Evaluating scenarios for school reopening under COVID19.

BACKGROUND: Thousands of school systems have struggled with the decisions about how to deliver education safely and effectively amid the COVID19 pandemic. This study evaluates the public health impact of various school reopening scenarios (when, and how to return to in-person instruction) on the spread of COVID19. METHODS: An agent-based simulation model was adapted and used to project the impact of various school reopening strategies on the number of infections, hospitalizations, and deaths in the state of Georgia during the study period, i.e., February 18th-November 24th, 2020. The tested strategies include (i) schools closed, i.e., all students receive online instruction, (ii) alternating school day, i.e., half of the students receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iii) alternating school day for children, i.e., half of the children (ages 0-9) receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iv) children only, i.e., only children receive in-person instruction, (v) regular, i.e., all students return to in-person instruction. We also tested the impact of universal masking in schools. RESULTS: Across all scenarios, the number of COVID19-related deaths ranged from approximately 8.8 to 9.9 thousand, the number of cumulative infections ranged from 1.76 to 1.96 million for adults and 625 to 771 thousand for children and youth, and the number of COVID19-related hospitalizations ranged from approximately 71 to 80 thousand during the study period. Compared to schools reopening August 10 with a regular reopening strategy, the percentage of the population infected reduced by 13%, 11%, 9%, and 6% in the schools closed, alternating school day for children, children only, and alternating school day reopening strategies, respectively. Universal masking in schools for all students further reduced outcome measures. CONCLUSIONS: Reopening schools following a regular reopening strategy would lead to higher deaths, hospitalizations, and infections. Hybrid in-person and online reopening strategies, especially if offered as an option to families and teachers who prefer to opt-in, provide a good balance in reducing the infection spread compared to the regular reopening strategy, while ensuring access to in-person education.

Reflecting on prediction strategies for epidemics: Preparedness and public health response.

OBJECTIVE: To provide an overview of the literature on respiratory infectious disease epidemic prediction, preparedness, and response (including pharmaceutical and nonpharmaceutical interventions) and their impact on public health, with a focus on respiratory conditions such as asthma. DATA SOURCES: Published literature obtained through PubMed database searches. STUDY SELECTIONS: Studies relevant to infectious epidemics, asthma, modeling approaches, health care access, and data analytics related to intervention strategies. RESULTS: Prediction, prevention, and response strategies for infectious disease epidemics use extensive data sources and analytics, addressing many areas including testing and early diagnosis, identifying populations at risk of severe outcomes such as hospitalizations or deaths, monitoring and understanding transmission and spread patterns by age group, social interactions geographically and over time, evaluating the effectiveness of pharmaceutical and nonpharmaceutical interventions, and understanding prioritization of and access to treatment or preventive measures (eg, vaccination, masks), given limited resources and system constraints. CONCLUSION: Previous epidemics and pandemics have revealed the importance of effective preparedness and response. Further research and implementation need to be performed to emphasize timely and actionable strategies, including for populations with particular health conditions (eg, chronic respiratory diseases) at risk for severe outcomes.

Estimating the impact of self-management education, influenza vaccines, nebulizers, and spacers on health utilization and expenditures for Medicaid-enrolled children with asthma.

INTRODUCTION: We quantify the effect of a set of interventions including asthma self-management education, influenza vaccination, spacers, and nebulizers on healthcare utilization and expenditures for Medicaid-enrolled children with asthma in New York and Michigan. METHODS: We obtained patients' data from Medicaid Analytic eXtract files and evaluated patients with persistent asthma in 2010 and 2011. We used difference-in-difference regression to quantify the effect of the intervention on the probability of asthma-related healthcare utilization, asthma medication, and utilization costs. We estimated the average change in outcome measures from pre-intervention/intervention (2010) to post-intervention (2011) periods for the intervention group by comparing this with the average change in the control group over the same time horizon. RESULTS: All of the interventions reduced both utilization and asthma medication costs. Asthma self-management education, nebulizer, and spacer interventions reduced the probability of emergency department (20.8-1.5%, 95%CI 19.7-21.9% vs. 0.5-2.5%, respectively) and inpatient (3.5-0.8%, 95%CI 2.1-4.9% vs. 0.4-1.2%, respectively) utilizations. Influenza vaccine decreased the probability of primary care physician (6-3.5%, 95%CI 4.4-7.6% vs. 1.5-5.5%, respectively) visit. The reductions varied by state and intervention. CONCLUSIONS: Promoting asthma self-management education, influenza vaccinations, nebulizers, and spacers can decrease the frequency of healthcare utilization and asthma-related expenditures while improving medication adherence.

Return on investment of self-management education and home visits for children with asthma.

OBJECTIVE: Priorities of the Centers for Disease Control and Prevention's 6|18 Initiative include outpatient asthma self-management education (ASME) and home-based asthma visits (home visit) as interventions for children with poorly-controlled asthma. ASME and home visit intervention programs are currently not widely available. This project was to assess the economic sustainability of these programs for state asthma control programs reimbursed by Medicaid. METHODS: We used a simulation model based on parameters from the literature and Medicaid claims, controlling for regression to the mean. We modeled scenarios under various selection criteria based on healthcare utilization and age to forecast the return on investment (ROI) using data from New York. The resulting tool is available in Excel or Python. RESULTS: Our model projected health improvement and cost savings for all simulated interventions. Compared against home visits alone, the simulated ASME alone intervention had a higher ROI for all healthcare utilization and age scenarios. Savings were primarily highest in simulated program participants who had two or more asthma-related emergency department visits or one inpatient visit compared to those participants who had one or more asthma-related emergency department visits. Segmenting the selection criteria by age did not significantly change the results. CONCLUSIONS: This model forecasts reduced healthcare costs and improved health outcomes as a result of ASME and home visits for children with high urgent healthcare utilization (more than two emergency department visits or one inpatient hospitalization) for asthma. Utilizing specific selection criteria, state based asthma control programs can improve health and reduce healthcare costs.

Homebound by COVID19: the benefits and consequences of non-pharmaceutical intervention strategies.

BACKGROUND: Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household). METHODS: An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020. RESULTS: Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119 K-248 K, 465 K-499 K, 5388 K-5389 K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound. CONCLUSIONS: Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place.

A comparison of first trimester prenatal screening strategies for Down Syndrome with maternal age and preferences considerations.

OBJECTIVE: Screening for Down Syndrome (DS) includes traditional ultrasound and serum-based and cell-free DNA (cfDNA) testing. While cfDNA testing usually has superior performance, it is significantly more costly. As an alternative, a hybrid strategy combining contingent cfDNA with traditional testing is recommended when universal cfDNA screening is not feasible. This study compares the efficacy of traditional, contingent cfDNA, and universal cfDNA screening strategies at various cut-offs based on maternal age and parents' preferences, which may improve testing outcomes and patients' satisfaction. METHOD: Decision trees were used to analyze a cohort of 3 855 500 pregnancies from 12 to 50 years old. The performance of the strategies was compared using the number of adverse outcomes (undetected DS live births and euploid procedure-related losses). RESULTS: Universal cfDNA results in the fewest number of adverse outcomes in every scenario. Contingent cfDNA performs better than traditional screening when the cut-offs used to identify high-risk cases for cfDNA testing are relatively low. These cut-offs change depending on the maternal age and parents' preference. CONCLUSION: Maternal age and parents' preferences should be considered when choosing cut-offs for contingent cfDNA to remain as an effective strategy compared to traditional screening and to improve patient satisfaction.

Using machine learning to estimate survival curves for patients receiving an increased risk for disease transmission heart, liver, or lung versus waiting for a standard organ.

INTRODUCTION: Over 19% of deceased organ donors are labeled increased risk for disease transmission (IRD) for viral blood-borne disease transmission. Many potential organ recipients need to decide between accepting an IRD organ offer and waiting for a non-IRD organ. METHODS: Using machine learning and simulation, we built transplant and waitlist survival models and compared the survival for patients accepting IRD organ offers or waiting for non-IRD organs for the heart, liver, and lung. The simulation consisted of generating 20 000 different scenarios of a recipient either receiving an IRD organ or waiting and receiving a non-IRD organ. RESULTS: In the simulations, the 5-year survival probabilities of heart, liver, and lung recipients who accepted IRD organ offers increased on average by 10.2%, 12.7%, and 7.2%, respectively, compared with receiving a non-IRD organ after average wait times (190, 228, and 223 days, respectively). When the estimated waitlist time was at least 5 days for the liver, and 1 day for the heart and lung, 50% or more of the simulations resulted in a higher chance of 5-year survival when the patient received an IRD organ versus when the patient remained on the waitlist. We also developed a simple equation to estimate the benefits, in terms of 5-year survival probabilities, of receiving an IRD organ versus waiting for a non-IRD organ, for a particular set of recipient/donor characteristics. CONCLUSION: For all three organs, the majority of patients are predicted to have higher 5-year survival accepting an IRD organ offer compared with waiting for a non-IRD organ.

A mathematical model to describe survival among liver recipients from deceased donors with risk of transmitting infectious encephalitis pathogens.

BACKGROUND: Between 2002 and 2013, the organs of 13 deceased donors with infectious encephalitis were transplanted, causing infections in 23 recipients. As a consequence, organs from donors showing symptoms of encephalitis (increased probability of infectious encephalitis (IPIE) organs) might be declined. We had previously characterized the risk of IPIE organs using data available to most transplant teams and not requiring special diagnostic tests. If the probability of infection is low, the benefits of a transplant from a donor with suspected infectious encephalitis might outweigh the risk and could be lifesaving for some transplant candidates. METHODS: Using organ transplant data and Cox Proportional Hazards models, we determined liver donor and recipient characteristics predictive of post-transplant or waitlist survival and generated 5-year survival probability curves. We also calculated expected waiting times for an organ offer based on transplant candidate characteristics. Using a limited set of actual cases of infectious encephalitis transmission via transplant, we estimated post-transplant survival curves given an organ from an IPIE donor. RESULTS: 54% (1256) of patients registered from 2002-2006 who died or were removed from the waiting list because of deteriorated condition within 1 year could have had an at least marginal estimated benefit by accepting an IPIE liver with some probability of infection, with the odds increasing to 86% of patients if the probability of infection was low (5% or less). Additionally, 54% (1252) were removed from the waiting list prior to their estimated waiting time for a non-IPIE liver and could have benefited from an IPIE liver. CONCLUSION: Improved allocation and utilization of IPIE livers could be achieved by evaluating the patient-specific trade-offs between (a) accepting an IPIE liver and (b) remaining on the waitlist and accepting a non-IPIE liver after the estimated waiting time.

Assessment of risk for transplant-transmissible infectious encephalitis among deceased organ donors.

BACKGROUND: There were 13 documented clusters of infectious encephalitis transmission via organ transplant from deceased donors to recipients during 2002-2013. Hence, organs from donors diagnosed with encephalitis are often declined because of concerns about the possibility of infection, given that there is no quick and simple test to detect causes of infectious encephalitis. METHODS: We constructed a database containing cases of infectious and non-infectious encephalitis. Using statistical imputation, cross-validation, and regression techniques, we determined deceased organ donor characteristics, including demographics, signs, symptoms, physical exam, and laboratory findings, predictive of infectious vs non-infectious encephalitis, and developed a calculator which assesses the risk of infection. RESULTS: Using up to 12 predictive patient characteristics (with a minimum of 3, depending on what information is available), the calculator provides the probability that a donor may have infectious vs non-infectious encephalitis, improving the prediction accuracy over current practices. These characteristics include gender, fever, immunocompromised state (other than HIV), cerebrospinal fluid elevation, altered mental status, psychiatric features, cranial nerve abnormality, meningeal signs, focal motor weakness, Babinski's sign, movement disorder, and sensory abnormalities. CONCLUSION: In the absence of definitive diagnostic testing in a potential organ donor, infectious encephalitis can be predicted with a risk score. The risk calculator presented in this paper represents a prototype, establishing a framework that can be expanded to other infectious diseases transmissible through solid organ transplantation.

Age-based differences in the predictive accuracy of a one-size-fits-all risk-cutoff value in prenatal integrated screening for Down syndrome.

OBJECTIVE: The objective of this study is to assess variation in detection and false positive rates and adverse pregnancy outcomes across different age groups when a one-size-fits-all risk-cutoff value, such as 1/270, is used in integrated screening for Down syndrome. METHOD: A Monte Carlo simulation was utilized to estimate the detection and false positive rates as well as adverse pregnancy outcomes. RESULTS: Using a one-size-fits-all risk-cutoff value, such as 1/270, can result in considerably high variations in detection and false positive rates across maternal ages and lead to a higher than the minimum possible total number of adverse outcomes. CONCLUSION: Our findings indicate that the one-size-fits-all risk-cutoff value of 1/270, commonly used in DS screening, should be revisited and alternative (possibly age-based) cutoff values and strategies should be considered. © 2017 John Wiley & Sons, Ltd.

An Airway Risk Assessment Score for Unplanned Extubation in Intensive Care Pediatric Patients.

OBJECTIVE: As a result of a workshop to identify common causes of unplanned extubation, Children's Healthcare of Atlanta developed a scoring tool (Risk Assessment Score) to stratify patients into groups of low, moderate, high, and extreme risk. This tool could be used to institute appropriate monitoring or interventions for patients with high risks of unplanned extubation to enhance safety. The objective of this study is to test the hypothesis that the Risk Assessment Score will correlate with the occurrence rate of unplanned extubation in pediatric patients. DESIGN: Retrospective review of 2,811 patients at five ICUs conducted between December 2012 and July 2014. SETTING: Five ICUs at two freestanding pediatric hospitals within a large children's healthcare system in the United States. PATIENTS: All intubated pediatric patients. INTERVENTIONS: Data of intubations and Risk Assessment Score were collected. Extubation outcomes and severity levels were compared across demographic groups and with the maximum Risk Assessment Score of each intubation. MEASUREMENTS AND MAIN RESULTS: Out of 4,566 intubations, 244 were unplanned extubations (5.3%). The occurrence rates of unplanned extubations in those less than 1, 1-6, and more than 6 years old were 6.7%, 3.6%, and 2.7%, respectively, corresponding to a rate of 0.59, 0.53, and 0.58 unplanned extubation every 100 ventilator days. The occurrence rates were 13.6% for patients weighing less than 1 kg (0.59 unplanned extubation per 100 ventilation days) and 3.8% for patients weighing greater than or equal to 1 kg (0.58 unplanned extubation per 100 ventilation days). For intubations with maximum risk score falling in risk categories of low, moderate, high, and extreme, the occurrence rates were 4.7%, 7.7%, 12.0%, and 8.3%, respectively, which corresponded to rates of 0.54, 0.62, 0.95, and 0.92 unplanned extubation per 100 ventilator days. CONCLUSIONS: Higher Risk Assessment Scores are associated with occurrence rates of unplanned extubation.

Staff planning for operating rooms with different surgical services lines.

We present a two-phase model for a staff planning problem in a surgical department. We consider the setting where staff, in particular nurse circulators and surgical scrub technicians, are assigned to one of different service lines, and while they can be 'pooled' and temporally assigned to other service line if needed, these re-assignments should belimited. In Phase I, we decide on the number of staff hours to budget for each service line, considering policies limiting staff pooling and overtime, and different demand scenarios. In Phase II, we determine how these budgeted staff hours should be allocated across potential work days and shifts, given estimated staff requirements and shift-related scheduling restrictions. We propose a heuristic to speed the model's Phase II solution time. We implement the model using a hospital's surgical data and compare the model's results with the hospital's current practices. Using a simulation model for the surgical operations, we find that our two-phase model reduces the delays caused by staff unavailability as well as staff pooling, without increasing the workforce size. Finally, we briefly describe a decision-support tool we developed with the objective of fine-tuning staff planning decisions.

Automated medical resident rotation and shift scheduling to ensure quality resident education and patient care.

At academic teaching hospitals around the country, the majority of clinical care is provided by resident physicians. During their training, medical residents often rotate through various hospitals and/or medical services to maximize their education. Depending on the size of the training program, manually constructing such a rotation schedule can be cumbersome and time consuming. Further, rules governing allowable duty hours for residents have grown more restrictive in recent years (ACGME 2011), making day-to-day shift scheduling of residents more difficult (Connors et al., J Thorac Cardiovasc Surg 137:710-713, 2009; McCoy et al., May Clin Proc 86(3):192, 2011; Willis et al., J Surg Edu 66(4):216-221, 2009). These rules limit lengths of duty periods, allowable duty hours in a week, and rest periods, to name a few. In this paper, we present two integer programming models (IPs) with the goals of (1) creating feasible assignments of residents to rotations over a one-year period, and (2) constructing night and weekend call-shift schedules for the individual rotations. These models capture various duty-hour rules and constraints, provide the ability to test multiple what-if scenarios, and largely automate the process of schedule generation, solving these scheduling problems more effectively and efficiently compared to manual methods. Applying our models on data from a surgical residency program, we highlight the infeasibilities created by increased duty-hour restrictions placed on residents in conjunction with current scheduling paradigms.

Preference-sensitive risk-cutoff values for prenatal-integrated screening test for Down syndrome.

OBJECTIVE: For a pregnant woman considering prenatal screening for early detection of Down Syndrome (DS), there are at least two major outcomes of interest: undetected DS live births and euploid procedure-related fetal losses. The risk-cutoff value of 1/270 has been commonly used for recommending a diagnostic test. The objective of this study was to assess the impact of women's preferences for different pregnancy outcomes on the optimal risk-cutoff values for integrated screening. METHOD: We built a Monte Carlo simulation model of 100,000 singleton second-trimester pregnancies to assess the probabilities of DS live births and euploid procedure-related fetal losses for various risk-cutoff values. To capture how undesirable some women may view an undetected DS live birth relative to a euploid procedure-related fetal loss, we used a ratio W1 : W2 of weights (penalties) assigned to these two adverse pregnancy outcomes. RESULTS: As the relative weight changes, the optimal risk-cutoff value changes significantly. CONCLUSION: A one-size-fits-all risk-cutoff value, such as 1/270, may not always be the best choice, depending on the preferences of women. Preference-sensitive risk-cutoff values for DS screening have the potential to improve the pregnancy outcomes and patient satisfaction.

A mixed integer programming model for vaccine pricing within a group purchasing organization.

BACKGROUND: Setting prices for life-saving medical or pharmaceutical products needs to consider multiple factors, e.g., affordability and health outcomes across different populations. When a group of buyers (e.g., countries) combine their purchasing power (e.g., via a group purchasing organization), the average procurement price decreases in the total volume. Decisions about what price to then charge to each member in a group are particularly challenging, considering the disparities in their respective ability and willingness to pay. Tiered pricing can be an effective way to set prices for a group of buyers, but its performance needs to be quantified and evaluated. METHODS: We modeled the decision of setting prices of a medical product (for example, a vaccine) for a group of buyers using a mixed integer programming model, considering the buyers' ability and willingness to pay. The objective is to minimize the unit price disparity adjusted by the buyers' willingness to pay, subject to the constraint that the prices decrease in the buyers' ability to pay. We also developed an analogous subsidy allocation model that applies if the group receives philanthropic donations to support procurement. The models were illustrated with two case studies based on the Bacillus Calmette-Guerin (BCG) vaccine procurement by Gavi, the Vaccine Alliance and Pan American Health Organization, and the performances of uniform, tiered, and differentiated pricing schemes were examined. RESULTS: The adjusted unit price disparity is non-increasing in the number of price tiers allowed. The biggest decrease in the adjusted price disparity occurs when switching to two-tier pricing from uniform pricing. Tiered pricing performs better in the Gavi group compared to the PAHO group, in part because the ability to pay and willingness to pay have a higher degree of rank correlation within the former group of countries. CONCLUSIONS: This work provides a model for price-setting (subsidy allocation) decisions for a group of buyers and provides a quantitative comparison of different pricing schemes. The results of the case studies suggest that the performance of tiered pricing depends on various factors, including the disparities in the ability and willingness to pay across the buyers. FUNDING: This research has been supported in part by the Center for Health and Humanitarian Systems, the William W. George endowment, and the following benefactors at Georgia Tech: Andrea Laliberte, Richard Rick E. and Charlene Zalesky, and Claudia and Paul Raines.

Potential Impact of a Diagnostic Test for Detecting Prepatent Guinea Worm Infections in Dogs.

Chad has seen a considerable reduction in cases of Guinea worm disease (or dracunculiasis) in domestic dogs in recent years. Tethering of dogs and application of Abate® larvicide to water sources appear to have contributed to this progress, but with 767 reported dog cases in 2021, accelerating elimination of the disease in Chad may require additional tools. We investigate the potential benefits of a hypothetical diagnostic test that could be capable of detecting prepatent infections in dogs. We adapt an agent-based simulation model for forecasting the impact of interventions on guinea worm disease in dogs to examine the interaction of multiple test factors including test accuracy, when the test can detect infection, dog selection, and dog-owner compliance with tethering recommendations. We find that a diagnostic test could be successful if used in conjunction with existing interventions, and elimination can be achieved within 2 years with 80% or higher test sensitivity, 90% or higher specificity, systematic testing of each dog twice per year, and more than 90% long-term tethering compliance when a dog tests positive or a worm is emerging. Because of the long incubation period of Guinea worm disease (10-14 months) and the fact that no treatment exists, the benefits of the test rely on the testing rollout and response of dog owners. If the test could estimate the timing of worm emergence, long-term tethering could be eliminated and infected dogs could be tethered only when the worms are expected, minimizing the related resources (human and financial) to support the intervention.

Modeling the Impact of Proactive Community Case Management on Reducing Confirmed Malaria Cases in Sub-Saharan African Countries.

Malaria continues to be a major source of morbidity and mortality in sub-Saharan Africa. Timely, accurate, and effective case management is critical to malaria control. Proactive community case management (ProCCM) is a new strategy in which a community health worker "sweeps" a village, visiting households at defined intervals to proactively provide diagnostic testing and treatment if indicated. Pilot experiments have shown the potential of ProCCM for controlling malaria transmission; identifying the best strategy for administering ProCCM in terms of interval timings and number of sweeps could lead to further reductions in malaria infections. We developed an agent-based simulation to model malaria transmission and the impact of various ProCCM strategies. The model was validated using symptomatic prevalence data from a ProCCM pilot study in Senegal. Various ProCCM strategies were tested to evaluate the potential for reducing parasitologically confirmed symptomatic malaria cases in the Senegal setting. We found that weekly ProCCM sweeps during a 21-week transmission season could reduce cases by 36.3% per year compared with no sweeps. Alternatively, two initial fortnightly sweeps, seven weekly sweeps, and finally four fortnightly sweeps (13 sweeps total) could reduce confirmed malaria cases by 30.5% per year while reducing the number of diagnostic tests and corresponding costs by about 33%. Under a highly seasonal transmission setting, starting the sweeps early with longer duration and higher frequency would increase the impact of ProCCM, though with diminishing returns. The model is flexible and allows decision-makers to evaluate implementation strategies incorporating sweep frequency, time of year, and available budget.

Modeling the spread of circulating vaccine-derived poliovirus type 2 outbreaks and interventions: A case study of Nigeria.

Background: Despite the successes of the Global Polio Eradication Initiative, substantial challenges remain in eradicating the poliovirus. The Sabin-strain (live-attenuated) virus in oral poliovirus vaccine (OPV) can revert to circulating vaccine-derived poliovirus (cVDPV) in under-vaccinated communities, regain neurovirulence and transmissibility, and cause paralysis outbreaks. Since the cessation of type 2-containing OPV (OPV2) in 2016, there have been cVDPV type 2 (cVDPV2) outbreaks in four out of six geographical World Health Organization regions, making these outbreaks a significant public health threat. Preparing for and responding to cVDPV2 outbreaks requires an updated understanding of how different factors, such as outbreak responses with the novel type of OPV2 (nOPV2) and the existence of under-vaccinated areas, affect the disease spread. Methods: We built a differential-equation-based model to simulate the transmission of cVDPV2 following reversion of the Sabin-strain virus in prolonged circulation. The model incorporates vaccinations by essential (routine) immunization and supplementary immunization activities (SIAs), the immunity induced by different poliovirus vaccines, and the reversion process from Sabin-strain virus to cVDPV. The model's outcomes include weekly cVDPV2 paralytic case counts and the die-out date when cVDPV2 transmission stops. In a case study of Northwest and Northeast Nigeria, we fit the model to data on the weekly cVDPV2 case counts with onset in 2018-2021. We then used the model to test the impact of different outbreak response scenarios during a prediction period of 2022-2023. The response scenarios included no response, the planned response (based on Nigeria's SIA calendar), and a set of hypothetical responses that vary in the dates at which SIAs started. The planned response scenario included two rounds of SIAs that covered almost all areas of Northwest and Northeast Nigeria except some under-vaccinated areas (e.g., Sokoto). The hypothetical response scenarios involved two, three, and four rounds of SIAs that covered the whole Northwest and Northeast Nigeria. All SIAs in tested outbreak response scenarios used nOPV2. We compared the outcomes of tested outbreak response scenarios in the prediction period. Results: Modeled cVDPV2 weekly case counts aligned spatiotemporally with the data. The prediction results indicated that implementing the planned response reduced total case counts by 79% compared to no response, but did not stop the transmission, especially in under-vaccinated areas. Implementing the hypothetical response scenarios involving two rounds of nOPV2 SIAs that covered all areas further reduced cVDPV2 case counts in under-vaccinated areas by 91-95% compared to the planned response, with greater impact from completing the two rounds at an earlier time, but it did not stop the transmission. When the first two rounds were completed in early April 2022, implementing two additional rounds stopped the transmission in late January 2023. When the first two rounds were completed six weeks earlier (i.e., in late February 2022), implementing one (two) additional round stopped the transmission in early February 2023 (late November 2022). The die out was always achieved last in the under-vaccinated areas of Northwest and Northeast Nigeria. Conclusions: A differential-equation-based model of poliovirus transmission was developed and validated in a case study of Northwest and Northeast Nigeria. The results highlighted (i) the effectiveness of nOPV2 in reducing outbreak case counts; (ii) the need for more rounds of outbreak response SIAs that covered all of Northwest and Northeast Nigeria in 2022 to stop the cVDPV2 outbreaks; (iii) that persistent transmission in under-vaccinated areas delayed the progress towards stopping outbreaks; and (iv) that a quicker outbreak response would avert more paralytic cases and require fewer SIA rounds to stop the outbreaks.

Evaluating the Effectiveness of Potential Interventions for Guinea Worm Disease in Dogs in Chad Using Simulations.

Guinea worm (GW) disease (or dracunculiasis) is currently transmitted among dogs in Chad, which presents risks for the human population. We studied how interventions implemented at different levels might reduce the spread of GW disease (geographically and over time) and what levels of interventions might accelerate elimination. We built a multiple-water-source agent-based simulation model to analyze the disease transmission among dogs in Chad, as well as in geographic district clusters, and validated it using local infection data. We considered two interventions: 1) tethering, where infected dogs are kept on a leash during periods of infectivity, and 2) Abate®, under which the water source is treated to reduce infectivity. Our results showed that elimination (0 dog infections) is most likely achieved within 5 years with extremely high levels of tethering (95%) and Abate (90%), when intervention levels are uniform across district clusters. We used an optimization model to determine an improved strategy, with intervention levels which minimize the number of dogs newly infected in the 6th year, under limitations on intervention levels across clusters; the number of dogs infected after 5 years of intervention could be reduced by approximately 220 dogs with an optimized strategy. Finally, we presented strategies that consider fairness based on intervention resource levels and outcomes. Increased tethering and Abate resources above historical levels are needed to achieve the target of GW disease elimination; optimization methods can inform how best to target limited resources and reach elimination faster.

Split or whole liver transplantation? Utilization and posttransplant survival.

BACKGROUND: Split liver transplantation (SLT), where a single donor liver is divided for transplantation to 2 recipients, has the potential to increase the availability of size-matched livers for pediatric candidates and expand the supply of donor organs available for adult candidates. Although SLT is a well-established technique, the number of SLTs has remained flat during the past 2 decades, partly due to concerns about the posttransplant survival of SLT recipients compared with whole liver transplantation (WLT) recipients. Prior work on SLT versus WLT survival analysis had limitations because, for pediatric recipients, it did not consider the correlations between donor age/weight and the allograft type, and for adult recipients, it may have included records where the donor livers did not meet the split liver criteria (splittable). METHODS: Using the Organ Procurement and Transplantation Network's database (2003-2019), this study analyzes and compares (i) key characteristics of donors and recipients, (ii) donor-recipient match dynamics (organ offers and accept/decline decisions), and (iii) recipient posttransplant survival, for SLT and WLT. RESULTS AND CONCLUSIONS: The results in this study show that the posttransplant survival of SLT and WLT recipients is similar (controlling for other confounding factors that may impact posttransplant survival), highlighting the importance of SLT for increasing the liver supply and potential benefits for both pediatric and adult candidates.