Migration of households from New York City and the Second Peak in Covid-19 cases in New Jersey, Connecticut and New York Counties
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Abstract
The five boroughs of New York City (NYC) were early epicenters of the Covid-19 pandemic in the United States, with over 380,000 cases by May 31. High caseloads were also seen in nearby counties in New Jersey (NJ), Connecticut (CT) and New York (NY). The pandemic started in the area in March with an exponential rise in the number of daily cases, peaked in early April, briefly declined, and then, showed clear signs of a second peak in several counties. We will show that despite control measures such as lockdown and restriction of movement during the exponential rise in daily cases, there was a significant net migration of households from NYC boroughs to the neighboring counties in NJ, CT and NY State. We propose that the second peak in daily cases in these counties around NYC was due, in part, to the movement of people from NYC boroughs to these counties. We estimate the movement of people using “Change of Address” (CoA) data from the US Postal Service, provided under the “Freedom of Information Act” of 1967. To identify the timing of the second peak and the number of cases in it, we use a previously proposed SIR model, which accurately describes the early stages of the coronavirus pandemic in European countries. Subtracting the model fits from the data identified, we establish the timing and the number of cases, N CS , in the second peak. We then related the number of cases in the second peak to the county population density, P, and the excess Change of Address, E CoA, into each county using the simple model which fits the data very well with α = 0.68, β = 0.31 (R 2 = 0.74, p = 1.3e-8). We also find that the time between the first and second peaks was proportional to the distance of the county seat from NY Penn Station, suggesting that this migration of households and disease was a directed flow and not a diffusion process. Our analysis provides a simple method to use change of address data to track the spread of an infectious agent, such as SARS-Cov-2, due to migrations away from epicenters during the initial stages of a pandemic.
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SciScore for 10.1101/2021.03.29.21254583: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We now list some caveats and limitations of our study, some of which can be mitigated by additional data and future research. First, the USPS CoA data we analyzed did not include data for months when the moves out of or into the county totaled less than 10 per month. We do not expect this to affect our results by much, as the Excess …
SciScore for 10.1101/2021.03.29.21254583: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We now list some caveats and limitations of our study, some of which can be mitigated by additional data and future research. First, the USPS CoA data we analyzed did not include data for months when the moves out of or into the county totaled less than 10 per month. We do not expect this to affect our results by much, as the Excess CoA ranges over three orders of magnitude (Table I and Figure 6c) and the average excess CoA from the 15 counties with the highest population density was 298 changes per month from March–June 2020. A more serious limitation is the fact that the data only reports movements of “households” and does not specify the number of people that moved per household, and the latter will vary from county to county. It may be possible to correct this using data on average household sizes in each county and cell phone use data, especially with voice tagging. A large number of people commute into NYC each day from counties close by [37]. In particular, there is a lot of commuter traffic into NYC from Westchester, Nassau, Suffolk Counties in NY, Bergen, Essex, Middlesex and Hudson Counties in NJ and Fairfield County in CT (Columns P and Q in Table I). It is possible that when households relocated out of NYC into these counties, some individuals in these households continued to commute into the city daily possibly because of their status as essential workers. Such individuals would increase the risk of Covid-19 infections into the counties. It should be possible to ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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