key: cord-1019323-kbnz1sy3 authors: Zhao, X.; Tatapudi, H. A.; Corey, G.; Gopalappa, C. title: Threshold analyses on rates of testing, transmission, and contact for COVID-19 control in a university setting date: 2020-07-25 journal: nan DOI: 10.1101/2020.07.21.20158303 sha: 6f7f544074d3a7fe4427e0f13783117967026974 doc_id: 1019323 cord_uid: kbnz1sy3 We simulated epidemic projections of a potential COVID-19 outbreak in a university population of 38,000 persons, under varying combinations of mass test rate (0% to 10%), contact trace and test rate (0% to 50%), transmission rate (probability of transmission per contact per day), and contact rate (number of contacts per person per day). We simulated four levels of transmission rate, 14% (average baseline), 8% (average for face mask use), 5.4% (average for 3ft distancing), and 2.5% (average for 6ft distancing and face mask use), interpolating results to the full range to understand the impact of uncertainty in effectiveness, feasibility, and adherence of face mask use and physical distancing. We evaluated contact rates between 1 and 25, to identify the threshold that, if exceeded, could lead to several deaths. When transmission rate was 8%, for trace and test at 50%, the contact rate threshold was 8. However, any time delays in trace, test, and isolation quickly raised the number of deaths. Keeping contact rate to 3 or below was more robust to testing delays, keeping deaths below 1 up to a delay of 5 days from the time of infection to diagnosis and isolation. For a contact rate of 3, the number of trace and tests peaked to about 70 per day and relaxed to 25 with the addition of 10% mass test. When transmission rate was 5.4%, for trace and test at 50%, the contact rate threshold was 10. However, keeping contact rate to 4 or below was more robust to delays in testing, keeping deaths below 1 up to a delay of 6 days from the time of infection to diagnosis and isolation. For contact rate of 4, the number of trace and tests peaked at 50 per day and relaxed to 10 per day with the addition of 10% mass test. Threshold estimates can help develop on-campus scheduling and indoor-spacing plans in conjunction with plans for asymptomatic testing for COVID-19. Combination thresholds should be selected specific to the setting based on an assessment of the feasibility and resource 48 availability for testing and quarantine. The COVID-19 pandemic caused by the SARS-CoV-2 virus has caused significant disease and 50 economic burdens since its first outbreak in December 2019. In the absence of an effective 51 vaccine, the main intervention for the prevention of COVID-19 transmissions has been social 52 distancing. The most effective social distancing being lockdowns of non-essential organizations 53 and services, as adopted by several states since March, 2020, in efforts to immediately slow 54 down the pandemic [1] . However, lockdowns are a huge threat to the economic stability of a 55 nation as seen by the unprecedented rise in unemployment rates [2] [3] . Therefore, while 56 lockdowns are a good short-term strategy, for a long-term strategy or until a vaccine becomes 57 widely available, it has become necessary to identify alternate strategies and lifestyles that 58 control the disease burden while minimizing the economic burden. Interventions that are 59 effective include the use of face masks, physical distancing between persons at a recommended 60 6ft, and contact tracing and testing or mass testing to enable early diagnosis in the asymptomatic 61 stage of infection [4] . However, removal of lockdowns should be strictly accompanied by a 62 reopening plan that rapidly and efficiently enables the adoption of the above interventions, to 63 avoid an epidemic rebound. In addition to public health agencies, all members of a community, 64 in both public and private sectors, play a key role in the development and implementation of a 65 reopening plan that is most suited for their organization [5] . Among these sectors, universities 66 and colleges bear a special burden to develop a reopening plan that include changes to a range of 67 activities related to teaching, research, dining, housing, and extra-curricular activities [6] . 68 69 We developed a compartmental differential equations model to simulate epidemic projections of 70 a potential COVID-19 outbreak in a population of 38,000 individuals representative of 71 undergraduate and graduate students, faculty and staff in a residential university in the United 72 States. We simulated epidemic projections, of potential outbreaks, under varying combinations 73 of contact tracing and testing and mass testing, to identify combinations that would reduce the 74 effective reproduction number to a value below the epidemic threshold of 1. is directly 75 proportional to the duration of infectiousness, transmission rate (the probability of transmission 76 per contact per day, representing the infectiousness of the virus), and contact rate (the number of 77 contacts per person per day) [7] . Trace and test and universal mass test lead to early diagnosis in 78 the asymptomatic phase of the infection and, if persons diagnosed with infection are successfully 79 quarantined, they reduce the duration of exposed infectiousness [8] [9] [10] and thus reduce . 80 Physical distancing by the recommended 6ft and use of face masks can reduce transmission rate, 81 and thus reduce [11] [12] . Reducing contact rate directly reduces , however, the expected 82 contact rates are dependent on the planned facility layout and scheduling of classrooms, housing, 83 dining, research labs, and offices. 84 85 While COVID-19 simulation models have typically used a product of transmission rate and 86 contact rate as one metric, we evaluate these separately, as it could help inform scheduling and 87 facility layout decisions. While physical distancing by the recommended 6ft and use of face 88 masks can reduce transmission rate, there is considerable uncertainty in the expected reduction, 89 with studies showing that it is likely to be less than 100% even if used properly [11] [12] [13] 90 [14] . That is, a person who is at a six feet distance is also a contact for potential transmission, 91 although the transmission rate (per contact) would be low. Therefore, if not properly planned, the 92 movement of students between multiple indoor locations, such as shared student housing or 93 dorms, classrooms, dining halls, and shared research lab and office spaces, could create a high 94 contact rate, even in the absence of large social gatherings. This can be further exacerbated by 95 the ease of transmission of the virus. While it is known that indoor gatherings such as events, 96 bars, and restaurants have contributed to a significant portion of the cases [ studies that have examined the impact of school closures and stay at home policies. In the past, 120 some of these studies examined the impact of policies during the H1N1 pandemic [25] [26] . 121 However, these studies targeted closure of schools or several classes in a school for certain 122 periods of time and do not explore partial re-opening or closure [25] [26] [27] . In more recent 123 times, a Johns Hopkins study provided an ethical framework for the COVID-19 reopening 124 process [28] . This framework is intended for policy makers on the state level to develop plans for 125 reopening of their state. In the framework, they highlight the long-term closure of schools can 126 have a detrimental effect on children but warns policy makers to not shy away from re-imposing 127 social distancing measures if hospitalizations or cases cross over a benchmark. A literature 128 review on school closure policies states that policy makers need to strongly consider 129 combinations of social distancing policies when planning to reopen [29] . One study examined 130 the impact of social distancing, contact tracing and household quarantine in a Boston 131 metropolitan area [30] . They tested the effectiveness of these policies to avoid a second wave of 132 the pandemic using a granular agent-based model. In our search, we found only one study that 133 researched the impact of school closure policies during the COVID-19 pandemic for K-12 134 schools [31] . In this study, the authors study the impact of partial, complete and progressive 135 reopening of schools. They also study social distancing interventions, testing, and isolation, and 136 estimate hospital bed capacity needs if schools were to be reopened. 137 While it is generally known that increasing contact tracing and testing is necessary, studies 139 directly observing the number of tests needed at an organizational level, such as university, are 140 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint only recently emerging. One study analyzing combination interventions, in generalized 141 populations, that include contact tracing estimated that reducing 0 of 1.5 to an of 1 requires 142 more than 20% of contact tracing [32] . For an 0 of 2.5, at least 80% of contact tracing and for 143 an 0 of 3.5, more than 100% of contact tracing. A modeling study applied to the Boston area 144 [30] should not be used as a metric for selection of a strategy. Selection of a scenario should be done 160 after a feasibility assessment. The estimates for number of tests and quarantines, along with the 161 uncertainty in the transmission rates, under each scenario, could help in the feasibility 162 assessment, based on the resource needs such as personnel, equipment, and infrastructure, and 163 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 25, 2020. Simulation methodology 167 We developed a compartmental model for simulating epidemic projections overtime. The 168 epidemic flow diagram is depicted in Figure 1 . Each box is an epidemic state, and each arrow 169 represents a transition from one state to another. Note, each compartment is further split by age 170 and gender, but for clarity of notations, we do not include it in the equations below. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint use the proportion hospitalized as a proxy for severe cases; the denominator is based on 208 the assumption that the duration of exposed phase is equal to the difference between the 209 duration of the incubation period and the latent period. never show symptoms and thus directly go from exposed to recovered. 221 1 , which assumes that person with mild 222 cases that did not get diagnosed through symptom-based testing have a chance of getting 223 tested through additional testing options, and self-quarantine upon diagnosis. 224 , theoretically, , should be the 226 same as , , however, as the rate of transitioning from to is fixed to proportion 227 hospitalized under symptom-based tests, if extensive testing is conducted, the number of 228 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . We simulate the epidemic over time using the following system of differential equations 238 where, = a matrix of transition rates between states (arrows in Figure 1 ), and = time-step. 240 We use a time-unit of per day for the transition rates in and set = 1 10 , and thus, the model 241 simulates every 10 th of a day. 242 The expansion of the system of differential equations are as follows: 243 CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint We can further expand by substitution of the rate terms with their equations as follows: 254 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . Input data assumptions and sources for simulation model 280 We used natural disease progression estimates from other studies in the literature. The 281 description of the data, sources, and values (with ranges and medians where applicable) for all 282 parameters are available in the Supplemental Appendix. Briefly, we assumed an incubation 283 period duration of 5.4 days [37] , the first 2.5 days in stage L (not infectious and asymptomatic) 284 [38] , and the remaining in stage E (infectious and asymptomatic). We assumed about 65% of 285 cases develop medium to severe symptoms [37] [39] [40] and, in the absence of test and trace or 286 mass test, can be diagnosed through symptom-based testing. We assumed the remaining 35% of 287 cases show mild to no symptoms and can be diagnosed only through trace and test, or universal 288 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint mass test. We assumed an average duration of 3.5 days from the time of onset of symptoms to 289 hospitalization [41] , with the proportion hospitalized varying as a function of age. For mild 290 cases, we assumed an average duration of 7 days from the time of onset of symptoms to recovery 291 [41] . We assumed case fatality rates vary as a function of age and gender. 292 293 Interventions 294 We evaluated mass testing at 5% and 10% of the population. We assumed a maximum contact 295 tracing rate of 50%, modeling the rate as the inverse of the time to find, test, and isolate infected 296 contacts from the time of their infection. To test the sensitivity of delays associated with trace 297 and/or test, we evaluated trace and test rates of 10%, 17%, 20%, 25%, 33%, and 50%, equivalent 298 of 10, 6, 5, 4, 3, and 2 days, respectively, from time of infection to effective isolation. We 299 assumed trace and test would initiate within the first 5 cases of diagnosis. To test the sensitivity 300 of delays in initiation of trace and test, we also evaluated scenarios by delaying the initiating of 301 trace and test to after diagnoses of 20 cases. We evaluated transmission rates ( ) of 14% 302 (baseline), 8% (mid), 5.4% (lower-mid), and 2.5% (lowest). The baseline value of corresponds 303 to an average estimate under no physical distancing and no face masks [12] [42] . Transmission 304 rate of 8% relative to baseline corresponds to expected relative risk under use of face masks in 305 non-health care settings [12] . Transmission rates of 5.4% and 2.5% correspond to expected rates 306 under 3ft and 6ft physical distancing, respectively [12] . We evaluated contact rates between 1 307 and 25 ( ), we did not separate between on-campus and off-campus contact rates. In all 308 scenarios, we applied baseline symptom-based testing and 14-day quarantine for diagnosed 309 persons. For diagnosis in asymptomatic stages, we assumed a test sensitivity of 0.9 for trace and 310 test and universal (mass) testing [43] . 311 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. Massachusetts Amherst, Amherst, MA, to determine the population size of undergraduate and 314 graduate students and their age and gender distributions [44] . For faculty and staff, we used the 315 age distribution of persons 25 years and older from the Town of Amherst, MA, where the 316 university is located [45] . To initiate an outbreak, we assumed 4 to 5 infected cases on day 1 317 based on the following. We assumed that the proportion of incoming students who are infected 318 would be equal to the prevalence of Massachusetts. We also assumed that all incoming students 319 would be tested, and about 10% of infected cases would be false negatives. Prevalence is 320 unknown, as not all cases are diagnosed and diagnosed cases are not specifically tracked. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint The contact rate thresholds, which represent the values that, if exceeded, lead to greater than 1 333 death, under varying combinations of transmission rate, trace and test, and universal test are 334 presented in Figure 2 . initiating trace and test to after diagnoses of 20 cases, and trace and test rates of 33% and lower 344 (equivalent to more than 2 days to find and isolate infected contacts from the time of their 345 infection) reduced the contact rate threshold to 1 ( Figure 2D ). With maximum testing of 10% 346 mass test, 50% trace and test, and initiating trace and test within 5 cases of diagnoses, the 347 threshold contact rate was 7 (Figure 2A) . 348 349 When transmission rate was 8%, with only symptom-based test, only 10% mass test, or only 350 50% trace and test, the contact rate thresholds were 1, 3, and 8, respectively (Figure 2A ). With 351 both 10% mass test and 50% trace and test, the contact rate threshold was 12 (Figure 2A ). With 352 10% (and 33%) trace and test, equivalent to 10 (and 3) days to find and isolate infected contacts 353 from the time of their infection, the contact rate threshold was 2 (and 6) ( Figure 2B ). Delays in 354 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint initiating trace and test to after diagnoses of 20 cases, and trace and test rates of 33% and lower 355 (equivalent to more than 2 days to find and isolate contacts from the time of their infection), 356 reduced the contact rate threshold to between 2 and 3 ( Figure 2D ). 357 358 When transmission rate was 5.4%, with only symptom-based testing, only 10% mass test, or only 359 50% trace and test, the contact rate threshold were 2, 4, and 12, respectively (Figure 2A ). With 360 both 10% mass test and 50% trace and test, the contact rate threshold was 18 (Figure 2A ). With 361 10% (and 33%) trace and test, equivalent to 10 (and 3) days to find and isolate infected contacts 362 from the time of their infection, the contact rate threshold was 4 (and 9) ( Figure 2B ). Delays in 363 initiating trace and test to after diagnoses of 20 cases, and trace and test rates of 33% and lower 364 (equivalent to more than 2 days to find and isolate contacts from the time of their infection), 365 reduced the contact rate thresholds to between 3 and 5 ( Figure 2D ). 366 367 When transmission rate was 2.5%, with only symptom-based test, or only 10% mass test, the 368 threshold contact rates were 5, and 9, respectively ( Figure 2A ). With 50% trace and test the 369 epidemic was under control up until a contact rate of 25. Delays in initiating trace and test to 370 after diagnoses of 20 cases, and trace and test rates of 33% and lower (equivalent to more than 2 371 days to find and isolate contacts from the time of their infection), reduced the contact rate 372 threshold to between 5 and 12 ( Figure 2D ). 373 The number of deaths under varying combinations of universal mass test, trace and test, 375 transmission rate, and contact rate are presented in Figures S1 and S2 of the Appendix. All 376 scenarios with less than 1 death are also presented in the Appendix Figures S1 and S2 . 377 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07. 21.20158303 doi: medRxiv preprint For all scenarios with 50% trace and test that resulted in less than 1 death, Figure 3 presents the 378 total number of trace and tests over the duration of the semester, the peak number of trace and 379 tests per day, and the peak number quarantined (excluding false positives), interpolated over the 380 transmission rate range. Both metrics increased as contact rate increased, and decreased with the 381 addition of universal testing, as expected. When contact rate was 5 reaching 60 and 80 when contact rate was 6 and 7, respectively, and transmission rate was 8%. 387 388 Delay in initiating trace and test to after diagnosis of 20 cases, would increase the peak number 389 of trace and tests or increase deaths (Figure 4 ). When contact rate was 5 and the transmission rate 390 was below 5.4%, total trace and tests varied from about 180 (50% trace and test + 10% mass test) 391 to 400 (50% trace and test), and the peak trace and tests per day varied from about 20 (50% trace 392 and test + 10% mass test) to 85 (50% trace and test). For combinations of transmission rate 393 above 5.4% and contact rate above 5, the number of trace and tests per day peak above 50 and 394 rise rapidly, e.g., reaching 200 and 230 (50% trace and test) when contact rate was 6 and 7, 395 respectively, and transmission rate was 8% (not shown in Figure) . These two scenarios also 396 generated 2 deaths. Adding 10% mass test reduces deaths to below 1, while also relaxing the 397 peak trace and tests to 45 and 60 when contact rate was 6 and 7, respectively. 398 399 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07. 21.20158303 doi: medRxiv preprint For scenarios with 50% trace and test, transmission rate below 5.4%, and contact rate at 5 or 400 below, the peak quarantines per day (excluding false positives), went up to 25 if trace and test 401 initiated within diagnosis of 5 cases (Figure 3) , and up to 60 if delay in initiating trace and test to 402 after diagnosis of 20 cases (Figure 4) . As test specificity ranges from 96.5% and 99% [43] test, and delays in trace and test initiation) that meet the contact rate thresholds. When 415 transmission rate was 8%, about 60% of scenarios had contact rate threshold of 3 or higher, and 416 when transmission rate was 5.4%, about 73% of scenarios had contact rate threshold of 4 or 417 higher. Specifically, when transmission rate was 8%, a contact rate of 3 kept deaths below 1 if 418 trace and test rates were 20% or above (equivalent to finding and isolating infected contacts 419 within 5 days from the time of their infection), even with a delay in initiating trace and test to 420 after diagnosis of 20 cases (Appendix Figures S3 and S4 ). When transmission rate was 5.4%, a 421 contact rate of 4 kept deaths below 1 if trace and test rates were 17% or above (equivalent to 422 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint finding and isolating infected contacts within 6 days from the time of their infection), even with 423 a delay in initiating trace and test to after diagnosis of 20 cases (Appendix Figures S3 and S4) . In 424 these scenarios, the number of trace and tests peaked to between 20 and 70 when contact rate 425 was 3 and transmission rate was 8%, and to between 20 and 50 when contact rate was 4 and 426 transmission rate was 5.4% (Appendix Figures S3 and S4) . It is difficult to determine how the threshold contact rate estimates compare to those expected at 439 a university, as the data on contact rates prior to the COVID-19 outbreak are unavailable. One 440 study, conducted prior to the COVID-19 outbreak, estimated an average rate of 21 among 441 middle-aged adults in Portland, Oregon [46] , however, contact rates in a university are likely to 442 be different. After the COVID-19 outbreak, more studies to estimate contact rates are emerging, 443 however, they are under the context of state-issued stay-at-home orders, and thus are 444 representative of the lower bounds. In one such poll [47] of U.S. adults, those who self-asses 445 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint their status as 'completely isolated' (about 27%) reported a median (and mean) non-household 446 contact rate of 0 (and 1.5), those who self-asses their status as 'mostly isolated' (about 47%), 447 reported a median (and mean) non-household contact rate of 2 (and 5.4), and those who self-448 asses their status as 'partially isolated' (about 17%), reported a median and mean non-household 449 contact rate of 2 (and 5.4). Among those listing their work sector as 'education' (which included 450 library and training services), reported a median (and mean) work contact rate of 1 (and 2.6). 451 Another study [48] reported a median (and mean) contact rate of 2 (and 2.7) among a nationally 452 representative survey of the US population, with 85% of surveyors reporting four or fewer 453 contacts. The above studies were under maximum lockdowns, and thus, how these would change 454 upon reopening, and specifically at a university, are unknown, but can be expected to be higher 455 thus requiring careful control. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint Given the above challenges, in this study we attempted to separate out contact rates from 469 transmission rates. Considering the uncertainty in the baseline transmission rate, and in the 470 expected reductions from face mask use and physical distancing [12] , uncertainties rising from 471 inherent features, feasibility, and compliance, we interpolated results for the full range of 472 transmission rate. The transmission rates simulated here cover the range of estimates from the 473 literature for baseline 14% (11.6%-17%), and expected reductions from use of surgical or cloth 474 face masks in non-health care settings 7.8% (5.6%-11.1%), 3ft physical distancing 6.9% (3.6%-475 13%), and 6ft physical distancing 2.8%(1.4%-5.7%), calculated using relative risks from a meta-476 analysis study that, in addition to SARS-CoV-2, evaluated viruses of similarly high virulence 477 [12] (see Supplemental Appendix Table S2 ). 478 The contact rate thresholds in this study can help inform decisions related to planning of indoor 480 spacing and personnel scheduling, by eliminating scenarios that generate contact rates that cross 481 the threshold. However, the selection of contact rates should be considered in conjunction with 482 testing rates and transmission rates. The contact rate thresholds were low when trace and test 483 rates were low or transmission rates were higher than 5.4%. Though the contact rate thresholds 484 under low transmission rates and 50% trace and test were higher, going above 5, the feasibility of 485 finding, testing, and isolating infected contacts within 2 days of infection (50% trace and test) 486 should be evaluated specific to the setting of implementation. When transmission rate was 5.4% 487 or below and contact rate was higher than 5, any delays in trace and test generated rapid 488 accumulation of the number of persons to trace and test with the peak numbers per day going 489 above 50, and eventually leading to several deaths if testing delays continued. When 490 transmission rate was 5.4%, a contact rate threshold of 4 was more robust to testing delays, and 491 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. Our work is subject to limitations. Our model is deterministic. We did not specifically model 503 false positives hence, the estimates here only provide a lower bound. We used an average contact 504 rate for all persons, in order to help decisions related to designing a controlled environment, such 505 as controlling workplace scheduling and layouts and issuing uniform guidelines. We did not 506 model contact rates to be representative of actual expected behaviors of individuals or to be 507 representative of actual expected networks between individuals. We did not explicitly model 508 other interventions that could reduce transmission rate such as controlled ventilation, filtering air 509 and controlling air flow, which are likely to impact transmissions [53] . The transmission rates 510 evaluated should be used with caution. The baseline estimate of 14% is an average estimate, and 511 the estimates for face masks and physical distancing are relative to these estimates. For a 512 different baseline transmission rate, the interpolated values of transmission rates should be used 513 to determine expected reduction. We did not model other flu like illnesses and thus we did not 514 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 25, 2020. indoor spacing and personnel scheduling scenarios that exceed these contact rate thresholds that 524 have a high chance of an epidemic. The threshold contact rates can only help in elimination of 525 strategies and should not be used as a metric for selection of a strategy. The uncertainty ranges in 526 results suggest that selection of a strategy should collectively consider indoor spacing, personnel 527 scheduling, testing, and quarantining. Selection of a scenario should be done after a feasibility 528 assessment that compares resource needs under each scenario to excepted resource availability 529 for testing and quarantining, and risk assessment to determine the ability to control transmission 530 rate through use of face masks and physical distancing, including its feasibility and compliance. 531 532 Acknowledgements 533 We would like to acknowledge Sonza Singh, Shifali Bansal, Seyedeh Nazanin Khatami, and 534 Arman Mohseni Kabir for their assistance in data collection in initial stages of the study, and Dr. 535 Laura Balzer, Dr. Michael Ash, and Dr. Hari Balasubramanian for their comments and inputs. 536 537 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 25, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 25, 2020. and trace and test (T), that generated less than 1 deaths; S: symptom-based testing; 5%U, and 554 10%U: mass test rate per day, equivalent to test once every 20 days, and 10 days, respectively; 555 10%T, 17%T, 20%T, 25%T, 33%T, 50%T: %trace and test rate, representing 10 days, 6 days, 5 556 days, 4 days, 3 days, and 2 days, respectively, from the time of infection to diagnosis and 557 isolation; Transmission rates from the literature (see Supplement Appendix Table S2 ) for 558 baseline: 15% (11.6%-17%), surgical or cloth face masks use in non-health care setting 7.8% 559 (5.6%-11.1%), 3ft physical distancing 5.4% (3.6%-13%), and 6ft physical distancing 560 2.8%(1.4%-5.7%). Solid markers are simulated cases. Dotted lines are interpolations . 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint 27 Figure 3 : Resource needs under varying combinations of mass test, trace and test (50%), contact rate, and transmission rate that generated less than 1 deaths, when trace and test was initiated within 5 cases of diagnoses. Solid markers are simulated. Dotted lines are interpolations. Figure 4 : Resource needs under varying combinations of mass test, trace and test (50%), contact rate, and transmission rate that generated less than 1 deaths, when trace and test was initiated after diagnoses of 20 cases. Solid markers are simulated cases. Dotted lines are interpolations. Figure 5 : Among all the testing scenarios evaluated (varying levels of mass test, trace and test, and delays in trace and test initiation), the proportion of scenarios (y-axis) with contact rate threshold higher than the value on the x-axis. (e.g., for transmission rate (p) = 8%, about 60% of scenarios had contact rate threshold of 3 or higher, and for p=5.4%, about 73% of scenarios had contact rate threshold of 4 or higher). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 25, 2020. . https://doi.org/10.1101/2020.07.21.20158303 doi: medRxiv preprint Timeline of COVID-19 policies, cases, and deaths in your state The COVID-19 crisis: How do U.S. employment and health outcomes compare to other OECD countries? 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