COVID19 [
According to Teutsch and Churchill [
The Great COVID19 Shutdown refers to the variety of “lockdown” [
In response to the large death toll exacted by the epidemic, states independently implemented public health guidelines [
The conventional approach to modeling the spread of diseases such as COVID19 is to posit an underlying contagion model and then to seek accurate direct measurement of the model parameters such as effective transmission rates or other parameters, often through laborintensive methods relying on contact tracing to determine the spread of the virus in a sample population. For viral epidemics with an incubation period of up to 14 days, it takes weeks if not months to generate accurate parameter estimates even for simple contagion models [
In contrast, we take an empirical approach that focuses on statistical modeling of widely available empirical data such as the number of confirmed cases or the number of tests conducted that can inform estimates of the current value of critical parameters like the infection rate or reproduction rate. We explicitly recognize that the data generating process for the reported data contain an underlying contagion component, a politicaleconomic component such as availability of accurate test kits, a social component such as how strongly people adhere to socialdistancing and shelterinplace policies, and a sometimes inaccurate data reporting process that may obscure the underlying contagion process. We therefore seek a statistical approach that can provide meaningful information despite the complex and sometimes obfuscating data generation process. Our approach is consistent with the principles of evidencebased medicine, including controlling for complex pathways that may include socioeconomic factors such as mediating variables, and policy recommendations “based on the best available knowledge, derived from diverse sources and methods” (pg S58) [
There are two primary advantages to this empirical approach. First, we can apply the empirical model relatively quickly to a short data set. This advantage stems from the panel nature of the model. We used US states as the crosssectional variable, so that a week’s data from all US states provides a reasonable sample size. In addition to enabling parameter estimation early in a pandemic, using this property we tested to see if there has been a shift in the transmission or reproductive rates of the transmission process in the past week, that is, whether there is statistical evidence that the US pandemic is peaking.
The second advantage is that the approach directly measures and informs policyrelevant variables. For example, the White House issued guidance on reopening the US economy that depends on a decrease in the documented number of cases and in the proportion of positive test results over a 14day period, among other criteria and considerations [
This study has two objectives: (1) to create a proofofconcept COVID19 surveillance system using the United States as a prototype for a global system; and (2) to validate novel surveillance metrics/techniques including speed, acceleration, and jerk to better inform public health leaders how the pandemic is spreading or changing course.
First, we will provide standard surveillance metrics including new counts of SARSCoV2 infections, moving 7day averages of SARSCoV2 infections, rates of SARSCoV2 infections per 100,000 population, new numbers of COVID19 deaths, moving 7day averages of COVID19 deaths, and rates of COVID19 deaths per 100,000 population plus testing and positive testing ratios. Standard surveillance metrics are useful and allow us to compare data even though standard techniques are limited to more severe cases and suffer from data contamination.
Second, to address these data limitations we will validate novel surveillance metrics of (1) speed, (2) acceleration, and (3) jerk (change in acceleration). The basic question we are trying to inform is: how are we doing this week relative to previous weeks? From a public health perspective, in the midst of a pandemic, we would like (at least) three affirmative responses: (1) there are fewer new cases per day this week than last week, (2) the number of new cases is declining from day to day, and (3) the daytoday decline in the number of cases is even bigger this week than last week. Additionally, we would like some indicative information about significant shifts in how the pandemic is progressing — positive shifts could be the first indicators of the emergence of a new or recurrent hotspot, and positive shifts could be first indicators of successful public health policy.
This study derives indicators to inform the three questions specified in the study objective above. Next, we provide a regressionbased decomposition of the indicators. While it is beyond the scope of this study to determine the underlying causes of the pandemic and its trajectory over time, we provide a decomposition into proximate contributory factors such as whether an acceleration is due to a “natural” progression of the pandemic (eg, due to an increasing infectious population) or to a shift in an underlying model parameter (eg, a parameter shift that could be associated with reopening, other health policy changes, a viral mutation, the end of summer vacation for K12 schools, or other underlying causes). Other factors can affect acceleration by “shifting” the underlying parameters (eg, the virus can mutate to become more or less infectious, states can impose lockdowns, social pressures can encourage or discourage people from wearing masks and social distancing, etc). Therefore, we use the regression analysis to provide a decomposition of speed, acceleration, and jerk into proximate contributory factors. Finally, this study is an innovation over traditional agnostic surveillancesystems in that we go beyond presenting metrics of the transmission of COVID19 by providing probable scenarios regarding the context in which the disease is spreading.
The COVID Tracking Project [
ArellanoBond estimation of difference equations has several statistical advantages: (1) it allows for statistical examination of the model’s predictive ability and the validity of the model specification; (2) it corrects for autocorrelation and heteroscedasticity; (3) it has good properties for data with a small number of time periods and large number of states; (4) it corrects for omitted variables issues and provides a statistical test of correction validity. With these advantages, the method is applicable to ascertaining and statistically validating changes in the evolution of the pandemic within a period of one week or less, such as changes in the reproduction rate [
The basic indicator of the pandemic’s status on a given day is the number of new cases on that day. Since new cases per day is a rate (value per unit of time), we will adopt physics nomenclature and refer to this as the speed of the pandemic. This is consistent with heuristic descriptions of the pandemic as spreading rapidly (ie, a large number of new cases per day) or slowly (ie, a small number of new cases per day). The public health ideal is to bring the speed of the pandemic to zero.
We report the number of new cases for each state both as a number per day and as a number per 100,000 population per day (table and column references).
For mathematical formality, we write:
where we have suppressed the
We are also interested in whether the number of cases per day is increasing, peaking, or decreasing, and why. Again, we will adopt physics nomenclature and refer to this datum as the acceleration. Since acceleration is difficult to ascertain on a daily basis, and there are weekend effects, etc, in the data, we report the weekly average for the acceleration as:
where
The number of positives per day in Illinois, according to the COVID19 Dashboard of the Center for Systems Science and Engineering [
We provide a regressionbased decomposition of accelerations into proximate components. That is, this is the systematic component of changes in acceleration, where
where we have suppressed the error terms and added a term for the “weekend effect.” We refer to the term containing
The analogous expression for 1 week prior and 2 weeks prior are:
The expression for 2 weeks prior,
The expressions for
We now address the question of whether the daytoday increase (or decrease) in new cases the current week is bigger or smaller than the daytoday increase (or decrease) in new cases of the past week.
Formally, for the current week we are interested in is:
The first term to the right of the definitional equality is the average growth in the number of daily positive cases for the current week ending at time
Using equation four, for the most recent week ending at time
The top row contains the 1day persistence effect’s contribution to the jerk. The first term on the right side of the equation represents the natural progression of the 1day persistence effect on acceleration due to changes across weeks in the daily change in the number of new cases per day. The last term in the first row represents structural shifts in the 1day persistence effect. The second row is analogous to the first row, except that it represents the 7day persistence effect’s contribution to the jerk. The third row represents the contribution of contemporaneous effects to the jerk.
The analogous equation for the prior week is:
Equations 6 and 7 are easily averaged over the week to provide a decomposition of jerk as defined by equation 5.
We group the states according to Census region and present regression results for each region below. The biweekly surveillance products will be based on these regressions.
For Region 1 (Northeast), the regression Wald statistic shows that the model was statistically significant (
ArellanoBond dynamic panel data modeling of the number of daily infections reported by state, August 230, 2020.
Variable  Region 1  Region 2  Region 3  Region 4  

Coefficient  Coefficient  Coefficient  Coefficient  
L1Pos  0.084  .22  –0.102  .02  –0.012  .77  0.273  <.001  
L1shiftAug17  –0.129  .16  0.069  .24  0.221  .02  –0.213  .03  
L1shiftAug24  –0.112  .19  0.093  .10  –0.021  .84  –0.737  <.001  
L7Pos  0.151  .02  0.288  <.001  0.269  <.001  0.006  .93  
L7shiftAug17  –0.014  .87  –0.024  .67  –0.334  <.001  0.018  .79  
L7shiftAug24  0.004  .96  0.046  .49  –0.265  .003  0.397  .02  
Tests  0.003  .12  0.047  <.001  0.091  <.001  0.017  .048  
Tests_squared  7.12E09  .61  –5.05E07  <.001  –4.89E07  <.001  2.74E08  .48  
Tests_per_10K  1.072  .04  8.023  .002  –15.986  <.001  —^{a}  —  
Weekend  –14.751  .20  23.581  .28  –33.948  .58  51.977  .34  
Constant  124.637  <.001  46.461  .89  429.167  <.001  397.678  <.001  
Wald statistic for regression  <.001  <.001  <.001  <.001  
Sargan statistic for validity  .38  .09  .89  .46 
^{a}Region 4 did not include the
The coefficient on the first lag of the dependent variable is not statistically significant, nor are the shift parameters for the weeks of August 17 and August 24 for this coefficient. The coefficient on the 7th lag is positive and statistically significant (0.151,
For Region 2 (Midwest), the regression Wald statistic shows that the model was statistically significant (
The coefficient on the first lag of the dependent variable is not statistically significant. The shift for the week of August 17 for this coefficient is positive and statistically significant (0.221,
For Region 3 (South), the regression Wald statistic shows that the model was statistically significant (
The coefficient on the first lag of the dependent variable is negative and statistically significant (–0.102,
For Region 4 (West), the regression Wald statistic shows that the model was statistically significant (
The coefficient on the first lag of the dependent variable is negative and statistically significant (0.273,
Region 1 appears to be fairly calm, with the only statistically significant persistence effect being a small 7day lag effect. Region 2 is slightly less calm, but with a larger and statistically significant persistence effect and a noticeable positive effect of both the number of tests and the number of tests per 10,000. Region 3 has the largest constant (average of statespecific effects) and the largest coefficient on tests, suggesting that the number of people newly tested for the virus is an important explanatory factor for the number of new cases. Region 4 has a high constant (average statespecific value) and significant shifts in both the 1day and 7day persistence values.
A significant advantage of the panel data approach is that it can provide statistically valid quantifications of shifts in a fairly short period such as 1 week. Perhaps the biggest pandemic issue during the week of August 24 was the high number of cases reported on university campuses as they reopened. We address this with an additional regression analysis. Six states in Region 3 reported 500 or more cases; at least one other university in these states reported 200 or more cases (Alabama, Florida, Georgia, North Carolina, South Carolina, and Texas). To inform this university effect, we split Region 3 into two groups of states—one with a high prevalence of university COVID19 positives (denoted as group 3a) and another comprising the remaining Region 3 states (denoted as group 3b)—and then ran the regression analysis on the two groups (
ArellanoBond dynamic panel data modeling of the number of daily infections reported by states in Region 3, grouped by the university effect, August 230, 2020.
Variable  Group 3a (with university effect)  Group 3b (without university effect)  

Coefficient  Coefficient  
L1Pos  –0.023  .76  0.037  .34  
L1shiftAug17  0.249  .13  –0.029  .74  
L1shiftAug24  –0.075  .69  0.005  .95  
L7Pos  0.268  <.001  0.213  <.001  
L7shiftAug17  –0.364  .007  –0.100  .22  
L7shiftAug24  –0.252  .12  0.092  .26  
Tests  0.121  <.001  0.029  <.001  
Tests_squared  –6.59E09  <.001  –5.61E07  <.001  
Tests_per_10K  –39.704  .005  –4.402  .06  
Constant  910.482  .008  245.307  <.001  
Wald statistic for regression  <.001  <.001  
Sargan statistic for validity  .81  .63 
For each group, the Wald statistic shows that the model was statistically significant (
Without belaboring the individual coefficients, there are two important differences between the two groups. First is the coefficient on
The larger coefficient on
These results may also help to explain spikes in other states, such as Iowa, Kansas, North Dakota, and South Dakota (which is also potentially affected by the Sturgis Motorcycle Rally), which all had significant numbers of COVID19 cases at universities.
Surveillance results are presented in
The innovation of this study is the novel metrics we derived to measure how COVID19 is spreading and changes in terms of transmission rates. These measures should be considered in combination with traditional static numbers including transmission rates and death rates. These novel metrics measure how fast the rates are changing, accounting for their data limitations.
As an example, we tracked the transmission of COVID19 for the state of Illinois for the week from August 17 to 23, 2020. Illinois had a weekly average of 48,181 COVID19 tests daily, also expressed as a weekly average of 380 tests per 100,000 population per day. Illinois had a weekly average of 2026 positive tests per day. The speed of the COVID19 transmission is measured as an increase of 15.99 persons infected per 100,000 population per day. For the week of August 17 to 23 in Illinois, COVID19 acceleration was 0.37, which means that every day there were .37 more new cases per 100,000 than the day before, or 2.6 more cases per day per 100,000 over the course of the week. The jerk is 0.17, which means that acceleration was increasing: this increased acceleration accounted for 1.4 of the 2.6 additional cases per day per 100,000. Finally, the 7day lag effect for speed is 3.58, which means that persistence or echo effects accounted for 3.58 or 22% of the 15.99 new daily positive cases per 100,000, which indicates an important but moderate persistence or echo effect for the week of August 17.
We see significant differences in COVID19 transmission the following week (August 2430, 2020). Illinois experienced a decrease in weekly average tests to 44,719 daily COVID19 tests, also expressed as a weekly average of 353 tests per 100,000 population per day. This is 27 fewer tests per 100,000 population from last week. Illinois had a weekly average of 1923 positive tests per day, a decrease from the prior week, also expressed as a speed of 15.18 persons newly infected per day per 100,000 population. During the week of August 2430, the acceleration decreased from the previous week to 0.11 and the jerk was negative (–0.26), indicating a leveling off of growth in new cases. Finally, the 7day lag effect on speed is 5.35, which means that the persistence or echo effects accounted for 5.35 or over onethird of the 15.18 new daily positive cases per 100,000. The increased importance of echo effects rather than new cases from other (new) causes is consistent with a leveling off of COVID19 growth in Illinois during the week of August 2430.
In summary, the week of August 1723 showed an increasing COVID19 speed with positive acceleration and jerk. The week of August 2430 exhibited a moderation in speed with lower acceleration and negative jerk. This is indicative of a leveling off or an inflection point: the pandemic in Illinois may be starting to decline, or this could be simply a pause before a continued increase in COVID19 speed.
Surveillance metrics for the week of August 1723, 2020.
State  Tests per day, n (weekly average)  Daily tests per 100K people, n (daily average for the week)  Positives, n (reported number of new positive test results or confirmed cases per day per 100K people, weekly average)  Speed, n (daily positives per 100K people, weekly average)  Acceleration (daytoday change in the number of positives per day, weekly average, per 100K people)  Jerk (weekoverweek change in acceleration, per 100K people)  7day persistence effect on speed (number of new cases per day per 100K people)  







CT  16,936  475  127  3.56  0.49  –0.16  0.32 

ME  3068  228  24  1.78  –0.06  –0.15  0.18 

MA  14,815  215  309  4.48  –0.63  –0.57  0.61 

NH  1591  117  17  1.25  0.07  0.13  0.23 

NJ  22,687  255  291  3.28  0.39  0.93  0.59 

NY  78,995  406  604  3.11  –0.03  –0.09  0.47 

PA  13,737  107  655  5.12  –0.05  0.07  0.86 

RI  5884  555  107  10.10  0.18  0.16  1.12 

VT  1273  204  6  0.96  –0.05  –0.07  0.18 







IL  48,181  380  2026  15.99  0.37  0.17  3.58 

IN  10,136  151  788  11.71  –0.26  0.38  3.41 

IA  4398  139  550  17.42  –0.43  –1.01  4.35 

KS  4654  160  594  20.40  6.63  –2.55  4.20 

MI  30,346  304  650  6.51  0.41  0.49  2.09 

MN  9467  168  633  11.23  –0.06  0.09  2.85 

MO  9888  161  1086  17.69  –0.61  –3.11  6.20 

NE  2498  129  220  11.37  –0.73  –1.56  3.89 

ND  1584  208  184  24.16  –0.06  –1.09  4.91 

OH  22035  189  931  7.96  0.03  0.35  2.40 

SD  1141  129  143  16.18  –0.24  –0.69  2.85 

WI  8511  146  708  12.17  –0.54  –0.73  3.51 







AL  10,749  219  947  19.31  –0.95  5.15  –1.33 

AR  6236  207  558  18.50  –1.41  1.77  –1.12 

DE  1637  168  63  6.51  0.18  0.44  –0.83 

DC  3313  469  53  7.49  –0.10  0.69  –0.61 

FL  28,001  130  3879  18.06  –0.54  1.09  –1.74 

GA  23,802  224  2417  22.76  –0.18  1.58  –1.77 

KY  5339  119  602  13.47  2.81  2.87  –0.89 

LA  15,107  325  718  15.44  0.13  4.65  –1.29 

MD  12927  214  556  9.19  0.14  1.09  –0.72 

MS  2015  68  823  27.64  1.18  1.88  –1.53 

NC  21,975  210  1452  13.84  0.31  0.59  –0.77 

OK  8220  208  689  17.41  0.08  –0.13  –1.11 

SC  6362  124  784  15.24  0.22  1.22  –1.08 

TN  26,836  393  1461  21.40  –0.22  0.12  –1.48 

TX  32,712  113  5994  20.67  –1.16  –2.07  –1.56 

VA  16,720  196  897  10.51  –0.07  –0.14  –0.71 

WV  5836  338  101  5.85  –0.17  0.03  –0.46 







AK  3704  506  71  9.73  –0.82  –0.94  0.28 

AZ  8414  116  652  8.96  –1.32  –1.46  0.31 

CA  106,128  269  6015  15.22  –0.40  –0.22  0.59 

CO  10,060  175  292  5.07  –0.01  0.32  0.15 

HI  2412  170  219  15.45  0.02  –0.49  0.36 

ID  2008  112  312  17.47  –0.09  2.06  0.57 

MT  1261  118  97  9.07  –0.51  –0.88  0.26 

NV  3824  124  614  19.92  –0.77  –0.24  0.57 

NM  5696  272  143  6.81  0.44  0.50  0.19 

OR  4432  105  239  5.67  –0.06  0.00  0.16 

UT  3758  117  352  10.98  –0.13  0.07  0.27 

WA  11,587  152  419  5.50  –0.17  –0.08  0.17 

WY  685  118  42  7.23  –0.57  –1.11  0.14 
Surveillance metrics for the week of August 2430, 2020.
State  Tests per day, n (weekly average)  Daily tests per 100K people, n (daily average for the week)  Positives, n (reported number of new positive test results or confirmed cases per day per 100K people, weekly average)  Speed, n (daily positives per 100K people, weekly average)  Acceleration (daytoday change in the number of positives per day, weekly average, per 100K people)  Jerk (weekoverweek change in acceleration, per 100K people)  7day persistence effect on speed (number of new cases per day per 100K people)  







CT  21,027  590  195  5.48  0.81  0.33  0.55  

ME  3994  297  25  1.88  0.05  0.12  0.28  

MA  24,300  353  410  5.95  0.41  1.04  0.69  

NH  1890  139  21  1.54  –0.07  –0.15  0.19  

NJ  26,762  301  302  3.40  0.05  –0.34  0.51  

NY  82,233  423  623  3.20  0.09  0.12  0.48  

PA  13,769  108  637  4.97  0.06  0.10  0.79  

RI  4963  469  60  5.66  –1.19  –1.36  1.57  

VT  1890  303  8  1.35  0.16  0.21  0.15  







IL  44,719  353  1923  15.18  0.11  –0.26  5.35  

IN  12,508  186  1054  15.66  0.56  0.82  3.92  

IA  5017  159  921  29.18  1.95  2.38  5.83  

KS  7366  253  838  28.78  7.63  1.00  6.82  

MI  30,189  302  817  8.18  0.90  0.48  2.18  

MN  8822  156  801  14.20  0.54  0.60  3.75  

MO  8486  138  1226  19.97  1.51  2.11  5.92  

NE  2798  145  282  14.57  1.20  1.93  3.80  

ND  1297  170  261  34.23  1.46  1.52  8.08  

OH  30,424  260  1066  9.12  0.35  0.32  2.66  

SD  1270  144  292  33.04  3.86  4.10  5.41  

WI  8464  145  728  12.50  0.22  0.76  4.07  







AL  8485  173  1454  29.65  2.38  3.33  0.09  

AR  6712  222  612  20.27  0.49  1.90  0.08  

DE  1831  188  66  6.81  –0.98  –1.16  0.03  

DC  3149  446  53  7.47  –0.45  –0.34  0.03  

FL  24,425  114  3002  13.98  –0.26  0.28  0.08  

GA  22,229  209  2146  20.21  –0.69  –0.51  0.10  

KY  9483  212  643  14.40  –2.59  –5.40  0.06  

LA  14,987  322  703  15.13  1.23  1.10  0.07  

MD  12,335  204  527  8.72  –0.19  –0.34  0.04  

MS  4988  168  683  22.95  0.10  –1.08  0.13  

NC  23,543  224  1573  15.00  –0.57  –0.88  0.06  

OK  7438  188  694  17.53  0.36  0.29  0.08  

SC  7220  140  905  17.58  1.06  0.84  0.07  

TN  20,545  301  1311  19.20  –2.13  –1.91  0.10  

TX  36,669  126  4688  16.17  –0.28  0.87  0.09  

VA  14,649  172  969  11.35  0.07  0.15  0.05  

WV  4990  289  120  6.92  0.46  0.63  0.03  







AK  2771  379  75  10.31  –0.25  0.57  3.92  

AZ  6939  95  508  6.98  0.33  1.65  3.61  

CA  98,685  250  5177  13.10  –0.26  0.14  6.14  

CO  9257  161  308  5.35  –0.07  –0.06  2.05  

HI  2536  179  255  17.99  0.25  0.23  6.23  

ID  2435  136  288  16.11  0.00  0.09  7.04  

MT  5130  480  130  12.17  0.48  0.99  3.66  

NV  3065  100  472  15.34  –0.39  0.37  8.03  

NM  6766  323  125  5.97  –0.48  –0.93  2.75  

OR  4789  114  231  5.48  0.12  0.17  2.29  

UT  4382  137  391  12.20  0.66  0.79  4.42  

WA  11,760  154  380  4.99  1.80  1.96  2.22  

WY  1486  257  34  5.95  0.00  0.57  2.92 
The dynamic panel data model is a statistically validated analysis of reported COVID19 transmissions and an important addition to the epidemiological toolkit for understanding the progression of the pandemic. It is important to recognize that surveillance systems require a variety of metrics. Systematic surveillance with standardized measures of decreases and increases in COVID19 transmission coupled with health policies and guidelines add a critical tool to the epidemiologic arsenal to combat COVID19.
The specific findings of the modeling exercise confirm that SARSCoV2 infection rates are persistent but changeable, and for most states increasing during the period between June 1319, 2020. We find that for every 100 new COVID19 cases from June 1319, the following day would result in 26 new cases, meaning there is a significant reduction each day. However, it is important to recognize that this is an average across states and that state and local experiences will vary, which we measured. From June 2026, on average in the United States, every 100 new cases on Monday was associated with 65 new cases on Tuesday, indicating the contagion increased 2.5fold the rate from the prior week. The American pandemic has been ramping up in the past 2 weeks.
Remarkably, the US states diverged into three distinct patterns: (1) decline, (2) constant, and (3) increases consistent with outbreaks. In the 30 states with increasing cases, over the course of 2 weeks, there was a 3.6fold increase in new infections while the states that had sustained declines in cases decreased by 2.5fold. Again, these are averages among the three classifications of decline, constant, and increases, but these data could be further refined to show how much each state contributed to increases and decreases. Further investigation could usefully model state and local differences in infection rates, as well as ascertain quickly whether the pandemic will continue to reemerge in the United States, or whether infection rates will reverse track and decline again even though states reopen.
The strengths of this study are the derived new metrics of the transmission of COVID19. The limitation of this proofofconcept surveillance system is that it includes only dynamic cases of COVID19 infections; a full surveillance system should also include static cases. For example,
Based on the empirical evidence that our metrics of the COVID19 contagion is a good standardization of increases and decreases for public health surveillance purposes, our future work will focus on the surveillance of 195 countries in eight global regions as defined by the World Bank. When possible, we will provide subcountrylevel metrics of the COVID19 contagion beginning with US states and Canadian provinces. Our surveillance system will include estimates of speed, acceleration, and jerk in acceleration along with traditional surveillance metrics.
None declared.