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COVID beats flu!

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Title:
COVID beats flu!
Creator:
Lu, Dongdong

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Abstract:
COVID and flu transmit in similar ways. While this year’s quarantine methods were implemented to combat COVID, it also significantly reduces the transmission of flu. Influenza typically has its outbreak during winter seasons, while it has been reported that this year’s outbreak disappears in southern-hemisphere countries. But the quarantine effect in the US remained unknown. In this project, I use Bayesian statistics to evaluate main covariates affecting the confirmed cases of flu counts of each state and evaluate the COVID’s impact on the transmission of influenza in the USA and Australia.
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Collected for Auraria Institutional Repository by the Self-Submittal tool. Submitted by Dongdong Lu.

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Auraria Library
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COVID and flu transmit in similar ways. While this year s quarantine methods were implemented to combat COVID, it also significantly reduces the transmission of flu. Influenza typically has its outbreak during winter seasons, while it has been reported that this year s outbreak disappears in southern hemisp here countries. But the quarantine effect in the US remained unknown. In this project, I use Bayesian statistics to evaluate main covariates affecting the confirmed cases of flu counts of each state and evaluate the COVID s impact on the transmission of influenza in the USA and Australia.



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COVI D beats FI u ! Dongdong Lu

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Two Questions • Do the COVID-induced social behavior changes reduce flu transmissions this year? • What are the key demographic factors affecting the mortality rate of COVID for each state? ,

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Datasets • WHO Flumart Output • 2020 Presidential Election Results • Temperature, Latitude and Longitude Data for 50 states • 2020 US State by Race • Gross Domestic Product (GOP) Summary • Crillle Data Explorer CCDE) ,

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Methods • Pearson's chi-squared test (X2 test) is a statistical test applied to sets of categorical data to evaluate how likely the difference between the expected frequencies and the observed frequencies occurred by chance? • For test of independence, df = (Rows -1) * (eois -1) • Rows corresponds to number of categories in one variable • Cols corresponds to number of categories in the second variable. ,

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Methods • The value of the test-statistic is (0. )2 2 = (Oi-E i)2 = N lIf-Pi X Lil=l E. Lil=l p. l l • X2: Pearson's cumulative statistic • 0i: the number of observations of type i. • N: total number of observations • Ei = NPi: the expected count of type i, if the null hypothesis that the fraction of type i in the population is Pi • n: the number of cells in the table. ,

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Findings Table 1 USA Flu Positive Counts by Weeks YEAR 12 13 14 15 16 17 18 19 20 W1-4 1184 24437 17474 37272 6270 33815 91773 33782 75582(66001) W5-8 3891 11552 8182 20066 22834 57094 91484 64584 96058(77116) W9-12 9027 6092 3849 10259 38615 36807 31865 64361 54525(52416) W 13-16 5418 2931 3622 6175 17612 19775 15778 24757 W1720 2695 1095 2471 2293 6179 5001 4449 4815 221(5997) W21-24 1400 564 1341 904 1627 2300 847 2107 76(2292) W25-28 530 306 688 501 491 1017 372 1126 66(1046) W29-32 550 254 382 334 392 769 354 991 53(837) W33-36 447 349 462 407 561 954 582 1494 57(1090) W3740 426 561 809 518 1080 1594 1134 2090 p-value < 2.2e-16 "

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Findings Table 2 Australia Flu Positive Counts by Weeks YEAR 12 13 14 15 16 17 18 19 20 W1-4 44 53 95 64 75 135 169 411 271(27) W5-8 38 58 92 92 99 175 215 445 293(31) W9-12 85 79 92 81 105 115 119 416 322(29) W13-16 70 27 73 112 67 90 92 501 51(22) W17-20 87 15 52 147 105 92 114 1074 7(35) W21-24 271 31 66 222 192 150 117 3147 0(86) W25-28 1172 80 290 430 501 624 276 3422 3(140) W29-32 1647 298 866 644 1361 2301 351 1870 2(192) W33-36 1061 502 1076 781 2336 3541 738 1557 0(239) W37-40 237 463 519 642 1311 2466 845 671 0(147) p-value = 3.4e-16 ,

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Methods • Normal error regression models • The response variable Y is continuous. • p explanatory variables, which we think might give information about the response Y. • A coefficient vector f3 = (P1} f32} ... } Pp) • ydxi'P, (J2-N(xT p, (J2) and Xl1X 2 , ... ,Xp are independent. • The normal regression models are: • y -Po + X1Pl + X 2P2 + ... + XpPp + c, c-N(O, (J2). ,

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Methods • The normal regression models are: Y -f3o + X 1f31 + X 2f32 + ... + Xpf3p + E, E-N(O, 0"2). • In Bayesian analysis of regression model, we specify the following prior distributions for parameters: ,

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Methods • Y: COVID mortality rate per 100, 000 residents (for each state) • f3i: population density • f32: GDP per capita • f33: Biden's support rate in 2020 presidential campaign • f341 f3s: Latitude and longitude of the state's capitol • f361 f37: White and asian percentage* • f38: poverty rates; f39: violent crime rate • f3i0: the average elevation; f3ii: the average temperature ,

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Findings 0 .003 0 .002 0 .001 0 .000 "'"'-==::......----r-==.J 15 10 5 o 500 o 10 5 -0. "16 . "10 .0li00.06. 1 0 600 00 200 00. 1 -0. 0 O:;r) . 0 000.002).00 1 . 5 1 . 0 0 . 5 0 .006 0 .00 0 .002 beta[7] 40 30 20 10 0 . 6 O . 0 . 2 O . 0 0 .002 0 .001 -3 -2 -1 0 1 0 .15 0 .10 0 .05 0 .00 -15 -1 0 0 . 2 0 . 1 0 . 0 --.....===--r-------r:=:...J 0 .0015 0 .0010 0 .0005 -10 -5 o 0 . 0 0 .000 0 .000 0 .0000 ""1......::;;::::""'-.--..-........===;:=..J -1.GO.50.00.51. 0 -500 -250 0 250 -100G-500 0 500 -500 0 50 0 .0020 0 .0015 0 .0010 0 .0005 sigmasq 0 .0000 01'.600 value model D model1 D model 2 D model 3 ,

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200 50 o 0 b -a 1:: 0 r-r-OO ";1 > 0 0 0 0 0 .. .. 5 0 .. 0 .. 0 .. 0.0 5 o. 00 0 0 0 0 .. .. .. .. 0 .. .. o. 25 po etly_rates o .. o .. o. 50 o. 5 o o o Red ta es Blue States ,

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Findings • "Programs such as Medicaid are governed by each state, allowing more conservative states to limit access to lifesaving coverage based on income levels that can already make living difficult before being told you have a chronic health condition." Healthcare in America (published before COVID)

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Findings 1 o -0.5 0 . 0 0 . 5 Rbsq mode l D m o d el1 D m o d e l 2 D m o d e l 3 , -

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Findings • /321/331 f39 has its major density in the positive area: COVID mortality rate is positively influenced by population density, GOP, violent crime rates. • /341 /3S1 f371 f381 f312 has its major density in the negative area: COVID mortality is negatively influenced with Biden support rate, latitude, asian/white population, temperature • Other /3s straddle zero for two tails (no significant findings) ,

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Interpretations • Southern states has significantly higher mortality than the northern states. Think about the south open its bars/restaurants earlier than the north ... • White/asian on average has higher social economic status which gives them better access to the costly medical care? How about Latino/black communities? • Misinformation hurts! Biden's support rate is negatively correlated with the COVID mortality implies Trump's support rate is positively correlated with the COVID mortality rate. Think about the misinformation he keeps giving about COVID ... And how would this influence the behaviors of his supporters? And the mortality of "red" states? ,

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Suggestions • To lower the mortality rate of COVID, we can: • Give public health guidance based on SCIENCEI • Extend the closures of the indoor population-dense areas, such as bars, restaurants. • Provide better insurance for lower income communities. ,

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Thank you!