Last data update: May 06, 2024. (Total: 46732 publications since 2009)
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COVID-19 cases and hospitalizations averted by case investigation and contact tracing in the United States (preprint)
Rainisch G , Jeon S , Pappas D , Spencer KD , Fischer LS , Adhikari BB , Taylor MM , Greening B , Moonan PK , Oeltmann JE , Kahn EB , Washington ML , Meltzer MI . medRxiv 2021 21 Importance: Evidence of the impact of COVID-19 Case Investigation and Contact Tracing (CICT) programs is lacking. Policymakers need this evidence to assess its value. Objective(s): Estimate COVID-19 cases and hospitalizations averted nationwide by US states' CICT programs. Design(s): We combined data from US CICT programs (e.g., proportion of cases interviewed, contacts notified or monitored, and days to case and contact notification) with incidence data to model CICT impacts over 60 days period (November 25, 2020 to January 23, 2021) during the height of the pandemic. We estimated a range of impacts by varying assumed compliance with isolation and quarantine recommendations. Setting(s): US States and Territories Participants: Fifty-nine state and territorial health departments that received federal funding supporting COVID-19 pandemic response activities were eligible for inclusion. Of these, 22 states and 1 territory reported all measures necessary for the analysis. These 23 jurisdictions covered 42.5% of the US population (140 million persons), spanned all 4 census regions, and reported data that reflected all 59 federally funded CICT programs. Intervention(s): Public health case investigation and contact tracing Main Outcomes and Measures: Cases and hospitalizations averted; percent of cases averted among cases not prevented by vaccination and other non-pharmaceutical interventions (other NPIs). Result(s): We estimated 1.11 million cases and 27,231 hospitalizations were averted by CICT programs under a scenario where 80% of interviewed cases and monitored contacts, and 30% of notified contacts fully complied with isolation and quarantine guidance, eliminating their contributions to future transmission. As many as 1.36 million cases and 33,527 hospitalizations could have been prevented if all interviewed cases and monitored contacts had entered into and fully complied with isolation and quarantine guidelines upon being interviewed or notified. Across all scenarios and jurisdictions, CICT averted a median of 21.2% (range: 1.3% - 65.8%) of the cases not prevented by vaccination and other NPIs. Conclusions and Relevance: CICT programs likely had a substantial role in curtailing the pandemic in most jurisdictions during the winter 2020-2021 peak. Differences in impact across jurisdictions indicate an opportunity to further improve CICT effectiveness. These estimates demonstrate the potential benefits from sustaining and improving these programs. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license. |
Patient flow time data of COVID-19 vaccination clinics in 23 sites, United States, April and May 2021.
Cho BH , Athar HM , Bates LG , Yarnoff BO , Harris LQ , Washington ML , Jones-Jack NH , Pike JJ . Vaccine 2022 41 (3) 750-755 INTRODUCTION: Public health department (PHD) led COVID-19 vaccination clinics can be a critical component of pandemic response as they facilitate high volume of vaccination. However, few patient-time analyses examining patient throughput at mass vaccination clinics with unique COVID-19 vaccination challenges have been published. METHODS: During April and May of 2021, 521 patients in 23 COVID-19 vaccination sites counties of 6 states were followed to measure the time spent from entry to vaccination. The total time was summarized and tabulated by clinic characteristics. A multivariate linear regression analysis was conducted to evaluate the association between vaccination clinic settings and patient waiting times in the clinic. RESULTS: The average time a patient spent in the clinic from entry to vaccination was 9 min 5 s (range: 02:00-23:39). Longer patient flow times were observed in clinics with higher numbers of doses administered, 6 or fewer vaccinators, walk-in patients accepted, dedicated services for people with disabilities, and drive-through clinics. The multivariate linear regression showed that longer patient waiting times were significantly associated with the number of vaccine doses administered, dedicated services for people with disabilities, the availability of more than one brand of vaccine, and rurality. CONCLUSIONS: Given the standardized procedures outlined by immunization guidelines, reducing the wait time is critical in lowering the patient flow time by relieving the bottleneck effect in the clinic. Our study suggests enhancing the efficiency of PHD-led vaccination clinics by preparing vaccinators to provide vaccines with proper and timely support such as training or delivering necessary supplies and paperwork to the vaccinators. In addition, patient wait time can be spent answering questions about vaccination or reviewing educational materials on other public health services. |
Assessment of the Costs of Implementing COVID-19 Vaccination Clinics in 34 Sites, United States, March 2021.
Yarnoff BO , Pike JJ , Athar HM , Bates LG , Tayebali ZA , Harris LQ , Jones-Jack NH , Washington ML , Cho BH . J Public Health Manag Pract 2022 28 (6) 624-630 OBJECTIVES: To estimate the costs to implement public health department (PHD)-run COVID-19 vaccination clinics. DESIGN: Retrospectively reported data on COVID-19 vaccination clinic characteristics and resources used during a high-demand day in March 2021. These resources were combined with national average wages, supply costs, and facility costs to estimate the operational cost and start-up cost of clinics. SETTING: Thirty-four PHD-run COVID-19 vaccination clinics across 8 states and 1 metropolitan statistical area. PARTICIPANTS: Clinic managers at 34 PHD-run COVID-19 vaccination clinics. INTERVENTION: Large-scale COVID-19 vaccination clinics were implemented by public health agencies as part of the pandemic response. MAIN OUTCOMES MEASURED: Operational cost per day, operational cost per vaccination, start-up cost per clinic. RESULTS: Median operational cost per day for a clinic was $10 314 (range, $637-$95 163) and median cost per vaccination was $38 (range, $9-$206). There was a large range of operational costs across clinics. Clinics used an average of 99 total staff hours per 100 patients vaccinated. Median start-up cost per clinic was $15 348 (range, $1 409-$165 190). CONCLUSIONS: Results show that clinics require a large range of resources to meet the high throughput needs of the COVID-19 pandemic response. Estimating the costs of PHD-run vaccination clinics for the pandemic response is essential for ensuring that resources are available for clinic success. If clinics are not adequately supported, they may stop functioning, which would slow the pandemic response if no other setting or approach is possible. |
Estimated COVID-19 Cases and Hospitalizations Averted by Case Investigation and Contact Tracing in the US.
Rainisch G , Jeon S , Pappas D , Spencer KD , Fischer LS , Adhikari BB , Taylor MM , Greening BJr , Moonan PK , Oeltmann JE , Kahn EB , Washington ML , Meltzer MI . JAMA Netw Open 2022 5 (3) e224042 IMPORTANCE: Evidence of the impact of COVID-19 case investigation and contact tracing (CICT) programs is lacking, but policy makers need this evidence to assess the value of such programs. OBJECTIVE: To estimate COVID-19 cases and hospitalizations averted nationwide by US states' CICT programs. DESIGN, SETTING, AND PARTICIPANTS: This decision analytical model study used combined data from US CICT programs (eg, proportion of cases interviewed, contacts notified or monitored, and days to case and contact notification) with incidence data to model outcomes of CICT over a 60-day period (November 25, 2020, to January 23, 2021). The study estimated a range of outcomes by varying assumed compliance with isolation and quarantine recommendations. Fifty-nine state and territorial health departments that received federal funding supporting COVID-19 pandemic response activities were eligible for inclusion. Data analysis was performed from July to September 2021. EXPOSURE: Public health case investigation and contact tracing. MAIN OUTCOMES AND MEASURES: The primary outcomes were numbers of cases and hospitalizations averted and the percentage of cases averted among cases not prevented by vaccination and other nonpharmaceutical interventions. RESULTS: In total, 22 states and 1 territory reported all measures necessary for the analysis. These 23 jurisdictions covered 42.5% of the US population (approximately 140 million persons), spanned all 4 US Census regions, and reported data that reflected all 59 federally funded CICT programs. This study estimated that 1.11 million cases and 27 231 hospitalizations were averted by CICT programs under a scenario where 80% of interviewed cases and monitored contacts and 30% of notified contacts fully complied with isolation and quarantine guidance, eliminating their contributions to future transmission. As many as 1.36 million cases and 33 527 hospitalizations could have been prevented if all interviewed cases and monitored contacts had entered into and fully complied with isolation and quarantine guidelines upon being interviewed or notified. Across both scenarios and all jurisdictions, CICT averted an estimated median of 21.2% (range, 1.3%-65.8%) of the cases not prevented by vaccination and other nonpharmaceutical interventions. CONCLUSIONS AND RELEVANCE: These findings suggest that CICT programs likely had a substantial role in curtailing the pandemic in most jurisdictions during the 2020 to 2021 winter peak. Differences in outcomes across jurisdictions indicate an opportunity to further improve CICT effectiveness. These estimates demonstrate the potential benefits from sustaining and improving these programs. |
A mathematical model of contact tracing during the 20142016 West African Ebola outbreak
Burton D , Lenhart S , Edholm CJ , Levy B , Washington ML , Greening BR Jr , JaneWhite KA , Lungu E , Chimbola O , Kgosimore M , Chirove F , Ronoh M , HelenMachingauta M . Mathematics 2021 9 (6) The 20142016 West African outbreak of Ebola Virus Disease (EVD) was the largest and most deadly to date. Contact tracing, following up those who may have been infected through contact with an infected individual to prevent secondary spread, plays a vital role in controlling such outbreaks. Our aim in this work was to mechanistically represent the contact tracing process to illustrate potential areas of improvement in managing contact tracing efforts. We also explored the role contact tracing played in eventually ending the outbreak. We present a system of ordinary differential equations to model contact tracing in Sierra Leonne during the outbreak. Using data on cumulative cases and deaths, we estimate most of the parameters in our model. We include the novel features of counting the total number of people being traced and tying this directly to the number of tracers doing this work. Our work highlights the importance of incorporating changing behavior into ones model as needed when indicated by the data and reported trends. Our results show that a larger contact tracing program would have reduced the death toll of the outbreak. Counting the total number of people being traced and including changes in behavior in our model led to better understanding of disease management. |
Stunting: an overlooked problem in Myanmar - an economic evaluation
Aye SKK , Mar SL , Lwin NN , Hnin ZL , Hlaing LM , Washington ML , Harris JR . Int J Technol Assess Health Care 2020 36 (2) 1-6 OBJECTIVES: Stunting increases a child's susceptibility to diseases, increases mortality, and is associated over long term with reduced cognitive abilities, educational achievement, and productivity. We aimed to assess the most effective public health nutritional intervention to reduce stunting in Myanmar. METHODS: We searched the literature and developed a conceptual framework for interventions known to reduce stunting. We focused on the highest impact and most feasible interventions to reduce stunting in Myanmar, described policies to implement them, and compared their costs and projected effect on stunting using data-based decision trees. We estimated costs from the government perspective and calculated total projected cases of stunting prevented and cost per case prevented (cost-effectiveness). All interventions were compared to projected cases of stunting resulting from the current situation (e.g., no additional interventions). RESULTS: Three new policy options were identified. Operational feasibility for all three options ranged from medium to high. Compared to the current situation, two were similarly cost-effective, at an additional USD 598 and USD 667 per case of stunting averted. The third option was much less cost-effective, at an additional USD 27,741 per case averted. However, if donor agencies were to expand their support in option three to the entire country, the prevalence of 22.5 percent would be reached by 2025 at an additional USD 667 per case averted. CONCLUSIONS: A policy option involving immediate expansion of the current implementation of proven nutrition-specific interventions is feasible. It would have the highest impact on stunting and would approach the WHO 2025 target. |
The cost of influenza-associated hospitalizations and outpatient visits in Kenya
Emukule GO , Ndegwa LK , Washington ML , Paget JW , Duque J , Chaves SS , Otieno NA , Wamburu K , Ndigirigi IW , Muthoka PM , Van Der Velden K , Mott JA . BMC Public Health 2019 19 471 Background: We estimated the cost-per-episode and the annual economic burden associated with influenza in Kenya. Methods: From July 2013-August 2014, we recruited patients with severe acute respiratory illness (SARI) or influenza-like illness (ILI) associated with laboratory-confirmed influenza from 5 health facilities. A structured questionnaire was used to collect direct costs (medications, laboratory investigations, hospital bed fees, hospital management costs, transportation) and indirect costs (productivity losses) associated with an episode of influenza. We used published incidence of laboratory-confirmed influenza associated with SARI and ILI, and the national population census data from 2014, to estimate the annual national number of influenza-associated hospitalizations and outpatient visits and calculated the annual economic burden by multiplying cases by the mean cost. Results: We enrolled 275 patients (105 inpatients and 170 outpatients). The mean cost-per-episode of influenza was US$117.86 (standard deviation [SD], 88.04) among inpatients; US$114.25 (SD, 90.03) for children < 5 years, and US$137.45 (SD, 76.24) for persons aged ≥5 years. Among outpatients, the mean cost-per-episode of influenza was US$19.82 (SD, 27.29); US$21.49 (SD, 31.42) for children < 5 years, and US$16.79 (SD, 17.30) for persons aged ≥5 years. National annual influenza-associated cost estimates ranged from US$2.96-5.37 million for inpatients and US$5.96-26.35 million for outpatients. Conclusions: Our findings highlight influenza as causing substantial economic burden in Kenya. Further studies may be warranted to assess the potential benefit of targeted influenza vaccination strategies. |
Estimating weekly call volume to a national nurse telephone triage line in an influenza pandemic
Adhikari BB , Koonin LM , Mugambi ML , Sliger KD , Washington ML , Kahn EB , Meltzer MI . Health Secur 2018 16 (5) 334-340 Telephone nurse triage lines, such as the Centers for Disease Control and Prevention's (CDC) Flu on Call((R)), a national nurse triage line, may help reduce the surge in demand for health care during an influenza pandemic by triaging callers, providing advice about clinical care and information about the pandemic, and providing access to prescription antiviral medication. We developed a Call Volume Projection Tool to estimate national call volume to Flu on Call((R)) during an influenza pandemic. The tool incorporates 2 influenza clinical attack rates (20% and 30%), 4 different levels of pandemic severity, and different initial "seed numbers" of cases (10 or 100), and it allows variation in which week the nurse triage line opens. The tool calculates call volume by using call-to-hospitalization ratios based on pandemic severity. We derived data on nurse triage line calls and call-to-hospitalization ratios from experience with the 2009 Minnesota FluLine nurse triage line. Assuming a 20% clinical attack rate and a case hospitalization rate of 0.8% to 1.5% (1968-like pandemic severity), we estimated the nationwide number of calls during the peak week of the pandemic to range from 1,551,882 to 3,523,902. Assuming a more severe 1957-like pandemic (case hospitalization rate = 1.5% to 3.0%), the national number of calls during the peak week of the pandemic ranged from 2,909,778 to 7,047,804. These results will aid in planning and developing nurse triage lines at both the national and state levels for use during a future influenza pandemic. |
Modeling in real time during the Ebola response
Meltzer MI , Santibanez S , Fischer LS , Merlin TL , Adhikari BB , Atkins CY , Campbell C , Fung IC , Gambhir M , Gift T , Greening B , Gu W , Jacobson EU , Kahn EB , Carias C , Nerlander L , Rainisch G , Shankar M , Wong K , Washington ML . MMWR Suppl 2016 65 (3) 85-9 To aid decision-making during CDC's response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in this report would not have been possible without collaboration with many U.S. and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html). |
Estimating Ebola treatment needs, United States
Rainisch G , Asher J , George D , Clay M , Smith TL , Kosmos C , Shankar M , Washington ML , Gambhir M , Atkins C , Hatchett R , Lant T , Meltzer MI . Emerg Infect Dis 2015 21 (7) 1273-5 By December 31, 2014, the Ebola epidemic in West Africa had resulted in treatment of 10 Ebola case-patients in the United States; a maximum of 4 patients received treatment at any one time (1). Four of these 10 persons became clinically ill in the United States (2 infected outside the United States and 2 infected in the United States), and 6 were clinically ill persons medically evacuated from West Africa (Technical Appendix 1 Table 6). | To plan for possible future cases in the United States, policy makers requested we produce a tool to estimate future numbers of Ebola case-patients needing treatment at any one time in the United States. Gomes et al. previously estimated the potential size of outbreaks in the United States and other countries for 2 different dates in September 2014 (2). Another study considered the overall risk for exportation of Ebola from West Africa but did not estimate the number of potential cases in the United States at any one time (3). |
Modeling the effect of school closures in a pandemic scenario: exploring two different contact matrices
Fung IC , Gambhir M , Glasser JW , Gao H , Washington ML , Uzicanin A , Meltzer MI . Clin Infect Dis 2015 60 Suppl 1 S58-63 BACKGROUND: School closures may delay the epidemic peak of the next influenza pandemic, but whether school closure can delay the peak until pandemic vaccine is ready to be deployed is uncertain. METHODS: To study the effect of school closures on the timing of epidemic peaks, we built a deterministic susceptible-infected-recovered model of influenza transmission. We stratified the U.S. population into 4 age groups (0-4, 5-19, 20-64, and ≥65 years), and used contact matrices to model the average number of potentially disease transmitting, nonphysical contacts. RESULTS: For every week of school closure at day 5 of introduction and a 30% clinical attack rate scenario, epidemic peak would be delayed by approximately 5 days. For a 15% clinical attack rate scenario, 1 week closure would delay the peak by 9 days. Closing schools for less than 84 days (12 weeks) would not, however, reduce the estimated total number of cases. CONCLUSIONS: Unless vaccine is available early, school closure alone may not be able to delay the peak until vaccine is ready to be deployed. Conversely, if vaccination begins quickly, school closure may be helpful in providing the time to vaccinate school-aged children before the pandemic peaks. |
Effectiveness of Ebola treatment units and community care centers - Liberia, September 23-October 31, 2014
Washington ML , Meltzer ML . MMWR Morb Mortal Wkly Rep 2015 64 (3) 67-9 Previous reports have shown that an Ebola outbreak can be slowed, and eventually stopped, by placing Ebola patients into settings where there is reduced risk for onward Ebola transmission, such as Ebola treatment units (ETUs) and community care centers (CCCs) or equivalent community settings that encourage changes in human behaviors to reduce transmission risk, such as making burials safe and reducing contact with Ebola patients. Using cumulative case count data from Liberia up to August 28, 2014, the EbolaResponse model previously estimated that without any additional interventions or further changes in human behavior, there would have been approximately 23,000 reported Ebola cases by October 31, 2014. In actuality, there were 6,525 reported cases by that date. To estimate the effectiveness of ETUs and CCCs or equivalent community settings in preventing greater Ebola transmission, CDC applied the EbolaResponse model to the period September 23-October 31, 2014, in Liberia. The results showed that admitting Ebola patients to ETUs alone prevented an estimated 2,244 Ebola cases. Having patients receive care in CCCs or equivalent community settings with a reduced risk for Ebola transmission prevented an estimated 4,487 cases. Having patients receive care in either ETUs or CCCs or in equivalent community settings, prevented an estimated 9,100 cases, apparently as the result of a synergistic effect in which the impact of the combined interventions was greater than the sum of the two interventions. Caring for patients in ETUs, CCCs, or in equivalent community settings with reduced risk for transmission can be important components of a successful public health response to an Ebola epidemic. |
Estimating the future number of cases in the Ebola epidemic - Liberia and Sierra Leone, 2014-2015
Meltzer MI , Atkins CY , Santibanez S , Knust B , Petersen BW , Ervin ED , Nichol ST , Damon IK , Washington ML . MMWR Suppl 2014 63 (3) 1-14 The first cases of the current West African epidemic of Ebola virus disease (hereafter referred to as Ebola) were reported on March 22, 2014, with a report of 49 cases in Guinea. By August 31, 2014, a total of 3,685 probable, confirmed, and suspected cases in West Africa had been reported. To aid in planning for additional disease-control efforts, CDC constructed a modeling tool called EbolaResponse to provide estimates of the potential number of future cases. If trends continue without scale-up of effective interventions, by September 30, 2014, Sierra Leone and Liberia will have a total of approximately 8,000 Ebola cases. A potential underreporting correction factor of 2.5 also was calculated. Using this correction factor, the model estimates that approximately 21,000 total cases will have occurred in Liberia and Sierra Leone by September 30, 2014. Reported cases in Liberia are doubling every 15-20 days, and those in Sierra Leone are doubling every 30-40 days. The EbolaResponse modeling tool also was used to estimate how control and prevention interventions can slow and eventually stop the epidemic. In a hypothetical scenario, the epidemic begins to decrease and eventually end if approximately 70% of persons with Ebola are in medical care facilities or Ebola treatment units (ETUs) or, when these settings are at capacity, in a non-ETU setting such that there is a reduced risk for disease transmission (including safe burial when needed). In another hypothetical scenario, every 30-day delay in increasing the percentage of patients in ETUs to 70% was associated with an approximate tripling in the number of daily cases that occur at the peak of the epidemic (however, the epidemic still eventually ends). Officials have developed a plan to rapidly increase ETU capacities and also are developing innovative methods that can be quickly scaled up to isolate patients in non-ETU settings in a way that can help disrupt Ebola transmission in communities. The U.S. government and international organizations recently announced commitments to support these measures. As these measures are rapidly implemented and sustained, the higher projections presented in this report become very unlikely. |
A tool for the economic analysis of mass prophylaxis operations with an application to H1N1 influenza vaccination clinics
Cho BH , Hicks KA , Honeycutt AA , Hupert N , Khavjou O , Messonnier M , Washington ML . J Public Health Manag Pract 2011 17 (1) E22-E28 This article uses the 2009 H1N1 influenza vaccination program experience to introduce a cost analysis approach that may be relevant for planning mass prophylaxis operations, such as vaccination clinics at public health centers, work sites, schools, or pharmacy-based clinics. These costs are important for planning mass influenza vaccination activities and are relevant for all public health emergency preparedness scenarios requiring countermeasure dispensing. We demonstrate how costs vary depending on accounting perspective, staffing composition, and other factors. We also describe a mass vaccination clinic budgeting tool that clinic managers may use to estimate clinic costs and to examine how costs vary depending on the availability of volunteers or donated supplies and on the number of patients vaccinated per hour. Results from pilot tests with school-based H1N1 influenza vaccination clinic managers are described. The tool can also contribute to planning efforts for universal seasonal influenza vaccination. |
Evaluating the capability and cost of a mass influenza and pneumococcal vaccination clinic via computer simulation
Washington ML . Med Decis Making 2009 29 (4) 414-423 OBJECTIVE: To determine if a mass influenza/pneumococcal vaccination clinic could vaccinate 15,000 clients in 17 h; optimize personnel configuration to maximize number of clients vaccinated; and estimate costs (opportunity and clinic) and revenue. METHOD: The author used a discrete event simulation model to estimate the throughput of the vaccination clinic as the number of clients (arrival intensity) increased and as staff members were reassigned to different workflows. We represented workflows for 3 client types: "Medicare," "Special," and "Cash," where "Special" designates Medicare clients who needed assistance moving through the clinic. The costs of supplies, staff salaries, and client waiting time were included in the model. We compared the "original" model based on the staffing and performance of an actual clinic to an "optimized" model in which staff were reassigned to optimize number of clients vaccinated. RESULTS: A maximum of 13,138 and 15,094 clients in the original and optimized models, respectively, were vaccinated. At the original arrival rate (8300 clients vaccinated in 17 h), supplies cost about $191,000 and were the most expensive component of the clinic operation in both models. However, as the arrival intensity increased to 140%, the "Medicare" client opportunity cost increased from $23,887 and $21,474 to $743,510 and $740,760 for the simulated original and optimized models, respectively. CONCLUSION: The clinic could reach their target of 15,000 vaccinees with 2 fewer staff members by rearranging staff assignments from "Special" to "Medicare" and "Cash" stations. Computer simulation can help public health officials determine the most efficient use of staff, machinery, supplies, and time. |
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