M A J O R A R T I C L E e2908 • cid 2021:73 (1 November) • Neilan et al Clinical Infectious Diseases Received 23 July 2020; editorial decision 14 September 2020; published online 18 September 2020. aP. K. and A. L. C. contributed equally to this work. Correspondence: A. M. Neilan, Medical Practice Evaluation Center, 100 Cambridge St, Suite 1600, Boston, MA 02114 (aneilan@mgh.harvard.edu). Clinical Infectious Diseases® 2021;73(9):e2908–17 © The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/ by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com DOI: 10.1093/cid/ciaa1418 Clinical Impact, Costs, and Cost-effectiveness of Expanded Severe Acute Respiratory Syndrome Coronavirus 2 Testing in Massachusetts Anne M. Neilan,1,2,3,4, Elena Losina,4,5,6,7 Audrey C. Bangs,3 Clare Flanagan,3 Christopher Panella,3 G. Ege Eskibozkurt,3 Amir Mohareb,2,3,4 Emily P. Hyle,2,3,4,8 Justine A. Scott,3 Milton C. Weinstein,9 Mark J. Siedner,2,3,4,10 Krishna P. Reddy,3,4,11 Guy Harling,10,12,13,14 Kenneth A. Freedberg,2,3,4,9,15 Fatma M. Shebl,3,4 Pooyan Kazemian,3,4,a and Andrea L. Ciaranello2,3,4,8,a 1Division of General Academic Pediatrics, Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA, 2Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA, 3Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, USA, 4Harvard Medical School, Boston, Massachusetts, USA, 5Orthopedic and Arthritis Center for Outcomes Research, Department of Orthopedic Surgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA, 6Policy and Innovation eValuation in Orthopedic Treatments Center, Department of Orthopedic Surgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA, 7Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA, 8Harvard University Center for AIDS Research, Cambridge, Massachusetts, USA, 9Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA, 10Africa Health Research Institute, KwaZulu-Natal, South Africa, 11Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA, 12Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA, 13Institute for Global Health, University College London, London, United Kingdom, 14Medical Research Council/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), University of the Witwatersrand, Johannesburg, South Africa, and 15Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA (See the Editorial commentary by Rosenberg and Holtgrave on pages e2918–20.) Background. We projected the clinical and economic impact of alternative testing strategies on coronavirus disease 2019 (COVID-19) incidence and mortality in Massachusetts using a microsimulation model.  Methods. We compared 4 testing strategies: (1) hospitalized: polymerase chain reaction (PCR) testing only for patients with severe/critical symptoms warranting hospitalization; (2) symptomatic: PCR for any COVID-19–consistent symptoms, with self- isolation if positive; (3) symptomatic + asymptomatic once: symptomatic and 1-time PCR for the entire population; and (4) sympto- matic + asymptomatic monthly: symptomatic with monthly retesting for the entire population. We examined effective reproduction numbers (Re = 0.9–2.0) at which policy conclusions would change. We assumed homogeneous mixing among the Massachusetts population (excluding those residing in long-term care facilities). We used published data on disease progression and mortality, trans- mission, PCR sensitivity/specificity (70%/100%), and costs. Model-projected outcomes included infections, deaths, tests performed, hospital-days, and costs over 180 days, as well as incremental cost-effectiveness ratios (ICERs, $/quality-adjusted life-year [QALY]).  Results. At Re = 0.9, symptomatic + asymptomatic monthly vs hospitalized resulted in a 64% reduction in infections and a 46% reduction in deaths, but required >66-fold more tests/day with 5-fold higher costs. Symptomatic + asymptomatic monthly had an ICER <$100 000/QALY only when Re ≥1.6; when test cost was ≤$3, every 14-day testing was cost-effective at all Re examined.  Conclusions. Testing people with any COVID-19–consistent symptoms would be cost-saving compared to testing only those whose symptoms warrant hospital care. Expanding PCR testing to asymptomatic people would decrease infections, deaths, and hos- pitalizations. Despite modest sensitivity, low-cost, repeat screening of the entire population could be cost-effective in all epidemic settings. Keywords. COVID-19; testing; PCR; cost-effective; SARS-CoV-2. Massachusetts experienced a major coronavirus disease 2019 (COVID-19) outbreak beginning in March 2020 after a bi- otechnology convention, which was subsequently fueled by transmission in communities living in multigenerational and multifamily housing [1]. In the United States, restricted testing capacity early in the pandemic led states such as Massachusetts to test only severely symptomatic people and/or those with a known exposure [2]. While some have argued that testing must be highly sensitive in order to be of value to guide reopening [3], others have argued that sensitivity can be sacrificed if tests are rapid, low-cost, and frequent [4, 5]. Despite the variable clinical sensitivity of severe acute respiratory syndrome co- ronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing, expanded testing programs could reduce transmissions by increasing isolation of infectious people, thereby reducing hospitalizations and deaths. Widely available testing could also allow for the safer resumption of economic and social activity D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 mailto:aneilan@mgh.harvard.edu?subject= http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ https://orcid.org/0000-0001-7915-4974 COVID-19 Testing in Massachusetts • cid 2021:73 (1 November) • e2909 by providing surveillance for any “second wave” of infection [6]. Such resumptions of public life may also benefit those with non–COVID-19–related health issues who may avoid seeking care due to concerns about acquiring COVID-19 [7]. To date, no national testing strategy has been articulated [8]. Since new infections peaked in late April 2020 [9], Massachusetts has used test positivity rates as a key indicator to guide gradual reopening, after implementing strategies to reduce transmis- sion risk [6]. In Massachusetts and elsewhere, planning is es- sential for utilization of key limited resources, such as testing and hospital beds, since mitigation strategies need to be able to pivot rapidly as epidemic growth scenarios change. Our goal was to examine the clinical and economic impact of screening strategies on COVID-19 in Massachusetts. METHODS Analytic Overview We developed a dynamic state-transition microsimulation model, the Clinical and Economic Analysis of COVID-19 Interventions (CEACOV) model, to reflect the natural history, diagnosis, and treatment of COVID-19. We modeled 4 testing strategies for all Massachusetts residents (excluding those res- iding in long-term care facilities): (1) hospitalized: PCR testing only of those who develop severe illness (ie, warranting hospital care), reflecting common practices in Massachusetts through late April 2020 [2]; (2) symptomatic: hospitalized and PCR for people with any COVID-19–consistent symptoms who self- isolate if positive; (3) symptomatic + asymptomatic once: symp- tomatic and a 1-time PCR for the entire population; and (4) symptomatic + asymptomatic monthly: symptomatic + asymp- tomatic once and retesting every 30 days of those who test neg- ative and remain asymptomatic (Supplementary Figure 1). For those who are not hospitalized, we assume that a positive PCR test leads to self-isolation in the community. We projected clin- ical outcomes (infections, COVID-19–related mortality, quality- adjusted life-years [QALYs]), and COVID-19–related resource utilization (tests, hospital and intensive care unit [ICU] beds, self-isolation days), and costs for Massachusetts (6.9 million people, excluding long-term care facility residents) over a 180- day horizon. We report incremental cost-effectiveness ratios (ICERs: difference in cost divided by difference in QALYs [$ / QALY]) from a healthcare sector perspective (Supplementary Methods). The threshold at which interventions are considered cost-effective is a normative value that varies by setting; for the sake of interpretability, we define a strategy as “cost-effective” if its ICER is below $100 000/QALY [10]. CEACOV Model Structure Cohort and Disease Progression At model start, a closed preintervention cohort is seeded with a user-defined proportion of age-stratified individuals (0–19, 25–59, ≥60 years) who are either infected with or susceptible to SARS-CoV-2. If infected, individuals face daily age-stratified probabilities of disease progression through 7 health/disease states, including latent infection, asymptomatic illness, mild/ moderate illness, severe illness (warranting hospitalization), critical illness (warranting intensive care), recuperation, and recovery (Supplementary Figure 2). We assume that recovered individuals are immune from repeat infection for the 180-day modeled horizon [11]. Susceptible and recovered individ- uals may also present for testing with symptoms due to non– COVID-19 conditions (“COVID-19–like illness”). Testing Individuals can experience a daily probability of undergoing SARS-CoV-2 testing. Each PCR testing strategy includes test sensitivity/specificity, turnaround time, and testing frequency. Transmission In the model, infected individuals have an equal probability of contacting susceptible individuals and transmitting SARS- CoV-2. The effective reproduction number (Re) captures the av- erage number of secondary cases per infected individual in the cohort; based on Massachusetts data, this was estimated to be 0.9 in late April 2020 (Supplementary Methods and Supplementary Table 1). People with a positive test result or symptom screen can isolate in the community or in the hospital, which further decreases transmission. Resource Use The model tallies tests, COVID-19–related use of hospital and ICU bed-days, as well as days spent self-isolating. Model Inputs Cohort and Disease Progression We derived the initial distribution of COVID-19 disease se- verity by age from the Massachusetts Census and Department of Public Health (Table  1) [12, 13]. Disease progression and COVID-19–related mortality are derived from data from China and Massachusetts and calibrated from mid-March to 1 May 2020 to deaths in Massachusetts (excluding those occurring in long-term care facilities) (Table 1 and Supplementary Table 1) [13–18]. Testing and Associated Transmission Reduction PCR test sensitivity and specificity are assumed to be 70% and 100%, respectively (Table 1) [20, 21]. In all strategies, patients with severe or critical illness are eligible for diagnostic testing and are hospitalized regardless of PCR test result. Transmission is reduced by 90% for hospitalized people due to infection control and isolation practices (Table  1 and Supplementary Methods). In the expanded PCR-based strategies, self-isolation among those in the community with a positive PCR test leads to a 65% transmission reduction [29]; those who test negative do not self-isolate (incorporating the potential for transmissions D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data e2910 • cid 2021:73 (1 November) • Neilan et al Table 1. Input Parameters for a Model of Coronavirus Disease 2019 and Severe Acute Respiratory Syndrome Coronavirus 2 Testing in Massachusetts Parameter Value Cohort characteristics Initial age distribution of cohort, % [12] 0–19 y 25 20–59 y 56 ≥60 y 19 Initial distribution of health states on 1 May 2020, % [13]a Susceptible 89.38 Latent 0.52 Asymptomatic 0.91 Mild/moderate illness 1.49 Severe illness 0.04 Critical illness 0.02 Recuperation 0.01 Recovered 7.63 Health state transition probabilities, by ultimate stage of disease, daily [14–16, 18]b Asymptomatic Latent to asymptomatic 0.565 Asymptomatic to recovered 0.099 Mild/moderate Latent to asymptomatic 0.565 Asymptomatic to mild/moderate 0.221 Mild/moderate to recovered 0.095 Severe With Hospital Care Without Hospital Care Latent to asymptomatic NA 0.565 Asymptomatic to mild/moderate NA 0.221 Mild/moderate to severe NA 0.143 Severe to recovered .091 0.063 Critical Latent to asymptomatic NA 0.565 Asymptomatic to mild/moderate NA 0.221 Mild/moderate to severe NA 0.284 Severe to recovered 0.026 0.000 Severe to critical 0.105 0.143 Critical to recuperation 0.049 0.000 Recuperation to recovered 0.161 0.000 COVID-19–related mortality while critically ill, probability, daily [19] With hospital care Without hospital care 0–19 y 0.00001 0.118 20–59 y 0.004 0.166 ≥60 y 0.050 0.203 Development of COVID-19–like illness symptoms among susceptible and recovered, probability, daily [19] Mild/moderate illness 0–19 y 0.00005 20–59 y 0.00005 ≥60 y 0.00008 Severe illness 0–19 y 0.00032 20–59 y 0.00036 ≥60 y 0.00053 Critical illness 0–19 y 0.00009 20–59 y 0.00010 ≥60 y 0.00015 Presentation to hospital care with severe symptoms, probabilityc 0.80 Test characteristics PCR test [20, 21] Sensitivityd, % 70 Specificity, % 100 Turnaround time, d 1 Test acceptance, probability Asymptomatic/mild illness/moderate illness 0.80 Critical/severe illness 1.00 D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 COVID-19 Testing in Massachusetts • cid 2021:73 (1 November) • e2911 associated with false-negative tests). PCR test acceptance is as- sumed to be 80% for those who are asymptomatic or have mild/ moderate illness at the time of testing, and 100% for those with severe or critical illness. Epidemic Scenarios The analysis of screening strategies begins after the period of model validation and calibration (mid-March through late April; Supplementary Methods). For the first month of the simulation, corresponding to 1 May 2020 to 31 May 2020, Re remains 0.9 (Supplementary Table 1). To account for the uncertain trajectory of the epidemic as reopening plans are implemented, we model 3 scenarios representing epidemics with distinct Re values in the absence of expanded testing (ie, hospitalized), beginning on 1 June 2020: (1) slowing (1 June 2020, Re  =  0.9), suggesting epidemic growth would re- main the same as during May (eg, stay-at-home advisory and nonessential business closures); (2) intermediate (1 June 2020, Re = 1.3), suggesting modest increase in epidemic growth; and (3) surging (1 June 2020, Re  =  2.0), suggesting an Re closer to late March/early April Massachusetts estimates (Re = 2.6– 5.9; Supplementary Table 1). We also identified threshold values for the Re at which policy conclusions would change. Transmission probabilities are based on time spent in each health state (Table 1). Costs and Cost-effectiveness PCR test cost is $51 [25]. Patients requiring hospitalization ac- crue per-day costs (hospital: $1640; ICU: $2680) [26–28]. We use projected deaths to estimate quality-adjusted discounted life-years lost per strategy (Supplementary Methods) [30]. Sensitivity and Scenario Analyses In each of the 3 epidemic growth scenarios, we vary PCR sen- sitivity (30%–100%), test acceptance (15%–100% for asymp- tomatic or mild/moderate symptoms), transmission reduction after a positive test (33%–100%), presentation to hospital with severe disease (50%–100%), ICU survival (20%–80%), testing program costs (including additional outreach costs of offering PCR testing even if declined, $1–$26), and hospital care costs ($820–$3880). In multiway sensitivity analyses, we vary key parameters simultaneously. In additional analyses, we examined implementation of these testing strategies on 1 April 2020 vs 1 May 2020; the Re threshold at which conclusions about the pre- ferred strategy shifted (Re = 1.3–2.0); the frequency of retesting in symptomatic + asymptomatic monthly (up to daily); patterns of presenting with COVID-19–like illness; and, the impact of costs associated with lost productivity due to hospitalization or positive PCR test results and averted mortality. Further details of the methods, as well as model calibration and validation, are shown in the Supplementary Materials. Parameter Value Transmissions Re 1–30 May 2020 0.9 By health state, probability, daily [22–24]e Latent 0.0000 Asymptomatic 0.2024 Mild/moderate illness 0.1948 Severe illness 0.0135 Critical illness 0.0107 Recuperation 0.0135 Recovery 0.0000 Transmission reduction after test result, %f Test Positive Test Negative Asymptomatic 65 0 Mild/moderate illness 65 0 Severe/critical/recuperationf 90 90 Costs (2020 USD) SARS-CoV-2 PCR assay [25] 51 Hospital bed, daily [26–28] 1640 Intensive care unit, daily [26–28] 2680 Abbreviations: COVID-19, coronavirus disease 2019; NA, not applicable; PCR, polymerase chain reaction; Re, effective reproduction number; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; USD, United States dollars. aDerived from model validation and calibration as described in the Supplementary Materials. bAverage days spent in each health state stratified by clinical disease progression severity are presented in Supplementary Table 1. Health state transitions are shown in Supplementary Figure 2. cAssumption; includes those with COVID-19 disease and those with COVID-19–like illness. dTest sensitivity is 0% in the latent phase and otherwise does not vary by disease states. eDaily transmission rates contribute to Re. fAssumptions for transmission reductions following test result are detailed in the Supplementary Materials. In severe/critical/recuperation states, transmission reduction is due to hospital- ization and thus is applied to all patients regardless of test result. Table 1. Continued D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data e2912 • cid 2021:73 (1 November) • Neilan et al RESULTS Base Case Outcomes Clinical Outcomes All of the expanded screening strategies would reduce infections and deaths compared to the hospitalized strategy. In all epi- demic scenarios, symptomatic + asymptomatic monthly would lead to the most favorable clinical outcomes, and hospitalized would lead to the least favorable outcomes; in the slowing sce- nario, symptomatic + asymptomatic monthly vs hospitalized resulted in 209 500 vs 577 700 infections (64% reduction) and 1700 vs 3100 deaths (46% reduction) (Table 2). As Re increases, compared to hospitalized, more expansive screening strat- egies would lead to greater reductions in infections and deaths (Table  2). As Re increases, the expanded screening strategies, compared with hospitalized, would result in a greater reduction in peak prevalence and lower reduction in the susceptible pro- portion of the population (Figure 1A–C). Resource Utilization and Costs In all epidemic growth scenarios, symptomatic would lead to lower total costs compared to hospitalized. In the slowing sce- nario, symptomatic + asymptomatic monthly would lead to the greatest reduction in cumulative bed-days compared to hospi- talized (77 300 vs 126 000 hospital bed-days [39% reduction] and 45 600 vs 76 600 ICU bed-days [40% reduction]) but would require >66-fold times more tests/day (192 200 vs 2900) at 5-fold higher total costs ($2.0 billion vs $439 million) (Tables 2 and 3). In the slowing and intermediate scenarios, peak hospital bed use is similar across all strategies. In the surging scenario, how- ever, all other PCR-based strategies would reduce peak hospital and ICU bed use compared to hospitalized: hospital beds (7100 vs 2300–4600) and ICU beds (4100 vs 1200–2500) (Table  3). Supplementary Table 2 reports results per million people. Cost-effectiveness Outcomes Under all epidemic growth scenarios considered, symptomatic would be clinically superior and cost-saving compared to hospi- talized (Table 2). Symptomatic + asymptomatic monthly would have an ICER <$100 000/QALY compared to symptomatic only in the surging scenario ($33 000/QALY). ICERs increase steeply as Re declines (Table 2). Sensitivity and Scenario Analyses Clinical Outcomes and Resource Use The impact of variation in clinical model input parameters on infections and deaths would be greatest in the surging scenario (Supplementary Figure 3A–F). Varying rates of presentation to hospital care and ICU survival would lead to large changes in mortality, which remain substantial (slowing scenario: 1300– 2400 deaths/180 days) even under optimistic assumptions (ie, 100% presentation to hospital with severe illness or 80% ICU survival) (Supplementary Figure 3D–F). If expanded PCR testing started 1 April 2020, compared to 1 May 2020, we project that PCR-based strategies would have averted 103 000–176 900 Table 2. Clinical and Cost-effectiveness Outcomes for a Model of Coronavirus Disease 2019 Infection and Testing in Massachusetts Scenario Undiscounted Undiscounted Discounted Undiscounted Discounted Incident Infections, No.a Deaths, No.a Total QALYs Lost, No.b Healthcare Costs, USDa,c ICER, USD/QALYc Slowing scenario (1 June 2020, Re = 0.9) Symptomatic 315 700 2200 11 900 342 787 000 … Hospitalized 577 700 3100 16 400 439 495 000 Dominated Symptomatic + asymptomatic once 268 100 2000 10 500 605 505 000 194 000 Symptomatic + asymptomatic monthly 209 500 1700 8900 2 024 106 000 908 000 Intermediate scenario (1 June 2020, Re = 1.3) Symptomatic 680 600 3400 18 300 488 896 000 … Symptomatic + asymptomatic once 579 200 3000 16 100 727 290 000 110 000 Hospitalized 1 696 800 6800 36 100 849 882 000 Dominated Symptomatic + asymptomatic monthly 333 700 2100 11 400 2 091 084 000 287 000 Surging scenario (1 June 2020, Re = 2.0) Symptomatic 3 374 200 13 700 72 600 1 608 128 000 … Symptomatic + asymptomatic once 3 258 100 13 000 68 800 1 831 196 000 Dominated Hospitalized 4 444 300 18 300 97 200 2 090 289 000 Dominated Symptomatic + asymptomatic monthly 1 884 000 7100 37 700 2 757 024 000 33 000 Strategies are listed in order of increasing cost as per cost-effectiveness analysis convention. Infections, deaths, and life-years lost are rounded to the nearest 100. Costs and ICERs are rounded to the nearest 1000. In-text results describing percentages are calculated from unrounded results. Abbreviations: ICER, incremental cost-effectiveness ratio; QALY, quality-adjusted life-year; Re, effective reproduction number; USD, United States dollars. aIncludes 180-day horizon between simulated days 1 May 2020 and 1 November 2020. b Total life-years lost were estimated from coronavirus disease 2019–related deaths occurring over 180 days. Details are shown in the Supplementary Materials. cIncremental cost-effectiveness ratios are calculated by dividing the difference in total healthcare-related costs by the difference in total QALYs lost compared to the next most expen- sive strategy. Dominated strategies are either more expensive and less effective than another strategy (strong dominance) or a combination of 2 other strategies (weak dominance). Total QALYs lost are discounted at 3%/year; because all healthcare costs occur in year 1, costs are not discounted in the base case. Additional details of calculating ICERs are shown in the Supplementary Materials. D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data COVID-19 Testing in Massachusetts • cid 2021:73 (1 November) • e2913 infections (Supplementary Figure 4A–C) and 90–260 deaths in April alone (Supplementary Figure 4D–F). Cost-effectiveness In 1-way sensitivity analyses, the economically preferred strategy in each epidemic scenario was most sensitive to test acceptance, the transmission reduction after a positive PCR test, and PCR test costs (Supplementary Tables 3–11). In the surging scenario, symptomatic + asymptomatic monthly would not be cost-effec- tive if we assume low test acceptance (15%), half the transmis- sion reduction after a positive test (33%), or triple PCR test costs ($154). Symptomatic + asymptomatic monthly would become cost-effective in the intermediate and slowing scenarios only with reductions in test costs (intermediate: ≤$13; slowing: ≤$5). If costs decrease for PCR assays, many combinations of program and assay costs symptomatic + asymptomatic monthly strategy would be cost-effective or cost-saving (Supplementary Figure 5). Holding other parameters equal to the base case, sympto- matic + asymptomatic monthly would become cost-effective at an Re ≥1.6 (Supplementary Table 12). The frequency of re- peat testing with symptomatic + asymptomatic monthly is also influential; in the surging scenario, symptomatic + asympto- matic monthly would no longer be cost-effective if tests occur more frequently than every 30 days (Supplementary Table 13); however, if test costs were ≤$3, then testing as frequently as every 14 days would be cost-effective in all epidemic scenarios (Figure  2). While total costs would vary widely with rates of COVID-19–like illness, cost-effectiveness conclusions would not change (Supplementary Table 14). Conclusions are similar even when costs associated with lost productivity or averted COVID-19–related mortality are included (Supplementary Table 15). DISCUSSION Using a microsimulation model, we projected the COVID-19 epidemic in Massachusetts from 1 May 2020 to 1 November 2020 under slowing, intermediate, and surging epidemic Prevalent cases Susceptible (%) Hospitalized Symptomatic Symptomatic + asymptomatic once Symptomatic + asymptomaticmonthly 0% 20% 40% 60% 80% 100% 0 250 000 500 000 750 000 1 000 000 1-May 30-Jun 29-Aug 28-Oct S u scep tib le co h o rt (% ) P re v al en t ca se s (N o .) Day of simulation A 1-July 1-Sept 1-Nov 0% 20% 40% 60% 80% 100% 0 250 000 500 000 750 000 1 000 000 1-May 30-Jun 29-Aug 28-Oct S u scep tib le co h o rt (% ) P re v al en t ca se s (N o .) Day of simulation B 1-July 1-Sept 1-Nov 0% 20% 40% 60% 80% 100% 0 250 000 500 000 750 000 1 000 000 1-May 30-Jun 29-Aug 28-Oct S u scep tib le co h o rt (% ) P re v al en t ca se s (N o. ) Day of simulation C 1-July 1-Sept 1-Nov Figure 1. Model-projected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection prevalence and proportion of susceptible cohort. For the modeled strategies, prevalent coronavirus disease 2019 cases over time are plotted as solid lines on the left vertical axis, while the percentages of the cohort remaining suscep- tible to infection over time are plotted as dotted lines on the right vertical axis. People with SARS-CoV-2 are no longer considered prevalent when they have recovered (Supplementary Figure 1). Results shown represent the population of Massachusetts. Testing strategies are denoted by different shaded lines. A, Slowing scenario in which the effective reproduction number (Re) on 1 June 2020 is 0.9. B, Intermediate scenario in which Re on 1 June 2020 is 1.3. C, Surging scenario in which Re on 1 June 2020 is 2.0. Abbreviation: Re, effective reproduction number. D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data http://academic.oup.com/cid/article-lookup/doi/10.1093/cid/ciaa1418#supplementary-data e2914 • cid 2021:73 (1 November) • Neilan et al Table 3. Clinical and Resource Utilization Outcomes for a Model of Coronavirus Disease 2019 Infection and Testing in Massachusetts Scenario PCR Tests per Simulation, d, Mean PCR Tests, Total Hospital Bed-days ICU Bed-days Cumulative Self-isolation DaysCumulative Peak Cumulative Peak Slowing scenario (1 June 2020, Re = 0.9) Hospitalized 2900 521  800 126  300 2200 76  600 1000 … Symptomatic 4800 861  500 91  200 2200 55  500 900 1  731  000 Symptomatic + asymptomatic once 35 100 6  318  200 87  100 2200 51  600 900 1  948  900 Symptomatic + asymptomatic monthly 192 200 34  593 900 77  300 2200 45  600 900 2  251  900 Intermediate scenario (1 June 2020, Re = 1.3) Hospitalized 2900 530  400 257  500 2200 149  100 1000 … Symptomatic 5900 1  053  100 133  100 2200 80  700 900 2  802  000 Symptomatic + asymptomatic once 36  300 6  534  100 123  200 2200 70  800 900 2  897  300 Symptomatic + asymptomatic monthly 193  500 34  823 700 93  400 2200 56  300 900 2  942  600 Surging scenario (1 June 2020, Re = 2.0) Hospitalized 3100 549  300 639  800 7100 377  300 4100 … Symptomatic 13  900 2  498  800 469  200 4600 264  600 2500 10  974 100 Symptomatic + asymptomatic once 46  800 8  418  900 442  900 4300 250  600 2500 11  326 700 Symptomatic + asymptomatic monthly 209  300 37  672 900 265  700 2300 144  600 1200 10  694 400 Includes events occurring during the 180-day horizon between simulated days 1 May 2020 and 1 November 2020. Strategies are listed by increasing number of tests utilized. PCR tests, hos- pital bed-days, ICU bed-days, and self-isolation days are rounded to the nearest 100. In-text results describing percentages are calculated from unrounded results. Cumulative self-isolation days are estimated in addition to the hospitalized strategy. Abbreviations: ICU, intensive care unit; PCR, polymerase chain reaction; Re, effective reproduction number. PCR test cost $1 $3 $5 $13 $26 $51 $103 P C R t es t fr e q u en cy Monthly 14-day 3-day Daily C Legend: Cost-saving < $100 000/YLS SLY/000051$-000001$ > $150 000/YLS PCR test cost $1 $3 $5 $13 $26 $51 $103 P C R t es t fr eq u e n c y Monthly 14-day 3-day Daily A PCR test cost $1 $3 $5 $13 $26 $51 $103 P C R t es t fr eq u e n c y Monthly 14-day 3-day Daily B Figure 2. Two-way sensitivity analyses: polymerase chain reaction (PCR) test cost and frequency. In this 2-way sensitivity analysis, PCR test cost and frequency were varied. Incremental cost-effectiveness ratios are reported in $/quality-adjusted life-year for symptomatic + asymptomatic monthly testing vs the next least costly strategy. “X” represents the base case. A, Slowing scenario in which the effective reproduction number (Re) on 1 June 2020 is 0.9. B, Intermediate scenario in which Re on 1 June 2020 is 1.3. C, Surging scenario in which Re on 1 June 2020 is 2.0. Abbreviations: PCR, polymerase chain reaction; YLS, years-of-life saved. D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 COVID-19 Testing in Massachusetts • cid 2021:73 (1 November) • e2915 growth scenarios, to examine the clinical and economic impact of 4 testing strategies. Expanded PCR testing beyond those with severe symptoms would reduce morbidity and mortality across a range of ep- idemic scenarios. In all Re scenarios, we estimate substantial reductions in mortality (1.8- to 2.6-fold lower) with sympto- matic + asymptomatic monthly compared to hospitalized. Our Re values encompass published estimates for Massachusetts during the study period [31–33]. Importantly, the slowing sce- nario likely reflects Massachusetts’s response through June 2020 [9], and the surging scenario provides important insight for elsewhere in the United States where infections are increasing. We further estimate that if expanded PCR testing had been widely available in Massachusetts from 1 April 2020 to 1 May 2020, 103  000–176 900 infections and 90–260 deaths would have been averted during that 1  month alone. Given the av- erage time from infection to hospitalization and death (~9 days and ~28 days, respectively), earlier expanded testing might also have facilitated timely recognition of epidemic trends and clo- sure policies. Policies that reduce Re at scale (eg, stay-at-home advisories), as occurred in Massachusetts even while PCR testing was scarce, are likely to be more effective than any of the modeled testing strategies [34, 35]. Similar to conclusions from other studies [22, 31, 36–38], our findings suggest that looser restrictions on social distancing regulations (which can lead to a higher Re) would require more aggressive testing, paired with individual behavioral measures, to control the epidemic. All the expanded screening strategies would lead to reductions in key hospital resource use as well as fewer days spent self-isolating compared to hospitalized. In Massachusetts, an estimated 9500 hos- pital beds and 1500 ICU beds were available at the peak of the surge capacity, of which 3800 and 1440 were used [9, 39]. None of the modeled scenarios exceeded peak hospital bed capacity; however, we projected that 23%–75% of available hospital beds would be needed by people with COVID-19. In all scenarios, we projected peak ICU bed use close to or exceeding capacity (1200–4100). While some assumptions are uncertain (eg, proportion of people presenting to the hospital with severe disease, probability of ICU survival), the substantial burden of severe and critical illness we project in all scenarios has important implications for healthcare globally, as resources redirected for COVID-19–related illness may jeopardize the ability to care for other diseases. In all examined epidemic growth scenarios, symptomatic testing would be cost-saving compared to hospitalized. At any Re >1.6, symptomatic + asymptomatic monthly would be the most efficient use of resources, unless test acceptance is very low (15%). Importantly, at these higher Re values, screening the entire population only once would be an inefficient strategy without repeat screening for those testing negative. ICERs were highly sensitive to PCR test costs. If low-cost testing were avail- able at $5/test, it would be cost-effective or cost-saving to offer repeat testing in all epidemic scenarios. In the absence of rapid, low-cost, widely available testing, states will also need to pre- pare themselves to pivot testing strategies as the epidemic shifts. In the slowing and intermediate scenarios, as of July 2020, Massachusetts would have test capacity to conduct the economi- cally preferred symptomatic strategy (approximately 12 000/day es- timated tests conducted statewide vs 4800–5900 model-projected tests) [9]. However, in the surging scenario, the projected average of 203 100 tests/day (36.6 million/180  days) required to conduct the cost-effective symptomatic + asymptomatic monthly strategy would greatly exceed current capacity; notably, daily testing of the entire population in this scenario led to >3 million projected tests/ day. Large-scale testing has been achieved early in the epidemic in some settings: In March 2020, South Korea was testing 20 000 people/day [40]. Newer high-throughput machines may process thousands of tests per day, rendering such an approach potentially feasible in the near future [41]. Additionally, the number of tests used for people without COVID-19 is uncertain. We assumed high rates of COVID-19–like illness (adding approximately 2800 tests/ day) in the base case; however, it is likely, particularly in summer months, that fewer people would seek testing. Given that the eco- nomically preferred strategy changes depending on Re, implemen- tation of the most cost-effective testing strategy will require careful planning and real-time epidemic monitoring in each setting to adapt to changing Re. Furthermore, while currently an aspiration, low-cost, rapid turnaround testing, even with current imperfect test sensitivity, would be cost-effective even in low Re settings. While critical supply chain issues and other factors precluded widespread testing in the United States early in the pandemic, even now, ex- panding testing capacity must remain a focus of national efforts. Given that scaling current technologies may not be feasible in all settings, additional innovative strategies including pooled, rapid an- tigen, and home self-testing should be examined [42, 43]. The impact of any testing strategy depends on the actions that policymakers, employers, and individuals take in response. Compared to testing only those with severe symptoms, monthly routine testing averted only 58%–64% of infections, whereas daily testing averted 75%–91% of infections. Our results em- phasize how policies that support isolating people infected with COVID-19 are essential; when an individual is less adherent to self-isolation after a positive test (ie, lower transmission re- duction), the benefits of testing are greatly reduced. In Iceland, broad testing led to only 6% of the population being tested, with 34% of an invited random sample presenting for testing [44]. In the surging scenario, at low test acceptance rates (15%) among those with no or mild symptoms, symptomatic + asymptomatic monthly would no longer be cost-effective. In Massachusetts, SARS-CoV-2 testing often does not require co-pays, and suffi- cient personal protective equipment permits safe testing [1, 2]. Nevertheless, people may avoid testing due to concerns such as physical discomfort, missing work, or stigma. While the Family Medical and Leave Act may provide support for those eligible who test positive (or if family members test positive), not all D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 e2916 • cid 2021:73 (1 November) • Neilan et al workers may be aware of their rights or have compliant em- ployers [45]. Federal and setting-specific incentives for infected people to self-isolate should be considered (eg, childcare or workplace incentives) [46]. This analysis has important limitations. First, we assume ho- mogenous population mixing. This assumption may over- or underestimate the benefits of PCR testing; however, we have cali- brated our model to reflect observed data, using a transmission multiplier. When relevant, we selected values or made assump- tions that would provide a conservative estimate of the benefits of testing (PCR sensitivity, test cost, transmission reduction after a negative test) and then varied these values widely in sensitivity analyses. Second, we do not address supply chain lapses, which could impact the feasibility of implementing these strategies. Third, we exclude several factors that may result from expanded testing that would render these strategies even more cost-ef- fective, including averting quality-of-life reductions due to COVID-19–related morbidity or self-quarantine-related mental health issues [47], preventing school closure-related workforce gaps [48], increasing economic purchasing, and enabling eco- nomic activity to reopen due to reduced COVID incidence [36]. We also assume that transmissions vary with a constant daily rate by disease state; emerging data suggest that infectivity may be highest early after acquisition of the virus [49]. If true, testing strategies that diagnose people in early or asymptomatic stages of infection would be of higher value. Finally, we do not model contact tracing, which is likely to be a critical tool to respond to a patchwork of surging outbreaks over time. Testing people with any COVID-19–consistent symptoms would be cost-saving compared to testing only those whose symptoms warrant hospital care. Expanding SARS-CoV-2 PCR testing to asymptomatic people would reduce infections, deaths, and hospital resource use. Despite modest sensitivity, low-cost, repeat screening of the entire population could be cost-effective in all epidemic settings. Supplementary Data Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author. Notes Author contributions. All authors contributed substantively to this man- uscript in the following ways: study and model design (all authors), data analysis (A. M. N., A. C. B.), interpretation of results (all authors), drafting the manuscript (A. M. N., A. C. B., A. M., P. K.), critical revision of the man- uscript (all authors), and final approval of submitted version (all authors). Acknowledgments. The authors gratefully acknowledge Christopher Alba, Giulia Park, and Tijana Stanic for their assistance in preparing the manuscript for publication. Disclaimer. The content is solely the responsibility of the authors; the study’s findings and conclusions do not necessarily represent the official views of the National Institutes of Health (NIH), the Wellcome Trust, or other funders. Financial support. This work was supported by the Eunice Kennedy Shriver National Institute for Child Health and Human Development, National Institutes of Health (NIH) (grant number K08 HD094638 to A. M. N.); the National Institute of Allergy and Infectious Diseases, NIH (grant numbers T32 AI007433 to A. M. and R37AI058736-16S1 to K. A. F.); and the Wellcome Trust (210479/Z/18/Z to G. H.). Potential conflicts of interest. E. L. reports advisory fees for osteoarthritis- related work from Pfizer and Lilly, outside the submitted work. M. S.  re- ports an investigator-initiated research grant from ViiV Pharmaceuticals, outside the submitted work. M.  W.  reports personal fees from Quadrant Health Economics and Precision HEOR, outside the submitted work. All other authors report no potential conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. References 1. Boston Public Health Commission. Mayor Walsh, Massachusetts General Hospital announce results of antibody and COVID-19 testing for Boston resi- dents. 2020. Available at: https://www.bphc.org/onlinenewsroom/Blog/Lists/ Posts/Post.aspx?ID=1297. Accessed 29 May 2020. 2. Massachusetts Department of Public Health. Testing of persons with suspect COVID-19. 2020. Available at: https://www.mass.gov/doc/covid-19-pui-criteria/ download. Accessed 19 May 2020. 3. Woloshin S, Patel N, Kesselheim AS. False negative tests for SARS-CoV-2 infec- tion—challenges and implications. N Engl J Med 2020; 383:e38. 4. Rockefeller Foundation. COVID-19 national testing and tracing action plan. Available at: https://www.rockefellerfoundation.org/national-covid-19-testing- and-tracing-action-plan/. Accessed 13 August 2020. 5. Rapid Tests. Why rapid tests? Available at: https://www.rapidtests.org. Accessed 13 August 2020. 6. Mass.gov. Reopening Massachusetts. 2020. Available at: https://www.mass.gov/ doc/reopening-massachusetts-may-18–2020/download. Accessed 7 July 2020. 7. Lange  SJ, Ritchey  MD, Goodman  AB, et  al. Potential indirect effects of the COVID-19 pandemic on use of emergency departments for acute life-threatening conditions—United States, January–May 2020. MMWR Morb Mortal Wkly Rep 2020; 69:795–800. 8. Centers for Disease Control and Prevention. CDC activities and initiatives sup- porting the COVID-19 response and the president’s plan for opening America up again. 2020. Available at: https://www.cdc.gov/coronavirus/2019-ncov/down- loads/php/CDC-Activities-Initiatives-for-COVID-19-Response.pdf. Accessed 24 August 2020. 9. Massachusetts Department of Public Health. Massachusetts Department of Public Health COVID-19 dashboard. 2020. Available at: https://www.mass.gov/ doc/covid-19-dashboard-may-1-2020/download. Accessed 15 July 2020. 10. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness—the curious resilience of the $50 000-per-QALY threshold. New Engl J Med 2014; 371:796–7. 11. Bao L, Deng W, Gao H, et al. Lack of reinfection in rhesus macaques infected with SARS-CoV-2. bioRxiv [Preprint]. May 1, 2020 [cited 2020 May 21]. Available at: http://biorxiv.org/lookup/doi/10.1101/2020.03.13.990226. 12. US Census Bureau. American Community Survey 1-year estimates (2018). 2018. Available at: http://censusreporter.org/profiles/04000US25-massachusetts/. Accessed 16 April 2020. 13. Massachusetts Department of Public Health. Archive of COVID-19 cases in Massachusetts. Available at: https://www.mass.gov/info-details/archive-of-covid- 19-cases-in-massachusetts. Accessed 16 April 2020. 14. He X, Lau EHY, Wu P, et al. Author correction: temporal dynamics in viral shed- ding and transmissibility of COVID-19. Nat Med 2020; 26:1491–3. 15. Hu Z, Song C, Xu C, et al. Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China. Sci China Life Sci 2020. doi:10.1007/s11427-020-1661-4. 16. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020; 395:1054–62. 17. CDC COVID-19 Response Team. Severe outcomes among patients with coro- navirus disease 2019 (COVID-19)—United States, February 12-March 16, 2020. MMWR Morb Mortal Wkly Rep 2020; 69:343–6. 18. World Health Organization. Report of the WHO-China joint mission on co- ronavirus disease 2019 (COVID-19). 2020. Available at: https://www.who.int/ publications-detail/report-of-the-who-china-joint-mission-on-coronavirus- disease-2019-(covid-19). Accessed 16 April 2020. D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 https://www.bphc.org/onlinenewsroom/Blog/Lists/Posts/Post.aspx?ID=1297 https://www.bphc.org/onlinenewsroom/Blog/Lists/Posts/Post.aspx?ID=1297 https://www.mass.gov/doc/covid-19-pui-criteria/download https://www.mass.gov/doc/covid-19-pui-criteria/download https://www.rockefellerfoundation.org/national-covid-19-testing-and-tracing-action-plan/ https://www.rockefellerfoundation.org/national-covid-19-testing-and-tracing-action-plan/ https://www.rapidtests.org https://www.mass.gov/doc/reopening-massachusetts-may-18–2020/download https://www.mass.gov/doc/reopening-massachusetts-may-18–2020/download https://www.cdc.gov/coronavirus/2019-ncov/downloads/php/CDC-Activities-Initiatives-for-COVID-19-Response.pdf https://www.cdc.gov/coronavirus/2019-ncov/downloads/php/CDC-Activities-Initiatives-for-COVID-19-Response.pdf https://www.mass.gov/doc/covid-19-dashboard-may-1-2020/download https://www.mass.gov/doc/covid-19-dashboard-may-1-2020/download http://biorxiv.org/lookup/doi/10.1101/2020.03.13.990226 http://censusreporter.org/profiles/04000US25-massachusetts/ https://www.mass.gov/info-details/archive-of-covid-19-cases-in-massachusetts https://www.mass.gov/info-details/archive-of-covid-19-cases-in-massachusetts https://doi.org/10.1007/s11427-020-1661-4 https://www.who.int/publications-detail/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19 https://www.who.int/publications-detail/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19 https://www.who.int/publications-detail/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19 COVID-19 Testing in Massachusetts • cid 2021:73 (1 November) • e2917 19. Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases. Overall percentages of visits for ILI and percentage of visits for ILI by age group reported by a subset of ILINet providers. 2020. Available at: https://www.cdc.gov/coronavirus/2019-ncov/covid-data. Accessed 4 July 2020. 20. Yang Y, Yang M, Shen C, et al. Evaluating the accuracy of different respiratory speci- mens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections. medRxiv [Preprint]. February 17, 2020 [cited 2020 May 2020]. Available at: http://medrxiv.org/lookup/doi/10.1101/2020.02.11.20021493. 21. Wang W, Xu Y, Gao R, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA 2020; 323:1843–4. 22. Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID- 19 is higher compared to SARS coronavirus. J Travel Med 2020; 27:taaa021. 23. Chen  X, Yu  B. First two months of the 2019 coronavirus disease (COVID-19) epidemic in China: real-time surveillance and evaluation with a second derivative model. Glob Health Res Policy 2020; 5:7. doi:10.1186/s41256-020-00137-4 24. Sanche S, Lin YT, Xu C, Romero-Severson E, Hengartner N, Ke R. High conta- giousness and rapid spread of severe acute respiratory syndrome coronavirus 2. Emerg Infect Dis 2020; 26:1470–7. 25. Centers for Medicare and Medicaid Services. Medicare administrative contractor (MAC) COVID-19 test pricing, 2020. Available at: https://www.cms.gov/files/ document/mac-covid-19-test-pricing.pdf. Accessed 21 May 2020. 26. Cox  C, Rudowitz  R, Neuman  T, Cubanski  J, Rae  M. How health costs might change with COVID-19. Peterson-Kaiser Family Foundation (KFF) Health System Tracker. 2020. Available at: https://www.healthsystemtracker.org/brief/ how-health-costs-might-change-with-covid-19/. Accessed 4 June 2020. 27. Rae M, Claxton G, Kurani N, McDermott D, Cox C. Potential costs of COVID- 19 treatment for people with employer coverage. Peterson-Kaiser Family Foundation (KFF) Health System Tracker. 2020. Available at: https://www. healthsystemtracker.org/brief/potential-costs-of-coronavirus-treatment-for- people-with-employer-coverage/. Accessed 4 June 2020. 28. FAIR Health. COVID-19: the projected economic impact of the COVID-19 pan- demic on the US healthcare system. 2020. Available at: https://s3.amazonaws.com/ media2.fairhealth.org/brief/asset/COVID-19%20-%20The%20Projected%20 Economic%20Impact%20of%20the%20COVID-19%20Pandemic%20on%20 the%20US%20Healthcare%20System.pdf. Accessed 7 July 2020. 29. Wolf MS, Serper M, Opsasnick L, et al. Awareness, attitudes, and actions related to COVID-19 among adults with chronic conditions at the onset of the U.S. out- break: a cross-sectional survey. Ann Intern Med 2020; 173:100–9. 30. Sullivan PW, Ghushchyan V. Preference-based EQ-5D index scores for chronic conditions in the United States. Med Decis Making 2006; 26:410–20. 31. Unwin H, Mishra S, Bradley V, et al; Imperial College London. Report 23: state- level tracking of COVID-19 in the United States. 2020. Available at: http://spiral. imperial.ac.uk/handle/10044/1/79231. Accessed 26 May 2020. 32. Systrom  K, Vladeck  T. Massachusetts. Available at: https://rt.live/us/MA. Accessed 15 July 2020. 33. Abbott S, Hellwell J, Thompson RN, et al. National and subnational estimates for the United States of America. Available at: https://epiforecasts.io/covid/posts/na- tional/united-states/. Accessed 15 July 2020. 34. Abouk R, Heydari B. The immediate effect of COVID-19 policies on social distancing behavior in the United States. medRxiv [Preprint]. April 28, 2020 [cited 2020 June 22]. Available at: https://www.medrxiv.org/content/10.1101/2020.04.07.20057356v2. 35. Dave D, Friedson A, Matsuzawa K, Sabia J. When do shelter-in-place orders fight COVID-19 best? Policy heterogeneity across states and adoption time. National Bureau of Economic Research. 2020. Available at: http://www.nber.org/papers/ w27091. Accessed 22 June 2020. 36. Eichenbaum  MS, Rebelo  S, Trabandt  M. The macroeconomics of epidemics. National Bureau of Economic Research. 2020. Available at: http://www.nber.org/ papers/w26882. Accessed 22 June 2020. 37. Kucharski AJ, Klepac P, Conlan AJK, et al. Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS- CoV-2 in different settings: a mathematical modelling study [manuscript published online 15 June  2020]. Lancet Infect Dis 2020. doi:10.1016/ S1473-3099(20)30457-6. 38. Firth JA, Hellewell J, Klepac P, et al. Combining fine-scale social contact data with epidemic modelling reveals interactions between contact tracing, quarantine, testing and physical distancing for controlling COVID-19. medRxiv [Preprint]. July 2, 2020 [cited 2020 June 22]. Available at: https://www.medrxiv.org/content/ 10.1101/2020.05.26.20113720v2. 39. Massachusetts Department of Public Health. Baker-Polito administration pro- vides update on hospital surge capacity. 2020. Available at: https://www.mass.gov/ news/baker-polito-administration-provides-update-on-hospital-surge-capacity. Accessed 16 April 2020. 40. Pancevski  B. Some nations look to mass testing for faster way out of coro- navirus crisis. Wall Street Journal 2020. Available at: https://www.wsj.com/ articles/some-nations-look-to-mass-testing-for-faster-way-out-of-coronavirus- crisis-11585758518. Accessed 15 July 2020. 41. Broad Institute. COVID-19 diagnostic processing dashboard. Available at: https:// covid19-testing.broadinstitute.org/. Accessed 30 June 2020. 42. Lim KL, Johari NA, Wong ST, et al. A novel strategy for community screening of SARS-CoV-2 (COVID-19): sample pooling method. PLoS One 2020; 15:e0238417. 43. Dinnes J, Deeks JJ, Adriano A, et al. Rapid, point-of-care antigen and molecular- based tests for diagnosis of SARS-CoV-2 infection. Cochrane Database Syst Rev 2020; 8:CD013705. 44. Gudbjartsson  DF, Helgason  A, Jonsson  H, et  al. Spread of SARS-CoV-2 in the Icelandic population. N Engl J Med 2020; 382:2302–15. 45. US Department of Labor. Families First Coronavirus Response Act: employee paid leave rights. Available at: https://www.dol.gov/agencies/whd/pandemic/ ffcra-employee-paid-leave. Accessed 7 July 2020. 46. Centers for Disease Control and Prevention. Case investigation and contact tracing: part of a multipronged approach to fight the COVID-19 pandemic. 2020. Available at: https://www.cdc.gov/coronavirus/2019-ncov/php/principles- contact-tracing.html. Accessed 30 June 2020. 47. Hawryluck L, Gold WL, Robinson S, Pogorski S, Galea S, Styra R. SARS control and psychological effects of quarantine, Toronto, Canada. Emerg Infect Dis 2004; 10:1206–12. 48. Bayham J, Fenichel EP. Impact of school closures for COVID-19 on the US health- care workforce and net mortality: a modelling study. Lancet Public Health 2020; 5:e271–8. 49. Arons  MM, Hatfield  KM, Reddy  SC, et  al; Public Health–Seattle and King County and CDC COVID-19 Investigation Team. Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. N Engl J Med 2020; 382:2081–90. D ow nloaded from https://academ ic.oup.com /cid/article/73/9/e2908/5908298 by U N IV O F W ITW ATER SR AN D user on 08 D ecem ber 2021 https://www.cdc.gov/coronavirus/2019-ncov/covid-data http://medrxiv.org/lookup/doi/10.1101/2020.02.11.20021493 https://doi.org/10.1186/s41256-020-00137-4 https://www.cms.gov/files/document/mac-covid-19-test-pricing.pdf https://www.cms.gov/files/document/mac-covid-19-test-pricing.pdf https://www.healthsystemtracker.org/brief/how-health-costs-might-change-with-covid-19/ https://www.healthsystemtracker.org/brief/how-health-costs-might-change-with-covid-19/ https://www.healthsystemtracker.org/brief/potential-costs-of-coronavirus-treatment-for-people-with-employer-coverage/ https://www.healthsystemtracker.org/brief/potential-costs-of-coronavirus-treatment-for-people-with-employer-coverage/ https://www.healthsystemtracker.org/brief/potential-costs-of-coronavirus-treatment-for-people-with-employer-coverage/ https://s3.amazonaws.com/media2.fairhealth.org/brief/asset/COVID-19%20-%20The%20Projected%20Economic%20Impact%20of%20the%20COVID-19%20Pandemic%20on%20the%20US%20Healthcare%20System.pdf https://s3.amazonaws.com/media2.fairhealth.org/brief/asset/COVID-19%20-%20The%20Projected%20Economic%20Impact%20of%20the%20COVID-19%20Pandemic%20on%20the%20US%20Healthcare%20System.pdf https://s3.amazonaws.com/media2.fairhealth.org/brief/asset/COVID-19%20-%20The%20Projected%20Economic%20Impact%20of%20the%20COVID-19%20Pandemic%20on%20the%20US%20Healthcare%20System.pdf https://s3.amazonaws.com/media2.fairhealth.org/brief/asset/COVID-19%20-%20The%20Projected%20Economic%20Impact%20of%20the%20COVID-19%20Pandemic%20on%20the%20US%20Healthcare%20System.pdf http://spiral.imperial.ac.uk/handle/10044/1/79231 http://spiral.imperial.ac.uk/handle/10044/1/79231 https://rt.live/us/MA https://epiforecasts.io/covid/posts/national/united-states/ https://epiforecasts.io/covid/posts/national/united-states/ https://www.medrxiv.org/content/10.1101/2020.04.07.20057356v2 http://www.nber.org/papers/w27091 http://www.nber.org/papers/w27091 http://www.nber.org/papers/w26882 http://www.nber.org/papers/w26882 https://www.medrxiv.org/content/10.1101/2020.05.26.20113720v2 https://www.medrxiv.org/content/10.1101/2020.05.26.20113720v2 https://www.mass.gov/news/baker-polito-administration-provides-update-on-hospital-surge-capacity https://www.mass.gov/news/baker-polito-administration-provides-update-on-hospital-surge-capacity https://www.wsj.com/articles/some-nations-look-to-mass-testing-for-faster-way-out-of-coronavirus-crisis-11585758518 https://www.wsj.com/articles/some-nations-look-to-mass-testing-for-faster-way-out-of-coronavirus-crisis-11585758518 https://www.wsj.com/articles/some-nations-look-to-mass-testing-for-faster-way-out-of-coronavirus-crisis-11585758518 https://covid19-testing.broadinstitute.org/ https://covid19-testing.broadinstitute.org/ https://www.dol.gov/agencies/whd/pandemic/ffcra-employee-paid-leave https://www.dol.gov/agencies/whd/pandemic/ffcra-employee-paid-leave https://www.cdc.gov/coronavirus/2019-ncov/php/principles-contact-tracing.html https://www.cdc.gov/coronavirus/2019-ncov/php/principles-contact-tracing.html